From 5dcb88fe336e9dd844e1f71a44f8d7da28fff5bf Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Sun, 5 Nov 2023 08:52:45 +0100 Subject: [PATCH 1/8] Add common and tensorflow methods in progress --- notebooks/docs/0.4-modelling-libraries.ipynb | 1610 +- ....1-modelling-libraries_preprocessing.ipynb | 360 + ...0.4.2-modelling-libraries_tensorflow.ipynb | 1698 ++ .../docs/code block\nTime series.ipynb" | 17422 ++++++++++++++++ src/ontime/time_series/test.py | 80 + 5 files changed, 21167 insertions(+), 3 deletions(-) create mode 100644 notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb create mode 100644 notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb create mode 100644 "notebooks/docs/code block\nTime series.ipynb" create mode 100644 src/ontime/time_series/test.py diff --git a/notebooks/docs/0.4-modelling-libraries.ipynb b/notebooks/docs/0.4-modelling-libraries.ipynb index 3ef4a0b..e164b9a 100644 --- a/notebooks/docs/0.4-modelling-libraries.ipynb +++ b/notebooks/docs/0.4-modelling-libraries.ipynb @@ -63,6 +63,1594 @@ "ts = ts.astype(np.float32)" ] }, + { + "cell_type": "markdown", + "id": "1d4bec6b-eedb-4a88-ba68-dbeae5f0644e", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "b7ab9b51-6c63-4068-ac53-98790bf55fde", + "metadata": {}, + "source": [ + "- [x] Normalize\n", + "- [x] Split train, test, val\n", + "- [ ] Feature engineering\n", + " - add weather for location\n", + " - add day of the week, month, year, etc.\n", + " - add whatever\n", + "- [x] Windowing\n", + "- [x] Windowing - Split (parts to train as X, parts to predict as y)\n", + "- [ ] Windowing - to tf.data.Dataset\n", + "- [ ] Windowing - to Pytorch DataLoaders" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from darts.dataprocessing.transformers import Scaler\n", + "\n", + "def normalize(ts: on.TimeSeries, type='minmax', return_transformer=False):\n", + " match type:\n", + " case 'minmax':\n", + " scaler = MinMaxScaler()\n", + " case 'zscore':\n", + " scaler = StandardScaler()\n", + " transformer = Scaler(scaler)\n", + " ts_transformed = transformer.fit_transform(ts)\n", + " if return_transformer:\n", + " return ts_transformed, transformer\n", + " else:\n", + " return ts_transformed" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "de144fa1-d419-46ae-9da1-102db4da92bb", + "metadata": {}, + "outputs": [], + "source": [ + "def train_test_split(ts: on.TimeSeries, test_split=None, train_split=None) -> tuple:\n", + " \"\"\"\n", + " Description\n", + " \n", + " :param ts: TimeSeries to split\n", + " :param test_split: float, int or pd.TimeStamp\n", + " :param train_split: float, int or pd.TimeStamp\n", + " \"\"\"\n", + " \n", + " if train_split is not None and test_split is not None:\n", + " raise Exception('Only one of those two parameters can be set : train_split, test_split.')\n", + "\n", + " if train_split is None and test_split is None:\n", + " test_split = 0.25\n", + " \n", + " # split ts in subts : train, test\n", + " if test_split is not None: \n", + " train_set, test_set = ts.split_after(1-test_split)\n", + " \n", + " if train_split is not None:\n", + " train_set, test_set = ts.split_after(train_split)\n", + "\n", + " return train_set, test_set" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a297972-1588-4539-8168-05ec379c794d", + "metadata": {}, + "outputs": [], + "source": [ + "def split_by_n(ts, n, drop_last=True):\n", + "\n", + " # Get DataFrame\n", + " df = ts.pd_dataframe()\n", + " \n", + " # Calculate the total number of splits needed\n", + " total_splits = -(-len(df) // n) # Ceiling division to get the number of parts\n", + " \n", + " # Initialize a list to hold the DataFrame splits\n", + " splits_df = []\n", + " \n", + " # Loop through the DataFrame and split it\n", + " for split in range(total_splits):\n", + " start_index = split * n\n", + " end_index = start_index + n\n", + " # Append the part to the list, using slicing with .iloc\n", + " splits_df.append(df.iloc[start_index:end_index])\n", + "\n", + " # If the last dataframe has a different length, then drop it.\n", + " if drop_last:\n", + " last_df = splits_df[-1]\n", + " second_last = splits_df[-2] \n", + " if len(last_df) != len(second_last):\n", + " splits_df = splits_df[:-1]\n", + "\n", + " # Change the data sctructure from DataFrame to TimeSeries\n", + " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9614843a-70c2-4213-8d03-e2df030236c1", + "metadata": {}, + "outputs": [], + "source": [ + "def split_inputs_from_targets(ts_list, input_len, target_len):\n", + "\n", + " # Change inner data structure to DataFrame\n", + " dfs = [ts.pd_dataframe() for ts in ts_list]\n", + "\n", + " # Create initial arrays\n", + " input_series_list = []\n", + " target_series_list = []\n", + " \n", + " # Iterate over each DataFrame in the list\n", + " for df in dfs:\n", + " # Check if the DataFrame is large enough to accommodate input_len and label_len\n", + " if len(df) >= input_len + target_len:\n", + " # Get the first input_len items\n", + " input_series = df.iloc[:input_len]\n", + " input_series_list.append(input_series)\n", + " \n", + " # Get the last label_len items\n", + " target_series = df.iloc[-target_len:]\n", + " target_series_list.append(target_series)\n", + " else:\n", + " raise Exception('input_len + label_len is longer that the total length of the DataFrame')\n", + "\n", + " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", + " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", + " \n", + " return input_ts_list, target_ts_list" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", + "metadata": {}, + "outputs": [], + "source": [ + "def to_numpy(ts_list):\n", + " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "312a3eb7-162f-4d7e-a68e-78b6d6842493", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "68e883a6-a762-4a81-bf1c-6bb20a4c157c", + "metadata": {}, + "source": [ + "### Test" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a4b12f07-8a97-403a-a554-89e166574120", + "metadata": {}, + "outputs": [], + "source": [ + "ts_t = normalize(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "84301c56-5e2f-4eea-ad98-a7d0b89c039c", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = train_test_split(ts_t, train_split=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "46e3a480-390f-446e-ab08-824f95467ddd", + "metadata": {}, + "outputs": [], + "source": [ + "train_list = split_by_n(train, 6)\n", + "test_list = split_by_n(test, 6)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a45e871d-ba2b-4de6-93bc-baf9b26104ec", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, y_train = split_inputs_from_targets(train_list, 4, 2)\n", + "X_test, y_test = split_inputs_from_targets(test_list, 4, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "9993a67f-41ff-4bb4-b104-df1df61bf16c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "48\n", + "48\n", + "12\n", + "12\n" + ] + } + ], + "source": [ + "print(len(X_train))\n", + "print(len(y_train))\n", + "print(len(X_test))\n", + "print(len(y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b1adc175-b981-4804-819a-9be32d41977b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(48, 4, 1)\n", + "(12, 2, 1)\n" + ] + } + ], + "source": [ + "print(to_numpy(X_train).shape)\n", + "print(to_numpy(y_test).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a0bc351b-9789-4f0c-914d-6e94d160e613", + "metadata": {}, + "outputs": [], + "source": [ + "X_train = to_numpy(X_train)\n", + "y_train = to_numpy(y_train)\n", + "X_test = to_numpy(X_test)\n", + "y_test = to_numpy(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "0ef9e79a-7c69-446b-a31a-cac8ebce99de", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "\n", + "class WindowGenerator:\n", + " def __init__(self, input_width, target_width, shift, ts, target_columns=None):\n", + " # Store the raw data.\n", + " self.ts = ts\n", + " self.df = ts.pd_dataframe()\n", + "\n", + " # Work out the target column indices.\n", + " self.target_columns = target_columns\n", + " if target_columns is not None:\n", + " self.target_columns_indices = {name: i for i, name in\n", + " enumerate(target_columns)}\n", + " self.column_indices = {name: i for i, name in\n", + " enumerate(self.df.columns)}\n", + "\n", + " # Work out the window parameters.\n", + " self.input_width = input_width\n", + " self.target_width = target_width\n", + " self.shift = shift\n", + "\n", + " self.total_window_size = input_width + shift\n", + "\n", + " self.input_slice = slice(0, input_width)\n", + " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", + "\n", + " self.target_start = self.total_window_size - self.target_width\n", + " self.targets_slice = slice(self.target_start, None)\n", + " self.target_indices = np.arange(self.total_window_size)[self.targets_slice]\n", + "\n", + " def __repr__(self):\n", + " return '\\n'.join([\n", + " f'Total window size: {self.total_window_size}',\n", + " f'Input indices: {self.input_indices}',\n", + " f'Target indices: {self.target_indices}',\n", + " f'Target column name(s): {self.target_columns}'])\n", + "\n", + " def split_window(self, features):\n", + " inputs = features[:, self.input_slice, :]\n", + " targets = features[:, self.targets_slice, :]\n", + " if self.target_columns is not None:\n", + " targets = tf.stack(\n", + " [targets[:, :, self.column_indices[name]] for name in self.target_columns],\n", + " axis=-1)\n", + "\n", + " # Slicing doesn't preserve static shape information, so set the shapes\n", + " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", + " inputs.set_shape([None, self.input_width, None])\n", + " targets.set_shape([None, self.target_width, None])\n", + "\n", + " return inputs, targets\n", + "\n", + " def make_dataset(self, data):\n", + " data = np.array(data, dtype=np.float32)\n", + " ds = tf.keras.utils.timeseries_dataset_from_array(\n", + " data=data,\n", + " targets=None,\n", + " sequence_length=self.total_window_size,\n", + " sequence_stride=1,\n", + " shuffle=True,\n", + " batch_size=32,)\n", + "\n", + " ds = ds.map(self.split_window)\n", + "\n", + " return ds\n", + "\n", + " @property\n", + " def dataset(self):\n", + " return self.make_dataset(self.df)\n", + "\n", + " @property\n", + " def example(self):\n", + " \"\"\"Get and cache an example batch of `inputs, targets` for plotting.\"\"\"\n", + " result = getattr(self, '_example', None)\n", + " if result is None:\n", + " # No example batch was found, so get one from the dataset\n", + " result = next(iter(self.dataset))\n", + " # And cache it for next time\n", + " self._example = result\n", + " return result\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "3b376cac-1262-485b-9c58-d8971c81bd13", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = train_test_split(ts_t, train_split=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a88057c4-033b-4bb5-81bc-edd7b6781e1a", + "metadata": {}, + "outputs": [], + "source": [ + "train_w = WindowGenerator(\n", + " input_width=5, \n", + " target_width=1, \n", + " shift=1, \n", + " ts=train)\n", + "\n", + "test_w = WindowGenerator(\n", + " input_width=5, \n", + " target_width=1, \n", + " shift=1, \n", + " ts=test)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "5b2ba14d-8ab9-4b87-adf9-cd5cc06682f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 6\n", + "Input indices: [0 1 2 3 4]\n", + "Target indices: [5]\n", + "Target column name(s): None" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_w" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "0d28062b-709f-4bfd-8b6e-1259df0608e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorSpec(shape=(None, 5, 1), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_w.dataset.element_spec" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "30d1b98e-bee2-427b-9dc7-29381b15a740", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorSpec(shape=(None, 5, 1), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_w.dataset.element_spec" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "16f5c827-d04f-4caf-9e57-8846a8b6ccef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<_MapDataset element_spec=(TensorSpec(shape=(None, 5, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))>" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "dc152b42-9150-46f3-9c26-eba81aab82f1", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "def make_dataset(ts):\n", + " data = ts.pd_dataframe().to_numpy()\n", + " ds = tf.keras.utils.timeseries_dataset_from_array(\n", + " data=data,\n", + " targets=None,\n", + " sequence_length=6,\n", + " sequence_stride=1,\n", + " shuffle=False,\n", + " batch_size=32,)\n", + " return ds" + ] + }, + { + "cell_type": "markdown", + "id": "8625cae5-9e25-4629-9288-e8843df3e1b7", + "metadata": {}, + "source": [ + "Typically, data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features." + ] + }, + { + "cell_type": "markdown", + "id": "43da64a5-1660-4fbd-b9ca-7905059c47a4", + "metadata": {}, + "source": [ + "All shapes are: (batch, time, features)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "54d03b85-a8f1-4c3c-b53d-41c241c7a5bd", + "metadata": {}, + "outputs": [], + "source": [ + "ds = make_dataset(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "0a84a5fb-4cda-461e-a237-e3a2eea28318", + "metadata": {}, + "outputs": [], + "source": [ + "l = ds.take(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4de6ecde-7e3d-42eb-b65e-349f38e58b68", + "metadata": {}, + "outputs": [], + "source": [ + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e3c20b74-2dd6-4632-8865-6f38ebb21d11", + "metadata": {}, + "outputs": [], + "source": [ + "X_train_ds = make_dataset(X_train)\n", + "y_train_ds = make_dataset(y_train)\n", + "X_test_ds = make_dataset(X_test)\n", + "y_test_ds = make_dataset(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0e56ce44-b648-4195-ab6c-88a3dc31e757", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[[[0.18770352]\n", + " [0.17028235]\n", + " [0.14567316]\n", + " [0.12912521]]\n", + "\n", + " [[0.00774153]\n", + " [0. ]\n", + " [0.0388972 ]\n", + " [0.1035383 ]]\n", + "\n", + " [[0.14808738]\n", + " [0.17584604]\n", + " [0.2224158 ]\n", + " [0.22889872]]\n", + "\n", + " [[0.30970636]\n", + " [0.2859744 ]\n", + " [0.26491043]\n", + " [0.2591856 ]]\n", + "\n", + " [[0.33711928]\n", + " [0.310189 ]\n", + " [0.24795601]\n", + " [0.24181467]]\n", + "\n", + " [[0.29296172]\n", + " [0.3365885 ]\n", + " [0.37652487]\n", + " [0.40132314]]]\n", + "\n", + "\n", + " [[[0.00774153]\n", + " [0. ]\n", + " [0.0388972 ]\n", + " [0.1035383 ]]\n", + "\n", + " [[0.14808738]\n", + " [0.17584604]\n", + " [0.2224158 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" [[0.84634775]\n", + " [0.8446894 ]\n", + " [0.84923244]\n", + " [0.8388753 ]]]\n", + "\n", + "\n", + " [[[0.4156444 ]\n", + " [0.4161064 ]\n", + " [0.43091118]\n", + " [0.4790907 ]]\n", + "\n", + " [[0.51657224]\n", + " [0.5306219 ]\n", + " [0.58437765]\n", + " [0.61388934]]\n", + "\n", + " [[0.60717833]\n", + " [0.6835155 ]\n", + " [0.68071043]\n", + " [0.7439119 ]]\n", + "\n", + " [[0.82856643]\n", + " [0.83584756]\n", + " [0.83007276]\n", + " [0.80015856]]\n", + "\n", + " [[0.84634775]\n", + " [0.8446894 ]\n", + " [0.84923244]\n", + " [0.8388753 ]]\n", + "\n", + " [[0.8552274 ]\n", + " [0.8433504 ]\n", + " [0.8835554 ]\n", + " [0.86441237]]]], shape=(32, 6, 4, 1), dtype=float32)\n" + ] + } + ], + "source": [ + "for element in X_train_ds.take(1): # Just taking one sample from the dataset\n", + " print(element)" + ] + }, { "cell_type": "markdown", "id": "6bb9090a-bc1c-4a06-9b6d-ddee9ac64a9a", @@ -72,6 +1660,14 @@ "## Models" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "46cda348-38b4-4672-9f99-a0e0757f00a1", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "58cf0f06-a12e-4e60-905c-eca5f7b734f9", @@ -90,7 +1686,7 @@ "id": "8e991124-59fd-4bde-84d0-c1622e37173a", "metadata": {}, "source": [ - "## Darts models" + "### Darts models" ] }, { @@ -609,7 +2205,7 @@ "id": "2534d1df-b474-4b09-a471-a66cfa211880", "metadata": {}, "source": [ - "## Scikit-learn API compatible models" + "### Scikit-learn API compatible models" ] }, { @@ -1063,6 +2659,14 @@ "model.fit(ts)\n", "model.predict(5)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad2ca1b9-288f-4b73-b9d1-d135b01d09e6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1086,4 +2690,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb new file mode 100644 index 0000000..6f58695 --- /dev/null +++ b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb @@ -0,0 +1,360 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", + "metadata": {}, + "source": [ + "# Modelling Libraries - Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", + "metadata": {}, + "outputs": [], + "source": [ + "# Import to be able to import python package from src\n", + "import sys\n", + "sys.path.insert(0, '../src')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import ontime as on\n", + "from darts.datasets import EnergyDataset" + ] + }, + { + "cell_type": "markdown", + "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", + "metadata": {}, + "source": [ + "---\n", + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9a96d79-0423-4d79-b01d-726193216238", + "metadata": {}, + "outputs": [], + "source": [ + "ts = EnergyDataset().load()\n", + "ts = ts.astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "1d4bec6b-eedb-4a88-ba68-dbeae5f0644e", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "b7ab9b51-6c63-4068-ac53-98790bf55fde", + "metadata": {}, + "source": [ + "- [x] Normalize\n", + "- [x] Split train, test, val\n", + "- [ ] Feature engineering\n", + " - add weather for location\n", + " - add day of the week, month, year, etc.\n", + " - add whatever\n", + "- [x] Windowing\n", + "- [x] Windowing - Split (parts to train as X, parts to predict as y)\n", + "- [x] Windowing - to tf.data.Dataset\n", + "- [ ] Windowing - to Pytorch DataLoaders" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from darts.dataprocessing.transformers import Scaler\n", + "\n", + "def normalize(ts: on.TimeSeries, type='minmax', return_transformer=False):\n", + " match type:\n", + " case 'minmax':\n", + " scaler = MinMaxScaler()\n", + " case 'zscore':\n", + " scaler = StandardScaler()\n", + " transformer = Scaler(scaler)\n", + " ts_transformed = transformer.fit_transform(ts)\n", + " if return_transformer:\n", + " return ts_transformed, transformer\n", + " else:\n", + " return ts_transformed" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "de144fa1-d419-46ae-9da1-102db4da92bb", + "metadata": {}, + "outputs": [], + "source": [ + "def train_test_split(ts: on.TimeSeries, test_split=None, train_split=None) -> tuple:\n", + " \"\"\"\n", + " Description\n", + " \n", + " :param ts: TimeSeries to split\n", + " :param test_split: float, int or pd.TimeStamp\n", + " :param train_split: float, int or pd.TimeStamp\n", + " \"\"\"\n", + " \n", + " if train_split is not None and test_split is not None:\n", + " raise Exception('Only one of those two parameters can be set : train_split, test_split.')\n", + "\n", + " if train_split is None and test_split is None:\n", + " test_split = 0.25\n", + " \n", + " # split ts in subts : train, test\n", + " if test_split is not None: \n", + " train_set, test_set = ts.split_after(1-test_split)\n", + " \n", + " if train_split is not None:\n", + " train_set, test_set = ts.split_after(train_split)\n", + "\n", + " return train_set, test_set" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9a297972-1588-4539-8168-05ec379c794d", + "metadata": {}, + "outputs": [], + "source": [ + "def split_by_n(ts, n, drop_last=True):\n", + "\n", + " # Get DataFrame\n", + " df = ts.pd_dataframe()\n", + " \n", + " # Calculate the total number of splits needed\n", + " total_splits = -(-len(df) // n) # Ceiling division to get the number of parts\n", + " \n", + " # Initialize a list to hold the DataFrame splits\n", + " splits_df = []\n", + " \n", + " # Loop through the DataFrame and split it\n", + " for split in range(total_splits):\n", + " start_index = split * n\n", + " end_index = start_index + n\n", + " # Append the part to the list, using slicing with .iloc\n", + " splits_df.append(df.iloc[start_index:end_index])\n", + "\n", + " # If the last dataframe has a different length, then drop it.\n", + " if drop_last:\n", + " last_df = splits_df[-1]\n", + " second_last = splits_df[-2] \n", + " if len(last_df) != len(second_last):\n", + " splits_df = splits_df[:-1]\n", + "\n", + " # Change the data sctructure from DataFrame to TimeSeries\n", + " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9614843a-70c2-4213-8d03-e2df030236c1", + "metadata": {}, + "outputs": [], + "source": [ + "def split_inputs_from_targets(ts_list, input_len, target_len):\n", + "\n", + " # Change inner data structure to DataFrame\n", + " dfs = [ts.pd_dataframe() for ts in ts_list]\n", + "\n", + " # Create initial arrays\n", + " input_series_list = []\n", + " target_series_list = []\n", + " \n", + " # Iterate over each DataFrame in the list\n", + " for df in dfs:\n", + " # Check if the DataFrame is large enough to accommodate input_len and label_len\n", + " if len(df) >= input_len + target_len:\n", + " # Get the first input_len items\n", + " input_series = df.iloc[:input_len]\n", + " input_series_list.append(input_series)\n", + " \n", + " # Get the last label_len items\n", + " target_series = df.iloc[-target_len:]\n", + " target_series_list.append(target_series)\n", + " else:\n", + " raise Exception('input_len + label_len is longer that the total length of the DataFrame')\n", + "\n", + " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", + " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", + " \n", + " return input_ts_list, target_ts_list" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", + "metadata": {}, + "outputs": [], + "source": [ + "def to_numpy(ts_list):\n", + " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " + ] + }, + { + "cell_type": "markdown", + "id": "9b508ee5-7c7e-4793-904e-45a40df354db", + "metadata": {}, + "source": [ + "### Test with common functions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a4b12f07-8a97-403a-a554-89e166574120", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:479: RuntimeWarning: All-NaN slice encountered\n", + " data_min = np.nanmin(X, axis=0)\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:480: RuntimeWarning: All-NaN slice encountered\n", + " data_max = np.nanmax(X, axis=0)\n" + ] + } + ], + "source": [ + "ts_t = normalize(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "84301c56-5e2f-4eea-ad98-a7d0b89c039c", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = train_test_split(ts_t, train_split=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "46e3a480-390f-446e-ab08-824f95467ddd", + "metadata": {}, + "outputs": [], + "source": [ + "train_list = split_by_n(train, 6)\n", + "test_list = split_by_n(test, 6)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a45e871d-ba2b-4de6-93bc-baf9b26104ec", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, y_train = split_inputs_from_targets(train_list, 4, 2)\n", + "X_test, y_test = split_inputs_from_targets(test_list, 4, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a0bc351b-9789-4f0c-914d-6e94d160e613", + "metadata": {}, + "outputs": [], + "source": [ + "X_train = to_numpy(X_train)\n", + "y_train = to_numpy(y_train)\n", + "X_test = to_numpy(X_test)\n", + "y_test = to_numpy(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0ef9e79a-7c69-446b-a31a-cac8ebce99de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4675, 4, 28)\n", + "(4675, 2, 28)\n", + "(1168, 4, 28)\n", + "(1168, 2, 28)\n" + ] + } + ], + "source": [ + "print(X_train.shape)\n", + "print(y_train.shape)\n", + "print(X_test.shape)\n", + "print(y_test.shape)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb b/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb new file mode 100644 index 0000000..9278d2d --- /dev/null +++ b/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb @@ -0,0 +1,1698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", + "metadata": {}, + "source": [ + "# Modelling Libraries - Tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", + "metadata": {}, + "outputs": [], + "source": [ + "# Import to be able to import python package from src\n", + "import sys\n", + "sys.path.insert(0, '../src')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import ontime as on\n", + "from darts.datasets import EnergyDataset" + ] + }, + { + "cell_type": "markdown", + "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", + "metadata": {}, + "source": [ + "---\n", + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "e9a96d79-0423-4d79-b01d-726193216238", + "metadata": {}, + "outputs": [], + "source": [ + "ts = EnergyDataset().load()\n", + "ts = ts.astype(np.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "1d4bec6b-eedb-4a88-ba68-dbeae5f0644e", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "markdown", + "id": "b7ab9b51-6c63-4068-ac53-98790bf55fde", + "metadata": {}, + "source": [ + "- [x] Normalize\n", + "- [x] Split train, test, val\n", + "- [ ] Feature engineering\n", + " - add weather for location\n", + " - add day of the week, month, year, etc.\n", + " - add whatever\n", + "- [x] Windowing\n", + "- [x] Windowing - Split (parts to train as X, parts to predict as y)\n", + "- [ ] Windowing - to tf.data.Dataset\n", + "- [ ] Windowing - to Pytorch DataLoaders" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", + "from darts.dataprocessing.transformers import Scaler\n", + "\n", + "def normalize(ts: on.TimeSeries, type='minmax', return_transformer=False):\n", + " match type:\n", + " case 'minmax':\n", + " scaler = MinMaxScaler()\n", + " case 'zscore':\n", + " scaler = StandardScaler()\n", + " transformer = Scaler(scaler)\n", + " ts_transformed = transformer.fit_transform(ts)\n", + " if return_transformer:\n", + " return ts_transformed, transformer\n", + " else:\n", + " return ts_transformed" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "de144fa1-d419-46ae-9da1-102db4da92bb", + "metadata": {}, + "outputs": [], + "source": [ + "def train_test_split(ts: on.TimeSeries, test_split=None, train_split=None) -> tuple:\n", + " \"\"\"\n", + " Description\n", + " \n", + " :param ts: TimeSeries to split\n", + " :param test_split: float, int or pd.TimeStamp\n", + " :param train_split: float, int or pd.TimeStamp\n", + " \"\"\"\n", + " \n", + " if train_split is not None and test_split is not None:\n", + " raise Exception('Only one of those two parameters can be set : train_split, test_split.')\n", + "\n", + " if train_split is None and test_split is None:\n", + " test_split = 0.25\n", + " \n", + " # split ts in subts : train, test\n", + " if test_split is not None: \n", + " train_set, test_set = ts.split_after(1-test_split)\n", + " \n", + " if train_split is not None:\n", + " train_set, test_set = ts.split_after(train_split)\n", + "\n", + " return train_set, test_set" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "9a297972-1588-4539-8168-05ec379c794d", + "metadata": {}, + "outputs": [], + "source": [ + "def split_by_n(ts, n, drop_last=True):\n", + "\n", + " # Get DataFrame\n", + " df = ts.pd_dataframe()\n", + " \n", + " # Calculate the total number of splits needed\n", + " total_splits = -(-len(df) // n) # Ceiling division to get the number of parts\n", + " \n", + " # Initialize a list to hold the DataFrame splits\n", + " splits_df = []\n", + " \n", + " # Loop through the DataFrame and split it\n", + " for split in range(total_splits):\n", + " start_index = split * n\n", + " end_index = start_index + n\n", + " # Append the part to the list, using slicing with .iloc\n", + " splits_df.append(df.iloc[start_index:end_index])\n", + "\n", + " # If the last dataframe has a different length, then drop it.\n", + " if drop_last:\n", + " last_df = splits_df[-1]\n", + " second_last = splits_df[-2] \n", + " if len(last_df) != len(second_last):\n", + " splits_df = splits_df[:-1]\n", + "\n", + " # Change the data sctructure from DataFrame to TimeSeries\n", + " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "9614843a-70c2-4213-8d03-e2df030236c1", + "metadata": {}, + "outputs": [], + "source": [ + "def split_inputs_from_targets(ts_list, input_len, target_len):\n", + "\n", + " # Change inner data structure to DataFrame\n", + " dfs = [ts.pd_dataframe() for ts in ts_list]\n", + "\n", + " # Create initial arrays\n", + " input_series_list = []\n", + " target_series_list = []\n", + " \n", + " # Iterate over each DataFrame in the list\n", + " for df in dfs:\n", + " # Check if the DataFrame is large enough to accommodate input_len and label_len\n", + " if len(df) >= input_len + target_len:\n", + " # Get the first input_len items\n", + " input_series = df.iloc[:input_len]\n", + " input_series_list.append(input_series)\n", + " \n", + " # Get the last label_len items\n", + " target_series = df.iloc[-target_len:]\n", + " target_series_list.append(target_series)\n", + " else:\n", + " raise Exception('input_len + label_len is longer that the total length of the DataFrame')\n", + "\n", + " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", + " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", + " \n", + " return input_ts_list, target_ts_list" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", + "metadata": {}, + "outputs": [], + "source": [ + "def to_numpy(ts_list):\n", + " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "312a3eb7-162f-4d7e-a68e-78b6d6842493", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "\n", + "class WindowGenerator:\n", + " def __init__(self, input_width, target_width, offset, ts, target_columns=None):\n", + " # Store the raw data.\n", + " self.ts = ts\n", + " self.df = ts.pd_dataframe()\n", + "\n", + " # Work out the target column indices.\n", + " self.target_columns = target_columns\n", + " if target_columns is not None:\n", + " self.target_columns_indices = {name: i for i, name in\n", + " enumerate(target_columns)}\n", + " self.column_indices = {name: i for i, name in\n", + " enumerate(self.df.columns)}\n", + "\n", + " # Work out the window parameters.\n", + " self.input_width = input_width\n", + " self.target_width = target_width\n", + " self.offset = offset\n", + "\n", + " self.total_window_size = input_width + offset\n", + "\n", + " self.input_slice = slice(0, input_width)\n", + " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", + "\n", + " self.target_start = self.total_window_size - self.target_width\n", + " self.targets_slice = slice(self.target_start, None)\n", + " self.target_indices = np.arange(self.total_window_size)[self.targets_slice]\n", + "\n", + " def __repr__(self):\n", + " return '\\n'.join([\n", + " f'Total window size: {self.total_window_size}',\n", + " f'Input indices: {self.input_indices}',\n", + " f'Target indices: {self.target_indices}',\n", + " f'Target column name(s): {self.target_columns}'])\n", + "\n", + " def split_window(self, features):\n", + " inputs = features[:, self.input_slice, :]\n", + " targets = features[:, self.targets_slice, :]\n", + " if self.target_columns is not None:\n", + " targets = tf.stack(\n", + " [targets[:, :, self.column_indices[name]] for name in self.target_columns],\n", + " axis=-1)\n", + "\n", + " # Slicing doesn't preserve static shape information, so set the shapes\n", + " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", + " inputs.set_shape([None, self.input_width, None])\n", + " targets.set_shape([None, self.target_width, None])\n", + "\n", + " return inputs, targets\n", + "\n", + " def make_dataset(self, data):\n", + " data = np.array(data, dtype=np.float32)\n", + " ds = tf.keras.utils.timeseries_dataset_from_array(\n", + " data=data,\n", + " targets=None,\n", + " sequence_length=self.total_window_size,\n", + " sequence_stride=1,\n", + " shuffle=True,\n", + " batch_size=32,)\n", + " return ds.map(self.split_window)\n", + "\n", + " @property\n", + " def dataset(self):\n", + " return self.make_dataset(self.df)\n", + "\n", + " @property\n", + " def example(self):\n", + " \"\"\"Get and cache an example batch of `inputs, targets` for plotting.\"\"\"\n", + " result = getattr(self, '_example', None)\n", + " if result is None:\n", + " # No example batch was found, so get one from the dataset\n", + " result = next(iter(self.dataset))\n", + " # And cache it for next time\n", + " self._example = result\n", + " return result\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "68e883a6-a762-4a81-bf1c-6bb20a4c157c", + "metadata": {}, + "source": [ + "### Test with WindowGenerator" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "dde4ea44-58ad-4f5f-8d0b-773f431d232f", + "metadata": {}, + "outputs": [], + "source": [ + "df = ts.pd_dataframe()\n", + "df = df.interpolate()\n", + "ts = on.TimeSeries.from_dataframe(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "19717f00-b1d5-4ba2-8b07-6feed1a30659", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:479: RuntimeWarning: All-NaN slice encountered\n", + " data_min = np.nanmin(X, axis=0)\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:480: RuntimeWarning: All-NaN slice encountered\n", + " data_max = np.nanmax(X, axis=0)\n" + ] + } + ], + "source": [ + "ts_t = normalize(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "3b376cac-1262-485b-9c58-d8971c81bd13", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = train_test_split(ts_t, train_split=0.8)\n", + "train, val = train_test_split(train, train_split=0.8)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "a88057c4-033b-4bb5-81bc-edd7b6781e1a", + "metadata": {}, + "outputs": [], + "source": [ + "target_columns = ['generation solar']\n", + "\n", + "train_window = WindowGenerator(\n", + " input_width=5, \n", + " target_width=1, \n", + " offset=1, \n", + " target_columns=target_columns,\n", + " ts=train)\n", + "\n", + "val_window = WindowGenerator(\n", + " input_width=5, \n", + " target_width=1, \n", + " offset=1, \n", + " target_columns=target_columns,\n", + " ts=val)\n", + "\n", + "test_window = WindowGenerator(\n", + " input_width=5, \n", + " target_width=1, \n", + " offset=1, \n", + " target_columns=target_columns,\n", + " ts=test)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "1f8d9be9-5c31-4676-8682-74c482b6b592", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 6\n", + "Input indices: [0 1 2 3 4]\n", + "Target indices: [5]\n", + "Target column name(s): ['generation solar']" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_window" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "0d28062b-709f-4bfd-8b6e-1259df0608e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorSpec(shape=(None, 5, 28), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_window.dataset.element_spec" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "30d1b98e-bee2-427b-9dc7-29381b15a740", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorSpec(shape=(None, 5, 28), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_window.dataset.element_spec" + ] + }, + { + "cell_type": "markdown", + "id": "85351a17-2601-4265-b397-817d0c8c02cd", + "metadata": {}, + "source": [ + "## TensorFlow Modelling" + ] + }, + { + "cell_type": "markdown", + "id": "6444e27d-c2c8-4d96-abd9-056675c2a829", + "metadata": {}, + "source": [ + "### Define data" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "0789d98b-1a85-4e6d-852e-92b83967f78e", + "metadata": {}, + "outputs": [], + "source": [ + "dataset = {\n", + " 'train': train_window.dataset,\n", + " 'val': val_window.dataset,\n", + " 'test': test_window.dataset,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "b82bd723-29b8-422f-ab05-444417125b74", + "metadata": {}, + "source": [ + "### Define model" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "45c410da-7ea6-4f07-a18f-6539854904fc", + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Dense(units=64, activation='relu'),\n", + " tf.keras.layers.Dense(units=64, activation='relu'),\n", + " tf.keras.layers.Dense(units=1)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "d6705888-c015-4247-a43d-60f88441c736", + "metadata": {}, + "source": [ + "### Training" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "5bd01720-b468-453a-8769-0080d787a336", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "absl WARNING At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "702/702 [==============================] - 1s 827us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 2/20\n", + "702/702 [==============================] - 1s 730us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 3/20\n", + "702/702 [==============================] - 1s 731us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 4/20\n", + "702/702 [==============================] - 1s 746us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 5/20\n", + "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 6/20\n", + "702/702 [==============================] - 1s 771us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 7/20\n", + "702/702 [==============================] - 1s 758us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 8/20\n", + "702/702 [==============================] - 1s 729us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 9/20\n", + "702/702 [==============================] - 1s 737us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 10/20\n", + "702/702 [==============================] - 1s 765us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 11/20\n", + "702/702 [==============================] - 1s 750us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 12/20\n", + "702/702 [==============================] - 1s 779us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 13/20\n", + "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 14/20\n", + "702/702 [==============================] - 1s 734us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 15/20\n", + "702/702 [==============================] - 1s 731us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 16/20\n", + "702/702 [==============================] - 1s 753us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 17/20\n", + "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 18/20\n", + "702/702 [==============================] - 1s 812us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 19/20\n", + "702/702 [==============================] - 1s 769us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "Epoch 20/20\n", + "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n" + ] + } + ], + "source": [ + "MAX_EPOCHS = 20\n", + "\n", + "early_stopping = tf.keras.callbacks.EarlyStopping(\n", + " monitor='val_loss',\n", + " patience=2,\n", + " mode='min'\n", + ")\n", + "\n", + "model.compile(\n", + " loss=tf.keras.losses.MeanSquaredError(),\n", + " optimizer=tf.keras.optimizers.Adam(),\n", + " metrics=[tf.keras.metrics.MeanAbsoluteError()]\n", + ")\n", + "\n", + "history = model.fit(\n", + " dataset['train'], \n", + " epochs=MAX_EPOCHS,\n", + " validation_data=dataset['val'],\n", + " #callbacks=[early_stopping]\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "36ffc316-1e9c-468f-97f3-32df5a302f58", + "metadata": {}, + "source": [ + "### Evaluate" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "81b8a1a9-95dc-4266-8213-f4e6b9f61108", + "metadata": {}, + "outputs": [], + "source": [ + "performance = model.evaluate(dataset['test'], verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "af94a073-55af-454e-a8e1-ce919ce37365", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[nan, nan]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "performance" + ] + }, + { + "cell_type": "markdown", + "id": "6bb9090a-bc1c-4a06-9b6d-ddee9ac64a9a", + "metadata": {}, + "source": [ + "---\n", + "## Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e17ddc34-ed99-4946-ba23-c781b1eab631", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46cda348-38b4-4672-9f99-a0e0757f00a1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "58cf0f06-a12e-4e60-905c-eca5f7b734f9", + "metadata": {}, + "source": [ + "- [x] Darts\n", + "- [x] Scikit-learn API compatible regressor\n", + "- [ ] GluonTS\n", + "- [ ] Kats\n", + "- [ ] Custom PyTorch\n", + "- [ ] Custom TensorFlow" + ] + }, + { + "cell_type": "markdown", + "id": "8e991124-59fd-4bde-84d0-c1622e37173a", + "metadata": {}, + "source": [ + "### Darts models" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a1b679c1-4334-4d10-9ef1-019e81a36b90", + "metadata": {}, + "outputs": [], + "source": [ + "from darts.models import BlockRNNModel" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eaec176b-c27c-4f8b-a4b1-967c258bd944", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "darts.models.forecasting.torch_forecasting_model INFO Train dataset contains 348 samples.\n", + "darts.models.forecasting.torch_forecasting_model INFO Time series values are 32-bits; casting model to float32.\n", + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------------------\n", + "0 | criterion | MSELoss | 0 \n", + "1 | train_metrics | MetricCollection | 0 \n", + "2 | val_metrics | MetricCollection | 0 \n", + "3 | rnn | RNN | 2.0 K \n", + "4 | fc | Sequential | 156 \n", + "---------------------------------------------------\n", + "2.2 K Trainable params\n", + "0 Non-trainable params\n", + "2.2 K Total params\n", + "0.009 Total estimated model params size (MB)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.29it/s, train_loss=4.480]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`Trainer.fit` stopped: `max_epochs=50` reached.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.16it/s, train_loss=4.480]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: True (mps), used: True\n", + "TPU available: False, using: 0 TPU cores\n", + "IPU available: False, using: 0 IPUs\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicting DataLoader 0: 100%|██████████████████████████████████| 1/1 [00:00<00:00, 124.78it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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\n", + " View on TensorFlow.org\n", + " \n", + " Run in Google Colab\n", + " \n", + " View source on GitHub\n", + " \n", + " Download notebook\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GU8C5qm_4vZb" + }, + "source": [ + "This tutorial is an introduction to time series forecasting using TensorFlow. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs).\n", + "\n", + "This is covered in two main parts, with subsections: \n", + "\n", + "* Forecast for a single time step:\n", + " * A single feature.\n", + " * All features.\n", + "* Forecast multiple steps:\n", + " * Single-shot: Make the predictions all at once.\n", + " * Autoregressive: Make one prediction at a time and feed the output back to the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XVhK72Pu1cJL" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "7rZnJaGTWQw0" + }, + "outputs": [], + "source": [ + "import os\n", + "import datetime\n", + "\n", + "import IPython\n", + "import IPython.display\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "\n", + "mpl.rcParams['figure.figsize'] = (8, 6)\n", + "mpl.rcParams['axes.grid'] = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TokBlnUhWFw9" + }, + "source": [ + "## The weather dataset\n", + "\n", + "This tutorial uses a weather time series dataset recorded by the Max Planck Institute for Biogeochemistry.\n", + "\n", + "This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in 2003. For efficiency, you will use only the data collected between 2009 and 2016. This section of the dataset was prepared by François Chollet for his book Deep Learning with Python." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "xyv_i85IWInT" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip\n", + "13568290/13568290 [==============================] - 1s 0us/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 5103616/13568290 [==========>...................] - ETA: 0s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 8396800/13568290 [=================>............] - ETA: 0s" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "13568290/13568290 [==============================] - 0s 0us/step\n" + ] + } + ], + "source": [ + "zip_path = tf.keras.utils.get_file(\n", + " origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n", + " fname='jena_climate_2009_2016.csv.zip',\n", + " extract=True)\n", + "csv_path, _ = os.path.splitext(zip_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R81Wx8WP4c3G" + }, + "source": [ + "This tutorial will just deal with **hourly predictions**, so start by sub-sampling the data from 10-minute intervals to one-hour intervals:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "TX6uGeeeWIkG" + }, + "outputs": [], + "source": [ + "df = pd.read_csv(csv_path)\n", + "# Slice [start:stop:step], starting from index 5 take every 6th record.\n", + "df = df[5::6]\n", + "\n", + "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VdbOWXiTWM2T" + }, + "source": [ + "Let's take a glance at the data. Here are the first few rows:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "ojHE-iCCWIhz" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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p (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wd (deg)
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" + ], + "text/plain": [ + " p (mbar) T (degC) Tpot (K) Tdew (degC) rh (%) VPmax (mbar) \\\n", + "5 996.50 -8.05 265.38 -8.78 94.4 3.33 \n", + "11 996.62 -8.88 264.54 -9.77 93.2 3.12 \n", + "17 996.84 -8.81 264.59 -9.66 93.5 3.13 \n", + "23 996.99 -9.05 264.34 -10.02 92.6 3.07 \n", + "29 997.46 -9.63 263.72 -10.65 92.2 2.94 \n", + "\n", + " VPact (mbar) VPdef (mbar) sh (g/kg) H2OC (mmol/mol) rho (g/m**3) \\\n", + "5 3.14 0.19 1.96 3.15 1307.86 \n", + "11 2.90 0.21 1.81 2.91 1312.25 \n", + "17 2.93 0.20 1.83 2.94 1312.18 \n", + "23 2.85 0.23 1.78 2.85 1313.61 \n", + "29 2.71 0.23 1.69 2.71 1317.19 \n", + "\n", + " wv (m/s) max. wv (m/s) wd (deg) \n", + "5 0.21 0.63 192.7 \n", + "11 0.25 0.63 190.3 \n", + "17 0.18 0.63 167.2 \n", + "23 0.10 0.38 240.0 \n", + "29 0.40 0.88 157.0 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WRzj1inMfgcO" + }, + "source": [ + "Here is the evolution of a few features over time:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "Vg5XIc5tfNlG" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_cols = ['T (degC)', 'p (mbar)', 'rho (g/m**3)']\n", + "plot_features = df[plot_cols]\n", + "plot_features.index = date_time\n", + "_ = plot_features.plot(subplots=True)\n", + "\n", + "plot_features = df[plot_cols][:480]\n", + "plot_features.index = date_time[:480]\n", + "_ = plot_features.plot(subplots=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wXWLG0_WBhZS" + }, + "source": [ + "### Inspect and cleanup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yhmZXJew6GlS" + }, + "source": [ + "Next, look at the statistics of the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "h510pgKVrrai" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
p (mbar)70091.0989.2128428.358886913.60984.20989.57994.7201015.29
T (degC)70091.09.4504828.423384-22.763.359.4115.48037.28
Tpot (K)70091.0283.4930868.504424250.85277.44283.46289.530311.21
Tdew (degC)70091.04.9564716.730081-24.800.245.2110.08023.06
rh (%)70091.076.00978816.47492013.8865.2179.3089.400100.00
VPmax (mbar)70091.013.5765767.7398830.977.7711.8217.61063.77
VPact (mbar)70091.09.5339684.1836580.816.228.8612.36028.25
VPdef (mbar)70091.04.0425364.8985490.000.872.195.30046.01
sh (g/kg)70091.06.0225602.6558120.513.925.597.80018.07
H2OC (mmol/mol)70091.09.6404374.2348620.816.298.9612.49028.74
rho (g/m**3)70091.01216.06123239.9742631059.451187.471213.801242.7651393.54
wv (m/s)70091.01.70256765.447512-9999.000.991.762.86014.01
max. wv (m/s)70091.02.96304175.597657-9999.001.762.984.74023.50
wd (deg)70091.0174.78909586.6194310.00125.30198.10234.000360.00
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% 50% \\\n", + "p (mbar) 70091.0 989.212842 8.358886 913.60 984.20 989.57 \n", + "T (degC) 70091.0 9.450482 8.423384 -22.76 3.35 9.41 \n", + "Tpot (K) 70091.0 283.493086 8.504424 250.85 277.44 283.46 \n", + "Tdew (degC) 70091.0 4.956471 6.730081 -24.80 0.24 5.21 \n", + "rh (%) 70091.0 76.009788 16.474920 13.88 65.21 79.30 \n", + "VPmax (mbar) 70091.0 13.576576 7.739883 0.97 7.77 11.82 \n", + "VPact (mbar) 70091.0 9.533968 4.183658 0.81 6.22 8.86 \n", + "VPdef (mbar) 70091.0 4.042536 4.898549 0.00 0.87 2.19 \n", + "sh (g/kg) 70091.0 6.022560 2.655812 0.51 3.92 5.59 \n", + "H2OC (mmol/mol) 70091.0 9.640437 4.234862 0.81 6.29 8.96 \n", + "rho (g/m**3) 70091.0 1216.061232 39.974263 1059.45 1187.47 1213.80 \n", + "wv (m/s) 70091.0 1.702567 65.447512 -9999.00 0.99 1.76 \n", + "max. wv (m/s) 70091.0 2.963041 75.597657 -9999.00 1.76 2.98 \n", + "wd (deg) 70091.0 174.789095 86.619431 0.00 125.30 198.10 \n", + "\n", + " 75% max \n", + "p (mbar) 994.720 1015.29 \n", + "T (degC) 15.480 37.28 \n", + "Tpot (K) 289.530 311.21 \n", + "Tdew (degC) 10.080 23.06 \n", + "rh (%) 89.400 100.00 \n", + "VPmax (mbar) 17.610 63.77 \n", + "VPact (mbar) 12.360 28.25 \n", + "VPdef (mbar) 5.300 46.01 \n", + "sh (g/kg) 7.800 18.07 \n", + "H2OC (mmol/mol) 12.490 28.74 \n", + "rho (g/m**3) 1242.765 1393.54 \n", + "wv (m/s) 2.860 14.01 \n", + "max. wv (m/s) 4.740 23.50 \n", + "wd (deg) 234.000 360.00 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe().transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TzOTnWOoWMGK" + }, + "source": [ + "#### Wind velocity" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i47LiW5DCVsP" + }, + "source": [ + "One thing that should stand out is the `min` value of the wind velocity (`wv (m/s)`) and the maximum value (`max. wv (m/s)`) columns. This `-9999` is likely erroneous.\n", + "\n", + "There's a separate wind direction column, so the velocity should be greater than zero (`>=0`). Replace it with zeros:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "qFOq0_80vF4d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wv = df['wv (m/s)']\n", + "bad_wv = wv == -9999.0\n", + "wv[bad_wv] = 0.0\n", + "\n", + "max_wv = df['max. wv (m/s)']\n", + "bad_max_wv = max_wv == -9999.0\n", + "max_wv[bad_max_wv] = 0.0\n", + "\n", + "# The above inplace edits are reflected in the DataFrame.\n", + "df['wv (m/s)'].min()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vtmu2IBPgPG8" + }, + "source": [ + "### Feature engineering\n", + "\n", + "Before diving in to build a model, it's important to understand your data and be sure that you're passing the model appropriately formatted data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FYyEaqiD6j4s" + }, + "source": [ + "#### Wind\n", + "The last column of the data, `wd (deg)`—gives the wind direction in units of degrees. Angles do not make good model inputs: 360° and 0° should be close to each other and wrap around smoothly. Direction shouldn't matter if the wind is not blowing.\n", + "\n", + "Right now the distribution of wind data looks like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "YO7JGTcWQG2z" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Wind Velocity [m/s]')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(df['wd (deg)'], df['wv (m/s)'], bins=(50, 50), vmax=400)\n", + "plt.colorbar()\n", + "plt.xlabel('Wind Direction [deg]')\n", + "plt.ylabel('Wind Velocity [m/s]')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yWnf5dwMU1_g" + }, + "source": [ + "But this will be easier for the model to interpret if you convert the wind direction and velocity columns to a wind **vector**:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "6GmSTHXw6lI1" + }, + "outputs": [], + "source": [ + "wv = df.pop('wv (m/s)')\n", + "max_wv = df.pop('max. wv (m/s)')\n", + "\n", + "# Convert to radians.\n", + "wd_rad = df.pop('wd (deg)')*np.pi / 180\n", + "\n", + "# Calculate the wind x and y components.\n", + "df['Wx'] = wv*np.cos(wd_rad)\n", + "df['Wy'] = wv*np.sin(wd_rad)\n", + "\n", + "# Calculate the max wind x and y components.\n", + "df['max Wx'] = max_wv*np.cos(wd_rad)\n", + "df['max Wy'] = max_wv*np.sin(wd_rad)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7iI0zDoxWDyB" + }, + "source": [ + "The distribution of wind vectors is much simpler for the model to correctly interpret:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "bMgCG5o2SYKD" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-11.305513973134667, 8.24469928549079, -8.27438540335515, 7.7338312955467785)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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XHH7ZglFQDh63bNmi559/Xl27dg10VwAAABqVoBs8Hj9+XCNGjNCf//xnNW3a9LyxFRUVKi0t9doAAABqy22dmTRj3xbos7owQTd4zM7O1tChQzVgwACfsbNmzVJ8fLxny8jIqIMeAgAANFxBNWHm5Zdf1ocffqgtW7YYxefk5GjSpEmer0tLSxlAAgCAWnP7YcKM3e3VlaAZPBYWFur+++/XqlWrFBlpUFNNUkREhCIiDGuXAQAAwKegGTzm5+eruLhYl19+uWefy+XSu+++qz/+8Y+qqKhQiM1FcwEAACTJLYfcNs+Otru9uhI0g8drr71W27dv99o3duxYdejQQVOmTGHgCFwMh+GtE9MVLUzbCxCH0+wXtuW273OxDJ+MdxjeLbEqq8zaM/3dGGr258ARHWXWXpVZ/2SZfS7u42VGcY5Is8/PYbiyjfvECYPG7P1+N71mpt9Txlixpt6bP3++5s+fry+//FKSdNlll2natGkaMmSIJKm8vFwPPfSQXn75ZVVUVGjQoEGaN2+eUlJSPG3s27dPEyZM0Lp16xQTE6PRo0dr1qxZCjX8HSAF0eAxNjZWnTt39toXHR2tZs2anbUfAADATi7LIZfNywnWtr0WLVroySef1KWXXirLsvTiiy9q2LBh+uijj3TZZZfpwQcf1FtvvaVXXnlF8fHxmjhxom6++Wa9//77p47ncmno0KFKTU3Vxo0bdejQIY0aNUphYWF64oknjPsRNINHAACAQKkPE2ZuvPFGr68ff/xxzZ8/X5s2bVKLFi20cOFCLVmyRP3795ckLVq0SB07dtSmTZt01VVX6Z133tEnn3yi1atXKyUlRd27d9djjz2mKVOmaMaMGQoPDzfqR/2+t+RDbm6u5syZE+huAAAAXLDv16SuqKjw+R6Xy6WXX35ZZWVlyszMVH5+vqqqqrxKGXbo0EEtW7ZUXl6eJCkvL09dunTxuo09aNAglZaWaufOncb9DerBIwAAQF1wy+4C4Wcm4GRkZHjVpZ41a1aN/di+fbtiYmIUERGhu+++W0uXLlWnTp1UVFSk8PBwJSQkeMWnpKSoqKhIklRUVOQ1cDz9+unXTHHbGgAAIIAKCwsVFxfn+fp8ZQbbt2+vbdu2qaSkRP/4xz80evRorV+/vi666cHgEQAAwAfLD6V6rP+0FxcX5zV4PJ/w8HBdcsklkqSePXtqy5Yt+sMf/qBbb71VlZWVOnr0qFf28fDhw0pNTZUkpaam6oMPPvBq7/Dhw57XTHHbGgAAIEi53W5VVFSoZ8+eCgsL05o1azyvFRQUaN++fcrMzJQkZWZmavv27SouLvbErFq1SnFxcerUqZPxMck8AgAA+HD6OUW726yNnJwcDRkyRC1bttSxY8e0ZMkS5ebmauXKlYqPj9e4ceM0adIkJSYmKi4uTvfee68yMzN11VVXSZIGDhyoTp06aeTIkXrqqadUVFSkqVOnKjs7u1Yr8jF4BGDO7uLfhkWJnRFmS5K6KysvpjdnMS3W7K6q9hnjNCyBYcoRHmYU54yNMWuwSROzOINZoJIkw2LdKjdrz5nUzKw9w8LZlknxb5kXbTdi+O1p+n1nOuww/bkwLk7uchkeGXYrLi7WqFGjdOjQIcXHx6tr165auXKlrrvuOknSM888I6fTqeHDh3sVCT8tJCREy5Yt04QJE5SZmano6GiNHj1aM2fOrFU/GDwCAAD4UB/qPC5cuPC8r0dGRmru3LmaO3dujTGtWrXS8uXLa3Xc72PwCAAA4EN9uG1dXzBhBgAAAMbIPAIAAPjg9kOpHrvbqytkHgEAAGCMzCMAAIAPPPN4BplHAAAAGCPzCAAA4AOZxzMYPAINWYCKehsf1zDO7qLEjlCzAtumxw2Jib6Y7nhzmn0mDtMi3KFmv+atGLNC7I5Is2LnlsPsj6LDaVaYWmGGf65Kj5kd187i3y7DnwvTOKfhgMKwILopy+b20HAxeAQAAPCBzOMZDB4BAAB8YPB4BhNmAAAAYIzMIwAAgA+W7C/qHaxPmZJ5BAAAgDEyjwAAAD7wzOMZZB4BAABgjMwjAACAD2Qez2DwCDRkhkW9neFmhZ/dVdVGcQ7TIsc2FzEPiY4yijMuOm4YZtKe07CQuHXipNlBDc9VhufqNiz+bcoVbVaIXY4Yo7CQ41VGcc4Isz9rjq++NYozaivc7FwdpgXbKyuN4twnThjFGTNdBACNHoNHAAAAH8g8nsHgEQAAwAcGj2cwYQYAAADGyDwCAAD4YFkOWTZnCu1ur66QeQQAAIAxMo8AAAA+uOWwfXlCu9urK2QeAQAAYIzMIwAAgA/Mtj6DzCMAAACMkXkEGjBHSIhRnOnKMcbHNVyxxjI8rjMy4mK6cxbTz8UZZbhijcnKHJZl1JYjPs4oThVmq5BYzeKN4lwxZqukVMabxbnNPmK5w8wyL+FNzBqM+MbsuM7kRKM4R6XB92iZ4apAJ8uNwtyGcZbb7HvKeOUY0xWfGulKNMy2PoPMIwAAAIyReQQAAPCBZx7PYPAIAADgA7etz+C2NQAAAIyReQQAAPDB8sNtazKPAAAAaPDIPAIAAPhgybjiVq3aDEZkHgEAAGCMzCMQjAyL+ZoWEXY4zZ67MS2aLadZ/xwRZsW/HeFmhanlMDsPq6rKrL1Qs8LUjlCD84gwLJweHWkUJ7dZoeaT6TFmxzUs1u0KN4s7kWT2PdDkG7PzCKk0i3NHmP1ZswyvbUipQQHwEyfMjmlaTDxQRbgbafFvU2455JDNpXpsbq+ukHkEAACAMTKPAAAAPlDn8QwGjwAAAD64LYccrDAjKYhuW8+aNUtXXHGFYmNjlZycrJtuukkFBQWB7hYAAECjEjSDx/Xr1ys7O1ubNm3SqlWrVFVVpYEDB6qsrCzQXQMAAA2cZflnC0ZBc9t6xYoVXl8vXrxYycnJys/PV58+fQLUKwAAgMYlaAaP31dSUiJJSkxMrDGmoqJCFRUVnq9LS0v93i8AANDwMGHmjKC5bf1dbrdbDzzwgH70ox+pc+fONcbNmjVL8fHxni0jI6MOewkAANDwBGXmMTs7Wzt27NCGDRvOG5eTk6NJkyZ5vi4tLWUAifrNsPi3aTFfZ4RZwWlHdBOzw5ZX+A6S5IyLNYpTmGHxb9Oi3obtOUINf/VVV5vFxfgunm6FmRWldsWaXbOqGLNzOJFqFneyuVkGxG1W61xOs28VVcSZfc+fbGZWUD66yGUU1+SrSqM4R4Xv7wGr0vD70/Dn1nKZnYPx7wvYgszjGUE3eJw4caKWLVumd999Vy1atDhvbEREhCIMV7AAAACAb0EzeLQsS/fee6+WLl2q3NxctW7dOtBdAgAAjQR1Hs8Impx3dna2/vrXv2rJkiWKjY1VUVGRioqKdPKk4VqhAAAAF6g+lOoxqXndr18/ORwOr+3uu+/2itm3b5+GDh2qqKgoJScna/Lkyao2fUxHQZR5nD9/vqRTH8p3LVq0SGPGjKn7DgEAANSh0zWvr7jiClVXV+tXv/qVBg4cqE8++UTR0dGeuPHjx2vmzJmer6OizjyX7XK5NHToUKWmpmrjxo06dOiQRo0apbCwMD3xxBNG/QiawaMVrJU0AQBA0DuVKbR7wkzt4k1rXkdFRSk1NfWcbbzzzjv65JNPtHr1aqWkpKh79+567LHHNGXKFM2YMUPh4b5nxQXNbWsAAICGqLS01Gv7bo3q86mp5vVLL72k5s2bq3PnzsrJydGJEyc8r+Xl5alLly5KSUnx7Bs0aJBKS0u1c+dOo+MGTeYRAAAgUPxZquf7ZQSnT5+uGTNmnPe9NdW8/vnPf65WrVopPT1dH3/8saZMmaKCggK9+uqrkqSioiKvgaMkz9dFRUVG/WbwCAAAEECFhYWKi4vzfG1SZrCmmtd33XWX5/+7dOmitLQ0XXvttdqzZ4/atm1rS38ZPAI1sbNgt83FfJ1NzIp6O0LMClMbHzc5ySwwwqxYtzvarA6r5TT7/KxQs7iQ4+VGcaYFu0OO+77FVJ4WY9SW5TTLbBxvYfYZHzdcF6GyqVkBa1POSrPzqIw3i4s5YHZc0+LfYUVmy9W69x80CDJ7cM1dZT6bFfWP9Z/N7jYlKS4uzmvw6Ettal737t1bkrR79261bdtWqamp+uCDD7xiDh8+LEk1Pif5fTzzCAAAEAQsy9LEiRO1dOlSrV271qjm9bZt2yRJaWlpkqTMzExt375dxcXFnphVq1YpLi5OnTp1MuoHmUcAAAAf6sPyhNnZ2VqyZIlef/11T81rSYqPj1eTJk20Z88eLVmyRNdff72aNWumjz/+WA8++KD69Omjrl27SpIGDhyoTp06aeTIkXrqqadUVFSkqVOnKjs723hVPjKPAAAAvlh+2mph/vz5KikpUb9+/ZSWlubZ/v73v0uSwsPDtXr1ag0cOFAdOnTQQw89pOHDh+vNN9/0tBESEqJly5YpJCREmZmZuuOOOzRq1CivupC+kHkEAAAIAr5qXmdkZGj9+vU+22nVqpWWL19+wf1g8AgAAOCLH25bi7WtAQAA0NCReQQAAPDh1PKE9rcZjMg8AgAAwBiZRwAAAB/qQ6me+oLBI3CRHKG+V/qwe6UXh2EtLke42SokCjX7VWDFmK244jKMO5lqFlcZZ3aTxBVu9ovYWR1lFBd9yGxFkIpmvq9HeaK93wPliWZxFS2qzAJPGt6IamK2Eo3ptYj4xux7z/RahB49aRRnFRX7DpIkh+/zcFf6XmHIL0xWtwL8gMEjAACAL5bD/tnRZB4BAAAaJibMnMGEGQAAABgzyjx+/PHHtW64U6dOCjV8jgoAAKBeu4DlBI3aDEJGo7vu3bvL4XD4XBbnNKfTqc8++0xt2rS5qM4BAACgfjFODW7evFlJSUk+4yzLUufOnS+qUwAAAPUJpXrOMBo89u3bV5dccokSEhKMGu3Tp4+aNGlyMf0CAABAPWQ0eFy3bl2tGl2+fPkFdQYAAKDeCtJnFO120TNaXC6Xtm/frlatWqlp06Z29AmNhcNwsn+gCuEaHtdy+Y4xLRLuCDP7kXREhBvFKcysSLjVNNYozhVtVpz8RLpZ8e+qGLPvgePpZrd23L2OGcUNv2SbUdw7BzoYxZVu9f1IjymX4aWNvuyIUVy3hG+N4koqzK7ZtyfN7ipV5JtVMY/Zb/bXOHpvqVGc9h0yCnMdLzOKM/rZpVg3Gplal+p54IEHtHDhQkmnBo59+/bV5ZdfroyMDOXm5trdPwAAgIA7/cyj3VswqvXg8R//+Ie6desmSXrzzTe1d+9e7dq1Sw8++KB+/etf295BAACAgLP8tAWhWg8ev/76a6Wmpko69WzjLbfconbt2unOO+/U9u3bbe8gAAAA6o9aDx5TUlL0ySefyOVyacWKFbruuuskSSdOnFCI4XNdAAAAwcXhpy341HrCzNixY/Wzn/1MaWlpcjgcGjBggKRTdSA7dDB7uBwAAADBqdaDxxkzZqhz584qLCzULbfcooiIU7MvQ0JC9Mgjj9jeQQAAgIBjeUIP48HjqFGjNGzYMA0aNEg//elPz3p99OjRtnYMAAAA9Y/xM4+XXHKJnnjiCSUlJWnIkCGaP3++Dhw44M++AQAA1A/MtvYwzjxOmzZN06ZN0/79+/XGG2/otdde04MPPqjLLrtMw4YN049//GN1797dj11FgxOowrqmxckNOSN9F862qqqN2nIYtCVJCjX70TUu/h1rViC6zLD4d1mq2WfsMmtOJzpUGsU92mWFUdzI2K+N4h5L3mEUpx5mYXaad7SFUdye8mSjuJNNzArKryjsbBSXVmD28x2/86hRnA4cNgqzyivM2jNkVVfZ2h7QENT6r2iLFi10zz33aOXKlfrqq680ZcoUFRQUqH///mrVqpUmTpyonTt3+qOvAAAAgWE5/LMFoYtKwcTGxupnP/uZXnrpJX311Vd64YUXFBISory8PLv6BwAAEHCW5Z8tGF302tanhYSE6Nprr9W1115rV5MAAACoZ2o9ePzmm280bdo0rVu3TsXFxXK7vZ9rOXLkiG2dAwAAqBco1eNR68HjyJEjtXv3bo0bN04pKSlyOILzfj0AAABqr9aDx/fee08bNmxQt27d/NEfAACA+scfE1way4SZDh066OTJk/7oCwAAAOq5Wg8e582bp1//+tdav369vvnmG5WWlnptAAAADY3D8s8WjGp92zohIUGlpaXq37+/137LsuRwOORyuWzrHBo402LdpsXEbS7+7QgJMQt0+/7pd8ZEmx0zItzsmDFRRmHVCU2M4iqamhWIPtrO7DMub2b2GzG0ZZlR3O+6vWEUd0tMiVFcQ3Bd9C6juPxjrYzi1n3awSiu2ftm36Px/zIrxK6DhsW/q8yKdbsrys2Oa/PvC6AxqfXgccSIEQoLC9OSJUuYMAMAABoHZlt71HrwuGPHDn300Udq3769P/oDAABQ/zBhxqPWeftevXqpsLDQH30BAABAPVfrzOO9996r+++/X5MnT1aXLl0UFub9rFTXrl1t6xwAAEC9wG1rj1oPHm+99VZJ0p133unZ53A4mDADAADQCNR68Lh3715/9AMAAKD+IvPoUevBY6tWZmUf/GXu3Ll6+umnVVRUpG7duum5557TlVdeGdA+AQAANBZGE2beeOMNVRnW2JKk5cuX+2UVmr///e+aNGmSpk+frg8//FDdunXToEGDVFxcbPuxAAAAPCw/bUHIKPP4k5/8REVFRUpKSjJq9LbbbtO2bdvUpk2bi+rc9/3+97/X+PHjNXbsWEnSggUL9NZbb+mFF17QI488YuuxUAdMi3/bzLT4t7NJpH0Hra42i4syK+pthZmdg8Nl9pvJafiocqRh3eeyNmbnO7S1WaHrxlT8O7+y0ijuV1/cahT3+b5Uo7iED8yKfzf9zCwx4Cj+xijObXi+lmGcsQD9/gEaAqPBo2VZGjNmjCIiIowaLS83rPBfC5WVlcrPz1dOTo5nn9Pp1IABA5SXl3fO91RUVKiiosLzNcsnAgCAC0KdRw+jwePo0aNr1eiIESMUFxd3QR2qyddffy2Xy6WUlBSv/SkpKdq169zZi1mzZunRRx+1tR8AAACNmdHgcdGiRf7uh1/k5ORo0qRJnq9LS0uVkZERwB4BAIBg5LBObXa3GYxqPds6UJo3b66QkBAdPnzYa//hw4eVmnruZ3oiIiKMb7UDAADUiFI9HrVenjBQwsPD1bNnT61Zs8azz+12a82aNcrMzAxgzwAAABqPoBk8StKkSZP05z//WS+++KI+/fRTTZgwQWVlZZ7Z1wAAAA3VrFmzdMUVVyg2NlbJycm66aabVFBQ4BVTXl6u7OxsNWvWTDExMRo+fPhZd2337dunoUOHKioqSsnJyZo8ebKqTauCKMgGj7feeqtmz56tadOmqXv37tq2bZtWrFhx1iQaAACAhmb9+vXKzs7Wpk2btGrVKlVVVWngwIEqKyvzxDz44IN688039corr2j9+vU6ePCgbr75Zs/rLpdLQ4cOVWVlpTZu3KgXX3xRixcv1rRp04z7YfzM47x583TPPfcYN+wvEydO1MSJEwPdDQAA0Ig45IcJM7WMX7FihdfXixcvVnJysvLz89WnTx+VlJRo4cKFWrJkifr37y/p1KTnjh07atOmTbrqqqv0zjvv6JNPPtHq1auVkpKi7t2767HHHtOUKVM0Y8YMhYf7rvlqnHmcOnWqBg0apIMHD9byVAEAAFCT0tJSr+27NarPp6Tk1AIKiYmJkqT8/HxVVVVpwIABnpgOHTqoZcuWnprYeXl56tKli9dd20GDBqm0tFQ7d+40Oq5x5nHHjh0aP368OnfurGeffVZ33HGH6VuBc3OY/dvF4TT7t5nlNvsnoXF7LrNlVxxhYb6DDNuSZXgO1YarYxh+JpbhPyMrDcu3OqrNPuPt36aZNZhuFlaf7ak+bhR3+8uTjeIizBZwUeoBs++B2C/KfAdJCj1odmDL8I+fu9wsjhVhEHB+LBL+/TKC06dP14wZM877VrfbrQceeEA/+tGP1LlzZ0lSUVGRwsPDlZCQ4BWbkpKioqIiT8y5amaffs2E8eAxPT1db731lhYvXqz77rtPS5cu1a9//WuFhno30bVrV9MmAQAAGr3CwkKvxVVMygxmZ2drx44d2rBhgz+7dk61rvM4ZswYtWjRQoMHD9Zrr70my7LkcDg8/3WZZlgAAACChR/rPMbFxdVqZb6JEydq2bJlevfdd9WiRQvP/tTUVFVWVuro0aNe2cfv1sROTU3VBx984NXe6dnYNdXN/r5az7b+/e9/r2HDhumOO+7QZ599pr179+qLL77w/BcAAKDBsfy01aYLlqWJEydq6dKlWrt2rVq3bu31es+ePRUWFuZVE7ugoED79u3z1MTOzMzU9u3bVVxc7IlZtWqV4uLi1KlTJ6N+GGcev/jiC40ePVqff/65lixZomHDhpm+FQAAABcpOztbS5Ys0euvv67Y2FjPM4rx8fFq0qSJ4uPjNW7cOE2aNEmJiYmKi4vTvffeq8zMTF111VWSpIEDB6pTp04aOXKknnrqKRUVFWnq1KnKzs42XpXPePDYtWtXDR48WEuXLlXz5s0v4JQBAACCU31Y23r+/PmSpH79+nntX7RokcaMGSNJeuaZZ+R0OjV8+HBVVFRo0KBBmjdvnic2JCREy5Yt04QJE5SZmano6GiNHj1aM2fONO6H8eBxwYIFzLAGAAAIEMugIkdkZKTmzp2ruXPn1hjTqlUrLV++/IL7YTx4ZOAIAAAaLT9OmAk2QbU8IQAAAAKr1qV6ANsYFv21DKs/OUINinXLvPi3cWFvA854wxIMTrN/z1khZoVqQ05UmrXnMHtIusnXZv9Mrow3/NXS1iysIRi6eYJRnGnx7+jDhsW/954wijMt/u3++ohRnFxmP9/mRfvNDgv4DZlHDzKPAAAAMEbmEQAAwIf6MNu6vjAaPN58883GDb766qsX3BkAAIB6yY9rWwcbo9vW8fHxni0uLk5r1qzR1q1bPa/n5+drzZo1io+P91tHAQAAEHhGmcdFixZ5/n/KlCn62c9+pgULFigkJESS5HK5dM8999RqXUYAAICgwYQZj1pPmHnhhRf0y1/+0jNwlE5VK580aZJeeOEFWzsHAACA+qXWg8fq6mrt2rXrrP27du2S221WmgEAACCYnJ4wY/cWjGo923rs2LEaN26c9uzZoyuvvFKStHnzZj355JMaO3as7R0EAABA/VHrwePs2bOVmpqq//mf/9GhQ4ckSWlpaZo8ebIeeugh2zuIBsxhc5lRw6Ljpsd1fOfRjPM357s9d0mpUVvO5olmx6yoNoqrTo4yinOHmX0mlbFmMwMdhpei8OumRnG3fDHAKO6VNqvNDhwAltvss4soMWsv+lCVUVzIsXKzBk8axhmsrStJ7kqzAvXGP7dAoPHMo0etB49Op1MPP/ywHn74YZWWnvqDyEQZAACAxuGiioQzaAQAAI2CP55RDNLMY63vGx4+fFgjR45Uenq6QkNDFRIS4rUBAAA0OJaftiBU68zjmDFjtG/fPv3mN79RWlqaHI7grI4OAACA2qv14HHDhg1677331L17dz90BwAAoB5iwoxHrW9bZ2RkyDKcbQcAAICGpdaDxzlz5uiRRx7Rl19+6YfuAAAA1D8UCT+j1retb731Vp04cUJt27ZVVFSUwsLCvF4/cuSIbZ0DAABA/VLrweOcOXP80A00KKbFv20uDmy5zOIchkUBrGqz/jnCfP8YOWOizQ5abXYSVS3NymRZoWbXorypWVz4MbN/JlckGk6kK2xiFJZ/4odGcTPjOhrFTWv+qVGcnaq+jjQLNPzoQsvMCsU7jp0wirNchj9ApnGmAvT7AsCFq/XgcfTo0f7oBwAAAIKA0eCxtLTUUxD89KoyNaFwOAAAaHCYbe1hNHhs2rSpDh06pOTkZCUkJJyztqNlWXI4HHLZfUsDAAAgwPwxwaVBT5hZu3atEhMTPf9PYXAAAIDGyWjw2LdvX+3du1etW7dWv379/NwlAACAeihIM4V2M67z2LZtW7Vu3Vp33nmn/vrXv2r//v3+7BcAAADqIePZ1mvXrlVubq5yc3P1t7/9TZWVlWrTpo369++vrKwsZWVlKSUlxZ99BQAACAwmzHgYDx779evnuWVdXl6ujRs3egaTL774oqqqqtShQwft3LnTX30FAABAgNW6zqMkRUZGqn///rrmmmuUlZWlt99+W88//7x27dpld/8AAAACjtnWZ9Rq8FhZWalNmzZp3bp1ys3N1ebNm5WRkaE+ffroj3/8o/r27euvfiKYmK4EYbqyhM0st9lPa0h0lH0HNaxQYCWa1Ul1R5gtk3OsRbhRnMssTJWxZucRbfhI9IlUs/aqXGG+gyStPFh/V5hxuAy/Bwx/LJxllWaBLrOfR6vspFGcu9LwuAAaLOPBY//+/bV582a1bt1affv21S9+8QstWbJEaWlp/uwfAABA4PHMo4fx4PG9995TWlqa+vfvr379+qlv375q1qyZP/sGAABQL3Db+gzj+4ZHjx7Vn/70J0VFRel3v/ud0tPT1aVLF02cOFH/+Mc/9NVXX/mznwAAAKgHjDOP0dHRGjx4sAYPHixJOnbsmDZs2KB169bpqaee0ogRI3TppZdqx44dfussAABAQHDb2uOCZyxER0crMTFRiYmJatq0qUJDQ/Xpp3X/EDoAAADqjnHm0e12a+vWrcrNzdW6dev0/vvvq6ysTD/4wQ+UlZWluXPnKisry599BQAACAwyjx7Gg8eEhASVlZUpNTVVWVlZeuaZZ9SvXz+1bdvWn/0DAABAPWI8eHz66aeVlZWldu3a+bM/AAAA9Q6zrc8wHjz+4he/8Gc/zuvLL7/UY489prVr16qoqEjp6em644479Otf/1rh4YbVjVF3TIt/21xM3BFiVjjbNM60sLejWaLvoAizItdlbROM4kwLSYcfM/uMq6LNPpOIo2a/6Syn2WcXXmIUJrfhj/nB/QbXQpK7q9nn4jR8LHxLRZXPmLBSs7bi9vpuS5IcldVGcdax42ZxLpdRnO0/3wCCzgUtT1jXdu3aJbfbreeff16XXHKJduzYofHjx6usrEyzZ88OdPcAAEBDxzOPHkExePxuiSBJatOmjQoKCjR//nwGjwAAwP8YPHoExeDxXEpKSpSYeP5bVBUVFaqoqPB8XVpa6u9uAQAANGgXXOcxkHbv3q3nnnvO53OYs2bNUnx8vGfLyMioox4CAICG5PSEGbu3YBTQweMjjzwih8Nx3m3Xrl1e7zlw4IAGDx6sW265RePHjz9v+zk5OSopKfFshYWF/jwdAACABi+gt60feughjRkz5rwxbdq08fz/wYMHlZWVpauvvlp/+tOffLYfERGhiIiIi+0mAABo7Hjm0SOgg8ekpCQlJSUZxR44cEBZWVnq2bOnFi1aJKczKO+4AwAABLWgGIEdOHBA/fr1U8uWLTV79mx99dVXKioqUlFRUaC7BgAAGoH68Mzju+++qxtvvFHp6elyOBx67bXXvF4fM2bMWY//fbdajSQdOXJEI0aMUFxcnBISEjRu3DgdP25WD/a0oJhtvWrVKu3evVu7d+9WixYtvF6zrCDN+TZkNhf/NmVcJDzM8NveNC7SdwXr8ox4o6aqm5gV1z7R3Oyziz5sdi2ii8wKRIdWGP68ucziKuPNrlmTI2afS1GS2efybrnZcftFmp3Hk/uv9xmT8JlZW03+bVY53VF2wijOZfhHwRFqVsjeqjYrYg7AfmVlZerWrZvuvPNO3XzzzeeMGTx4sBYtWuT5+vuP740YMUKHDh3SqlWrVFVVpbFjx+quu+7SkiVLjPsRFIPHMWPG+Hw2EgAAwG/8+Mzj90sJ1jRnY8iQIRoyZMh5m4yIiFBqauo5X/v000+1YsUKbdmyRb169ZIkPffcc7r++us1e/ZspaenG3U7KG5bAwAABJTlp01SRkaGV2nBWbNmXXA3c3NzlZycrPbt22vChAn65ptvPK/l5eUpISHBM3CUpAEDBsjpdGrz5s3GxwiKzCMAAEBDVVhYqLi4OM/XF1opZvDgwbr55pvVunVr7dmzR7/61a80ZMgQ5eXlKSQkREVFRUpOTvZ6T2hoqBITE2s1j4TBIwAAgA+O/2x2tylJcXFxXoPHC3Xbbbd5/r9Lly7q2rWr2rZtq9zcXF177bUX3f5p3LYGAABogNq0aaPmzZtr9+7dkqTU1FQVFxd7xVRXV+vIkSM1Pid5LgweAQAAfPHjM4/+sn//fn3zzTdKS0uTJGVmZuro0aPKz8/3xKxdu1Zut1u9e/c2bpfb1gAAAEHg+PHjniyiJO3du1fbtm1TYmKiEhMT9eijj2r48OFKTU3Vnj179PDDD+uSSy7RoEGDJEkdO3bU4MGDNX78eC1YsEBVVVWaOHGibrvtNuOZ1hKZRwAAAJ/qQ5HwrVu3qkePHurRo4ckadKkSerRo4emTZumkJAQffzxx/rxj3+sdu3aady4cerZs6fee+89rwk4L730kjp06KBrr71W119/va655hqjJZ+/i8wj6j1nuO8i3KcCDR9lDjX7tnc0TTCKq06M9hnjDjHsm+EvkoTdZoWandVmDTorzYqEhx8+ZhTnjok0a6/ErFh3RTOzmYdNt5td24U9+hjFNUtbYRT36TuX+IxJP1Bh1JbDbVhkv+ykWXuGxb/dFeVmxzUt7m+6WAAAY/369Tvv4igrV6702UZiYmKtCoKfC4NHAAAAX/xYJDzYMHgEAAAwEaSDPbvxzCMAAACMkXkEAADw4UImuJi0GYzIPAIAAMAYmUcAAABfmDDjQeYRAAAAxsg8AgAA+MAzj2eQeQQAAIAxMo8IGEeI2eoiCjH7N44zNsasvegoozBXM7P2Kpv6XgEn5ITZCi4R35r9MzSs1GyFmZDjZquGOMrN2tOx40ZhzlLDOMNr4agyW62kKtr3aj+StOn9jkZx/90xySiu6We++xd21GyFGVWbfa+4j5eZtWf3Si+sHIPGimcePcg8AgAAwBiZRwAAAB945vEMBo8AAAC+cNvag9vWAAAAMEbmEQAAwBcyjx5kHgEAAGCMzCMAAIAPTJg5g8wjAAAAjJF5hDmH2b81TIt/O5wOozjj4t9hYUZhVoTvot6S5I4w+/EIPV7tMybs25NGbanKrEC0w7CQtEqPGYVZ5WYFrB3hZp+xTK+ZIWel789Ykpp8bVbsPPaLCKM412dmRcITdx31GeMsMSzqfdLwe8WweL77hFmheNOfb4qEo9HimUcPMo8AAAAwRuYRAADAB4dlyWHZmyq0u726wuARAADAF25be3DbGgAAAMbIPAIAAPhAqZ4zyDwCAADAGJlHAAAAX3jm0YPMIwAAAIyReUTghBp++xkWHZdh8W+FmBUnDyk1K5xt8k8wR5lhoebjZoWkrSqzYtimxb/lMiz87DIsYu40/Hep4feAaVH0iBNm5xsf1sysvSNm1835banPGOvIUaO2rCqzgujuykqjOFOmRfstw/r0QEPDM49nkHkEAACAMTKPAAAAvvDMoweDRwAAAB+4bX0Gt60BAABgjMwjAACAL9y29iDzCAAAAGNkHgEAAAwE6zOKdiPzCAAAAGNkHiE5zP4N4Qw3K8JtGRaSdjZpYhRn7NhxozCHK8os7uTJi+mNF/Ni3WafnWVaINrw2trN9HwdEYb/jA8LM4sz/Fya/LvEKM5xwux7wPrWd3umPxemxb8dhsXzrWqzAvCm/QMaLcs6tdndZhAi8wgAAABjZB4BAAB8oM7jGUGXeayoqFD37t3lcDi0bdu2QHcHAAA0BpaftiAUdIPHhx9+WOnp6YHuBgAAQKMUVLet3377bb3zzjv65z//qbfffttnfEVFhSoqzjy4X1pa6s/uAQCABsrhPrXZ3WYwCprM4+HDhzV+/Hj97//+r6KizGbLzpo1S/Hx8Z4tIyPDz70EAABo2IJi8GhZlsaMGaO7775bvXr1Mn5fTk6OSkpKPFthYaEfewkAABosnnn0COjg8ZFHHpHD4TjvtmvXLj333HM6duyYcnJyatV+RESE4uLivDYAAABcuIA+8/jQQw9pzJgx541p06aN1q5dq7y8PEVERHi91qtXL40YMUIvvviiH3sJAAAaO0r1nBHQwWNSUpKSkpJ8xj377LP67W9/6/n64MGDGjRokP7+97+rd+/e/uxiUHOEGq7KYcrpMAtrEm3vcauqzOJCDb+dDVeOcRuuWCOX7yeeTVcNcYaZnYPpaiCWu9ooznS1EtPzCIk3y/Jb1Wb907dHjcIcEWarIDmchquzfPOtUZwJt40rFknmK8cAgN2C4pnHli1bqnPnzp6tXbt2kqS2bduqRYsWAe4dAABo8E4vT2j3VgvvvvuubrzxRqWnp8vhcOi11177XhctTZs2TWlpaWrSpIkGDBigzz//3CvmyJEjGjFihOLi4pSQkKBx48bp+HHDZMl/BMXgEQAAIJBO37a2e6uNsrIydevWTXPnzj3n60899ZSeffZZLViwQJs3b1Z0dLQGDRqk8vJyT8yIESO0c+dOrVq1SsuWLdO7776ru+66q1b9CKo6j6f98Ic/lBWki4kDAAB81/frUEdERJw1z0OShgwZoiFDhpyzDcuyNGfOHE2dOlXDhg2TJP3lL39RSkqKXnvtNd1222369NNPtWLFCm3ZssVTvea5557T9ddfr9mzZxsvwkLmEQAAwBc/lurJyMjwqks9a9asWndv7969Kioq0oABAzz74uPj1bt3b+Xl5UmS8vLylJCQ4FX2cMCAAXI6ndq8ebPxsYIy8wgAANBQFBYWepUTPFfW0ZeioiJJUkpKitf+lJQUz2tFRUVKTk72ej00NFSJiYmeGBMMHgEAAHzwZ6meYKtFzW1rAACAIJeamirp1HLO33X48GHPa6mpqSouLvZ6vbq6WkeOHPHEmGDwCAAA4Es9KNVzPq1bt1ZqaqrWrFnj2VdaWqrNmzcrMzNTkpSZmamjR48qPz/fE7N27Vq53e5a1c3mtnV94jAby5sWdDZmGRYbdht+kxsWfrYMi47rpGGRcEPGBbarDAtY28i0CLcxw+8pq9rwMzZsz328zKw9Q85Is+d/jIuOf/2NUZhpYW/L9GfDhOFnbPxzC6DBOH78uHbv3u35eu/evdq2bZsSExPVsmVLPfDAA/rtb3+rSy+9VK1bt9ZvfvMbpaen66abbpIkdezYUYMHD9b48eO1YMECVVVVaeLEibrtttuMZ1pLDB4BAAB8qg/LE27dulVZWVmerydNmiRJGj16tBYvXqyHH35YZWVluuuuu3T06FFdc801WrFihSIjIz3veemllzRx4kRde+21cjqdGj58uJ599tla9YPBIwAAgC/fKa1ja5u10K9fv/PWuXY4HJo5c6ZmzpxZY0xiYqKWLFlSuwN/D888AgAAwBiZRwAAAB/qw23r+oLMIwAAAIyReQQAAPDFbZlXHalNm0GIzCMAAACMkXkEAADwpR7Mtq4vGDzWJ4ZFfy2zGtfGnOHhZoEhNieqK80KU7vLK4ziTIt/2y4QxZrtLiRtWqDetLC7IdOC98bFustOmB3X8Dwo/g0AZ2PwCAAA4INDfphtbW9zdYbBIwAAgC82r0XtaTMIMWEGAAAAxsg8AgAA+ECR8DPIPAIAAMAYmUcAAABfKNXjQeYRAAAAxsg8AgAA+OCwLDlsnh1td3t1hcFjMLK7iLBh8W+rwqxYtwwLP5uyqs2KidfrIsx29y1AhaRNi2Y7ww2Lf1dWGsWZFvU2LTpue0F5CnsDaEQYPAIAAPji/s9md5tBiMEjAACAD9y2PoMJMwAAADBG5hEAAMAXSvV4kHkEAACAMTKPAAAAvljWqc3uNoMQmUcAAAAYI/MIAADgg8M6tdndZjAi8wgAAABjZB4vkiM0zCjO9hUtDBivtmG6cowh01VDjJmuzlKfBWoFkgCtumO8KpBpe8Y/P2ZxAVuJBkDw4plHjwbwVxkAAAB1hcwjAACADw73qc3uNoMRg0cAAABfuG3twW1rAAAAGCPzCAAA4AvLE3qQeQQAAIAxMo8AAAA+OCxLDpufUbS7vbpC5hEAAADGyDxeJFuLIdtcDNvuQs0BK9YdqALbDUGAPju7i2vbXdSb4t8Aao3Z1h5kHgEAAGCMzCMAAIAvliS7b+YEZ+KRwSMAAIAvTJg5I6huW7/11lvq3bu3mjRpoqZNm+qmm24KdJcAAAAalaDJPP7zn//U+PHj9cQTT6h///6qrq7Wjh07At0tAADQGFjyw4QZe5urK0ExeKyurtb999+vp59+WuPGjfPs79SpUwB7BQAA0PgExW3rDz/8UAcOHJDT6VSPHj2UlpamIUOG+Mw8VlRUqLS01GsDAACotdOleuzeglBQDB6/+OILSdKMGTM0depULVu2TE2bNlW/fv105MiRGt83a9YsxcfHe7aMjIy66jIAAECDFNDB4yOPPCKHw3HebdeuXXK7T82N//Wvf63hw4erZ8+eWrRokRwOh1555ZUa28/JyVFJSYlnKywsrKtTuzCW294tUP0DbGa5LaMNAPzG7actCAX0mceHHnpIY8aMOW9MmzZtdOjQIUnezzhGRESoTZs22rdvX43vjYiIUEREhC19BQAAQIAHj0lJSUpKSvIZ17NnT0VERKigoEDXXHONJKmqqkpffvmlWrVq5e9uAgCARo46j2cExTOPcXFxuvvuuzV9+nS98847Kigo0IQJEyRJt9xyS4B7BwAAGrx6MGFmxowZZz3e16FDB8/r5eXlys7OVrNmzRQTE6Phw4fr8OHDdn8SwVGqR5KefvpphYaGauTIkTp58qR69+6ttWvXqmnTpoHuGgAAQJ247LLLtHr1as/XoaFnhnIPPvig3nrrLb3yyiuKj4/XxIkTdfPNN+v999+3tQ9BM3gMCwvT7NmzNXv27EB3BQAANDb+KK1zAe2FhoYqNTX1rP0lJSVauHChlixZov79+0uSFi1apI4dO2rTpk266qqrLrq7pwXFbWsAAICG6vs1qSsqKmqM/fzzz5Wenq42bdpoxIgRnonD+fn5qqqq0oABAzyxHTp0UMuWLZWXl2drfxk8AgAA+OLHZx4zMjK86lLPmjXrnF3o3bu3Fi9erBUrVmj+/Pnau3ev/uu//kvHjh1TUVGRwsPDlZCQ4PWelJQUFRUV2fpRBM1tawAAgIaosLBQcXFxnq9rKjM4ZMgQz/937dpVvXv3VqtWrfR///d/atKkid/7eRqZRwD1HwXqAQSaH4uEx8XFeW2mNaoTEhLUrl077d69W6mpqaqsrNTRo0e9Yg4fPnzOZyQvBoNHAACAIHT8+HHt2bNHaWlp6tmzp8LCwrRmzRrP6wUFBdq3b58yMzNtPS63rQEAAHyoD0XCf/nLX+rGG29Uq1atdPDgQU2fPl0hISG6/fbbFR8fr3HjxmnSpElKTExUXFyc7r33XmVmZto601pi8AgAAOBbPSjVs3//ft1+++365ptvlJSUpGuuuUabNm3yrNb3zDPPyOl0avjw4aqoqNCgQYM0b948e/ssBo8AAABB4eWXXz7v65GRkZo7d67mzp3r134weAQAAPDFbUkOmzOPbta2BgAAQANH5hEAAMCXevDMY31B5hEAAADGyDwCAAD45IfMo8g8AgAAoIEj8wgAAOALzzx6MHgEAADwxW3J9tvMlOoBAABAQ0fmEQAAwBfLfWqzu80gROYRAAAAxsg8AgAA+MKEGQ8yjwAAADBG5hEAAMAXZlt7kHkEAACAMTKPAAAAvvDMoweDRwAAAF8s+WHwaG9zdYXb1gAAADBG5hEAAMAXblt7kHkEAACAMTKPAAAAvrjdkmxeTtDN8oQAAABo4Mg8AgAA+MIzjx5kHgEAAGCMzCMAAIAvZB49GDwCAAD4wtrWHty2BgAAgDEyjwAAAD5YlluWZW9pHbvbqytkHgEAAGCMzCMAAIAvlmX/M4pBOmGGzCMAAACMkXkEAADwxfLDbGsyjwAAAGjoyDwCAAD44nZLDptnRwfpbGsGjwAAAL5w29qD29YAAAAwRuYRAADAB8vtlmXzbWuKhAMAAKDBI/MIAADgC888egRN5vGzzz7TsGHD1Lx5c8XFxemaa67RunXrAt0tAACARiVoBo833HCDqqurtXbtWuXn56tbt2664YYbVFRUFOiuAQCAhs5t+WcLQkExePz666/1+eef65FHHlHXrl116aWX6sknn9SJEye0Y8eOQHcPAACg0QiKZx6bNWum9u3b6y9/+Ysuv/xyRURE6Pnnn1dycrJ69uxZ4/sqKipUUVHh+bq0tLQuugsAABoay5Jkd5Hw4Mw8BsXg0eFwaPXq1brpppsUGxsrp9Op5ORkrVixQk2bNq3xfbNmzdKjjz5ahz0FAABo2AJ62/qRRx6Rw+E477Zr1y5ZlqXs7GwlJyfrvffe0wcffKCbbrpJN954ow4dOlRj+zk5OSopKfFshYWFdXh2AACgobDcll+2YOSwrMDlTL/66it98803541p06aN3nvvPQ0cOFDffvut4uLiPK9deumlGjdunB555BGj45WWlio+Pl79NEyhjrCL6jsAAKgb1VaVcvW6SkpKvMYBdeH02CEr5Gbbxw7VVpXWuV4NyHldjIDetk5KSlJSUpLPuBMnTkiSnE7vRKnT6ZTbHZzV2QEAAIJRUMy2zszMVNOmTTV69Gj961//0meffabJkydr7969Gjp0aKC7BwAAGrj6ctt67ty5+uEPf6jIyEj17t1bH3zwgR/O9vyCYvDYvHlzrVixQsePH1f//v3Vq1cvbdiwQa+//rq6desW6O4BAAD43d///ndNmjRJ06dP14cffqhu3bpp0KBBKi4urtN+BPSZx7rGM48AAASf+vDMoz/GDrU9r969e+uKK67QH//4R0mS2+1WRkaG7r33XuP5H3YIilI9djk9Tq5Wle3LUwIAAP+oVpWkM3/HA9YHmw9/+ry+X4c6IiJCERERXvsqKyuVn5+vnJwczz6n06kBAwYoLy/P3o750KgGj8eOHZMkbdDyAPcEAADU1rFjxxQfH1+nxwwPD1dqaqo2FPln7BATE6OMjAyvfdOnT9eMGTO89n399ddyuVxKSUnx2p+SkqJdu3b5pW81aVSDx/T0dBUWFio2NlYOhyMgfSgtLVVGRoYKCwuDalq+HRrzuUuN+/wb87lLjfv8OffGee6SvedvWZaOHTum9PR0m3pnLjIyUnv37lVlZaVf2rcs66wxyfezjvVNoxo8Op1OtWjRItDdkCTFxcU1yl8mUuM+d6lxn39jPnepcZ8/5944z12y7/zrOuP4XZGRkYqMjAzY8aVTk4dDQkJ0+PBhr/2HDx9WampqnfYlKGZbAwAANGbh4eHq2bOn1qxZ49nndru1Zs0aZWZm1mlfGlXmEQAAIFhNmjRJo0ePVq9evXTllVdqzpw5Kisr09ixY+u0Hwwe61hERISmT59e759n8IfGfO5S4z7/xnzuUuM+f869cZ67xPn7w6233qqvvvpK06ZNU1FRkbp3764VK1acNYnG3xpVnUcAAABcHJ55BAAAgDEGjwAAADDG4BEAAADGGDwCAADAGINHmz3++OO6+uqrFRUVpYSEhHPG7Nu3T0OHDlVUVJSSk5M1efJkVVdXn7fdI0eOaMSIEYqLi1NCQoLGjRun48eP++EM7JObmyuHw3HObcuWLTW+r1+/fmfF33333XXYc3v88Ic/POs8nnzyyfO+p7y8XNnZ2WrWrJliYmI0fPjwswrCBoMvv/xS48aNU+vWrdWkSRO1bdtW06dP97lCQzBf+7lz5+qHP/yhIiMj1bt3b33wwQfnjX/llVfUoUMHRUZGqkuXLlq+PPiWTZ01a5auuOIKxcbGKjk5WTfddJMKCgrO+57FixefdY0DXXz5Qs2YMeOsc+nQocN539MQrrt07t9vDodD2dnZ54xvSNcdDB5tV1lZqVtuuUUTJkw45+sul0tDhw5VZWWlNm7cqBdffFGLFy/WtGnTztvuiBEjtHPnTq1atUrLli3Tu+++q7vuussfp2Cbq6++WocOHfLa/vu//1utW7dWr169zvve8ePHe73vqaeeqqNe22vmzJle53HvvfeeN/7BBx/Um2++qVdeeUXr16/XwYMHdfPNN9dRb+2za9cuud1uPf/889q5c6eeeeYZLViwQL/61a98vjcYr/3f//53TZo0SdOnT9eHH36obt26adCgQSouLj5n/MaNG3X77bdr3Lhx+uijj3TTTTfppptu0o4dO+q45xdn/fr1ys7O1qZNm7Rq1SpVVVVp4MCBKisrO+/74uLivK7xv//97zrqsf0uu+wyr3PZsGFDjbEN5bpL0pYtW7zOe9WqVZKkW265pcb3NKTr3uhZ8ItFixZZ8fHxZ+1fvny55XQ6raKiIs+++fPnW3FxcVZFRcU52/rkk08sSdaWLVs8+95++23L4XBYBw4csL3v/lJZWWklJSVZM2fOPG9c3759rfvvv79uOuVHrVq1sp555hnj+KNHj1phYWHWK6+84tn36aefWpKsvLw8P/Swbj311FNW69atzxsTrNf+yiuvtLKzsz1fu1wuKz093Zo1a9Y543/2s59ZQ4cO9drXu3dv6xe/+IVf++lvxcXFliRr/fr1NcbU9LsxGE2fPt3q1q2bcXxDve6WZVn333+/1bZtW8vtdp/z9YZ03WFZZB7rWF5enrp06eJV0HPQoEEqLS3Vzp07a3xPQkKCV7ZuwIABcjqd2rx5s9/7bJc33nhD33zzjVEl/JdeeknNmzdX586dlZOToxMnTtRBD+335JNPqlmzZurRo4eefvrp8z6ekJ+fr6qqKg0YMMCzr0OHDmrZsqXy8vLqort+VVJSosTERJ9xwXbtKysrlZ+f73XdnE6nBgwYUON1y8vL84qXTv0eCPbrXFJSIkk+r/Px48fVqlUrZWRkaNiwYTX+7gsGn3/+udLT09WmTRuNGDFC+/btqzG2oV73yspK/fWvf9Wdd94ph8NRY1xDuu6NHSvM1LGioqKzKsGf/rqoqKjG9yQnJ3vtCw0NVWJiYo3vqY8WLlyoQYMGqUWLFueN+/nPf65WrVopPT1dH3/8saZMmaKCggK9+uqrddRTe9x33326/PLLlZiYqI0bNyonJ0eHDh3S73//+3PGFxUVKTw8/KxnZVNSUoLqOp/L7t279dxzz2n27NnnjQvGa//111/L5XKd8+d6165d53xPTb8Hgvk6u91uPfDAA/rRj36kzp071xjXvn17vfDCC+ratatKSko0e/ZsXX311dq5c6fP3w31Te/evbV48WK1b99ehw4d0qOPPqr/+q//0o4dOxQbG3tWfEO87pL02muv6ejRoxozZkyNMQ3pukPctjYxZcoUS9J5t08//dTrPTWl6MePH28NHDjQa19ZWZklyVq+fPk5j//4449b7dq1O2t/UlKSNW/evAs/sQt0IZ9HYWGh5XQ6rX/84x+1Pt6aNWssSdbu3bvtOoULdiHnftrChQut0NBQq7y8/Jyvv/TSS1Z4ePhZ+6+44grr4YcftvU8LtSFnP/+/futtm3bWuPGjav18erTta/JgQMHLEnWxo0bvfZPnjzZuvLKK8/5nrCwMGvJkiVe++bOnWslJyf7rZ/+dvfdd1utWrWyCgsLa/W+yspKq23bttbUqVP91LO68+2331pxcXHW//t//++crzfE625ZljVw4EDrhhtuqNV7GtJ1b4zIPBp46KGHzvsvKklq06aNUVupqalnzcI8PZs2NTW1xvd8/8H76upqHTlypMb3+NOFfB6LFi1Ss2bN9OMf/7jWx+vdu7ekU9mrtm3b1vr9drqY74XevXururpaX375pdq3b3/W66mpqaqsrNTRo0e9so+HDx8OyHU+l9qe/8GDB5WVlaWrr75af/rTn2p9vPp07WvSvHlzhYSEnDUr/nzXLTU1tVbx9d3EiRM9E/lqm0UKCwtTjx49tHv3bj/1ru4kJCSoXbt2NZ5LQ7vukvTvf/9bq1evrvXdgYZ03RsjBo8GkpKSlJSUZEtbmZmZevzxx1VcXOy5Fb1q1SrFxcWpU6dONb7n6NGjys/PV8+ePSVJa9euldvt9vxxrUu1/Twsy9KiRYs0atQohYWF1fp427ZtkySlpaXV+r12u5jvhW3btsnpdJ71CMJpPXv2VFhYmNasWaPhw4dLkgoKCrRv3z5lZmZecJ/tVJvzP3DggLKystSzZ08tWrRITmftH7GuT9e+JuHh4erZs6fWrFmjm266SdKpW7hr1qzRxIkTz/mezMxMrVmzRg888IBn36pVq+rNdTZlWZbuvfdeLV26VLm5uWrdunWt23C5XNq+fbuuv/56P/Swbh0/flx79uzRyJEjz/l6Q7nu37Vo0SIlJydr6NChtXpfQ7rujVKgU58Nzb///W/ro48+sh599FErJibG+uijj6yPPvrIOnbsmGVZllVdXW117tzZGjhwoLVt2zZrxYoVVlJSkpWTk+NpY/PmzVb79u2t/fv3e/YNHjzY6tGjh7V582Zrw4YN1qWXXmrdfvvtdX5+F2L16tU13s7dv3+/1b59e2vz5s2WZVnW7t27rZkzZ1pbt2619u7da73++utWmzZtrD59+tR1ty/Kxo0brWeeecbatm2btWfPHuuvf/2rlZSUZI0aNcoT8/1zt6xTt/5atmxprV271tq6dauVmZlpZWZmBuIULsr+/futSy65xLr22mut/fv3W4cOHfJs341pKNf+5ZdftiIiIqzFixdbn3zyiXXXXXdZCQkJnqoKI0eOtB555BFP/Pvvv2+FhoZas2fPtj799FNr+vTpVlhYmLV9+/ZAncIFmTBhghUfH2/l5uZ6XeMTJ054Yr5/7o8++qi1cuVKa8+ePVZ+fr512223WZGRkdbOnTsDcQoX5aGHHrJyc3OtvXv3Wu+//741YMAAq3nz5lZxcbFlWQ33up/mcrmsli1bWlOmTDnrtYZ83WFZDB5tNnr06HM+B7Zu3TpPzJdffmkNGTLEatKkidW8eXProYcesqqqqjyvr1u3zpJk7d2717Pvm2++sW6//XYrJibGiouLs8aOHesZkNZ3t99+u3X11Vef87W9e/d6fT779u2z+vTpYyUmJloRERHWJZdcYk2ePNkqKSmpwx5fvPz8fKt3795WfHy8FRkZaXXs2NF64oknvJ53/P65W5ZlnTx50rrnnnuspk2bWlFRUdZPfvITrwFXsFi0aFGNz0Se1tCu/XPPPWe1bNnSCg8Pt6688kpr06ZNntf69u1rjR492iv+//7v/6x27dpZ4eHh1mWXXWa99dZbddzji1fTNV60aJEn5vvn/sADD3g+p5SUFOv666+3Pvzww7rvvA1uvfVWKy0tzQoPD7d+8IMfWLfeeqvX87kN9bqftnLlSkuSVVBQcNZrDfm6w7IclmVZdZjoBAAAQBCjziMAAACMMXgEAACAMQaPAAAAMMbgEQAAAMYYPAIAAMAYg0cAAAAYY/AIAAAAYwweAQAAYIzBIwBb5ebmyuFw6OjRoxfVzpgxYzxrRdtpxowZcjgccjgcmjNnju3tn8+YMWM8x37ttdfq9NgAYBcGjwDOacGCBYqNjVV1dbVn3/HjxxUWFqZ+/fp5xZ4eMO7Zs0dXX321Dh06pPj4eL/2b/78+UpISFBhYaHX/nvvvVft2rXTiRMnanzvZZddpkOHDumuu+6ypS8vvviirrnmGp9xf/jDH3To0CFbjgkAgcLgEcA5ZWVl6fjx49q6datn33vvvafU1FRt3rxZ5eXlnv3r1q1Ty5Yt1bZtW4WHhys1NVUOh8Ov/bv77rt15ZVXaty4cZ59a9as0fz587V48WJFRUXV+N7Q0FClpqaeN6Y2Xn/9df34xz/2GRcfH6/U1FRbjgkAgcLgEcA5tW/fXmlpacrNzfXsy83N1bBhw9S6dWtt2rTJa39WVpbn/79723rx4sVKSEjQypUr1bFjR8XExGjw4MFeGTiXy6VJkyYpISFBzZo108MPPyzLss7bP4fDoYULF2rz5s1asGCBSktLdeedd2rSpEm6+uqra32+DodDzz//vG644QZFRUWpY8eOysvL0+7du9WvXz9FR0fr6quv1p49e7zeV15ernfeecczeJw3b54uvfRSRUZGKiUlRT/96U9r3RcAqM8YPAKoUVZWltatW+f5et26derXr5/69u3r2X/y5Elt3rzZM3g8lxMnTmj27Nn63//9X7377rvat2+ffvnLX3pe/5//+R8tXrxYL7zwgjZs2KAjR45o6dKlPvuXkZGhOXPmaPLkybrjjjsUExOjxx577ILP97HHHtOoUaO0bds2dejQQT//+c/1i1/8Qjk5Odq6dassy9LEiRO93rNmzRr94Ac/UIcOHbR161bdd999mjlzpgoKCrRixQr16dPngvsDAPVRaKA7AKD+ysrK0gMPPKDq6mqdPHlSH330kfr27auqqiotWLBAkpSXl6eKiorzDh5Px7dt21aSNHHiRM2cOdPz+pw5c5STk6Obb75Z0qnnLVeuXGnUx7Fjx+pPf/qT3nzzTW3evFkREREXeroaO3asfvazn0mSpkyZoszMTP3mN7/RoEGDJEn333+/xo4d6/We796y3rdvn6Kjo3XDDTcoNjZWrVq1Uo8ePS64PwBQH5F5BFCjfv36qaysTFu2bNF7772ndu3aKSkpSX379vU895ibm6s2bdqoZcuWNbYTFRXlGThKUlpamoqLiyVJJSUlOnTokHr37u15PTQ0VL169TLq47/+9S99+OGHioqK0nvvvXeBZ3pK165dPf+fkpIiSerSpYvXvvLycpWWlkqSLMvSm2++6Rk8XnfddWrVqpXatGmjkSNH6qWXXjrvxB0ACEYMHgHU6JJLLlGLFi20bt06rVu3Tn379pUkpaenKyMjQxs3btS6devUv3//87YTFhbm9bXD4fD5TKOJyspKjRo1SiNGjNC8efM0depUFRQUXHB73+3n6Qk/59rndrslSR988IGqq6s9z1jGxsbqww8/1N/+9jelpaVp2rRp6tat20WXLQKA+oTBI4DzysrKUm5urnJzc71K9PTp00dvv/22Pvjgg/PesvYlPj5eaWlp2rx5s2dfdXW18vPzfb535syZOnLkiJ555hmNHj1a1113ncaOHesZ3Pnb66+/rqFDhyokJMSzLzQ0VAMGDNBTTz2ljz/+WF9++aXWrl1bJ/0BgLrA4BHAeWVlZWnDhg3atm2bJ/MoSX379tXzzz+vysrKixo8SqeeJXzyySf12muvadeuXbrnnnt8Zuu2bNmi3/3ud1q4cKGnpuTzzz+vgoICPfPMMxfVH1NvvPGGV4meZcuW6dlnn9W2bdv073//W3/5y1/kdrvVvn37OukPANQFJswAOK+srCydPHlSHTp08DwHKJ0aPB47dsxT0udiPPTQQzp06JBGjx4tp9OpO++8Uz/5yU9UUlJyzviKigqNHj1aY8eO1cCBAz3709LS9Nxzz2ncuHG64YYb/Dpo27Nnj3bv3u2ZTCNJCQkJevXVVzVjxgyVl5fr0ksv1d/+9jdddtllfusHANQ1h2XHg0cAECRmzJih1157Tdu2bbuodn7/+99r9erVWr58ea3f63A4tHTpUr8svwgA/sZtawCNzvbt2xUTE6N58+ZdcBstWrRQTk5Ord5z9913KyYm5oKPCQD1AZlHAI3KkSNHdOTIEUlSUlKS39fg/q7i4mJPmZ+0tDRFR0fX2bEBwC4MHgEAAGCM29YAAAAwxuARAAAAxhg8AgAAwBiDRwAAABhj8AgAAABjDB4BAABgjMEjAAAAjDF4BAAAgLH/DyBsfvi/OxvsAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(df['Wx'], df['Wy'], bins=(50, 50), vmax=400)\n", + "plt.colorbar()\n", + "plt.xlabel('Wind X [m/s]')\n", + "plt.ylabel('Wind Y [m/s]')\n", + "ax = plt.gca()\n", + "ax.axis('tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_8im1ttOWlRB" + }, + "source": [ + "#### Time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7YE21HKK40zQ" + }, + "source": [ + "Similarly, the `Date Time` column is very useful, but not in this string form. Start by converting it to seconds:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "LIFf-VjMfnh3" + }, + "outputs": [], + "source": [ + "timestamp_s = date_time.map(pd.Timestamp.timestamp)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EC_pnM1D5Sgc" + }, + "source": [ + "Similar to the wind direction, the time in seconds is not a useful model input. Being weather data, it has clear daily and yearly periodicity. There are many ways you could deal with periodicity.\n", + "\n", + "You can get usable signals by using sine and cosine transforms to clear \"Time of day\" and \"Time of year\" signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "MBfX6CDwax73" + }, + "outputs": [], + "source": [ + "day = 24*60*60\n", + "year = (365.2425)*day\n", + "\n", + "df['Day sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n", + "df['Day cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n", + "df['Year sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n", + "df['Year cos'] = np.cos(timestamp_s * (2 * np.pi / year))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "mXBbTJZfuuTC" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Time of day signal')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.array(df['Day sin'])[:25])\n", + "plt.plot(np.array(df['Day cos'])[:25])\n", + "plt.xlabel('Time [h]')\n", + "plt.title('Time of day signal')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HiurzTGQgf_D" + }, + "source": [ + "This gives the model access to the most important frequency features. In this case you knew ahead of time which frequencies were important. \n", + "\n", + "If you don't have that information, you can determine which frequencies are important by extracting features with Fast Fourier Transform. To check the assumptions, here is the `tf.signal.rfft` of the temperature over time. Note the obvious peaks at frequencies near `1/year` and `1/day`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "EN4U1fcMiTYs" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fft = tf.signal.rfft(df['T (degC)'])\n", + "f_per_dataset = np.arange(0, len(fft))\n", + "\n", + "n_samples_h = len(df['T (degC)'])\n", + "hours_per_year = 24*365.2524\n", + "years_per_dataset = n_samples_h/(hours_per_year)\n", + "\n", + "f_per_year = f_per_dataset/years_per_dataset\n", + "plt.step(f_per_year, np.abs(fft))\n", + "plt.xscale('log')\n", + "plt.ylim(0, 400000)\n", + "plt.xlim([0.1, max(plt.xlim())])\n", + "plt.xticks([1, 365.2524], labels=['1/Year', '1/day'])\n", + "_ = plt.xlabel('Frequency (log scale)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2rbL8bSGDHy3" + }, + "source": [ + "### Split the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qoFJZmXBaxCc" + }, + "source": [ + "You'll use a `(70%, 20%, 10%)` split for the training, validation, and test sets. Note the data is **not** being randomly shuffled before splitting. This is for two reasons:\n", + "\n", + "1. It ensures that chopping the data into windows of consecutive samples is still possible.\n", + "2. It ensures that the validation/test results are more realistic, being evaluated on the data collected after the model was trained." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "ia-MPAHxbInX" + }, + "outputs": [], + "source": [ + "column_indices = {name: i for i, name in enumerate(df.columns)}\n", + "\n", + "n = len(df)\n", + "train_df = df[0:int(n*0.7)]\n", + "val_df = df[int(n*0.7):int(n*0.9)]\n", + "test_df = df[int(n*0.9):]\n", + "\n", + "num_features = df.shape[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-eFckdUUHWmT" + }, + "source": [ + "### Normalize the data\n", + "\n", + "It is important to scale features before training a neural network. Normalization is a common way of doing this scaling: subtract the mean and divide by the standard deviation of each feature." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mxbIic5TMlxx" + }, + "source": [ + "The mean and standard deviation should only be computed using the training data so that the models have no access to the values in the validation and test sets.\n", + "\n", + "It's also arguable that the model shouldn't have access to future values in the training set when training, and that this normalization should be done using moving averages. That's not the focus of this tutorial, and the validation and test sets ensure that you get (somewhat) honest metrics. So, in the interest of simplicity this tutorial uses a simple average." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "Eji6njXvHusN" + }, + "outputs": [], + "source": [ + "train_mean = train_df.mean()\n", + "train_std = train_df.std()\n", + "\n", + "train_df = (train_df - train_mean) / train_std\n", + "val_df = (val_df - train_mean) / train_std\n", + "test_df = (test_df - train_mean) / train_std" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G6ufs8kk9JQw" + }, + "source": [ + "Now, peek at the distribution of the features. Some features do have long tails, but there are no obvious errors like the `-9999` wind velocity value." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "T0UYEnkwm8Fe" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ly/qvll8cvj47d6jl_123hgbpdr0000gp/T/ipykernel_81691/3214313372.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " _ = ax.set_xticklabels(df.keys(), rotation=90)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_std = (df - train_mean) / train_std\n", + "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n", + "plt.figure(figsize=(12, 6))\n", + "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n", + "_ = ax.set_xticklabels(df.keys(), rotation=90)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZBBmdxZ2HgfJ" + }, + "source": [ + "## Data windowing\n", + "\n", + "The models in this tutorial will make a set of predictions based on a window of consecutive samples from the data. \n", + "\n", + "The main features of the input windows are:\n", + "\n", + "- The width (number of time steps) of the input and label windows.\n", + "- The time offset between them.\n", + "- Which features are used as inputs, labels, or both. \n", + "\n", + "This tutorial builds a variety of models (including Linear, DNN, CNN and RNN models), and uses them for both:\n", + "\n", + "- *Single-output*, and *multi-output* predictions.\n", + "- *Single-time-step* and *multi-time-step* predictions.\n", + "\n", + "This section focuses on implementing the data windowing so that it can be reused for all of those models.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YAhGUVx1jtOy" + }, + "source": [ + "Depending on the task and type of model you may want to generate a variety of data windows. Here are some examples:\n", + "\n", + "1. For example, to make a single prediction 24 hours into the future, given 24 hours of history, you might define a window like this:\n", + "\n", + " ![One prediction 24 hours into the future.](images/raw_window_24h.png)\n", + "\n", + "2. A model that makes a prediction one hour into the future, given six hours of history, would need a window like this:\n", + "\n", + " ![One prediction one hour into the future.](images/raw_window_1h.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sa2BbfNZt8wy" + }, + "source": [ + "The rest of this section defines a `WindowGenerator` class. This class can:\n", + "\n", + "1. Handle the indexes and offsets as shown in the diagrams above.\n", + "1. Split windows of features into `(features, labels)` pairs.\n", + "2. Plot the content of the resulting windows.\n", + "3. Efficiently generate batches of these windows from the training, evaluation, and test data, using `tf.data.Dataset`s." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rfx3jGjyziUF" + }, + "source": [ + "### 1. Indexes and offsets\n", + "\n", + "Start by creating the `WindowGenerator` class. The `__init__` method includes all the necessary logic for the input and label indices.\n", + "\n", + "It also takes the training, evaluation, and test DataFrames as input. These will be converted to `tf.data.Dataset`s of windows later." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "Kem30j8QHxyW" + }, + "outputs": [], + "source": [ + "class WindowGenerator():\n", + " def __init__(self, input_width, label_width, shift,\n", + " train_df=train_df, val_df=val_df, test_df=test_df,\n", + " label_columns=None):\n", + " # Store the raw data.\n", + " self.train_df = train_df\n", + " self.val_df = val_df\n", + " self.test_df = test_df\n", + "\n", + " # Work out the label column indices.\n", + " self.label_columns = label_columns\n", + " if label_columns is not None:\n", + " self.label_columns_indices = {name: i for i, name in\n", + " enumerate(label_columns)}\n", + " self.column_indices = {name: i for i, name in\n", + " enumerate(train_df.columns)}\n", + "\n", + " # Work out the window parameters.\n", + " self.input_width = input_width\n", + " self.label_width = label_width\n", + " self.shift = shift\n", + "\n", + " self.total_window_size = input_width + shift\n", + "\n", + " self.input_slice = slice(0, input_width)\n", + " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", + "\n", + " self.label_start = self.total_window_size - self.label_width\n", + " self.labels_slice = slice(self.label_start, None)\n", + " self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n", + "\n", + " def __repr__(self):\n", + " return '\\n'.join([\n", + " f'Total window size: {self.total_window_size}',\n", + " f'Input indices: {self.input_indices}',\n", + " f'Label indices: {self.label_indices}',\n", + " f'Label column name(s): {self.label_columns}'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yVJgblsYzL1g" + }, + "source": [ + "Here is code to create the 2 windows shown in the diagrams at the start of this section:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "IsM5kRkz0UwK" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 48\n", + "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", + "Label indices: [47]\n", + "Label column name(s): ['T (degC)']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w1 = WindowGenerator(input_width=24, label_width=1, shift=24,\n", + " label_columns=['T (degC)'])\n", + "w1" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "viwKsYeAKFUn" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 7\n", + "Input indices: [0 1 2 3 4 5]\n", + "Label indices: [6]\n", + "Label column name(s): ['T (degC)']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w2 = WindowGenerator(input_width=6, label_width=1, shift=1,\n", + " label_columns=['T (degC)'])\n", + "w2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kJaUyTWQJd-L" + }, + "source": [ + "### 2. Split\n", + "\n", + "Given a list of consecutive inputs, the `split_window` method will convert them to a window of inputs and a window of labels.\n", + "\n", + "The example `w2` you define earlier will be split like this:\n", + "\n", + "![The initial window is all consecutive samples, this splits it into an (inputs, labels) pairs](images/split_window.png)\n", + "\n", + "This diagram doesn't show the `features` axis of the data, but this `split_window` function also handles the `label_columns` so it can be used for both the single output and multi-output examples." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "W4KbxfzqkXPW" + }, + "outputs": [], + "source": [ + "def split_window(self, features):\n", + " inputs = features[:, self.input_slice, :]\n", + " labels = features[:, self.labels_slice, :]\n", + " if self.label_columns is not None:\n", + " labels = tf.stack(\n", + " [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n", + " axis=-1)\n", + "\n", + " # Slicing doesn't preserve static shape information, so set the shapes\n", + " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", + " inputs.set_shape([None, self.input_width, None])\n", + " labels.set_shape([None, self.label_width, None])\n", + "\n", + " return inputs, labels\n", + "\n", + "WindowGenerator.split_window = split_window" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G6U6VtVuM15s" + }, + "source": [ + "Try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "YeCWbq6KLmL7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All shapes are: (batch, time, features)\n", + "Window shape: (3, 7, 19)\n", + "Inputs shape: (3, 6, 19)\n", + "Labels shape: (3, 1, 1)\n" + ] + } + ], + "source": [ + "# Stack three slices, the length of the total window.\n", + "example_window = tf.stack([np.array(train_df[:w2.total_window_size]),\n", + " np.array(train_df[100:100+w2.total_window_size]),\n", + " np.array(train_df[200:200+w2.total_window_size])])\n", + "\n", + "example_inputs, example_labels = w2.split_window(example_window)\n", + "\n", + "print('All shapes are: (batch, time, features)')\n", + "print(f'Window shape: {example_window.shape}')\n", + "print(f'Inputs shape: {example_inputs.shape}')\n", + "print(f'Labels shape: {example_labels.shape}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xtMk1ffk2Mmd" + }, + "source": [ + "Typically, data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features.\n", + "\n", + "The code above took a batch of three 7-time step windows with 19 features at each time step. It splits them into a batch of 6-time step 19-feature inputs, and a 1-time step 1-feature label. The label only has one feature because the `WindowGenerator` was initialized with `label_columns=['T (degC)']`. Initially, this tutorial will build models that predict single output labels." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tFZukGXrJoGo" + }, + "source": [ + "### 3. Plot\n", + "\n", + "Here is a plot method that allows a simple visualization of the split window:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "fmgd1qkYUWT7" + }, + "outputs": [], + "source": [ + "w2.example = example_inputs, example_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "jIrYccI-Hm3B" + }, + "outputs": [], + "source": [ + "def plot(self, model=None, plot_col='T (degC)', max_subplots=3):\n", + " inputs, labels = self.example\n", + " plt.figure(figsize=(12, 8))\n", + " plot_col_index = self.column_indices[plot_col]\n", + " max_n = min(max_subplots, len(inputs))\n", + " for n in range(max_n):\n", + " plt.subplot(max_n, 1, n+1)\n", + " plt.ylabel(f'{plot_col} [normed]')\n", + " plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n", + " label='Inputs', marker='.', zorder=-10)\n", + "\n", + " if self.label_columns:\n", + " label_col_index = self.label_columns_indices.get(plot_col, None)\n", + " else:\n", + " label_col_index = plot_col_index\n", + "\n", + " if label_col_index is None:\n", + " continue\n", + "\n", + " plt.scatter(self.label_indices, labels[n, :, label_col_index],\n", + " edgecolors='k', label='Labels', c='#2ca02c', s=64)\n", + " if model is not None:\n", + " predictions = model(inputs)\n", + " plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n", + " marker='X', edgecolors='k', label='Predictions',\n", + " c='#ff7f0e', s=64)\n", + "\n", + " if n == 0:\n", + " plt.legend()\n", + "\n", + " plt.xlabel('Time [h]')\n", + "\n", + "WindowGenerator.plot = plot" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HXvctEuK68vX" + }, + "source": [ + "This plot aligns inputs, labels, and (later) predictions based on the time that the item refers to:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "XjTqUnglOOni" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w2.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UqiqcPOldPG6" + }, + "source": [ + "You can plot the other columns, but the example window `w2` configuration only has labels for the `T (degC)` column." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "EBRe4wnlfCH8" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "w2.plot(plot_col='p (mbar)')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xCvD-UaUzYMw" + }, + "source": [ + "### 4. Create `tf.data.Dataset`s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kLO3SFR9Osdf" + }, + "source": [ + "Finally, this `make_dataset` method will take a time series DataFrame and convert it to a `tf.data.Dataset` of `(input_window, label_window)` pairs using the `tf.keras.utils.timeseries_dataset_from_array` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "35qoSQeRVfJg" + }, + "outputs": [], + "source": [ + "def make_dataset(self, data):\n", + " data = np.array(data, dtype=np.float32)\n", + " ds = tf.keras.utils.timeseries_dataset_from_array(\n", + " data=data,\n", + " targets=None,\n", + " sequence_length=self.total_window_size,\n", + " sequence_stride=1,\n", + " shuffle=True,\n", + " batch_size=32,)\n", + "\n", + " ds = ds.map(self.split_window)\n", + "\n", + " return ds\n", + "\n", + "WindowGenerator.make_dataset = make_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LvsxQwJaCift" + }, + "source": [ + "The `WindowGenerator` object holds training, validation, and test data.\n", + "\n", + "Add properties for accessing them as `tf.data.Dataset`s using the `make_dataset` method you defined earlier. Also, add a standard example batch for easy access and plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "2jZ2KkqGCfzu" + }, + "outputs": [], + "source": [ + "@property\n", + "def train(self):\n", + " return self.make_dataset(self.train_df)\n", + "\n", + "@property\n", + "def val(self):\n", + " return self.make_dataset(self.val_df)\n", + "\n", + "@property\n", + "def test(self):\n", + " return self.make_dataset(self.test_df)\n", + "\n", + "@property\n", + "def example(self):\n", + " \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n", + " result = getattr(self, '_example', None)\n", + " if result is None:\n", + " # No example batch was found, so get one from the `.train` dataset\n", + " result = next(iter(self.train))\n", + " # And cache it for next time\n", + " self._example = result\n", + " return result\n", + "\n", + "WindowGenerator.train = train\n", + "WindowGenerator.val = val\n", + "WindowGenerator.test = test\n", + "WindowGenerator.example = example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fF_Vj6Iw3Y2w" + }, + "source": [ + "Now, the `WindowGenerator` object gives you access to the `tf.data.Dataset` objects, so you can easily iterate over the data.\n", + "\n", + "The `Dataset.element_spec` property tells you the structure, data types, and shapes of the dataset elements." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "daJ0-U383YVs" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorSpec(shape=(None, 6, 19), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Each element is an (inputs, label) pair.\n", + "w2.train.element_spec" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XKTx3_Z7ua-n" + }, + "source": [ + "Iterating over a `Dataset` yields concrete batches:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:08.954732Z", + "iopub.status.busy": "2023-10-27T05:28:08.954485Z", + "iopub.status.idle": "2023-10-27T05:28:09.094541Z", + "shell.execute_reply": "2023-10-27T05:28:09.093757Z" + }, + "id": "6gtKXEgf4Iml" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inputs shape (batch, time, features): (32, 6, 19)\n", + "Labels shape (batch, time, features): (32, 1, 1)\n" + ] + } + ], + "source": [ + "for example_inputs, example_labels in w2.train.take(1):\n", + " print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n", + " print(f'Labels shape (batch, time, features): {example_labels.shape}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LyuGuJUgjUK3" + }, + "source": [ + "## Single step models\n", + "\n", + "The simplest model you can build on this sort of data is one that predicts a single feature's value—1 time step (one hour) into the future based only on the current conditions.\n", + "\n", + "So, start by building models to predict the `T (degC)` value one hour into the future.\n", + "\n", + "![Predict the next time step](images/narrow_window.png)\n", + "\n", + "Configure a `WindowGenerator` object to produce these single-step `(input, label)` pairs:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:09.098464Z", + "iopub.status.busy": "2023-10-27T05:28:09.097851Z", + "iopub.status.idle": "2023-10-27T05:28:09.102956Z", + "shell.execute_reply": "2023-10-27T05:28:09.102372Z" + }, + "id": "G5QX1G1JTPCr" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 2\n", + "Input indices: [0]\n", + "Label indices: [1]\n", + "Label column name(s): ['T (degC)']" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "single_step_window = WindowGenerator(\n", + " input_width=1, label_width=1, shift=1,\n", + " label_columns=['T (degC)'])\n", + "single_step_window" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RKTm8ajVGw4N" + }, + "source": [ + "The `window` object creates `tf.data.Dataset`s from the training, validation, and test sets, allowing you to easily iterate over batches of data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:09.106468Z", + "iopub.status.busy": "2023-10-27T05:28:09.105880Z", + "iopub.status.idle": "2023-10-27T05:28:09.242161Z", + "shell.execute_reply": "2023-10-27T05:28:09.241552Z" + }, + "id": "Do4ILUaBF8oc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inputs shape (batch, time, features): (32, 1, 19)\n", + "Labels shape (batch, time, features): (32, 1, 1)\n" + ] + } + ], + "source": [ + "for example_inputs, example_labels in single_step_window.train.take(1):\n", + " print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n", + " print(f'Labels shape (batch, time, features): {example_labels.shape}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D1bbPiR3VAm_" + }, + "source": [ + "### Baseline\n", + "\n", + "Before building a trainable model it would be good to have a performance baseline as a point for comparison with the later more complicated models.\n", + "\n", + "This first task is to predict temperature one hour into the future, given the current value of all features. The current values include the current temperature. \n", + "\n", + "So, start with a model that just returns the current temperature as the prediction, predicting \"No change\". This is a reasonable baseline since temperature changes slowly. Of course, this baseline will work less well if you make a prediction further in the future.\n", + "\n", + "![Send the input to the output](images/baseline.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:09.245879Z", + "iopub.status.busy": "2023-10-27T05:28:09.245206Z", + "iopub.status.idle": "2023-10-27T05:28:09.249843Z", + "shell.execute_reply": "2023-10-27T05:28:09.249208Z" + }, + "id": "9TybQaIsi3yg" + }, + "outputs": [], + "source": [ + "class Baseline(tf.keras.Model):\n", + " def __init__(self, label_index=None):\n", + " super().__init__()\n", + " self.label_index = label_index\n", + "\n", + " def call(self, inputs):\n", + " if self.label_index is None:\n", + " return inputs\n", + " result = inputs[:, :, self.label_index]\n", + " return result[:, :, tf.newaxis]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0vb3f948i8p8" + }, + "source": [ + "Instantiate and evaluate this model:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:09.253150Z", + "iopub.status.busy": "2023-10-27T05:28:09.252653Z", + "iopub.status.idle": "2023-10-27T05:28:11.066664Z", + "shell.execute_reply": "2023-10-27T05:28:11.065941Z" + }, + "id": "IS3-QKc4sX0D" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/439 [..............................] - 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1s 2ms/step - loss: 0.0128 - mean_absolute_error: 0.0785\n" + ] + } + ], + "source": [ + "baseline = Baseline(label_index=column_indices['T (degC)'])\n", + "\n", + "baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", + " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", + "\n", + "val_performance = {}\n", + "performance = {}\n", + "val_performance['Baseline'] = baseline.evaluate(single_step_window.val)\n", + "performance['Baseline'] = baseline.evaluate(single_step_window.test, verbose=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nhBxQcCSs7Ec" + }, + "source": [ + "That printed some performance metrics, but those don't give you a feeling for how well the model is doing.\n", + "\n", + "The `WindowGenerator` has a plot method, but the plots won't be very interesting with only a single sample.\n", + "\n", + "So, create a wider `WindowGenerator` that generates windows 24 hours of consecutive inputs and labels at a time. The new `wide_window` variable doesn't change the way the model operates. The model still makes predictions one hour into the future based on a single input time step. Here, the `time` axis acts like the `batch` axis: each prediction is made independently with no interaction between time steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.071282Z", + "iopub.status.busy": "2023-10-27T05:28:11.070474Z", + "iopub.status.idle": "2023-10-27T05:28:11.076109Z", + "shell.execute_reply": "2023-10-27T05:28:11.075407Z" + }, + "id": "C8jNR5uuJ5Zp" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 25\n", + "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", + "Label indices: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]\n", + "Label column name(s): ['T (degC)']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wide_window = WindowGenerator(\n", + " input_width=24, label_width=24, shift=1,\n", + " label_columns=['T (degC)'])\n", + "\n", + "wide_window" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZAnj7CFZkuYv" + }, + "source": [ + "This expanded window can be passed directly to the same `baseline` model without any code changes. This is possible because the inputs and labels have the same number of time steps, and the baseline just forwards the input to the output:\n", + "\n", + "![One prediction 1h into the future, ever hour.](images/last_window.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.079555Z", + "iopub.status.busy": "2023-10-27T05:28:11.078996Z", + "iopub.status.idle": "2023-10-27T05:28:11.208572Z", + "shell.execute_reply": "2023-10-27T05:28:11.207768Z" + }, + "id": "sGKdvdg087qs" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 24, 19)\n", + "Output shape: (32, 24, 1)\n" + ] + } + ], + "source": [ + "print('Input shape:', wide_window.example[0].shape)\n", + "print('Output shape:', baseline(wide_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SKqQHX1K0JW-" + }, + "source": [ + "By plotting the baseline model's predictions, notice that it is simply the labels shifted right by one hour:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.212395Z", + "iopub.status.busy": "2023-10-27T05:28:11.211685Z", + "iopub.status.idle": "2023-10-27T05:28:11.648458Z", + "shell.execute_reply": "2023-10-27T05:28:11.647749Z" + }, + "id": "jQyAPVLgWTOZ" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wide_window.plot(baseline)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e93TLUhfAVg2" + }, + "source": [ + "In the above plots of three examples the single step model is run over the course of 24 hours. This deserves some explanation:\n", + "\n", + "- The blue `Inputs` line shows the input temperature at each time step. The model receives all features, this plot only shows the temperature.\n", + "- The green `Labels` dots show the target prediction value. These dots are shown at the prediction time, not the input time. That is why the range of labels is shifted 1 step relative to the inputs.\n", + "- The orange `Predictions` crosses are the model's prediction's for each output time step. If the model were predicting perfectly the predictions would land directly on the `Labels`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E4aOJScj52Yu" + }, + "source": [ + "### Linear model\n", + "\n", + "The simplest **trainable** model you can apply to this task is to insert linear transformation between the input and output. In this case the output from a time step only depends on that step:\n", + "\n", + "![A single step prediction](images/narrow_window.png)\n", + "\n", + "A `tf.keras.layers.Dense` layer with no `activation` set is a linear model. The layer only transforms the last axis of the data from `(batch, time, inputs)` to `(batch, time, units)`; it is applied independently to every item across the `batch` and `time` axes." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.652522Z", + "iopub.status.busy": "2023-10-27T05:28:11.652259Z", + "iopub.status.idle": "2023-10-27T05:28:11.661267Z", + "shell.execute_reply": "2023-10-27T05:28:11.660553Z" + }, + "id": "6341OXuQ5xA9" + }, + "outputs": [], + "source": [ + "linear = tf.keras.Sequential([\n", + " tf.keras.layers.Dense(units=1)\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.664721Z", + "iopub.status.busy": "2023-10-27T05:28:11.664202Z", + "iopub.status.idle": "2023-10-27T05:28:11.845036Z", + "shell.execute_reply": "2023-10-27T05:28:11.844350Z" + }, + "id": "KwaOM8RucUSn" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 1, 19)\n", + "Output shape: (32, 1, 1)\n" + ] + } + ], + "source": [ + "print('Input shape:', single_step_window.example[0].shape)\n", + "print('Output shape:', linear(single_step_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OMZTYIj3bYLg" + }, + "source": [ + "This tutorial trains many models, so package the training procedure into a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.848956Z", + "iopub.status.busy": "2023-10-27T05:28:11.848338Z", + "iopub.status.idle": "2023-10-27T05:28:11.853315Z", + "shell.execute_reply": "2023-10-27T05:28:11.852692Z" + }, + "id": "CbCL6VIrk-Gt" + }, + "outputs": [], + "source": [ + "MAX_EPOCHS = 20\n", + "\n", + "def compile_and_fit(model, window, patience=2):\n", + " early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n", + " patience=patience,\n", + " mode='min')\n", + "\n", + " model.compile(loss=tf.keras.losses.MeanSquaredError(),\n", + " optimizer=tf.keras.optimizers.Adam(),\n", + " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", + "\n", + " history = model.fit(window.train, epochs=MAX_EPOCHS,\n", + " validation_data=window.val,\n", + " callbacks=[early_stopping])\n", + " return history" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OobVjM-schwj" + }, + "source": [ + "Train the model and evaluate its performance:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:11.856499Z", + "iopub.status.busy": "2023-10-27T05:28:11.856003Z", + "iopub.status.idle": "2023-10-27T05:28:40.532673Z", + "shell.execute_reply": "2023-10-27T05:28:40.531745Z" + }, + "id": "9agbz2qB9bLS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/1534 [..............................] - ETA: 17:22 - loss: 2.0953 - mean_absolute_error: 1.2495" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 21/1534 [..............................] - ETA: 3s - loss: 2.6312 - mean_absolute_error: 1.3650 " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1698384492.579925 449804 device_compiler.h:186] Compiled cluster using XLA! 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ETA: 0s - loss: 0.0092 - mean_absolute_error: 0.0701" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "1534/1534 [==============================] - 4s 3ms/step - loss: 0.0092 - mean_absolute_error: 0.0702 - val_loss: 0.0090 - val_mean_absolute_error: 0.0706\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/20\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/1534 [..............................] - ETA: 55s - loss: 0.0051 - mean_absolute_error: 0.0605" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 21/1534 [..............................] - 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1s 2ms/step - loss: 0.0088 - mean_absolute_error: 0.0697\n" + ] + } + ], + "source": [ + "history = compile_and_fit(linear, single_step_window)\n", + "\n", + "val_performance['Linear'] = linear.evaluate(single_step_window.val)\n", + "performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7U9XukYh8beN" + }, + "source": [ + "Like the `baseline` model, the linear model can be called on batches of wide windows. Used this way the model makes a set of independent predictions on consecutive time steps. The `time` axis acts like another `batch` axis. There are no interactions between the predictions at each time step.\n", + "\n", + "![A single step prediction](images/wide_window.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:40.537236Z", + "iopub.status.busy": "2023-10-27T05:28:40.536479Z", + "iopub.status.idle": "2023-10-27T05:28:40.561691Z", + "shell.execute_reply": "2023-10-27T05:28:40.560926Z" + }, + "id": "K9UVM5Sw9KQN" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 24, 19)\n", + "Output shape: (32, 24, 1)\n" + ] + } + ], + "source": [ + "print('Input shape:', wide_window.example[0].shape)\n", + "print('Output shape:', linear(wide_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X-CGj85oKaOG" + }, + "source": [ + "Here is the plot of its example predictions on the `wide_window`, note how in many cases the prediction is clearly better than just returning the input temperature, but in a few cases it's worse:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:40.565380Z", + "iopub.status.busy": "2023-10-27T05:28:40.564918Z", + "iopub.status.idle": "2023-10-27T05:28:41.009006Z", + "shell.execute_reply": "2023-10-27T05:28:41.007992Z" + }, + "id": "bCC8VVo-OvwV" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wide_window.plot(linear)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Is51vU8EMl6c" + }, + "source": [ + "One advantage to linear models is that they're relatively simple to interpret.\n", + "You can pull out the layer's weights and visualize the weight assigned to each input:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:41.013850Z", + "iopub.status.busy": "2023-10-27T05:28:41.013143Z", + "iopub.status.idle": "2023-10-27T05:28:41.211704Z", + "shell.execute_reply": "2023-10-27T05:28:41.210787Z" + }, + "id": "d4uCTbsmK8VI" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(x = range(len(train_df.columns)),\n", + " height=linear.layers[0].kernel[:,0].numpy())\n", + "axis = plt.gca()\n", + "axis.set_xticks(range(len(train_df.columns)))\n", + "_ = axis.set_xticklabels(train_df.columns, rotation=90)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ylng7215boIY" + }, + "source": [ + "Sometimes the model doesn't even place the most weight on the input `T (degC)`. This is one of the risks of random initialization. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W18e6da1cNbw" + }, + "source": [ + "### Dense\n", + "\n", + "Before applying models that actually operate on multiple time-steps, it's worth checking the performance of deeper, more powerful, single input step models.\n", + "\n", + "Here's a model similar to the `linear` model, except it stacks several a few `Dense` layers between the input and the output: " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:28:41.216061Z", + "iopub.status.busy": "2023-10-27T05:28:41.215349Z", + "iopub.status.idle": "2023-10-27T05:29:33.717496Z", + "shell.execute_reply": "2023-10-27T05:29:33.716656Z" + }, + "id": "Z86WkYp7cNAD" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/1534 [..............................] - ETA: 33:19 - loss: 2.2005 - mean_absolute_error: 1.1882" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 16/1534 [..............................] - ETA: 5s - loss: 0.7024 - mean_absolute_error: 0.5700 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 31/1534 [..............................] - ETA: 5s - loss: 0.4030 - mean_absolute_error: 0.4007" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 46/1534 [..............................] - 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It can't see how the input features are changing over time. To address this issue the model needs access to multiple time steps when making predictions:\n", + "\n", + "![Three time steps are used for each prediction.](images/conv_window.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zac-ti8agbJ7" + }, + "source": [ + "The `baseline`, `linear` and `dense` models handled each time step independently. Here the model will take multiple time steps as input to produce a single output.\n", + "\n", + "Create a `WindowGenerator` that will produce batches of three-hour inputs and one-hour labels:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gtN4BwZ37niR" + }, + "source": [ + "Note that the `Window`'s `shift` parameter is relative to the end of the two windows.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:29:33.721895Z", + "iopub.status.busy": "2023-10-27T05:29:33.721637Z", + "iopub.status.idle": "2023-10-27T05:29:33.727158Z", + "shell.execute_reply": "2023-10-27T05:29:33.726511Z" + }, + "id": "lBh0j5djUKY2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 4\n", + "Input indices: [0 1 2]\n", + "Label indices: [3]\n", + "Label column name(s): ['T (degC)']" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CONV_WIDTH = 3\n", + "conv_window = WindowGenerator(\n", + " input_width=CONV_WIDTH,\n", + " label_width=1,\n", + " shift=1,\n", + " label_columns=['T (degC)'])\n", + "\n", + "conv_window" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:29:33.730290Z", + "iopub.status.busy": "2023-10-27T05:29:33.730057Z", + "iopub.status.idle": "2023-10-27T05:29:34.316259Z", + "shell.execute_reply": "2023-10-27T05:29:34.315487Z" + }, + "id": "dCQ5gvs68Xkd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Given 3 hours of inputs, predict 1 hour into the future.')" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "conv_window.plot()\n", + "plt.title(\"Given 3 hours of inputs, predict 1 hour into the future.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "We0HdMxKeqB_" + }, + "source": [ + "You could train a `dense` model on a multiple-input-step window by adding a `tf.keras.layers.Flatten` as the first layer of the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:29:34.320517Z", + "iopub.status.busy": "2023-10-27T05:29:34.320269Z", + "iopub.status.idle": "2023-10-27T05:29:34.332247Z", + "shell.execute_reply": "2023-10-27T05:29:34.331632Z" + }, + "id": "oNQnUOkOnC1G" + }, + "outputs": [], + "source": [ + "multi_step_dense = tf.keras.Sequential([\n", + " # Shape: (time, features) => (time*features)\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(units=32, activation='relu'),\n", + " tf.keras.layers.Dense(units=32, activation='relu'),\n", + " tf.keras.layers.Dense(units=1),\n", + " # Add back the time dimension.\n", + " # Shape: (outputs) => (1, outputs)\n", + " tf.keras.layers.Reshape([1, -1]),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:29:34.335199Z", + "iopub.status.busy": "2023-10-27T05:29:34.334970Z", + "iopub.status.idle": "2023-10-27T05:29:34.390377Z", + "shell.execute_reply": "2023-10-27T05:29:34.389704Z" + }, + "id": "cayD74luo4Vq" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 3, 19)\n", + "Output shape: (32, 1, 1)\n" + ] + } + ], + "source": [ + "print('Input shape:', conv_window.example[0].shape)\n", + "print('Output shape:', multi_step_dense(conv_window.example[0]).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:29:34.393844Z", + "iopub.status.busy": "2023-10-27T05:29:34.393261Z", + "iopub.status.idle": "2023-10-27T05:30:06.869401Z", + "shell.execute_reply": "2023-10-27T05:30:06.868569Z" + }, + "id": "fu91yEbRo9-J" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/438 [..............................] - ETA: 35s - loss: 0.0052 - mean_absolute_error: 0.0573" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 26/438 [>.............................] - ETA: 0s - loss: 0.0066 - mean_absolute_error: 0.0539 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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0.0066 - mean_absolute_error: 0.0565" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "411/438 [===========================>..] - ETA: 0s - loss: 0.0066 - mean_absolute_error: 0.0565" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "438/438 [==============================] - 1s 2ms/step - loss: 0.0066 - mean_absolute_error: 0.0568\n" + ] + } + ], + "source": [ + "history = compile_and_fit(multi_step_dense, conv_window)\n", + "\n", + "IPython.display.clear_output()\n", + "val_performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.val)\n", + "performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.test, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:06.873786Z", + "iopub.status.busy": "2023-10-27T05:30:06.873215Z", + "iopub.status.idle": "2023-10-27T05:30:07.348247Z", + "shell.execute_reply": "2023-10-27T05:30:07.347568Z" + }, + "id": "tnqdXYT6pkEh" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "conv_window.plot(multi_step_dense)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gWfrsP8mq8lV" + }, + "source": [ + "The main down-side of this approach is that the resulting model can only be executed on input windows of exactly this shape. " + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:07.352337Z", + "iopub.status.busy": "2023-10-27T05:30:07.351690Z", + "iopub.status.idle": "2023-10-27T05:30:07.384287Z", + "shell.execute_reply": "2023-10-27T05:30:07.383653Z" + }, + "id": "j-q6tz5Yq8Jk" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 24, 19)\n", + "\n", + "ValueError:Exception encountered when calling layer 'sequential_2' (type Sequential).\n", + "\n", + "Input 0 of layer \"dense_4\" is incompatible with the layer: expected axis -1 of input shape to have value 57, but received input with shape (32, 456)\n", + "\n", + "Call arguments received by layer 'sequential_2' (type Sequential):\n", + " • inputs=tf.Tensor(shape=(32, 24, 19), dtype=float32)\n", + " • training=None\n", + " • mask=None\n" + ] + } + ], + "source": [ + "print('Input shape:', wide_window.example[0].shape)\n", + "try:\n", + " print('Output shape:', multi_step_dense(wide_window.example[0]).shape)\n", + "except Exception as e:\n", + " print(f'\\n{type(e).__name__}:{e}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bvvajm3ip_8V" + }, + "source": [ + "The convolutional models in the next section fix this problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CrpU6gwSJome" + }, + "source": [ + "### Convolution neural network\n", + " \n", + "A convolution layer (`tf.keras.layers.Conv1D`) also takes multiple time steps as input to each prediction." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cdLBwoaHmsWb" + }, + "source": [ + "Below is the **same** model as `multi_step_dense`, re-written with a convolution. \n", + "\n", + "Note the changes:\n", + "* The `tf.keras.layers.Flatten` and the first `tf.keras.layers.Dense` are replaced by a `tf.keras.layers.Conv1D`.\n", + "* The `tf.keras.layers.Reshape` is no longer necessary since the convolution keeps the time axis in its output." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:07.387646Z", + "iopub.status.busy": "2023-10-27T05:30:07.387379Z", + "iopub.status.idle": "2023-10-27T05:30:07.397169Z", + "shell.execute_reply": "2023-10-27T05:30:07.396589Z" + }, + "id": "5azaMBj4ac9t" + }, + "outputs": [], + "source": [ + "conv_model = tf.keras.Sequential([\n", + " tf.keras.layers.Conv1D(filters=32,\n", + " kernel_size=(CONV_WIDTH,),\n", + " activation='relu'),\n", + " tf.keras.layers.Dense(units=32, activation='relu'),\n", + " tf.keras.layers.Dense(units=1),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ftaH6B5ECRiK" + }, + "source": [ + "Run it on an example batch to check that the model produces outputs with the expected shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:07.400740Z", + "iopub.status.busy": "2023-10-27T05:30:07.400166Z", + "iopub.status.idle": "2023-10-27T05:30:07.445983Z", + "shell.execute_reply": "2023-10-27T05:30:07.445389Z" + }, + "id": "5YNgt1-e98lH" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conv model on `conv_window`\n", + "Input shape: (32, 3, 19)\n", + "Output shape: (32, 1, 1)\n" + ] + } + ], + "source": [ + "print(\"Conv model on `conv_window`\")\n", + "print('Input shape:', conv_window.example[0].shape)\n", + "print('Output shape:', conv_model(conv_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5m4kC-jGCY3x" + }, + "source": [ + "Train and evaluate it on the ` conv_window` and it should give performance similar to the `multi_step_dense` model." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:07.449331Z", + "iopub.status.busy": "2023-10-27T05:30:07.448798Z", + "iopub.status.idle": "2023-10-27T05:30:42.379916Z", + "shell.execute_reply": "2023-10-27T05:30:42.379026Z" + }, + "id": "QDVWdm4paUW7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/438 [..............................] - 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1s 2ms/step - loss: 0.0077 - mean_absolute_error: 0.0638\n" + ] + } + ], + "source": [ + "history = compile_and_fit(conv_model, conv_window)\n", + "\n", + "IPython.display.clear_output()\n", + "val_performance['Conv'] = conv_model.evaluate(conv_window.val)\n", + "performance['Conv'] = conv_model.evaluate(conv_window.test, verbose=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sYRipDeXs0Kr" + }, + "source": [ + "The difference between this `conv_model` and the `multi_step_dense` model is that the `conv_model` can be run on inputs of any length. The convolutional layer is applied to a sliding window of inputs:\n", + "\n", + "![Executing a convolutional model on a sequence](images/wide_conv_window.png)\n", + "\n", + "If you run it on wider input, it produces wider output:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:42.384632Z", + "iopub.status.busy": "2023-10-27T05:30:42.383907Z", + "iopub.status.idle": "2023-10-27T05:30:42.501246Z", + "shell.execute_reply": "2023-10-27T05:30:42.500484Z" + }, + "id": "hoqccxx9r5jF" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wide window\n", + "Input shape: (32, 24, 19)\n", + "Labels shape: (32, 24, 1)\n", + "Output shape: (32, 22, 1)\n" + ] + } + ], + "source": [ + "print(\"Wide window\")\n", + "print('Input shape:', wide_window.example[0].shape)\n", + "print('Labels shape:', wide_window.example[1].shape)\n", + "print('Output shape:', conv_model(wide_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "h_WGxtLIHhRF" + }, + "source": [ + "Note that the output is shorter than the input. To make training or plotting work, you need the labels, and prediction to have the same length. So build a `WindowGenerator` to produce wide windows with a few extra input time steps so the label and prediction lengths match: " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:42.505045Z", + "iopub.status.busy": "2023-10-27T05:30:42.504414Z", + "iopub.status.idle": "2023-10-27T05:30:42.510238Z", + "shell.execute_reply": "2023-10-27T05:30:42.509583Z" + }, + "id": "_VPvJ_VwTc0f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 27\n", + "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25]\n", + "Label indices: [ 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]\n", + "Label column name(s): ['T (degC)']" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LABEL_WIDTH = 24\n", + "INPUT_WIDTH = LABEL_WIDTH + (CONV_WIDTH - 1)\n", + "wide_conv_window = WindowGenerator(\n", + " input_width=INPUT_WIDTH,\n", + " label_width=LABEL_WIDTH,\n", + " shift=1,\n", + " label_columns=['T (degC)'])\n", + "\n", + "wide_conv_window" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:42.513238Z", + "iopub.status.busy": "2023-10-27T05:30:42.512977Z", + "iopub.status.idle": "2023-10-27T05:30:42.705644Z", + "shell.execute_reply": "2023-10-27T05:30:42.704880Z" + }, + "id": "gtqlWYXeKXej" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wide conv window\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 26, 19)\n", + "Labels shape: (32, 24, 1)\n", + "Output shape: (32, 24, 1)\n" + ] + } + ], + "source": [ + "print(\"Wide conv window\")\n", + "print('Input shape:', wide_conv_window.example[0].shape)\n", + "print('Labels shape:', wide_conv_window.example[1].shape)\n", + "print('Output shape:', conv_model(wide_conv_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yzxbbS56cSBV" + }, + "source": [ + "Now, you can plot the model's predictions on a wider window. Note the 3 input time steps before the first prediction. Every prediction here is based on the 3 preceding time steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:42.709185Z", + "iopub.status.busy": "2023-10-27T05:30:42.708889Z", + "iopub.status.idle": "2023-10-27T05:30:43.224828Z", + "shell.execute_reply": "2023-10-27T05:30:43.224036Z" + }, + "id": "gR7VyL45UuEe" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wide_conv_window.plot(conv_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H4crpOcoMlSe" + }, + "source": [ + "### Recurrent neural network\n", + "\n", + "A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series data. RNNs process a time series step-by-step, maintaining an internal state from time-step to time-step.\n", + "\n", + "You can learn more in the [Text generation with an RNN](https://www.tensorflow.org/text/tutorials/text_generation) tutorial and the [Recurrent Neural Networks (RNN) with Keras](https://www.tensorflow.org/guide/keras/rnn) guide.\n", + "\n", + "In this tutorial, you will use an RNN layer called Long Short-Term Memory (`tf.keras.layers.LSTM`)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vfQbHSMb1ATa" + }, + "source": [ + "An important constructor argument for all Keras RNN layers, such as `tf.keras.layers.LSTM`, is the `return_sequences` argument. This setting can configure the layer in one of two ways:\n", + "\n", + "1. If `False`, the default, the layer only returns the output of the final time step, giving the model time to warm up its internal state before making a single prediction: \n", + "\n", + "![An LSTM warming up and making a single prediction](images/lstm_1_window.png)\n", + "\n", + "2. If `True`, the layer returns an output for each input. This is useful for:\n", + " * Stacking RNN layers. \n", + " * Training a model on multiple time steps simultaneously.\n", + "\n", + "![An LSTM making a prediction after every time step](images/lstm_many_window.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:43.229414Z", + "iopub.status.busy": "2023-10-27T05:30:43.229121Z", + "iopub.status.idle": "2023-10-27T05:30:43.243949Z", + "shell.execute_reply": "2023-10-27T05:30:43.242875Z" + }, + "id": "DXKLCJy8nWNU" + }, + "outputs": [], + "source": [ + "lstm_model = tf.keras.models.Sequential([\n", + " # Shape [batch, time, features] => [batch, time, lstm_units]\n", + " tf.keras.layers.LSTM(32, return_sequences=True),\n", + " # Shape => [batch, time, features]\n", + " tf.keras.layers.Dense(units=1)\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F124B00KZcLC" + }, + "source": [ + "With `return_sequences=True`, the model can be trained on 24 hours of data at a time.\n", + "\n", + "Note: This will give a pessimistic view of the model's performance. On the first time step, the model has no access to previous steps and, therefore, can't do any better than the simple `linear` and `dense` models shown earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:43.247234Z", + "iopub.status.busy": "2023-10-27T05:30:43.246965Z", + "iopub.status.idle": "2023-10-27T05:30:43.780907Z", + "shell.execute_reply": "2023-10-27T05:30:43.779886Z" + }, + "id": "eZEROCQVYV6q" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: (32, 24, 19)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output shape: (32, 24, 1)\n" + ] + } + ], + "source": [ + "print('Input shape:', wide_window.example[0].shape)\n", + "print('Output shape:', lstm_model(wide_window.example[0]).shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:30:43.784848Z", + "iopub.status.busy": "2023-10-27T05:30:43.784120Z", + "iopub.status.idle": "2023-10-27T05:32:12.903783Z", + "shell.execute_reply": "2023-10-27T05:32:12.902995Z" + }, + "id": "uvdWRl1e9WJl" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/438 [..............................] - ETA: 37s - loss: 0.0045 - mean_absolute_error: 0.0469" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 18/438 [>.............................] - ETA: 1s - loss: 0.0055 - mean_absolute_error: 0.0512 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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0.0056 - mean_absolute_error: 0.0517" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "438/438 [==============================] - 1s 3ms/step - loss: 0.0056 - mean_absolute_error: 0.0517\n" + ] + } + ], + "source": [ + "history = compile_and_fit(lstm_model, wide_window)\n", + "\n", + "IPython.display.clear_output()\n", + "val_performance['LSTM'] = lstm_model.evaluate(wide_window.val)\n", + "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:32:12.907984Z", + "iopub.status.busy": "2023-10-27T05:32:12.907330Z", + "iopub.status.idle": "2023-10-27T05:32:13.360680Z", + "shell.execute_reply": "2023-10-27T05:32:13.360021Z" + }, + "id": "NwAOWCVgB26e" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wide_window.plot(lstm_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pYglOCKehi8F" + }, + "source": [ + "### Performance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2pCk0_rwhi8H" + }, + "source": [ + "With this dataset typically each of the models does slightly better than the one before it:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:32:13.364669Z", + "iopub.status.busy": "2023-10-27T05:32:13.364422Z", + "iopub.status.idle": "2023-10-27T05:32:13.545906Z", + "shell.execute_reply": "2023-10-27T05:32:13.545288Z" + }, + "id": "JjEkt488hi8I" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.arange(len(performance))\n", + "width = 0.3\n", + "metric_name = 'mean_absolute_error'\n", + "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", + "val_mae = [v[metric_index] for v in val_performance.values()]\n", + "test_mae = [v[metric_index] for v in performance.values()]\n", + "\n", + "plt.ylabel('mean_absolute_error [T (degC), normalized]')\n", + "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", + "plt.bar(x + 0.17, test_mae, width, label='Test')\n", + "plt.xticks(ticks=x, labels=performance.keys(),\n", + " rotation=45)\n", + "_ = plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:32:13.549415Z", + "iopub.status.busy": "2023-10-27T05:32:13.548913Z", + "iopub.status.idle": "2023-10-27T05:32:13.552614Z", + "shell.execute_reply": "2023-10-27T05:32:13.552030Z" + }, + "id": "cBMCpsdphi8L" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline : 0.0852\n", + "Linear : 0.0686\n", + "Dense : 0.0595\n", + "Multi step dense: 0.0589\n", + "Conv : 0.0661\n", + "LSTM : 0.0521\n" + ] + } + ], + "source": [ + "for name, value in performance.items():\n", + " print(f'{name:12s}: {value[1]:0.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b5rUJ_2YMWzG" + }, + "source": [ + "### Multi-output models\n", + "\n", + "The models so far all predicted a single output feature, `T (degC)`, for a single time step.\n", + "\n", + "All of these models can be converted to predict multiple features just by changing the number of units in the output layer and adjusting the training windows to include all features in the `labels` (`example_labels`):" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:32:13.556270Z", + "iopub.status.busy": "2023-10-27T05:32:13.555679Z", + "iopub.status.idle": "2023-10-27T05:32:13.691337Z", + "shell.execute_reply": "2023-10-27T05:32:13.690576Z" + }, + "id": "9Gk0Z91xjOwv" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Inputs shape (batch, time, features): (32, 24, 19)\n", + "Labels shape (batch, time, features): (32, 24, 19)\n" + ] + } + ], + "source": [ + "single_step_window = WindowGenerator(\n", + " # `WindowGenerator` returns all features as labels if you \n", + " # don't set the `label_columns` argument.\n", + " input_width=1, label_width=1, shift=1)\n", + "\n", + "wide_window = WindowGenerator(\n", + " input_width=24, label_width=24, shift=1)\n", + "\n", + "for example_inputs, example_labels in wide_window.train.take(1):\n", + " print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n", + " print(f'Labels shape (batch, time, features): {example_labels.shape}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XmcjHfDskX1N" + }, + "source": [ + "Note above that the `features` axis of the labels now has the same depth as the inputs, instead of `1`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9k7S5IHNhSNF" + }, + "source": [ + "#### Baseline\n", + "\n", + "The same baseline model (`Baseline`) can be used here, but this time repeating all features instead of selecting a specific `label_index`:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:32:13.695235Z", + "iopub.status.busy": "2023-10-27T05:32:13.694902Z", + "iopub.status.idle": "2023-10-27T05:32:13.709220Z", + "shell.execute_reply": "2023-10-27T05:32:13.708464Z" + }, + "id": "sqqB9W-pjr5i" + }, + "outputs": [], + "source": [ + "baseline = Baseline()\n", + "baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", + " metrics=[tf.keras.metrics.MeanAbsoluteError()])" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:32:13.712327Z", + "iopub.status.busy": "2023-10-27T05:32:13.712010Z", + "iopub.status.idle": "2023-10-27T05:32:15.096415Z", + "shell.execute_reply": "2023-10-27T05:32:15.095437Z" + }, + "id": "ltQdgaqQjQWu" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/438 [..............................] - 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Every model trained in this tutorial so far was randomly initialized, and then had to learn that the output is a a small change from the previous time step.\n", + "\n", + "While you can get around this issue with careful initialization, it's simpler to build this into the model structure.\n", + "\n", + "It's common in time series analysis to build models that instead of predicting the next value, predict how the value will change in the next time step. Similarly, residual networks—or ResNets—in deep learning refer to architectures where each layer adds to the model's accumulating result.\n", + "\n", + "That is how you take advantage of the knowledge that the change should be small.\n", + "\n", + "![A model with a residual connection](images/residual.png)\n", + "\n", + "Essentially, this initializes the model to match the `Baseline`. For this task it helps models converge faster, with slightly better performance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yP58A_ORx0kM" + }, + "source": [ + "This approach can be used in conjunction with any model discussed in this tutorial. \n", + "\n", + "Here, it is being applied to the LSTM model, note the use of the `tf.initializers.zeros` to ensure that the initial predicted changes are small, and don't overpower the residual connection. There are no symmetry-breaking concerns for the gradients here, since the `zeros` are only used on the last layer." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:35:45.749796Z", + "iopub.status.busy": "2023-10-27T05:35:45.749063Z", + "iopub.status.idle": "2023-10-27T05:35:45.754041Z", + "shell.execute_reply": "2023-10-27T05:35:45.753181Z" + }, + "id": "7YlfnDQC22TQ" + }, + "outputs": [], + "source": [ + "class ResidualWrapper(tf.keras.Model):\n", + " def __init__(self, model):\n", + " super().__init__()\n", + " self.model = model\n", + "\n", + " def call(self, inputs, *args, **kwargs):\n", + " delta = self.model(inputs, *args, **kwargs)\n", + "\n", + " # The prediction for each time step is the input\n", + " # from the previous time step plus the delta\n", + " # calculated by the model.\n", + " return inputs + delta" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:35:45.757468Z", + "iopub.status.busy": "2023-10-27T05:35:45.756999Z", + "iopub.status.idle": "2023-10-27T05:36:44.543243Z", + "shell.execute_reply": "2023-10-27T05:36:44.542445Z" + }, + "id": "NNeH02pspc9B" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/438 [..............................] - ETA: 35s - loss: 0.0517 - mean_absolute_error: 0.1111" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 17/438 [>.............................] - ETA: 1s - loss: 0.0598 - mean_absolute_error: 0.1159 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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0.0617 - mean_absolute_error: 0.1178" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "438/438 [==============================] - 1s 3ms/step - loss: 0.0617 - mean_absolute_error: 0.1178\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CPU times: user 2min 8s, sys: 26.9 s, total: 2min 35s\n", + "Wall time: 58.8 s\n" + ] + } + ], + "source": [ + "%%time\n", + "residual_lstm = ResidualWrapper(\n", + " tf.keras.Sequential([\n", + " tf.keras.layers.LSTM(32, return_sequences=True),\n", + " tf.keras.layers.Dense(\n", + " num_features,\n", + " # The predicted deltas should start small.\n", + " # Therefore, initialize the output layer with zeros.\n", + " kernel_initializer=tf.initializers.zeros())\n", + "]))\n", + "\n", + "history = compile_and_fit(residual_lstm, wide_window)\n", + "\n", + "IPython.display.clear_output()\n", + "val_performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.val)\n", + "performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.test, verbose=0)\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I42Er9Du6co1" + }, + "source": [ + "#### Performance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LZxR38P_6pUi" + }, + "source": [ + "Here is the overall performance for these multi-output models." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:36:44.547056Z", + "iopub.status.busy": "2023-10-27T05:36:44.546788Z", + "iopub.status.idle": "2023-10-27T05:36:44.717796Z", + "shell.execute_reply": "2023-10-27T05:36:44.717147Z" + }, + "id": "6XgTK9tnr7rc" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.arange(len(performance))\n", + "width = 0.3\n", + "\n", + "metric_name = 'mean_absolute_error'\n", + "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", + "val_mae = [v[metric_index] for v in val_performance.values()]\n", + "test_mae = [v[metric_index] for v in performance.values()]\n", + "\n", + "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", + "plt.bar(x + 0.17, test_mae, width, label='Test')\n", + "plt.xticks(ticks=x, labels=performance.keys(),\n", + " rotation=45)\n", + "plt.ylabel('MAE (average over all outputs)')\n", + "_ = plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:36:44.720860Z", + "iopub.status.busy": "2023-10-27T05:36:44.720629Z", + "iopub.status.idle": "2023-10-27T05:36:44.724382Z", + "shell.execute_reply": "2023-10-27T05:36:44.723777Z" + }, + "id": "URz3ajCc6kBj" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Baseline : 0.1638\n", + "Dense : 0.1333\n", + "LSTM : 0.1206\n", + "Residual LSTM : 0.1193\n" + ] + } + ], + "source": [ + "for name, value in performance.items():\n", + " print(f'{name:15s}: {value[1]:0.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_Vt2MJhNxwPU" + }, + "source": [ + "The above performances are averaged across all model outputs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eYokb7Om2YbK" + }, + "source": [ + "## Multi-step models\n", + "\n", + "Both the single-output and multiple-output models in the previous sections made **single time step predictions**, one hour into the future.\n", + "\n", + "This section looks at how to expand these models to make **multiple time step predictions**.\n", + "\n", + "In a multi-step prediction, the model needs to learn to predict a range of future values. Thus, unlike a single step model, where only a single future point is predicted, a multi-step model predicts a sequence of the future values.\n", + "\n", + "There are two rough approaches to this:\n", + "\n", + "1. Single shot predictions where the entire time series is predicted at once.\n", + "2. Autoregressive predictions where the model only makes single step predictions and its output is fed back as its input.\n", + "\n", + "In this section all the models will predict **all the features across all output time steps**.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WFsDAwVt4_rq" + }, + "source": [ + "For the multi-step model, the training data again consists of hourly samples. However, here, the models will learn to predict 24 hours into the future, given 24 hours of the past.\n", + "\n", + "Here is a `Window` object that generates these slices from the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:36:44.727613Z", + "iopub.status.busy": "2023-10-27T05:36:44.727206Z", + "iopub.status.idle": "2023-10-27T05:36:45.241450Z", + "shell.execute_reply": "2023-10-27T05:36:45.240709Z" + }, + "id": "1cFYtsz6XiGw" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Total window size: 48\n", + "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", + "Label indices: [24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]\n", + "Label column name(s): None" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "OUT_STEPS = 24\n", + "multi_window = WindowGenerator(input_width=24,\n", + " label_width=OUT_STEPS,\n", + " shift=OUT_STEPS)\n", + "\n", + "multi_window.plot()\n", + "multi_window" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5lg8SInh9Jzd" + }, + "source": [ + "### Baselines" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "axwpoWYOApJL" + }, + "source": [ + "A simple baseline for this task is to repeat the last input time step for the required number of output time steps:\n", + "\n", + "![Repeat the last input, for each output step](images/multistep_last.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:36:45.244898Z", + "iopub.status.busy": "2023-10-27T05:36:45.244651Z", + "iopub.status.idle": "2023-10-27T05:36:47.157824Z", + "shell.execute_reply": "2023-10-27T05:36:47.157089Z" + }, + "id": "_5iaHSaJ9Rxv" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 1:13 - loss: 0.6018 - mean_absolute_error: 0.5035" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 29/437 [>.............................] - ETA: 0s - loss: 0.6245 - mean_absolute_error: 0.4990 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 57/437 [==>...........................] - ETA: 0s - loss: 0.6219 - mean_absolute_error: 0.4985" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 85/437 [====>.........................] - ETA: 0s - loss: 0.6225 - mean_absolute_error: 0.4995" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "112/437 [======>.......................] - ETA: 0s - loss: 0.6267 - mean_absolute_error: 0.5003" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class MultiStepLastBaseline(tf.keras.Model):\n", + " def call(self, inputs):\n", + " return tf.tile(inputs[:, -1:, :], [1, OUT_STEPS, 1])\n", + "\n", + "last_baseline = MultiStepLastBaseline()\n", + "last_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", + " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", + "\n", + "multi_val_performance = {}\n", + "multi_performance = {}\n", + "\n", + "multi_val_performance['Last'] = last_baseline.evaluate(multi_window.val)\n", + "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(last_baseline)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AvHZ93ObAfMA" + }, + "source": [ + "Since this task is to predict 24 hours into the future, given 24 hours of the past, another simple approach is to repeat the previous day, assuming tomorrow will be similar:\n", + "\n", + "![Repeat the previous day](images/multistep_repeat.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:36:47.162098Z", + "iopub.status.busy": "2023-10-27T05:36:47.161536Z", + "iopub.status.idle": "2023-10-27T05:36:48.984400Z", + "shell.execute_reply": "2023-10-27T05:36:48.983674Z" + }, + "id": "L8Y1uMhGwIRs" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 1:08 - loss: 0.4046 - mean_absolute_error: 0.3896" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 28/437 [>.............................] - ETA: 0s - loss: 0.4342 - mean_absolute_error: 0.3962 " + ] + }, + { + "name": "stdout", + 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0.4263 - mean_absolute_error: 0.3955" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "437/437 [==============================] - 1s 2ms/step - loss: 0.4270 - mean_absolute_error: 0.3959\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class RepeatBaseline(tf.keras.Model):\n", + " def call(self, inputs):\n", + " return inputs\n", + "\n", + "repeat_baseline = RepeatBaseline()\n", + "repeat_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", + " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", + "\n", + "multi_val_performance['Repeat'] = repeat_baseline.evaluate(multi_window.val)\n", + "multi_performance['Repeat'] = repeat_baseline.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(repeat_baseline)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tbndS-ct9C2Q" + }, + "source": [ + "### Single-shot models\n", + "\n", + "One high-level approach to this problem is to use a \"single-shot\" model, where the model makes the entire sequence prediction in a single step.\n", + "\n", + "This can be implemented efficiently as a `tf.keras.layers.Dense` with `OUT_STEPS*features` output units. The model just needs to reshape that output to the required `(OUTPUT_STEPS, features)`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NCKS4m1VKrDQ" + }, + "source": [ + "#### Linear\n", + "\n", + "A simple linear model based on the last input time step does better than either baseline, but is underpowered. The model needs to predict `OUTPUT_STEPS` time steps, from a single input time step with a linear projection. It can only capture a low-dimensional slice of the behavior, likely based mainly on the time of day and time of year.\n", + "\n", + "![Predict all timesteps from the last time-step](images/multistep_dense.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:36:48.989133Z", + "iopub.status.busy": "2023-10-27T05:36:48.988843Z", + "iopub.status.idle": "2023-10-27T05:37:19.728629Z", + "shell.execute_reply": "2023-10-27T05:37:19.727949Z" + }, + "id": "kfRz_WVhIQcd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 34s - loss: 0.3062 - mean_absolute_error: 0.3227" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_linear_model = tf.keras.Sequential([\n", + " # Take the last time-step.\n", + " # Shape [batch, time, features] => [batch, 1, features]\n", + " tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n", + " # Shape => [batch, 1, out_steps*features]\n", + " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", + " kernel_initializer=tf.initializers.zeros()),\n", + " # Shape => [batch, out_steps, features]\n", + " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", + "])\n", + "\n", + "history = compile_and_fit(multi_linear_model, multi_window)\n", + "\n", + "IPython.display.clear_output()\n", + "multi_val_performance['Linear'] = multi_linear_model.evaluate(multi_window.val)\n", + "multi_performance['Linear'] = multi_linear_model.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(multi_linear_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zi2TMHk2IRrh" + }, + "source": [ + "#### Dense\n", + "\n", + "Adding a `tf.keras.layers.Dense` between the input and output gives the linear model more power, but is still only based on a single input time step." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:37:19.733015Z", + "iopub.status.busy": "2023-10-27T05:37:19.732769Z", + "iopub.status.idle": "2023-10-27T05:38:07.271768Z", + "shell.execute_reply": "2023-10-27T05:38:07.271056Z" + }, + "id": "jezm-BKaGj91" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 34s - loss: 0.2548 - mean_absolute_error: 0.3072" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + " 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_dense_model = tf.keras.Sequential([\n", + " # Take the last time step.\n", + " # Shape [batch, time, features] => [batch, 1, features]\n", + " tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n", + " # Shape => [batch, 1, dense_units]\n", + " tf.keras.layers.Dense(512, activation='relu'),\n", + " # Shape => [batch, out_steps*features]\n", + " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", + " kernel_initializer=tf.initializers.zeros()),\n", + " # Shape => [batch, out_steps, features]\n", + " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", + "])\n", + "\n", + "history = compile_and_fit(multi_dense_model, multi_window)\n", + "\n", + "IPython.display.clear_output()\n", + "multi_val_performance['Dense'] = multi_dense_model.evaluate(multi_window.val)\n", + "multi_performance['Dense'] = multi_dense_model.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(multi_dense_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "icsBAjCzMaMl" + }, + "source": [ + "#### CNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "34lCZrWYNBwd" + }, + "source": [ + "A convolutional model makes predictions based on a fixed-width history, which may lead to better performance than the dense model since it can see how things are changing over time:\n", + "\n", + "![A convolutional model sees how things change over time](images/multistep_conv.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:38:07.276523Z", + "iopub.status.busy": "2023-10-27T05:38:07.276266Z", + "iopub.status.idle": "2023-10-27T05:38:54.043730Z", + "shell.execute_reply": "2023-10-27T05:38:54.042918Z" + }, + "id": "0xJoIP6PMWMI" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 34s - loss: 0.2273 - 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CONV_WIDTH = 3\n", + "multi_conv_model = tf.keras.Sequential([\n", + " # Shape [batch, time, features] => [batch, CONV_WIDTH, features]\n", + " tf.keras.layers.Lambda(lambda x: x[:, -CONV_WIDTH:, :]),\n", + " # Shape => [batch, 1, conv_units]\n", + " tf.keras.layers.Conv1D(256, activation='relu', kernel_size=(CONV_WIDTH)),\n", + " # Shape => [batch, 1, out_steps*features]\n", + " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", + " kernel_initializer=tf.initializers.zeros()),\n", + " # Shape => [batch, out_steps, features]\n", + " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", + "])\n", + "\n", + "history = compile_and_fit(multi_conv_model, multi_window)\n", + "\n", + "IPython.display.clear_output()\n", + "\n", + "multi_val_performance['Conv'] = multi_conv_model.evaluate(multi_window.val)\n", + "multi_performance['Conv'] = multi_conv_model.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(multi_conv_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "weBjeZAFJOP4" + }, + "source": [ + "#### RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8022xOKxOO92" + }, + "source": [ + "A recurrent model can learn to use a long history of inputs, if it's relevant to the predictions the model is making. Here the model will accumulate internal state for 24 hours, before making a single prediction for the next 24 hours.\n", + "\n", + "In this single-shot format, the LSTM only needs to produce an output at the last time step, so set `return_sequences=False` in `tf.keras.layers.LSTM`.\n", + "\n", + "![The LSTM accumulates state over the input window, and makes a single prediction for the next 24 hours](images/multistep_lstm.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:38:54.048251Z", + "iopub.status.busy": "2023-10-27T05:38:54.047656Z", + "iopub.status.idle": "2023-10-27T05:40:01.913933Z", + "shell.execute_reply": "2023-10-27T05:40:01.913256Z" + }, + "id": "Bf1ks6RTzF64" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 35s - loss: 0.2475 - mean_absolute_error: 0.2971" + ] + }, + { + "name": "stdout", + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_lstm_model = tf.keras.Sequential([\n", + " # Shape [batch, time, features] => [batch, lstm_units].\n", + " # Adding more `lstm_units` just overfits more quickly.\n", + " tf.keras.layers.LSTM(32, return_sequences=False),\n", + " # Shape => [batch, out_steps*features].\n", + " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", + " kernel_initializer=tf.initializers.zeros()),\n", + " # Shape => [batch, out_steps, features].\n", + " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", + "])\n", + "\n", + "history = compile_and_fit(multi_lstm_model, multi_window)\n", + "\n", + "IPython.display.clear_output()\n", + "\n", + "multi_val_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.val)\n", + "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(multi_lstm_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d5n-1cDW12Vo" + }, + "source": [ + "### Advanced: Autoregressive model\n", + "\n", + "The above models all predict the entire output sequence in a single step.\n", + "\n", + "In some cases it may be helpful for the model to decompose this prediction into individual time steps. Then, each model's output can be fed back into itself at each step and predictions can be made conditioned on the previous one, like in the classic Generating Sequences With Recurrent Neural Networks.\n", + "\n", + "One clear advantage to this style of model is that it can be set up to produce output with a varying length.\n", + "\n", + "You could take any of the single-step multi-output models trained in the first half of this tutorial and run in an autoregressive feedback loop, but here you'll focus on building a model that's been explicitly trained to do that.\n", + "\n", + "![Feedback a model's output to its input](images/multistep_autoregressive.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PKRreBbULRXY" + }, + "source": [ + "#### RNN\n", + "\n", + "This tutorial only builds an autoregressive RNN model, but this pattern could be applied to any model that was designed to output a single time step.\n", + "\n", + "The model will have the same basic form as the single-step LSTM models from earlier: a `tf.keras.layers.LSTM` layer followed by a `tf.keras.layers.Dense` layer that converts the `LSTM` layer's outputs to model predictions.\n", + "\n", + "A `tf.keras.layers.LSTM` is a `tf.keras.layers.LSTMCell` wrapped in the higher level `tf.keras.layers.RNN` that manages the state and sequence results for you (Check out the [Recurrent Neural Networks (RNN) with Keras](https://www.tensorflow.org/guide/keras/rnn) guide for details).\n", + "\n", + "In this case, the model has to manually manage the inputs for each step, so it uses `tf.keras.layers.LSTMCell` directly for the lower level, single time step interface." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:01.918165Z", + "iopub.status.busy": "2023-10-27T05:40:01.917903Z", + "iopub.status.idle": "2023-10-27T05:40:01.922575Z", + "shell.execute_reply": "2023-10-27T05:40:01.921871Z" + }, + "id": "s5tz3Nu0R5JG" + }, + "outputs": [], + "source": [ + "class FeedBack(tf.keras.Model):\n", + " def __init__(self, units, out_steps):\n", + " super().__init__()\n", + " self.out_steps = out_steps\n", + " self.units = units\n", + " self.lstm_cell = tf.keras.layers.LSTMCell(units)\n", + " # Also wrap the LSTMCell in an RNN to simplify the `warmup` method.\n", + " self.lstm_rnn = tf.keras.layers.RNN(self.lstm_cell, return_state=True)\n", + " self.dense = tf.keras.layers.Dense(num_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:01.925600Z", + "iopub.status.busy": "2023-10-27T05:40:01.925379Z", + "iopub.status.idle": "2023-10-27T05:40:01.937413Z", + "shell.execute_reply": "2023-10-27T05:40:01.936820Z" + }, + "id": "2OXVM9G1U7xR" + }, + "outputs": [], + "source": [ + "feedback_model = FeedBack(units=32, out_steps=OUT_STEPS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ph5uFSfTUNho" + }, + "source": [ + "The first method this model needs is a `warmup` method to initialize its internal state based on the inputs. Once trained, this state will capture the relevant parts of the input history. This is equivalent to the single-step `LSTM` model from earlier:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:01.940554Z", + "iopub.status.busy": "2023-10-27T05:40:01.940336Z", + "iopub.status.idle": "2023-10-27T05:40:01.944047Z", + "shell.execute_reply": "2023-10-27T05:40:01.943486Z" + }, + "id": "vM2K_LLdRjDZ" + }, + "outputs": [], + "source": [ + "def warmup(self, inputs):\n", + " # inputs.shape => (batch, time, features)\n", + " # x.shape => (batch, lstm_units)\n", + " x, *state = self.lstm_rnn(inputs)\n", + "\n", + " # predictions.shape => (batch, features)\n", + " prediction = self.dense(x)\n", + " return prediction, state\n", + "\n", + "FeedBack.warmup = warmup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6JkaSYaZ9eB7" + }, + "source": [ + "This method returns a single time-step prediction and the internal state of the `LSTM`:" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:01.947452Z", + "iopub.status.busy": "2023-10-27T05:40:01.946910Z", + "iopub.status.idle": "2023-10-27T05:40:02.113871Z", + "shell.execute_reply": "2023-10-27T05:40:02.113239Z" + }, + "id": "w9Fz6NTKXXwU" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TensorShape([32, 19])" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prediction, state = feedback_model.warmup(multi_window.example[0])\n", + "prediction.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S_ZdvPjdX3y3" + }, + "source": [ + "With the `RNN`'s state, and an initial prediction you can now continue iterating the model feeding the predictions at each step back as the input.\n", + "\n", + "The simplest approach for collecting the output predictions is to use a Python list and a `tf.stack` after the loop." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yotTad3nZXQU" + }, + "source": [ + "Note: Stacking a Python list like this only works with eager-execution, using `Model.compile(..., run_eagerly=True)` for training, or with a fixed length output. For a dynamic output length, you would need to use a `tf.TensorArray` instead of a Python list, and `tf.range` instead of the Python `range`." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:02.117766Z", + "iopub.status.busy": "2023-10-27T05:40:02.117346Z", + "iopub.status.idle": "2023-10-27T05:40:02.122428Z", + "shell.execute_reply": "2023-10-27T05:40:02.121847Z" + }, + "id": "g1GRDu3mZtr9" + }, + "outputs": [], + "source": [ + "def call(self, inputs, training=None):\n", + " # Use a TensorArray to capture dynamically unrolled outputs.\n", + " predictions = []\n", + " # Initialize the LSTM state.\n", + " prediction, state = self.warmup(inputs)\n", + "\n", + " # Insert the first prediction.\n", + " predictions.append(prediction)\n", + "\n", + " # Run the rest of the prediction steps.\n", + " for n in range(1, self.out_steps):\n", + " # Use the last prediction as input.\n", + " x = prediction\n", + " # Execute one lstm step.\n", + " x, state = self.lstm_cell(x, states=state,\n", + " training=training)\n", + " # Convert the lstm output to a prediction.\n", + " prediction = self.dense(x)\n", + " # Add the prediction to the output.\n", + " predictions.append(prediction)\n", + "\n", + " # predictions.shape => (time, batch, features)\n", + " predictions = tf.stack(predictions)\n", + " # predictions.shape => (batch, time, features)\n", + " predictions = tf.transpose(predictions, [1, 0, 2])\n", + " return predictions\n", + "\n", + "FeedBack.call = call" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ubop-YWp15XW" + }, + "source": [ + "Test run this model on the example inputs:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:02.125416Z", + "iopub.status.busy": "2023-10-27T05:40:02.125192Z", + "iopub.status.idle": "2023-10-27T05:40:02.229792Z", + "shell.execute_reply": "2023-10-27T05:40:02.229200Z" + }, + "id": "Xja83zEYaM2D" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output shape (batch, time, features): (32, 24, 19)\n" + ] + } + ], + "source": [ + "print('Output shape (batch, time, features): ', feedback_model(multi_window.example[0]).shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qMs0rYB8be9M" + }, + "source": [ + "Now, train the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:40:02.232874Z", + "iopub.status.busy": "2023-10-27T05:40:02.232620Z", + "iopub.status.idle": "2023-10-27T05:47:05.859323Z", + "shell.execute_reply": "2023-10-27T05:47:05.858639Z" + }, + "id": "VBRVG2hnNyrO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + " 1/437 [..............................] - ETA: 36s - loss: 0.2431 - mean_absolute_error: 0.3142" + ] + }, + { + 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loss: 0.2280 - mean_absolute_error: 0.3019\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = compile_and_fit(feedback_model, multi_window)\n", + "\n", + "IPython.display.clear_output()\n", + "\n", + "multi_val_performance['AR LSTM'] = feedback_model.evaluate(multi_window.val)\n", + "multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0)\n", + "multi_window.plot(feedback_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hGjcJsAQJUkI" + }, + "source": [ + "### Performance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sODAwr2ndtDB" + }, + "source": [ + "There are clearly diminishing returns as a function of model complexity on this problem:" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:47:05.863879Z", + "iopub.status.busy": "2023-10-27T05:47:05.863606Z", + "iopub.status.idle": "2023-10-27T05:47:06.038056Z", + "shell.execute_reply": "2023-10-27T05:47:06.037421Z" + }, + "id": "WZwWBA8S6B3L" + }, + "outputs": [ + { + "data": { + "image/png": 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TIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0ctj6goSEBG3YsEHHjh1TfHy8ChcurGrVqql06dKZUR8AAABwWxkOs2vWrNHnn3+u+fPnKykpSV5eXsqVK5fOnz+vhIQEBQQEqGfPnnr55ZeVN2/ezKwZAAAAkJTBYQbh4eGKiIiQv7+/fv/9d12+fFn//POPYmJiFB8fr4MHD2rQoEGKiorSI488oqVLl2Z23QAAAEDGemZbtGihuXPnKmfOnOluDwgIUEBAgLp06aI9e/bo5MmTdi0SAAAASE+GwuxLL72U4QNWqFBBFSpUuOeCAAAAgIyyeTWD48ePKyYmxvp848aN+s9//qPx48fbtTAAAADgbmwOsx06dNDy5cslSadOnVLTpk21ceNGDRw4UMOHD7d7gQAAAMDt2Bxmd+3apRo1akiSZs+erUqVKmnt2rWaPn26pkyZYu/6AAAAgNuyOcwmJSXJ3d1dkrRs2TKFh4dLksqXL8/ELwAAAGQpm8NsxYoVNW7cOK1atUpLly5VWFiYJOnEiRMqWLCg3QsEAAAAbsfmMPvRRx/p22+/1eOPP6727duratWqkqRff/3VOvwAAAAAyAo238728ccf17lz5xQbGytvb29re8+ePZU7d267FgcAAADcic09s40bN9bly5dTBVlJKlCggCIiIuxWGAAAAHA3NofZFStWKDExMU37tWvXtGrVKrsUBQAAAGREhocZ7Nixw/r1nj17dOrUKevz5ORkLV68WMWKFbNvdQAAAMAdZDjMBgUFyWKxyGKxqHHjxmm258qVS19++aVdiwMAAADuJMNh9siRIzLGKCAgQBs3blThwoWt29zc3OTj4yNXV9dMKRIAAABIT4bDbKlSpSRJKSkpmVYMAAAAYAubl+aaOnXqHbd37tz5nosBAAAAbGFzmO3bt2+q50lJSYqPj5ebm5s8PT0JswAAAMgyNi/NdeHChVSPK1euaP/+/apXr55mzJiRGTUCAAAA6bI5zKYnMDBQI0eOTNNrCwAAAGQmu4RZScqRI4dOnDhhr8MBAAAAd2XzmNlff/011XNjjE6ePKmvvvpKdevWtVthAAAAwN3YHGZbt26d6rnFYlHhwoXVuHFjjRo1yl51AQAAAHdlc5hlnVkAAAA8KO5rzKwxRsYYe9UCAAAA2OSewuykSZNUqVIleXh4yMPDQ5UqVdLEiRPtXRsAAABwRzaH2SFDhqhv375q1aqV5syZozlz5qhVq1Z6/fXXNWTIkHsqYuzYsfL395eHh4dq1qypjRs3Zuh1M2fOlMViSTOOFwAAAA8Hm8fMfvPNN5owYYLat29vbQsPD1eVKlXUu3dvDR8+3KbjzZo1S5GRkRo3bpxq1qypMWPGKDQ0VPv375ePj89tX3f06FH169dP9evXt/VbAAAAQDZhc89sUlKSQkJC0rQHBwfr+vXrNhcwevRo9ejRQ127dlWFChU0btw4eXp66rvvvrvta5KTk9WxY0cNGzZMAQEBNp8TAAAA2YPNYfb555/XN998k6Z9/Pjx6tixo03HSkxM1ObNm9WkSZP/K8jFRU2aNNG6detu+7rhw4fLx8dH3bt3v+s5EhISFBsbm+oBAACA7MHmYQbSjQlgv//+u2rVqiVJ2rBhg6Kjo9W5c2dFRkZa9xs9evQdj3Pu3DklJyerSJEiqdqLFCmiffv2pfua1atXa9KkSdq2bVuGah0xYoSGDRuWoX0BAADgXGwOs7t27dJjjz0mSTp06JAkqVChQipUqJB27dpl3c9isdipxP9z+fJlPf/885owYYIKFSqUodcMGDAgVcCOjY1ViRIl7F4bAAAAsp7NYXb58uV2O3mhQoXk6uqq06dPp2o/ffq0fH190+x/6NAhHT16VK1atbK23byJQ44cObR//36VKVMm1Wvc3d3l7u5ut5oBAADw4LB5zGy3bt10+fLlNO1xcXHq1q2bTcdyc3NTcHCwoqKirG0pKSmKiopS7dq10+xfvnx57dy5U9u2bbM+wsPD1ahRI23bto0eVwAAgIeMzT2z33//vUaOHKm8efOmar969aqmTp16x1UI0hMZGakuXbooJCRENWrU0JgxYxQXF6euXbtKkjp37qxixYppxIgR1hs03Cp//vySlKYd2Zv/2wuz5DxHR7bIkvMAAIB7k+EwGxsba7197eXLl+Xh4WHdlpycrEWLFt1xXdjbiYiI0NmzZzVkyBCdOnVKQUFBWrx4sXVSWHR0tFxc7uuuuwAAAMimMhxm8+fPL4vFIovFokceeSTNdovFcs+rBvTq1Uu9evVKd9uKFSvu+NopU6bc0zkBAADg/DIcZpcvXy5jjBo3bqy5c+eqQIEC1m1ubm4qVaqU/Pz8MqVIAAAAID0ZDrMNGzaUJB05ckQlS5bMlKW3AAAAAFvYPAHs2LFjOnbs2G23N2jQ4L4KAgAAADLK5jD7+OOPp2m7tZc2OTn5vgoCAAAAMsrmZQIuXLiQ6nHmzBktXrxY1atX1++//54ZNQIAAADpsrln1svLK01b06ZN5ebmpsjISG3evNkuhQEAAAB3Y7cFXIsUKaL9+/fb63AAAADAXdncM7tjx45Uz40xOnnypEaOHKmgoCB71QUAAADclc1hNigoSBaLRcaYVO21atWy+Va2AAAAwP2wOcweOXIk1XMXFxcVLlw41e1tAQAAgKxgc5gtVapUZtQBAAAA2OyeJoCtXLlSrVq1UtmyZVW2bFmFh4dr1apV9q4NAAAAuCObw+y0adPUpEkTeXp6qk+fPurTp49y5cqlJ554Qj/++GNm1AgAAACky+ZhBh988IE+/vhjvf7669a2Pn36aPTo0XrvvffUoUMHuxYIPDDeTbvGcuac51LWnAcAgGzA5p7Zw4cPq1WrVmnaw8PD00wOAwAAADKTzWG2RIkSioqKStO+bNkylShRwi5FAQAAABlh8zCDN954Q3369NG2bdtUp04dSdKaNWs0ZcoUff7553YvEAAAALgdm8PsK6+8Il9fX40aNUqzZ8+WJD366KOaNWuWnnrqKbsXCAAAANyOzWFWktq0aaM2bdrYuxYAAADAJve0ziwAAADwICDMAgAAwGkRZgEAAOC0CLMAAABwWoRZAAAAOK0MrWYQGRmZ4QOOHj36nosBAAAAbJGhMLt169ZUz7ds2aLr16+rXLlykqQDBw7I1dVVwcHB9q8QAAAAuI0Mhdnly5dbvx49erTy5s2r77//Xt7e3pKkCxcuqGvXrqpfv37mVAkAAACkw+Yxs6NGjdKIESOsQVaSvL299f7772vUqFF2LQ4AAAC4E5vDbGxsrM6ePZum/ezZs7p8+bJdigIAAAAywuYw26ZNG3Xt2lXz5s1TTEyMYmJiNHfuXHXv3l1t27bNjBoBAACAdGVozOytxo0bp379+qlDhw5KSkq6cZAcOdS9e3d98skndi8QAAAAuB2bw6ynp6e+/vprffLJJzp06JAkqUyZMsqdO7fdiwMAAADuxOYwe1Pu3LlVpUoVe9YCAAAA2MTmMBsXF6eRI0cqKipKZ86cUUpKSqrthw8ftltxAAAAwJ3YHGZffPFFrVy5Us8//7yKFi0qi8WSGXUBAAAAd2VzmP3tt9+0cOFC1a1bNzPqAQAAADLM5qW5vL29VaBAgcyoBQAAALCJzWH2vffe05AhQxQfH58Z9QAAAAAZZvMwg1GjRunQoUMqUqSI/P39lTNnzlTbt2zZYrfiAAAAgDuxOcy2bt06E8oAAAAAbGdzmB06dGhm1AEAAADYzOYxswAAAMCDwuae2eTkZH322WeaPXu2oqOjlZiYmGr7+fPn7VYcAAAAcCc298wOGzZMo0ePVkREhC5duqTIyEi1bdtWLi4uevfddzOhRAAAACB9NofZ6dOna8KECXrjjTeUI0cOtW/fXhMnTtSQIUO0fv36zKgRAAAASJfNYfbUqVOqXLmyJClPnjy6dOmSJKlly5ZauHChfasDAAAA7sDmMFu8eHGdPHlSklSmTBn9/vvvkqRNmzbJ3d3dvtUBAAAAd2BzmG3Tpo2ioqIkSb1799bgwYMVGBiozp07q1u3bnYvEAAAALgdm1czGDlypPXriIgIlSpVSmvXrlVgYKBatWpl1+IAAACAO7E5zP5brVq1VKtWLXvUAgAAANiEmyYAAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LZvD7PHjxxUTE2N9vnHjRv3nP//R+PHj7VoYAAAAcDc2h9kOHTpo+fLlkm7cDaxp06bauHGjBg4cqOHDh9u9QAAAAOB2bA6zu3btUo0aNSRJs2fPVqVKlbR27VpNnz5dU6ZMsXd9AAAAwG3ZHGaTkpKst61dtmyZwsPDJUnly5e33uYWAAAAyAo2h9mKFStq3LhxWrVqlZYuXaqwsDBJ0okTJ1SwYEG7FwgAAADcjs1h9qOPPtK3336rxx9/XO3bt1fVqlUlSb/++qt1+AEAAACQFWy+ne3jjz+uc+fOKTY2Vt7e3tb2nj17ytPT067FAQAAAHdyT+vMGmO0efNmffvtt7p8+bIkyc3NjTALAACALGVzz+yxY8cUFham6OhoJSQkqGnTpsqbN68++ugjJSQkaNy4cZlRJwAAAJCGzT2zffv2VUhIiC5cuKBcuXJZ29u0aaOoqCi7FgcAAADcic09s6tWrdLatWvl5uaWqt3f319///233QoDAAAA7sbmntmUlBQlJyenaY+JiVHevHntUhQAAACQETaH2WbNmmnMmDHW5xaLRVeuXNHQoUPVvHlze9YGAAAA3JHNwwxGjRql0NBQVahQQdeuXVOHDh108OBBFSpUSDNmzMiMGgEAAIB02Rxmixcvru3bt2vmzJnasWOHrly5ou7du6tjx46pJoQBAAAAmc3mMCtJOXLkUKdOnexdCwAAAGCTewqzJ06c0OrVq3XmzBmlpKSk2tanTx+7FAYAAADcjc1hdsqUKXrppZfk5uamggULymKxWLdZLBbCLAAAALKMzWF28ODBGjJkiAYMGCAXl3u6Gy4AAABgFzan0fj4eD333HMEWQAAADiczT2z3bt315w5c/T2229nRj0AHMT/7YVZcp6jI1tkyXkAAA8Hm7tXR4wYoZUrV+rxxx9X7969FRkZmepxL8aOHSt/f395eHioZs2a2rhx4233nTdvnkJCQpQ/f37lzp1bQUFB+uGHH+7pvAAAAHBuNvfMjhgxQkuWLFG5cuUkKc0EMFvNmjVLkZGRGjdunGrWrKkxY8YoNDRU+/fvl4+PT5r9CxQooIEDB6p8+fJyc3PTggUL1LVrV/n4+Cg0NNTm8wMAAMB53dMdwL777ju98MILdilg9OjR6tGjh7p27SpJGjdunBYuXKjvvvsu3aEMjz/+eKrnffv21ffff6/Vq1cTZgEAgMNlxbAthmz9H5uHGbi7u6tu3bp2OXliYqI2b96sJk2a/F9BLi5q0qSJ1q1bd9fXG2MUFRWl/fv3q0GDBunuk5CQoNjY2FQPAAAAZA8298z27dtXX375pb744ov7Pvm5c+eUnJysIkWKpGovUqSI9u3bd9vXXbp0ScWKFVNCQoJcXV319ddfq2nTpunuO2LECA0bNuy+awWQCd71yqLzXMqa8wAAspzNYXbjxo363//+pwULFqhixYrKmTNnqu3z5s2zW3G3kzdvXm3btk1XrlxRVFSUIiMjFRAQkGYIgiQNGDAg1cS02NhYlShRItNrBAAAQOazOczmz59fbdu2tcvJCxUqJFdXV50+fTpV++nTp+Xr63vb17m4uKhs2bKSpKCgIO3du1cjRoxIN8y6u7vL3d3dLvUCAADgwWJzmJ08ebLdTu7m5qbg4GBFRUWpdevWkqSUlBRFRUWpV69eGT5OSkqKEhIS7FYXAAAAnIPNYdbeIiMj1aVLF4WEhKhGjRoaM2aM4uLirKsbdO7cWcWKFdOIESMk3RgDGxISojJlyighIUGLFi3SDz/8oG+++caR3wYAAIDjZMUchAd0/kGGwuxjjz2mqKgoeXt7q1q1andcT3bLli02FRAREaGzZ89qyJAhOnXqlIKCgrR48WLrpLDo6OhUt86Ni4vTq6++qpiYGOXKlUvly5fXtGnTFBERYdN5AQAA4PwyFGafeuop67jTp5566p5ujnAnvXr1uu2wghUrVqR6/v777+v999+36/kBAADgnDIUZocOHWr9+t13382sWgAAAACb2HzThICAAP3zzz9p2i9evKiAgAC7FAUAAABkhM1h9ujRo0pOTk7TnpCQoJiYGLsUBQAAAGREhlcz+PXXX61fL1myRF5e/zdrLjk5WVFRUSpdurR9qwMAAADuIMNh9uY6sBaLRV26dEm1LWfOnPL399eoUaPsWhwAAABwJxkOsykpKZKk0qVLa9OmTSpUqFCmFQUAAABkhM03TThy5Ehm1AEAABzA/+2FmX6OoyNbZPo58PBy+B3AACC7yoqQIBEUADzcbF7NAAAAAHhQEGYBAADgtBhmAABwGozvBPBvGQqzsbGxGT5gvnz57rkYAAAAwBYZCrP58+eXxWK54z7GGFkslnTvDgYAyCLvet19n/s+x6XMPwcAZFCGwuzy5cszuw4AAPCw4E0X7ChDYbZhw4aZXQcAAABgswyF2R07dmT4gFWqVLnnYgAAeODQiwg80DIUZoOCgmSxWGSMueN+jJkFAABAVspQmOUWtgAAAHgQZSjMlipVKrPrAAAAAGx2zzdN2LNnj6Kjo5WYmJiqPTw8/L6LAgAAADLC5jB7+PBhtWnTRjt37kw1jvbmOrSMmQUAAEBWcbH1BX379lXp0qV15swZeXp6avfu3frjjz8UEhKiFStWZEKJAAAAQPps7pldt26d/ve//6lQoUJycXGRi4uL6tWrpxEjRqhPnz7aunVrZtQJAAAApGFzz2xycrLy5s0rSSpUqJBOnDgh6cYksf3799u3OgAAAOAObO6ZrVSpkrZv367SpUurZs2a+vjjj+Xm5qbx48crICAgM2oEAAAA0mVzmB00aJDi4uIkScOHD1fLli1Vv359FSxYULNmzbJ7gQAAAMDt2BxmQ0NDrV+XLVtW+/bt0/nz5+Xt7W1d0QAAAADICve8zuytChQoYI/DAAAAADaxeQIYAAAA8KAgzAIAAMBpEWYBAADgtGwKs0lJSerWrZuOHDmSWfUAAAAAGWZTmM2ZM6fmzp2bWbUAAAAANrF5mEHr1q31yy+/ZEIpAAAAgG1sXporMDBQw4cP15o1axQcHKzcuXOn2t6nTx+7FQcAAADcic1hdtKkScqfP782b96szZs3p9pmsVgIswAAAMgyNodZJn8BAADgQXHPS3MlJiZq//79un79uj3rAQAAADLM5jAbHx+v7t27y9PTUxUrVlR0dLQkqXfv3ho5cqTdCwQAAABux+YwO2DAAG3fvl0rVqyQh4eHtb1JkyaaNWuWXYsDAAAA7sTmMbO//PKLZs2apVq1aslisVjbK1asqEOHDtm1OAAAAOBObO6ZPXv2rHx8fNK0x8XFpQq3AAAAQGazOcyGhIRo4cKF1uc3A+zEiRNVu3Zt+1UGAAAA3IXNwww+/PBDPfnkk9qzZ4+uX7+uzz//XHv27NHatWu1cuXKzKgRAAAASJfNPbP16tXTtm3bdP36dVWuXFm///67fHx8tG7dOgUHB2dGjQAAAEC6bO6ZlaQyZcpowoQJ9q4FAAAAsInNPbNNmjTRlClTFBsbmxn1AAAAABlmc5itWLGiBgwYIF9fXz377LP673//q6SkpMyoDQAAALgjm8Ps559/rr///lu//PKLcufOrc6dO6tIkSLq2bMnE8AAAACQpWwOs5Lk4uKiZs2aacqUKTp9+rS+/fZbbdy4UY0bN7Z3fQAAAMBt3dMEsJtOnTqlmTNnatq0adqxY4dq1Khhr7oAAACAu7K5ZzY2NlaTJ09W06ZNVaJECX3zzTcKDw/XwYMHtX79+syoEQAAAEiXzT2zRYoUkbe3tyIiIjRixAiFhIRkRl0AAADAXdkcZn/99Vc98cQTcnG5p+G2AAAAgN3YHGabNm0qSTp79qz2798vSSpXrpwKFy5s38oAAACAu7C5ezU+Pl7dunVT0aJF1aBBAzVo0EB+fn7q3r274uPjM6NGAAAAIF02h9nXX39dK1eu1Pz583Xx4kVdvHhR//3vf7Vy5Uq98cYbmVEjAAAAkC6bhxnMnTtXP/30kx5//HFrW/PmzZUrVy61a9dO33zzjT3rAwAAAG7rnoYZFClSJE27j48PwwwAAACQpWwOs7Vr19bQoUN17do1a9vVq1c1bNgw1a5d267FAQAAAHdi8zCDzz//XKGhoSpevLiqVq0qSdq+fbs8PDy0ZMkSuxcIAAAA3I7NYbZSpUo6ePCgpk+frn379kmS2rdvr44dOypXrlx2LxAAAAC4HZvDrCR5enqqR48e9q4FAAAAsAm38QIAAIDTIswCAADAaRFmAQAA4LQIswAAAHBa9xRmL168qIkTJ2rAgAE6f/68JGnLli36+++/7VocAAAAcCc2r2awY8cONWnSRF5eXjp69Kh69OihAgUKaN68eYqOjtbUqVMzo04AAAAgDZt7ZiMjI/XCCy/o4MGD8vDwsLY3b95cf/zxh12LAwAAAO7E5jC7adMmvfTSS2naixUrplOnTtmlKAAAACAjbA6z7u7uio2NTdN+4MABFS5c2C5FAQAAABlhc5gNDw/X8OHDlZSUJEmyWCyKjo7WW2+9paefftruBQIAAAC3Y3OYHTVqlK5cuSIfHx9dvXpVDRs2VNmyZZU3b1598MEHmVEjAAAAkC6bw6yXl5eWLl2q+fPn64svvlCvXr20aNEirVy5Urlz576nIsaOHSt/f395eHioZs2a2rhx4233nTBhgurXry9vb295e3urSZMmd9wfAAAA2ZfNS3PdVK9ePdWrV+++C5g1a5YiIyM1btw41axZU2PGjFFoaKj2798vHx+fNPuvWLFC7du3V506deTh4aGPPvpIzZo10+7du1WsWLH7rgcAAADOw+Yw+8UXX6TbbrFY5OHhobJly6pBgwZydXXN0PFGjx6tHj16qGvXrpKkcePGaeHChfruu+/09ttvp9l/+vTpqZ5PnDhRc+fOVVRUlDp37mzjdwMAAABnZnOY/eyzz3T27FnFx8fL29tbknThwgV5enoqT548OnPmjAICArR8+XKVKFHijsdKTEzU5s2bNWDAAGubi4uLmjRponXr1mWonvj4eCUlJalAgQLpbk9ISFBCQoL1eXorMQAAAMA52Txm9sMPP1T16tV18OBB/fPPP/rnn3904MAB1axZU59//rmio6Pl6+ur119//a7HOnfunJKTk1WkSJFU7UWKFMnwmrVvvfWW/Pz81KRJk3S3jxgxQl5eXtbH3QI2AAAAnIfNYXbQoEH67LPPVKZMGWtb2bJl9emnn2rAgAEqXry4Pv74Y61Zs8auhaZn5MiRmjlzpn7++edUdyO71YABA3Tp0iXr4/jx45leFwAAALKGzcMMTp48qevXr6dpv379urU31c/PT5cvX77rsQoVKiRXV1edPn06Vfvp06fl6+t7x9d++umnGjlypJYtW6YqVarcdj93d3e5u7vftRYAAAA4H5t7Zhs1aqSXXnpJW7dutbZt3bpVr7zyiho3bixJ2rlzp0qXLn3XY7m5uSk4OFhRUVHWtpSUFEVFRal27dq3fd3HH3+s9957T4sXL1ZISIit3wIAAACyCZvD7KRJk1SgQAEFBwdbez1DQkJUoEABTZo0SZKUJ08ejRo1KkPHi4yM1IQJE/T9999r7969euWVVxQXF2dd3aBz586pJoh99NFHGjx4sL777jv5+/vr1KlTOnXqlK5cuWLrtwIAAAAnZ/MwA19fXy1dulT79u3TgQMHJEnlypVTuXLlrPs0atQow8eLiIjQ2bNnNWTIEJ06dUpBQUFavHixdVJYdHS0XFz+L3N/8803SkxM1DPPPJPqOEOHDtW7775r67cDAAAAJ3bPN00oX768ypcvb5cievXqpV69eqW7bcWKFameHz161C7nBAAAgPO7pzAbExOjX3/9VdHR0UpMTEy1bfTo0XYpDAAAALgbm8NsVFSUwsPDFRAQoH379qlSpUo6evSojDF67LHHMqNGAAAAIF02TwAbMGCA+vXrp507d8rDw0Nz587V8ePH1bBhQz377LOZUSMAAACQLpvD7N69e9W5c2dJUo4cOXT16lXlyZNHw4cP10cffWT3AgEAAIDbsTnM5s6d2zpOtmjRojp06JB127lz5+xXGQAAAHAXNo+ZrVWrllavXq1HH31UzZs31xtvvKGdO3dq3rx5qlWrVmbUCAAAAKTL5jA7evRo6w0Khg0bpitXrmjWrFkKDAxkJQMAAABkKZvCbHJysmJiYlSlShVJN4YcjBs3LlMKAwAAAO7GpjGzrq6uatasmS5cuJBZ9QAAAAAZZvMEsEqVKunw4cOZUQsAAABgE5vD7Pvvv69+/fppwYIFOnnypGJjY1M9AAAAgKxi8wSw5s2bS5LCw8NlsVis7cYYWSwWJScn2686AAAA4A5sDrPLly/PjDoAAAAAm9kcZhs2bJgZdQAAAAA2s3nMrCStWrVKnTp1Up06dfT3339Lkn744QetXr3arsUBAAAAd2JzmJ07d65CQ0OVK1cubdmyRQkJCZKkS5cu6cMPP7R7gQAAAMDt3NNqBuPGjdOECROUM2dOa3vdunW1ZcsWuxYHAAAA3InNYXb//v1q0KBBmnYvLy9dvHjRHjUBAAAAGWJzmPX19dVff/2Vpn316tUKCAiwS1EAAABARtgcZnv06KG+fftqw4YNslgsOnHihKZPn65+/frplVdeyYwaAQAAgHTZvDTX22+/rZSUFD3xxBOKj49XgwYN5O7urn79+ql3796ZUSMAAACQLpvDrMVi0cCBA9W/f3/99ddfunLliipUqKA8efJkRn0AAADAbdk8zGDatGmKj4+Xm5ubKlSooBo1ahBkAQAA4BA2h9nXX39dPj4+6tChgxYtWqTk5OTMqAsAAAC4K5vD7MmTJzVz5kxZLBa1a9dORYsW1Wuvvaa1a9dmRn0AAADAbdkcZnPkyKGWLVtq+vTpOnPmjD777DMdPXpUjRo1UpkyZTKjRgAAACBdNk8Au5Wnp6dCQ0N14cIFHTt2THv37rVXXQAAAMBd2dwzK0nx8fGaPn26mjdvrmLFimnMmDFq06aNdu/ebe/6AAAAgNuyuWf2ueee04IFC+Tp6al27dpp8ODBql27dmbUBgAAANyRzWHW1dVVs2fPVmhoqFxdXTOjJgAAACBDbA6z06dPz4w6AAAAAJvd0wSwuLg4rVy5UtHR0UpMTEy1rU+fPnYpDAAAALgbm8Ps1q1b1bx5c8XHxysuLk4FChTQuXPn5OnpKR8fH8IsAAAAssw93QGsVatWunDhgnLlyqX169fr2LFjCg4O1qeffpoZNQIAAADpsjnMbtu2TW+88YZcXFzk6uqqhIQElShRQh9//LHeeeedzKgRAAAASJfNYTZnzpxycbnxMh8fH0VHR0uSvLy8dPz4cftWBwAAANyBzWNmq1Wrpk2bNikwMFANGzbUkCFDdO7cOf3www+qVKlSZtQIAAAApMvmntkPP/xQRYsWlSR98MEH8vb21iuvvKKzZ89q/Pjxdi8QAAAAuB2be2ZDQkKsX/v4+Gjx4sV2LQgAAADIKJt7ZgEAAIAHRYbCbFhYmNavX3/X/S5fvqyPPvpIY8eOve/CAAAAgLvJ0DCDZ599Vk8//bS8vLzUqlUrhYSEyM/PTx4eHrpw4YL27Nmj1atXa9GiRWrRooU++eSTzK4bAAAAyFiY7d69uzp16qQ5c+Zo1qxZGj9+vC5duiRJslgsqlChgkJDQ7Vp0yY9+uijmVowAAAAcFOGJ4C5u7urU6dO6tSpkyTp0qVLunr1qgoWLKicOXNmWoEAAADA7di8msFNXl5e8vLysmctAAAAgE1YzQAAAABOizALAAAAp0WYBQAAgNMizAIAAMBpZTjMbty4UcnJybfdnpCQoNmzZ9ulKAAAACAjMhxma9eurX/++cf6PF++fDp8+LD1+cWLF9W+fXv7VgcAAADcQYbDrDHmjs9v1wYAAABkFruOmbVYLPY8HAAAAHBHTAADAACA07LpDmB79uzRqVOnJN0YUrBv3z5duXJFknTu3Dn7VwcAAADcgU1h9oknnkg1LrZly5aSbgwvMMYwzAAAAABZKsNh9siRI5lZBwAAAGCzDIfZUqVK3XWfXbt23VcxAAAAgC3uewLY5cuXNX78eNWoUUNVq1a1R00AAABAhtxzmP3jjz/UpUsXFS1aVJ9++qkaN26s9evX27M2AAAA4I5smgB26tQpTZkyRZMmTVJsbKzatWunhIQE/fLLL6pQoUJm1QgAAACkK8M9s61atVK5cuW0Y8cOjRkzRidOnNCXX36ZmbUBAAAAd5ThntnffvtNffr00SuvvKLAwMDMrAkAAADIkAz3zK5evVqXL19WcHCwatasqa+++oobJQAAAMChMhxma9WqpQkTJujkyZN66aWXNHPmTPn5+SklJUVLly7V5cuXM7NOAAAAIA2bVzPInTu3unXrptWrV2vnzp164403NHLkSPn4+Cg8PDwzagQAAADSdV/rzJYrV04ff/yxYmJiNGPGDHvVBAAAAGTIfd80QZJcXV3VunVr/frrr/Y4HAAAAJAhGV7NoFu3bnfdx2KxaNKkSfdVEAAAAJBRGQ6zU6ZMUalSpVStWjUZYzKzJgAAACBDMhxmX3nlFc2YMUNHjhxR165d1alTJxUoUCAzawMAAADuKMNjZseOHauTJ0/qzTff1Pz581WiRAm1a9dOS5YsoacWAAAADmHTBDB3d3e1b99eS5cu1Z49e1SxYkW9+uqr8vf315UrVzKrRgAAACBd97yagYuLiywWi4wxSk5OtmdNAAAAQIbYFGYTEhI0Y8YMNW3aVI888oh27typr776StHR0cqTJ889FTB27Fj5+/vLw8NDNWvW1MaNG2+77+7du/X000/L399fFotFY8aMuadzAgAAIHvIcJh99dVXVbRoUY0cOVItW7bU8ePHNWfOHDVv3lwuLvfWwTtr1ixFRkZq6NCh2rJli6pWrarQ0FCdOXMm3f3j4+MVEBCgkSNHytfX957OCQAAgOwjw6sZjBs3TiVLllRAQIBWrlyplStXprvfvHnzMnzy0aNHq0ePHuratav1HAsXLtR3332nt99+O83+1atXV/Xq1SUp3e0AAAB4uGQ4zHbu3FkWi8VuJ05MTNTmzZs1YMAAa5uLi4uaNGmidevW2e08CQkJSkhIsD6PjY2127EBAADgWDbdNMGezp07p+TkZBUpUiRVe5EiRbRv3z67nWfEiBEaNmyY3Y4HAACAB8c9r2bgLAYMGKBLly5ZH8ePH3d0SQAAALCTDPfM2luhQoXk6uqq06dPp2o/ffq0XSd3ubu7y93d3W7HAwAAwIPDYT2zbm5uCg4OVlRUlLUtJSVFUVFRql27tqPKAgAAgBNxWM+sJEVGRqpLly4KCQlRjRo1NGbMGMXFxVlXN+jcubOKFSumESNGSLoxaWzPnj3Wr//++29t27ZNefLkUdmyZR32fQAAAMAxHBpmIyIidPbsWQ0ZMkSnTp1SUFCQFi9ebJ0UFh0dnWoN2xMnTqhatWrW559++qk+/fRTNWzYUCtWrMjq8gEAAOBgDg2zktSrVy/16tUr3W3/Dqj+/v4yxmRBVQAAAHAG2X41AwAAAGRfhFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0CLMAAABwWoRZAAAAOC3CLAAAAJwWYRYAAABOizALAAAAp0WYBQAAgNMizAIAAMBpEWYBAADgtAizAAAAcFqEWQAAADgtwiwAAACcFmEWAAAAToswCwAAAKdFmAUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0CLMAAABwWoRZAAAAOC3CLAAAAJwWYRYAAABOizALAAAAp0WYBQAAgNMizAIAAMBpEWYBAADgtAizAAAAcFqEWQAAADgtwiwAAACcFmEWAAAAToswCwAAAKdFmAUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0CLMAAABwWoRZAAAAOC3CLAAAAJwWYRYAAABOizALAAAAp0WYBQAAgNMizAIAAMBpEWYBAADgtAizAAAAcFqEWQAAADgtwiwAAACcFmEWAAAAToswCwAAAKdFmAUAAIDTIswCAADAaRFmAQAA4LQIswAAAHBahFkAAAA4LcIsAAAAnNYDEWbHjh0rf39/eXh4qGbNmtq4ceMd958zZ47Kly8vDw8PVa5cWYsWLcqiSgEAAPAgcXiYnTVrliIjIzV06FBt2bJFVatWVWhoqM6cOZPu/mvXrlX79u3VvXt3bd26Va1bt1br1q21a9euLK4cAAAAjubwMDt69Gj16NFDXbt2VYUKFTRu3Dh5enrqu+++S3f/zz//XGFhYerfv78effRRvffee3rsscf01VdfZXHlAAAAcLQcjjx5YmKiNm/erAEDBljbXFxc1KRJE61bty7d16xbt06RkZGp2kJDQ/XLL7+ku39CQoISEhKszy9duiRJio2Nvc/qMy4lIT5LzpPqe0owWXJOZeF1vBXX1P64pvaXba+pg66nlDXXNM3fB67pfeOa2l92v6Y3vz9jMvB9GQf6+++/jSSzdu3aVO39+/c3NWrUSPc1OXPmND/++GOqtrFjxxofH5909x86dKiRxIMHDx48ePDgwcPJHsePH79rnnRoz2xWGDBgQKqe3JSUFJ0/f14FCxaUxWJxYGWZIzY2ViVKlNDx48eVL18+R5eTLXBN7YvraX9cU/vjmtof19T+svM1Ncbo8uXL8vPzu+u+Dg2zhQoVkqurq06fPp2q/fTp0/L19U33Nb6+vjbt7+7uLnd391Rt+fPnv/einUS+fPmy3Q+2o3FN7YvraX9cU/vjmtof19T+sus19fLyytB+Dp0A5ubmpuDgYEVFRVnbUlJSFBUVpdq1a6f7mtq1a6faX5KWLl162/0BAACQfTl8mEFkZKS6dOmikJAQ1ahRQ2PGjFFcXJy6du0qSercubOKFSumESNGSJL69u2rhg0batSoUWrRooVmzpypP//8U+PHj3fktwEAAAAHcHiYjYiI0NmzZzVkyBCdOnVKQUFBWrx4sYoUKSJJio6OlovL/3Ug16lTRz/++KMGDRqkd955R4GBgfrll19UqVIlR30LDxR3d3cNHTo0zdAK3DuuqX1xPe2Pa2p/XFP745raH9f0BosxGVnzAAAAAHjwOPymCQAAAMC9IswCAADAaRFmAQAA4LQIswAAAHBahFkAAICHzNGjR7Vr1y5Hl2EXhNmHEAtYZK7k5ORUz1NSUhxUiXPj5xQPuhMnTji6hGyJ//uZ79ixYwoICFCjRo20bds2R5dz3wizD5Fjx45JkiwWi4Mryb6SkpLk6uoqSfrtt98UHx+fap1k2G7Dhg06cuSIo8sAUtm5c6c6d+6sAwcOOLqUbOHYsWNasmSJJP5GZQUXFxcVL15cCQkJevLJJ/Xnn386uqT7wl/ZbOzAgQMaOnSoJGnOnDl6+umndfjwYQdXlX0tWrTIelvlyMhI9evXT5cvX3ZwVc7nZq+MxWLRkiVLVLt2bR08eFDXr193cGXO7eZ1jY2N1ZUrVxQXF5dmGzIuMTFRGzZssIYAruG9+/vvvxUcHKw333xTc+fOdXQ52Z4xRgUKFFDjxo318ssvq0WLFmratKlTB1qH3wEMmWfr1q167733tGvXLv3888+aPHmyAgICHF1WtlW0aFH9888/CggI0Pnz57Vx40brneyQcTd7ZU6dOqUzZ87o448/VrNmzRxclXMzxshisWj+/Pn69ttvdfDgQQUHB6tu3bp67bXX6AnLoJSUFLm4uMgYo+DgYPXr10/vvfee6tWrp5IlSzq6PKe1e/dunT9/XmXLltW0adN0/fp1RUREOLqsbOnm74LcuXMrPDxcPXv21KJFi5SUlKTQ0FAtXrxY1atXd3SZNqNnNhuLiIjQSy+9pJ9//lnNmzdXly5dJNGDkFmqVaumRo0a6ejRoypdurQeeeQRSYyZvRd//fWX/Pz81L9/f+XJk8fR5Tg9i8WiBQsWqF27dnr88cf16aefqkCBAurdu7fWrl3r6PKcRmJioqT/e8PVpEkT5cuXT1u2bJGUdrw8MqZZs2Zq166dkpKS5OLiokmTJumnn35ydFnZyqFDh7R161ZduHDB2ta2bVu1aNFCq1ev1hdffKEGDRroySef1KZNmxxY6b0hzGZDt4bVkiVLqnPnzlq2bJn69esn6cYv4n8HLAKufbRr104zZ87U1atXVadOHSUmJsrFxSXNR+Rc7zvz9fXVsGHDdOnSJetYb67ZvYuLi9OUKVM0bNgw9evXT7Vr19Yvv/yi1157TXXq1HF0eU7hzz//1KOPPqqpU6fqr7/+kiTVrVtXAQEBGjx4sCRZx8sj4xISEiRJ7du3V1BQkLp16yZ3d3d98803DDmwk5iYGAUGBqpevXp69dVX9cUXX1jfmNWsWVPTpk2Tl5eXZs2apQYNGig8PFzr1q1zcNU2MshWUlJSjDHGbNiwwfz2228mNjbWGGPM1KlTjZubm3njjTdS7b99+/YsrzG7SE5OTvX85rU3xphNmzaZsmXLmtq1a5vr169b26dNm2bi4+OzrEZndv78eTN8+HBjsVjMpEmTHF2OU4uPjzdVqlQxixYtMidOnDDFihUzPXr0sG6fPXu22bhxowMrfHDd/H+9adMm07dvX1OyZEnTqFEjM2jQIJOcnGz++usvU6dOHTN27FgHV+o8jh8/bhYuXJiqLTo62pQpU8Z8//33Jjo62rRs2dI88cQTZs6cOQ6qMnuIjY01x48fN0FBQSZnzpxm0KBBplSpUiY8PNwMGTLEXLlyxQQHB5svvvjCGGPMpUuXTNOmTU1AQIC5evWqg6vPOMJsNnLzl+7cuXONt7e3+eCDD8zhw4eNMcYkJiaaH374wbi7u5vXX3/dxMbGmqFDh5qaNWuaf/75x5FlO6Vbg+yUKVPMW2+9ZV599VWzbds2a/vmzZtNYGCgCQ4ONitXrjRNmjQxDRo0SBOCH3Y3f2737t1r1q9fb5YtW2bdduXKFTNkyBBjsVjM5MmTHVSh87n1jZUxxly9etVERESY9957z5QuXdr06NHDus/p06dNt27dzNSpU/nZvMXN63P58uVU7WvXrjWjR482Pj4+pm7duubll182TZs2NS+//LIjynQ6R48eNQULFjQWi8U888wzZt68eebYsWPGGGNmzJhhGjZsaC5cuGC2bNliWrVqZcLCwsz06dMdXLVz2rJli2natKn566+/zLFjx0zFihVNWFiY2blzp5kwYYJp1qyZKVOmjPHx8TFPPfWUNbxeunTJHD9+3MHV24Ywm80sW7bM5MuXz0yYMMFcu3bN2n7zF/OPP/5ocuTIYSpWrGi8vb3Nn3/+6ahSs4U333zTlChRwrRt29Y888wzxs3Nzfzyyy/W7Xv27DHBwcGmfPnypkGDBiYxMdEYkzZsPKxuXoeff/7ZlClTxjzyyCPG19fXtG3b1ly4cMEYY0xcXJwZMmSIcXNzM998840Dq3UON6/p77//bgYNGmT9JODzzz83FovFNG7cONXvhgEDBpjAwEBz5MgRR5T7QFuwYIFp1KiRadWqlRkyZIj1/68xN/7gDx482LRt29ZYLBbj5uZm/ZlF+q5fv262bdtmKlWqZGrWrGmCgoJMt27dTEBAgJk+fbqZNWuWCQ8PN8uXLzfG3OgQaNCggWndurX1U0ZkzNatW03OnDnNm2++aW07duyYKV26tHniiSdMTEyMMebGG4ju3bubH374wVGl2gVhNpvp1auX6dChgzHmRgjYuHGj6dWrl3njjTfMpk2bjDHGHDx40MycOdP6bhj3ZsKECaZ48eLWNwQLFy40FovFeHp6pvnFsGvXLmuvV1JSUpbX+iBbsmSJyZcvnxk/fryJjY01v/32m7FYLCY8PNycPn3aGHPjZ/mNN94wBQoUMBcvXnRwxQ++n376yRQoUMC89tprZseOHdb2d955x+TIkcP07t3b/Oc//zFdu3Y1+fLlM1u3bnVcsQ+odevWmZw5c5p+/fqZp59+2oSEhJgWLVqkCrTXr183V69eNd99953ZvXu3A6t98G3atMkEBgaapKQk89NPP5k2bdqYtm3bmvnz55upU6eaBg0amKeeespYLBbTqFEj6+/L7du3m+joaAdX71z27dtncufObT7++GNr2803uceOHTOBgYGmRo0a5tSpU8YYk+pn2lkRZrOJmz+ovXr1Mk8++aSZP3++ef75501YWJipWrWqCQsLM7Vq1bKGA9yfK1eumPfff986lvPXX381efPmNd9++63p27evyZ07t/npp5/SfGzLx7ipXbhwwfTs2dN88MEHxpgb4+ZKly5tIiIiTLFixUxoaKg5efKkMebGuM8zZ844slynsHHjRpM/f/4044xv/sGaNGmSefrpp039+vVN7969CWHp2LVrl/n+++/Np59+aowx5tq1a2bWrFmmWrVq5sknn7ReS96YZsy2bdtM3rx5zauvvmptmz17tmnWrJlp0aKFOXbsmLl48aJZvny5ady4sZk6daoDq3Vu27dvN97e3sZisZidO3caY278nN76aeCxY8fMI488YmrWrGn+/vtvR5VqV4TZbGb+/PnmscceM4ULFzYdOnQwv/76qzHGmMmTJ5v69esz+ege3fxFcOsvhK1bt5qjR4+agwcPmvLly5vPP//cGGPMihUrjMViMRaLxSxevNgh9TqL69evm2nTppl9+/aZc+fOmWrVqlknJv3www/GYrGYxx9/nBBrg8mTJ5uwsDBjzI1JdHPmzDHh4eEmODjYfP3118aYG8E2JSUl1eRE3HDs2DETHBxsvL29rf+njbkRaOfMmWOqVatmWrZsaRISEhxYpfPYs2ePyZMnj3nnnXeMManfAMydO9c0btzYtGjRwmzevNkYwxv++7Ft2zaTK1cu061bN9OlSxdTuHBhs27dOmNM2ut67NgxU6FCBfPoo4+aEydOOKJcu2JpLidl/v8yRbt379Zvv/2mhQsX6tq1a2rZsqXmzZunP/74Q9OnT1fLli0lSXv37lWOHDmUlJTkyLKdUkpKinVdyfj4eF27dk2SFBQUpFKlSunQoUPKnTu3nnrqKUmSp6en+vbtq2+//VZPPPGEw+p+EJl/La/l6uqqiIgIlStXTr///rs8PDw0aNAgSZKHh4eeeOIJnT9/PtXdqpDWrdc1Z86cWrJkiSZNmqQ2bdpoypQpKlSokKpWrarBgwfr0KFDypkzpywWC0tJpSN//vx67rnnVKBAAf3222/Wdnd3d4WHh2vQoEHavXu3Onbs6MAqncOOHTtUv359ubu7q3Xr1pKkHDlyWJcqbNu2rV577TUlJCRo6NCh+vPPP7n99z3av3+/qlWrpv79+2vSpEl688031bhxY4WHh2vDhg1ycXFJtSRnyZIltWDBAuXOndu6PJpTc3CYxj242Ts4b948U7p0aRMYGGiCgoJMUFCQOXfuXKp9N23aZPr372/y5cuXaqY9MubWd7OjRo0yYWFhpkGDBqZdu3bWCQlz5swxFovFrFq1yhw5csS0atXKdOzY0fo6Poq84dZl48aMGWNGjhyZauWCYcOGmYCAAOt+AwYMMIMGDcoW47kyy81rdeXKlVTPIyMjTWBgoOnZs6dZv369MebGuOPKlStbx87jhvQmY8bGxpqxY8eaRx55xHTv3j3VtoSEBPPf//7XulIM0rd161bj6elpevbsaapXr26aNWtm/ve//1m33/p7cd68eSYsLMzUr1+f8dv3ICUlxcTGxpqvvvoqVfvu3btNRESEKVy4sPX3wL97aLPL3yfCrJNatmyZ8fLyMuPHjzfJycnm999/NxaLxTz66KPWJTX27dtnnnrqKVOrVi3Wk71PAwYMMD4+Pmbs2LFm/vz5Jm/evKZhw4bWQNuhQwdjsVhMQECAqVKlCgHsNn766Sfj4+NjQkNDTbt27YzFYrFOUti/f7/Jnz+/eeyxx0zTpk1Nvnz5Uk1eQvpunXH/7rvvWv843ZzccdPbb79tHn30UcbN3+JmkF29erX56KOPzFtvvWWWLl1qjLkxrOCrr74ylStXThNocWeHDh0yOXPmNP379zfGGPPXX3+ZKlWqmGbNmllXKjAmdZCaMWOGadOmDZO9bHTo0CEzZMiQVG8Ubg2s6QXa7Di8iDDrhGJjY81rr71mnTTz999/m5IlS5oOHTqY6tWrm8DAQOug7j179lgn0ODe7N+/31SpUsXai7hw4UKTL1++NMtELVq0yPz+++/WXxTZ5R2vvezevdv4+flZx21GR0cbV1dX079//1QL03fq1Mn06dPH7Nq1y5HlOoV/z7gPDg5ONeM+JSXFLFiwwPTs2dMULFjQbNmyxcEVP3h++uknkydPHtOwYUNTs2ZNY7FYzOuvv25OnTplrl69ar788ksTHBxs2rVr5+hSnUJycrKJiooyU6ZMMcb8X3DKSKD995q+uLMdO3aY0qVLm06dOpnZs2en2nbrJw43A62fn59ZvXp1VpeZJQizTmr+/Pnmzz//NOfPnzePPfaYeemll4wxxsyaNctYLBZTpEgR6zpyuD9r1641JUuWNMbcuO558uQx48aNM8bcWGty4sSJaV6THd/53q8VK1aYxo0bG2OMOXz4sClevHiqheYPHjxojDFMTMqgO824b968uUlMTDTXr183Y8aMMc2bN+fNQToOHjxoSpYsaSZMmGD94z9jxgxTsGBB069fP2OMMRcvXjQfffSRqV+/fraYKJOZDh06ZEaOHGn27duXqv3m/+dDhw6lG2j5/267ffv2mQIFCpi3337bnD17Nt19br2ue/bsMU8++aQJDAw0V69ezXZrnRNmH3D/vp3cv38AFyxYYOrUqWNd8DwqKsq0bNnSPPXUU+bAgQNZVWa2cev1vfn1uXPnTKNGjczAgQNNnjx5zLfffmvdZ8uWLSY0NNRs2LAhy2t1Nr/99pupVKmS2bBhgylVqpTp2bOn9ZftH3/8YZ577jk+YsygjMy4Dw8Pt/bQXrp0yVGlPlDOnDljNm3aZJ05v3PnThMQEGC2bduW6v/+9OnTjYuLi1m1apUx5kaP4fnz5x1Ss7PYsWOHKVOmjHnyySfNjBkz0mz/d6Bt3ry5WbJkSVaXmS0kJCSYF198Mc1d52JjY81ff/1loqKi0n2DsG/fvmzbycW0wQfY33//rc6dO2v58uXWtpuz6m86duyYtm3bpuLFi0uSoqKiVLhwYc2aNUuBgYFZWq+zu3XVglvlyJFDBQoU0Mcff6yePXuqZ8+ekqRr165p0KBB8vDwUEhISFaX+0Az/1q1QJLKli2rQoUKqVmzZqpfv76+/fZb62z6X3/9VZcuXVLu3LmzulSndHPGvbe3921n3G/fvt064z5fvnyOKvWBsWfPHrVp00aDBw/WBx98oOTkZF27dk3Hjx/XtWvXZLFYrLO6O3TooAoVKmjDhg2SpDx58sjb29uR5T/Q9u/fr8aNG+uZZ57RjBkz9Nxzz6XZx9XVVdevX1dAQIB+/vln7dy5U99++63i4+MdULFzc3Nz05EjR+Tp6WltW7Bggfr166egoCC1bdtWderU0d9//y1J1tUjypUrp2LFijmk5syWw9EF4PYSEhIUExOjUaNGyc3NTXXr1k2zT+vWrTV27FgVK1ZMVapU0Zo1a7Rhwwa5u7s7oGLnZYyxLgkzevRobdu2TadOnVL37t3VqlUrjR49WgcPHtSff/6p/v37q3jx4vrll1907tw5bdmyxbrsCcvK3LiWFotFGzZs0MGDB2WM0fPPP6+yZcsqPDxcu3fvVrFixbRnzx5ZLBZNnjxZkyZN0h9//KECBQo4uvwH0s1rKt34w5QvXz69+uqrypMnj0aPHq0XX3xREydOlHTjD13Lli2VI0cOVa5c2ZFlPzB2796tevXq6dVXX9VLL72k4sWLy8XFRSEhIWrdurW6deum+fPnKyAgQJKUmJgod3d33gRkwPXr1/Xhhx+qVatWGjlypLX96tWrOnv2rK5evar8+fOrSJEi1mW5AgIC9McffyglJSVVIMPdGWN09epVFSpUSCdOnND8+fO1detWff/996pXr57GjBmjxx57TO3atVP//v31448/KkeOhyDqObJbGHd34MABExYWZkJDQ1MN3L45WzE5OdkcPHjQdOjQwXzwwQdmz549jirVad0683PgwIGmQIECpkePHqZt27Ymf/78pnv37ubkyZPmr7/+MpGRkaZKlSqmRYsW5uWXX7ZOXmCyV2o///yz8fDwMFWrVjU5cuQwzZo1s05EHDJkiKlevbrJkSOHCQ4ONpUrV2Y5nju4+fH30qVLzX/+8x/TpEkTM27cOHPo0CFz/fp189VXX5mqVasy4/42/vnnH1OvXj3Tp0+fVO03/9+vXr3ahIWFmXLlypmoqCizcuVKM3DgQFOoUCFz6NAhR5TsVBISEkyDBg3Ml19+aW1buHCh6dGjh8mTJ4/x9vY2zZo1Mxs3brRu58YI92/58uWmatWqpmzZssbHx8dMmTLFHD161Lr9lVdeMc2aNXNghVmLMOsE7hRok5KSTP/+/U3Hjh0ZF3efTp48aXr37m3++OMPa9uMGTNMlSpVzGuvvWaMuTHu6+bdk24iyN5w85rExsaa5s2bm++//96cP3/e7N692/j7+5vatWtbV9k4ceKEWb58udm3bx9398qAefPmmbx585qXX37ZDB482BQrVsyEhYWZ06dPm9jYWPPFF18w4/42du/ebcqUKWNWrlx52xC1ceNG07FjR+Pu7m7Kli1rKlasyMoPNggNDTUhISHmwIEDZtCgQSYgIMC0b9/ezJgxw8yaNcvUqlXLvP322+b69evZbuKRI8XExFhvBfxv7du3N717935o3jgQZp1EeoE2ISHB9OrVy1gsFnq27tPMmTONxWIxJUuWtN7+76YffvjBeHh4pHvTiYf9F/OOHTtSram7bNkyEx4ebtq2bZtqUfmYmBjj7++farIiMiY6OtpUqVLFuhRcSkqKyZs3r3nzzTetP39xcXHm448/NvXq1WPG/b9Mnz7d5MiRw3qtbv3jfnOSTFxcnNm7d685e/asOXbs2G1nhyO1m9d07dq1pnLlysbPz88ULlzYfPfdd6n+/z/11FMmNDTUUWU6tXPnztm02kN8fLwZOHCgKVKkiNm/f38mVvZgIcw6kVsD7fLly82bb75pcuXKRQ+CHRw+fNh644P//ve/xhiTKqSVLl3auj4qbvwRmzp1qvHz80v1icC6deuMl5eXcXd3t96o42Z4iImJMYGBgaZy5coEWhtER0ebatWqmStXrpgDBw6YYsWKmR49eli3r1u3ziQnJzPj/jbWrFljPDw8zE8//XTbfb744gvTtGlTc+3atSyszDndusLOrW/mL1++bLZu3ZrqLpQpKSkmKSnJdOrUybz55psswWWj2NhY4+PjYyIjIzN07aZNm2Z69Ohh/Pz8HrpcQJh1MgcOHDAtW7Y03t7exs3NzbrEDDLudh+7nDx50rRo0cIULFgwVU/32bNnjb+/v/nhhx+yqELncXOM1qlTp6xB4M8//zSFCxc2bdq0sX78dfOPXnR0tKlatWqqsV34P3Fxcebs2bPmf//7n4mJiTEXL140MTExplixYuZ///ufKVOmjOnRo4f1Z3j79u0mIiLC/Pnnnw6u/MEVExNjfHx8THh4eKqfu1uD2BtvvGHefvvth/6TlruJiYkxzz777G3vNvVvSUlJZtCgQcbPzy/N2rPImPHjxxsPDw8zePDgOwbaffv2mdatW5suXbqYvXv3ZmGFDwbCrBPat2+fCQ8PZxH0e3DrL97p06ebd9991wwePNi63uG5c+dM8+bNjbe3txk6dKj5+uuvTYsWLUzlypUZG3uLm79Uk5OTzbZt20yePHnMrFmzrIF2/fr1xtvb2zz77LPWntub157emfTt37/fdO7c2ZQvX954eHgYLy8v06FDB7Nz507z5ptvGovFYp555plUrxkwYICpXr06QwvuYu7cucbd3d08//zzZvfu3db2uLg4M2DAAFOqVKmH6iPZe3Xo0CFTu3Zt06JFi7veSWrixInmpZdeMoULF37oegntberUqcbV1fW2gfbatWtm+/bt5sKFCw/tXdQIs07q1o/AYbv+/fsbX19f06NHD9OiRQsTEBBghg8fboy50Xv4zDPPGIvFYp5//nkzceJE60drBNr0tW7d2hQqVMjMmzfPGmjXrVtnvL29Tfv27dOdoID/s337dlO0aFHz8ssvmylTppi9e/eat956y5QpU8aUL1/efPLJJ6Zz586mbNmyZunSpeann34yr7/+usmbN2+6Y7mRWnJyshk3bpzJkSOHKV++vOnatat55ZVXTHh4uPHx8SFs2eB2E5Jv7dXeu3evCQ8PNz179nwoewkzw62B9ta/QwkJCaZHjx7GYrE81GO9CbN46Pz666+mZMmSZv369caYG+OMPDw8zLRp06z73Pw4zcfHx/rL+GEeT3ezV/Xfd6S7tZegQ4cOJl++fKkC7YYNG4zFYjEvvPACH+Hexvbt242np6cZMGBAmjdLM2bMMDVq1DA1a9Y0EyZMMF27djVeXl6mcuXKpkmTJtZxyciYDRs2mGeeecYEBQWZ+vXrm7feeos7Jd6D2wVaY278rujdu7dp1KiRdTk+2Me/e2gTExNNr169TN68ec2mTZscXZ5DEWbx0BkzZowJCwszxhgzZ84ckzdvXutM8cuXL1t/KcTExJjmzZsbPz8/s3PnTofV+6BIb7ycMXcPtJs2bWK83G1ER0ebQoUKmWeffdbadnPSzE3jxo0zBQsWNOPHjzfGGHPw4EFz5coVExsbm+X1ZgcMc7GP262w89prrxlXV1dW2LlHd3vTfzPQDho0yPTq1cvkypWLuTPGGIsx6dx3Esgmli9frlWrViklJUV16tRRs2bNNG7cOG3ZskVPP/20nnnmGX3yySd6+eWXJUk//fSTtm/frn79+snLy0t///23nnvuOZ0+fVq7d+9Wzpw5HfwdOc7hw4fVqVMnFShQQAMGDEh1R7rk5GTrrWk7duyo33//XV999ZXCw8OVK1cuR5X8wDt69KjatWunokWLqn///qpXr551m7nlrl/169dX4cKFNW/evFTXGra79bre+jVsd/DgQfXp00fGGL399tv67bff9OWXX2rNmjWqVq2ao8tzGomJiUpKSsrw7bx//PFHderUSZK0efNmrrUkwiyyrYkTJ+qdd95R1apVdeDAARljNHHiRBUtWlRVq1aVJE2ePFldunSRJMXHx6tNmzYKCAjQ2LFjrbemPXHihJKTk1WiRAmHfS8Pilv/eA0ePNgaaI0xSklJsYasm7dRXbdunfLkyeOwep3Brdd00KBB1kB7a9Bq1KiRihUrpmnTpjmyVCCNgwcPKjIyUmvWrFFcXJzWrVunxx57zNFlOY19+/Zp8ODBOn78uIoXL65BgwapatWqd32TtWDBApUtW1bly5fPokofbNxIHtnSxIkT9dprr+nrr7/W0qVLNWXKFMXGxmr69OmqXLmyvvnmG7m6uurUqVPasGGD1q9frzZt2uj06dP68ssv5eLiopSUFEmSn58fQfb/CwwM1BdffCGLxaL33ntPa9askSRZLBa5uroqPj5eAwcOVN26dTV37lyCbAbcek3ff//9VNc0JSVFMTExypUrl5o2bSrpRsgFHhSBgYH69NNPVb9+fW3ZsoUga4Pt27erbt268vDwUNOmTbV+/Xr17t1bFy9evOtrW7ZsSZC9lSPGNgCZafny5cZisZhhw4alavfz8zN169Y1ly5dMufOnTOzZs0yhQoVMsWLFzdVqlQxYWFh1lUiGFd3Z3e7Ix0z7G136zVdtWqVtf2tt94yVatWNcePH3dgdcCdscKObXbs2GHy5s1r3nnnHWvbl19+aSwWi5k9e3aqfZk8e3cMM0C2c/DgQXXv3l3e3t4aPHiwQkJC1LZtWy1YsEBNmzbVpUuX5OXlpWeffVbe3t7y8/NTsWLF5OvrKxcXF12/fl05cuRw9LfxwGO8nP3dek1HjBihpUuX6r333tPq1autQ2MAOLfExETVqVNHhw4d0unTp+Xm5iZJ6tu3r7788ktNnjxZZcuWVd26dZWUlPRQz9XIKMIssqWbocDV1VWXLl1SfHy8Jk+erPLly2v16tXav3+/PvroI8XFxem5557Tl19+KUlKSUmxjpXF3TFezv5uXtONGzfqwoULWrdunYKDgx1dFgA7WrdunVq3bq2GDRtq9uzZ+uijjzR8+HA1b95chQsX1oIFC1SiRAkVK1ZMERERCgkJUalSpRxd9gOLMIts6+DBg3r11Ve1adMmjR8/Xu3atUu1/dKlS9q2bZvq1avH7PD7sH//fr355pv68MMPVbFiRUeXky1wTYHsb8OGDQoLC1PBggWtczpujo2Pj4/X1KlTtWjRIm3btk3r16+Xn5+fgyt+cBFmka0dOnRIr732mlxcXPTOO+9YZ4r/eygByx3dHz4Ksz+uKZD93PxbY/7/aiWbNm1SRESEfHx8tHLlSrm7u6f5hPDChQvy9vZ2YNUPPj5PRbZWpkwZffnllzLG6IMPPrDOFP/3mFiC7P0hdNkf1xTIHvbt26eBAwfq2LFj1pBqsVhkjFH16tU1c+ZMHThwQM8//7xiY2Ot+9xcUSd//vyOKt1p0DOLh8LBgwf1+uuv6/Tp05o0aZKqVKni6JIAANlcUlKS6tatqz///FNly5bVU089pRo1aujZZ59Ntd+GDRv05JNPKiwsTN988428vLwcVLFzIsziobF3715NnDhRn3zyCZO8AABZ4pNPPlGOHDlUqVIlrVmzRl988YVatGih2rVr65VXXrHeIGH9+vWqU6eOOnfurMmTJ3N3OhsQZvFQYtUCAEBWWLFihZ566ilFRUUpJCREJ0+e1Pjx4/Xxxx+rcuXK6t69uxo0aKBy5cpp8+bNypMnj8qVK+fosp0Kf83xUCLIAgCywuOPP66ePXtqzJgxunbtmooWLaq9e/eqZMmSKl++vKZNm6bKlStr1KhRCg4OJsjeA1aGBwAAyEQ1a9bU6NGj5ebmphdffFErVqxQVFSUKlasqP3792vJkiV64oknHF2m02KYAQAAQCZr2LChVq9eLV9fXy1atIi7+tkRn7UCAABkkpt9hm+99ZbKli2rsWPHqmrVqqIv0X4IswAAAJnk5qoEwcHBSklJ0ebNm1O14/4RZgEAADJZkSJFNHToUH322WfauHGjo8vJVgizAAAAWaBRo0aqXr26/Pz8HF1KtsIEMAAAgCxy7do1eXh4OLqMbIUwCwAAAKfFMAMAAAA4LcIsAAAAnBZhFgAAAE6LMAsAAACnRZgFAACA0yLMAgAAwGkRZgEAAOC0CLMAAABwWoRZAAAAOC3CLAAAAJzW/wM2yfu+u8meSQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.arange(len(multi_performance))\n", + "width = 0.3\n", + "\n", + "metric_name = 'mean_absolute_error'\n", + "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", + "val_mae = [v[metric_index] for v in multi_val_performance.values()]\n", + "test_mae = [v[metric_index] for v in multi_performance.values()]\n", + "\n", + "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", + "plt.bar(x + 0.17, test_mae, width, label='Test')\n", + "plt.xticks(ticks=x, labels=multi_performance.keys(),\n", + " rotation=45)\n", + "plt.ylabel(f'MAE (average over all times and outputs)')\n", + "_ = plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zq3hUsedCEmJ" + }, + "source": [ + "The metrics for the multi-output models in the first half of this tutorial show the performance averaged across all output features. These performances are similar but also averaged across output time steps. " + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "execution": { + "iopub.execute_input": "2023-10-27T05:47:06.041402Z", + "iopub.status.busy": "2023-10-27T05:47:06.041000Z", + "iopub.status.idle": "2023-10-27T05:47:06.044860Z", + "shell.execute_reply": "2023-10-27T05:47:06.044275Z" + }, + "id": "jKq3eAIvH4Db" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last : 0.5157\n", + "Repeat : 0.3774\n", + "Linear : 0.2990\n", + "Dense : 0.2776\n", + "Conv : 0.2739\n", + "LSTM : 0.2763\n", + "AR LSTM : 0.2944\n" + ] + } + ], + "source": [ + "for name, value in multi_performance.items():\n", + " print(f'{name:8s}: {value[1]:0.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MpBFwfnaHP23" + }, + "source": [ + "The gains achieved going from a dense model to convolutional and recurrent models are only a few percent (if any), and the autoregressive model performed clearly worse. So these more complex approaches may not be worth while on **this** problem, but there was no way to know without trying, and these models could be helpful for **your** problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pOzaIRYBhqwg" + }, + "source": [ + "## Next steps\n", + "\n", + "This tutorial was a quick introduction to time series forecasting using TensorFlow.\n", + "\n", + "To learn more, refer to:\n", + "\n", + "- Chapter 15 of Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition.\n", + "- Chapter 6 of Deep Learning with Python.\n", + "- Lesson 8 of Udacity's intro to TensorFlow for deep learning, including the exercise notebooks.\n", + "\n", + "Also, remember that you can implement any classical time series model in TensorFlow—this tutorial just focuses on TensorFlow's built-in functionality.\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "time_series.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/ontime/time_series/test.py b/src/ontime/time_series/test.py new file mode 100644 index 0000000..a6f0f1b --- /dev/null +++ b/src/ontime/time_series/test.py @@ -0,0 +1,80 @@ +import numpy as np +import tensorflow as tf + + +class WindowGenerator: + def __init__(self, input_width, target_width, offset, ts, target_columns=None): + # Store the raw data. + self.ts = ts + self.df = ts.pd_dataframe() + + # Work out the target column indices. + self.target_columns = target_columns + if target_columns is not None: + self.target_columns_indices = {name: i for i, name in + enumerate(target_columns)} + self.column_indices = {name: i for i, name in + enumerate(self.df.columns)} + + # Work out the window parameters. + self.input_width = input_width + self.target_width = target_width + self.offset = offset + + self.total_window_size = input_width + offset + + self.input_slice = slice(0, input_width) + self.input_indices = np.arange(self.total_window_size)[self.input_slice] + + self.target_start = self.total_window_size - self.target_width + self.targets_slice = slice(self.target_start, None) + self.target_indices = np.arange(self.total_window_size)[self.targets_slice] + + def __repr__(self): + return '\n'.join([ + f'Total window size: {self.total_window_size}', + f'Input indices: {self.input_indices}', + f'Target indices: {self.target_indices}', + f'Target column name(s): {self.target_columns}']) + + def split_window(self, features): + inputs = features[:, self.input_slice, :] + targets = features[:, self.targets_slice, :] + if self.target_columns is not None: + targets = tf.stack( + [targets[:, :, self.column_indices[name]] for name in self.target_columns], + axis=-1) + + # Slicing doesn't preserve static shape information, so set the shapes + # manually. This way the `tf.data.Datasets` are easier to inspect. + inputs.set_shape([None, self.input_width, None]) + targets.set_shape([None, self.target_width, None]) + + return inputs, targets + + def make_dataset(self, data): + data = np.array(data, dtype=np.float32) + ds = tf.keras.utils.timeseries_dataset_from_array( + data=data, + targets=None, + sequence_length=self.total_window_size, + sequence_stride=1, + shuffle=True, + batch_size=32,) + return ds.map(self.split_window) + + @property + def dataset(self): + return self.make_dataset(self.df) + + @property + def example(self): + """Get and cache an example batch of `inputs, targets` for plotting.""" + result = getattr(self, '_example', None) + if result is None: + # No example batch was found, so get one from the dataset + result = next(iter(self.dataset)) + # And cache it for next time + self._example = result + return result + From 8ec98c2fdee297cf7e96c54e6affc476c593b2c8 Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Fri, 10 Nov 2023 09:54:45 +0100 Subject: [PATCH 2/8] checkpoint --- .../docs/0.1-time-series-custom-class.ipynb | 4 +- ....1-modelling-libraries_preprocessing.ipynb | 55 ++-- ...0.4.2-modelling-libraries_tensorflow.ipynb | 238 +++++++++++------- src/ontime/context/dhn/__init__.py | 0 4 files changed, 178 insertions(+), 119 deletions(-) create mode 100644 src/ontime/context/dhn/__init__.py diff --git a/notebooks/docs/0.1-time-series-custom-class.ipynb b/notebooks/docs/0.1-time-series-custom-class.ipynb index 90a644e..5f3a8b8 100644 --- a/notebooks/docs/0.1-time-series-custom-class.ipynb +++ b/notebooks/docs/0.1-time-series-custom-class.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "52af59bb-083c-46c6-989a-bd4c65137a1a", "metadata": {}, "outputs": [], @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "d6fc731f-3f50-4e9a-a24c-b2ab01d4fa31", "metadata": {}, "outputs": [ diff --git a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb index 6f58695..142aba0 100644 --- a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb +++ b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", "metadata": {}, "outputs": [], @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "id": "de144fa1-d419-46ae-9da1-102db4da92bb", "metadata": {}, "outputs": [], @@ -150,16 +150,9 @@ " if train_split is not None:\n", " train_set, test_set = ts.split_after(train_split)\n", "\n", - " return train_set, test_set" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9a297972-1588-4539-8168-05ec379c794d", - "metadata": {}, - "outputs": [], - "source": [ + " return train_set, test_set\n", + "\n", + "\n", "def split_by_n(ts, n, drop_last=True):\n", "\n", " # Get DataFrame\n", @@ -187,16 +180,7 @@ "\n", " # Change the data sctructure from DataFrame to TimeSeries\n", " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9614843a-70c2-4213-8d03-e2df030236c1", - "metadata": {}, - "outputs": [], - "source": [ + "\n", "def split_inputs_from_targets(ts_list, input_len, target_len):\n", "\n", " # Change inner data structure to DataFrame\n", @@ -223,12 +207,15 @@ " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", " \n", - " return input_ts_list, target_ts_list" + " return input_ts_list, target_ts_list\n", + "\n", + "def to_numpy(ts_list):\n", + " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", "metadata": {}, "outputs": [], @@ -247,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "a4b12f07-8a97-403a-a554-89e166574120", "metadata": {}, "outputs": [ @@ -268,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "84301c56-5e2f-4eea-ad98-a7d0b89c039c", "metadata": {}, "outputs": [], @@ -278,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "46e3a480-390f-446e-ab08-824f95467ddd", "metadata": {}, "outputs": [], @@ -289,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "a45e871d-ba2b-4de6-93bc-baf9b26104ec", "metadata": {}, "outputs": [], @@ -300,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "a0bc351b-9789-4f0c-914d-6e94d160e613", "metadata": {}, "outputs": [], @@ -313,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "id": "0ef9e79a-7c69-446b-a31a-cac8ebce99de", "metadata": {}, "outputs": [ @@ -334,6 +321,14 @@ "print(X_test.shape)\n", "print(y_test.shape)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54b0dfbd-be2f-4a3e-b152-f0bab31bb372", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb b/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb index 9278d2d..c17e8cd 100644 --- a/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb +++ b/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 1, "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", "metadata": {}, "outputs": [], @@ -22,10 +22,21 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], "source": [ "import pandas as pd\n", "import numpy as np\n", @@ -44,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 3, "id": "e9a96d79-0423-4d79-b01d-726193216238", "metadata": {}, "outputs": [], @@ -88,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 4, "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", "metadata": {}, "outputs": [], @@ -112,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 5, "id": "de144fa1-d419-46ae-9da1-102db4da92bb", "metadata": {}, "outputs": [], @@ -144,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 6, "id": "9a297972-1588-4539-8168-05ec379c794d", "metadata": {}, "outputs": [], @@ -181,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 7, "id": "9614843a-70c2-4213-8d03-e2df030236c1", "metadata": {}, "outputs": [], @@ -217,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 8, "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", "metadata": {}, "outputs": [], @@ -228,7 +239,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 9, "id": "312a3eb7-162f-4d7e-a68e-78b6d6842493", "metadata": {}, "outputs": [], @@ -325,40 +336,61 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 10, "id": "dde4ea44-58ad-4f5f-8d0b-773f431d232f", "metadata": {}, "outputs": [], "source": [ "df = ts.pd_dataframe()\n", - "df = df.interpolate()\n", + "df = df.interpolate()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "c9a69edc-e303-4eec-bf1f-f069adf117ac", + "metadata": {}, + "outputs": [], + "source": [ + "to_drop = []\n", + "for k, v in df.isna().sum().items():\n", + " if v != 0:\n", + " to_drop.append(k)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "65c2c0f7-2138-487d-b676-67e5cb075b34", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop(to_drop, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "08a228d2-bae2-4a32-b5d8-2478c0957ad5", + "metadata": {}, + "outputs": [], + "source": [ "ts = on.TimeSeries.from_dataframe(df)" ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 14, "id": "19717f00-b1d5-4ba2-8b07-6feed1a30659", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:479: RuntimeWarning: All-NaN slice encountered\n", - " data_min = np.nanmin(X, axis=0)\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/preprocessing/_data.py:480: RuntimeWarning: All-NaN slice encountered\n", - " data_max = np.nanmax(X, axis=0)\n" - ] - } - ], + "outputs": [], "source": [ "ts_t = normalize(ts)" ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 15, "id": "3b376cac-1262-485b-9c58-d8971c81bd13", "metadata": {}, "outputs": [], @@ -369,30 +401,32 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 16, "id": "a88057c4-033b-4bb5-81bc-edd7b6781e1a", "metadata": {}, "outputs": [], "source": [ "target_columns = ['generation solar']\n", + "input_width=24\n", + "target_width=12\n", "\n", "train_window = WindowGenerator(\n", - " input_width=5, \n", - " target_width=1, \n", + " input_width=input_width, \n", + " target_width=target_width, \n", " offset=1, \n", " target_columns=target_columns,\n", " ts=train)\n", "\n", "val_window = WindowGenerator(\n", - " input_width=5, \n", - " target_width=1, \n", + " input_width=input_width, \n", + " target_width=target_width, \n", " offset=1, \n", " target_columns=target_columns,\n", " ts=val)\n", "\n", "test_window = WindowGenerator(\n", - " input_width=5, \n", - " target_width=1, \n", + " input_width=input_width, \n", + " target_width=target_width, \n", " offset=1, \n", " target_columns=target_columns,\n", " ts=test)" @@ -400,20 +434,20 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 17, "id": "1f8d9be9-5c31-4676-8682-74c482b6b592", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Total window size: 6\n", - "Input indices: [0 1 2 3 4]\n", - "Target indices: [5]\n", + "Total window size: 25\n", + "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", + "Target indices: [13 14 15 16 17 18 19 20 21 22 23 24]\n", "Target column name(s): ['generation solar']" ] }, - "execution_count": 78, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -424,18 +458,18 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 18, "id": "0d28062b-709f-4bfd-8b6e-1259df0608e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(TensorSpec(shape=(None, 5, 28), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + "(TensorSpec(shape=(None, 24, 26), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 12, 1), dtype=tf.float32, name=None))" ] }, - "execution_count": 79, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -446,18 +480,18 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 19, "id": "30d1b98e-bee2-427b-9dc7-29381b15a740", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(TensorSpec(shape=(None, 5, 28), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" + "(TensorSpec(shape=(None, 24, 26), dtype=tf.float32, name=None),\n", + " TensorSpec(shape=(None, 12, 1), dtype=tf.float32, name=None))" ] }, - "execution_count": 80, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -484,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 20, "id": "0789d98b-1a85-4e6d-852e-92b83967f78e", "metadata": {}, "outputs": [], @@ -506,18 +540,53 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 22, "id": "45c410da-7ea6-4f07-a18f-6539854904fc", "metadata": {}, "outputs": [], "source": [ "model = tf.keras.Sequential([\n", - " tf.keras.layers.Dense(units=64, activation='relu'),\n", - " tf.keras.layers.Dense(units=64, activation='relu'),\n", + " tf.keras.layers.Conv1D(filters=32,\n", + " kernel_size=(6,),\n", + " activation='relu'),\n", + " tf.keras.layers.Dense(units=32, activation='relu'),\n", + " tf.keras.layers.Dense(units=32, activation='relu'),\n", " tf.keras.layers.Dense(units=1)\n", "])" ] }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3af2167c-04ce-4161-a93b-3f5587d94bd2", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'OUT_STEPS' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[23], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mSequential([\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Shape [batch, time, features] => [batch, lstm_units].\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Adding more `lstm_units` just overfits more quickly.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mLSTM(\u001b[38;5;241m32\u001b[39m, return_sequences\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Shape => [batch, out_steps*features].\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mDense(\u001b[43mOUT_STEPS\u001b[49m\u001b[38;5;241m*\u001b[39mnum_features,\n\u001b[1;32m 7\u001b[0m kernel_initializer\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39minitializers\u001b[38;5;241m.\u001b[39mzeros()),\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Shape => [batch, out_steps, features].\u001b[39;00m\n\u001b[1;32m 9\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mReshape([OUT_STEPS, num_features])\n\u001b[1;32m 10\u001b[0m ])\n", + "\u001b[0;31mNameError\u001b[0m: name 'OUT_STEPS' is not defined" + ] + } + ], + "source": [ + "model = tf.keras.Sequential([\n", + " # Shape [batch, time, features] => [batch, lstm_units].\n", + " # Adding more `lstm_units` just overfits more quickly.\n", + " tf.keras.layers.LSTM(32, return_sequences=False),\n", + " # Shape => [batch, out_steps*features].\n", + " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", + " kernel_initializer=tf.initializers.zeros()),\n", + " # Shape => [batch, out_steps, features].\n", + " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", + "])\n", + "\n" + ] + }, { "cell_type": "markdown", "id": "d6705888-c015-4247-a43d-60f88441c736", @@ -528,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 50, "id": "5bd01720-b468-453a-8769-0080d787a336", "metadata": {}, "outputs": [ @@ -544,45 +613,23 @@ "output_type": "stream", "text": [ "Epoch 1/20\n", - "702/702 [==============================] - 1s 827us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0822 - mean_absolute_error: 0.2424 - val_loss: 0.0813 - val_mean_absolute_error: 0.2346\n", "Epoch 2/20\n", - "702/702 [==============================] - 1s 730us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0796 - mean_absolute_error: 0.2395 - val_loss: 0.0808 - val_mean_absolute_error: 0.2464\n", "Epoch 3/20\n", - "702/702 [==============================] - 1s 731us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0786 - mean_absolute_error: 0.2382 - val_loss: 0.0801 - val_mean_absolute_error: 0.2416\n", "Epoch 4/20\n", - "702/702 [==============================] - 1s 746us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0780 - mean_absolute_error: 0.2374 - val_loss: 0.0800 - val_mean_absolute_error: 0.2431\n", "Epoch 5/20\n", - "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0774 - mean_absolute_error: 0.2367 - val_loss: 0.0801 - val_mean_absolute_error: 0.2313\n", "Epoch 6/20\n", - "702/702 [==============================] - 1s 771us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0773 - mean_absolute_error: 0.2365 - val_loss: 0.0798 - val_mean_absolute_error: 0.2433\n", "Epoch 7/20\n", - "702/702 [==============================] - 1s 758us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0770 - mean_absolute_error: 0.2361 - val_loss: 0.0798 - val_mean_absolute_error: 0.2444\n", "Epoch 8/20\n", - "702/702 [==============================] - 1s 729us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", + "701/701 [==============================] - 1s 1ms/step - loss: 0.0767 - mean_absolute_error: 0.2357 - val_loss: 0.0798 - val_mean_absolute_error: 0.2420\n", "Epoch 9/20\n", - "702/702 [==============================] - 1s 737us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 10/20\n", - "702/702 [==============================] - 1s 765us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 11/20\n", - "702/702 [==============================] - 1s 750us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 12/20\n", - "702/702 [==============================] - 1s 779us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 13/20\n", - "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 14/20\n", - "702/702 [==============================] - 1s 734us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 15/20\n", - "702/702 [==============================] - 1s 731us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 16/20\n", - "702/702 [==============================] - 1s 753us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 17/20\n", - "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 18/20\n", - "702/702 [==============================] - 1s 812us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 19/20\n", - "702/702 [==============================] - 1s 769us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n", - "Epoch 20/20\n", - "702/702 [==============================] - 1s 735us/step - loss: nan - mean_absolute_error: nan - val_loss: nan - val_mean_absolute_error: nan\n" + "701/701 [==============================] - 1s 1ms/step - loss: 0.0767 - mean_absolute_error: 0.2356 - val_loss: 0.0802 - val_mean_absolute_error: 0.2317\n" ] } ], @@ -605,7 +652,7 @@ " dataset['train'], \n", " epochs=MAX_EPOCHS,\n", " validation_data=dataset['val'],\n", - " #callbacks=[early_stopping]\n", + " callbacks=[early_stopping]\n", ")\n" ] }, @@ -619,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 123, "id": "81b8a1a9-95dc-4266-8213-f4e6b9f61108", "metadata": {}, "outputs": [], @@ -629,17 +676,17 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 124, "id": "af94a073-55af-454e-a8e1-ce919ce37365", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[nan, nan]" + "[0.05250326171517372, 0.15813955664634705]" ] }, - "execution_count": 85, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -648,6 +695,23 @@ "performance" ] }, + { + "cell_type": "markdown", + "id": "8f6f79f5-efb5-40dd-9293-a9699923f60d", + "metadata": {}, + "source": [ + "Concepts\n", + "--------\n", + "\n", + "- Single-step models : Predict one value\n", + " - Single-output models : Predict one value of one feature\n", + " - Multi-output models : Predict one value of many features\n", + "\n", + "- Multi-step models : Predict many values\n", + " - Single-output models : Predict many values of one feature\n", + " - Multi-output models : Predict many values of many features" + ] + }, { "cell_type": "markdown", "id": "6bb9090a-bc1c-4a06-9b6d-ddee9ac64a9a", diff --git a/src/ontime/context/dhn/__init__.py b/src/ontime/context/dhn/__init__.py new file mode 100644 index 0000000..e69de29 From 9097037164812183fa260101ff2fa9cb51b20e8a Mon Sep 17 00:00:00 2001 From: vincent magnin Date: Mon, 13 Nov 2023 13:23:02 +0100 Subject: [PATCH 3/8] Documentation update --- src/ontime/detectors/registry/threshold.py | 19 ++++++++++-- .../time_series/resticted_time_series.py | 30 +++++++++---------- 2 files changed, 32 insertions(+), 17 deletions(-) diff --git a/src/ontime/detectors/registry/threshold.py b/src/ontime/detectors/registry/threshold.py index 35a256d..6d76cbe 100644 --- a/src/ontime/detectors/registry/threshold.py +++ b/src/ontime/detectors/registry/threshold.py @@ -1,5 +1,6 @@ -from darts.ad.detectors.threshold_detector import ThresholdDetector +from typing import Sequence, Union +from darts.ad.detectors.threshold_detector import ThresholdDetector from ...abstract import AbstractBaseDetector from ...time_series import TimeSeries, BinaryTimeSeries @@ -9,7 +10,21 @@ class Threshold(ThresholdDetector, AbstractBaseDetector): Wrapper around Darts ThresholdDetector. """ - def __init__(self, low_threshold=None, high_threshold=None): + def __init__( + self, + low_threshold: Union[int, float, Sequence[float], None] = None, + high_threshold: Union[int, float, Sequence[float], None] = None, + ): + """ + + :param low_threshold: (Sequence of) lower bounds. + If a sequence, must match the dimensionality of the series + The lower bound is included in the valid interval. So if the lower bound is 0, the value 0 is valid. + + :param high_threshold: (Sequence of) upper bounds. + If a sequence, must match the dimensionality of the series + The upper bound is included in the valid interval. So if the upper bound is 10, the value 10 is valid. + """ super().__init__(low_threshold, high_threshold) def detect(self, ts: TimeSeries) -> BinaryTimeSeries: diff --git a/src/ontime/time_series/resticted_time_series.py b/src/ontime/time_series/resticted_time_series.py index 385c441..8b585be 100644 --- a/src/ontime/time_series/resticted_time_series.py +++ b/src/ontime/time_series/resticted_time_series.py @@ -29,7 +29,7 @@ def check(self, xa: xr.DataArray) -> bool: raise NotImplementedError @classmethod - def from_darts(cls, ts: DartsTimeSeries): + def from_darts(cls, ts: DartsTimeSeries) -> T: """ Convert a Darts TimeSeries to an OnTime TimeSeries @@ -51,7 +51,7 @@ def from_dataframe( fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: ts = super().from_dataframe( df, time_col, @@ -77,7 +77,7 @@ def from_group_dataframe( fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, - ): + ) -> T: raise NotImplementedError @classmethod @@ -88,7 +88,7 @@ def from_series( freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - ): + ) -> T: ts = super().from_series( pd_series, fill_missing_dates, freq, fillna_value, static_covariates ) @@ -104,11 +104,11 @@ def from_values( fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: raise NotImplementedError @classmethod - def from_pickle(cls, path: str): + def from_pickle(cls, path: str) -> T: raise NotImplementedError @classmethod @@ -122,7 +122,7 @@ def from_times_and_values( fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: raise NotImplementedError @classmethod @@ -137,7 +137,7 @@ def from_csv( static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, **kwargs, - ): + ) -> T: raise NotImplementedError @classmethod @@ -146,7 +146,7 @@ def from_json( json_str: str, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, hierarchy: Optional[Dict] = None, - ): + ) -> T: raise NotImplementedError @classmethod @@ -156,7 +156,7 @@ def from_xarray( fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, - ) -> Type[T]: + ) -> T: cls.check(cls, xa) return super().from_xarray(xa, fill_missing_dates, freq, fillna_value) @@ -182,7 +182,7 @@ def concatenate( ignore_time_axis: bool = False, ignore_static_covariates: bool = False, drop_hierarchy: bool = True, - ) -> Type[T]: + ) -> T: # TODO : if return super().concatenate the type is TimeSeries. I choose to raise an error. See what is the best raise NotImplementedError @@ -213,7 +213,7 @@ def prepend_values(self, values: np.ndarray) -> T: """ raise NotImplementedError - def rescale_with_value(self, value_at_first_step: float) -> "TimeSeries": + def rescale_with_value(self, value_at_first_step: float) -> T: """ Rescales the time series so that the first value is equal to the given value. @@ -223,7 +223,7 @@ def rescale_with_value(self, value_at_first_step: float) -> "TimeSeries": """ raise NotImplementedError - def stack(self, other: "TimeSeries") -> "TimeSeries": + def stack(self, other: "TimeSeries") -> T: """ Stacks this time series with another one, along the time axis. @@ -233,7 +233,7 @@ def stack(self, other: "TimeSeries") -> "TimeSeries": """ return NotImplementedError - def sum(self, axis: int = 2) -> "TimeSeries": + def sum(self, axis: int = 2) -> T: """ Sums the values along the given axis. @@ -250,7 +250,7 @@ def window_transform( forecasting_safe: Optional[bool] = True, keep_non_transformed: Optional[bool] = False, include_current: Optional[bool] = True, - ): + ) -> T: raise NotImplementedError def with_values(self, values: np.ndarray) -> T: From 7ed28061782475ed9310661249969350ec55f519 Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Thu, 16 Nov 2023 15:00:38 +0100 Subject: [PATCH 4/8] Add common preprocessing for modelling tasks --- ....1-modelling-libraries_preprocessing.ipynb | 173 +++--------------- src/ontime/__init__.py | 3 +- src/ontime/context/common/generic_detector.py | 4 +- .../context/common/generic_predictor.py | 2 +- src/ontime/{model => modelling}/__init__.py | 0 .../libs/darts/__init__.py | 0 .../libs/darts/forecasting_model.py | 0 .../libs/skforecast/__init__.py | 0 .../libs/skforecast/forecaster_autoreg.py | 0 src/ontime/{model => modelling}/model.py | 2 +- .../modelling/preprocessing/__init__.py | 1 + src/ontime/modelling/preprocessing/common.py | 136 ++++++++++++++ src/ontime/time_series/test.py | 80 -------- 13 files changed, 165 insertions(+), 236 deletions(-) rename src/ontime/{model => modelling}/__init__.py (100%) rename src/ontime/{model => modelling}/libs/darts/__init__.py (100%) rename src/ontime/{model => modelling}/libs/darts/forecasting_model.py (100%) rename src/ontime/{model => modelling}/libs/skforecast/__init__.py (100%) rename src/ontime/{model => modelling}/libs/skforecast/forecaster_autoreg.py (100%) rename src/ontime/{model => modelling}/model.py (96%) create mode 100644 src/ontime/modelling/preprocessing/__init__.py create mode 100644 src/ontime/modelling/preprocessing/common.py delete mode 100644 src/ontime/time_series/test.py diff --git a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb index 142aba0..3258a1a 100644 --- a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb +++ b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb @@ -92,136 +92,7 @@ " - add day of the week, month, year, etc.\n", " - add whatever\n", "- [x] Windowing\n", - "- [x] Windowing - Split (parts to train as X, parts to predict as y)\n", - "- [x] Windowing - to tf.data.Dataset\n", - "- [ ] Windowing - to Pytorch DataLoaders" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", - "from darts.dataprocessing.transformers import Scaler\n", - "\n", - "def normalize(ts: on.TimeSeries, type='minmax', return_transformer=False):\n", - " match type:\n", - " case 'minmax':\n", - " scaler = MinMaxScaler()\n", - " case 'zscore':\n", - " scaler = StandardScaler()\n", - " transformer = Scaler(scaler)\n", - " ts_transformed = transformer.fit_transform(ts)\n", - " if return_transformer:\n", - " return ts_transformed, transformer\n", - " else:\n", - " return ts_transformed" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "de144fa1-d419-46ae-9da1-102db4da92bb", - "metadata": {}, - "outputs": [], - "source": [ - "def train_test_split(ts: on.TimeSeries, test_split=None, train_split=None) -> tuple:\n", - " \"\"\"\n", - " Description\n", - " \n", - " :param ts: TimeSeries to split\n", - " :param test_split: float, int or pd.TimeStamp\n", - " :param train_split: float, int or pd.TimeStamp\n", - " \"\"\"\n", - " \n", - " if train_split is not None and test_split is not None:\n", - " raise Exception('Only one of those two parameters can be set : train_split, test_split.')\n", - "\n", - " if train_split is None and test_split is None:\n", - " test_split = 0.25\n", - " \n", - " # split ts in subts : train, test\n", - " if test_split is not None: \n", - " train_set, test_set = ts.split_after(1-test_split)\n", - " \n", - " if train_split is not None:\n", - " train_set, test_set = ts.split_after(train_split)\n", - "\n", - " return train_set, test_set\n", - "\n", - "\n", - "def split_by_n(ts, n, drop_last=True):\n", - "\n", - " # Get DataFrame\n", - " df = ts.pd_dataframe()\n", - " \n", - " # Calculate the total number of splits needed\n", - " total_splits = -(-len(df) // n) # Ceiling division to get the number of parts\n", - " \n", - " # Initialize a list to hold the DataFrame splits\n", - " splits_df = []\n", - " \n", - " # Loop through the DataFrame and split it\n", - " for split in range(total_splits):\n", - " start_index = split * n\n", - " end_index = start_index + n\n", - " # Append the part to the list, using slicing with .iloc\n", - " splits_df.append(df.iloc[start_index:end_index])\n", - "\n", - " # If the last dataframe has a different length, then drop it.\n", - " if drop_last:\n", - " last_df = splits_df[-1]\n", - " second_last = splits_df[-2] \n", - " if len(last_df) != len(second_last):\n", - " splits_df = splits_df[:-1]\n", - "\n", - " # Change the data sctructure from DataFrame to TimeSeries\n", - " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", - "\n", - "def split_inputs_from_targets(ts_list, input_len, target_len):\n", - "\n", - " # Change inner data structure to DataFrame\n", - " dfs = [ts.pd_dataframe() for ts in ts_list]\n", - "\n", - " # Create initial arrays\n", - " input_series_list = []\n", - " target_series_list = []\n", - " \n", - " # Iterate over each DataFrame in the list\n", - " for df in dfs:\n", - " # Check if the DataFrame is large enough to accommodate input_len and label_len\n", - " if len(df) >= input_len + target_len:\n", - " # Get the first input_len items\n", - " input_series = df.iloc[:input_len]\n", - " input_series_list.append(input_series)\n", - " \n", - " # Get the last label_len items\n", - " target_series = df.iloc[-target_len:]\n", - " target_series_list.append(target_series)\n", - " else:\n", - " raise Exception('input_len + label_len is longer that the total length of the DataFrame')\n", - "\n", - " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", - " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", - " \n", - " return input_ts_list, target_ts_list\n", - "\n", - "def to_numpy(ts_list):\n", - " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", - "metadata": {}, - "outputs": [], - "source": [ - "def to_numpy(ts_list):\n", - " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " + "- [x] Windowing - Split (parts to train as X, parts to predict as y)" ] }, { @@ -234,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "a4b12f07-8a97-403a-a554-89e166574120", "metadata": {}, "outputs": [ @@ -250,58 +121,58 @@ } ], "source": [ - "ts_t = normalize(ts)" + "ts_t = on.modelling.preprocessing.common.normalize(ts)" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "84301c56-5e2f-4eea-ad98-a7d0b89c039c", + "execution_count": 6, + "id": "8b67892d-db8c-4f12-93b6-147016da4186", "metadata": {}, "outputs": [], "source": [ - "train, test = train_test_split(ts_t, train_split=0.8)" + "train, test = on.modelling.preprocessing.common.train_test_split(ts_t, train_split=0.8)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "46e3a480-390f-446e-ab08-824f95467ddd", + "execution_count": 7, + "id": "500e954a-82d6-4eff-bbdd-0b889c2a10f8", "metadata": {}, "outputs": [], "source": [ - "train_list = split_by_n(train, 6)\n", - "test_list = split_by_n(test, 6)" + "train_list = on.modelling.preprocessing.common.split_by_length(train, 6)\n", + "test_list = on.modelling.preprocessing.common.split_by_length(test, 6)" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "a45e871d-ba2b-4de6-93bc-baf9b26104ec", + "execution_count": 9, + "id": "f7897c44-71ba-4752-86c6-547387245ae4", "metadata": {}, "outputs": [], "source": [ - "X_train, y_train = split_inputs_from_targets(train_list, 4, 2)\n", - "X_test, y_test = split_inputs_from_targets(test_list, 4, 2)" + "X_train, y_train = on.modelling.preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n", + "X_test, y_test = on.modelling.preprocessing.common.split_inputs_from_targets(test_list, 4, 2)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "a0bc351b-9789-4f0c-914d-6e94d160e613", + "execution_count": 10, + "id": "a4ab9cfa-289d-4d8e-be40-d5d4247f5ab5", "metadata": {}, "outputs": [], "source": [ - "X_train = to_numpy(X_train)\n", - "y_train = to_numpy(y_train)\n", - "X_test = to_numpy(X_test)\n", - "y_test = to_numpy(y_test)" + "X_train = on.modelling.preprocessing.common.timeseries_list_to_numpy(X_train)\n", + "y_train = on.modelling.preprocessing.common.timeseries_list_to_numpy(y_train)\n", + "X_test = on.modelling.preprocessing.common.timeseries_list_to_numpy(X_test)\n", + "y_test = on.modelling.preprocessing.common.timeseries_list_to_numpy(y_test)" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "0ef9e79a-7c69-446b-a31a-cac8ebce99de", + "execution_count": 11, + "id": "1b0a2843-6d02-4b08-96f8-91712e521bf5", "metadata": {}, "outputs": [ { diff --git a/src/ontime/__init__.py b/src/ontime/__init__.py index 1a88547..4747b95 100644 --- a/src/ontime/__init__.py +++ b/src/ontime/__init__.py @@ -4,7 +4,8 @@ from .context import * from .detectors import detectors from .generators import generators -from .model import Model +from .modelling import Model +from .modelling import preprocessing from .plots import plots from .processors import processors from .time_series import TimeSeries diff --git a/src/ontime/context/common/generic_detector.py b/src/ontime/context/common/generic_detector.py index d048e3e..a96559a 100644 --- a/src/ontime/context/common/generic_detector.py +++ b/src/ontime/context/common/generic_detector.py @@ -2,7 +2,7 @@ from darts.utils.statistics import check_seasonality from ...time_series import BinaryTimeSeries from ...detectors import Quantile -from ...model import Model +from ...modelling import Model class GenericDetector: @@ -53,7 +53,7 @@ def detect(self, ts) -> BinaryTimeSeries: def predetect(self, n) -> BinaryTimeSeries: """ - Predict n steps into the future and detect anomalies + Predict length steps into the future and detect anomalies Can raise a ValueError if the model has not been fitted diff --git a/src/ontime/context/common/generic_predictor.py b/src/ontime/context/common/generic_predictor.py index 9ba991c..f590b7e 100644 --- a/src/ontime/context/common/generic_predictor.py +++ b/src/ontime/context/common/generic_predictor.py @@ -31,7 +31,7 @@ def fit(self, ts): def predict(self, n): """ - Predict n steps into the future + Predict length steps into the future :param n: Int number of steps to predict :return: TimeSeries """ diff --git a/src/ontime/model/__init__.py b/src/ontime/modelling/__init__.py similarity index 100% rename from src/ontime/model/__init__.py rename to src/ontime/modelling/__init__.py diff --git a/src/ontime/model/libs/darts/__init__.py b/src/ontime/modelling/libs/darts/__init__.py similarity index 100% rename from src/ontime/model/libs/darts/__init__.py rename to src/ontime/modelling/libs/darts/__init__.py diff --git a/src/ontime/model/libs/darts/forecasting_model.py b/src/ontime/modelling/libs/darts/forecasting_model.py similarity index 100% rename from src/ontime/model/libs/darts/forecasting_model.py rename to src/ontime/modelling/libs/darts/forecasting_model.py diff --git a/src/ontime/model/libs/skforecast/__init__.py b/src/ontime/modelling/libs/skforecast/__init__.py similarity index 100% rename from src/ontime/model/libs/skforecast/__init__.py rename to src/ontime/modelling/libs/skforecast/__init__.py diff --git a/src/ontime/model/libs/skforecast/forecaster_autoreg.py b/src/ontime/modelling/libs/skforecast/forecaster_autoreg.py similarity index 100% rename from src/ontime/model/libs/skforecast/forecaster_autoreg.py rename to src/ontime/modelling/libs/skforecast/forecaster_autoreg.py diff --git a/src/ontime/model/model.py b/src/ontime/modelling/model.py similarity index 96% rename from src/ontime/model/model.py rename to src/ontime/modelling/model.py index 86dd05d..a92421a 100644 --- a/src/ontime/model/model.py +++ b/src/ontime/modelling/model.py @@ -35,7 +35,7 @@ def fit(self, ts, **params): def predict(self, n, **params): """ - Predict n steps into the future + Predict length steps into the future :param n: Integer :param params: Parameters to pass to the predict method :return: TimeSeries diff --git a/src/ontime/modelling/preprocessing/__init__.py b/src/ontime/modelling/preprocessing/__init__.py new file mode 100644 index 0000000..e4193cf --- /dev/null +++ b/src/ontime/modelling/preprocessing/__init__.py @@ -0,0 +1 @@ +from . import common diff --git a/src/ontime/modelling/preprocessing/common.py b/src/ontime/modelling/preprocessing/common.py new file mode 100644 index 0000000..8b496cd --- /dev/null +++ b/src/ontime/modelling/preprocessing/common.py @@ -0,0 +1,136 @@ +import numpy as np + +from ontime.time_series import TimeSeries + +from sklearn.preprocessing import MinMaxScaler, StandardScaler +from darts.dataprocessing.transformers import Scaler + + +def normalize(ts: TimeSeries, type='minmax', return_transformer=False) -> tuple | TimeSeries: + """ + Normalize a TimeSeries + + :param ts: TimeSeries to normalize + :param type: str type of normalization to apply + :param return_transformer: bool whether to return the transformer + :return: TimeSeries + """ + match type: + case 'minmax': + scaler = MinMaxScaler() + case 'zscore': + scaler = StandardScaler() + transformer = Scaler(scaler) + ts_transformed = transformer.fit_transform(ts) + if return_transformer: + return ts_transformed, transformer + else: + return ts_transformed + + +def train_test_split(ts: TimeSeries, test_split=None, train_split=None) -> tuple: + """ + Split a TimeSeries into train and test sets + + :param ts: TimeSeries to split + :param test_split: float, int or pd.TimeStamp + :param train_split: float, int or pd.TimeStamp + :return: tuple of TimeSeries + """ + + if train_split is not None and test_split is not None: + raise Exception('Only one of those two parameters can be set : train_split, test_split.') + + if train_split is None and test_split is None: + test_split = 0.25 + + # split ts in subts : train, test + if test_split is not None: + train_set, test_set = ts.split_after(1-test_split) + + if train_split is not None: + train_set, test_set = ts.split_after(train_split) + + return train_set, test_set + + +def split_by_length(ts: TimeSeries, length: int, drop_last: bool = True) -> list: + """ + Split a TimeSeries into parts of a given length + + :param ts: TimeSeries to split + :param length: int length of each part + :param drop_last: bool, whether to drop the last part if it is shorter than n + :return: list of TimeSeries + """ + + # Get DataFrame + df = ts.pd_dataframe() + + # Calculate the total number of splits needed + total_splits = -(-len(df) // length) # Ceiling division to get the number of parts + + # Initialize a list to hold the DataFrame splits + splits_df = [] + + # Loop through the DataFrame and split it + for split in range(total_splits): + start_index = split * length + end_index = start_index + length + # Append the part to the list, using slicing with .iloc + splits_df.append(df.iloc[start_index:end_index]) + + # If the last dataframe has a different length, then drop it. + if drop_last: + last_df = splits_df[-1] + second_last = splits_df[-2] + if len(last_df) != len(second_last): + splits_df = splits_df[:-1] + + # Change the data structure from DataFrame to TimeSeries + return list(map(TimeSeries.from_dataframe, splits_df)) + +def split_inputs_from_targets(ts_list: list, input_length: int, target_length: int) -> tuple: + """ + Split a list of TimeSeries into input and target TimeSeries + + :param ts_list: list of TimeSeries + :param input_length: int length of the input TimeSeries + :param target_length: int length of the target TimeSeries + :return: tuple of list of TimeSeries + """ + + # Change inner data structure to DataFrame + dfs = [ts.pd_dataframe() for ts in ts_list] + + # Create initial arrays + input_series_list = [] + target_series_list = [] + + # Iterate over each DataFrame in the list + for df in dfs: + # Check if the DataFrame is large enough to accommodate input_length and label_len + if len(df) >= input_length + target_length: + # Get the first input_length items + input_series = df.iloc[:input_length] + input_series_list.append(input_series) + # Get the last label_len items + target_series = df.iloc[-target_length:] + target_series_list.append(target_series) + else: + raise Exception('input_length + label_len is longer that the total length of the DataFrame') + + input_ts_list = list(map(TimeSeries.from_dataframe, input_series_list)) + target_ts_list = list(map(TimeSeries.from_dataframe, target_series_list)) + + return input_ts_list, target_ts_list + + +def timeseries_list_to_numpy(ts_list: list) -> np.array: + """ + Convert a list of TimeSeries into a numpy array + + :param ts_list: list of TimeSeries + :return: np.array + """ + return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) \ No newline at end of file diff --git a/src/ontime/time_series/test.py b/src/ontime/time_series/test.py deleted file mode 100644 index a6f0f1b..0000000 --- a/src/ontime/time_series/test.py +++ /dev/null @@ -1,80 +0,0 @@ -import numpy as np -import tensorflow as tf - - -class WindowGenerator: - def __init__(self, input_width, target_width, offset, ts, target_columns=None): - # Store the raw data. - self.ts = ts - self.df = ts.pd_dataframe() - - # Work out the target column indices. - self.target_columns = target_columns - if target_columns is not None: - self.target_columns_indices = {name: i for i, name in - enumerate(target_columns)} - self.column_indices = {name: i for i, name in - enumerate(self.df.columns)} - - # Work out the window parameters. - self.input_width = input_width - self.target_width = target_width - self.offset = offset - - self.total_window_size = input_width + offset - - self.input_slice = slice(0, input_width) - self.input_indices = np.arange(self.total_window_size)[self.input_slice] - - self.target_start = self.total_window_size - self.target_width - self.targets_slice = slice(self.target_start, None) - self.target_indices = np.arange(self.total_window_size)[self.targets_slice] - - def __repr__(self): - return '\n'.join([ - f'Total window size: {self.total_window_size}', - f'Input indices: {self.input_indices}', - f'Target indices: {self.target_indices}', - f'Target column name(s): {self.target_columns}']) - - def split_window(self, features): - inputs = features[:, self.input_slice, :] - targets = features[:, self.targets_slice, :] - if self.target_columns is not None: - targets = tf.stack( - [targets[:, :, self.column_indices[name]] for name in self.target_columns], - axis=-1) - - # Slicing doesn't preserve static shape information, so set the shapes - # manually. This way the `tf.data.Datasets` are easier to inspect. - inputs.set_shape([None, self.input_width, None]) - targets.set_shape([None, self.target_width, None]) - - return inputs, targets - - def make_dataset(self, data): - data = np.array(data, dtype=np.float32) - ds = tf.keras.utils.timeseries_dataset_from_array( - data=data, - targets=None, - sequence_length=self.total_window_size, - sequence_stride=1, - shuffle=True, - batch_size=32,) - return ds.map(self.split_window) - - @property - def dataset(self): - return self.make_dataset(self.df) - - @property - def example(self): - """Get and cache an example batch of `inputs, targets` for plotting.""" - result = getattr(self, '_example', None) - if result is None: - # No example batch was found, so get one from the dataset - result = next(iter(self.dataset)) - # And cache it for next time - self._example = result - return result - From 1213dd3a6b111f09303b397182cb55baee63ee9f Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Thu, 16 Nov 2023 15:02:37 +0100 Subject: [PATCH 5/8] Roll back to model --- notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb | 2 +- src/ontime/__init__.py | 4 ++-- src/ontime/context/common/generic_detector.py | 2 +- src/ontime/{modelling => model}/__init__.py | 0 src/ontime/{modelling => model}/libs/darts/__init__.py | 0 .../{modelling => model}/libs/darts/forecasting_model.py | 0 src/ontime/{modelling => model}/libs/skforecast/__init__.py | 0 .../libs/skforecast/forecaster_autoreg.py | 0 src/ontime/{modelling => model}/model.py | 0 src/ontime/{modelling => model}/preprocessing/__init__.py | 0 src/ontime/{modelling => model}/preprocessing/common.py | 0 11 files changed, 4 insertions(+), 4 deletions(-) rename src/ontime/{modelling => model}/__init__.py (100%) rename src/ontime/{modelling => model}/libs/darts/__init__.py (100%) rename src/ontime/{modelling => model}/libs/darts/forecasting_model.py (100%) rename src/ontime/{modelling => model}/libs/skforecast/__init__.py (100%) rename src/ontime/{modelling => model}/libs/skforecast/forecaster_autoreg.py (100%) rename src/ontime/{modelling => model}/model.py (100%) rename src/ontime/{modelling => model}/preprocessing/__init__.py (100%) rename src/ontime/{modelling => model}/preprocessing/common.py (100%) diff --git a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb index 3258a1a..f123e27 100644 --- a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb +++ b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb @@ -121,7 +121,7 @@ } ], "source": [ - "ts_t = on.modelling.preprocessing.common.normalize(ts)" + "ts_t = on.model.preprocessing.common.normalize(ts)" ] }, { diff --git a/src/ontime/__init__.py b/src/ontime/__init__.py index 4747b95..0d1c19b 100644 --- a/src/ontime/__init__.py +++ b/src/ontime/__init__.py @@ -4,8 +4,8 @@ from .context import * from .detectors import detectors from .generators import generators -from .modelling import Model -from .modelling import preprocessing +from .model import Model +from .model import preprocessing from .plots import plots from .processors import processors from .time_series import TimeSeries diff --git a/src/ontime/context/common/generic_detector.py b/src/ontime/context/common/generic_detector.py index a96559a..5dc64f4 100644 --- a/src/ontime/context/common/generic_detector.py +++ b/src/ontime/context/common/generic_detector.py @@ -2,7 +2,7 @@ from darts.utils.statistics import check_seasonality from ...time_series import BinaryTimeSeries from ...detectors import Quantile -from ...modelling import Model +from ...model import Model class GenericDetector: diff --git a/src/ontime/modelling/__init__.py b/src/ontime/model/__init__.py similarity index 100% rename from src/ontime/modelling/__init__.py rename to src/ontime/model/__init__.py diff --git a/src/ontime/modelling/libs/darts/__init__.py b/src/ontime/model/libs/darts/__init__.py similarity index 100% rename from src/ontime/modelling/libs/darts/__init__.py rename to src/ontime/model/libs/darts/__init__.py diff --git a/src/ontime/modelling/libs/darts/forecasting_model.py b/src/ontime/model/libs/darts/forecasting_model.py similarity index 100% rename from src/ontime/modelling/libs/darts/forecasting_model.py rename to src/ontime/model/libs/darts/forecasting_model.py diff --git a/src/ontime/modelling/libs/skforecast/__init__.py b/src/ontime/model/libs/skforecast/__init__.py similarity index 100% rename from src/ontime/modelling/libs/skforecast/__init__.py rename to src/ontime/model/libs/skforecast/__init__.py diff --git a/src/ontime/modelling/libs/skforecast/forecaster_autoreg.py b/src/ontime/model/libs/skforecast/forecaster_autoreg.py similarity index 100% rename from src/ontime/modelling/libs/skforecast/forecaster_autoreg.py rename to src/ontime/model/libs/skforecast/forecaster_autoreg.py diff --git a/src/ontime/modelling/model.py b/src/ontime/model/model.py similarity index 100% rename from src/ontime/modelling/model.py rename to src/ontime/model/model.py diff --git a/src/ontime/modelling/preprocessing/__init__.py b/src/ontime/model/preprocessing/__init__.py similarity index 100% rename from src/ontime/modelling/preprocessing/__init__.py rename to src/ontime/model/preprocessing/__init__.py diff --git a/src/ontime/modelling/preprocessing/common.py b/src/ontime/model/preprocessing/common.py similarity index 100% rename from src/ontime/modelling/preprocessing/common.py rename to src/ontime/model/preprocessing/common.py From e1455558558cd090ac854a25e7b6876da69c1e4b Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Thu, 16 Nov 2023 15:04:06 +0100 Subject: [PATCH 6/8] format --- ....1-modelling-libraries_preprocessing.ipynb | 32 +++++++++---------- src/ontime/model/preprocessing/common.py | 25 ++++++++++----- 2 files changed, 33 insertions(+), 24 deletions(-) diff --git a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb index f123e27..88560fd 100644 --- a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb +++ b/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "e9a96d79-0423-4d79-b01d-726193216238", "metadata": {}, "outputs": [], @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "a4b12f07-8a97-403a-a554-89e166574120", "metadata": {}, "outputs": [ @@ -126,52 +126,52 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "8b67892d-db8c-4f12-93b6-147016da4186", "metadata": {}, "outputs": [], "source": [ - "train, test = on.modelling.preprocessing.common.train_test_split(ts_t, train_split=0.8)" + "train, test = on.model.preprocessing.common.train_test_split(ts_t, train_split=0.8)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "500e954a-82d6-4eff-bbdd-0b889c2a10f8", "metadata": {}, "outputs": [], "source": [ - "train_list = on.modelling.preprocessing.common.split_by_length(train, 6)\n", - "test_list = on.modelling.preprocessing.common.split_by_length(test, 6)" + "train_list = on.model.preprocessing.common.split_by_length(train, 6)\n", + "test_list = on.model.preprocessing.common.split_by_length(test, 6)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "f7897c44-71ba-4752-86c6-547387245ae4", "metadata": {}, "outputs": [], "source": [ - "X_train, y_train = on.modelling.preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n", - "X_test, y_test = on.modelling.preprocessing.common.split_inputs_from_targets(test_list, 4, 2)" + "X_train, y_train = on.model.preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n", + "X_test, y_test = on.model.preprocessing.common.split_inputs_from_targets(test_list, 4, 2)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "a4ab9cfa-289d-4d8e-be40-d5d4247f5ab5", "metadata": {}, "outputs": [], "source": [ - "X_train = on.modelling.preprocessing.common.timeseries_list_to_numpy(X_train)\n", - "y_train = on.modelling.preprocessing.common.timeseries_list_to_numpy(y_train)\n", - "X_test = on.modelling.preprocessing.common.timeseries_list_to_numpy(X_test)\n", - "y_test = on.modelling.preprocessing.common.timeseries_list_to_numpy(y_test)" + "X_train = on.model.preprocessing.common.timeseries_list_to_numpy(X_train)\n", + "y_train = on.model.preprocessing.common.timeseries_list_to_numpy(y_train)\n", + "X_test = on.model.preprocessing.common.timeseries_list_to_numpy(X_test)\n", + "y_test = on.model.preprocessing.common.timeseries_list_to_numpy(y_test)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "1b0a2843-6d02-4b08-96f8-91712e521bf5", "metadata": {}, "outputs": [ diff --git a/src/ontime/model/preprocessing/common.py b/src/ontime/model/preprocessing/common.py index 8b496cd..86fb23f 100644 --- a/src/ontime/model/preprocessing/common.py +++ b/src/ontime/model/preprocessing/common.py @@ -6,7 +6,9 @@ from darts.dataprocessing.transformers import Scaler -def normalize(ts: TimeSeries, type='minmax', return_transformer=False) -> tuple | TimeSeries: +def normalize( + ts: TimeSeries, type="minmax", return_transformer=False +) -> tuple | TimeSeries: """ Normalize a TimeSeries @@ -16,9 +18,9 @@ def normalize(ts: TimeSeries, type='minmax', return_transformer=False) -> tuple :return: TimeSeries """ match type: - case 'minmax': + case "minmax": scaler = MinMaxScaler() - case 'zscore': + case "zscore": scaler = StandardScaler() transformer = Scaler(scaler) ts_transformed = transformer.fit_transform(ts) @@ -39,14 +41,16 @@ def train_test_split(ts: TimeSeries, test_split=None, train_split=None) -> tuple """ if train_split is not None and test_split is not None: - raise Exception('Only one of those two parameters can be set : train_split, test_split.') + raise Exception( + "Only one of those two parameters can be set : train_split, test_split." + ) if train_split is None and test_split is None: test_split = 0.25 # split ts in subts : train, test if test_split is not None: - train_set, test_set = ts.split_after(1-test_split) + train_set, test_set = ts.split_after(1 - test_split) if train_split is not None: train_set, test_set = ts.split_after(train_split) @@ -90,7 +94,10 @@ def split_by_length(ts: TimeSeries, length: int, drop_last: bool = True) -> list # Change the data structure from DataFrame to TimeSeries return list(map(TimeSeries.from_dataframe, splits_df)) -def split_inputs_from_targets(ts_list: list, input_length: int, target_length: int) -> tuple: + +def split_inputs_from_targets( + ts_list: list, input_length: int, target_length: int +) -> tuple: """ Split a list of TimeSeries into input and target TimeSeries @@ -118,7 +125,9 @@ def split_inputs_from_targets(ts_list: list, input_length: int, target_length: i target_series = df.iloc[-target_length:] target_series_list.append(target_series) else: - raise Exception('input_length + label_len is longer that the total length of the DataFrame') + raise Exception( + "input_length + label_len is longer that the total length of the DataFrame" + ) input_ts_list = list(map(TimeSeries.from_dataframe, input_series_list)) target_ts_list = list(map(TimeSeries.from_dataframe, target_series_list)) @@ -133,4 +142,4 @@ def timeseries_list_to_numpy(ts_list: list) -> np.array: :param ts_list: list of TimeSeries :return: np.array """ - return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) \ No newline at end of file + return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) From 501ab36c77ded59b34f2b56fd718f4ac2249971a Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Fri, 17 Nov 2023 15:25:50 +0100 Subject: [PATCH 7/8] new project structure and deep clean --- notebooks/demo.ipynb | 686 - .../docs/0.1-time-series-custom-class.ipynb | 799 - notebooks/docs/0.2-detectors-generators.ipynb | 720 - notebooks/docs/0.4-modelling-libraries.ipynb | 2693 --- .../0_core/0.1-time-series-custom-class.ipynb | 670 + .../0_core/0.2-detectors-generators.ipynb | 720 + .../docs/{ => 0_core}/0.3-processing.ipynb | 174 +- .../0.4-modelling.ipynb} | 769 +- .../0.5-plots.ipynb} | 0 .../0.6-anomaly-frequency.ipynb} | 0 .../1.0-preprocessing-common.ipynb} | 113 +- .../2.0-context-common.ipynb} | 168 +- .../docs/code block\nTime series.ipynb" | 17422 ---------------- notebooks/getting-started.ipynb | 1712 +- src/ontime/__init__.py | 12 +- src/ontime/abstract/__init__.py | 4 - src/ontime/{context/dhn => api}/__init__.py | 0 src/ontime/api/modular.py | 47 + src/ontime/config/__init__.py | 2 - src/ontime/config/colors.py | 14 - src/ontime/config/constants.py | 25 - src/ontime/context/__init__.py | 2 +- src/ontime/context/common/__init__.py | 9 +- ...es_frequencies.py => anomaly_frequency.py} | 15 +- src/ontime/context/common/generic_detector.py | 7 +- .../context/common/generic_predictor.py | 5 +- src/ontime/context/common/profiler.py | 7 +- src/ontime/core/__init__.py | 14 + .../{detectors => core/detector}/__init__.py | 2 + .../detector/abstract_detector.py} | 4 +- .../{detectors => core/detector}/detectors.py | 0 .../detector}/registry/__init__.py | 0 .../detector}/registry/quantile.py | 5 +- .../detector}/registry/threshold.py | 5 +- .../generator}/__init__.py | 2 + .../generator/abstract_generator.py} | 4 +- .../generator}/generators.py | 0 .../generator}/registry/__init__.py | 0 .../generator}/registry/constant.py | 4 +- .../generator}/registry/gaussian.py | 4 +- .../generator}/registry/holiday.py | 4 +- .../generator}/registry/linear.py | 4 +- .../generator}/registry/random_walk.py | 4 +- .../generator}/registry/sine.py | 4 +- src/ontime/{ => core}/model/__init__.py | 2 + .../model/abstract_model.py} | 8 +- .../{ => core}/model/libs/darts/__init__.py | 0 .../model/libs/darts/forecasting_model.py | 39 + .../model/libs/skforecast/__init__.py | 0 .../libs/skforecast/forecaster_autoreg.py | 6 +- src/ontime/{ => core}/model/model.py | 21 +- src/ontime/core/plot/__init__.py | 1 + src/ontime/{plots => core/plot}/plots.py | 18 +- .../processor}/__init__.py | 2 + .../processor/abstract_processor.py} | 5 +- .../processor}/processors.py | 0 .../processor}/registry/correlation.py | 20 +- .../processor}/registry/filler.py | 4 +- .../processor}/registry/mapper.py | 4 +- .../processor}/registry/windower.py | 4 +- src/ontime/{ => core}/time_series/__init__.py | 7 + .../time_series/binary_time_series.py | 5 +- .../time_series/probabilistic_time_series.py | 4 +- .../time_series/resticted_time_series.py | 6 +- .../{ => core}/time_series/time_series.py | 7 +- src/ontime/{ => core}/utils/__init__.py | 0 src/ontime/{ => core}/utils/dynamic_class.py | 3 + src/ontime/{ => core}/utils/registry.py | 4 + src/ontime/{ => core}/utils/utils.py | 0 .../model/libs/darts/forecasting_model.py | 20 - src/ontime/model/preprocessing/__init__.py | 1 - src/ontime/module/__init__.py | 1 + src/ontime/module/preprocessing/__init__.py | 1 + .../{model => module}/preprocessing/common.py | 7 +- src/ontime/plots/__init__.py | 1 - 75 files changed, 2411 insertions(+), 24645 deletions(-) delete mode 100644 notebooks/demo.ipynb delete mode 100644 notebooks/docs/0.1-time-series-custom-class.ipynb delete mode 100644 notebooks/docs/0.2-detectors-generators.ipynb delete mode 100644 notebooks/docs/0.4-modelling-libraries.ipynb create mode 100644 notebooks/docs/0_core/0.1-time-series-custom-class.ipynb create mode 100644 notebooks/docs/0_core/0.2-detectors-generators.ipynb rename notebooks/docs/{ => 0_core}/0.3-processing.ipynb (56%) rename notebooks/docs/{0.4.2-modelling-libraries_tensorflow.ipynb => 0_core/0.4-modelling.ipynb} (56%) rename notebooks/docs/{0.6-plots.ipynb => 0_core/0.5-plots.ipynb} (100%) rename notebooks/docs/{0.7-anomaly-frequency.ipynb => 0_core/0.6-anomaly-frequency.ipynb} (100%) rename notebooks/docs/{0.4.1-modelling-libraries_preprocessing.ipynb => 1_module/1.0-preprocessing-common.ipynb} (64%) rename notebooks/docs/{0.5-context.ipynb => 2_context/2.0-context-common.ipynb} (95%) delete mode 100644 "notebooks/docs/code block\nTime series.ipynb" delete mode 100644 src/ontime/abstract/__init__.py rename src/ontime/{context/dhn => api}/__init__.py (100%) create mode 100644 src/ontime/api/modular.py delete mode 100644 src/ontime/config/__init__.py delete mode 100644 src/ontime/config/colors.py delete mode 100644 src/ontime/config/constants.py rename src/ontime/context/common/{anomalies_frequencies.py => anomaly_frequency.py} (71%) create mode 100644 src/ontime/core/__init__.py rename src/ontime/{detectors => core/detector}/__init__.py (89%) rename src/ontime/{abstract/abstract_base_detector.py => core/detector/abstract_detector.py} (76%) rename src/ontime/{detectors => core/detector}/detectors.py (100%) rename src/ontime/{detectors => core/detector}/registry/__init__.py (100%) rename src/ontime/{detectors => core/detector}/registry/quantile.py (88%) rename src/ontime/{detectors => core/detector}/registry/threshold.py (92%) rename src/ontime/{generators => core/generator}/__init__.py (95%) rename src/ontime/{abstract/abstract_base_generator.py => core/generator/abstract_generator.py} (74%) rename src/ontime/{generators => core/generator}/generators.py (100%) rename src/ontime/{generators => core/generator}/registry/__init__.py (100%) rename src/ontime/{generators => core/generator}/registry/constant.py (92%) rename src/ontime/{generators => core/generator}/registry/gaussian.py (93%) rename src/ontime/{generators => core/generator}/registry/holiday.py (93%) rename src/ontime/{generators => core/generator}/registry/linear.py (93%) rename src/ontime/{generators => core/generator}/registry/random_walk.py (93%) rename src/ontime/{generators => core/generator}/registry/sine.py (94%) rename src/ontime/{ => core}/model/__init__.py (54%) rename src/ontime/{abstract/abstract_base_model.py => core/model/abstract_model.py} (68%) rename src/ontime/{ => core}/model/libs/darts/__init__.py (100%) create mode 100644 src/ontime/core/model/libs/darts/forecasting_model.py rename src/ontime/{ => core}/model/libs/skforecast/__init__.py (100%) rename src/ontime/{ => core}/model/libs/skforecast/forecaster_autoreg.py (81%) rename src/ontime/{ => core}/model/model.py (71%) create mode 100644 src/ontime/core/plot/__init__.py rename src/ontime/{plots => core/plot}/plots.py (91%) rename src/ontime/{processors => core/processor}/__init__.py (93%) rename src/ontime/{abstract/abstract_base_processor.py => core/processor/abstract_processor.py} (69%) rename src/ontime/{processors => core/processor}/processors.py (100%) rename src/ontime/{processors => core/processor}/registry/correlation.py (90%) rename src/ontime/{processors => core/processor}/registry/filler.py (95%) rename src/ontime/{processors => core/processor}/registry/mapper.py (97%) rename src/ontime/{processors => core/processor}/registry/windower.py (97%) rename src/ontime/{ => core}/time_series/__init__.py (63%) rename src/ontime/{ => core}/time_series/binary_time_series.py (97%) rename src/ontime/{ => core}/time_series/probabilistic_time_series.py (97%) rename src/ontime/{ => core}/time_series/resticted_time_series.py (99%) rename src/ontime/{ => core}/time_series/time_series.py (90%) rename src/ontime/{ => core}/utils/__init__.py (100%) rename src/ontime/{ => core}/utils/dynamic_class.py (86%) rename src/ontime/{ => core}/utils/registry.py (78%) rename src/ontime/{ => core}/utils/utils.py (100%) delete mode 100644 src/ontime/model/libs/darts/forecasting_model.py delete mode 100644 src/ontime/model/preprocessing/__init__.py create mode 100644 src/ontime/module/__init__.py create mode 100644 src/ontime/module/preprocessing/__init__.py rename src/ontime/{model => module}/preprocessing/common.py (97%) delete mode 100644 src/ontime/plots/__init__.py diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb deleted file mode 100644 index 8bbc87b..0000000 --- a/notebooks/demo.ipynb +++ /dev/null @@ -1,686 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "70a32352-80c9-40b7-8f68-1aeecfc52658", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')\n", - "\n", - "import pandas as pd\n", - "import ontime as on\n", - "\n", - "from darts.datasets import EnergyDataset" - ] - }, - { - "cell_type": "markdown", - "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", - "metadata": {}, - "source": [ - "# **onTime** Common Context Demo" - ] - }, - { - "cell_type": "markdown", - "id": "520ed047-e840-4bc3-8b0e-32c1a9e0fda3", - "metadata": {}, - "source": [ - "## Load data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e75060cc-c514-4210-b359-585f4f51e873", - "metadata": {}, - "outputs": [], - "source": [ - "ts = EnergyDataset().load()" - ] - }, - { - "cell_type": "markdown", - "id": "e766b6d8-985a-44ae-9d52-a74ee7511561", - "metadata": {}, - "source": [ - "## Process the data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4d355f16-5c6d-477a-802c-9b1dbf3718f0", - "metadata": {}, - "outputs": [], - "source": [ - "df = ts.pd_dataframe()\n", - "df = df.interpolate()\n", - "cols = ['generation biomass', 'generation solar', 'generation nuclear']\n", - "df = df[cols]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c1cca8db-e15f-4e40-936b-8ef18e7a63c3", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.TimeSeries.from_dataframe(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ebe23c8b-82ca-4ba4-aba7-969ee926e802", - "metadata": {}, - "outputs": [], - "source": [ - "ts_uni = ts['generation solar'].slice(pd.Timestamp('2015'), pd.Timestamp('2016'))\n", - "ts_multi = ts.slice(pd.Timestamp('2015'), pd.Timestamp('2016'))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "ef3cde59-c483-407e-821b-dfb566ca51f5", - "metadata": {}, - "outputs": [], - "source": [ - "train, test = ts_uni.split_after(pd.Timestamp('2015-09-01'))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "26560ca6-f072-4e06-bec7-2e2516f998c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train.plot()" - ] - }, - { - "cell_type": "markdown", - "id": "5c1c15b8-a54b-400d-8a6f-323c66d6fd2e", - "metadata": {}, - "source": [ - "---\n", - "## Context" - ] - }, - { - "cell_type": "markdown", - "id": "3cb667de-932c-400b-a062-1b652c67b828", - "metadata": {}, - "source": [ - "### Profiler" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "06d4cfb7-132b-4dd4-9a0d-273a8dc0c234", - "metadata": {}, - "outputs": [], - "source": [ - "profiler = on.context.common.Profiler()" - ] - }, - { - "cell_type": "markdown", - "id": "2f16554a-3511-498b-a052-aceabd381d7c", - "metadata": {}, - "source": [ - "#### Daily aggregation" - ] - }, - { - "cell_type": "markdown", - "id": "d54a3472-5359-4f8e-9f56-b63d9719bdab", - "metadata": {}, - "source": [ - "What does the common day looks like ?" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "4d79ec2f-86a9-4265-89c9-deabc1afea30", - "metadata": {}, - "outputs": [], - "source": [ - "day_mean = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEAN)\n", - "day_median = profiler.profile(ts_uni, profiler.Period.DAILY, profiler.Aggregation.MEDIAN)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "64bc19dd-217e-4d35-9269-cd27847b397f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "day_mean.plot();\n", - "day_median.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "f6f51cbe-7535-49c2-88b0-430b7747995e", - "metadata": {}, - "source": [ - "#### Weekly aggreagation" - ] - }, - { - "cell_type": "markdown", - "id": "d582ef08-0ab7-4719-8ba7-7c3a6ca39c9e", - "metadata": {}, - "source": [ - "What does the common weeks looks like ?" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "e1b23b1e-6af9-45b1-9bc2-53ae65d73556", - "metadata": {}, - "outputs": [], - "source": [ - "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN)\n", - "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "b6090438-5348-45be-b355-3a14ac287ec4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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W/5zvD6ppO9dffz2+/e1vy/cXLVqEF198ES+//DJ++MMfKuTc/fbbDw899BDeeOMN3H333Rg2jLY79ng8uOmmm/D666/jmWeewUknnaTWJZoCkTIVF9a3X6+1XDqYTcprj+HII4/E289eL//t2Xfqd/EqiIu79PHWBnp7d1jzyzEcu8Mi7niE3HbYgf29fwfeGYlILI9v30pTg+fPn48LL7wQbW1tmD9/PoYMGYJAIIDp06fjhRde6PP9N2zYAEEQ8OGHH8p/C4fDEAQBr7zyivy31atXY+7cuZgyZQqGDRuG8847D7t379biKyuwlfkIlrhMGkntqkVc6h+/fwS48k4R864WccWf8sjlROzoFHHlX/M451d5bNlVvzaRxZ7tmOMQUbZybhFx6erqktPOWMVlR5cOF2YAJDcRALz31gqEw2GgeyWQJVGKK95B3RalSzIJU70RF1Zx2dVd+ni94dYHqPr27a8BT91zFpDtAMIv4/n3gS83AZ2dnVixYgUWLlyIaDSKuXPn4sUXX8QHH3yAk046CfPmzcPGjRurvoZwOIw5c+Zg2rRpWLZsGZYvX44dO3ZolsHIgnUVKYkLvf3V5vpcCxYo3vuc/sZ/fAw44lIRE84WcftDwIMvAH94dM+YAxZx4Qys4iK5itrb23HGGWegubkZc+bMQT6fR0uIln6v1xgXVkkI7yq0xBbTQJh0/d0VVhZpqyewiounF1fRnqS45HIi/kYKb8PjAq79loAxo5qx1wEnAbseBAC0bQEee+wxtLa24phjjsHUqVNxySWXYMqUKZg0aRJuvPFGTJgwAf/5z3+qvo4///nPmDZtGm6++WZMmDAB06ZNw+LFi/Hyyy/jyy+1nYgScfF7lQcaJXHR9BIscIANRZUP3vsciDF7xqfr9b0eo1BTHRcL6kM6VbqcgMMu4te//g1uuukmuZbNq6++ijfeeAMzZ87E4EYR2zvrmLiwSkJmNzweD5LJJNC1AmglMVMr3gEO2duY69MSA7mK/F7A7SLpwbtVVlwO+U6+/zklArncCNjtAAT1Ck8NbQbev7v0LLV5Fw1aP/FQYMQg4h455sSF+PL2i4GJf8GmnR48cP/9WLBgAWw2G6LRKK6//no888wz2LZtG7LZLBKJRE2Ky0cffYSXX34ZoVAIoigq3N9tbW3Ya6+9+nl1bdhW+D2GNUPxuc0hAc0hEvtjEZf6hiiKciB+c4jE+XVFiOtUBJDLAW17SIC2RVw4g2Sggz7giSeewC9+8YuS5zz44IOYOXMmhrYQ0rKjk7hM9EyJ1gNK4tKBBx98EJdddhm2hV+T//xZuwigvr43ACQGcBUJgoBBDSI27yLKk5rY3gls2TXQs/QzHW1b6O2JI+jtU06Zh7tuF4HOZ7D68+l4/fXX8fvfk0I/P/3pT/H888/j9ttvx8SJE+H1enHGGWeUVNmWIKVysmX0M5mM4jnRaBTz5s3Db37zG2zZsgUjRoyQX8fG6amNeFJEd5TcHtZL3ZpJI4F3PgM27QQSKRFed/2tBwuEpEgH24P2Ahb/TMAzbwPHHQyc9gsRq9cDG7YThdJur+85YBEXziBNzICX1MqRcMEFF+Dhhx9GIpHAo48+ij/96U8Y2kyMZjYHdPaQzJt6goK4ZDswd+5cnHzyyVh83/3yn9cbUzRWc7AxLr25igDye2/eRcapWAGoBWz8VK8QgVwuC7vdoSpn7Otz2VPkhBH0AyeO8gCtpwE7H8AbL7Vh8uTJOOiggwAAb7zxBi644AKcdtppAAjp6K9Z66BBgwAA27Ztw7Rp0wBAEagLAAcddBAef/xxjB07Fk6nE2PGjNGldkVf8S0SJOICEJI3Zbzml2TBALBuorFDgVFDBHyXCM+YMIIQl3QG2LIbGD3EmGvUCxZx4QwScQl6gbVr18p/v+aaaxCLxfDoo49i9+7dePHFFzGk6Xj58R1d9Uhc6Ol3UJMAl8tFiMvixUBqK+AeXrfERXIVuV3oU0mT4lyyOaA7CjQGe31axejNXcMin8+jvX2Lbht32xY6DyYMp38fNRjAoHOAT7+Oz9/9DD//ybnyY5MmTcLSpUsxb948CIKA6667rt9+Kl6vF4cffjhuueUWjBs3Djt37sS1116reM7ll1+Ou+++G+eccw7OPfdcZLNZrFu3Dg899BDuuece2O32Pt69NgxMXARIxem+2mwRl3oFW69pzBClTWDXRduW+icuVnAuR8jlRHnDCvqAr74iAal2ux1jx47FggUL5Oc+9NBDRbVc9LxSfbC9IyffHjWURCoff/zxZINIkii0HZ1ESq83SPOgL7UFKK7lounlGAql4kJvB3wCGkfNAZzNSIS/wDnnnCM/dscdd6CpqQlHHnkk5s2bhxNPPFFWY/rC4sWLkc1mcfDBB+OKK67ATTfdpHh8+PDheOONN5DL5XD++edj6tSpuOKKK9DY2KgpgWOJy/DWUhJrBejuGdiwnd4eW+SZZJXIPSHOxVJcOEK0KKNodUFxGTNmDFwuF+bOnYtgMIhIJIKlS5fi2uPvgvQT1mMtly07kpAaSU4YTeSFhoYGzJgxA6/t2ABgBgCgfTuwz1hDLlEzSMSlt+JzEhSZRd3AxJF9P9fMkGJc7PbSk+SYoTaED9sMpwMYM4Ya77Fjx+Kll15SPPfyyy9X3C92He2zzz548803FX9jY14AouQ8/vjjaG9v58ZVxMb9kJTo+o5v2FPRvp3OxTFF62A8o7is21r/c8BSXDgCWzXXZU8hEiH1SiZNmgSAFOeTfPY9PT3YvvEj+fn1qLjs6CwoLvkMJo2jK3Xu3LlAcoN8vx7dRTJxYQJz8/k8LrvsMuyzzz54++23MaiRGie1A3R5gSiK8gly9GDA6VAa5NGDyf+ZbP3WM+qraq4EVnFZu6X0cQv1AYXiMlT5WLGrqN5hEReOwCou+QzV/idOnCjfPvvss+Xbq955Xr7dHas/d0lnpLBJZXdj3Lix8t+PPfbYuicuUnAuS1z+9Kc/4c4778Tnn3+Oq6++ukRxqUd09gA9hWbyrJtIAqvAbKzTnk19Vc2V0BgU5LlguYrqFxJxcdiB4a3Kx8YMpXW99gRXkUVcOAKruKQTVEKRFBeAbNoejwcA0L5utfx3ybjXEyLJwq6d2a3oVzVhwgQ5xgUANmyvP9KWKCIuX3zxBa655hr58TfeeAM+F50w9Upc2NMje6qUMGowVWA27dThggzAQK4igKouW3bVZ8yXBeISB0hQut0uIJ/Po62tDblcDi6nIKuPluJiQVewiksiSq0wq7g4nU6MHj0aALBjK806YklPPSCeFJHNFwI8iohLY2MjfHY6PvWmuGSyInIFL5nHBeRyOVxwwQWk+F4B2WwW7V+tku/vCtfnZtVXKrSEPUFxkYiL2wU09ZE5ZrmL6hvhiIhwoZbP2KHEbXzGGWdg4sSJco9AKc4lHAU6e+rTHkiwiAtHYMlHJEx3Y1ZxASATl0SEOj176oy4FNdwGTWKtsEVBAFjhtoBkezu9UZciqvm/vGPf8Tbb78NAPD7/fJjH75PmwbuqYqLkrjUp7Huq2ouC0WzxU16XJUFPcGmQo8dBtx555144oknAABLlizB6tWrFa7UdXXuLrKIC0dgiUvXbmJ9bDabQm0AKHFBLiL/rd5cRWz/Hb8rAbdbWT52zOjhQIqM0bqt6pWd5wEK4uIC7rvvPgBk01q2bJnsKnzrtafk59Vrv6K2rZSMjO/VVURv16OrKJUW0VEgpX25iYA9LyVaFEWsXLkSu3YNWOK5LtDOBOYGHJ248sorFY/ffvvtmDCcSYmuc9XNIi4cgXUV7d5BYjikVGgWMnHJUuJSb66iLbvo7t0ULCUmo0ePluNcwlEbuqP1c9ou7gwtFSLca6+9cOyxx+KYY44BAGzfRGOc6rVDtEJx6SU4d3gLDUrcWIfEhc0WLJ+41M9a6A35fB7nnHMOZs6ciRkzZiCVSg38IpODzSha/sRf5d51Eh544AE0eulkqfcAXYu4cASWfCRj5CRR7CYCCJkhyMFpJ/1U6s1V9EUbjUgc3FQ6TQlx2SDfZxe22cEqLmIuIce2TJgwAQBw8sknFx7MwuMgj9Wr4rKu4AYc1AgEfaVuEodDwIhChkU9xriUE5gLKF1m9VgagcUvf/lLPPTQQwBIkc7XX3/d4CvSHmwCQtunpD7RhAkT8MMf/hAA6av15ov/ps/ZUt/k1SIuHCGSYCZbjkRisYG5EmTFBYDTRjauelNc2jZSCWHkEE/J46NHjwZSG+T79RTnwhKXVIKOQwlxAYAsyZWtxxiXREqUmz1KaosoitiwYQM6OuiOLm3au8LkNfWEgarmSmgI0NvddeY2ZvHvf/+7pKLx008/bdDV6AfWVSQd2P7+97/j6quvlhX5Jx64XX7Kujqyh73BIi4cIcqSj5yy+BwLlrjY8oTg1FuMy8Zt9AuNG1maSlGiuNTRQmWJSzJOj88ScZk4caI8L5I9JM4nHCXZSPUElowGHTtx2mmnYejQoRg3bhyGDBmCd999F4AyzmVznbmLylVcnA5BTp2XOknXG97931p86/d7Awe+DbjHyn9/+umnSyoc1xtkRVnMAqnNsNvtmDlzJoYOHYpvfetbAIBIeDP8LuJCsmJcLOiGCOu2LMSv9Ka4jBxJHdpitkd+bT0t3m27MvLtSeNK2wazMS4AsH5b/Xz3BBPjEu2h1cck4gIwqkuGPt5RZ6oLa3xXvvBPPPnkk9i5kzCTXC4nBy2PZohLvcW5DFQ1l0VDIeGsXhWXm+9uh+g/CAhOR8vMlzFz1gkAgLa2Nnz55ZcGX522kIiLkNkGIIfRo0fD6XQCAC699FL5eR6Q4JbNu0hgd73CIi4cQeHuKSgpvSkuHo8HQ4YQfTyTJEeyXE55Ujc7WNfHfpNKW52OGDGibl1FbHBupJvuxCxxOeKII8gNhrjUm7uIJS6Jzk8BkHRwqT/QypUrAQCjmU659RbnUlw1N5VK4dlnn8VVV12FJUuWKJ4bKhCXeot3k/DlFpqk0JEajcQI6hqpZ3dRJC6ik5xPISbWAVDagn322Ue+LRQOc6JYXzaxGBZx4QhsVhFykV5ToSVIAbrpONWS6ynOpTvulG8fsE9pHqzL5SIn0Dxha/W0SFkCGu4kX0wQBIwbN07+u+wuzNB00HrrV8SmQiPZBgD4/ve/L3d5Xr16Nbq6uuq6CB3rKrrr/65Ha2sr5s6di9/+9rdYtGiRopGkpLj0xIB8vv5O29u6mxT339+0HzD0OwDqm7go41s2AlAq8V6vVz7IJrvX0NfV2VpgYREXjqAgHtkIRo8eXVK/RAKt5dIj/62e4lziGS+5kU+iqaH3FsljRo8Cku0ASNR9vbjKWOLSuZtIvyNGjJDrtwCgBfnqWHFRGmxy0pw8eTKOOuoo+c9vvvlmUS2X+pgDEqTic3abiDv/dCOiUWUAi+QuA2iArigWHYLqAKIooidXmPN5WkFamPhHwDsJK1euRDgcNubiNIaCjKeIvWMVFwDyoSbatUH+WziCuoVFXDiCTFzELCCmeo1vkdBbEbp6UVwSiQSyaAQAuITuPquFsplF0YRQNzEeLHGJFWJcxo8fr3jOsGHDYLfb65q4dLKGt/A999prLwVxWblyZV0rLjsLHa+DnjgAQsrmzJmDxsZGAMDSpUtlMtNAiyrXXYDu+q0ZiDYSpB8S38f3vkH+LgpuYNJdyGZzeO655wy8Qu2gcP1lyYQoJi6yMp+lRiBcZ3OAhUVcOIJ8SioE5g4f3kup0AJoETpGcakT4rJhQzvgJMU5fK6+j46lAbqaX5ouUMQq5cn3LzZUDoeDzI8sJS715iqSDK8NCUAkwdqTJ0/GjBkz5OesXLkSTUHAXxDo6i04N1YQF4Q8lVOvvvpqnHXWWQCAeDwul35XEJc6Ul8B4OV36A87LNSFW78r0ErKDbOAod/BM888Y8zFaYyYIoSAGPm+FBeWuNTbHGBhEReOICsmBRVFOlX1BrkIXR0qLmu+2gzYiHuo0Zft83lEcaERnDu6NL80XcAG5/ZFXICCu0ihuNSXm0RWDQrGuKWlBS0tLRg6dKisRr777rtIpVJobSBP7aozeVwisdkU/WJTpkzBeeedJ9//17/+BaC+a7m8/Qk9oE0akYHPI+DvVzJK7Lhb8M4H9dnrIJZk7uTID1uswMqKSy4s/607Vl/2gIVFXDiCTDwKGUVNTU19PreeY1w2bqVfpCmY6/N5o0ePVi7UOpFGFUXU+iEuo0ePruvgXElxyadIhOpee+0lPya5i9LpNFatWoVgQXGpp9iOXE5EulAVIJkIAyA2YejQoZgxY4Z8yn7xxRexdetWNPjpRl4va0HC6jba9mPaXuRQc+zBAhbNLfzR0YB1+fMNuDLtoSAu+RiGDBmCQCCgeE5viosV42JBc+TzIp2g2YEVF0pcqIWK1InR3rab1nBp7qVPkYTRo0crXGX14tNVuoqU5f5ZlCouWl+ZfshmRcZ1GgZA3EQSiuNcAj5yO54kG349IEWXATJJ8uNOmTIFgiBAEASce+65AEjvngceeKCuXUUbdvnk20dOo3Wdfne5AAFEosy4D0R3dx0tggJiiorqsV5jHy1XkQVDEE+SbAAAZbmKWlpa4PV6lTEudTJRd3RSstIS6rvMOSEu9bdQE2W6ioji1APkyQvqibgofssc+WL9ERdJcQHqR3XpjcBOmTJF/pNEXABSCr9eXUWiKGJ3bBC5k1yPA/ajbpKmoAC/ozDxXUOxceNGA65QWxQrLn0dYgRBUCjQ9XKQ6w0WceEEiviUgh+zP+IiCEJh46J6YE+d+DTDEUZxaXD0+bzm5ma4HXSXCtdJh2jFhpVLoLGxEc3NpdWDi1Oi68lVpDC62VListdee6G1lQRwv/HGG7LiAtRPrFdvQdr77bef/Ke99toL06ZNAwB89NFHcDvoDldPrqLNO4EsiJxkS36BYcOGKR5v8BW+t7MVbes36X15mqM4xqU34uJ2u0lRzjo8yPUGi7hwguLic0D/xAUoBOgyMS71YrB7ojQgtzHUew0XgJC3YYOoPl4vJ4zi4NzeDBXAZpbRRov1UstGSVzCAJQxLoIgyKpLV1cXxAxdB3uK4gIAU6dOlW/3dNLg1HoKzPx4HVVgmz3bSsojDG6gj3/21W7UG5RZRb0TF6AQoCtm5MyjeiKvxbCICyeI9NJgcSDiIrsKCqiXdOhIjAbkNjeUdoZmMXIYbcC4qyvdzzPNg+KTdl+GqlhxSaXrZ9MuJi42m63Et8+WOs9l6EmzXgi8wmWYK1VcAKUK1bFznXy7njattz8Ky7dHt5R+sRGD7fLtL9bXkb+0gHJcRQAT51JwrdbLQa43WMSFEygCa3MDZxUBUoxH/aVDxxJMjEuTt59nAuNG0THasTvZzzPNg+KTdnHqo4SWlhZSTTdbf5u2IiMiF8bYsWNLqkiPGDGCPiUVlm/XyxgkiwjskCFDZPeYBJa4bN9CGw3Wk5vg/TV0Xe8zplRJGjeC2ogNW+qEuTModhX1VZi0uAhdPc2BYljEhRNU4yoqUVzqZKLGktQ4NTf6+nkmMHYUjf3o7Ok7ddpMKFdxoXFO9GhVn4pLt2KDlsB2SU8lqIugXohLcZB2sZsIUBKXzRs+lW/Xiy0AgC82UUXlkP0CJY/vNTYk396yqz5sAAvWVRT029HS0nubcJpZFAZA5kC9ZNgVwyIunKDYVSQIAoLBYJ/PB4ChQ4fWZTo0u3E3BPoOzgWA5qYGObMqkqiP6Vwc49JXo02g4C5iqqpG62TTLnYVsfEtEljFJRml9WzqhbwVK2/FbiKAZJtJ3bLXf/Wh/Pd6OW2LoogtXQViklyP/fYeU/Kc0UNpHNyubnvJ42ZHVEqHzicxccLYPlug0CJ09Rf3WIz6sPR1gOKsooaGBtkg9QXiSsrL5KVeTlnJNF2Yvv5DXNDQ0CD7dGPJ/kmOWSCftPMpAGKvGUUS6lVxUQSXZsO9Ki4scYmEab+HeiTwfSkubrdbPmmv/XI1XIWm6vVCXHZ2AelcwQjEP+/VTTKUWR49SR+y2b6rbZsR3VKyQj+BuUCp4gLUzzwohkVcOEGxq2ggNxHAuJIKrqV6YdfJDD01eXtvji2joaFB9unG004tL0s3yBtWIZOkv1inUaNGKYhLrD7CfEoUl96Iy+DBg+FwELLa00VbP9TNOihS3npTXADqLorFYgh6iaukXoJzWbso5MI0IJ3BUNZz4hyCrVu3an9hOiIaL8T8DUBcRo4cSRqv7gGNFi3iwgmKXUUVEZeCq6Qesory+TwyOaqc+AYgLo2NjfIJI5NzIZM1v0+XEhditQds/VCHiovC4ObCmDRpUslz7Ha7XNOjc1e7/PdI3PxzACjPVQQo41w8DvKiejlps1Vjg367TFRZDGGXh3MI2tvbS55jZiRShW06HyPhAX3A4XAUDjJMLReLuFjQEgpjm4uWRVwaGgqd5RjFxex1PKLRKGCjAbnluYqoT7ceFipLXARBQCgU6vO5xFBRxlo3xIXNKsp292mwpQDdni56yq6XMWCJS2PITdd7Efbee2/5tl0suI3rwBYAwJbtYfl2Ux81ndwuAV5nQWqss+q5oigimS0o0LnYgPvCuHHjLFeRBf1Q7CoaKBUaIP5tr9crb9z5PGkdYGZ0d3cDNpreWImrCKiPhSq7CPKJAWOdil1F9bJps4qLz52F09m7G1COc6nDsgBxptlmY6hvBs8qLvlMGACQyxUVLjMpdu6mv2uon0D95kBh0dQZcUmlAVGkiktf5FXC2LFjLVeRBf1QjasIKLhK2Mwikxvt7u5uwE6Jy0CuIkJcwvL9elio8kk7lxiQwJKsIibGpQ42K4D5HXOJflPi5ZToXP0Rl2iMtr4I+PvetFnikk7Q7Kp6IPG7u+iP6ff2vV3JAbr2ANau36HxVekHZQ2XeHnV1LOWq8iCTlAqLuW5igApxqN+queGw2HZVSQgL2dJ9AU2qwgw/0LNZkVkpVIU+eSAxCUQCCCgaDBofvcAwBKXcL9jICsudUhcunqor6gh0HfriyFDhsjuxHgPza6qB+LSGaY7d9DXd6ozmxLdtsnkRoBBcfG5gfaF5uZmhT2sh4Ncb7CICyeoTXFhGy2qelm6g3UVOe2ZPmsWSHC5XHCADp7ZjXVx0bFyXIZDB9MYmHoJTJUbZmb7Jy5Ucak/d1l3hCEuwf57dkmqSzRMs6vMTuKBIvIW7PsUM2Y4daVt3F4frT+AIgW1DFdRU1NTkeu8PuxBMSziwglk4iLmgfzAkqAEQlzqp+AQIS5EcXE5yquC6XNTQ2X2E0ZxJklZxGUQrSa6O5zq55nmQDYrIhIvENYBiIusuIgp2EDmi9nXgIRupkt6U0P/rS9kd1GdxXuFe+ja7o+4DG+lB5xtu8W6CEwGKldcCHEJy/frYQ70Bou4cAI2IBMYuNy/hGLiUheKi50QF7czP8CzCfxuWnDK7KfM4tod5cyD1kYaCMRudmaFwt2Z7S5PcQFgF8jaqRfiEonTeT1Qs1GZuNRZhl13lCFvob4D3tgidMl8A7EjdYBi4tJfhiFQIC6Wq8iCXkhLNipPdq5yTtpAqavI7FVDWVeR11UecQn66PNkF4NJUVwttZx50NLEEhfz92rpLio+198YDB8+XL4tFIKU68VVFGOIS0uzv9/nUsWlfuLdgPI7xbPEBc76ySxiXUUuR6bXOjYsSlxFFnGxoCXS0sFCJMSlXMWFTNR6i3EhiovX3X98i4QGPyUrOzvN7d+uxlU0iNnUeuLlkT2eUVx8rr8xcLvdGDRoEAAgn5F6Vml5dfqBbTba2lQmcamjQHWAqRoLoKWp7+wyBXGpo5RoVnHxlHGQI4pLDwk5gKW4WNAYsuJSIXEpUVxMfsrqDEcAG/Fl+z3lEZfGIJ3Gu8P1RFzKU1xam/2yoaoHtaG4M/RAYyDFuWRTXQDIKTWfN7fyBgAJhrgMau2/4eqYMYXmg3UWmMmSt0Et/RAXtuy/a2jdVM9liYt/gGKcgKTUi/KeYMW4WNAUsuKSr4a4sPKwuY1VV5juvAFfedOzJUTTJDu7ze0qKSYu5cyDpqZGOasmniyP7PGM4j5FA42BHOfCKI/1QODYDLPBLf0Tl1AoRNwIufoKzmULag7qx13WEgJsQkGRcA7Bzp07Nb4yfdATpfbM3398NgCmKGkhQLceVLfeYBEXTmApLgSd3XTnDvRTt4HFoGaaKtoVMTdxKw7OLUdxYYsQJtLljRnPqFZxYddBPRCXVKZAQsUcBrU29vtcQRDQ0tJSd/ENbKf4wf2oTjabgOZgwYi6hpJ6UHWAjjLr2LBgA3QtV5EFTcEqLjabDYFAoN/nSygpQGfyU1Z3hO7coUB53Z5bm7yASIxWT8zcioNCcSmjci5QmAN58sOnsv0H75kBlcS4AL3XcjE7gQeAtNSjJp8gqtoAaG1trbt06FSWbtYhf/8b9+DGguLiGoKurvrIKmIPcg2BCohLYR4k00Aqbe7DXG+wiAsnYBWXxsbGAQuvSag3xYVN5w2W6SpqbGyQyVskYe4prShAJ5YXnEtOWGTTTmf7LlRmFigywwbIKgLqt3qu3CW93Fin1ta6cxWlGSI+UMPVYS0Fmyk4sLPL3C5jCeXWsWFRWoRO9csyHOa28nWCXE5ETi7zni47FRroLcZF3WvTGz0xmgI6kKGSwPYriqXMrTgoFZdkWS5D1lWUhwPpjLlPWMrO0JUoLvVFXLL5wkaVT8HjGXgxtLS0kDpQeUL+68FVlMnRzXqghqsjB9O1v7vb/C5TAOiOsrV8yjuUFNdyqYd5UAyLuHCATJa5U1BcykW9NVmMxulJyVumeMD2K0qkXaaumllNcC6ZA/RYFTN5h/Di4NzyFZf6KvufE8kCsAupshTY1tbWwgvJWjD7STuTySAPwlYEMQOno/8xGDGYkpWOqPmVRwDoUdSxGYC5FbAnVM+1iAsHSCuIS6Yi4kJ6V4hyRoWZY1zy+bxi0/WVmQ5NFBdirHOiXbn5mwxscK7HJcLpHFgeDoVCcowLAERNTl5Z4uJxJOF292+w61VxEQWy+TqE8qohy8QlWx/Eha2ibRcGLnMwrJnai55E36nTZkKUyRJtbS7vOxW7iuoxQNciLhwgzdqlfGWKi8vlgs/nk422mQ12NBoFBCqJl+sqIgHK9SGNsqQr4C/P7WW32+G00ReaXW1gN9ym0MAmKhQKkWD2OlIes9mcXEHaYc8O8GyCEsUlClOrj2yneIdtYPI2hClCF82Ul9zAO9i13NpURj40LFeRBZ2QrsFVBCjjXMwc48JWzQWqcxUB5j5pJlJ0owmWSVwAwOOkk6huXEX5FJobyztlDh48uK4Ulx276Hx2OcqrhtzSUqjCViDx2RxMrT6Gw2FZcXE5BiYuTUy2dDrrQTZbHuHjGfEUVZGGDCqPjFmKiwVdUIviAiiJSyRu3lMWKw0DlQbn1sdCZRvrNQTK99N7XNQXHjF5EcKwVIunjPgWCc3NzUUxLuYegx27aMC9x1ned6GKS320AGEVF3cZ5C3Iclx7ED09PX0+1yxIpgtbdD6DQS0NZb3GyiqyoAuKFZdKsooASRokxkoUlY25zASiuFTuKgqFQkAuTN/HxAuVrWPTGCpzAAD4PXRz6+w2d4foriqIC7sGAPMrLjt3043H466QuNTJphUOdwN2Ui23nD49oSLiUg9F6OS6TPlYIZ5xYBDXeVi+Xw+tH4phERcOoIriwjZaNKnRrtZV5HQ64RQoWzOzTzcSpZOhqZ9uuMUIMO7vnR3m3a3yeRGRREEer1hxqZ8u6bs66CQut9locYwLYO61sKuD/p7eMsibQnFxhOqCuKSzheD8XKyyxrvMHFCUF6gTWMSFAyiIS9UxLuaXh6t1FQGAz00H0cyuIraOTUuZwXiAslhfR5d5g1x6YoAoFjbqXHeFxrp+FJddnXQR+z1l9uwqinEBzK24KMdgYPJW7CqqB+KSEQsZdbnyFRfLVWRBF6gSnJunltqsAXlEcaGbdSXEJeChg2jmhcrWsWlu7LupXDEagkzxLRMTl0pruEgojXFR+cJ0RkcXXc/+MnvUyI0W62TT2t1Jx6Cchqus6gh7AF1dXRpclb7IiQUjKMZJ88QyYNVxsaALVHEV5elmlTJpiEOxq8hXXr0lAEDQR6Xkrkh5WRg8Ip6k1z6opfyUzkamHHhnz8A1L3hFtcSl3hSXrjD9AuVmlwmCUCj7T4NSzewq6uqurMGg3S7ALWUf1YHiks6IgEB+e0eZRQiBXlxFJp4DfcEiLhxAHcWFLvKkSfetYlfRQCW+WTQw4sSuTpMOAJTp0INb+u6GWwy2HHg4Yt400JoUl3xSbrZpeuLSQ9dzyF9ejxqg4C6qkyrKXUyDwXIbrnpdhblvN3+MC/vbOe3l2zSv1wuXMw/kyWvMTF77gkVcOIA6igtd5HWjuFTgKmoO0dPI7rB5N26ZdObTaGlpLPt1rU2U5fVEzdtgribFBZDdRWYnLuEeup4bQ+UzeKK40EE0M3EJR5jOyGU2GAx4CoplHSgubNVct70yo97MqC6W4mJBE9SaDl2iuNRLjEsFiktzA5XTO3vMu3EnM1LdhvL6FEkYxJQDN3MdF8XpMNddBXEh7iKzx7j0MNllDcEKiQvb/sHE9WzYBoNNZZK3gLQMHEF0dYXVvygdsYuJ8WHrNJUDNkDXinGxoAlYxcUm5MoOwpLQ2NgIiJStmNtVRL97Ja6iQU2sq8S8xjqdLfjy88mKCOzgFuorM/Omrbj2XKwyVxFQF60vACVxqSQtvkRxMfFciDDEJRQoL84n5C8or4JDEeBsRuzYTRlHOengLBTEJWpee9gXLOLCAVjFxeVE2UFYEuo1OLcS4tLaHJDHoCde2fjxhGxOKjiVqIi4DGXKgbNlws0GBemugLzJz8tSxSWfN6/BjiaoUWhuLP8gUxzjYm4SSwPVy1Vfm4JMh+iwSaXnAth6TP5qiEsh0zSXF5DJmnct9AaLuHAAVnFxld+eRgYhLtTim1pxKbiKPC7AZit/A2bL/kcS5aWP8oisWB1xGTaEBvIm0+b9/grSXQFxkRWXfH3Ed8TidNMOeMv/PespxiWWpJttufFuzQ1sdp15Y90AYHcnZZ3lpIOzIMSFvj5u4nnQGyziwgGKFZdKUY+KSyVqC6AkLvFUFeyPE+QLBacEMQWPp3wXweDWRjmjJpWtYhJxgmSablZOR75st6nP54PT6QSyTC0XE3sK4kx2maf8llW9xLioeVX6IZfLIckoh+UqLg0BuqV1mTjWDQA6wvTHC1ZDXHL09Wat7dUXLOLCAVjFxe2sXOavrxgXQlwqySgCpA7RpH5FMuM0ZaPJXE6EKBDS4bBVdloMhYLySTudq2Cn4wwpZu4G/OV/D0EQ6qbsvyiKio2mIpdpncS49PT0VFVFm+1X1BMzbz0nAOjqpouhocwYHwmsqwgA4hZxsaA2FMTFVTlxcTgcilNZyoTERRRFYqwKrqJKMoqAAnEpLFQRNmRMqBKzm5XTXtkXEAQBgki+f1askPVxBFYtrKQ7NlA/ReiSySTyIlXNKiEuLS0txEUgkk3brK6iaksjBH3UfkZMHOsGAF1MIUm2MnY5KHYVWYqLBdWRSFFJ0+2q7icJ+OjEZuV2syCZTCKfz8vGqirFhXGXmdGny/YpcjsrPy06QAxVHuYlLmzl4FCgMvZaL2X/u7q6FGUBKlZcADlA16xjEA6Hq6qizfYrSmVdyGRM6jeHMh28uaGytVCiuJjQHvaHmojLzTffjBNPPBGzZs3CN7/5Tbz22mvyY0uWLMFxxx2HOXPm4I9//KNCuv/000+xYMECzJgxAxdffDG2bdsmP5ZMJnHdddfh6KOPximnnIIVK1bUcommQJzJIPBUSVyCzOnUjDEu8XgcgCCnQ1ca40IaTZpbGt25m5ZqdzsrJ58OGzmhiTY/IYEmRCRGT5mNocoIWL0oLmTTpsSl4hgXQI5zMaviEg6Hq6qiHWLbe9mDRLkxKSKMq6u5sQrikjN//7q+UBNxWbhwIZ566im8+uqr+H//7//huuuuQzgcxsqVK/Hoo49iyZIleOSRR/Dmm29i2bJlAIB0Oo2rrroKCxYswEsvvYSpU6fiuuuuk9/zrrvuQjgcxvLly3HLLbfg1ltvxYYNG2r6krwjnqTExeuu7idhT6exhPk2rXg8XnXxOUDpKgLMuVB37qabrtdVOXGR+7QIDnSGzblrR2OUdTc2+vp5ZilKYlzMOQQ1KS7BYJAEKReUp7pRXMp2FTF37AFTV8+NMJllrU2VrYWSrCIT2sP+UFP6xdixY+XbgiAgm81i165dWL58OU477TSMHDkSAHDuuefiqaeewqmnnopVq1bB6XTi1FNPBQBcdNFFOPbYY7FlyxaMGDECy5cvx6233opAIID9998fs2bNwn//+19ccsklvV5DOp1GOq0M6nA4HHC51A1QlE6wWpxkYwlqrD1uW1WfEQq4gUIyQU80hXxefS+glmMQjUZLjHUlnxMIBBQLNZoQNanjoeUYsIqL1y1U/BluZxYoLIXN28JornDjLxdajkEkxhZe81X0GUR1oxk13THzzQEA6OzsBGx0p3Y7K/seLS0t2F4Yh1hCm+vUegy6uroUiounzDHwswTHEUJnZ6dm16j1GLBqWWuzt6LPKT7Imcke2mwD7101543ecssteOqpp5BKpTBjxgxMnDgR69evx4knnig/Z+LEiWhrawMArFu3DpMmTZIf83g8GDlyJNatW4dgMIiOjg5MnDhR8dqPP/64z8+/7777cPfddyv+duaZZ+Kss86q9av1ik2bNqn+nrs7bAAaAABiPo329vaK34ONidixqxvt7TvVurwSaDEGbW1tCkMlZmNob99d0Xs4bBlI2tW6DdsQqqAxWaXQYgw2bNwOYAoAwCZUPg9cNnqs+vCTr9Dg09ZnqMUYdHXbAZCaLE57rqIxsNlsCsVl09ZOtLdH+nlFbdDi+wPA2rVrAdve8v3dOzYiXUH101AohO0FxSWZBtatb4ddo2hGrcZg/fr1gO1g+X7H7k0Q0gNvjvGIC8AwcscexBdffIFBgwZpco0StBqDKKO45DORitZCLBZTHOQ2bdmN9nbtav+rOQbjxo0b8Dk1E5err74aV155JVatWoW2tjYIgoB4PA6/nzob/X4/EgkyiIlEQvGY9Hg8Hi/EOaDP1/aGRYsWYeHChcovpZHismnTJowaNaosRlgJ7I5d8u2W5iDGjBlT8XsMGdQEhAt3BDfGjBmqzsUx0HIMNm/erJCGW5v9GDPG388rSuFx5SGFZjY0DUMVwzggtBwDh2u7fDsUcFc8D0KBHYB0SrMFqppH5UDLMciJO+Tb48eOqOg7jBs3Dsh9Kd93upsxZkyzqtcHaPv9AWK/YKfSwV4TR8NdgTkbPnw4vtxNT9utg8coYz9UgNZjYLPZFArs5AmjaB+ifhBhS7fYg3C5XKZcBwCQybfJwRxTp0zEmOHlFyIcNGiQoo6LL9iKMWNa1b5EzcegL6hSqctut+PQQw/Fgw8+iFGjRsHn8xHGV0AsFpMLSXm9XsVj0uM+nw8+n0++HwgESl7bG1wul+okpT/YbDbVfyA2C8jvdVb1/qEgHYNEKq/pJNJkDJJJhaHye8uTDFl4XaJMXJJpoaLKu5VCizGIJdhYJ6Hi92dTQXd3JTU3JFqvhUEtwYren5S7pwpLNFH5HKoEWnx/QBmcK0CEx22rqA1IS0sLsINmV8VTAhqD2qwFrcaArekEAH5veeu5ISACKMwhewg9PT2mXAcAkMo4gEJ8U2PQUZE98/v9sNsykHhcyoT2sN/PU/PNcrkcNm/ejHHjxhG5s4C2tjZMmDABADB+/HjFY8lkEps3b8b48eMRCoXQ0tLS52vrFSlGAvV7q+OSIT9LXMyXDp1IJJRZBFVwUQ8T0GrGrrixOHXteD1VkFc/PZHt7jJnVKZc00jMIRSsIiCRiXExdUZNgbi4nPmKe5eRInTMOJhwKrB1XFyOPOz28sagOKvIzMG5mTw1gv4KKxwIgoCAl9oQKx26gGg0ihUrViAejyObzeKFF17A+++/j2nTpmHu3LlYunQpNm/ejI6ODtx///2YO3cuAODggw9GKpXCsmXLkE6nsXjxYuyzzz4YMWIEAGDu3LlYvHgxYrEYVq9ejVdffVURL1OPSDLExeetrlx7KEhTD8xYx4VkFVWeRcCCzcTpjpgvjJ5trOdzV95vqCFAX9PZbb7vDwCpbMEk5VOyAlsumpubFX59U1eQLgTneqrILiNl/81dz4bNKqokwzDIivMmzyqSiYuYq8hVKIE9yCRMuCf0h5pcRU888QRuueUWiKKIUaNG4aabbsLkyZMxefJknHHGGTj//PORz+dx6qmnYv78+QCIa+e3v/0tbrzxRtx2223Yd999ceONN8rveckll+Cmm27CSSedhFAohKuuukqRvVSPSDGTKuCrzu3VEKQ7vRkr55akQ3sqlzW9bqBQgw3hSBpA+V11eUAszhCXChrrSWgMMQ3muk04CQBkZOKSrJi4kBRQerQ0Y0o8UAisLKyFapTHEsXFhKdtto5LJbbA4RDgduQIAXaETEtcstksRIHMATuSEITgAK8oRcBPt3czqm79oWriEggEcNddd/X5+KJFi7Bo0aJeH9tvv/3w0EMP9fqYx+PBTTfdVO1lmRJsyf+AvzrFxe/3AfkUYHMjlTFfqet4PK7sTVJhHReA+MEl4tIdMV8VvniSRhb6qyAuLUx1zXDEhD0PAGSlNH5xz1VcWBJfSfE5CSQtfL1834yKS3d3N+Aiv7/fW5k9C/jySPXYTO0qInOA+L3sQgpAFcTFa4OUZtkTzUAOmKkDWCX/OUAqwyou1U0uv98vnzbTWZMSF8ZVVGnlXEBp4MhCNRcSKSbWyVf5maKliQ5aT9R8RQgBIJsrfO98quzO0BLqpT8LS1x8VcQ6BYNB08e4sAeZSt3GISlI3ezExU6Ii9NW3UQOMB2lI3FzHmT6gkVcOECGIRqVdMRlQYgLmeDprPl+VmKsqYWqJsaFVSkiMfMt1CTTpyfgq1x5G9xCIxPNGJwMANl84TeswlXkdDoVhM+sxCUai8lroRqXaSgUkkv+A+ZUXGLxJB2DCg8xoUDB/tnN6ypSEBd7dYewIOMqisRy/TzTfDDfDleHYDsZByoNHy+AVVwyucrdDEajJDi3CsUl6GOIS9x8CzXBuDaqiXVSEhc1rkh/5KSuyFUE5wJAU6Nf7oxsVldRLJ4BBGKaqyEuJYqLCWNcYkzxtYobrvoLY2ZzoTOsXdE1LRGJ0oOcq8JO8RKCihgXcyqwfcEiLhwgkysstHwKPl91AaV+vx8QyRFTPrWaCMUxLtW4ikIBcy/UJJPGHgpUrri0NFKyk0ybbw6Ioog8Ct8hn6zYVQQALc3NMoE3q+LCzt2q1kEoZPou2WxvnUoPMWwWfVe3+VzGABDupj+ay1HdIayRqe0VNaE97A8WceEAmZyUSZGuylgDSsUll1elrqCuUCMduoHZ7GMmdJWwsU7BKlyGbAG6VLa6IG8jwSqP1QTnAso4FzZmyEyIJ+k8qCY4t1RxMddayOfzSKbpXK7UFrDEJVxBqwSeEO6hMpnLWd08blAUJa35kriCRVw4QFYiLqI6xCUrmm/TqrU7NKBcqHETFuFjs8HYujzlIsBMnXROv2rSakHh2smn4XZXPgZsZpHZNmwJtSouwWBQWcfFZF2yk8mkMsOw4uBcejuVdSKVMt+u3ROl1+yu8hwaCvqAPFlU9dYd2iIuHCCXV4u4SLPThmzWXEa7JB26CsWlMUStfCJlvsyqNEtcApUPgJ+ZOlnRfKmPKUbVtwmZiivGAspaLgmTEhe2gGTVxMXEMS61HmIUBZftQZJabTL0MAU0qyk+B0h7AmGtSRPaw/5gERcOQDMp1FFcAPMFJqqRDt1kduKSZYlL5apZQEFcvBBFc23cSeZU6LBVF5DIKi5mrGeUzWaRydEjdjUF6Ox2OzwuGhdhthiXWt3GrMsU9hC6urpUujL90B2lBrwaWwiQWmt0LdTXVl9f38akyIsF4lKD4uJ2uwGRTvaUyWLS1HAVNTUw1YNNmBLOZoN5XJVvuk6HAAGFOWDzm04iZ+esw15dQCKruKQyNtORt+J1UE2MCwAEmRoe5lRcqj/EFCsukUikz+fyikiM2vJqbAFQOMzmiOKSypovWL8/mM+61yFyolR0q3riIggC7Mwp1ZSKS42uolAowGxa5luoctVYAO4qw5ScQuF4bQ8iGo32/2TOwBIXp6064sIqLiIEZcCvCVBMXKo9bYf8dC6ZUnFRVNGubOMubrRotnUAAFGmDlU1DVcBpeKSzpkvYaM/WMSFA+Slzgs1KC6A0tibkrjUaLDZE0bGhAs1y1xztX5tucqmw3wnTXbOOh3VZVKYvV9RcSFGb4WbtgRlaYCaL0tX1OwqYk2oI0h6P5kMUabSrb9K4sLGuGRyDtOpj/3BIi4GQxRFiJLiImbgdFafEcTK62ZzFSUSCZm4OOzE7VEpiheq2ZAVWVdRde/hdhR2ahOeNGMJOn9dVRKXZqaOC2BW4lK74tIQCsgkvidmrrTwWCxWU9+yYleR2dYBAMQS1ID7PNWpx6ziAtgUPfHMDou4GIxcDnKVTJuQrSqTQgJr7JNmNNhV9iaRQIgLWajZvPnSgWWXIap3FXkchZOazYMuk1UNjTApoK4qeWdjY6OpGy2ynaGBGmJcgkG57L/Z2j+Udoqv7PX14CpiSXw1fcsASYGma6GeUqIt4mIw0owP3i7UVqbe5aAGKpE2o7EixKXaUybrKsqarBNqJpMBQHcpV5XExeuiE2pXp7l8BGwmhctZ3fwlqcDmbbSoWowLUz3XnMRlz1Zc4oq+ZdURF6K40CI+cZMFafcHi7gYDFa+q5W4uBlj391jnlmaz+dJ0SmpI26Vxtrr9TKBmS7k8+Yx2Gxsgw3pqpU3n4fOod1d5tq1WcWlJqVBNHtZALoAqiUubC2XeNJcaeG1BuoriUvIlMSFrfpcTcNVQKlAk/es+bK4gUVcDIZCcakyk0ICG9DZHTEPcUkkCovLVpurSBAE2AW6U5lpobIblk2o3hkd8FCy1mGyPi09UXq91aaABgIBUysuxa6iauq4ABJxIRt2MmMzIYlXp3KuWRUXtnhi0F8DcckxiovJ1kJ/sIiLwUgralfUFkTnZYx9d8Q8R814vLC4pBiXGrw8doGuTjNtWvF4HBDIF69FeQswRrvTZMSFDUisNpumuBBjwjzLAICkNqjkKirEuIiiYL61oFZwriNgSuLCKoWhQHXs1el0wmbSg9xAsIiLwWDLezvstZ2KPIyxj8TMM0vJpu0CBBI9X62xBgCnjW5+ZjphkKwq8sWrrRoLAA1+OgfCEXMVMYnGWeJSnWmy2+1w2k1ez0it4FyTlv2vVXFxuwRqS03qKmLnbTUNVyW4mLVgxbhYUA0swXDWqrgwxIWV3XkHOWHRVAB/la4iAHAyLeDNtFDZGJdqq8YCQEOALunuqMnSYOP0e1dbdAsA3Ew3XbOdMtUNzqXExUxF6GoNzgWAoLcwB0zqKmKVeJ+3+rXgYuyh2dZCf7CIi8FgAxKdNZYeYfP9ozGTEZcaTlgs2BNGNGGejZuNcamFwDaF6CTqiZknrgEA4klqZKutXQEoY73MpriQGBe2AF1171OiuJiIuJTUcanCHsj9ikxKXFJM37JqVTfyWmpLzKRADwSLuBgMVnFh05mrgd/LEJeEyYgLY6hqUVzYhdrVbR7JhY1xqY240EC+aMJk2SQMcWHncqVgN3uznTLVUlxIHRe6YZtPcam+jgsAhCSXqT1kysq56WztxSgBwMOojzGTpcX3B4u4GIyogrjU9l5soSJWducdxFBRV1Etigu7UDtNRFwi0ThgI6SjFgLb0kitXDRhruUdZxQyf5UpoIAyPiZqonUAqBfjUuwqMnOMSzVj0BCQiIsXETOxtgIyudr7lgFK4ttjIhV+IJjLstUhWJeOy1nbCZnN948nzROYqabi4nXTTT/cYx4/QQ/jMnRXWXwNAFqbqKWKp8y1vJNpSlyCVRbdAgC/h431MpfkUpIOXZOryMSKSyHmzesSq6ppxGYWmc1lCgDZnDqKC3sINJM9HAjmsmx1iFicTiZ3lbUrJLD5/vGkeRarmooLa+jDPebZtJTEpfr3GdxMBy+RMVe/pkSKztlaFBe/jxr97oi5TplqFaBj06EBc8W4sIqLv8qesyxxiZjMZZrNZpFnqmjXQlz8TGCvmRI2BoJFXAwGW7uiduJCZzhbeZF3lCou1Y+Dz8PWsjHPQo1EWQJb/fu0NlHikszUwIAMgBq1KwAgwBCXiMnkca3Soc2quPi9VXbHZohLLGmubY6NdwNqO8iwxKXbIi4W1AJLXDyu2n6OUJAlLnum4hLw0TGMxMzjLmPb2FdbNRYAGgP0temcufo1pZiaRrUQF9bNxI6rGcASF7ezOjcJUD91XKp1G7OviycFiKLZ7CH9Ap4alrFyLZgr3qs/WMTFYMQT7IZV28/REKAzPGWiJotqxrgEmWyUHjMRF0YZqKWGCSuRm464ZOn3Dgaqv/ZggB5RIyYz1myMSy2FGB0OB1wOKmGZSXGJMYpL1Z3iWReTzUfbipgAJcSlBgU26GfVR3Othf5gEReDwQbRemrYsACgIcS4CUwUh0WqxqpTxyXgZ08Y5nGXsVVjfVVWjQUKBlsk3zsrVhkgYBDSGaouNASr37VZAh9PmMtYsyX/q217IIEt3GamVNh4Ig8IZA1Ur7gwY2f3m6qWCyEulK3U4ipqYEh8zER1rQaCRVwMRoKpXeF1V1+7AlAa+1TGPD+tmopLiCEuMRNtWjFGefPWUHxNEATYQHo/5QTfAM/mC5kc3WwaQ9VPgoYQNfpmClIHlKftWhQXAAh46Xc3leLCbLDV9i1THH5MRlyURQjzNRUmbQiaM2FjIJhnd6tTJBTVQmvLAmlqoCfsTNY8kfRqxriE2BOGiRYqqwywWTHVwAGp23YQ6bR5pLcMkwLaEKx+EjQyr42bKNYLUM9VBABBJt7LLDEuoigq0virzSpSHH5s5iIuivYftmzVcU4A0BhiyyPUfGncwCIuBiORUpG4NNITdjprnp9WTcWlqYE9bZuIvCmqxtY2D5y2wi5lD5qqamiWKbrV1FC9WtTcSHc7M7lMgUJ8R4G41BLbAAAhv/n6VqXTaYgCNQBqBOfC7jUfcRGkhqu1qcYscUmmzWMPB4J5drc6RZJJW/bVuGE1N1DVIpM3z0+rpuKiXKi1XJW+iCeZqrE1zgOXvfDF7QFEIuYx2Nl84XvnM/BXe9RGMXExl7GOJzKqdEkHgAam/YNZAtWLG66qE5zrNxWBZxWXWhvvmnkt9Afz7G51CrZaqM9bW90NNhsllzdP8TFVFRdFgLJ5pjdLYAM1FF8DALezQFwEG3Z2mMdgy3NWTMJur95d1hDyAyLZqNMmivXKZDKKiqneGhWXRia+occkikvxIUYdxcVcriI2xoXt7lwNggE/kCcKrJniHgdC/XwTk4JNW66lWihAAjOlSZo1G3FhFZcaTprNjWwBNvOcMBLsPKihwSAAeJ30dL2r0zxRmTmRzFlBrE0qIzVMyPdO58xj4tRqsCihMeQD8mQsoybJKlLrEFMvMS61Nt4NBAJAjgTrp7Lm2RMGgnlWdZ0ilaETs9aTNgAIIIYqJ5qnamqJsaohi7elib44baKFmmL26loK0AGAz01Pabs6zRORJ5U5F1Bbhc9AIACIhMBnTDQH1KqaK4E0WiQbtlmyikrdxtWtBYUNMZniwrZ9cNXQtwwA/H4/kCc/fiZnnrUwECziYjDSjI0O+Gq0VABsBeLC9rrgHcXdYGtRXJoaAoBINu60iRZqipkHtZT8BwC/h7oFOsJmIi6EbEtzuFqQU2bBWJtOeVRPcQkGg3K/onjKHOpjcYzLnukqoopLreQ1EAgAeaK4ZPPmOcwOBIu4GAwFcfHXXunUXjitinCapsw1q7i4nIDDUb2RDQT8sjRqpoXKzoNaCk4BQJCp39HZbZ7+JKJArLRdqO2ayYZNiIvplEemYmqtxIVVXBImifci8R21F6P0mdhVxDZcrZW4+P2MPRTNc5gdCOaYzXWMDBPsH6yhP4sEu63whoLHNDU8WHm4FrUFANxuN3PCMM9CZcvd12qsgn5K/Dp7zJFNks/ngUIarF2o7Zq9Xq/sKsqZyFizNVwAlRSXQr+iVMZhioOMJjEuJlNcIjFqt2t1GxPFhZD4PNzI5/mfA+XAIi4GI81UCw3Wkk5TgGz0bW7TpACyxqqWVGiABCgLICeWHMzRq0cURWQY4lKr4tIQoO8VjpijenA8npTLnNdau8Jms8nuJlFwm2LDBpTl/gEVCGwwKCsuedGmiKPiFcVuY3UK0Jmrjgvbt8xXYxsYl8sFiDTAyUwlIvqDRVwMRpapcFtLYzkJTnvB6Ns8iEZNRFwKiosK3A22wmnbLHE+mUwGIihbqTXGpTHANpo0x6Yd7qHG1WGvnWw5GHeTWYx1aYxLbaftUCgkq4+AOarnltRxqdIkOhwCnFJGjskUF7aLc63ERRAExVpImCfkrV9YxMVgZPOsi6D2ADq5YJFgQ3cP/8Qlk8kgk8moprgAgEMgq1MUvKY4bbNZBEDtJ+2mBhqQGon380SOwBIXpxrExU7dTWYhLiWuIlUUFzoB4iYkLrUcZPzuwto3WYyLouFqDX3LJDhsdAGYYQ6UA4u4GAy2zLlLhThCl4NmlHR1858DSdrN2+SgRDUUF4cc5+NQxBDxiuKgzFpdRS1M24NowhzZJN0RalGdNdauAAAnQ1zMcsrUJDiXUVwSJiBwJRmGtRAXiQPazVU5N6boW1Z7Vhx7EKiXfkUWcTEYrOLiUiFzk837ZzcDXlEcjKeG4uK00xNLNM5/xdBEIiH3JgFqJy6tTfS9YilzLPFwD52rLPmuFux7dEdNsGNDq3ToPVdxCXgLc99kriK2/UegxmKUAOBymI/EDwRzWLU6Rk6kE1MNxcXNEBd2M+AViURCGYynMnHZ3cW/r6TYVVRrjMugZjqIiVTthk8PRBhy4a6x6BZ5D2r8O00wB4BSxUWd4FyqupqGuKgQnKt4rclcRQmm/YenxjgnQLkWuqPmKY/QHyziYjDyIpVZ1FBc3EycTHeEf3qtVlM1Fm7mtL27k/9NS+0Yl8EtTGO1jDnqmESY2hW1Kk4A4GHIT2eYf5cpoFE6tBldRXZ1ilHKtsTmVMwv3pFgFJdabQEAeFgSHzYBey0DFnExEKIoUuIi5mC3186u2YC+HhMs1pITlhrExUl9ul3d/C9UtWNchrTQ8UxlzZFZ1RNVr3YFeQ962wyxXoD6MS4ulwt2wVyBmYS8qXOQYW1JJG6OsgCAMphcjbXgdVES39XD/55QDiziYiDS6TQgFMqc11h0S4LXTX/SiAl8+2qlP7LwutmFyr+1jsfjqsa4NAbpHEjnzVHLhs2kUOOUyfa4CZvEWKsd4wIAHmbTMgNxYRUXjysPm636jZslLjFzcFcAQJJpuKqG+sjOI7OshYFgERcDQeI7iJW2QSXiwuT9sxUYeYWaPm0JitN2j1nGgF50rTEuLqcA5ImByuZVGFAdEInT+e9x126W2HVgBpcpUOoqUoPAsadtM2SUqFlFm7Ul6ZyDlF0wAVJMV3s15oDfy4YP8G8Py4FFXAwEySYpEBeVFBe/lyUu/OcClyoutUujrLzcY4KFWhKcq8IpyyaS9M8sfAM8kw/E4upVCwUAv4K48D8HAG0UFy+zFsyQUcLag1oPMcrqueZIic5ms8gxPdY8KswBxVqwgnMt1AqWuNgFdXywPg+N8GU3A16hheLCLtRwxCxjUOjTY8vVJI9LsKPQn0TwD/BMPhBPqFctFACCfroOemL8zwGgtOR/rQXoACDAnLZ7zHKQKdgDf43zwIz9ikrUVxUOMQG/uQ6z5cAiLgaCdRXZa+zPIiHAFCxiCxnxCi1iXFhpNGKCTYuNcZErH9cIp63g1LcHkMvxPw9iSabolrf29Dp2HUTj5jDWagfnAkoF1gzxDTE1FRf29SZJiVY7JR4AgsxaiMT4twXlwCIuBoJVXBw2dTasgI9S9HiS/0laLI+robiwp20zZBMQAkt2KZcKVWMBwGUrbFI2D8Ld/EvkCsVFBeLCzoGoCeYAoE2MS9DH9K2K8k/g4vEMIJBrrjXDUOF2Nkn1XC2ISyjAHmb5L8hZDiziYiBYxcWh0kk7GKDEJZE0SZ8elRWXoJ8aazNUzmWNlUuF4msA4HbQuI7tu/k32IkU/d5+FaqFNgSZ6sEmIS5aKC4K4mICN0EsSclGrTWdzOsqUpe4NDB7gkVcLNQMheJiV2fDCvrpTI+n+J+kWsS4hPzmWqhscK5bJcXF46Qusp0d/OeCJhniEvTX7thvCNJ1EEvyPwcAbYJz2YOMGeIb2MynWhUXpavIZwriEovFVC2NAChJvBkyy8qBRVwMRDxOFRenSsQlxExSth4Ar9BCcWkMsu4yk4xBwVjVmgotweuim9SuTv4LeCTTlFyw5LtaNIboRDJDNg0guYok5Q2qBGk3BFiXGf8Ejq01o67iYg7iooXi0hiib2KWtTAQLOJiIKIxOovU6IgLKAOxkibIAtWici572o6n+O+OzCoualTKBAC/h2l70MW/tWLnqhqKS2OI7dfEP3kFlIqLGmoLUKw88T0OmUwGWaZgorqKi0ldRSrMg+YGZi2k62PLr49vYVJEGOKiRoNFQLnxpcxCXFTuVdTELFQzkLdYjAbnelWoYwMAfi/dpDrC/GdWsUW3QoHarXVzIyXDyTT/5BVQEhc1TtqAUnniXX1MJBKKPkU1ExfTxrio6ypqbKAMLs2/KSgLFnExEFGmsq0aDRYBpcFLZ/k32FooLs2NLHHhf4pH40yfHhWqxgJAkKk719XDv7VKM+EXrLuzWrQ0MsbaBOsgk8kgm81SxUUl4qI4bXOuPrJVc4E9MziXdRcC6hDYpgZqDMywFsoB/1a9jsFuWG6VXARuBXHh/+clpyx1FRfWWKeytWeoaI1Ygu7aXpWIS2OALcLHf1ZNOkOvl63BUi3YgMRUhv91IKfqFjYt1VxFIR8gkvnFu/JUrL76PbVdb124ilQmLhkT7AnloD6+hUnBVrZ1O9UxKuxEN8MkTSaTyjouqigu7AmD/zFQu2osADQEKGHrjvIflJnJ0e+tRpwP22Qxk1NJztQQ8Xic3FA5xiUYDAA58t4pzteC2uqrItDfJIqL2g1XAaCpMSDfzuT4P8iVA75ncp0jnqQnbbdLnZ+Cneg50cF91dRkMimfsgRBnayaEJMCaoaFyhYKVCurqDHIFuHjO7YBUP5OahhrlsBn82YhLnbARr68WjEugUAAyJN0+FSG73FQO97NjOnQxTEuqnRK93mBPEnXyub5t4flwCIuBiLBEBe1skkUE93moSc5TkEUF3LK8nkAQVD3tJ3NqRT1rCHiTJ0RNTZtoCizygQVlFmDqkpXZOa0nc3zPwdisZiyT5FqiksQyBMbwLvyRIiLVsG55iAuxTEuahxkBEEARJIIkhP5ngPlwiIuBkJ50lZfcTENcZF6k6jgJgIAhx2ASMY2a4KFmmDngUp7LJuZY4Z0YJa4qGGsbTYBgkhiyHKiE6LI9xhoUXwOKCguBVdRhnMCRzZtSlxqVVy8bkAQCr+7SbpDs4qL055X5SAHAIJIwhIs4mKhZiSZDUutbBJF3r/g5n6xKhQXlYy1IAgQ5BMG38YaAFJM0o96abBsNgnfMS6iKCp+J7XGwCYUgt9tHjLPOIYW5f4BpasoJ7q4JnClwbm1vZ8gCNSmmCnGpTAP3Cq1/wAAO8hayJvAHpYDi7gYCHZD8brV8T0Wu4rMRFzUKPcvQdq08lBpF9QQbNaLaq6iEB3MFOcVlDOZjNz6AlBvDBxCgRHavNxvWiWKi6oxLkRxEWFHhuOq/1q0/5A7xZuRuKhoumwC+eFFwSIuFmpEMs0qLuoQF6WryMU9cUkwriK1FBcAsKOwaQlusjFyinw+jyyTUaNacC5LXDImSINlMinUKsbosEnExYNIJKLOm2oE0nBV3c7QAOD1emXFBVCW1OcNWrT/kFUbk6RDs72KPCpyDLutwFgFN6kXZHJYxMVAsL2EfB51fI9OBwAUlBwzKC4pERDINFRTcbHb6Gmb5zFIpVJFWQTqkIwmRbVMvokL2bTJDmNDWjW/vtNeOBiYQHEpLguglqvIZrPBLtB6UTz3qikuQKeGPZDjZEyiuJB5QC7ao1IVbQBw2KS1wP+eUA4s4mIgUkxjOa9XHcVFEAQ6STmPcRFFEaksJWxqKi5O6YTB+UIlhkrdug2Ast8P77VsCHEhY2AX1DsNuhzUWPO+abEbFqAecQEAp40qjjx3By7OKlJVcbH7EeF8DgBKEq9W+w+ABPoCAAQHeiL82sNywbdFq3OwsQd+lRQXgD1p8r1pZ7NZiKDGWk3FxWGSMSjesNQiLopChDm+lzk7BrKkrQJcUuNSmxvhbnO5itTctFwOOqY8u4rIGKgc48IE+EbjOa6Dk4GC61zlhqsAQ1wAdHbxaw/LBd8Wrc7BNrxSK8YFAJwOc7iK2FRoQF3FxeWgJ4zuHn7HIJFIKCtlqhTbwBIg3otOJZNJeQwcKhIXj4sa667uRD/PNB7FriK1YlwARnkC364iYg9UVlyKitAlErzPgywgkPWqRmdoCS4mQyncw/cYlAOLuBiIVIZOJrUCEgHALRMXvuM72IwiQL06LoDSWHeG+V2oxa4itTYslgDxXjmWdRXJbk4VwI5l2GTERU1XkcdJCVx3lN/ATDIPyEHG5cjDbq9dcVDYFBME6LLFKNUlryyJ57u2Vzmomrik02nccMMNOOWUUzBr1ixccMEF+Pjjj+XHlyxZguOOOw5z5szBH//4R4VE9+mnn2LBggWYMWMGLr74Ymzbtk1+LJlM4rrrrsPRRx+NU045BStWrKj2ErlHhunUqVZ3aIDJ/+e8AF2J4qIicWFrIPB82iYblgapwEwRvrzoQD7Pby0X1lXEStq1gnW3dHZz7COB1GxUI+LiYtcCv+PA2gOvWx2Xjtk6RLPFItUkLqzbqTvC7xwoF1UTl1wuh+HDh+Pee+/Fyy+/jLPPPhs/+tGPEI/HsXLlSjz66KNYsmQJHnnkEbz55ptYtmwZAEJ4rrrqKixYsAAvvfQSpk6diuuuu05+37vuugvhcBjLly/HLbfcgltvvRUbNmyo+YvyiDRz+FFTcZEn/B6suLCLvjvCrz5O3CTqx7gIggCbXMfEzbVEzqpOahIXtvUDz3MAKFXe1KrjAigPBDwTOGUxSpWIS1GHaJ7tIQAkU+q3/wCUCmx3D79zoFxUTVy8Xi++853vYOjQobDZbDjxxBPhdDrR3t6O5cuX47TTTsPIkSPR2tqKc889F8uXLwcArFq1Ck6nE6eeeircbjcuuugirFmzBlu2bAEALF++HBdddBECgQD2339/zJo1C//973/V+bacQSvFRT6t2ZyIRDnfsBSKi3rBaMoTBr+bVklWkYoblpyhI/CdVRONJQGBLACnQ03iQmN7eqLpfp5pPLSq4wIAPje7FvgdB9YeqKW+mk1xSTKlC9ScA16mMntPlF97WC5U2y43btyInp4ejBo1CuvXr8eJJ54oPzZx4kS0tbUBANatW4dJkybJj3k8HowcORLr1q1DMBhER0cHJk6cqHgt64IqRjqdRjqtXIwOhwMul7oVUyWpXU3JPZOjk9RhF5HPq3PKYGXm7khKtWtWewyKK2X63NqMQbgnyfkYMMXXHOqNgV3IIiMCsLnR09ODQYMGqfK+ao9BDyNdu52iau8rV00FEO5JczsHAIm4DJHvu13qzQOfV+kyU+O6tRiDeDyh6FumxnsrXG52P3p6eridB6IoKopFup3qvbe76CDH6xgApPbQQFCFuEhxKRdccAECgQDi8Tj8fqaQkN8vS9WJRELxmPR4PB6X4zH6em1vuO+++3D33Xcr/nbmmWfirLPOqvl79YZNmzap9l65PP2Bwl070N6ujoRnQ0C+vX1nN9rb21V5XwlqjcGGDRsUiks8uhvt7epIuYJIx3LLtk5ux2DTpk2KrKKuzu1ob1fnRGQXCqTQ5sZXX62Bw6FukK5qY7BlF70jplT7rQSRGtNdHT3czgEA6OzsBGz7yPd7wirOA7BroQvt7erlZKg5Bl3dUVl6dtrSaG/fUvN7phJBAM3kjs2H9evXczsP0uk0RDD1l1IRtLd3qvLeQp7OpW3bO7gdAwAYN27cgM+p2ZJls1lcffXVGDVqFL7zne8AAHw+n8KXGIvFSOlpEBdTsZ8xFovB5/PB5/PJ9wOBQMlre8OiRYuwcOFC5ZfSSHHZtGkTRo0aVRYjHAjZbFbR8Grk8CEYM6bmtwUAtDRRQ5WDC2NUemO1x+DLL79UyOOjR7ZizJjWmt8XAFqa1sm3BbuH2zEIBoOAjSqGY0YNVW0euBw7EcsBsHkQDAa5HQOnOyjfDvqdGDNmdM3vCQBDB4fl2zmR33UAkJgktp7PWBXnwZDWICDtKzavKuOgxRjkQW1BS5M61zlyGHPH7ofXq877AuqPQU9Pj2IODGoJYsyYYD+vKB8tzUy2ns3N7RiUi5qISz6fx3XXXQdBEHD99dfLpbrHjRuHtWvXYtasWQCAtrY2TJgwAQAwfvx4PPbYY/J7JJNJbN68GePHj0coFEJLSwvWrl2LAw88sOS1vcHlcqlOUvqDzWZT5QfKZDLKYDy3AJtNnRiPoI/69mPxnOoTSq0xSKfTCsUl4FVxDJjKsdyPgUbzwOXIAykAghvxeJzbMYgnaZS6xyWodp0NQTqu8SS/cwCQWj/Qjduv4loIBehaiCbyqo6DmmOQSAFST1RiC1Q4GPhEAFKWpY/rdUBsgbJ6slrXGvAx9jDB91oo6/NqefGvf/1rdHR04JZbblHI0HPnzsXSpUuxefNmdHR04P7778fcuXMBAAcffDBSqRSWLVuGdDqNxYsXY5999sGIESPk1y5evBixWAyrV6/Gq6++qoiXqRdoVb8DAAI++lvEErynwWqTVRRgyFs0oV5tELXB1jABVK7d4KSVYyMRfoMSY8zvo2a10FCADmaM4zkAaFuAriHIEJc4v/Ygkabb0Z4YnFtSjNKp3lpQ7AlxfpvOlouqFZdt27bhySefhNvtxnHHHSf//U9/+hOOOuoonHHGGTj//PORz+dx6qmnYv78+QCIQvLb3/4WN954I2677Tbsu+++uPHGG+XXX3LJJbjppptw0kknIRQK4aqrrsLYsWOr/4acgq0WCqibTcJm58SS/BoqLeu4KBQX3smb0CTfVzUFUnovwYbuHn7r+cQZUqFmqXtirAl5iyc5L/VeUvJfvfduDFLjwvM4JFKUuKh1iDETcSlu/6HqYZaxh/Ek3yS+HFRNXIYNG4b333+/z8cXLVqERYsW9frYfvvth4ceeqjXxzweD2666aZqL8s00Kq5HqA0egneiYtGigtLXPgfA/XruABKMsxzme+YgrioJzezhj+Z4nfDBrRtstgYou/Lc5PFZJaqpKopLkV1XKLRbX0+12hoSVyCfpa8mp+4WCX/DYKWxIWd8Im0eidYtZFKpTRTXFg3QZx74sK4ilStmMqmA/NbdCqRoobU59GGuCTMRlxU3LSaG+ik4rlXUTpDz9FqHWJ8RenQPCsuxW5jNfeEUICdA3yvhXJgEReDoFXFVEAptyf5rTelqeLCBmYm0vwuVK1K/gPmKTrFGlKvR72GkCxxYetj8AgtC9C1NNM1lkzza/LTOZa4qPN7FTdZ5Llyrl6KSyLF70GuXPA7i+scWp60WZk5mRa4beVOYly0MdZBPzWCKY5VJy0JLKtecE1cGEXM79WGuKSzNv77NRXWgsMOOBzqzdmWRkpcUlk+TX4ul0M2Tw3XnhicWxz3qGrlXMYWpDg+zJYLPmfxHgAtT9rshBfhLqkszAtK+rOo6iZhPofj0zY7BnabqEpHXAk+hgREOC55z6qCfq96RfIUhr+QEs4r2KwiNdcBALQ0UXdsOstnp/Bit7Emwbmcd4cucRWpSFzY/SXJsQJdLiziYhBKsoo0Cs6Fzc2tPMqqDYIgwqmiTS0+bfMKlriwHa3VQIAhAZEYvymQKebS/D6NiIvNze2mJYpiYdMia0Ft4hIKBYAcIW2ZHJ/EpTgdXJPgXLuP2zkAaOsqUthDjg9y5YJfi17nYDcsm5BX9aStJC78dohmF6rLIcoFDNWAcqHyO81ZAqtmh3AA8LNFpziu3cBK12yhrFqhOAzY+G00mc1miRursHGruWEBpJI58gXiktevWGclKC6NoJbi4nQI9EDEuauIJa+AuvOAXQvprEVcLFQJdtN22NRNT1OeNHknLtqoDSx5y+TUi5tQG6yxUlN1A5S1G3guwpdkOFVATcVFQeA9iEQiqr23mkgmCxlfGrmK7Ha73LsrJ3JMXNiGqyoG6sskiHNXkZaKC+t2yub4jvcqBxZxMQjspu20q7upeIsMNt/ERdq01SUuijgfwRxxPmqftNlA13gi288zjUWGOQGqWTlXSeD5VVzkJrIaERcAsBUaLeahwZurgOKsKp+Klym7izhXXLQskVGsPvIc71UOLOJiEFgXgdOhLvv1mkpxIcRF7U27eNPiewzISVOtFFAJLAnguegUG4OklV+fZ+JCFBebHKyvZg0XCXYQ4p6HilKGiiCuIqq4qEneZBJk83I7BwCp5L/2riKe473KhUVcDIJCcXFopzbAbhbiovamzdzhmLgkmDRYn1e7MeC51DtLXNTMpDAVcdGoaq4Eh63gj7O5kcvxNxeKg3NVJS6yq8iHVCpFGtxyCL1cRRD4TdgoFxZxMQgscXGpTFwUi17gVxZkF6qaPWrI+zF3ON60EsksIJC4DrVP2ooUSI6LTmVzDHHRqCwAaTTJZ4yLlsXnJDjtdLOOxPlT3zQlLtJ72b0AbNxu2iWuIktx6RMWcTEIivgOLYkLx4pLgiUuqrtJmDscp4SzJdjVDEgESms38FqIMJunAbmayeMCv+RVy01bgstOY5x2d/J3kCmJcVFxLSjei2N3kV5ZRTzbw3JhEReDkEymKHHRNL6DZ+JCVQC1FReSAll4f47HgC3BrvaGpQxQdpEiXxwim6dBxGoqLna7AIdNmgOcExe7tsTF7aQqy+4u/ohLyRioaBOV1XP5LfuvpctQWYzRxe1aKBcWcTEI8QSVbtUmLmbJKmJ71KgtjwuCQNPMOR6DJNOOQM1MCqDUr82jscrlcsiLlK2ovRZcTkpcuHYVCdrGuLBZe51hTomL1jEuAGDjtwidXp3iLcXFQtWIMempagemWsSFQE4zF/gdg1RGO8XFDH7tVCqlmTwOMPFjnH5/QNueXRK8bpa48NcpnCUuDltO3dYXCtc5v8SFdZe5nSJsNvXGwGEHBPC/FsqFRVwMApueqjZxcTpICX0AXLtJ2FLvahdfAwCXgyouvC5Utumd2jEuZsiq0bJ2BcCMAaffH+hFbdCAuPg4Jy5k0ybp0PK6VQlKxYXfWi5a1rUSBAEOO/8KdLmwiItBiDPxHR6V4zsEQYCbkch5naRscz0tTpkuqT4OpwHKmUxGUVdDdVeRIjiVz1OWlgGJ5P0Ka4t3V5HGwblsp/BwhL9ijCx5c6lc18osiouy0ab6Zfmd9sK4cmoLKoFFXAwCG5jqc6v/M3icZlBc2Iqp6r+/fGrh1FVU6tdX11i5zaK4CNp0SQeYbDVOvz+g/TwAAD9TI6ibV+Jil3o1qUtc2O/Oc4wLS2A1ySxj3KY82sNKYBEXg5Bg6mr4POr30qESOb/EJa0xcWHdBDyOgZb9WQBzdEfWsnYFwCgNJiIuas8DAAj6qI3pifLX/oFdCx6nhooLx8RFy7pWAOByWjEuFmpEMs2kAmtAXNiTJo8F6LLZLHJMNok2AYmFGzYnItGE+h9QI4qJi5YF6Hg1VqyrSEAeDpWXgrwBCA70RPhbB0BhDJhy92q7DAEg4Ke1ciIx/grQJRKUvKneHbsoHZrHdQAoFRdtFOjCDatyroVqkWRKang96v8Mct8bThWXVCqlWV8OCeyppYdzeRywgnPttiwEQbtChJEYz6XetVVcGhjiwmOn8Fg8AwjEDqr9/c0SnJtIZgAbYRdauIrkWEpODzGVwCIuBiHJ2FCPBhk1Xiluxu5DlEPiUtqXQ31plA1I7I7yV3xN64qpZgjOZZuNOmzquzBY4hKN8+ciAXpxGWqwaTUE6WSIxvlr/xBNsMUo1X1vZXAux5VzNS4PQQPVLcXFQpVIMQKA2n59QLn4ozFO1QYNs0kAZexQlMPTdslJW8sCdJyesth5IFe5VREK4sLhHAB6cRVpoLg0huhAxDhsuBljkxVUbv9RXICO102bJS5aKC7yYZZjt2m5sIiLQUiziosGagM78WPcNlVjqkRqQFwCTEAij6dt4ipiYlw0LPnPK3Ehfv1Cl3S7+vOUHYNkWkQux+ta0G4eAEBTA11rCf7ER0X3cr/KrnN/EXHhcR0A2mdZKg5ycT5JfLmwiItBSGfpJNWi+Bo78dnTDC8ozibRYqEGfHz79bWObTCNq6hAYNWu3wEUByjznF2mnfIGAM0N9P0TafUPSrWCJS7sgUMNmCE4VxRFpDWsog0o64VFovyp8JXAIi4GIc1UTNWCuCgUl8SeSVxYyTnO4Riw1UIB9Y2V3S7AbuM7BZKNcXGq3CUdMEeAsh6uopYmSlxSaf7MPqsCqU5cFOnQfAbn6tEhnN1n2JYzZgR/M3gPQYYlLhrHuKSzNu4kcj1iXBSqkxkUFy2b63G6acfj1FXk0oG48Fg9V+t6PgDQ3EjnWTKjfvmFWsE2G90TFRc97CFLXCxXkYWKkcvlkBPp4tTaVQSbl7taLnoTFzbwjRcUx7hosWG5XXynQMYSaTkN1u3aMxUXPbKKQn5qb9I5/sw+S1z8Kq8DMxSgKy6NoEW/KoU9TPJ3kKsE/M3gPQCkI662bhJvkcHmzbdPXAT61XFJcOjS1UMepgXYOCUuTNC0NgSeiecQ+CQuxFWkbR0X9j0zOQ0GukYkFfEd6sbg+L3MHU4Vl0QiobSHWqivRQq0KPJ3mCsXFnExAKxfH9A+xoVHxYWQN/0Ul2Ra4G6h6nHSlucWp2oDK1lrkV1nRleR1geZbF6DiVYjUoz7Sm0CX2wLeVwHpYqL+muhOFg/meSvS3i5sIiLASjetLWIcVFsAhxWz9XbVSTChXSaL9lF14A8bl1FVLLWg7jwOAbFpd5tNvXHweEQIIhk/udE/ohLOkuJi9qKk9MhwCklGNpJcC6fhxhqC7SOceHVHpQLi7gYgJLGcporLpy6ijQnb8wdDjct9pTlsOdht2uwccv9mvj7/gAQZ3ztWjSW8xStAx7HgI110sJNJMEOcsIWbV5kMnwFZ2ZytHSBFvEd8rjafBBFkZBFjsD27AI0OsQU1XXibU+oBBZxMQD6E5c9U3FRjgF/C5V1EbhV7ogrgVVcIhxu2vEk22xUfXNUXISPd1eRFu5CCXZbQXG0+bgbh0yeIS4ajIE8roVx5tMWaJ1VxMZ7WYqLhQpRHOOitZtkTyUuxYoLn2NAFBev1sQFQDotcnfSjjOuIp9bfXNkBnmcreejpeLilIiLna9aJvl8Hrk8/aE0IS7SuBaULZ6+P6CT4lK0Fnizh5XAIi4GoERx2WPVhj2bvLGFxzwapAKT92Xu8EjeUkyPGq/69UV4dxcC+rmK3I4CabX5uVJcSMyftllVfsZVBPBHXKwYl8pgERcDUBLfoUMdF+42LN2JC4ebNqu4aOQiKDZWPG1YgDJN3a8DceHt+4uiiGQyI68FLV1FbkdB3bI50dnFz6ZV0rNLyxgXuw+AwN2mXbwnaFPyn7kj8HeYrQQWcTEAVoxLaR0XPQKUeTNWiQSNbfBqdNJWqHkCf8RFqbg4+nlmdVAQFw7ruKRSKc1ruEjwuKhbblcnP+UR9Miu8xXZQ97mAZtZBliKy0CwiIsBKKnjormriFe1gVhpzTJqOFdc4smsXDXW79Gm8R3vrpIUE3Kjh+LC2/cv2bQ1sAXye7upO7Kji5+smuJNW+sCfDwWodNDceFdfa0EFnExAFZ8h3Khyv10VEbxGPBmrOJM/Se/VxviUmysenp6NPmcapHK0O+9J9Zx0aNPkfzezGbY1ZPq+4k6o3gMtFdceCUu+iouFnGxUBHIJKUzc491FRWIi0t9DwEA/uMb4klK2LRQG4DSdGDuiAsT46J9rBd/c0CPztASAgw57uzmpxijHq4iRdl/DomL7nVcBP5sQSWwiIsBKI7vcGlBXExx0iRjoEtGjeBBd3e3Jp9TLRLMoTfo1WYpKk9ZHu6MVZrpkq5HgDaf60Dbtg8Sgn461uEezoiLndpDLcZA8Z6cpYMDOlfRBrg8xFQCi7gYANZV5LDlIAgaS+R2L3eTVElctHGTFMf58DYGCaYjrl7BubyRtwzTqVgTxYXzAG09XUVBHx3r7mi2n2fqC7aODZDX5CCnGFcOFRd9CtAxdyziYqFSsMTF6dCm8Fixq4i3ScouVK1SgXl3kyQZ4qLVSVvZs4q/Mchk2RgX9d+fd79+iatIQ8WlIUh9st3RXD/P1Bes2uC0ZTU5yCkVF/6Iix6uIo/lKrJQC9isIpdDGzdJcQE63iYpKTpFLtKrQcVUoMgFZ/NypzYk0/R761PHhT/VKZOjsT3a96vij7gUuwh8GmWXAUBjkE6GaJxP4uJyaKMEKcaVw5YHxfNAC/WxuFcRb/awEljExQCwiotWxKU4o4a3hZpIZgCBnAC1aK4HAIIg0PgZDjftFBPfodWGxbM8nM1mkQe9QC2MtdMByAd4wYN0Ok1IMyfQI6NGQlOIvnmMn2xoRQE6l10bQlWcVcSbPVQkKzhFTTqE836IqQQWcTEAeqQC2+1MK3cOXUVscz2PhsZa3gg4XKjprLYdcYHiapl8jUEqldK8Zxchr4U7hTXH00lTT1dRcyNDXJL9PFFnKBQXpzbERZFVZPdztQ4AZS0bj0Z7As+HmEphERcDoFBcNDhlSuB5006m6OLUYsOi7104uXA4BuksdZNoFZTJs7Eq9utrobgA4Jq4lLqKtPuslkb65vGUdi6pSsFu2lod5Ior5/K0DgB9Yv6sdGgLNYGNcdF20y7cKCxUUdTGKFQDtkeNXmPA04YliiKyefrF9YlxcXFlrEoKMWodpM0tcdFHcQn5KVFm46uMRjxB4908GnVJL66cy6erqKC4aJRlyfMhplLwM3v3ICSTacBGZpFWkxRgFRcv8vk84nGO+pNoXHhMArtp8bRQSXCytrUrgNI6JjyNgR7NRoFS4hIOh7X5oCqgZwE6P/PeyYxGVR+rQCRGA3K1qulUnA7N0zoAlOqjPvFuLkSjUeRy/ARpVwKLuBiAeJJOFo9GgakAEzdRWBA8nTKUpd61+5xidxkvqlNJR9w9UB7Wo9koYDLFRUviwsR5pHP8EBc2w0mrdVCcDs3TOgCKFRdtPqPYFgDgLi28XFjExQCwgalapQIDjPReWBA8LVa9iAvdtFwQRX7a2esV21CcDszTpp1IJDQPzlW8L7fEhZkHGrqK2PfO5DSUOSsES1y0+v5KxcXP1SEOAOIJqsJrRd4cdsAmbTeFAwNPe0IlsIiLAUgk2ROGdj+Bl9m0ARs3kzSfzyPL1O/QhbgAXPl19Ypt6C0FkivViXUVaX3StLkBCFwRl2JXkZbp0KzikocXmUym7yfriFiCzket3CT+ohgX3tLiEzokKwiCQO2BRVwsVAp2kmpKXDgtec8WnwP0JC78ZBPotWEVB+Tlcjny2RxAj2ajQHHFUBdXxEVXVxGnAaqsAu3TqGdXcYwLwNemzcb8aUle5TVW6JXH0xhUAou4GIBkWp8aJrxu2sVNJnUJUAa4Im+GuIoEvk5ZuqdDA4CNr2abemYVKdcCP+6SOCN8BLQiLkUF6AB+1gGgbLiqKXFRqI98jUElsIiLAWBrmGiZUVPcr4gXQ6VHQ7Fe35ujTUuviqnuou8P8GOs2LIANiEHu10bAsvrHAAk5U2fOi42mwCHUNghOeqQHE9Sexjw2ft5ZvVQpkOT8ebFHgL6xfwVu4p4WguVwCIuBiDJTFL9iAtvaoMxxIWvMdA+KLPYVQRwRlykZqMalXoH+CYuxQRWy7UAAE57wSfBUdl7tku6VsTF6QDs0lvb/AD4WQfZbBa5PF2o+riK+LIFlcIiLgYgpXcNE4A/V5EOQZkA52PAnLR16ZDNmbFiXUVOuzaFxwAzEBcpDVabHjUs5CaGdn5cRSmGuAT92qRpC4JAY3zsfLmKim2BnooLL2NQKSziYgDSTANULTdtxWZo52zTNiQ4l59Nq1hx0as7NMCPsVIoLo49k7iwQdo+t/bZXm5HQdniiLgkM3QbCmlEXABG1eQsxqX4IKdV3zLAinGxUAOU/kwdCtABXLlJiqvG6lKADuBqDFgXgcOW1eykrYxxIXe4GgNB2y7pAN/EhZ0HWnVJZ+F1FQiizYeeHj6IS4ohLn6NgnMBJs6lQBS5IW5FhxhdEjYEOwA7N7agUljERWeIoohsjg67pq4iKzhXSQy5Iy6FjriO7ADPrh78Ky7kmiziAvi92hMXn7tAXAQburr5aBHNNhvVUm3gVXEpzq7zaniY7a2ukxlhERedUaw2aBqcq9i0eXMVWcG50snPpaGbxGYT4JTUd84yCYjBlpqNakdcio01L98fKHIVaZhRJIH9jE4OiYuWY0AVFz8AgS9bYNM5xgXgqiBnpbCIi85g5XFAxxgX3jZtHUq9l7w3Rx2iFUGZGnXElUDlYb782gmmK7Dbqd0ps1h1S6VSZPw5QCKZkcdAyxouEgI+OhZd3XxUjs3olFGjLELn4VaB1qWOC2ARFwvlQ6/GcgDnGTU6KS5BH3PHHuRmDIjaQC7O49KWuNBMAr5cRbEELTmvW2aZwJnqxHAHPRSXoI+a/HCEj5L/2TwNyNW05QGnHaKLXUW6KS6cNV2tBBZx0RnFZc733MBUfRZqY4C542jgZgwSCeoq0tJNAvCbSRBL0NgeLQNTPUXrAOCJuDB9enRQXII+ShK6I9rFVlUCtuGjpk0mi1oe8LIOdFVcLFeRhWpQ4irSsQAdr9KolqdtBXGxN3KzYUUVm7a2xMXDKXGJJ3Tqkt5L9WBe5kEyTb+3HopLQ5ASl564tkpfORBFEXlQQ6Wpq6io7D8v64AoLjrFuFiuIgvVwCq+BsRiMd0UlwaF4tLIzRhEmU3Dp3EaLK+N1eIpfbqkF3cIBzgiLkwqsJabtoSmED0pRTkgLiRZQft6RkCx4sJRHRsdFZditykv66BSWMRFZ7CZFIC+vYri8TiyWePlYT2JSyOnxCXGqA1an7R5rZaZYLsCe/Y8xSWXyyGbpxenh6uoKUQ/jwfiolezUYBfxcXIrKJIJIJ83vh5UCks4qIzyKZtjKsI4KPoknHEpYGbhRqJUbXB79WmP4sE6ipygac00ESKIS4ajgGvxIWsA6YztB7BuX46zvGU9nVjBkJxk0kt67j4vcwdjohLSR0X3WJcPBBFkcxDk8EiLjojFovplgqsOGHYyQ7Ow2ItJm9aZxUJkn22N0AURS664kZiVPkK+bUlLrz6tdku6XtijEs0GpUDtAF9FBc2s4YH4qJX6wugyCXLUXBuNBrVbQzcitIAfCmwlcAiLjqjRHHRcNMO+Zk79iAA/hQXQRBpgTQNYLMJdBwcjQD4WKisq0irxnISiv3aPHx/AIgzxEU/eZwf4kLWAesm0aNyLr2dTGtLmMsBS1xsyMJu124MlHVcSHdsUdS+P9RAKJ4HeqZDA3zYw0phERedQdi1PpVzlcQlBICPScqqTm6nCEHQ1mDL7iKOiEuUIS6hgIaTAKV+7XQ6zUUBtniCusv0C1Lnh7gQW6Cz4sK4S1JZbQlzOWCJi8OW1vSzlAq0nxs3SfGeoGc6NMCHPawUNRGXxx57DAsXLsRhhx2Gu+66S/HYU089hblz52LWrFm44YYbkMnQYkebN2/GhRdeiBkzZmDhwoX48ssv5cfy+Tx+97vfYfbs2TjhhBNw//3313KJ3EHPGJcg69N18OYqIgtVy9OFBEpcGgDwMQZxtvCYXllFAFdF6BJMVpGWzUZ5LUBX4irSIcaFdRXlRLfCLhsBNs7Hadc2cUCpuBDjyMM60FNxKc40BfgYg0pRE3FpbW3FxRdfjDlz5ij+vnbtWtxxxx347W9/i2eeeQY7duzAPffcIz/+85//HIcddhheeuklnHbaabjyyivlbJfHH38cq1atwtKlS3HPPffg3//+N959991aLpMrFMe4aHnSdLtA3TC8KS6FDdSt4YYloUFSnmweblIAE4zgofWG1Vs6MA/zIMkcsPWrIM2P4lK8YemRDl1cPdZo1zEhb2QMnPbcAM+uDUrFhZ8O0URx0aeWTXGyAsCHLagUNRGX2bNnY9asWQgGg4q/r1ixAnPmzMF+++2HQCCACy+8EM888wwAYMOGDVi/fj0WLVoEt9uNM844A/l8Hh9++CEAYPny5Tj33HPR3NyM0aNH49RTT5VfWw/QMzBVEJj4Do6ICyuNatkJVQKPKdHJNP3eWhMXb1EaKGD8PMhms8hkqfnRs9EmwAdxMdpVxEMtEzYw1e3UlrgUZxUBxq8DoDQ4V0sS3xxi7jiaAPAxBpVCEyfnunXrcOihh8r3J06ciO3btyMej2P9+vUYPXo0XC6X4vG2tjYccsghWLduHSZNmqR4bOXKlX1+VjqdRjqt9I06HA7F+6sBKYW21lTaSCSiKPnvtIvI57ULEAt6gY5uyMG5PT09VX8HtcYgFosBTdRVpHV6ckPRKSMcDhs+BmzhMbdT4zmg6NdELJfRYxCJRBTKo9Oh3Ri4+gjONXoORCIRhavI49Z2HgBF6cZ2f9X2QK0x6O7ukUmE25HX1BYoCSz5TKPXAaA8yLmdYuE9tZkHDWzco6MZAB9rgYXNNrCeoglxSSQS8PvpCAUCZOeIx+OIx+OKxwDA7/eTXPZeXuv3+xGPx/v8rPvuuw9333234m9nnnkmzjrrrJq/R2/YtGlTTa/fsWOHXMEUALZva4dDw+B+j3MYABfgIBvWxo0b0d7eXtN71joG3d3dQAsZAxvSaG/fVtP7DQR7vglA4ajhaMSGDRsMHYN8Po9MjqmnEdmF9va+53itEDMhAOR0JcnDbW1tGDduXE3vW8sY7NixQxGQGO3ZjfZ2bQIlibo1GgBgd/qRA7Br1y7D18HGjRsBG7V1PV3b0d6ubcfmcNQGYBS5Y/Pjq6++ku1zNah1DDZs3AoIZKNy2NI1/yb9oafLCWA4uVM4yLW1tWHixIk1vW+tY9DZ2SkrLi5HHu3tm2t6v/6QiDgAjCB3CooLD3sCi3LskibExev1KqK1pboZPp8PPp+vJJI7FovB6/X2+tpYLAafz4e+sGjRIixcuFDxN60Ul02bNmHUqFFlMcK+IAiC7CqyCSImjB+j1iX2itZGAJtQiO9wwmazYcyY6j5TrTFIptLyGIQCrqqvp1yMHMbcsTfC4XAYOgbFLoJRIwZByyEYM4K5YyfExe12GzoGpNQ7XaMjhrVizJjWqt5rILCHQYcrgBzIIcrodeB2uxUugvFjh2o6DwBgKCtO233w++1VjYNaY2BzUOIWCjgxZszIqt9rIOTZLcFBiIvH4zF8HmSzWcBFSLzfW93vUS5CzcwdJ7kjCILhY1ApNCEu48ePx9q1a+X7bW1tGDp0KHw+H8aNG4dNmzYhnU7L5KKtrU0mH9JrJXdRW1sbxo8f3+dnuVwu1UlKf7DZbDX9QPF4XN603c7yZLFaEPQxVtseRCQSqfkzax2DWJxmD3jc2o9BU1AEUJBeHY2GjwGZA3TDCngF2Gzaxfo0Kr4/IS7RaJSDMWBTQLUbA5sNcDnzSGcAm4OMe3d3t+HrIB6PA/Yh8n2t5wFA3FE2IYe8aANsPsRinbURjxrHoDtCs5p8HkFTW9AYYNZBwWVq9DqQrgGtZF56XNraw6agCEEQIYqQFRej7WFVn1fLi7PZLFKpFPL5PHK5HFKpFHK5HE466SS89NJLWLNmDaLRKBYvXoxTTjkFADB27FiMHTsWS5YsQTqdxtKlSyEIAg488EAAwMknn4x//etf6OrqwqZNm/Dkk0/Kr60HRKNR2bevRypwcS0XowOxcrkc0llqnHVNhwYAR4PhYxCLxRSxDVpnkyj92nxkEhRnUmgZkMi+v1DIYOGhlo0RwbmCIMDlKJAFDoJzu6NUAvJ7td34eivIafQ6AJRZllrbAptNYMpD7KHBuffee68ivmTx4sX45S9/iXnz5uFHP/oRfvzjHyMWi2HOnDm46KKL5OfdfPPN+OUvf4l//OMfGDNmDG677TY4HORSzjjjDGzatAmnnXYanE4nzj//fEWgr9nBZhXpkQpcvFiNNlR6tjyQULxxd3dv0P5D+4GeJb6BouBkOx/ERe954HEBkTgUKk93dzc8Hh2Kp/SBYuKiRzo0AHicOSQzAGx+w9tf9ESp4qJ1zy6nQ4DXLSKRghzzZ/Q6AJT2QA972BwCuiKQg3N5GINKURNxueSSS3DJJZf0+ti8efMwb968Xh8bNWoUFi9e3OtjNpsNP/nJT/CTn/yklkvjFrFYDAhJNUy0/zxlETrjFZfiBotan7QBoJHN1rcbnw5NxoAJXvf282QV0JviYnQ6cEkFaR2ICwCIDFnq7u7GkCFD+niF9iDKmz6dkVl4XXmE45CziowE27Mr6NO+BUHID0JcCq4iow9yoigiGksCNmII9SCvzUGgDShUEuen6WolsEr+6wyF4qLDph3ys43F+CMu+ruKOCEudnpRRhAXo8eg2FWkh+ICAHnQD+KDvDHp0DqF6sk9kex+dHR06POhfSAap2m/DUHtWxCEpOHmpK5VIpHQXYFukg5ygo0L13k1sIiLzlD26dH+84pdRUZPUuOJSwMfG5aexMUEriKt14I0z3Ii/SAu5kHBReB1i5oH5koI+gpm3+bBjp27dfnMvhBNUOLSFNTeINKGq3wQF7ZyMKCT4lJUhM7oMagGFnHRGRHmpKnHpq0oPsahq0j/GBdeFBd6UVq7CEJFcwDgxGDr6DKU1kE27wIEcrI3mriwQdpa96ti0RCgysbO3cbGuMSS9Hs36EFcpLUgOACb13BXESHw+jRYlNDEus4dzYbbgmpgERcdkcvlkE7n5IJLesS4KDatQnCuka3cDSEuRYqD0QuVVVzcjozmJ22HQ6DlzjnpkK23q6hFccpsAWA8cWFdRXrFtwBKl4zRxCWRoltQ0Kd3soLxB7lixUWX4FwFcSGKi5F7QjWwiIuOMCIwtdhVlM1mDU0DNYK4OIs27kgkonmbgf7Axrh4XNr2Z5EgqU6CsxGA8Zt2yVrQeB60NjJ3nHs2cQl4KUHY1ZnQ74N7QSJNtyC/DmOgsIe8KNC6Ky4MQXQ0I5fLyZXrzQKLuOgIvf36QO99aoxcrKXERR+JXHYXFYJTjZSIWeLidelDoOTvX4hx2b3b4NgGIxUXJ6nQazRxYV1FeqVCA8pmg53d2rYYGAjJDM0k8msc6wUUK9DGExdDFBfFWiAp0Z2dndp/sIqwiIuO0LvoFsBffIMRigvABOgWXCW7du3S54N7AZkH5IL8Hn0kWsldJtr8AGzo7u42VHkjhRjpj6/1WmhtYE+ZfCgukWhCtgd6FJ+TwCobiZRg6DxIZ+kPr7viYg8ZHuNSHOvl1d1V1AjAWHtYDSzioiPYVGiAlLvXGr1VizRcbWBVJ72Jiz0ACA7S5M8gRKIJ+ZSlG3EpksgBGDoGxQbbpTFxaWlg7hQUl3A4rO2H9oNcLocUs2kH+27HpjoUbim73zD1LZ1OI8+4SXQhLmwcjSOEeDxOegUZBLInMIqLAcG5ALBz507tP1hFWMRFR/DiKjLypFmyUPUmLgBgDxm6aYeZ/ix6BCQCRcSl4C4ycgxY5c3tFEnzUQ3RyhKXgrHu6urS9DP7A3ET0R1ET+Li9zBjbfMZdtomc4ApC6DDGBQrLoCxB7lSxUV7e1CcDg1YxMVCPzAiODdYlFUEGBvfUJwKrHUNEwkNRUXojNy0e6I0IDfk12cJNhTVsgE4UFwK81EP8tZbjIuRxpr9/oDexIW5Y/cbRlzIGFBboL+riBMFWueDnKW4WKgIxT1q9MqokQP/Cq3ct2/frv0H94Hik6ZexKW4eq6hxCVGA3IbAtqXOQf4U1yi0ajsstJj02YVF5d/GAAOFCeWuOi0DoCiIFibca4iQ4gLZzF/JYqL7gXoGgFYxMVCPyg2Vgr2ryGCRWWujTfYlEXoddIsJi6GnrYT+pY5B4AGtvUDB4oLuxb0WAdsjIvTZzxx4UdxMc5VVNzyQJesol5cRcYTF30r53rdAo0tLGQVWcG5FvpELBaTVQ9Av/iG4v4cPCkuehlsxcZtbzB2004w1UKNcBVxoLhEokn5pKnHHGAzKWzuQQDIXIzFYtp/eC8obvugly0AilROe4gbxUWPzKridGjA2CBto+yhvB6sGBcLA8GoSSqfMuz8uYqMUlwMJS5MmfOAEcG5PGQVMeRNDzeJwyHIc0C0N8t/N2oMjAzOHdbC3HEP5yLGxWnLwG7XuXIuD+vAIOVNdhdZMS4WBkLxJA3pNEnlxWBzAjaPRVwcxiouiTSNa9EtOLmXDtFGzYNMJoNMnvor9JoDUpxLBtTJb9Q8KFVc9PvskYOYO65RhikubFaRy6FPSnJvrqJt27bp8tm9Qe9O8RLkAF27HxBcFnGx0DcMU1yK5FHDYxsc+i/UxqKiS4YSF7ZaqE6l3llXkdNLdi5e1Aa9Yr2kOJdUzg+A/AbGEhdjFJfBTYDTUYizco/kQnHxOHUiLr1kWRp5kDNMcSnqV2TFuFjoE8Rg09OeXga7OAVwx44dhvXqYU9Zdrt+dVyKs2oikYgh/TlEUUQqQ/PgjVBcvIEhAPa8TVtRy8VJfPtGbVpGuopsNgEjBxXcMrwQF51aX7hdgFOKh3fsuTF/ipRoZzPi8bhh8V7VwCIuOqIkOFenTatYHs1kMoYV32IXatALzQuPSSiOcQGM2bhTqRREJpPCiDo2roLiEg6HkUrp36vGKOKi7BBNarlwQ950TIcGGHeRswW7OozZsCIRpsmkW58K0oIgMDF/xhOX4nmglz0wexE6i7joCKMMtsIoGnzKYMmbnqdMXohLSbVQAxQXu5sGpxphrMgcoJYz6NWHvCoVFxKhyg1x0XEtAMCowfR2R9RriAIb7kkBAtmC9FoHAHUXCU4yIYyMcSnOrNJPcVF2iAYs4mKhDxjnKmJTgYmx5CG+QU9j3VBU8h8wcNM2IBiPnWuSwQaMmQeGKS5so0WnsYpLaWkEfT+fDdAVXSMMUWC7mNYXfp3IK8BmWRobpA4o7SFxY+kzDsUxLoC5arlYxEVHGFV8rbdIeiMWaz6fRzyekMdAz1OWx8U08jNQcSnOJtGjPwtADKLUXC8nUKvFwxjoReCV/Yr4Ulz0XAsAMGows0G6RhmyabGtL/SsYyMpLqLgAgQXotEo+T0MADsP9JwDlqvIQtlgFRebTZ8qiUARQTLQVZRIJAyRRQHi25bdRUa7igzo1QRQd1FWpANvxDwwKiCRjXHxBEcAMFp51F95kzCScRXBPcIY4hJje3bp0/qCfBZzx8BaLqIoKuaBnnFOTb0oLhZxsdArFGXOffoFpipTAMkiMW7TNk4e54G4lCguBhCXZJYyZuPGgIlx0SurqJHe9oZGAeBDcfG6RDh0chFIGKUgLsbUcokoenbp0/oC4KeWSzKZJLFFBrjOm4uyigCLuFjoA6SxnP6TlBdXkdHEpUGRTSDseYpLgS8l0k5IS98w4mJAdh2ruLj8wwGQzsDxeFyfC2CgcBHovA6AYuJiTEp0NEGJS2PI2c8z1UVvZf+NsIfEPWUH7GQB6GkPe+sQbcW4WOgVrKtIL78+ULQgeCIuOsvjsuIi2AwrQkeaqhXq2Ag5uJz6nbQbiur5AHwob0bEuNjdNDrV6DEI6RjfIaG1AXDYC64a90hDFJcYU0bJaFeRcfbQGPXVinGxUDaiMRrjoaviwnyWlFHCA3HR268/bhhzxzvZwA2LGCu3TtVCJSgyqwzsEG14fxYAeYP7FbFjENTxECPBZhMwuKGQ1WNQcG48RbcfvSpIA0VE0WjFxSAFWlEewnIVWegL6XQa2TyNLdBTbWBPGB6/ceXeS7Oq9D1pThnPfJ5/iuFj4HHmBni2umAVF4+B1XONMtguJy0+loWxKeGRaBqwkbLReiuPEoa3Flw1ziZs2xnR/fMTaYa4GGQPjYxxMTIl3m4X5IOM3U1KA1jExUIJjKrhAigXhNND/Zm5nL4bp9ExLvuPZ+74pqCrqwvpdFrXa2CDc31u44hL06BxAIx3k9gEUbfsOoDGuSRydDAMIW9Mh3C914GEscNoQOxmA8IbUhn6+boqLpy4ili3MaC/Ai0H6DIxLqKoTwXjWmERF51gJLtmF4TN1QiA1FTRWx42mrjsN465498PgP4BadEoVVx8Hn2NREOAbpYNzSSrprOz0yDyRuaB3yvqll0H0EaLibQbhgYoJ4wnLhNG0EZhO7r0C44FCj27spS4GFE5F+DAdW5gEUIpQDdvI+Qtk8mgu7tb34uoEhZx0QkliovOTdXkRcEQB70Xq1EF+CQMahQwRApt8O8PQP9NqzuSAgQSiBjQ8ZQJKBUXf8Nw+bbeEjFLXEI+fcmbFKCbFwXD0uJzuRzSWUoajCIubBG6zpi+F5FIJOQ+RYBxriJ/iAS+8RHjoq/rXIr5EmGXqwibxV1kERedYLTaIH1eTjBOIjc6OBdg3EXOQYBzsO5jEI7QgFy9DZWyQ/RQ+bbeY0DSoQvZdTqPgaLRYqFfkSEE3sCTtgQ2Jbon1dD3EzVAcY8ePV1FynVABmHHjh26u86NdhWNZlPivXsBsIiLhSIYza4lhSeTpxZiT1NcAGCKwl20v+6bdg9TdCvo13f5KTpE+4xLB47G4vI8CAX0HQNl2X9j+hUVb1hGERe2X1HWNkTXejbFhRiNinGR1kEul0NHR4d+FwHjCezUicweFJgKwCIuFopgZHAu+3mpDJWojSEuxp4092czi3z7GUBc6KmuIaBf7Qqg7w7RhpI3necA22jR22BM9dzSAnz613EBgFFDmDtufVOiCXljFBeDYlwcrib5tt72sJi86Z1ddsAE5o7/AADmKUJnERedQNi1cacs6fNECLLB2BOJi1Jx0T8lOsoU3WoM6VfmHFASF8FhXDpwlDnY6xnrBSgVl2CzMZlVPKwDgIyFXSjUcnGPwOeff67bZxe7inQNzu2jU7rRCrTeFZSnTmTuFGL+LMXFggJGBueWfJ5BjcV4MNj7jmXu+AwmLkGdiQvjKpIyCQBg8+bNul5HLEWVJt0VFybGxRcaCQDo6elBMpnU7RpKTtoGERdBENAaLHxv9yi88cYbun02sQVMcK6OriKfhzS5BYC8QH8HvWu5lIQP6Ky4NAUFGudUUFws4mJBASOrJAJ89Csysk+P/Jk+AWOHFlwV/v2wbZu+xCWepEsu4DEuOJdVXN555x1dryOWNI64sIqL1K8I0JfE80DgJYyXUqIdjXjl9VW6fW6xq0jPWj6CINCYP5EaoT1RgZbdRY5GwD3aIi4WlDB6krKf5y5UzzXGp0vGwG4T4XEN8AKNcMCEwrS3B/D+6t26ZhOw1UL1Jm6s4hJPO7HffqSWzQcffFBo+KY90uk0cqBfXP8YF3rb7qZpFXoSF6MPMSwmjqaM4d3VEWQyGV0+l1WdXPYMbDadkxUKnCmdo9/f6BgXIw5ySnfRVMO6pVcKi7joBKODc5VVUycBALZu3aprpUSWvAW80LXwGAu2gm4kNxoffPCBbp+dZKqF6u3TdjoE+WTbHQWOOuooACSj4t1339XlGkoaLOqcXTe4kd5OCcakhPNEXI7an45/KjgPH374oS6fy8a4uHVufQFQ13k8RdejkQc5wCDFRdEG5QCsWrVKN/JaCyziohOM7oy871g6Qf3DjgEAdHd36xqQx6b/6U3cWBT3LHrppZd0++xUllYoNeKEJblKNu0CjpxxlPz3lStX6vL5xFhTAq+3sR7aAgwnWdDY0jMKAHFbrVmzRrdrMFp9ZXHGbMBuKxCHQQvw2uv6xLmwriKvywDiUrA/RAElc0DvGBceykMoFZcDEI1GdXcdVwOLuOgEkgJJJ6neG/dRB9DbQsNM+faKFSt0uwal4mKM2gIUZRb59CMuuVxO0WjTCOIyfW/yf3cUGDRmtvx3fYmLccZaEAQcTUpWEBIZOBAA8OKLL+p2DcUnbSPmgYTmkICZ+8XIHfcIPPlyly6fyyouPnd+gGerD0X13AZjqueya8FhB1z6dl0AAEwaCeqyLwTovvDCC/pfSIWwiItOYF1FNkGET+dy76OHCBhdqNuwpWckIBCJVE/iwhYeM/KUOXk04JYWa+gIvPb667r06ylJfzRgwzrqAEoY13cMx/DhJED1rbfeQjab7etlqoEHeXwmMwYNo+cBAF5//XWkUildPt9o8laM755OFbD3N+6li/s4EqGB+nrbQkCZZTloKIlQ3bp1K/J5/UgUe5AL+oxxndvtAqZIrnPvRMDmw/PPP6/7dVQKi7joBHaS+j36NpaTcBRJ1UcibcPg8ScCAF577TXSN0QHROPUIBpprJ0OATMlBcozBglxlC7yKA/BeNIcAIA3PgFmzJgBgFzbJ598ovnn8+AmkRQXAAiNOgUA6Z3z1ltv6fL5xWOgZypwb/j6DAEOkOI6Sf9cfLqmTfPP7I7Sg4IR6iuruIybSIxBJBLRbQ4AShJvpD2cKmUWCTbAPwXvvPMOenp6jLugMmARF53AKi5Bg+I72NP2+APPAwAkk0m8+uqrunx+jEkFNvqUeeJ0xlg2naCLu4gHxeXASfSEu/ITGqALQJc6HmyfIkD/ekYAqeUjNZjryu0LgMwFvdxFbOVcnzune0ZNMbxuAQeMKJAVRwPufHij5p/J9uwK6BygDSjn3ZFHnyzffvTRR3W7BvYgY6Q9PGACG/M3FblcDq+88oph11MOLOKiE9hTlt6ZFBLY07YYPFK+rYe7SBRFxNPG1e8oxomHMneaTtRl0yquXWEEcXE6BBy2D7m9cQcwYd/Z8mN6xLnw4Cqy2QR5LURTHsBHBkQv3z47BgGvvt2x+8LC4+ntp99t1PzzeqJM6wu/vq0vABLbI2Hc3jPhchHf8aOPPqqLu0gURYXr3Mg4p94q6PIe52IRF50QidKMmoaAMcRlv3G0lkfb7mGw2YnB0IO4JJNJQzuhFmPKeGBYS+FOwyy89c7/CLnUEDwoLoAyULsrty/8fkKmVq5cqXl8Aw+uIgA4eipdg0MnnwUAeO+999Dd3a35ZytcBAavAwkXn7UXkN4KANgY2RtvvvOxpp8XYXp2hQL6VpAGgIP2orc/Xu/DiScS1/nWrVvx5ptvav75qVQKeQPrGbFQ9CwKkhOdRVwsAOAjvsNmEzBjCrm9u9uGaUecCQD44osvsGHDBk0/uyT1z2CDLQgCTpheuGP3Ies7THOXWXHlYCOCEgFl7Y63PrXhiCOOAABs2bIFX331laafzYPiAhTFuYycC4BkfenhNt3Q3i6PQUNQf7WhNwQCXkxqLpRGsHlw8tl34Msvv9Ts8yIJag8bDCAuR+wHSGGGb6wGzjrrLPmxRx55RPPP58keNgUFmmkZOBhwDsGaNWt0bwVSCSziohN6mMZyRk5SNs5l9JRz5NvPPvuspp+7e/fuog3LWL8+AJx4KHMNjSfg+9//vqat7Xfs2GFotVAJh+9He7Ws/ASYPXu2/Nj555+vaYYVL6nA0ybRjsQd2X3lv2t90szn81jbtgUQCGEJ+Y1fBxJu+9kM+XaP8xgcd9xxmm1eMSYfQO9CjADQGBSw31hy+8O1wDHHzZPdRY899pjm1bS3bt2qXAcGu86/LoW6CTag+WsAgH/84x/GXdAAsIiLDojH49jdSVMtjSy+NpNxE2T9h8u3f/GLX+B///ufZp+7du1aLk7aLI4/BBCEwsmv6QSsW7cOZ5xxhmaVI7/88kuZuBhRu0JCyC/ImQSfrAMWfutSjBkzBgDw9ttv4yc/+Ylmn719+3Z5HnhdOdjtxmzcDoeAI0nHA3REvRC8JCf0+eef19RdtnXrViTYWC9OXEUAcPIRHgSlmJvmudi0aSuuuuoqTT4rkaJbj1/nnl0Sjiyoz7kc8MWWEE466SQApBCd1oHqX3zxhaEFSYsxfwbzG7SQEgHXXXcdli5datAV9Q+LuOiAtrY2bjbtQybTQkerN7VgzpzjAABdXV049thj8f7772vyuV999RU3YyChtVHAwZMLCzYwFXAOxSuvvIIf/OAHmnweS1yCfmOXnhTnIorA51ub8Pjjj8PtJsXx/vznP+Nf//qXJp9LxsD4FFBAGecyZtplAIDPP/8cl112GfL5PN5//30cddRR2HvvvfGzn/0MH3zwQc2khsd1IMHtEnDy4YUxcbYADUfh4YcfJvZLReRyOXT1sOnQqr592ZjBuEyL3UVXX301du3apdlnE+LCTy2fQ/amMX+OQScBNh9EUcTChQt1ifmpFBZx0QFfffWVHJgLGDtJPW4a57J+G7DgB8vklNhwOIzjjjtOk3oexGAbH5hajBOn09uOwSTW4W9/+xueeeYZ1T/rq6++kgOUG4P6+/VZsHEuL/1PxMEHH4w///nP8t8uuOACxX21wG7cDQFj4ztOpQWkkR30bTno4W9/+xsOO+wwHHbYYXjjjTfwxRdf4LbbbsNBBx2E448/vqYgbp6JCwDMP4o9ec9HPp/HbbfdpupntLe3I5OnHVaNqmMzQ1HTSMS8efPQ0kJ277feegvTp0/Hxx9rE6RcrLgYWUkcIPGP8wqJptm8E7NPuxkASar4+te/zl28i0VcdAA5ZbK1K4ydpD8/j37+Tf/2YOmTz2LWrFkASP+ir33ta6qXvy4mLrwY7BOYei6HnXClfPvyyy9HPB7v7SVVQRRFfPFlG2AnjM3oGJ/Z04BCUhnufx7I5UR8+9vfxsUXXwyAxGJ8//vfxw9+8APVKuqmUikSmFqo42KkyxQgPaukIN3NnUH8/NbnYC8Myvvvv0/SYhtmA61nQDKVL774Ir773e9Wrbzwug4kzD2clJ8HAKH1VADAkiVLsGXLFtU+4/PPPwds9Iv7DTrEjB8ODGkmt9/6FAgEgli+fDmGDSMtANrb23HEEUdg8eLF8u/94Ycf4v777yeu7xrAKo8AH/OAJa1jpn0Pxx57LACgo6MDCxcu1DzupxJYxEUH8HbKOu4QAScdRm5v3AEsXuHHM888g0MPJalwGzduxPz581WtqMvbGEg4dB/qOtuZ3Atz5swBQIzWjTfeqNrnbN26FYkUNQxGK06DmwScUghx2robeO49cvvOO+/E1VdfLT/v//7v/zB//nxEIpGaP3PdunUQ4ZYDU3mYA5efRn+TNeE5eOyxx+QgTeewbwIHvAjs8zAOWPAV/CHSM+OBBx6o2pVWumHxE5wLkKDV2dPIbdE9BvDtj3Q6jTvuuEO1z1izZo0iu84oxUUQaJxTTwz4dD1w6KGH4r333sMhhxwCgMQnXnTRRTj99NNx/PHHY9q0aTj33HMxadIkHHPMMXjyyScr3tBFUeTOVQQAcw6iJHL5OzY89NCjGDVqFABSYf2mm24y8OqUsIiLDmBjGwDjT5oAcNt3BTmz5Nf/FpHI+LBs2TJ5or777rs4/fTTVVFekskkNm7cyCVx8bgFufHgV5uBG35zl7xx3X777fj0009V+RweXWUXzqWb5uLl5ERps9nwm9/8BosXL4bDQdxZy5cvx1FHHUV+wxpQsmlzMAanHU19+8veAA4+Yj7efvtt/PTav8CzPyUnH28Zi0HHfga4RgAAbr755qrKw/NK4FmwgZqOId8AQNxnatV7WrNmjaIQo5EtD4rjXABgxIgReO211/Dtb39bfuyJJ54oyTh77bXX8OMf/xiHH354RS1Ddu3ahXA4zJ098LgF2XW+Kwx8sa0RDz74oKxC/upXv8KyZct06WU1ECziogOIsaKuIh6M1f4TBCwqVLruiQG/+beIoUOH4umnn0YgQBbUs88+i7333ht33nlnTTLhunXryGTn1GCzFYW3xyfgZz/7GQAgm83iuOOOw3333VdzNc1i8sqDoZp7BDC4idxethLYHaYGadGiRXjuuefQ1ESe8PHHH2P69On417/+VfVY8LhpOx0CLvk62bzyeeCPj4nYZ98D8fqu7yKSUMbgbNjdiIajPwBsXmQyGXzjG9+oyGWQy+W4CtTvC6ccQW8P24cErMbjcZx88sk4//zzay4Z8PnnnyvWglGuIqA0zkWC1+vF3XffjUcffRSNjY3y38ePH48rr7wSkydPlv/2v//9D4cffjguvfTSslRquT4Oh/Pg6wxpffxVETNmzMD1118PgLiPTz31VEybNg2LFy/Wrcddb7CIi8bo6ekp1O/gb5L+6iJBbmn+96eA7qiIAw44AEuXLkVzM3H+dnd347LLLsPs2bOr9uvKRc04qN/RG2Yy2SUrPxZxzTXXYNKkSQBI+u6FF16IQw45pKZ08S+//NLwcv/FcDoEfIsUDEUmCzxQVMLkmGOOwVtvvYUJE0ju9M6dO/Gtb30LM2bMwEcffVTx5xXHevGyDi6eR+M6fvcwEDhJxDufkfvjhwNv3SlgHAl7QHeqBfseRdLFd+7ciRNOOAHbtm0r63M2bdpEauRwUnisL4wdBgwtxH505ybj2GNpP4B//vOfGDduHK699lp0dXVV/N6iKJa4ioxcCwftBdkGvvwBkEwp1YQzzjgDH3/8Ma6//no88sgjcqD2mjVr8PzzzysIzN/+9jfMnDkT7e3t/X7mF198QW5w5ioCgHkzAGchb+CRl4F8ntjDk0+m/Zw++ugjXHTRRTj88MMNU18s4lImXnvtNfziF7+ouMaHvGkb3FiuNwxvpRtXNAHcW0ikOf744/HFF1/gggsukJ+7cuVKTJs2Df/85z8rPnFT4kKNFS8LFSD1HKQqmq9/TE5bL774Ik477TT5OR988AEOP/xw3HrrrVWpTzy6igBgEeMuuvcZscQQTZ48Ge+88w7mz58v/01yI/7iF79AKpVCuSjOruPBZQoAw1oFLDiW3pd+XqcDePh6AYfvJ+CvP6bjdMDsn2OvvUjN+PXr1+Okk05CZ2fngJ9DbQF/hxgWgiDgCCn2Iy7g93etwD333IOGhgYApIvyb37zGxx99NH461//WtHmtXv3bjJWHMS4AIDLSQO0t+wCrryz9LuMGjUKv/zlL3HmmWfK7lNBEDBnzhw89dRTuOOOO+DzkR9y1apVOOSQQ/pt2kqJC38HueaQIPdx27KLdJC32+1YtmwZ/v3vf8txkABw5plnQhCMidGyiEsZ+Oyzz3DaaafhwQcfxNe//vWKAhV7Uxt4MlZXnEkn3p8eF5HNkoXb2tqK++67D6+88grGjyfFueLxOK6//nqceOKJA54qWBSPgd1OTzk8gC15/eFaIBIXMWrUKCxduhQvvfQSDjiAFD3JZDL4+c9/jgULFuCDDz6o6DO+/PJLBXn1G5z+KGHfsQIOKxSO/bgN+KCXKu8tLS148skn8dxzz2G//ciOls/nccstt2D69Ol47733yvosngNT/3yFgNsuFXDmMcDk0UBLA/D3KwUcsje5xtkHUpfGix+4ce/iJXLRvo8//hhHHHHEgCXyeXYRFOOIKfS3efszARdddBE+++wzXHLJJXA6STR7LBbD97//fXzta18jqnIZWLNmDbnBxrgYvGn/9jIB7oI9+vNS4MnXyydiDocDP/zhD/HWW2/JdnL37t04/vjj8bvf/a5XUtcbceFpHiyYQ3/7h14k1+90OrFw4UK88847ePvtt3HeeefJGYhGwCIuZeCrr76SU2Ofe+45zJo1q2x5uDdjxctJEwD2GUszjNq3A08WNQieNWsWPvroI1x22WXy31566SXsv//+uOeee8o6bRUTl6AXhjH1viBVFM7ngbdW078fc8wxeO+993DNNdfI17xq1SpMnz4dF154ITZt2jTge2ezWRLb4N1b/tvoIapefk3oLUi3Nxx//PH48MMPcfPNN8ub1yeffILDDjsM3/72t7Fz584+XxuNRkvKnPPkJmkICLjybAGP3GDD5/+2YfdTNlxwMh0Xj1vACSTRBLvCwI74aKxYsQKDBw8GQNb5YYcdhueee67Pz+D9EMNCUlwA4K1PyZwYPnw4/va3v2Ht2rW46KKL5MeXL1+O/fffv6zaR59/XuiHZGfSoQ1UXADggAkC/vB9+ltfeIuIjTsqc4EccMABeP/992WXSj6fx09/+lOcffbZJbEg0p5gc/LnNgVI+X/pYPnoK5APsxIOO+ww/POf/5TnvhGwiEsZmD9/Pp577jlZKpXcBp999tmArzWDsfoRo7r8/pHSBRsIBPCXv/wF//3vf+UaB5FIBN/5zndwyimnDFjjQRoDwUnGj7fvDyh7OK38RDkGLpcLv/71r/Hqq69i4kTSA14URdx3330YN24cvvGNb+DFF1/sk8Rt3LiRuBiDB8t/Y7vTGo1vzgG8pGguHnih1M/PwuFw4Oqrr8aTTz4pK1GiKOLee+/F5MmT8Z///KfX18nxURzGuJSLeUzg4osf+LDXXnvhnXfewZQppKJjOBzGiSeeiAULFmDdunUlr+c91ovFwZNprMObq5WPjR49Gn//+9+xePFiDBlCGPiuXbvwta99Dd/73vf6DdosVlwEgc49I3HJ14EzZpPbXRHgR/9XeexGU1MTnnrqKVx77bXy3x5++GH88Ic/lO/LhxgA3gDd+HmaB0GfgK8VitHtCpPYH95gEZcyMXPmTDz66KOyPLxx40bMmDFjwG6yvSkuRp8winH8dGC/gqvkzdXAe2t6X7THHXccnn32WSxatEj+27PPPospU6bgoYce6vU18XhcrrooFHz7PC1SCWwPp9f7KJY5c+ZMfPLJJ/j5z38uk9hcLocnnngCxx13HGbPno1Vq1aVvE6eA35SIMPjAvYdo+rl14SGgKAw2stW9vt0AMA+++yDd999F3fccQdCIUJGwuEw5s+fj+uvv74kDopu2nyVBagEcw+nsVAvfUgm8dixY/Hmm2/i61//uvy8hx9+GHvvvTduueUWxeulMbC56YbF6xh43QKmkfh0fLER6OwptQmzZ8/Ghx9+iK997Wvy3/7yl7/gsMMOw/r163t9X6q4kHng9/ChvgqCgLuvFOTU+KWvAZ9tqJy82O123HjjjXjyySfluJe7774br7zyCgASEyXFSTq9JALaZuODvLHozV3EEyziUgEmTpyIN954AwcddBAAYqhPOOEEPPDAA70+XxRFedNyeMmKCPpgWFfgviAIAn5wOr2mh1/qe6KGQiHcc889ePrpp2X1JRwO4+yzz8bFF19cctqifU4E5AWykHk8aY8cLGDsUHL77U+BVLr3MXC5XPj2t7+NL7/8Ev/v//0/eQwAEsA9ffp0XHTRRYpxkGM7fERmOWACafLHE8p1F7FwOp340Y9+hC+++AKnn366/PcbbrgBp59+uqLirkzenPxv2n1hSLOAQ/cht7/Y7EJ7ocRRMBjEE088gb/85S8YNGgQABIPdc011+D222+X7xMVxg6EiG92UCP5xytYd9HbfZQzGjx4MP7zn//gr3/9KzweciL75JNPMH369F4PdZLiIjjJIcbo+BYWjUEBP11A18Gt91e/Yc+fP1/RKkGyjWwclOQqCvr4IG8s5h5BD5hLXwNiCb7Ii0VcKsSwYcPw6quvyp1E0+k0Fi5ciIsvvrikh0lHRwcpNATA5iT1MHjctAEik0ol4J94HQPGrpxyyilYvXo1zjnnHPlvd999Nw477DBs2LBB/hvvGUUsZhayC5Jp4JeL+//+ra2tuOGGG9De3o6HHnpITp8WRRGLFy/G6aefLmfcfPXVV4D/QPm1PLmJJBw9laT+AsDz76MiH//QoUPx6KOP4rbbboOtUNXwySefxHXXXSc/R54HDTPkvx0wvvbr1hvzjqQbzNNM7zmbzYbLLrsMa9euxZVX0tYRV155Je677z5s2LCBZKMFD0ZeIJv2nIP427BYHLEfvTYpzqU3CIKASy+9FKtWrZKzrTo6OnDccccpKgzH43ES1O9ohOgkhfyGNGl08VXi4nlAc8Gbef8LwIZt1W/Yl156KY44ghTF+eqrr3D99dfTwFwAeYHYRJ5ivSR43YLcyyscBS67ozTj0EhYxKUKBAIB/Oc//1FUVrz77rtxyCGHKCqtysbaMx5pEMVF2hx4Q3NIwKzCxr1uK/BJqYu+9DXNzbj//vtx3333weslq++TTz7B7NmzZfIij4GLfnFe0sGLccUZglzP49YHgOffG3ihOp1OfPOb38Tq1atxxx13wO8nxujZZ5/F2WefjWw2S05ZgYPk1xy0F3+blc0myMGoogj8o8IiqYIg4Morr8Qzzzwjp4zecsstWL58OQCpjo0PCJDSnJNHkzRks2Ee5V3442PA6x8p50goFMJtt92GX/3qV/LfLrzwQjkeCI1z5L8fezDf3/+IKfT2W2UUkN53333xzjvv4MQTSY2FbDaLCy64AE888QQAJpum8Vi57cOxB/f6VoYh4BPwwzPI75LLAb99sPrN2maz4Z577pED2W+77TZcc8018uPpHPEP8eg6B4Drzhfka/vnf2m5DB5gEZcq4XQ68fe//x333nuv7Mv8/PPPMXPmTLz77rsAGHm8ea78ulMO59dYnTaTXtsTr5X/ugsuuADvv/++fNpqb2/HMcccg3fffZeWwm79hvz8Q/fhcwwOmizgNxfTazvvZhE7u8ozXC6XCz/60Y+wfPlymcQ98cQTGDlyJF5++WUFcTmYQ8UFAM4/icZw/HmpiK82VW60TzrpJEVsx3nnnYdVq1YRAhs6ErARIz77QDWuWH/sPx7YezS53bYVOPr7Ir75yzx6Ysqxuvbaa/GDH/xAvp9MJskNhrjMOQhcY9RgYHgruf3OZ6QR50BobGzE008/je9+97sASHbNggULsGLFCpo233SS/PyTDuXPFnzvG5RM3LscWP5W9WrDvvvuq1Ae0+m0fDuRJtsvrwr0XqME3H0V/X2+9wcRH37FieoiWigLuVxOXLdunZjL5Uoe+/zzz8WpU6eKAEQAYiAQEG+99Vb6tykrRMzMiZiZEz9pyxtw9eVh0468fJ1TF5V+z/7GQBRFcdu2beLee+8tj4Pi30Efyu+9fiu/Y5DL5cUTf5KTr/WwS3Lijs4883j/YyCKovjcc8+JLper6Pt/LGJmTnQekxOTKX6//9evpt992Kk5cc2G0msdaAzy+bw4f/780jkw5kb5vR94nt8xGAgfr82J+yxMyN8FM3PiN3/Z+3q56qqrxH333Vfcf//9xanTDhPtRydFzMyJY87Mifk8/2Nw+rX0O17w65y4dRe55oHmQC6XE88777zebcGhG0XMzIne43JiIsnnGPz0LznF7zv7Bzlx1efKay3HFogiWQ9LliwRZ8+eLQqCIAIQ9z/wcPm95/yw/9cbjct+R8dhzJk5cdvuyuyhFrAUFxUwefJkvP7665g9ezYAUrPiZz/7GSmLbg8ADUcDILU7pOwdHjFyMG04+NFaYP3Wytj10KFD8dJLLynKYAMAfFMAP2kKcuQUYOww/k5ZEmw2Af/4uSC3u3/nM+DQS0R80lb+WBx//PF4+umnMX36dAwbNgwtg0YDPjKwU8YBbhe/3/+enwnYvxB7sq0DmP1DEavXVTYPBEGQU8UVaJgl35x1YI0XaiD2Gwf854Zt+NtPgYZCktTDL5W6Fm02G2699VZ8+umn+Pjjj3HH3W8hJxLFiff4FgnHH0KvccmzwKSFIq69O4/d4f5fZ7PZcO+99yqyrQAQW+Am8S2zDyT1cXjEtd8ScBSTafjKB8D0S0RcdWce8WTl6+H888/Hyy+/jK1bt2LZsmW44Y7/yo/zqrhIuON7dF9o3w7Mu0Y0PFjXIi4qIRgMYvny5TjllFMUfx+2z3mAjfgyv3YE/8bqVMZdVFyMrhwMGzYML7/8Ms4991ycdtppuPLKK3Hy+f+QHz/7WL6/P0CyR569TZBl8vbtwJGXiXj6zcrIy7vvvoutW7fiPy9ukH36B0/u/3VGY1CjgJf+IODAQirsjk5g5vdErPy4MkPV1NSE9957DzfffDPOPPNMTJp8IIQQKRe+1yjSbsLMsNuA73wN+P336Pe47A6x3xo4L/6PPnbsQeb4/t+ZB9x2qSBngMUSwM3/AsYvAH79YCO29dNv0el04uGHH8bll1+OWbNmYf78+Tj4+F/Ij5/Msdu8ISDgtf8T8MgNAiYSnoV8Hvjtg8ABi0Rc+rs8bv4n8NTbPmzqu+5iCYYOHYrZx87Dj+6kZQFOOozfcQDIQevJXwsYVUgIfP9z4OwbxLJch1pBEEWOQoU5Rj6fR3t7O8aMGSNnTvSGTCaDW265BTt27MBZZ52FJW8ehftIfCKW3yZwvVgBYM0GEft+i0yJCSNIcbIRrQJm7A9MGSdi48aBx4CFKIoY/00RG7aTegVblwoY0sz3GEjYulvE168RsaoQUygIwG8vBU47tB1jx5Y/Bn9+XMT3/0jG9K8/FnDpqfx//84eESf9VMR7hbIbHhfw72sFfGMW+U3LWQssXnhfxPE/JmNw8TzgrivNe2ZibQEgYNYPRKws1P65fpGAXy7q/fc94tK8nFa8ZalgKvK2KyzihvtE3PUfIMu06nI7gYtOAa46R8CYoQN/nzk/zMsFzb68X8CkUfyPQToj4ncPAzcsEZFK9/6csUOBMUMJofW4gH3GkCD8Q/cBJo5UfscLb8nLe8LRU4GX/iDAbud/HFavEzHjchE9heTZy08D/vgDVLwnqAGLuJSJh17Mwydsw0lHDYPLVd4PlM+LGP4NETs6AZ8H6HhK4FYaZbH3uXl8sbH070OagKP2i+IHZwUwc6pQlnr01moRR15GptgJ04H//s5cG1Y8KeL8X4t47BX6txMOjmPB8T7M2F/AyEGkeFR/Y8Eaqrf/JuCwffmfAwDp2XT6tSKef5/+bfQQYN6RwJGTt+PM44fC6Szv97z27jxuLmTG3n+dgHOON8cY9IbiQ8zqdSKmXSQimyPk/BtHA5efRvpfOexko9+0k7gacjmyqX32L3OtAwnt20X89kER9zwNpJh+s4JA3F/nnUDSaBsCpb9vJC6i5WsiMlmSXbn2wfJsCC/4vF3EJbeLeK3CxuiH7Qt852sCBjUCL6wS8X+Pk78HvMAnSwSuXefFeGmViJOuJL/h3MOBx28Etm21iAuXSGdENJwsIpkm/sgjpwD7jSUbltctYJ8xpAbIoEY6AUVRxCsfAHOuIMM770jgP7eYw1j941kR375NVJysirHfOEJEWhsENAaIcU4WTiMNfjJOX2wS8firwKeFIpr3XSMo+r+YBfm8iBuWiPjVkt4fdzqApiAwuBEY0kwI3pBmMjYd3SLuf4G4XOx2ILJCgNcE5FVCOiNi0W9EPPBC6WPDWoD5R9G6F0472bAaA8DQZnICHT2YFBmb+T0Rb3xCnrf5cQEjBplnDIrRm/r6szvzuO3B8l5/+WnAn39kDlvQF7bsyuOGe7rxwMsNiCWVjznswIz9gZMPE3Dy4SQTK58H7nwSsvJ42anAX35szjHY3iFi005g404Rr74fxiftjXj7M2r/ysW9PxNw4SnmWwf/+q+IN1eL+L8fCrDZKldf1YBFXMrAm58QiWwgjB8O+NwkdH5bB9DZQx+766cCLv66eSZpOCJi406y4a5pB55/nxCxaN9tSPqF2wXseFLo9SRmFjz4gojv/o5KpZVi//HAx0vMZ6zzeRH/WEFKf7/8AZDJDvyavjBpJPDlA+YbAxa9EZdsVsRtDwL/97iI7Z39v/6F3wvc13AZCNIYBBrH4M5lAv6xQsS6rb0/d8QgUhto6276t//8RlD0fjIjil2GmSw5wHXHSKf19z8HHn1FxMdtpa/9/unAH39gLsWpN5QbQqE2LOJSBrbtFvH4qyJWvBXDqrX+AQ1TMbxuoO1BwZQFt1jE/3979x4WZZn/cfw9DCJnBBUQxSRxzbSDZK5phrWXtbXqXiuKZYefmUpFpltqWleX7tqWmh2urVw0TbfDlZm4ZrqVWlnkekKr1dQ8piSCoILIeZj79wfLpKapOczDMJ/XP+rIwPf+zPPMfLmfw13uZHZmIZn/acHabef/+jrtYmuP/f+fF862nKmk1MnyL/LYVxjLll1wrASKT8LRE5B/nHMeAw8OhNfHe/chEoATpYYV6wzzl5fxxdZgqqrP/5xTpQ2AjHGNr3GpU1VtWPIlLM0ylFbUfpDZgOjI2pm4G7rYTjsB3ludmYExhvXf1Ta3/14Pe35h3dVOl8Hmud4183g2F/KhbYxxNTBQezfiGzpDbHPvHnsdNS6nOH78OFOmTGHz5s1ER0czceJEunfvbmlNdS9Q27aX8UOejcNHa6cGi0/Chh2GrP/WHhJx/i/NiJDaNWmuvhwG9bFxfQO96drFOHUjzTli42B+7Qd20UkI8K89Kc2Y2t84iktr36h7Xw3xMd4/9jq/tKMaUzsbk3+8dqaqoKj2ENJlsdCmJQQ0aRw51GUQEXUZ/91no+Z/6ylWVtW+7sdL4FCh4UBe7W/Z5VW1+0qrqNqZR2/fHqx6s25IzpfB7hzDRxvgow21s3Q1NbXnRNx7m41+NzTcy6AvhrYD6zLw99hPugjTp0+nefPmrF69mg0bNjBp0iSWLFniWpHXSjYbtG9to33rnx5L6eP9O+HFuizWxmWxVlfRsNhsNiJCa+/t8Zt4q6upf83CoE/Xc237vrdPyE86xNvoEA+PDrJRVW0wpmHfv0i8S4NrE8vKylizZg1paWkEBgaSnJxM+/btz7rSqIiINGwBTWxqWsStGtyMy8GDBwkODiYmJsb1WGJi4v+WhP+5qqqq09Z/APD39ycgIMCtdTmdztP+9EXKQBmAMvD18YMyAGUA9ZPBhRxyanCNS3l5uWuF3TohISEUFxef9evnz5/P66+/ftpjgwcPJjU1tV7qy8nJqZfv602UgTIAZeDr4wdlAMoA3JvBz5YKOYsG17gEBQVRWnr69aalpaWuFZjPdP/993P33Xef9lh9zbjk5OQQHx/v0ydiKQNl4OsZ+Pr4QRmAMgDrMmhwjUvbtm0pKyvjyJEjREfXLo6wd+/en60BVCcgIMDtTcov8fPz89mNtI4yUAagDHx9/KAMQBmA5zNocGkHBweTnJzM7NmzqaioICsriz179pCcnHz+J4uIiEij1uAaF4CJEydSUFDA7373O1566SWeffbZBnEptIiIiFirwR0qAoiMjOTvf/+71WWIiIhIA9MgZ1xEREREzkaNi4iIiHgNNS4iIiLiNdS4iIiIiNdQ4yIiIiJeQ42LiIiIeA01LiIiIuI1bMYYY3URIiIiIhdCMy4iIiLiNdS4iIiIiNdQ4yIiIiJeQ42LiIiIeA01LiIiIuI11LiIiIiI11DjIiIiIl5DjYuIiIh4DTUuIiIi4jXUuIiIiIjXUONyBq2AIL7O4XAAUFNTY3El1ikuLra6BMvl5uZSUVFhdRmW2rBhA/v27QP02dCQqHEBDhw4wJdffgmA0+m0uBpr7Nu3j1mzZrF8+XLy8vKsLscSe/fuZfr06bz//vvs2bPH6nI8yhhDSUkJY8aMYfr06QDY7XaLq/K8ffv2kZqaysyZM60uxTL79+9n2LBhvPDCCxw+fNjqciyxd+9e0tLSeOSRR1ixYgUANpvN4qo8a8+ePUyZMoXZs2eTnZ1tdTmn8enGxel0MnfuXIYOHcpTTz1Ffn4+drvdp5qXmpoaXn75ZYYPH05VVRWLFy/mtddeY+vWrVaX5jEOh4Np06YxYsQIgoODyc7O5tVXX2XXrl1Wl+YxNpuNyspKdu7cyZdffsm3334L+M6si8PhYOrUqYwYMYLk5GSmTp1qdUmWOHToEE888QRJSUlMnz6dyy67DPCd2YaqqiqefvppHnjgAZKSkrj99tsJCgoCfOOX2rrXecmSJaSlpdGyZUt+/PFH5s2bx9q1ay2u7ic+3bgUFBRQUFDAxIkT6du3L6+++irgW531unXrOHLkCG+++SZjx45l8uTJ2Gw21/SoL9i6dSt2u52FCxcyevRohgwZwtGjRwkICLC6NI8qKCige/fuDB48mFdeeQXwnVmXDRs28MUXXzBhwgTS09MBKCoqsrYoC6xfv57OnTvz6KOP4u/vz/79+wHfeU+cN28eAJmZmaSlpXHVVVfx73//GwA/v8b/cVn3Om/atIlRo0aRnp7O6NGjad26dYOaffO3ugBPKy0tJTg4GJvNRnh4OEOHDiUmJob27dszadIksrOz6datGzU1NY32Tbu0tJSgoCD8/PyIiYnhjjvuoG3btjgcDhISEigqKuL48eNWl1mvTt0OOnToQKdOnQgMDCQrK4tnnnmGmpoa8vLyCA8PJyoqyupy3a60tJSQkBAA17YeFRVFbm4ud911F2vWrGHJkiUMHDgQh8OBv3/je6s4dT9ITEwkJSWFTZs2ERISwoIFC4iMjCQ+Pp4//OEPJCYmWl1uvTh1O6gTGhrKtm3bmDp1Kk2bNiUmJobf/va3DBo0CKfT2eg+wEtLSwkMDMRut3Pfffe58nA4HMTHxxMTE8MPP/xAu3btrC20Hp26HRw9epSamhpKSkoAiI6OpqCggOTkZCtLPE3jezc6h7y8PKZMmYKfnx/NmjVj/PjxREZGuqZCO3TowK233kpGRgZz587FbrdjjGlUv2mcmkFERAQTJkygQ4cOdOjQAfjpt+uQkBDatm1rZan15lzbAUBJSQkbN25k+PDh9OrViy1btvDWW28xadIk2rRpY3Hl7vFL48/JyaFjx45ceeWVDBgwgMzMTJKSkmjatCmtWrWyuHL3Odt+EBMTQ8+ePXnuuedYs2YNY8eOJSwsjBUrVjB79mzGjx9PdHS01aW7zbm2g/LycowxvPfee9x333307NmTVatWMXv2bK677joSEhIazfvimRmMGzfOtS/UNeuhoaEUFBT8rLlrLM62LzRv3pwePXqwadMmZs6cyfbt2/nhhx8wxvDJJ5/w8MMPExcXZ2ndjat1PoeKigr+8pe/0K5dOyZMmEBRUREzZsxg/fr1QO1xvSZNmvDHP/6R8vJyFi9eDDSu6dEzMyguLj4tg5qaGmw2G+Xl5ezatYuYmBiLK3a/820HISEhPP744wwZMoQ2bdrQu3dvIiIi+PDDDy2u3D3ONf5169YB0KJFC9d5PcnJyZSWljJ48GA2btzYaM5xONt+MG3aNDZv3sw111zDgw8+yDvvvEP//v3p06cP6enpBAQE8N///tfq0t3mbNvBtGnT2LZtG7fccgtfffUV33zzDb169SIyMpLU1FRuuukm3nvvPaBxvC+eLYPnn3+eDRs2AD/9EtelSxeqq6td+0hj2Q/g7PvCc889x5YtWxg4cCB//vOfOXr0KO3ateOzzz4jPT0du93Om2++aXXpvtG4FBYWYozhnnvuoV27dsyYMYPw8HBWrVpFXl6ea0eMjY1l4MCBZGZmAvDRRx/x/fffW1m625wvg7oddfv27TidTjp37gxAVlYWP/74o5Wlu835MvDz88PpdLouB46IiKCwsJCIiAiLK3ePc41/9erV5OXlUVFRQYcOHfj4448ZNWoUYWFhtGrVih49emCz2RrFm/bZMmjWrBkrVqygoKCAPn36EB0dTVVVFVA7Tf7dd9/RtGlTiyt3n7NlEBERQWZmJlFRUfTr14+goKDT9vsmTZrQsmVLoHF8eF/IZ4LT6aSyspLu3btTUFAANI6mrc7ZMoiMjGTFihXk5uYSHBxMUVERt99+OwAdO3YkNDSUsLAwiyv3kcbFZrOxc+dOAgMDgdpjuH379qWyspI1a9a4vs7f35+UlBRqamq4/vrrycjIoEmTJhZV7V4XmsHhw4e544472L59OwMHDmTevHmN5pj2hWTg5+fnOp/jm2++oby8nE6dOllVsluda/zl5eWsX7+e6OhoFi9ezLx58xg7diyzZ8+mU6dOjWoG8pe2gU8//RSo3QbqTszeunUrMTExrkPKjcHZMrj11lupqKhg5cqVDB06lNatW7Nw4UKys7PZuXMn3377Le3bt3c939td6HtB06ZNqa6u5tChQ0DjusruXBlUVFSwbt06/P39OXbsGAcOHKCkpIQ9e/awceNGLr/8cosr94HGxel00rp1a6666ioyMjJcjyclJREbG8uBAwdcN5sqKyvj3nvv5ciRI0yePJkPPvigQbxIl+pCMqi7giI7O5s5c+bw6KOPkpqayoIFCyw/nukOF5LBiRMnKCgoYPny5YwZM4bx48eTkpJC165dLazcPX5p/HFxcWzfvp3w8HDmzZvHW2+9xc0330xwcDCTJk1yXWXj7X4pg1atWrneC4qKivj8888ZM2YMTz75JP37928053z9UgatW7dm27ZtBAUFMXbsWOLj45k/fz6PPfYYAwcOpE+fPtYV7kYX+plQd/nz1Vdfzc6dO4HGc5Xd+faFuoZm6NChZGVlkZ6eTnp6Oqmpqa4ZGEuZRs7hcBhjjFm3bp3p16+f2b17t+v/srKyzKBBg4zT6TTGGFNaWmrmzJljqqurLam1vlxIBnWeeuopM2vWLJ/O4P333zevv/56o8rgfONPSUk57evr9onG5GK2gfnz5/vkfnDmdnDs2DFTU1Pj0Rrr28V8JhhjzPHjx30ug4EDB7r+XVZWZr7++mvXcxoCr59xWb16NSNHjnSdYFnXJRtjcDqdrg65Y8eO3HTTTfz1r391PTcxMZGgoCCOHj0KQHBwMCNHjvS6Sz/dkcGRI0cAePrpp3nooYd8MoO649h/+tOfGDFihFdlcKnjDw4Odu0H4J2HA9y5Ddxzzz0+uR+cuR1ERkZ63aFid2wHx44dcz3WrFkzn8sgJCSEwsJCAIKCgrj22msb1GyTd70ap6ipqWHp0qW8/PLL+Pv7u06ordvAbDYbfn5+bNu2jdGjR7Njxw5Gjx7NyZMnGTduHO+88w5jx46lY8eONG/e3Mqh/GruzKBFixYAXncSYn1k0JB20PNx5/i99X419bENeFvDou1AGYDvfC7ajPHOU8QdDgfZ2dlUVVURHh7OG2+8QXJysuvkWrvdztKlS8nIyGDAgAE8/PDDQO26RF9//TVfffUVXbp0YdiwYdYO5BIoA2Xg6+MHZQDKAJQB+FAGVh2j+jU+/vhjs3v3blNSUmKMMa4/q6urzcKFC01aWpo5duyY6+v3799vysvLXf8+9biltx6zVAbKwNfHb4wyMEYZGKMMjPHNDLxixmX37t2MGzeOpk2b0rx5cyoqKpgxY4brvgJQu6Lp3LlziYuLIz09/bRbUzscDux2u1cet6+jDJSBr48flAEoA1AG4NsZeMU5Llu2bCEpKYlFixbx0ksvERgYyBtvvOFaAAwgPj6e5ORktmzZwv79+/Hz8+PEiRMAXvvinEoZKANfHz8oA1AGoAzAtzPwisZl7dq1rnuJBAYGMmbMGAoKCli7dq3rLqf+/v5ce+21JCUlMWfOHKZMmcLzzz9PRUWF1744p1IGysDXxw/KAJQBKAPw7QwadONSd5fCpKQksrKyXI9fccUVXH311Wzfvp2DBw+6Ho+OjubQoUOsXr2a4uJiHn/8cdddAb2VMlAGvj5+UAagDEAZgDKABtS4lJWVAaffUrnustQbbriBwMBAVq1a5fq/fv36sWfPHtf19nXH9zZv3syCBQt46aWXaNasmecG4AbKQBn4+vhBGYAyAGUAyuBcLG9c8vLyGDZsGE8//TRw+j00qqurAWjdujXdu3dnyZIlrsXPoqKiiI6OZuPGjUDtVNnw4cP55JNPXAsEegtloAx8ffygDEAZgDIAZXA+ljYuL7/8MkOGDKFz58688MILrsfrLnSqW+Dw8OHDJCcn4+fnxzPPPENlZSUOhwObzUbPnj1dz6u7eZQ3UQbKwNfHD8oAlAEoA1AGF8Sq67BXrlxpbrvtNvPBBx+4HisrKzvta5YuXWquv/568+KLLxpjjMnJyTF33nmneeSRR8wtt9xixo4da0pLSz1atzspA2Xg6+M3RhkYowyMUQbGKIMLZdl9XA4fPszChQsJDg7mmmuu4a233qJFixZER0czdOhQbDYbU6dOZfDgwfTo0cP1vOLiYg4fPozD4aBLly5WlO42ykAZ+Pr4QRmAMgBlAMrgQnmkcSkqKiIzM5PbbruNNm3auB7/9NNPmT9/Prm5uQwfPpzY2FjefvttOnbsyMiRI11TXOaMhaG8kTJQBr4+flAGoAxAGYAyuBT1vpLYF198wSuvvMKBAwew2+3cddddroX8evToQXl5OUlJSa7r0Vu1asXbb7/N3r17adGihetOf9784igDZeDr4wdlAMoAlAEog0tV7yfnHj9+nDvvvJO//e1vrFy5kn379rn+LyQkhJtvvpm4uDjXWdGJiYl8/fXXVFRU1BboZcuJn40yUAa+Pn5QBqAMQBmAMrhUbp9xyc/Px2azER0dDcDvf/97ysrKiIqKYtWqVSxbtoz4+HhCQ0OB2hcJICAgAIAdO3bQvn17EhMT3V2axygDZeDr4wdlAMoAlAEoA3dzW+NSXV3N5MmT+eabb2jZsiW9e/fmjjvuIC4uznX5VlpaGk888QQ33ngjPXv2dN1yuKSkhOzsbFavXs1//vMfHnroIVq3bu2u0jxGGSgDXx8/KAN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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "week_mean.plot();\n", - "week_median.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "813f08f7-51aa-4413-92da-8455bc854aed", - "metadata": {}, - "source": [ - "### Generic Predictor" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e5df7b11-4c2d-4b26-9b9c-f1bf521b6300", - "metadata": {}, - "outputs": [], - "source": [ - "model = on.context.common.GenericPredictor()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "402c4d6e-dd2c-4920-9736-eee98fa3ab32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(train)" - ] - }, - { - "cell_type": "markdown", - "id": "eac89dd3-f5ac-42f4-9db0-29e07328151b", - "metadata": {}, - "source": [ - "What does the future looks like ?" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "33c52d1f-6983-4edb-945b-9578d954f738", - "metadata": {}, - "outputs": [], - "source": [ - "pred = model.predict(48)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c10dd2d7-7a48-4343-b18a-152175dcd799", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.plots.prediction(train[-96:], pred, test[:48])" - ] - }, - { - "cell_type": "markdown", - "id": "1eacbd84-bb31-48b1-bd5b-6ab7db392204", - "metadata": {}, - "source": [ - "## Generic Detector" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9715fd4d-24d8-4d95-a77a-3c347916f2aa", - "metadata": {}, - "outputs": [], - "source": [ - "model = on.context.common.GenericDetector()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "56eae67d-ae1a-438c-bbd4-e1b52c93c2c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.fit(train)" - ] - }, - { - "cell_type": "markdown", - "id": "cad58ac3-bc19-4281-8975-d31b13e5b7f5", - "metadata": {}, - "source": [ - "Does the current signal has problem ? " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3fcf48b4-6d60-411d-88e5-191d575967a7", - "metadata": {}, - "outputs": [], - "source": [ - "detected_test = model.detect(test)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "317ef652-cfa1-4d85-b873-a1944c13fefc", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.LayerChart(...)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.plots.anomalies(test[:72], detected_test[:72])" - ] - }, - { - "cell_type": "markdown", - "id": "2eef8370-d1d7-47dc-ac68-91fa9d375646", - "metadata": {}, - "source": [ - "What if we want to have an idea about the future problems ?" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "0e31b255-cf60-4470-910e-31fe826b9782", - "metadata": {}, - "outputs": [], - "source": [ - "predetected = model.predetect(72)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "fe406b99-9025-4553-8c3d-0237a670e513", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.LayerChart(...)" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.plots.anomalies(test[:72], predetected[:72])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/docs/0.1-time-series-custom-class.ipynb b/notebooks/docs/0.1-time-series-custom-class.ipynb deleted file mode 100644 index 5f3a8b8..0000000 --- a/notebooks/docs/0.1-time-series-custom-class.ipynb +++ /dev/null @@ -1,799 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "670316b8-460c-4009-a5da-94278f4ac9a9", - "metadata": {}, - "source": [ - "# Time Series, Custom Class" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "52af59bb-083c-46c6-989a-bd4c65137a1a", - "metadata": {}, - "outputs": [], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d6fc731f-3f50-4e9a-a24c-b2ab01d4fa31", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import ontime as on" - ] - }, - { - "cell_type": "markdown", - "id": "831f1944-599b-4761-a071-2a682346610a", - "metadata": {}, - "source": [ - "---\n", - "## Generation of random time series" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ef3e03e1-c247-4b5a-a27a-a13361e673b0", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "01962643-33af-4adf-8bfa-7d0163e4e41c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    static_covariates:  None\n",
-       "    hierarchy:          None
" - ], - "text/plain": [ - "\n", - "array([[[ 0.2723568 ]],\n", - "\n", - " [[-1.5569272 ]],\n", - "\n", - " [[ 0.00838568]],\n", - "\n", - " [[-2.1595223 ]],\n", - "\n", - " [[-2.14411014]]])\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n", - " * component (component) object 'random_walk'\n", - "Dimensions without coordinates: sample\n", - "Attributes:\n", - " static_covariates: None\n", - " hierarchy: None" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ts[0:5]" - ] - }, - { - "cell_type": "markdown", - "id": "0cbd8da5-81fd-4b2d-8b7d-8394ad87348b", - "metadata": {}, - "source": [ - "---\n", - "## Use `TimeSeries` object" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "1cbbd4f4-035d-43fa-93a7-6801b944835f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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L3NxcAFVVng8++MBpz0FE5I0YTCgg6AWT3bt3K8e5ubnYvn07gL8rJrZW5ACum/z6z3/+E5988gnS09NV1584ccJpz0FE5I0YTCgg6AUTra+//hqSJClLie2pmMTFxSnb0DtzKOe7777TvT47Oxu5ubnYsmWL0l4iIn/CYEIBwZ5g8tVXX6GoqEj5srcnmAQFBSEuLg6Acysm4tb2BoMBbdu2VS43adIEAwYMwIgRI7B8+XKnPScRkTdgMKGAIAYTcV6I6OTJk/j555+Vy/YEE+Dv4RxnVkzEkLN+/XqMGzdOuSzPOQGq5skQEfkTBhMKCGIwGTNmjMX7LV26VDm2Z44JANSpUwcAkJOTg5KSkmq28G9FRUXK4/Tt2xeDBg1C06ZNde+7adMmlJWV1fg5iYi8BYMJBQQxmEyaNEl1W/fu3ZXjzz//XDm2t2KSmpqqHJ8+fbq6TVSI1ZLExEQAsBhMCgoK8NNPP9X4OYmIvAWDCQUEMZg0bNhQtZqmZcuW6NixIwCozqFTq1Ytux67cePGyvGpU6dq1lAAly5dUo7ldmqDiRxYAGDdunU1fk4iIm/BYEIBQQwmsbGxqiGbKVOmoFOnTmY/I09qtUWsmGiX91aHWDGRg4kYRADgpZdeUo4PHDhQ4+ckIvIWDCYUEORgYjQaER0djYEDB2Ljxo1Yv349+vXrh/j4eLOfsTeY6FVMajLXRC+YyEuSZeJJCMWTDhIR+ToGEwoIcjCJiYlRvuRvuOEGDB48GIB+CKluMBk5ciTi4uKwcuXKarVVb44JAHz66aeoXbs2nnrqKdSrV085jw+DCRH5E54rhwKCHEzE/UFENQkmjRo1Uo63bNmC8+fPAwBuu+22au3UqjfHBADGjx+PcePGKcEqKioK+fn5qnkxRES+jhUTCgi2goneUI6l+2pFRkaibt26AKCEkprQG8qRiUM6kZGRAFgxISL/wmAS4CRJwty5c/Hcc885ZQ8Ob1RaWorS0lIArqmYAOrhnJqyFkxE8nJmVkyIyJ9wKCfAffHFF5gxYwYAoFmzZpg8ebJnG+QC2hU5emoaTFJTU/HLL7+YXV+dc9lYmmOixYoJEfkjVkwC3Msvv6wcv/XWWx5sieuIW7hbChs1WZUDWK6Y5Ofn2/0YMjmYGI1Gq22QKyYlJSUwmUwOPw8RkTdiMAlwhw4dUo5bt27twZa4TnUrJjExMXY/h6VgUp0T+8mTX+Pj42E0Wn6LijvTcjiHiPwFg0kAKywsRGVlpXJZXn7qb+wJJtqKSa1atRzqD0vBRFxhYy85zFibXwL8PZQDMJgQkf9gMAlg4pl0gaqT0Pkje4JJVFSUKog4MowDqHd/FTkaTMrLy5XhH1vBRKyYcJ4JEfkLBpMA9v3336suZ2dne6glriX+XrVr19a9j8FgUIURZwUTR4dyxLZam/gKqCsmDCZE5C8YTALYl19+qbrsrxWTixcvKsfWvuzFCoS9e5jIoqOjdR/b0WBiaXM1PZxjQkT+iMEkAEiShIMHD6KsrEy57siRI2Ynf/PXion4ZV+nTh2L9xM3L3Nk4qtMr2ri6FCOvXuYAKyYEJF/YjAJAC+99BI6dOiA/v37K/tqrFixwux+ubm5frns1N6KiRhMgoMd3+JHbwKsoxUTR4IJKyZE5I8YTALAzJkzAQA//fQTrly5AgDYsGGDcnubNm0AACaTqVr7bng7sWpha96GrDobozk7mNhqKye/EpE/YjAJMHIwkasIcXFx6NChg3K7P84zkYOJ0WjU3UhNJlZMqhNMnDGU48gcEy4XJiJ/xGDi58R5JcDfS2fFk9qJK1X8cZ6JHMISEhKsblgmBpPq6NKli9l1WVlZDg2PVXcoR6yYXL58GcuWLfPLf0si8n8MJn5Oe7ZbvWAiVhH8uWJibeKrM/Ts2ROLFy/G3LlzMWzYMABAcXExjh8/bvdjVHfyq1gxmTBhAsaOHYvbbrvN7uclIvIWDCZ+7syZM6rLeXl5qrPtxsTEqCom/hZMiouLlWqCrTkbs2bNUo6feOKJaj3fpEmT8PDDD6Nr167KdTNmzMAHH3xg1/BQTeeYFBcXY/369QCATZs2OdR2IiJv4JJgsnLlSowfPx7du3fHBx98oLrtm2++wdChQ9G3b1+8+OKLKC8vd0UT6C/aYJKfn6+a4KqtmPhb+d+Ria+jRo3CkiVLsGLFCvTp06dGz9u5c2fleP369bj33nuxatUqmz8nttfSZnAyveXCv/32m+o+4ikHiIh8gUuCSWJiIu655x70799fdf3x48fx1ltv4Y033sDatWuRlZWFBQsWuKIJ9Be9iol2i3Z/rpjYu4cJUDU59o477sCtt95a4+cVg4ls/vz5Nn9OrpjUqlULoaGhVu+rt1x4z549qvvk5uaiqKgIaWlpNp+biMgbOL5Zgx369esHoGp5qmj9+vXo378/2rVrBwCYMmUKZs6cifvuu0/3ccrKyswmbwYHB9v8wK4OeYKiv+3jkZmZqbqcm5urCh8xMTGqXU4vX75ssQ98sY+ysrKU44SEBLe1vX79+mbXnT592ubziyfws3XfiIgI5bigoAAmkwm7d+9W3ef8+fMYNmwYTp06hU8++QTjxo2z91dwK198bXka+8x+7CvHuKq/rC0+ELkkmFhy4sQJdOvWTbncvHlznD9/HkVFRaqytGzRokX46KOPVNeNHj0aY8aMcVkbMzIyXPbYnnDs2DHV5dOnT5tdV1JSohy/+eabiI6Oxh133GHxMX2pj/744w/l2Gg0Ij093W3PHR0djYKCAuXy8ePHsXv3btStW1f3/iaTSRlKq1Wrls22isNuly5dQnp6Onbu3Km6z4IFC3Dq1CkAwMSJE9GrV6/q/Cpu40uvLW/BPrMf+8oxzu6vJk2a2HU/twaT4uJiVfk5OjoaACwGkzvvvBPjx49XXefKiklGRgZSUlLsTnW+QLthmsFgUP2lnZKSolSwZP/5z3/w3HPPmT2WL/aRuAS4ZcuWFk+25wozZ87E448/rrru5MmTqomxouzsbOUvlPr169tsq/z+Aar2XUlISDAbsikuLlZddufv7whffG15GvvMfuwrx3i6v9waTCIiIlT7Lch/TeqFEgAIDQ11SQixxmg0+tULV2/yq7zJGlC1wVpKSgratm2Lw4cPA6iah2KtD3ypj8Tt6OvWrevWdj/00EOoU6cO8vPz8eCDDwIAduzYgbFjx+reXxxiS0xMtNlWMZgUFxcjPT3dbOWPdjKzt/+7+dJry1uwz+zHvnKMp/rLrc/YtGlT1Z4OaWlpqFevnsVgQjUjSZJdk1+NRiN27typnB+mtLS0WjufepuLFy9i4cKFyuWUlBS3Pn9QUBCuu+46jBw5UrnOWmnUkT1MACA8PFypCBUWFpr9WwNQhnGIiHyFS4JJRUUFSktLYTKZUFlZidLSUlRWVmLIkCHYsmULjhw5goKCAnz88ce46aabXNEEQtWcBu1W5XrBBKiaBNuzZ08AVYGmoqLCfQ11kaeeekrZYG7w4MHKOYHcTVyOnZuba/F+juxhAlQNU8mhvqioyGyiM1A1r4uIyJe4JJgsXLgQvXr1wurVq/Hxxx+jV69e+Pbbb9G8eXPMmDEDjz76KIYOHYo6dergrrvuckUTAtbPP/+Mbt26oVmzZmjdurXZ7fn5+brBBADCwsKUY3FCrK/asmULgKpltYsXL67xlvPVFR4ergxJWgsmjpwnRybP2bJUMdGeRNAf/l2JyL+5ZI7JtGnTMG3aNN3bbr75Ztx8882ueFoC8Prrr5stGRXl5eWpJsTGxMQox+Hh4cpxaWkpatWq5ZpGukFFRYVSQWjVqhXq1avnsbYYDAbExcXhwoULdldMHA0mRUVFusFE7zmSk5PtemwiIk/gLCA/o1fOB6omuQKWh3IAdcVE3rLeV2VmZiq7njZu3NizjcHf/e+qYFJQUGBXMHH0bMdERO7m1lU55HqWvnhSU1ORm5vr10M5kiRhzZo1aNiwoep39KZgkpeXB5PJpDvT3dE5JoC6YmLPngPaoR0iIm/Diomf0fviGTBggDJkU1paigsXLii3WRvK8TUfffQRhg8fju7du+OHH35QrvemYCJJkmq5tqg6c0zEJcPyxnmpqakICgqy+RxERN6IwcSPlJeXK/NH2rVrh1atWiEiIgLPP/+8qjIi/2UdHR2t+gLz9aEceV5TeXk53n//feV6b9hUTA4mgOXhnOoM5YjBRP43S05OVq0EsvQcRETeiMHEj4hfOk2bNsXhw4dx8eJF9OnTRxVM5L+axWoJ4NtDOUePHlVdPnfunHLsTRUTwHYwCQsLs3tvHzGYyBo2bGgxmLBiQkTejsHEj2jnKBiNRmUOghhMZNrrfHkoZ+nSpRZv87WKSWJiot1Lm8VTPMhYMSEiX8Zg4keszVEQvxhl2mDiy0M5Gzdu1L0+Pj5eN5S5m9j/4tbzMkmSlH8/e4dxAP2KSYMGDVgxISKfxWDiR6yt6ujQoYPZ/Rs0aKC67MtDOadPn9a93huGcQDrFZOjR49ixIgRKCsrA1DzYJKYmIjatWvr3p8VEyLydgwmfsRaxaR3795m9+/Xr5/qsq8O5VRWVqrmlIg6duzo5tbosxZMZs6ciW+++Ua5XNNgEhsbi6uvvlr3/unp6Zg+fTqefPJJZZ8XIiJvwmDiR6yt6mjYsKHZXIv+/furLvvqUM6FCxcsfsk+/vjjbm6NPmvB5H//+5/qsr17mAD6wSQuLg433nij7v2PHDmC+fPn44033sD69evtfh57nD9/Hvv27XPqYxJR4GEw8SNixUTvy6179+6qy23btlVd9tWhHHHH04YNGyrH/fv3R7t27TzRJDP2nsgPcKxiojf5NTY21uzfVs9vv/1m9/NYU1FRgWeeeQapqam45pprVEu1iYgcxWDiR2ztg9G+fXvluGnTpmYrP3x1KEcMJnfddRfGjx+PIUOG4NNPP/Vgq9SsVUy05/HRm6hsiaWhHIPBoHrciRMnmt3Pni3s7bF69Wq89tpryhyZrVu3OuVxiSgwMZj4EVtbmt99993K3iUffPCB2e2+OpQjnh8oNTUVn376KdatW4f69et7sFVq1oKJdifY4uJiux/X0lAOAHz55ZdK+HzsscfwxhtvqO534sQJu5/Hml27dqkui6cDICJyFM+V40fEoRy9VRn169fHiRMnkJ+fjyZNmpjd7qvBRPzL31vPnGspmJSXl6OwsFB131tuucXux9ULJnL47NGjB/bv3w+j0Yh27dqhY8eOGDFiBFq2bAkAOHnypAO/gWW//vqr6rKtoSoiImsYTPzE3Llzlb9c4+LiEBys/0+bkJBgcQ6DP8wx8dZgEh4ejrCwMJSWlqq+uMVjo9GI77//3q75ITJtMImKikJISIhyWbtMvEWLFmjXrh0OHTqEU6dOWTyhoL0kSTKbq8JgQkQ1waEcP3Dy5EnMmDFDuezI5EmRP8wxESe/ehu5apKdna1cJ262Nm7cON1l3dZoJ7/as5mcXC0rKyuzuMzaXpmZmarfB2AwIaKaYTDxA+fPn1ddvv3226v1OL4+lBMVFWV2/h9vUrduXQBAVlYWJEkCoP4St7RbqzXaiok9waRp06bKcU3nmWiHcQAGEyKqGQYTP1BUVKQcT5gwAS+//HK1HsdXh3Lkya/Jycl2n2PGE+TJuGVlZUqlRKyYOLIaR6YNJvY8hji/qKbzTPSWHJeWlvrU64eIvAuDiR8QJ0+2bt262o/ji0M5paWlKCgoAAAkJSV5uDXWiauE5CEUMZhUp2ISERGhuuxoxaSmweT48ePKcfPmzZVjrswhoupiMPEDYsVEb8Mte/niUI4YyvRWqHgTvWAiDntUp2Kinbhqz2OkpKQox2fPnnX4OUX5+fnKsViJ4XAOUfVcvHhRGeoNVAwmfkD8co6MjKz24/jiUI74u9cklLmDuOGZsyomWvZUTMTnqWllQ65WAeqJxwwmRI577bXXULduXYwfP97TTfEoBhM/4KyKiS8O5YhfjN4eTFwxlKNlT8XE2mZvjhI3hxPPVs1gQuS4Z555BgCwdOlSmEwmD7fGcxhM/IAYTJxVMfGVYBLoQzla9lRMYmJilEnCzgom0dHRqk39GEyIasaRHaD9DYOJH3DWcAaHclzLHRUTe8KZ0WhErVq1ANQ8QMgVq1q1aqmCFSe/EjlGWyERq8GBhsHEx0iSZDYxylkVk+DgYAQFBQHwnYoJh3LULO34qyWHCGdWTJw5REQUaMTPAgBmp6oIJAwmPiQnJwcdO3ZE+/btVefFcWbVQK6a+Eow8aWhnKioKKVSoR3KCQoKckr75WBpi61gYjKZ8N133+HgwYMWH0OSJCWYaCsmDCbkbc6ePYvp06djxYoVnm6KrosXL6ous2JCPuGFF17AgQMHcPjwYTz33HPK9c6qmAB/BxMO5biGXDXRVkzi4uKcsjmcoxUTS5uhLV26FIMHD0bXrl1VW/6LSktLUVlZCaAqFIrzWxhMyNvMnj0b77//PsaMGWO2W7Y3YDD5G4OJD/n555+V4z179ijHzvxyllfm+GLFxJeCSUFBATIyMpQv8JpMfH3wwQeV4+uvv96un7FV3bj33nsBVAXUuXPn6j6GuCKHFRPydl9++aVyLJ/wVKukpARHjx71yD4iDCZ/YzDxIeLkKLFk74qKia8EE1+aYwIALVu2VI579OihnACvJuf4efnll/HSSy9h9erVqk3OrLEVIsTXl7zlvxaDCfkqvVMpmEwmDBw4EK1bt8bMmTPd3qYLFy6oLjOYkEtduXIFGzduVAWI6pDL5oDrg4kvDuV4+xwTAHj66aeRnJwMQL3rqjz3pDpiY2Px3HPPYcSIEXb/jK0QIU7UtbQ7rPjBqR3K4aoc8ibaiaVixVm2adMmbN++HQDw0ksvuaVdss2bN+Ott95SXcdgQi510003YdCgQZg+fXqNHqeiokI5FoOJ/OVsNBpVS36rg0M5rtW0aVO89tprZte7O1SJwSQrKwsvvvgiPvjgA+U68bxD8nwYLW3FJDg4WPk9WDEhb6I9i/aePXvMhmvee+89iz9/4MABTJ06FVu2bHF62w4ePIhBgwbhzz//VF0fyMHEvplyVG2SJOHHH38EACxevBgLFy40O7+JvcQNd8QvBbliEhkZWeMJlOJQjiRJXn22XsD3hnIA/U3QalIxqQ4xmDz77LM4fPgwAODqq69G165dVdW5c+fO6b4WtMEEqFryXFBQoFo1RuRpaWlpqstZWVnIzMxUzhuVnp6ONWvWKLeLmwUCQL9+/ZCdnY0FCxbYnH9SUlKCoKAghISE2NW2//znP7q7vAZyMGHFxMW0QyJHjx6t9mOJY5B6y4Wd8cUsBxNJklBeXl7jx3M1X6uYAPohxJPBRA4lALBq1SoA6tBRUFCAy5cvmz2GdigHAOrWrQsAuHz5ckBvqU2el5OTgx49eqBr165YsGCB2e379u1TjpctW6YKHNpQIM8FA/6uXB86dAiPPvqoaljo1VdfRUxMDMLCwnDHHXfYNYlWPEO3iMGEXEb74hJX1jiipKREdSbXS5cuKS96sWJSU752vhxfm2MC6IcQTw7liOrUqQNAHUwA4NixY2b31auYyMGksrLSbFyfyJ0+//xz/Pzzz9izZw82b95sdru4ZHj58uWq28rKyix+/smv+wkTJmDOnDkYPXq08lk8d+5clJeXQ5IkfPLJJzh48CDS0tIwd+5c3SXKFRUV2LFjh+7zMJiQy2g/4H/55ZdqPU5WVpbqcklJiRJIXFExAXwjmPjiUI7eChxPVkxEcoVP+7rVjn8D6r7XBhPAfJUBkTvprbwRyVXAtLQ07N271+x2eQK3tvJ35coVZGRkKI9/6tQp/PHHH6isrDQbwty0aRP69u2LGTNmYMqUKWbPsW/fPosBhMGEXEb74qpuMNFL2xcvXoQkSU6tmPja+XLEiokzfn938OaKiVzl0L5uT548aXZfMbzI7ZcrLgCDCXnWgQMHrN4uBxNxbolIDibakJ6fn4+NGzeqrtuyZQtyc3PNQsyjjz6qbFC4bt06s+eQ5x/q4Zb05DLaD/j9+/dXqxKhrZgAVcM5YngIxIqJ/OYNDw+3ezt2T/PmiklOTg4qKirMzmyqN8fE2lAOwGBCnmMymZRg0rBhQ/Tq1QsA0L17d+U+8mtaDN2dOnVSjuVgoh2SvHLlCr777jvVdVu2bLFrwrd2zsmpU6cs3pcVE3IZbdquqKhQTaSyl6Vg4uyKgTjHxBcqJvKb11fmlwBARESE2cosbwkm2dnZuh+Itia/MpiQNzlx4oRSSe7SpQu2bNmC1atXq+aSyK9pcZ+e1q1bK8dyMNEufc/NzcWmTZtU123dutWu17v2+0D8XF+2bBmeeeYZ5TKDCbmM3ourOptPWQom4uZqzqiYiOGmphvCuYMz59e4i8FgMKuauDtYWdppNicnx+zDE7BdMdGuygEYTMhzxGGcDh06IDg4GFdddZVqfx75NS3u09OqVSvlWF5soK2Y7N692+z9kJOTg507d9psl3bbefFz/eabb8a///1vZVk+gwk5XWVlJebNm4d58+aZ3easYHLx4kWnV0zEL0hfeGP4YjABzCsk7q6YBAUF4aqrrjK7Picnx+6Kia2hHO2HMJG7/P7778qx+DoPCQlRQrm2YhIfH68KLnl5efj999/xxRdfqB5769atus8pnon79ttvB1C16WXjxo2V6y0Fk1q1aiEiIgIGg0H5LPOFz19XYTBxkc8++wwPPPCA7ou4OsFEb+mlKyomvhRMTCaT8vv70lAOYF6x8ET7169fj6effho9e/ZUrnOkYqI3lMPJr+QNxOXtbdu2Vd2WkJAAoOo1LUmSUjFp0KCBavPD33//HVdffbXZH5fbtm1TjgcNGqQci/sB3Xrrrdi5cyd+/vln3HXXXcr1loKJGIjkzwJv//x1JQYTFxHHCrWqs1233gztQK+YFBcXK5PJWDFxXP369fHqq69ix44daNOmDYCqOSbVGcqR+5/BhLyBGADE8z4BfweTnJwc5OTkKBO9tcFkzpw5qh2Q9QwdOlQ5PnTokHKcmJiIHj16oEuXLqr3hNiu0tJS5buAwUSNwcQDqlMx0Qsm58+fd2nFxNuXq/nirq8ybRDxdMUnPj4eQNWHoV517sqVKygrKzO7DqiazBscHKwcy78bgwl5irxCJigoyGyitxxMTCYTjhw5olxfv359h87y3bRpU9WcFHGxQGJionJsKayLxwwmagwmLmJtK+LqBBO9F2lmZqZTzywM+FbFxJeDifYD0BMVE5EcTAAgIyND9z7iajKxBK794JfnmTCYkKfIlYnExESzczzJwQRQT5LVVkxs6dKli2pOlchSMBErJuK8Qb1gUlZWZvbHQKBgMPGAmlRMwsPDlRNPnTlzxulfzr4STIqKivD4448rlz1dcXCUt1ZMAOD06dPKsfihLg7npKWlKZc7d+6seiz5wzonJ8cnzrdE/kWSJKViIoYCmRhMxAmr9evXdyiYXH311RaDiXgSwOoGE8D7q9auwmDiATUJJlFRUWjYsCGAqr9Ixb9iA6lisnjxYuWEc4DvV0zsPROpq4gfpGIwEVcUiMFk165dyrE4eRbgkmHyrMLCQmVYRaxcyJxVMWnatKluMImLi1OGNoGaBRNv/gx2JQYTF5HPQKmnJpNfxWACAN9++61ynJyc7PDjavnKm0JcDggAqampHmpJ9Xh66EbLUsXEnmDSo0cP1WM1atRIOdbbyp7IlcQdWB2tmDjyvkxJSUFoaKjZUKY2DMXHxyu7Uq9fvx6jRo1CaWkpKyZWMJi4gCRJVs+s6qyKCQBla+TQ0FBce+21Dj+ulq8EE3Er52nTpmHq1Kmea0w1ODLJzh0sBRMx8InBRN5MymAwoGvXrqrHatasmXKclpbm9LYSWSNWJWwFE7Hi3KBBAwQFBdk9rCoPqWurJtpgYjQaVdetXr0aW7ZssRhMxOqv3gq5QMBg4gJ5eXlWKyaOBhNJkpSQoA0msp49ezplOEN8DG8MJqtXr8bIkSOxYcMGAFXt/c9//uN1FQhbvK29YjARh1/0KibFxcXYv38/gKo9IrTl76ZNmyrHDCbkTg8//DC6deumXLY1lCOSQ4w9wznBwcGoV68eANvBBDDfIDMjI8NiMBH/aJF3nw00wbbvQo7S2/EyOjoapaWlKC8vdziYlJSUqPbr0AsmN9xwQ/Uaq9NOmbcFE0mSMGrUKNV1jRs3Npt17wu87UzIYjAR6QWTM2fOKPs7dOjQwexnWDEhT8jMzMQ777yjuk6vYiLOpxLJ78nY2FjljMCWyNUVQB0qgL8rKaJGjRqpKpHFxcWqrfDFxxCHhqoz7O8PWDFxAb2zTJaWlipJ/LfffsNDDz1k94tOHGeMjo7WDSYDBgyoXmM1vLliohfoxC9OX6I9iZ+nyX/9aekN5YhDOnp/HTZp0kQ5PnHihLOaSGSV3ueD3utTb6hGPLGmpRNcisTwoa2YjBgxwuz+Dz30kOpybm6usodKUlKSqk3i81dn2N8feNeno5/Qq5iUl5erSoTvvvuuWbq3RLskWBtMUlJSzMb5qyskJARhYWEAvC+YiH9hyHw1mHgb8ayqIjGYyEM84ri83l+fERERykRsVkzIXfTmY+hVTPSGvMUKpqWQLmrQoIHuzwJA//79ze7/2GOPYcGCBcrltLQ05f3Url071X3F7wlWTMhp9ComgHkSf+GFF+x6PG0w0W6x/Pjjj6uWp9WUnN69bUa4XjAR/zr3Jd42lFOrVi2zwJuUlISUlBRlzHv79u0oLy9XVUwsjdfLwzkXL14M2Al85F568zH0KiZ6wUS8TgwdQNUfP3v37lV9xoqf5REREcpxq1atLC79F1ev7dixQznWnsuHQzkMJi5h6ayq2klV9q7M0AaTkJAQ1QRD8SRRzuCtWyL7U8Vk2LBhSlXh888/93Brqmg/ILt164bg4GDcdNNNAKo+JL///nubFROAE2DJ/eytmOj9USBep912oX79+rj66qtVCxrE8DB58mRERkYiPDwcK1assNg+8fNffE9oKyYcymEwcQlLFZPqBhMxIMjJft68ebj55puxceNGp28uxmDiemFhYThy5Aj++OMP5RTpniafyE8mr24QJxx//PHHDlVMAM4zIffQq5jovT7DwsLM5niJwURbMdFbpSNe16xZM6SnpyMzM1N3MrjM0twVDuWY46ocF7BUMdGePycrKwsmk8niRMhLly6hoqJCd9v5IUOGYMiQIU5qsZo4lGOtfe7mT0M5QNXwiXgSME/Tq5gAwI033oiwsDCUlpZi6dKlqvtYqpiIw0Lnz593ckuJzOlVTOT5ciKDwYCoqCjds2MD5hUTOShMmjQJS5YsAWC+2EBvyEgrKioKQUFBZmcs5lCOOY9849xzzz249tprcd111+G6664zm7Hs68RSt6xp06Y4fvy46rry8nKz9e2yb7/9Fo0aNUJycjJ++ukn5Xp3bL0uBxNJkpRTgnsDbTB56qmnLH4xkuO0H5BdunQBUPV6uOeee3R/xtaeEIDloE7kTNpgMmnSJIv31X6O2lMxee211zB58mTMnj3bbLdjexgMBrPqS1JSktl7SLxPoA7leKxi8s9//hNDhw711NO7lPgG6d+/P44dO4YVK1bgX//6l2oLZKBqox3tZNbdu3cr4/oA8L///U85dsfJ3rR7mXjLeWjOnj2rHOfn53vdJmW+TjuUI4a+2bNnY+fOndizZ4/F+4gYTMjdxKGcBQsWYMqUKRbvq/1Ms1YxkYfc69Wrh0WLFtWojbGxsao/XPUqpuHh4QgNDUVZWRkrJuQ88twMo9GITZs24fTp07j66qvx8ssvm6VjcdMd2cyZM1WXxe3X3RESvHUvE7liEh0dzVDiAgkJCbjllltgMBgwZ84c1W0hISGqszkDVa9vS+PmPJEfuZv4B2Hnzp2tbrxorWKi/Wxx5MR+tmjfL+J5pWQGg0G5X6AGE49VTN566y289dZbaNmyJWbMmIEWLVqY3aesrAxlZWWq64KDgxEaGur09phMJtX/a0J+g9SqVUuZVyJJEtq0aYOMjAysWLFCKTOePn1a9ZynT5/G+vXrLT52RESEU9pojfimzc/PN+sbVz+/JXIwqV+/vsfa4AhP91d1LFu2DHl5eYiLizNrt/ZEifJusXq/nxjAL168aLMPfLGvPI19piYOe0RFRan6RdtXesHEUj/GxMQ4rY+1Iadhw4a6jx0bG4sLFy4gLy/PI/++rnpt2Ttf0SPB5KGHHkLTpk1hNBqxbNkyPPTQQ1i5cqXZi2XRokX46KOPVNeNHj0aY8aMcVnbMjIyavwYcsqNjIxEenq62e3ihKyDBw+q7vPaa69ZfTEUFBToPqYzic9//Phxs5TvjD5yVFFRkRL44uPjXd4HzuSJ/qopvbFteQtuWa1ataz+O0RERKC4uBhnzpyx+9/LF/vK09hnVcTKXF5enu5rTu4r7RdkRUWFxddoTk6O0z5vtH9UW/qOkPdGycvLw8mTJz22AMHZry17Fyt4JJi0b99eOZ40aRK+/vprHDhwwGxC0Z133onx48errnNlxSQjIwMpKSk1fhEUFRUBqCrbaf/KBNSrc/Ly8pT7PPvss/jwww8BVL1xjEaj2ckAmzZtqvuYziRO/oqOjlaez5l95Chx4nDjxo1d3gfO4Mn+coVGjRopq3OAqjF3a/8OdevWRXp6OnJzc23+e/lbX7kD+0xNXO3Stm1b1fCMtq+0Q+rWXsuhoaFO+7zR7irbqVMn3ceWh0IlSUJCQoLbz0bu6deWVywXtvSLh4aGuiSE2GpLTf4hxDMBR0dH6z5WSkoKDAYDJElCRkYGjEYjCgsLMWvWLOU+zz77LLZs2aLaIRCo+ivV1S8UcYy1qKjI7Plq2kfVIe6dkZSU5FMfxJ7oL1eJi4tTVpLFxcVZ/b3kYHLp0iVIkmRWcdHjT33lLuyzKvLkV6PRiKioKN05JnJfaRcRaD+rhwwZogypd+jQwWn9qz1ZZmpqqu5ji1Xq/Px8u87f4wqeem25/RmvXLmCXbt2oaysDOXl5fjss8+Qn5+vqqL4sqKiIqUiYmmCZkhIiJKI5XkT2dnZys8NHToUL7/8stnscMC9y4UB79mWXhxa8NSblNRj5OXl5VbvK6/MkSRJdwk9kTPJQ70xMTE2zzhubfIrULWqZ8CAAbj77rtVKyRrSvvZpXcmYu39AnECrNsrJhUVFZg3bx7S09MRHByMli1b4u2333bLMlh3EGeGW1s50qBBA2RlZeHcuXMwmUyqL1653Kd3FmF39JPYbm95U4j948xZ8uQYsaSst9OmSLsyR297cCJnERcd2KINItqgkpycjE2bNjmvcX/RfnZZ+iMr0PcycXswiY+PxyeffOLup3Ube4NJcnIyfv31V1RWVuLChQuqACC/WD1VMUlKSlKOvWXXTgYT7yAGE1sfmNzLhNxJDsr2zMewVTFxFe1nl6XKTqBXTDgw6WRiMLFW3RAnmJ49e1b3i1cvmLjjDSRu+Ka3DbwnMJh4h2effVY5/ve//231vmIw4V4m5EqVlZXKsLM9FRNPBRPtYgZLAj2YeMXkV38ibkhmayhHdvbsWVVZXP7i1Q7lhIeH2zWBsKYYTMiSAQMG4NNPP0VBQYHq5H56xKEcVkzIlez93JVZ2/nVlWzNfZEF+lAOKyZO5sgcE5m2YiKnZW0wET/oXal27drKaihxG3hPYjDxHuPHj8e0adNshmRWTMhdxM9dbx7KGTNmjPL5/uWXX1q8X6BXTBhMnKy6wUR88clfvKmpqejXrx+Aqo1pZs+e7dzGWmAwGJSqibsqJvPnz0dycjLee+893dsZTHyPGKQtnaySyBns/dyVeapiEhsbi+PHj+PIkSMYOXKkxfs5MsncH3Eox8mqG0zEk6HJX7wGgwGbN29GYWGh288NU79+fWUPirKyMpfuJ3Pp0iVMnz4dADBjxgw88MADZvcR35zu3myIqkecI3XmzBkPtoT8naOfD56qmABVp2uwdFZuWaAHE1ZMnMzeya/aD21LFQGj0eiRE9aJ80xc/deueNqBiooKlJSUmN2HFRPfk5SUhODgqr99uG06uZKvVEzsJX7GMZhQjdk7CSsxMVH50NYO5XjDBmLumgBbWVmJefPmqa7Tm9ciB5OgoCC3/nVD1Wc0GpUAzmBCrpSZmakce/OqHHs5sizfHzGYOJm9yd1oNCpf/paWC3uSGExcOQH21KlTZmV+vbK/3D+xsbF2z2wnz5N3tszOzlbOIUVUU0uWLMGbb76JoqIinD9/Hk899ZRyW5s2bWz+vC8Fk0CsmHCOiZM5UlJMTk5GRkYGLly4oEr83jCHwl0VE70N3GwFE/Id4pbbmZmZaNmypQdbQ/5g7969mDx5MgBgzZo1aNeunbLqa8iQIRg2bJjNx9AGE3efk82W0NBQhIeHo6SkJCCDCSsmTmbvHBMA6Ny5s3J84MABAFVvmJCQENc0zgHi5FxXBhO9+SvaYCJJEoOJjxKXvHM4h5xh//79yvH333+P+fPnA6haLLBo0SK7TjqnDSbeWIWV/0DlUA7VmCMVk+uuu87sOm/54nVXxcSeYFJcXKzsmOgt/UP2ESsmDCbkDJY+j9q1a6ecZ8wWbxu60SMHk+pUTEpLS1FZWensJrkNg4mTObIDoTcHE3ft2mnPUI63zb8h+2mHcohqylIw6d27t92P4QvBRP6sy8/PV848b48jR46gQYMGaNKkCdLT013VPJdiMHEyuWJiNBoRERFh9b4NGzZEamqq6jpv+eKNj49XjnNyclz2PHoVE+1kW+5h4rs4lEPOZimY9OrVy+7HcMepPWpK/qyrrKx0aOL4ihUrkJ2djYyMDNxwww0OhRpvwWDiZOKpt+0Zt9RWTbxhqTAAREREICwsDID7gwkrJv6DQznkbM4IJkDV/knXXHMN1qxZ44xmOV11V+YcOnRIOT5+/Dg6duyI3bt3O7VtrsZg4mRyMLE18VU2ZMgQ1WVv+uKVd6N1VzCRV2ycPXtWlfIZTHxXnTp1lBUPJ0+e9HBryB/IwaR27dq48cYbAVTNL2ncuLFDj3P33Xdjz549uOmmm5zdRKeo7iZr2s/rAwcO4MEHH3Rau9yBwcSJJEnCpUuXAEC1xbw1I0aMUF22NfzjTvJwjjuCSXx8PJo0aQKgauLW5cuXlfswmPguo9GItm3bAgCOHTvGvUyoRiRJUoJJ/fr1sWTJEnz44YdYu3atV66sqYnqbrKmN69k7969ujtqeysGEyfKyclBWVkZAPWqFmuio6NVS3PT0tJc0rbqkINJYWGh8ns5mxxMkpKSLC5RZjDxbfKyeJPJpCyLJ6qO3NxclJaWAqj6jK1Tpw6mTp1qNlfPH1RnKEeSJJw+fRoA0LZtW2W/l4qKCvz222/ObqLLMJg4kThp095gAkBZhw8AEydOdGqbasLVE2ALCwtRWFgIoCqY1KlTR7lNrjwB0D3zMvmOq6++Wjnet2+fB1tCvk78g8WRz1hfVJ1gcvHiRaUy0qhRI3Tr1k257ZdffnFuA12IO786kfimEf/6t2XEiBFYsGABzp07hwkTJriiadWiDSZicHAGcX6JNpiIS5TFY2e3gVyPwYScJZCCifhHmL1DOXK1BABSU1PRtWtX5bIvTYBlMHGimrxp7rrrLmc3p8ZcXTGpTjAR91ch39CxY0cYDAZIkoRff/3V080hHxZIwaQ6FRMxmDRq1AhXXXUVQkNDUVZW5lMVEw7lOJG/vWnECbyuCCbi0Je1YCKfBwNgMPFFUVFRaN26NYCqFQLl5eUebhH5Kn/7jLWmOsFEnPiampqK0NBQdOrUCUDV5HN56NzbMZg4kb+9aVxdMfnmm2+U49atW9sVTBISEpzeDnI9eSl4WVmZav4QkSPEnaL94TPWmuosF9ZWTACoTpwp3u7NGEycSKwAODLHxFu5MpgUFBRg5cqVAKregDfddJPNYJKQkIDgYI4++iJ37SRM/k3cRiAxMdGDLXG96iwX1gsm8v+1t3szBhMn8ueKSXZ2tlMfe9WqVUpZ8bbbbkN4eLjNOSYcxvFd4o7G4iorIkeIwcTfq6fVGcoR5+3JJzRkMAlwcjCJi4tDeHi4h1tTc678K3fnzp3K8e233w6gai6CvMGcHEbEJcUMJr6LFRNyBjGY2LuJpa8S3zP2Dn/Kn5uxsbHKKUUYTAKYdkdCf+DKLxN5635AfaI3uWoiv8G4Isc/sGJCziAHk5iYGISEhHi4Na4VGxurnNrE3vNMycPeYvVZ3HyOwSTA5OfnK9tt+0swceWqHHF2uHgKcvkNdfnyZZhMJtXEV+5h4rtYMSFnkIOJvw/jAIDBYFCqHadPn7Z5luCysjIl9It/xIkn0tTbrt4bMZg4ib/NLwFc+2UiBpOoqCjlWA4flZWVyMnJ4VJhP8GKCdWUyWRSPocCIZgAf4eKkpIS1TCWHnG4R/wjrlatWspnubWKSUVFRU2a6lQMJk4iltrEhOrLwsLClDkf7g4mQNUwDody/AMrJlRTubm5MJlMAAInmDgyP8TaZ6X8OJmZmaisrDT72UmTJiE+Ph6rVq2qSXOdhsHESfwxmAB/fwBkZGTovqCrSx72Cg0NVS0B1gYTVkz8AysmVFOBtCJHJgYTW/NMrA17y49TXl6OsWPH4uDBg8ptWVlZ+O9//4uCggLccsstzmh2jTGYOIm/BhP5JFB5eXlO3U5crpiI1RKAwcRfsWJCNRWIwUT8LnFGxQQAVq5ciY4dO2LZsmUA1AsRvAWDiZOIwURcZeLrbrjhBuV48+bNTntcOZiIE1+Bqq3pZefOnePkVz/BignVVCAGE0eGcqx9Vnbv3l112WQy4amnnsKqVauwdetWJ7TUubiNppP4a8Vk4MCByvGmTZswduxYpzyupYqJuLTt1KlTqi8xf9+3wJ9FRkYiODgYFRUVrJhQtQRiMBG/S2wN5Vg7C/v48eMRHR2NkpISzJgxA1lZWUhPT9cduiktLfX4UmwGEyeRXzTh4eF+9aZp1qwZUlNTkZ6eju3bt6OkpMTsPhUVFSgoKFD9VWyLpWDSpEkT5fjkyZMoKChQLstr+sn3GAwGxMfH4+LFi6yYULUEYjARq++OVEy0QzlGoxGjRo0CUFWxvP/++y0+TlZWlser/hzKcZLMzEwAVQnXYDB4uDXOYzAY0K9fPwBV6+SPHz+uur20tBRt27ZFUlKS3SXBsrIyZWmaNpg0bNgQRmPVy/LkyZNKgDEYDMoKIfJNcnBlxYSqIxCDSXh4uDK87cgcE2vD3iNGjLD6OOKJEj2FwcQJ8vLylAlE/jSMI2vTpo1yfPLkSdVtK1euxJ9//omysjIMHTrUrseztFQYAEJCQpQ+PHXqlGouij8FvkAkT4DNz89Xln0S2SsQgwnw9wlhs7KyrG6yJlZMrJ3gsEGDBujTp4/F28U9uTyFwcQJ/HV+iUw8bbY2mIgnl9Ib5tEjLxUGzIMJADRu3BhA1YkD5TeJ3v3It8gVE0mS7D5bKpEsUIOJPCxTUVFhdRhUrpjExcUhNDTU6mMuWbIEzZs3172NFRM/4e/BpFWrVsqxNpjYegPosbQdvUycZyKX/RlMfJ+4ZJjzTMhRgRpMxGEZsSoiEs/VZs+2Co0bN8bHH3+sexuDiZ84c+aMcuzpSUOu0KxZM2UY5cSJE6rbSktLHX48a0M5wN8VExEnvvo+Lhmm6pIkCQcOHACgPrldINDu7aTnwoULyudq06ZN7XpcS6scOZTjJ8Qk7497bYSFhSlh4eTJk6pxzuqU5G0FE7FiYu1+5FvEiskzzzzjVefmIO929OhR5Uu5d+/eATXfTKyAWAom4qKEZs2a2fW44vtRxIqJnwiEEqM8z6SgoEBVTtQGk/LycpuPVZ2KCYOJ7xP3qNmwYQPmz5/vwdaQL/nxxx+VY2sTN/2RPUM5aWlpyrGluSNaloIJKyZ+Ijs7Wzn2103AxAmwx44dU47Fya+A+gyXltgKJsnJyWbXMZj4vsmTJ6N3797KZe3ScyJLfvjhB+U40IKJqyomERERCA8PN7ueFRM/IVZMAi2YaCsmlt44InFVjt7kV70kz2Di+8LDw/H+++8rl8WASmSNHEwiIyNx9dVXe7g17qWtmKxZswbPPvus6rNWrJjYG0wA/c/ajIwMuz7HXYk7vzpBIFRMxBf7qVOnlGNtMLFUahTZqpjExMSYXcdg4h/Ef0cGE7JHSUmJsrlYp06dqrUS0JeJFZNjx47hww8/RGlpKQoKCvDOO+8A+LtiYjAY7J78ClR9X2mHbiRJwpo1a9C/f38ntL56WDFxAjmYRERE+O3upOKEVHFlTnUqJraCSXBwMGrVqqW6jsHEPzCYkKPECmtsbKwHW+IZYsVk9+7dykrIn3/+WblerpgkJyfrDs9YYmmeyddff12dpjoNg4kTyEM5/lotAdQTUl1dMQHM3zCBtDzQn4n/3uJ5kIgssbUho7+Ljo5GWFgYAHV1/ujRo5AkCbm5ucp3kL0TX2Xi52yDBg2U7e83btyI4uLimja92hhMakiSJOXF4q8rcoCq+QHy1siurpgAMDshYCB+IPmjiIgIZaknKyZkD1sbMvo7g8Ggu2laXl4eLly4gLVr1yrXiXMB7SEHHqDqu2z48OEAgOLiYmzfvr2aLa45BpMaKi4uVkpr/lwxAf7euEfczMdVFRMGE/9kMBiUf0sGE7KHrcnygcDS/lhHjx7F7Nmzlcvjxo1z6HGDgoKU48rKSowcORJNmjTBI488orttg7tw8msNBcKKHFnjxo2VFH3y5Em0a9fObLmwoxUTSx802qEcBhP/ERUVhYKCAg7lkF0YTCxvM79gwQL8+uuvAIAuXbo4vJRaG0yGDBmCtLQ0SJKE9PT06je4hlgxqSFxzM+fh3IA9VbHJ0+eREFBgdnZLrOysmw+jj1jxqyY+C95vhArJmSPQJ9jAliumCxbtkw5vu+++xzeEddo/DsCVFZWwmg0esWuugwmNRRoFRPZiRMndLejtydlcygnsPnbUE5lZaVZ5ZCcJ9DnmAD6m04CQFlZmXLcpk0bhx9XWzHxFgwmNRQIe5jIxIrJ4cOH8cknn5jd5+zZsza/cBhMApv8b1lSUuJVH4bVUVpaig4dOqBu3brYunWrp5vjE44cOeJQKOVQjvp0DpZU58z2Tz75pHK8ePFih3/eVTjHpIYCaSinbdu2yvGHH35o8X4nTpxAhw4dLN4ufihZ2veFy4X9l3YvE70N9XzF8uXLceTIEQDAhAkTVGcaJ3MLFizA1KlT0bRpU/zxxx8ICQmx+TMMJraDSVBQEOrXr+/w47Zt2xZbtmxBdnY2Ro4cWc3WOR8rJjUUSEM5CQkJFncVFMcqbZ0DRQ4mERERqp8TsWLiv8SQ6UvDOXv37sVvv/2muu7kyZPK8dmzZ93cIt8zdepUAFV/vOzYscPs9rS0NGzZsgUmk0m5jnNM9E9sKkpOTlYNyzji+uuvxy233GLxs9gTvKclHpSfn4+0tDQcPnzY4ZUCgTSUA8DieSrE620FE3k83tpfygwm/ssXd3/dvXs3unTpgs6dO6vCifj+J8eI8yMAICcnB506dcKAAQPw3//+V7mec0yARo0aWb29OsM43ozBBMC//vUvtGzZEsOGDcO+ffsc+tlACybXXHON7vViJcVWMJEnzVrbXprLhf2XL+7++s9//lM5nj59unIsnjyNHCPv/yT77LPPlNfDnXfeqVzPoZyq94y14WwGEyfIycnBww8/jN69e+P//u//8Msvv3iiGQrxvCz2zK7fuXMntmzZAqDqd5FZOu+AP7FUMRFP+GQtmJhMJly5cgWA9WDCion/8sWKiVjmFpfE//nnn6r7lZeXu61Nvi43N1d1WRtUZBzKqSIGE3kXbpmtioqv8Ugwef3115GQkIBNmzbh4YcfxjPPPKO79NRdxGAif2la8vvvv+Paa6/FgAEDsHHjRlW7tV+m/qhZs2bKZj9hYWHo0aMHbrjhBtxxxx1KMLP2V+SVK1eUvU8cCSb+enLEQOStc0zy8/OxfPly3U0CxWqo/MdIRUWF6vQMgH37+FAVbTCxtEKLFZMq4vtGOxmWFZMaKioqwrZt2zBt2jSEh4ejb9++aNasGb7//nt3N0UhBpO1a9fiscceU01qE7333nvK8cSJE5U3V1BQUECkeaPRiCVLlmDMmDHYsmULdu7ciY0bNyIiIkI5gdTp06fNxo9lYkXKkWDiDZv+kHN461DOPffcg9tuuw2jRo0yu018PcrDt+np6WYVEk6AtUx7Ujix2gxYrjZxjkkVMZhIkqTa28TfgonblwufPn0akZGRylkMgaozImr/8gCqJkdpv+CCg4MRGhrq1DaJH5RLly4FAGzbtg27d+82u6+4vC0rK0t5scTGxkKSJLOdUP2JPFP+hhtuwKBBg1TXAVD+TSVJwuXLl1X/xjLxwygmJkb18yJtyLN0P28mt9kX2+5K4pfLlStXYDKZvKKv5F00f/rpJ1RWVqrCsPZ9bTKZcPToUbPHyMzMRJcuXVzbUKEN4v+9nbYSlZ2drWq7dhhdvk0MJuHh4dX6fX2tr/QkJycrE6+jo6PRqlUrZXl6w4YNnfq7uaq/7F354/ZgUlxcbPalExUVpTuUs2jRInz00Ueq60aPHo0xY8Y4vU1a+/bt093FVFt+lJcLR0VFefTcAu6UkZGhe70YGA8dOoSSkhKz+4gf5gaDwe4+8+W+tdRfgUp8v2VkZKj+bb2lr44ePaoaPtSenPLUqVP48ssvzX7u4MGD6Ny5s8vbJ/KWPrNF3u9FlpmZqfq3177H5cvilgzZ2dk12pTPV/pKzyOPPIJ169bBZDLhoYcewq5du7BlyxbUrVvXZd8/zu6vJk2a2HU/tweTiIgIs3HlwsJC3RLdnXfeifHjx6uuc0XFpFmzZrrX621qo227HFQSExPt2p3Pl5lMJmRkZCAlJUU3+YqlxcjISN3+OHTokHLcsGFDq332wQcfYM6cOfjnP//pk31rq78ClfhvGRYWhtTUVK/rq9jYWNWGVdo2ZWZmYsmSJWY/V1pa6rbXqrf1mS3a4fHy8nJVX2mr4/JtYrWqVatWCAsLc/i5fa2v9KSmpuLPP/9EeXk5WrRogSFDhuCGG25Aq1atUK9ePac+l6f7y+3BpFGjRigqKsKFCxeUSZRpaWm46aabzO4bGhrq9BCix9JcB71/EO1fTuJj+OoL3lFGo1H3dxVXJeXn5+veR5xcHBcXZ7XP7rnnHtxzzz01bK3nWeqvQCXO6SoqKlL1jaf6SjtUk5+frwra2qrqww8/rMyJGDVqlFI9OXfunNvb7yuvL+2cktzcXFW7tbcDVb+bPPnVaDQiPDy8RvPNfKWvLBG3ZTAajbj++utd+nye6i+3P2NkZCT69u2LDz74ACUlJfjxxx9x/Phx9O3b191NUTiyJfb58+d1r7c2kTNQiMFE70MGsH/yK/kvb1wurF2qqh1aFleGAFDG+sPDw/HOO+8o13NLesu0m9Fph8W1t8t/xMh9HxkZyUnwAcIj0fHpp5/GxYsXMWDAAMyZMwevvPKKR7+kxL/grJEkyWIwCYSlwraIfaD90JGJH/gMJoFJXF3gDatyJEmyOEQrsxSgOnfujOTkZGUo+uDBg349Ab4mtMFD+8eL9nb5jxg5mATCqkeq4pFgEh8fj3feeQc//fQTVq1ahe7du3uiGQpLwUQ7yerKlSu6E2UBfskC9lVMGEzImyomJ06cQPfu3dGwYUPV9bYqJrIuXbrAYDAoJfWzZ886vHt0oBAnsQK2g4n8byC/RgJ5qXCg8d3BNicKCwvTPcul9sPI2uZJrJiwYkL28ZZgcuHCBTRr1gy7d+82W0Gmff1aCyYAMHz4cOW6r776yrkN9RN6QzUVFRUAgJKSErM+1lZMGEwCB4PJX/SqJtoPTUvDOAC/ZAEGE7KPGEy++OILbNiwwSPtWLBggcXb7K2YdO3aFQAwbNgw5bqvv/7aCa3zP9qKCfB3P+tVWPPy8iBJEoNJAGIw+Yu1YHLmzBl89dVXyMzMtPjzrJhw8ivZR3systtuu61Ge1NUl7X3s73BpGXLlgCqzl0iV0/2799vMZgHMr0zMcv9pHdbXl4eSktLlTk7nGMSOBhM/mIpmFRWVqJnz54YOXIkxo0bp9ymPXcLv2RZMSH7hIaGok2bNsrlvLw8j3yRW5ovBlgeyuncubNSHbn//vsRFBSk3Kdjx47KMc86bE4vfMh/wOjdlp+fz+3oAxSDyV/0gklBQQEuX76su/ud9iy7rJhU/SUsr3m3Nfk1JCSkWhslkX/YvHmz6ktd74vJ1awFEzFAl5eXK3uWREVF4csvv8Thw4cxd+5c1c+IGzUymJjTG8qxFkzy8vJ4Ar8AxWDyF0sVE0uT83r16qW6zL/+qzbjkQOarYpJbGws9yQIYPXr18f999+vXLYUZF3J3oqJ9ssxODgYbdq0MZswz2Bimclk0t2c0tZQjtj3HMoJHAwmf7EUTLRjy8HBwXjuuefMdtxjxaSKI8GEAps4J8nbKiZr165Fjx49cPnyZbv/aheDid5JSQNZTk6O7jwi+cR+mzdvNrstPz+fFZMA5fYt6b2VPcEkMTERP//8M5o2bYoDBw6o7ssv2iryl01ubi4kSVKqIpIk4ZNPPlG+gNhfVLt2beXY24IJAPz8888YOnSo3V+O4nbhrJioidWSOnXqKIEkKysLO3bswGeffWb2M3l5eZxjEqBYMfmLPcFkypQpyoePeB4NgF+0MrliUllZqdrV85dffsGkSZOUy+wvEoOJtw3lyH755RccPHhQuWztyzE+Pl4J5gwmauIeUFdddZVyfP78ecybN0+5/NxzzynHeXl52LFjh3LZ2SeqI+/FYPIXe+aYiB9KYhkagFtONugLxCGtpKQkpaR97Ngx1f3EFQwUmMRgsm7dOmzcuBEmk8ltz29PMNGy9Ve7PJyTkZFhdv6dQCZWTLTBRDzr8COPPKIc5+fn4/PPP1cujxw50qVtJO/BYPIXvRUi2oqJOPmKEzf1icGkuLgYH330EQD1jPxhw4bhtddec3fTyMuI4X7dunWYNm0aVq5c6bbnd2UwkSQJ6enp1WqXPxKDSbt27ZTPz/PnzyvVlNq1ayM+Ph7BwVUzDHbu3In9+/cDALp164bmzZu7udXkKQwmf9H7S00bTLQfSjt27MCECROwbds2VzfPZ8gfKrLDhw8DUAeTBx98kEuFSVUxkd13331ue/7qBBNbK0O4MkefOJSTnJyMxMREAFXBRN5ROykpCQaDAXXq1AEA1WkCbr/9dje2ljyNweQv1QkmPXv2xCeffIK+ffu6vH2+YsCAAarL8jyTS5cuKdclJCS4tU3knfSCiTvPzKs9P449bFVMGjVqpBzr7X8UqMSKSVJSkjJf5PTp08pnrHyd3lySnj17uqGV5C0YTP6it5TNVjAhc7fccgu+/PJL5fLZs2cBqCsmDCYEmM/TAty7V4UrhnJSUlKUYwaTv4kVk7p16+qGj6SkJAD6wUS72ID8G4PJX+TzXIi0k1+5wY9tRqMRI0eOVMaDGUzIkpCQELPz5rjrPWYymSxOTpVPzKfHkWBi7Vw8gUa7XNiRYGIwGJTbKDAwmPxl+PDhmDhxIrp166Zcx4pJ9TVo0ADA3+e7kIOJ3pcRBS7tcE51hleqw9rz/Pzzz7j33nt1b7P1GdCwYUPlmBWTv8nBJD4+HqGhobrBxNJQTlJSktkuu+TfuMHaXwwGA1588UXUqVNHWTrMYFJ9cjABgHPnzinBJCEhgSuaSKFdpn/hwgXVxnyuYm0Yx2AwWNwzw9ZnQFxcHKKiolBYWMhgIpCHcurWrQtAf7jGUsVE/CyhwMCKiUZERITyochgUn3ih8nZs2eVYCLPxicCoNqEDwBKS0vNrnMFW/NL6tevr3u9rc8Ag8GgDOdkZGS4dTKvtyoqKlL+Ta0FE0sVEwaTwMNgomEwGJQPH84xqT7xg/348ePKFwHnl5BIPIuvTN6u3JXEYKK3Oqi6wQT4e55JcXGxR7ba9xZHjhxBr169MGLECOU6axNcWTEhGYOJDjmAsGJSfeKHiXheIQYTEukFE72z0DqbGEz0JlbGxMTo/pze6j0trsypMmvWLOzYsQObNm1SrpMnFot9JGPFhGQMJjrkYHLq1CnVOTwYTOzHYEL20J6lG3B/xUQeXhC1atVK9+dSU1NtPjZX5lRZvHix2XUDBw4EADRv3hydOnVS3SZvrMZgQgwmOsQhmx9++EE5ZjCxn/hhIp4EjcGERPPmzUOTJk1U17m7YqJXHalXrx4++eQT3H333dixYwemTp2KJUuW2PUlyZU5Va677jrV5fj4eOUcWQaDQXVeHADKyhvthGgGk8DDYKJDb4JmaGio2XbrZJn4YSJursTJryRq3bo10tLSsGLFCuU6d1dMWrdurRwPHz5cOZ4wYQI++ugj9OzZEx9++CHuuOMOux5bDCZnzpxxQmt9k3bib7169WA0/v2VM3bsWOXzoE+fPsr12hVZDCaBh9+0Op5//nmz89+wWuKY6OhoxMbGms0hYMWEtAwGg2o4xd0Vk4SEBHz//ffYunWrxf1LHBEbG6sci5PnA01+fr7q8jPPPKO6HBYWhs2bN2PZsmWYPHmyxcfh5mqBhxUTHddffz2WL1+uuo7BxHF6E9wYTEiPPL8AcH/FJCIiAn369MELL7zglC/BiIgI5VicPB9oxGDy+uuvY/z48Wb3ueqqq/Dvf/8bLVq0UF0vn328R48eFldIkf9iMLFA+2bgUmHH6QUT8QuISCZWTNwxL0MbTJxJ/COmOufj8RdXrlwBUHXG5SeffFI1jGPLU089haNHj+KHH37ghowBiMHEAm0wYcXEceKZVmXav4yIgKodU+UTte3cudPlm6y5MpiwYlJFrphYWnptS8uWLbkVfYBiMLFAu2SNwcRx2opJQkICh3JIl8FgUJYOl5WVqfa+cAVWTFyrtLQU5eXlAMxX2RDZwmBiQVRUlKqEyGDiOG0wsbQ3BBEA9OvXTzleu3atS5+LFRPXEueXVLdiQoGLwcQKcXY955g4TjuUw2BC1vTs2RPh4eEAgG+//dal55lxZTCRfwft8wQSMZiwYkKOYjCxQgwmJpPJgy3xTdqKScuWLT3UEvIFERERyqZcZ8+exYkTJ1z2XK4MJkajUQkngVoxkSe+AqyYkOMYTKwQg4neOT3IOnGjKYAVE7JN3C10+/btLnseVwYT4O+hX1ZMGEzIcQwmVohvKO1mQWRbWFiY6jKDCdnSu3dv5fjHH3902fO4OpjIj8mKCYdyyHEMJlaIFRMGk+oJCgpSjps1a+bBlpAv6Nq1q7JElBUT38WKCdUEg4kV4qZP4hcs2W/Dhg3o1q0b5s+fb1ZBIdKKjIzENddcAwA4evQoTp48id9//93pE2HdFUwCtWLCYEI1wWBixQsvvKD89faf//zHw63xTQMGDMDPP/+M++67z9NNIR/Rq1cv5bhp06bo2LEj3nrrLac+R0lJiXLsyqGc0tLSgJw4z6EcqgkGEytSU1Nx6NAh7N69GzfccIOnm0MUEK6++mqz6x5//HGnPodYMRGX9zpLoG+yxooJ1QTPLmwDt1Ancq/OnTu7/DnkIZbQ0FCXDNOKVZji4uKA2weJFROqCVZMiMirtGzZ0iXDKyL5XDyu+tIUKyaBOM+EFROqCQYTIvIqQUFBuOqqq8yuLy0tddpzyMHEVZUMbcUk0DCYUE0wmBCR12nQoIHZdefPn3fa48vBJDo62mmPKQr0igmHcqgmGEyIyOu0bdvW7DpnBROTyaSEBVcFE1ZMeK4cqj4GEyLyOg8//DDq1aunuu7cuXNOeezi4mJlXxRXDeUEesUkNzcXQFU/cA8ochSDCRF5nTp16iA9PR3vvvuucp2zgok8jAOwYuIKJSUl+PPPPwFU7UND5CgGEyLySqGhoaovNmcFk8LCQuWYc0yc78CBA6ioqAAAZRdfIkcwmBCR16pfv75y7Kw5JqyYuNbevXuVY73N8ohsYTAhIq8lBhNXDOW4Y45JIAcTVkyoOhhMiMhr1alTB0Zj1ceUr84xCbShHDmYGI1GdOrUybONIZ/EYEJEXisoKEg5y/eZM2ec8pjunmOycOFCZGZmuuR5vE1ZWRkOHjwIAGjdunXAbcVPzsFgQkRerWXLlgCq5picOnWqxo/njqEcsWJy5MgRDBw4UFmi7M8uXryI8vJyAH//uxE5isGEiLzagAEDlONNmzbV+PHcMZQjVkwA4I8//kB2drZLnksmSRKWLVuGvn37omfPnjh9+rRLn0+PuLFabGys25+f/AODCRF5tYEDByrHvhJM9E5C6Oqg8Omnn2Ls2LH44YcfsGvXLnz22WcufT493IqenIHBhIi8WteuXZUTwW3evBkmk6lGj+fuOSYyVweTn3/+WXX50qVLLn0+PQwm5AwMJkTk1YKDg9GvXz8AVV+28q6i1eXuOSay06dPY86cObj++uuxe/dupz+ndtWSOKziLjyrMDkDgwkReb3U1FTlOCcnx+6f++WXX/CPf/wD6enpynWemGMCABs2bMCjjz6Kbdu24aWXXnL6c2o3oMvLy3P6c9jCigk5A4MJEXk98a9v8cvPlrFjx+KVV15B586dcfHiRQDuGcrRq5isXbtWOT5+/LjTn9MbKibivw0rJlRdDCZE5PXEv77tCSbnzp3DhQsXcPLkSQBVVZZJkyYBcM9QTnh4ODp37mzx9oyMDKcuH5Ykyaxi4umhHFZMqLrcHky6dOmC3r1747rrrsN1112Hjz/+2N1NICIf40gw2bt3L1JSUpCUlKS6ft26dTh16pRbhnIMBgO2bduGrVu3Ij4+3uz2wsJCh4akbMnPzzfb+t7TFRMGE6quYE886RdffGH2oUFEZIn4JWfrC3fSpEmorKzUvW3Tpk1uqZgAVUMZ/fr1Q/PmzXUnu2ZkZKB27dpOeS69Exx6umLCoRyqLo8EE3uVlZWhrKxMdV1wcDBCQ0Od/lzyEsSaLkX0Z+wjx7C/7Gerr8QAkZ+fb7VPrW1dv3HjRmWOSUREBAwGg8v/fVJSUnSDyalTp9ChQ4dqP67YZ3q/c15enttfe2IwiYqK8prXPt+LjnFVf8nnvbLFI8Fk0qRJMBgM6N69Ox555BHExcXp3m/RokX46KOPVNeNHj0aY8aMcVnbMjIyXPbY/oJ95Bj2l/0s9ZU4TJGZmalaZaNlqVoCVFVM5ImpERERVh/HWRISEpTjrl27KiFl//79uOqqq2r8+BkZGcr5aURXrlzByZMn7f4ycIasrCzlOC8vzy396wi+Fx3j7P5q0qSJXfdzezD56KOP0KFDB1y5cgWvv/46XnzxRcyZM0f3vnfeeSfGjx+vus6VFZOMjAykpKS49Y3sS9hHjmF/2c9WX124cEE5NhqNquXDWgaDweJt4rbwMTExVh/HWR599FHs3r0bzZo1w6RJkzB8+HAAVWcdrsnzi31WUVFhdrskSUhMTHTrXA8xFLZt29Zr5pnwvegYT/eXU4PJXXfdhf379+veNmXKFNx///3KTPX4+Hg8/vjjGDp0KEpLSxEWFmb2M6GhoS4JIdYYjUa+cG1gHzmG/WU/S30lnnflypUrVvtTb7VLs2bNkJaWprouOjraLf8urVu3xq+//gqg6pw5soyMDKc8v9FoVFUq4uPjlYm1BQUFbj1njXbyq7e97vledIyn+supwWThwoUO3V/+hQPhrJtEVH2OrMrR+zyZNm0a3njjDWUvE8C1E18tSUlJUY6dWSYX9zBp2bKlsj19fn4+kpOTnfY8tsj/Nt4YSsh3uPWVk5aWhmPHjqGyshL5+fmYPXs2unfvjvDwcHc2g4h8jCMbrOkFk7p16+Khhx5SXdeyZUvnNM4BUVFRykocZ547R1yV06pVK+XY3Stz5OfzliEc8k1unWOSnZ2NV199FRcuXEBUVBS6deuGF1980Z1NICIfJFY3qhNMoqOj8eCDD2L9+vX4888/MXbsWI999qSmpiI7OxsZGRkWh7EdJc+dMRqNqqqMu7elFysmRNXl1mDStWtXrFq1yp1PSUR+wGg0Ijo6GgUFBTaDid4Sx+joaMTGxmL79u2uaqLd2rRpg19//RUmkwlHjx61uDJnzZo1eO+999CzZ0/cf//9qFOnjsXHlPskJiZGNafEnRUTSZJU7SCqLg4CEpFPkP8Kt/ZlW1lZiZKSEos/6w3atWunHB86dMji/e6//35s2LABM2fORM+ePa0GMnEIRQwF7gwmhYWFSrXKm/qbfA+DCRH5BPkL19oXtLirq8hVW89XhxhMDh8+rHuf/Px81eTYtLQ0fPjhhxYfU6xUeCqYcDt6chYGEyLyCfKXXUFBge48EkmSLFYgvDWYWGqv3tmH33rrLZSWlppdX1lZqexmW6tWLY8N5XA7enIWBhMi8glyMDGZTCgqKjK7/Y477kCvXr2s/qw3aNKkibIS0VIw+fPPP82uO3v2LNatW2d2vVglYsWE/AGDCRH5BGt7mVRWVuLTTz+1+LPeVDEJCgpCmzZtAFRVRrRzYsrKynD06FHl8u23364c79u3z+zxxPChnWPizlU54r8JKyZUEwwmROQTrFUCzp49a/VnvW2vJHk4x2QyYefOncr1GRkZSE1NxQsvvKBcd+uttyrHv//+u9ljaQOBpyomYghixYRqgsGEiHyCtYqJrc3KrJ0/xxOGDBmiHL/zzjvK8fvvv6/aLA0ABg4cqOzjohdMtHM7PFUxyczMVI7r16/vtucl/8NgQkQ+wVow8baz2NoyevRoNGjQAADw1VdfKZNdtfusREVFoVatWujQoQMA4OTJk2ZVEO3cjtjYWCWIyefMcQcxHDZq1Mhtz0v+h8GEiHyCPwWT0NBQPPDAAwCqVhN9/vnnkCQJBw8eVN1PXm3TsWNH5boDBw6o7qOtmAQFBSE+Ph4AcOnSJZe0X48YTNxx1mbyXwwmROQTrJ0vx9eCCQCMGzdOOd6wYQOOHTumbC0ve/DBBwFAtTusdjhHbzVMQkICAODy5cvObbQV4r9Bw4YN3fa85H8YTIjIJ4jBRFsJ8MVgkpqaitatWwMAdu3ahbVr1yq3tW/fHg888ACeffZZAOpgol1irLcaJjExEUDVHJPy8nLX/AIacsUkKSnJ6yYbk29hMCEin9CiRQvl+OGHH0Zqaio+/PBD/PHHH9i8ebMHW1Z98iRYk8mEf//738r18+bNw7vvvot69eoBAJo2barcpp3oq10uDPwdTACYVWFcoaysDOfOnQPAYRyqOQYTIvIJHTt2VK2uOX36NKZNm4Y2bdq4rSrgbIMHD1aOxQDRuXNn1f2SkpIQHFx1zlVxq3pAv2IiD+UA7plnkpmZqezGy4mvVFMMJkTkE6Kjo1VVE0siIiLc0Brn6NOnD4KCglTXNWnSxGwfkKCgICQnJwMwDya2KibumGfCFTnkTAwmROQztJUErenTpyM3N1e17FbcoMzbREZGKkuBZdrLMnlC6eXLl1Vb8ntDxYQrcsiZGEyIyGdYCyYDBgzAjBkzEBoail69euFf//oXxo8fj3fffdeNLXRc165dVZfbt2+ve7+UlBTlWNzMzBsqJuLkY1ZMqKYYTIjIZ1iqJsyYMQObNm1Cs2bNlOv+8Y9/4NNPP1UmkHqrbt26qS5b+h0tBRPtSfwA91dMTp06pRwzmFBNMZgQkc+49tprdU8Qd+2113qgNc5RnYqJOM9ErpgEBQUpy3TdXTE5ceKEciyGQ6LqCPZ0A4iI7BUXF4dt27bh119/xe+//463334bsbGxuOGGGzzdtGqTT+gna9Wqle79LAUTeY5JTEyMsmrJ3RUTOZjUrl0bsbGxLn8+8m8MJkTkUzp37ozOnTujqKgInTp1QufOnREXF+fpZlVbcHAw7rzzTixatAhjxoxBSEiI7v1sVUzElTxixcTVwaSsrExpj7jfClF1MZgQkU+KjIzE5MmTPd0Mp1iwYAEee+wxZSdYPWIwEYdOxIqJrHbt2sqxq4dy0tPTlT1MGEzIGTjHhIjIw4xGI9q1a2e2p4moTp06iIqKAgBs2rQJjz76KC5cuKAsHRaHb4KDg5UqkqsrJmJIYjAhZ2AwISLyAQaDAc8995xy+e2338aCBQuUy71791bdXx7OcXUwOXnypHLMYELOwGBCROQjnnrqKbzyyivKZTGYDBw4UHVfeTgnLy8PJpPJZW1ixYScjcGEiMiHPPTQQ2aTfSMjI9GjRw/VdfHx8QAASZLwyCOP4JFHHlHtGKunsLAQ06dPR//+/fHnn3/a1R4GE3I2BhMiIh8SFRWFqVOnqq7r06cPwsLCVNeJ4eXdd9/F22+/jbfeesvi45aUlODqq6/G/PnzsXXrVrz33nt2tUfeXM1oNKom6BJVF4MJEZGPmTlzJkaNGqVcHjt2rNl95IqJaOHChRYfc+nSpTh27JhyWXuyQEuysrIAAHXr1lXOgExUE3wVERH5mMjISKxcuRIbN25EaGgo+vXrZ3Yfvb1drJ15+fDhw6rLubm5yMnJwf3334+EhATMnTvXLHhIkoQLFy4AqAomRM7AYEJE5KNatmyJ1NRUZcdXkaPBRDwRHwBkZ2fjs88+w//+9z8AQHJyMp555hnVffLy8lBWVgYASEpKcrT5RLo4lENE5If0hnIiIyMt3l88ER9QtTHbmjVrlMvPPvus2c/I1RKAFRNyHgYTIiI/5GjFRBtMsrOz0bhxY9V1r7zyCurVq4fHH38cAIMJuQaDCRGRH9KrmMhnH9YqKirCxYsXza47f/686rp//OMfyMrKwuzZs3H58mVl4ivAoRxyHgYTIiI/pFcxqaio0L2vdn6JLC0tzeLjHzp0iBUTcglOfiUi8kN6FZPi4mLd+2qHcWTWgsmBAwdU290zmJCzsGJCROSH9Comu3btwvTp0/Htt9+qrheDiRhoxCCjDR4HDx7kUA65BIMJEZEfio2NNbuupKQE8+fPx0033YR7770XkiQBUA/lXH311WY/17hxY/z444+YOXOmct2BAwc4lEMuwWBCROSHQkJCrN7+wQcfKEM1YjDp3Lmz2X3j4uLQsmVLvPDCC8q289qKCYMJOQuDCRFRgJJX3Ygrctq2bWt2P3FYqH379gCqNlfbt28fACAmJsbiih8iRzGYEBEFqJycHABVm6kBQHBwsNneJYB63kmHDh2UY/lsxayWkDMxmBARBSg5mMiraxITE5GQkGB2P7Fi0r17d7PbGUzImRhMiIgClLZikpiYiNq1a5vdT6yY9O7d2+z2Ll26uKiFFIgYTIiIAlROTg6KioqUZcEJCQk2KyZ169ZFq1atVLcPHz7cpe2kwMJgQkTkp/7v//7P6u05OTmqTdISExMRERFhNpFVu1lbjx49VJf79OlTw5YS/Y3BhIjIT7333nu4/fbbLd6ek5OjDOMAUKol7dq1U91Pu1mbuNdJmzZtbC5NJnIEgwkRkZ+qX78+Pv/8c9x55526t+tVTADzCoi2YjJx4kSkpKQgNDQUCxcudHKrKdAxmBAR+bnIyEjd67UVE0vBRFsxiY+Px/Hjx3Hp0iX07NnTuY2lgMeT+BER+bmIiAjd67UVE3koR7vyRu+8O6GhoQgNDXVeI4n+wooJEZGfs1Yx0RvKkf8v0wsmRK7CYEJE5OfsHcoRlwq/+eabAKrOndOgQQPXNpBIwGBCROTntEM58mTWkpISZGZmKteLlZJHH30UR48exc6dO2EwGNzTUCIwmBAR+T1txSQ1NVU5Pn78uHIsBhODwYCWLVsiLCzM9Q0kEjCYEBH5OWvB5M8//wRQdQK/mJgYt7aLSA+DCRGRn9MGk0aNGinHpaWlAIDatWtzyIa8AoMJEZGf084x0a66AdS7uRJ5EoMJEZGf01ZMtDu5AsDo0aPd1RwiqxhMiIj8nDaYtG/f3uw+I0eOdFNriKxzejB55ZVXMHLkSHTp0gV79uxR3WYymTB79mz069cPgwYNwmeffebspyciIg3t2YL79euHiRMnKpcHDx6M2rVru7tZRLqcviV9y5YtMWjQILz88stmt33xxRfYu3cvVq1ahYKCAkybNg0tWrRAt27dnN0MIiL6i8lkUl02GAxYuHAh4uLisGvXLsyaNctDLSMy5/Rgcuutt1Y9cLD5Q3/77beYMGECateujdq1a2PkyJFYu3atxWBSVlaGsrIydYODg11yfgb5jat9A9Pf2EeOYX/Zj33lOEf6LDk5WTnu06cPTCYTgoKCMHfuXLPH80d8fTnGVf1lNNo3SOPWk/idOHECLVq0UC43b94c27dvt3j/RYsW4aOPPlJdN3r0aIwZM8ZlbczIyHDZY/sL9pFj2F/2Y185zt4+++ijj7Bjxw7cfffdSE9Pd3GrvBNfX45xdn81adLErvu5NZgUFxcjKipKuRwVFYWioiKL97/zzjsxfvx41XWurJhkZGQgJSXF7lQXaNhHjmF/2Y995ThH+2zKlCmYMmWKG1rmffj6coyn+8uhYHLXXXdh//79urdNmTIF999/v9Wfj4iIQGFhoXK5sLDQ4smlAM+cVttoNPKFawP7yDHsL/uxrxzHPrMf+8oxnuovh4LJwoULa/RkTZs2xfHjx5XhnLS0NDRt2rRGj0lERET+w+lRqLy8HKWlpZAkCRUVFcoxANx444345JNPkJOTg4yMDKxevRo33XSTs5tAREREPsrpc0ymT5+Offv2AQAeeOABAMDXX3+NBg0a4NZbb0VGRgZGjRqFkJAQTJo0iUuFiYiISOH0YPLhhx9avM1oNOKxxx7DY4895uynJSIiIj/AWUBERETkNRhMiIiIyGswmBAREZHXYDAhIiIir8FgQkRERF6DwYSIiIi8BoMJEREReQ0GEyIiIvIaDCZERETkNQySfCIbIiIiIg9jxYSIiIi8BoMJEREReQ0GEyIiIvIaDCZERETkNRhMiIiIyGswmBAREZHXYDAhIiIir8FgQkRERF6DwYSIiIi8BoMJEREReQ0GE6Ia4lkd7FNRUeHpJhCRD2AwIUV2dranm+BTVq5cCQAwGAwebon3+/TTTzF37lyUlpZ6uik+o6CgwNNNIPIIvw8mmzZtwjPPPIODBw8CAEwmk4db5H2+/fZb/N///R9eeeUVvPXWW8jPz/d0k7za2rVrMXToUKxbtw4FBQV8TVnx7bff4sYbb8Tbb7+No0ePIiwsjP1lw/r16zF8+HA899xzmDNnDi5duuTpJnm1TZs2YerUqdi1axcAfsZb4yvfh8GeboCrlJeXY/ny5ViyZAkaNWqEjRs3on379jAa/T6L2a2goABz5szBnj17MGPGDDRt2hSTJ09G69atMXToUEiSxGqA4MqVK3jllVfw008/4dVXX0WvXr083SSvdf78eTz66KMoLCzEyy+/jGbNmmHs2LHIzc1FXFycp5vntX755RcsWLAAzzzzDOLi4jB//nzMnz8fkyZNQmpqqqeb51UqKyvxzTffYMGCBUhJScEXX3yBHj16wGg08rNLw9e+D72zVU4gSRISEhLw0ksvYfTo0Th//jy2bdum3EZVQxDXXHMNVq9ejX79+iEuLg4xMTE4e/ascjv9zWQyobS0FBMnTkSvXr1QUVGBn376CZmZmZ5umtcJCgrC8OHD8dVXX6FLly7Izc1FkyZNcOTIEU83zStVVlYCAH7//Xd0794dPXv2RJs2bTB16lSkp6dj1apVHm6hd6pXrx6efPJJTJs2DaWlpfjiiy8A8DNey9e+D/0qmHz//fc4f/48SkpKEBoaim7duqFHjx7o0aMHUlJS8P333+PKlSswGAxe+Y/hDmIfRUVF4frrr4fBYMDGjRsxePBgJCQkQJIk7NixA+fOnfN0cz1O7q/i4mLExsZi0KBBSEtLw6OPPoqbbroJK1aswKRJk7B48WJcvHjR0831KLGv6tSpg7Fjxyq3JSQk4MKFC8oXsLeWkN1N7rPy8nIAQG5uLtLS0pTb27Zti0uXLmHfvn3Yu3evp5rpNXJycpTjoKAgdOjQAX369EH79u3Rq1cvfPfdd8jJyYHRaAz415gvfx8aJG9rUTUcPnwYTzzxBKKiopCYmIiwsDDMmTNHdZ9du3bhm2++QadOnTB69GiYTCavLWO5gq0+2rVrFxo0aIBGjRrhyJEjWLZsGerWrYv77rsvICsn2v4KDQ3F3LlzYTKZMGvWLJw9exYPPvggWrRogc2bN2Pt2rW4/vrrcfPNN3u66W5n67VVWVmJoKAgPPvss4iIiMBzzz3nwdZ6B22fhYSE4O2330Zubi4GDx6MJ554AoMHD8Zvv/2GVatWoVGjRkhOTsaYMWM83XSP2LNnD55//nl07twZTz/9NGrVqmV2nxMnTmDhwoVo0KABpk+fHnCf8TJ/+D70npbUwI8//ohBgwZh+fLleOGFF3Dq1CnMmzcPubm5yn06deqEFi1aYN++fTh//jyMRiMKCws912g3s9RH8kqcHj16oFGjRqioqECbNm1Qv359HD9+HCUlJR5uuWdo+ys9PR1vv/02Kisrcffdd+OZZ55BixYtUFlZiQEDBiAmJgaHDx8G4J2lUVey9f6Tx/ybNWsGSZJQXFzs2QZ7AW2fnT59Gm+//Tbi4uLwwgsv4LvvvsMDDzyA2bNnY9KkSaisrFQmpQfa6+v48eP4+OOP0bNnT/z555/4/fffdfugUaNG6Nu3L/bt24eTJ0/CaDQG5ER+f/g+9Itgsm3bNjRo0AAAkJSUhH/+85/YvXs3fv31V6WcFx4ejh49eiAxMRHLly/Hiy++iCVLliglVH9nqY/279+vKnkGB1fNh46MjERQUBAiIiI80l5P0+uvffv2Yfv27UhISED9+vUBVJWTASA+Pl6pLAVahcnW+89gMMBgMCA6OhrHjx9HREREwH25all6fW3btg1Dhw7F/Pnz8cwzz2D16tXo1KkTQkJCEBoaCiDwXl/NmzfHzTffjOeeew69evXCypUrcfnyZbP7BQcHo1OnTrjmmmvw4YcfYubMmXjjjTcC7o8rf/g+9OlgIo9XX3vttarx12uuuQbt2rXDli1bVH+dtW7dGidOnMAnn3yCy5cvY/z48QgJCXF7u93Jnj4qKioCAGWOxOeff45ly5Zh0KBB7m+wh1nrr/bt22PLli3KXxbyX2NLly7F1q1bMWDAAPc32IPsff/JIaR///5IT0/Hn3/+GXBfrjJbr69NmzahoKAAwcHBaNGiBQBg0aJF2L59O6699lqPtNmT5NfOwIEDAQD33HMPzp07hx9++EF3w766desiMzMTmzZtQl5eHh577DGEh4e7tc2e4k/fhz4dTOS/Vtu2bYvy8nL88ssvym0TJ07EDz/8gAsXLgAA8vLy8Nxzz+HUqVNYsmQJ3nnnHcTGxnqk3e5kTx/JgWTHjh245ZZbsGbNGrzyyivKh0EgcaS/fvrpJwwbNgzffPMNXn75ZVxzzTUeabOn2Pv+k0PI5cuXMWbMGNSuXdsj7fUGtvrsxx9/VF5fJ06cwBNPPIG1a9fi+eefR/PmzT3SZk+SXzvBwcGoqKhAREQERo8eja+//hoZGRmqam9ZWRlef/117N27F4sXL8acOXMCamm6P30fen0wycrKwqpVq8xmpEuSpJSd2rRpg6SkJGzYsEFJ0fXq1UOLFi2we/duAEBUVBTuvvturF27Fm3btnXvL+FiNe0j+QU8YMAAPPPMM/j8889x1VVXufeXcCNn9Vfv3r2V/urQoYN7fwk3qWlf7dmzR/mZ1q1bY/r06UhISHDfL+ABzvrMSk1Nxb333ouVK1f67esLsN5fYlVEHma+5ZZbEBoaio0bN8JoNCrDOiEhIbjrrruwYcMGtGvXzn2/gBudP38eixcvxrZt21S7KPvb96FXB5N58+ZhzJgx+P333/H8889j7ty5yi6IBoNBKTuFhobi+uuvx8WLFzFv3jwAVZuHGY1GdOnSBUDVi9ofNyhyRh917doVABAdHa30l79yZn/VqlXLrzdZc0ZfBVoVyZmfWaGhoWjWrJlnfhE3sdVfchiR9wqSv2gff/xxbNy4EQ888ACGDBmCY8eOwWAwIDEx0TO/iBu8/fbbGDt2LM6fP4/3338fb7zxBvLy8gD44feh5KW+/PJL6b777pMyMzMlSZKk/fv3S2PGjJGOHTum3OeLL76QunTpIr3//vtSeXm59Ntvv0mDBg2SHn30Ualfv37SU089JRUXF3vqV3A59pFj2F/2c2ZfmUwmT/0absXXl2Ps7a9u3bpJ7777rupnV69eLXXp0kV68sknlZ/3Z9988430j3/8Q8rIyJAkSZK2bt0q3XrrrVJeXp5yn5UrV/rNa8urgkl5ebly/Mcff0jffPONJEmSVFpaKkmSJE2aNElatWqVJEmSdPr0aemOO+6Qdu7cqXqMc+fOSbt375Z+/fVX9zTazdhHjmF/2Y995Tj2mWOc0V+//PKLNGHCBLPr/Y3YV9nZ2dKVK1ckSZKkvXv3SjfffLM0YsQIad++fZIkVb2GJk6c6DevLa/YYC0nJwfz5s2DwWBA8+bNMWrUKGVpnKy8vBzTpk3DjBkzzMZbJUmCyWRSJv/4I/aRY9hf9mNfOY595hj2l/2s9VV6ejreffddtGjRAr1798YPP/wAg8GAsWPHKhN9/aGvPD7HZM2aNRg7dqyyPG7NmjV4/fXXAVRtWy1VVXVw+fJllJSUICYmRrUHQmVlJQwGg0//I9jCPnIM+8t+7CvHsc8cw/6yn7W+Aqo2kZs1axamTZuGdu3aoWvXrjhx4oQyydxf+sqjZxcuKCjAqVOnMH36dAwfPhwAcNVVV+Ef//gHsrOzUbt2bWWr3CNHjiAoKEiZsPPHH3+gXr16fr8cjH3kGPaX/dhXjmOfOYb9ZT9rfZWTk4P4+HgAVTspl5WVITQ0FFdddRWef/55XH/99QDg84FE5vZgkpWVBYPBgLp16yIiIgLXX389GjZsqNyel5eH2NhYREZGAoCyf//x48cxbNgwZGVl4cEHH0RUVBTeeOMNdzffLdhHjmF/2Y995Tj2mWPYX/azt6/kHbjlfV3koZ3Dhw+jYcOGymZ8/sJtwaS8vBwvvPACfvvtN9SpUwfXXXcdhg0bpqw3lyQJBoMBYWFhiIyMVJaJSZKEyspKHDp0CD///DPmz5+PiRMn4u6773ZX092GfeQY9pf92FeOY585hv1lv+r2FQBkZ2fj+++/V06Rce+99/rd5ntum2Oyfv165OXl4euvv8bEiRORmZmJV155xex+mzdvRoMGDZR/CHkt+9mzZzF48GCsW7fOb1+w7CPHsL/sx75yHPvMMewv+1W3rwCgdu3aOHHiBKKjo/HNN9/gtttuc2fT3cKlwaSkpESZxHT8+HHExMQgODgYAwYMwF133YVTp05hxYoVAKoSpCRJOHTokHKOlvXr12PlypUAgMWLF2PmzJlK+c9fsI8cw/6yH/vKcewzx7C/7OeMvlq1ahUA4KGHHsJTTz2F6Ohoz/wyLuaSoZzTp0/jzTffRGRkJCIiIvDkk0+iVq1aCAoKwpUrV1CrVi2kpKTgrrvuwvz585UthouKihAXF4fc3Fw8/PDDOHDgAJ588kkA8LsXK/vIMewv+7GvHMc+cwz7y36u6CtvOdmeqzi9YrJ69Wrce++9aNmyJSZMmICjR49i4cKFaN68OXbv3o2srCzlvv369UPTpk3xxRdfAKg6adWPP/6Il19+Gc2bN8eWLVswZMgQZzfR49hHjmF/2Y995Tj2mWPYX/ZjX1WP04PJ2bNncc899+CBBx5A+/bt8dprr+F///sfevXqhZiYGKxduxa5ubkAqlJfvXr1UFZWVtUYoxFTp07FV199hQcffNDZTfMa7CPHsL/sx75yHPvMMewv+7GvqsfpQzlyGQqoGicLCgpCkyZNUFFRgbvvvhtz5sxBamoqbrzxRkRGRiI3N1c53XLr1q298kyHzsY+cgz7y37sK8exzxzD/rIf+6p6nB5MkpKSAFQtdwoJCcGlS5dgMBgQGhqKzp07Y/jw4diwYQO2bNmCiooKnD17VlkiJa9n93fsI8ewv+zHvnIc+8wx7C/7sa+qx2X7mMgbwfzyyy9o0qSJsiPdLbfcgt69e+Onn37ClStXMHnyZFc1weuxjxzD/rIf+8px7DPHsL/sx75yjMuCSWVlJYKCgnDs2DEMHDgQALB8+XIUFBRgypQpuOWWW1z11D6DfeQY9pf92FeOY585hv1lP/aVY1xWKwoKCkJFRQVKSkqQlZWFqVOnYsmSJWjfvr2rntLnsI8cw/6yH/vKcewzx7C/7Me+coxLt6Q/ceIEdu3ahT///BPjxo3DHXfc4cqn80nsI8ewv+zHvnIc+8wx7C/7sa/sZ5DE80s7WUVFBZYtW4Zbb70VYWFhrnoan8Y+cgz7y37sK8exzxzD/rIf+8p+Lg0mRERERI4I3PVIRERE5HUYTIiIiMhrMJgQERGR12AwISIiIq/BYEJEREReg8GEiIiIvAaDCREREXkNBhMiIiLyGgwmRORSe/bsQZcuXdClSxecPXvW080hIi/HYEJETjNz5kx06dIF99xzj3JddHQ02rdvj/bt2yM0NNSDrSMiX+DSk/gREbVu3RqLFy/2dDOIyEfwXDlE5BQ333wzzp07Z3b9+++/j3vvvRcA8PXXX6NBgwaYOXMm1qxZg/r162PatGn4z3/+g4KCAgwfPhzTp0/HvHnz8PXXXyM6Ohp33nknbr31VuXxLl68iPnz52Pnzp3Izc1FUlISbr75ZkyePBnBwfxbi8jX8V1MRE7RqlUrFBcXIzc3F1FRUWjSpAkA4I8//rD4M5cuXcJrr72GxMREFBYWYunSpdi1axcuXLiA6OhoZGVlYdasWbjmmmvQpEkT5ObmYvLkycjKylKe48SJE3j//fdx5swZvPDCC+76dYnIRTjHhIic4s0330Tv3r0BVIWUxYsXY/HixWjdurXFnykvL8d7772HVatWISkpCQCQkZGBpUuXYsWKFQgLC4PJZMLevXsBAMuXL0dWVhYSEhKwevVqLF26FK+//joAYM2aNcjIyHDxb0lErsaKCRF5TExMDDp16gQAqFevHrKystCsWTM0aNAAABAfH4/z588jOzsbAHDo0CEAwOXLlzFw4EDVY0mShIMHDyIlJcV9vwAROR2DCRF5TFRUlHIcFBRkdp3BYABQFTq0PycPFYnCw8Nd0UwiciMGEyJyGjkYlJSUuOTx27Zti59++glBQUF45ZVXlMpKYWEhtm7diuuvv94lz0tE7sNgQkRO07hxYwDA4cOHcdtttyEiIgJTp0512uOPGTMGX331FS5cuIBbbrkFTZo0QWFhIbKyslBRUYFhw4Y57bmIyDM4+ZWInGb48OHo378/oqOjkZaWhoMHD8JkMjnt8ePj47Fo0SLcfPPNiI2NRVpaGkpLS9G5c2c8+uijTnseIvIc7mNCREREXoMVEyIiIvIaDCZERETkNRhMiIiIyGswmBAREZHXYDAhIiIir8FgQkRERF6DwYSIiIi8BoMJEREReQ0GEyIiIvIaDCZERETkNRhMiIiIyGv8P38dBUxLQmjAAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ts.plot();" - ] - }, - { - "cell_type": "markdown", - "id": "06a8a724-7142-4077-b8b6-afafa8950d7b", - "metadata": {}, - "source": [ - "---\n", - "## Custom Class Creation" - ] - }, - { - "cell_type": "markdown", - "id": "91a1218b-8e8f-488e-94e4-875ea28477d8", - "metadata": {}, - "source": [ - "### Create custom model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "42e2f870-fd46-4100-9fb0-180a283e3d1e", - "metadata": {}, - "outputs": [], - "source": [ - "from ontime.abstract import AbstractBaseModel\n", - "\n", - "class MyModel(AbstractBaseModel):\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " def fit(self, series):\n", - " super().fit(series)\n", - " print('I am fitted')\n", - "\n", - " def predict(self, n):\n", - " super().predict(n)\n", - " print('I predicted')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "bd48fbd8-f40b-4db8-a1b2-21e2ea3aa2ff", - "metadata": {}, - "source": [ - "Load custom model in OnTime" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "372be43f-b104-4dfd-a26f-0e70c23f0f2d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['arima', 'catboost', 'TCN']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.models.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2b8c0f2b-31b6-481c-8e58-d087eb3d1e30", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['arima', 'catboost', 'TCN', 'my_model']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.models.load('my_model', MyModel) \n", - "on.models.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "cce99e86-5cce-4d2e-811a-4938fa637b83", - "metadata": {}, - "outputs": [], - "source": [ - "m = on.models.my_model()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "aa9eb39f-f636-4bfa-a783-787096d52f3e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I am fitted\n" - ] - } - ], - "source": [ - "m.fit(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "47ef7030-f5cd-4d50-a74f-0a204eb47686", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I predicted\n" - ] - } - ], - "source": [ - "m.predict(5)" - ] - }, - { - "cell_type": "markdown", - "id": "cd126529-3b5e-4a22-a871-60d221b6df6d", - "metadata": {}, - "source": [ - "### Create custom detector" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "40a0dd53-0fe9-40a7-86d9-ee551e4c5e6e", - "metadata": {}, - "outputs": [], - "source": [ - "from ontime.abstract import AbstractBaseDetector\n", - "\n", - "class MyDetector(AbstractBaseDetector):\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " def detect(self, ts):\n", - " print('I detected')\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "55bc256f-0ca3-4087-b7a9-bf563f26ffe7", - "metadata": {}, - "source": [ - "Load custom detector in OnTime" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6a8dd074-6350-4c3a-a8a7-7d901b790f95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['threshold', 'quantile']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.detectors.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "91bdf719-f451-4d07-aaed-c2f5f7b4fa1b", - "metadata": {}, - "outputs": [], - "source": [ - "on.detectors.load('my_detector', MyDetector)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "bb636aa5-f155-46e4-8038-027d5f5db78a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['threshold', 'quantile', 'my_detector']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "on.detectors.get_all()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f16ac090-142d-4d1b-8351-40b443275c72", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I detected\n" - ] - } - ], - "source": [ - "on.detectors.my_detector().detect(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74a24176-7c9b-4790-9e5c-ac71e8517872", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/docs/0.2-detectors-generators.ipynb b/notebooks/docs/0.2-detectors-generators.ipynb deleted file mode 100644 index 8957294..0000000 --- a/notebooks/docs/0.2-detectors-generators.ipynb +++ /dev/null @@ -1,720 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", - "metadata": {}, - "source": [ - "# Detectors, Generators" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", - "metadata": {}, - "outputs": [], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import ontime as on" - ] - }, - { - "cell_type": "markdown", - "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", - "metadata": {}, - "source": [ - "---\n", - "## Generation of random time series" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e9a96d79-0423-4d79-b01d-726193216238", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d463df9c-4f02-4c1e-b1a5-7162b9ea8c63", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "\n",
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-       "\n",
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-       "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
-       "  * component  (component) object 'random_walk'\n",
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"cell_type": "code", - "execution_count": 5, - "id": "8310ade1-a382-4d2a-b139-0331b3b8ebed", - "metadata": {}, - "outputs": [], - "source": [ - "td = on.detectors.threshold(low_threshold=-2)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5b3d020e-18cc-47f2-a553-eb00ff972ef3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "td.detect(ts).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "ffbed9d6-d331-4708-8d50-25882c85e60d", - "metadata": {}, - "source": [ - "### Quantile" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "04f2a0c4-5744-46bf-b622-9abaaaf6b35c", - "metadata": {}, - "outputs": [], - "source": [ - "td = on.detectors.quantile(low_quantile=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "02f12ec0-d1cc-41db-ba53-c53a98f6d8f3", - "metadata": {}, - "outputs": [], - "source": [ - "td.fit(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d640d149-f0eb-4d19-9e2b-10926d6fa26f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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AGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBRb5WTVqlUaPny4unTpokWLFl10nMPh0IsvvqiYmBj16dNHb731VrnrU1JSdMcddyg6OlqTJk3S77///tdmDwAAahxb5aRRo0aKi4tTr169Ljlu9erV2rNnj5KSkrRkyRK9+eab2rlzpyTp5MmTevrpp/X4449r48aNqlu3rubMmfPXzwAAANQovnYGx8TESDq/8nEp69ev14gRIxQcHKzg4GDdcccdSk5OVufOnbVlyxa1bdtW0dHRkqS4uDgNGzZMTz/9tPz9/S+4v+LiYhUXF5efuK+v/Pz87Ez/sm666SYdPXpUPj4+lbrfmqa0tJSMbCAv15GVfWRmD3m5rmHDhvr6668rfb/e3pdfF7FVTlyVnp6uqKgo5+XIyEh9/vnnkqSDBw8qMjLSed0111wjX19fHTlypNz2P1q2bJkWL15cbtuwYcMUGxtbqfM+evSosrKyKnWfAABUR6WlpcrMzKz0/UZERFx2jFvKSWFhoYKCgpyXg4KCVFBQIEkqKChQaGhoufFBQUEqLCy86P5Gjx6t4cOHl9vmjpWTJk2aSBKt+jL4zcMe8nIdWdlHZvaQl+saNmyoZs2aubTSUdncUk4CAgKUn5/vvJyfn6/AwEBJUmBgYLnryq4PCAi46P78/PwqvYhcyK5du5SRkaHw8HCPfDGqA4fDQUY2kJfryMo+MrOHvFxXlpW3t7dHsnLLEVu2bKnU1FTn5bS0NLVs2VLS+eWcP1539OhRlZSUqGnTpu6YCgAAqGZslZOSkhIVFRXJ4XCotLRURUVFKi0trTDu9ttv1/Lly3Xq1CllZmZqzZo16t+/vySpZ8+e2r9/v7744gudPXtWixcvVu/evS/6YlgAAHBlsfW0ztKlS8u9MPW1117T9OnT1bRpU02cOFHbt2+XJA0dOlSZmZm68847VatWLY0aNUqdO3eWJAUHB+u5555TfHy8jh8/rs6dO+uZZ56pxFMCAADVmZdlWZanJ2EKno+8PDKyh7xcR1b2kZk95OU6T2fFVwcAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGMV2OTl16pQefvhhRUdHa/Dgwdq5c+cFx2VlZemRRx5RTEyMBg0apA0bNpS7/p133tGAAQPUo0cPxcXF6eDBg3/tDAAAQI1iu5zEx8crJCREGzdu1MMPP6wpU6bo9OnTFcZNmzZNLVq00MaNGzV79mzFx8fr0KFDkqT9+/frn//8p+bNm6fNmzerQ4cOmjlz5t8+GQAAUP352hlcUFCgrVu36oMPPpC/v7969OihVq1aadu2bRo0aFC5cV9//bVeeukl+fr6qnXr1oqJidH69ev14IMP6tixY2rVqpUiIyMlSX379tW777570eMWFxeruLi4/MR9feXn52dn+pflcDjK/YuKyMge8nIdWdlHZvaQl+vcmZW39+XXRWyVk8OHDyswMFChoaHObZGRkUpPTy83zrIsWZZVYVvZuC5duigxMVEHDhxQZGSk1q9fr65du170uMuWLdPixYvLbRs2bJhiY2PtTN9lmZmZbtlvTUJG9pCX68jKPjKzh7xc546sIiIiLjvGVjkpLCxUUFBQuW1BQUEVntYJCgrSDTfcoEWLFmnChAlKTU3Vpk2b1K5dO0lSYGCgbrnlFo0aNUqSFBoaWqF8/NHo0aM1fPjw8hN308pJZmammjVr5lKzuxKRkT3k5Tqyso/M7CEv13k6K1vlJCAgQPn5+eW25efnKzAwsMLY5557TvHx8erXr5+uueYa9e/fXwUFBZKkNWvWaMOGDXr//fd19dVXa8WKFXrsscf05ptvXvC4fn5+lV5ELsXb25s77mWQkT3k5Tqyso/M7CEv13kqK1tHbN68uQoKCpSTk+PclpaWppYtW1YYGxYWpnnz5mnjxo16/fXXlZub61w5+eWXX3TLLbeoSZMm8vX11ZAhQ3TgwAH9/vvvf/N0AABAdWernAQGBqpHjx5atGiRzp49q+3btys1NVU9evSoMDY9PV0FBQUqLi5WcnKy9u3b53zRbJs2bbR9+3bl5OSotLTUuYJSr169yjkrAABQbdl6WkeSnnrqKU2fPl29e/dWaGioZs2apfr16+ujjz7SsmXLtHLlSklSSkqKEhMTVVxcrOuuu04JCQnOp2YGDBiggwcP6p577lFhYaEiIiIUHx9fuWcGAACqJS/rz2+ruYI5HA5lZGQoPDyc5yMvgozsIS/XkZV9ZGYPebnO01nx1QEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARrFdTk6dOqWHH35Y0dHRGjx4sHbu3HnBcVlZWXrkkUcUExOjQYMGacOGDeWuP378uCZPnqwePXqod+/emj9//l87AwAAUKP42r1BfHy8QkJCtHHjRn311VeaMmWKkpKSVL9+/XLjpk2bprZt2+qFF15Qamqqxo8fr6ioKLVo0UKSNGnSJPXp00czZ86UJB05cuTvnw0AAKj2bK2cFBQUaOvWrRo3bpz8/f3Vo0cPtWrVStu2basw7uuvv9aYMWPk6+ur1q1bKyYmRuvXr5ckpaSkyM/PTyNGjJC/v7/8/f0VGRlZeWcFAACqLVsrJ4cPH1ZgYKBCQ0Od2yIjI5Wenl5unGVZsiyrwraycT/88IPCwsI0ceJE/fDDD/rHP/6hyZMnKyIi4oLHLS4uVnFxcfmJ+/rKz8/PzvQvy+FwlPsXFZGRPeTlOrKyj8zsIS/XuTMrb+/Lr4vYKieFhYUKCgoqty0oKEinT5+usO2GG27QokWLNGHCBKWmpmrTpk1q166dJOm3337Tp59+qpdeekmdO3fWihUr9Nhjj+m9996Tj49PheMuW7ZMixcvLrdt2LBhio2NtTN9l2VmZrplvzUJGdlDXq4jK/vIzB7ycp07srrYQsQf2SonAQEBys/PL7ctPz9fgYGBFcY+99xzio+PV79+/XTNNdeof//+KigokCTVrl1bHTp0UPfu3SVJI0eO1NKlS5WZmel8TcofjR49WsOHDy8/cTetnGRmZqpZs2YuNbsrERnZQ16uIyv7yMwe8nKdp7OyVU6aN2+ugoIC5eTk6Oqrr5YkpaWlqX///hXGhoWFad68ec7LTz/9tDp27ChJatWqlVJTU10+rp+fX6UXkUvx9vbmjnsZZGQPebmOrOwjM3vIy3WeysrWEQMDA9WjRw8tWrRIZ8+e1fbt25WamqoePXpUGJuenq6CggIVFxcrOTlZ+/bt06BBgyRJPXv21C+//KKvvvpKpaWlevvttxUSEqJmzZpVzlkBAIBqy/ZbiZ966ilNnz5dvXv3VmhoqGbNmqX69evro48+0rJly7Ry5UpJ59+Rk5iYqOLiYl133XVKSEhwrn40aNBA8fHxev7555WTk6Nrr71WL7zwwgVfbwIAAK4sXtaf31ZzBXM4HMrIyFB4eDhLfhdBRvaQl+vIyj4ys4e8XOfprPjqAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUygkAADAK5QQAABiFcgIAAIxCOQEAAEahnAAAAKNQTgAAgFEoJwAAwCiUEwAAYBQvy7IsT08CAACgDCsnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBRKCcAAMAolBMAAGAUyglQCfgrEK4rKSnx9BQAGI5yggpOnjzp6SlUG6tWrZIkeXl5eXgm1cObb76pefPmqaioyNNTqTby8vI8PQWgyl0x5WTjxo2aMmWK9u3bJ0lyOBwenpF51q9fr8GDB2vWrFl66aWX9Pvvv3t6SsZKTk5Wv3799NFHHykvL4/702WsX79et99+uxISEvTTTz+pdu3aZHYZH3/8sQYNGqSpU6dq7ty5On78uKenZKyNGzdq7Nix2rFjhyR+vl9OdXg89PX0BNzt3LlzWrlypV5//XU1b95cGzZsUPv27eXtfcX0ssvKy8vT3LlztXv3bj366KNq2bKl7r33XrVu3Vr9+vWTZVmsDPx/Z86c0axZs5SSkqL//d//Vffu3T09JaNlZWVp0qRJys/P17PPPqtWrVrp7rvvVm5urho0aODp6Rlr586dWrJkiaZMmaIGDRpo4cKFWrhwoUaNGqXw8HBPT88YpaWlWrt2rZYsWaJmzZpp9erV6tq1q7y9vfm5dQHV6fHQvBlVMsuyFBISopkzZ2rYsGHKysrS1q1bndfh/FMSN954o9asWaOYmBg1aNBA9erV09GjR53X4zyHw6GioiKNHDlS3bt3V0lJiVJSUnTkyBFPT81IPj4+GjRokD744AN16tRJubm5ioiI0I8//ujpqRmptLRUkvTdd9+pS5cuuvnmm9WmTRuNHTtWGRkZSkpK8vAMzdO4cWNNnjxZ48aNU1FRkVavXi2Jn+8XUp0eD2tkOdm2bZuysrJ09uxZ+fn5qXPnzuratau6du2qZs2aadu2bTpz5oy8vLyM+4JUlT9mFBQUpJ49e8rLy0sbNmzQbbfdppCQEFmWpS+++ELHjh3z9HQ9qiyrwsJC1a9fX3369FFaWpomTZqk/v3767333tOoUaOUmJio3377zdPT9bg/5nXVVVfp7rvvdl4XEhKinJwc54OwicvJnlCW2blz5yRJubm5SktLc17ftm1bHT9+XHv37tWePXs8NU0jnDp1yvl/Hx8fXXfddbrlllvUvn17de/eXZ9++qlOnTolb29v7l+qvo+HXpZJs/mb9u/fryeeeEJBQUFq1KiRateurblz55Ybs2PHDq1du1YdOnTQsGHD5HA4jFzScpfLZbRjxw41adJEzZs3148//qh3331XV199tR544IErbgXlz1n5+flp3rx5cjgcmj17to4ePaoJEyYoKipKmzZtUnJysnr27KmBAwd6euoecbn7VmlpqXx8fPRf//VfCggI0NSpUz04WzP8ObNatWopISFBubm5uu222/TEE0/otttu0zfffKOkpCQ1b95c11xzjWJjYz099Sq3e/duTZs2TR07dtRTTz2lunXrVhiTnp6upUuXqkmTJnrooYeuuJ/vf1TdHw/NmEUl2b59u/r06aOVK1dq+vTpOnTokBYsWKDc3FznmA4dOigqKkp79+5VVlaWvL29lZ+f77lJV7GLZVT2Dp2uXbuqefPmKikpUZs2bRQWFqbU1FSdPXvWwzOven/OKiMjQwkJCSotLdWYMWM0ZcoURUVFqbS0VL1791a9evW0f/9+SeYtkVaFy33/lb0OoFWrVrIsS4WFhZ6dsAH+nNnhw4eVkJCgBg0aaPr06fr00081fvx4vfjiixo1apRKS0udL1S/ku5jqampeu2113TzzTfrl19+0XfffXfB82/evLl69OihvXv36uDBg/L29r5iX9hf3R8Pa1Q52bp1q5o0aSJJCg0N1X//939r165d+vrrr53Le/7+/uratasaNWqklStX6plnntHrr7/uXE6t6S6W0bfffltuCdTX9/xrpQMDA+Xj46OAgACPzNeTLpTV3r179fnnnyskJERhYWGSzi8tS1LDhg2dq0tX2iqTdPnvPy8vL3l5ealOnTpKTU1VQEDAFfUAeyEXu49t3bpV/fr108KFCzVlyhStWbNGHTp0UK1ateTn5yfpyrqPRUZGauDAgZo6daq6d++uVatW6cSJExXG+fr6qkOHDrrxxhv1f//3f5oxY4bmzJlzRf5yVd0fD2tEOSl7/rpbt27lno+98cYb1a5dO23evLncb2mtW7dWenq6li9frhMnTmj48OGqVatWlc+7KrmSUUFBgSQ5Xzfx9ttv691331WfPn2qfsIedKms2rdvr82bNzt/uyj7rWzFihXasmWLevfuXfUT9jBXv//KikivXr2UkZGhX3755Yp6gP2jy93HNm7cqLy8PPn6+ioqKkqStGzZMn3++efq1q2bR+bsKWX3m1tvvVWSFBcXp2PHjumzzz674Af6XX311Tpy5Ig2btyo06dP67HHHpO/v3+VztmTasrjYY0oJ2W/ubZt21bnzp3Tzp07ndeNHDlSn332mXJyciRJp0+f1tSpU3Xo0CG9/vrrevnll1W/fn2PzLsquZJRWSn54osvNGTIEK1bt06zZs1y/lC4UtjJKiUlRQMGDNDatWv17LPP6sYbb/TInD3J1e+/siJy4sQJxcbGKjg42CPzNcHlMtu+fbvzPpaenq4nnnhCycnJmjZtmiIjIz0yZ08pu9/4+vqqpKREAQEBGjZsmD788ENlZmaWW/EtLi5WfHy89uzZo8TERM2dO/eKe8t6TXk8rDblJDs7W0lJSRVeqW5ZlnMJqk2bNgoNDdUnn3zibNSNGzdWVFSUdu3aJUkKCgrSmDFjlJycrLZt21btSbjZ382o7E7cu3dvTZkyRW+//bauv/76qj2JKlJZWUVHRzuzuu6666r2JKrQ381r9+7dztu0bt1aDz30kEJCQqruBDygsn5mhYeH6/7779eqVatq7H3sUln9cXWk7OnmIUOGyM/PTxs2bJC3t7fzKZ5atWrpvvvu0yeffKJ27dpV3QlUsaysLCUmJmrr1q3lPm25Jj0eVotysmDBAsXGxuq7777TtGnTNG/ePOenJXp5eTmXoPz8/NSzZ0/99ttvWrBggaTzHzDm7e2tTp06STp/566JH2JUGRnddNNNkqQ6deo486qJKjOrunXr1vgPYquMvK60FaXK/Jnl5+enVq1aeeZEqsDlsiorJGWfJVT2QPv4449rw4YNGj9+vPr27auff/5ZXl5eatSokWdOpIokJCTo7rvvVlZWll599VXNmTNHp0+fllTDHg8tw73//vvWAw88YB05csSyLMv69ttvrdjYWOvnn392jlm9erXVqVMn69VXX7XOnTtnffPNN1afPn2sSZMmWTExMdaTTz5pFRYWeuoU3I6MXEdW9lRmXg6Hw1OnUaW4j7nO1aw6d+5svfLKK+Vuu2bNGqtTp07W5MmTnbev6dauXWs9/fTTVmZmpmVZlrVlyxZr6NCh1unTp51jVq1aVSPuW0aWk3Pnzjn/f+DAAWvt2rWWZVlWUVGRZVmWNWrUKCspKcmyLMs6fPiwdc8991hffvlluX0cO3bM2rVrl/X1119XzaSrGBm5jqzsIS/7yMx1lZHVzp07rREjRlTYXhP9Ma+TJ09aZ86csSzLsvbs2WMNHDjQ+vd//3dr7969lmWdvw+NHDmyRty3jPoQtlOnTmnBggXy8vJSZGSk7rzzTufb5sqcO3dO48aN06OPPlrh+VfLsuRwOJwvCKqJyMh1ZGUPedlHZq4jK3sulVdGRoZeeeUVRUVFKTo6Wp999pm8vLx09913O18AXN3zMuY1J+vWrdPdd9/tfOvcunXrFB8fL+n8R1xb51d5dOLECZ09e1b16tUr9xkJpaWl8vLyqrZfCFeQkevIyh7yso/MXEdW9lwqL+n8h83Nnj1b48aNU7t27XTTTTcpPT3d+cLzmpCXEX+VOC8vT4cOHdJDDz2kQYMGSZKuv/56Pf300zp58qSCg4OdH6v7448/ysfHx/kingMHDqhx48Y1/u1iZOQ6srKHvOwjM9eRlT2XyuvUqVNq2LChpPOfuFxcXCw/Pz9df/31mjZtmnr27ClJ1bqUlPFYOcnOzpaXl5euvvpqBQQEqGfPnmratKnz+tOnT6t+/foKDAyUJOfn/aempmrAgAHKzs7WhAkTFBQUpDlz5njkHNyNjFxHVvaQl31k5jqyssfVvMo+qbvss1/KnubZv3+/mjZt6vzAvpqgysvJuXPnNH36dH3zzTe66qqr9K//+q8aMGCA8z3plmXJy8tLtWvXVmBgoPNtZJZlqbS0VD/88IO++uorLVy4UCNHjtSYMWOq+hTcjoxcR1b2kJd9ZOY6srLnr+YlSSdPntS2bducf1Lj/vvvr1Ef0Fflrzn5+OOPdfr0aX344YcaOXKkjhw5olmzZlUYt2nTJjVp0sT5xSh7v/vRo0d122236aOPPqqxd1wych1Z2UNe9pGZ68jKnr+alyQFBwcrPT1dderU0dq1a3XXXXdV5dTdrkrKydmzZ50vbkpNTVW9evXk6+ur3r1767777tOhQ4f03nvvSTrfJC3L0g8//OD8my4ff/yxVq1aJUlKTEzUjBkznMuBNQUZuY6s7CEv+8jMdWRlT2XklZSUJEmaOHGinnzySdWpU8czJ+NGbn1a5/Dhw3rhhRcUGBiogIAATZ48WXXr1pWPj4/OnDmjunXrqlmzZrrvvvu0cOFC50cSFxQUqEGDBsrNzdXDDz+s77//XpMnT5akGnenJSPXkZU95GUfmbmOrOxxR14m/IE+d3HbysmaNWt0//336x//+IdGjBihn376SUuXLlVkZKR27dql7Oxs59iYmBi1bNlSq1evlnT+D11t375dzz77rCIjI7V582b17dvXXVP1GDJyHVnZQ172kZnryMoe8rLPbeXk6NGjiouL0/jx49W+fXs9//zzeuedd9S9e3fVq1dPycnJys3NlXS+/TVu3FjFxcXnJ+XtrbFjx+qDDz7QhAkT3DVFjyMj15GVPeRlH5m5jqzsIS/73Pa0TtmSlHT+eTMfHx9FRESopKREY8aM0dy5cxUeHq7bb79dgYGBys3Ndf6p5tatWxv3FxLdgYxcR1b2kJd9ZOY6srKHvOxzWzkJDQ2VdP6tULVq1dLx48fl5eUlPz8/dezYUYMGDdInn3yizZs3q6SkREePHnW+farsPe81HRm5jqzsIS/7yMx1ZGUPednn9s85KfuwmJ07dyoiIsL5yXVDhgxRdHS0UlJSdObMGd17773unoqxyMh1ZGUPedlHZq4jK3vIy3VuLyelpaXy8fHRzz//rFtvvVWStHLlSuXl5ek///M/NWTIEHdPwXhk5Dqysoe87CMz15GVPeTlOrevF/n4+KikpERnz55Vdna2xo4dq9dff13t27d396GrDTJyHVnZQ172kZnryMoe8nJdlXx8fXp6unbs2KFffvlF//Ef/6F77rmnKg5brZCR68jKHvKyj8xcR1b2kJdrvKw//l1qNykpKdG7776roUOHqnbt2u4+XLVERq4jK3vIyz4ycx1Z2UNerqmScgIAAOCqK/M9SgAAwFiUEwAAYBTKCQAAMArlBAAAGIVyAgAAjEI5AQAARqGcAAAAo1BOAACAUSgnANxu9+7d6tSpkzp16qSjR496ejoADEc5AVCpZsyYoU6dOikuLs65rU6dOmrfvr3at28vPz8/D84OQHVQJX/4D8CVrXXr1kpMTPT0NABUE/xtHQCVZuDAgTp27FiF7a+++qruv/9+SdKHH36oJk2aaMaMGVq3bp3CwsI0btw4/fOf/1ReXp4GDRqkhx56SAsWLNCHH36oOnXqaPTo0Ro6dKhzf7/99psWLlyoL7/8Urm5uQoNDdXAgQN17733yteX37mA6o7vYgCV5tprr1VhYaFyc3MVFBSkiIgISdKBAwcuepvjx4/r+eefV6NGjZSfn68VK1Zox44dysnJUZ06dZSdna3Zs2frxhtvVEREhHJzc3XvvfcqOzvbeYz09HS9+uqr+vXXXzV9+vSqOl0AbsJrTgBUmhdeeEHR0dGSzheVxMREJSYmqnXr1he9zblz5zR//nwlJSUpNDRUkpSZmakVK1bovffeU+3ateVwOLRnzx5J0sqVK5Wdna2QkBCtWbNGK1asUHx8vCRp3bp1yszMdPNZAnA3Vk4AeFS9evXUoUMHSVLjxo2VnZ2tVq1aqUmTJpKkhg0bKisrSydPnpQk/fDDD5KkEydO6NZbby23L8uytG/fPjVr1qzqTgBApaOcAPCooKAg5/99fHwqbPPy8pJ0vnj8+XZlTxv9kb+/vzumCaAKUU4AVKqycnD27Fm37L9t27ZKSUmRj4+PZs2a5Vxhyc/P15YtW9SzZ0+3HBdA1aGcAKhULVq0kCTt379fd911lwICAjR27NhK239sbKw++OAD5eTkaMiQIYqIiFB+fr6ys7NVUlKiAQMGVNqxAHgGL4gFUKkGDRqkXr16qU6dOkpLS9O+ffvkcDgqbf8NGzbUsmXLNHDgQNWvX19paWkqKipSx44dNWnSpEo7DgDP4XNOAACAUVg5AQAARqGcAAAAo1BOAACAUSgnAADAKJQTAABgFMoJAAAwCuUEAAAYhXICAACMQjkBAABGoZwAAACjUE4AAIBR/h+q3rtA4u0qagAAAABJRU5ErkJggg==", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "on.generators.constant().generate(1, pd.Timestamp('2022-01-01'), pd.Timestamp('2022-12-31')).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "a389170d-8cce-4cd2-8bd2-a6bf2403213c", - "metadata": {}, - "source": [ - "### Gaussian Noise" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ae82840b-2bf5-4d9b-936c-7355cd7da95d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "on.generators.gaussian().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "bb922dbb-03eb-4888-ae64-e7b297dcb9ba", - "metadata": {}, - "source": [ - "### Random Walk" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "9802955b-6791-43bb-80d4-74ccb4119d70", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb55754b-215a-457e-9577-2571c6d81c6d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file diff --git a/notebooks/docs/0.4-modelling-libraries.ipynb b/notebooks/docs/0.4-modelling-libraries.ipynb deleted file mode 100644 index e164b9a..0000000 --- a/notebooks/docs/0.4-modelling-libraries.ipynb +++ /dev/null @@ -1,2693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", - "metadata": {}, - "source": [ - "# Modelling Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", - "metadata": {}, - "outputs": [], - "source": [ - "# Import to be able to import python package from src\n", - "import sys\n", - "sys.path.insert(0, '../src')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import ontime as on" - ] - }, - { - "cell_type": "markdown", - "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", - "metadata": {}, - "source": [ - "---\n", - "## Generation of random time series" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e9a96d79-0423-4d79-b01d-726193216238", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))\n", - "ts = ts.astype(np.float32)" - ] - }, - { - "cell_type": "markdown", - "id": "1d4bec6b-eedb-4a88-ba68-dbeae5f0644e", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", - "metadata": {}, - "source": [ - "## Preprocessing" - ] - }, - { - "cell_type": "markdown", - "id": "b7ab9b51-6c63-4068-ac53-98790bf55fde", - "metadata": {}, - "source": [ - "- [x] Normalize\n", - "- [x] Split train, test, val\n", - "- [ ] Feature engineering\n", - " - add weather for location\n", - " - add day of the week, month, year, etc.\n", - " - add whatever\n", - "- [x] Windowing\n", - "- [x] Windowing - Split (parts to train as X, parts to predict as y)\n", - "- [ ] Windowing - to tf.data.Dataset\n", - "- [ ] Windowing - to Pytorch DataLoaders" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", - "from darts.dataprocessing.transformers import Scaler\n", - "\n", - "def normalize(ts: on.TimeSeries, type='minmax', return_transformer=False):\n", - " match type:\n", - " case 'minmax':\n", - " scaler = MinMaxScaler()\n", - " case 'zscore':\n", - " scaler = StandardScaler()\n", - " transformer = Scaler(scaler)\n", - " ts_transformed = transformer.fit_transform(ts)\n", - " if return_transformer:\n", - " return ts_transformed, transformer\n", - " else:\n", - " return ts_transformed" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "de144fa1-d419-46ae-9da1-102db4da92bb", - "metadata": {}, - "outputs": [], - "source": [ - "def train_test_split(ts: on.TimeSeries, test_split=None, train_split=None) -> tuple:\n", - " \"\"\"\n", - " Description\n", - " \n", - " :param ts: TimeSeries to split\n", - " :param test_split: float, int or pd.TimeStamp\n", - " :param train_split: float, int or pd.TimeStamp\n", - " \"\"\"\n", - " \n", - " if train_split is not None and test_split is not None:\n", - " raise Exception('Only one of those two parameters can be set : train_split, test_split.')\n", - "\n", - " if train_split is None and test_split is None:\n", - " test_split = 0.25\n", - " \n", - " # split ts in subts : train, test\n", - " if test_split is not None: \n", - " train_set, test_set = ts.split_after(1-test_split)\n", - " \n", - " if train_split is not None:\n", - " train_set, test_set = ts.split_after(train_split)\n", - "\n", - " return train_set, test_set" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9a297972-1588-4539-8168-05ec379c794d", - "metadata": {}, - "outputs": [], - "source": [ - "def split_by_n(ts, n, drop_last=True):\n", - "\n", - " # Get DataFrame\n", - " df = ts.pd_dataframe()\n", - " \n", - " # Calculate the total number of splits needed\n", - " total_splits = -(-len(df) // n) # Ceiling division to get the number of parts\n", - " \n", - " # Initialize a list to hold the DataFrame splits\n", - " splits_df = []\n", - " \n", - " # Loop through the DataFrame and split it\n", - " for split in range(total_splits):\n", - " start_index = split * n\n", - " end_index = start_index + n\n", - " # Append the part to the list, using slicing with .iloc\n", - " splits_df.append(df.iloc[start_index:end_index])\n", - "\n", - " # If the last dataframe has a different length, then drop it.\n", - " if drop_last:\n", - " last_df = splits_df[-1]\n", - " second_last = splits_df[-2] \n", - " if len(last_df) != len(second_last):\n", - " splits_df = splits_df[:-1]\n", - "\n", - " # Change the data sctructure from DataFrame to TimeSeries\n", - " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9614843a-70c2-4213-8d03-e2df030236c1", - "metadata": {}, - "outputs": [], - "source": [ - "def split_inputs_from_targets(ts_list, input_len, target_len):\n", - "\n", - " # Change inner data structure to DataFrame\n", - " dfs = [ts.pd_dataframe() for ts in ts_list]\n", - "\n", - " # Create initial arrays\n", - " input_series_list = []\n", - " target_series_list = []\n", - " \n", - " # Iterate over each DataFrame in the list\n", - " for df in dfs:\n", - " # Check if the DataFrame is large enough to accommodate input_len and label_len\n", - " if len(df) >= input_len + target_len:\n", - " # Get the first input_len items\n", - " input_series = df.iloc[:input_len]\n", - " input_series_list.append(input_series)\n", - " \n", - " # Get the last label_len items\n", - " target_series = df.iloc[-target_len:]\n", - " target_series_list.append(target_series)\n", - " else:\n", - " raise Exception('input_len + label_len is longer that the total length of the DataFrame')\n", - "\n", - " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", - " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", - " \n", - " return input_ts_list, target_ts_list" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", - "metadata": {}, - "outputs": [], - "source": [ - "def to_numpy(ts_list):\n", - " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "312a3eb7-162f-4d7e-a68e-78b6d6842493", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "68e883a6-a762-4a81-bf1c-6bb20a4c157c", - "metadata": {}, - "source": [ - "### Test" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "a4b12f07-8a97-403a-a554-89e166574120", - "metadata": {}, - "outputs": [], - "source": [ - "ts_t = normalize(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "84301c56-5e2f-4eea-ad98-a7d0b89c039c", - "metadata": {}, - "outputs": [], - "source": [ - "train, test = train_test_split(ts_t, train_split=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "46e3a480-390f-446e-ab08-824f95467ddd", - "metadata": {}, - "outputs": [], - "source": [ - "train_list = split_by_n(train, 6)\n", - "test_list = split_by_n(test, 6)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "a45e871d-ba2b-4de6-93bc-baf9b26104ec", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, y_train = split_inputs_from_targets(train_list, 4, 2)\n", - "X_test, y_test = split_inputs_from_targets(test_list, 4, 2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9993a67f-41ff-4bb4-b104-df1df61bf16c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "48\n", - "48\n", - "12\n", - "12\n" - ] - } - ], - "source": [ - "print(len(X_train))\n", - "print(len(y_train))\n", - "print(len(X_test))\n", - "print(len(y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b1adc175-b981-4804-819a-9be32d41977b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(48, 4, 1)\n", - "(12, 2, 1)\n" - ] - } - ], - "source": [ - "print(to_numpy(X_train).shape)\n", - "print(to_numpy(y_test).shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a0bc351b-9789-4f0c-914d-6e94d160e613", - "metadata": {}, - "outputs": [], - "source": [ - "X_train = to_numpy(X_train)\n", - "y_train = to_numpy(y_train)\n", - "X_test = to_numpy(X_test)\n", - "y_test = to_numpy(y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "0ef9e79a-7c69-446b-a31a-cac8ebce99de", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tensorflow as tf\n", - "\n", - "\n", - "class WindowGenerator:\n", - " def __init__(self, input_width, target_width, shift, ts, target_columns=None):\n", - " # Store the raw data.\n", - " self.ts = ts\n", - " self.df = ts.pd_dataframe()\n", - "\n", - " # Work out the target column indices.\n", - " self.target_columns = target_columns\n", - " if target_columns is not None:\n", - " self.target_columns_indices = {name: i for i, name in\n", - " enumerate(target_columns)}\n", - " self.column_indices = {name: i for i, name in\n", - " enumerate(self.df.columns)}\n", - "\n", - " # Work out the window parameters.\n", - " self.input_width = input_width\n", - " self.target_width = target_width\n", - " self.shift = shift\n", - "\n", - " self.total_window_size = input_width + shift\n", - "\n", - " self.input_slice = slice(0, input_width)\n", - " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", - "\n", - " self.target_start = self.total_window_size - self.target_width\n", - " self.targets_slice = slice(self.target_start, None)\n", - " self.target_indices = np.arange(self.total_window_size)[self.targets_slice]\n", - "\n", - " def __repr__(self):\n", - " return '\\n'.join([\n", - " f'Total window size: {self.total_window_size}',\n", - " f'Input indices: {self.input_indices}',\n", - " f'Target indices: {self.target_indices}',\n", - " f'Target column name(s): {self.target_columns}'])\n", - "\n", - " def split_window(self, features):\n", - " inputs = features[:, self.input_slice, :]\n", - " targets = features[:, self.targets_slice, :]\n", - " if self.target_columns is not None:\n", - " targets = tf.stack(\n", - " [targets[:, :, self.column_indices[name]] for name in self.target_columns],\n", - " axis=-1)\n", - "\n", - " # Slicing doesn't preserve static shape information, so set the shapes\n", - " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", - " inputs.set_shape([None, self.input_width, None])\n", - " targets.set_shape([None, self.target_width, None])\n", - "\n", - " return inputs, targets\n", - "\n", - " def make_dataset(self, data):\n", - " data = np.array(data, dtype=np.float32)\n", - " ds = tf.keras.utils.timeseries_dataset_from_array(\n", - " data=data,\n", - " targets=None,\n", - " sequence_length=self.total_window_size,\n", - " sequence_stride=1,\n", - " shuffle=True,\n", - " batch_size=32,)\n", - "\n", - " ds = ds.map(self.split_window)\n", - "\n", - " return ds\n", - "\n", - " @property\n", - " def dataset(self):\n", - " return self.make_dataset(self.df)\n", - "\n", - " @property\n", - " def example(self):\n", - " \"\"\"Get and cache an example batch of `inputs, targets` for plotting.\"\"\"\n", - " result = getattr(self, '_example', None)\n", - " if result is None:\n", - " # No example batch was found, so get one from the dataset\n", - " result = next(iter(self.dataset))\n", - " # And cache it for next time\n", - " self._example = result\n", - " return result\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "3b376cac-1262-485b-9c58-d8971c81bd13", - "metadata": {}, - "outputs": [], - "source": [ - "train, test = train_test_split(ts_t, train_split=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "a88057c4-033b-4bb5-81bc-edd7b6781e1a", - "metadata": {}, - "outputs": [], - "source": [ - "train_w = WindowGenerator(\n", - " input_width=5, \n", - " target_width=1, \n", - " shift=1, \n", - " ts=train)\n", - "\n", - "test_w = WindowGenerator(\n", - " input_width=5, \n", - " target_width=1, \n", - " shift=1, \n", - " ts=test)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "5b2ba14d-8ab9-4b87-adf9-cd5cc06682f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 6\n", - "Input indices: [0 1 2 3 4]\n", - "Target indices: [5]\n", - "Target column name(s): None" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_w" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "0d28062b-709f-4bfd-8b6e-1259df0608e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(TensorSpec(shape=(None, 5, 1), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_w.dataset.element_spec" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "30d1b98e-bee2-427b-9dc7-29381b15a740", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(TensorSpec(shape=(None, 5, 1), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_w.dataset.element_spec" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "16f5c827-d04f-4caf-9e57-8846a8b6ccef", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "<_MapDataset element_spec=(TensorSpec(shape=(None, 5, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))>" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "dc152b42-9150-46f3-9c26-eba81aab82f1", - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "\n", - "def make_dataset(ts):\n", - " data = ts.pd_dataframe().to_numpy()\n", - " ds = tf.keras.utils.timeseries_dataset_from_array(\n", - " data=data,\n", - " targets=None,\n", - " sequence_length=6,\n", - " sequence_stride=1,\n", - " shuffle=False,\n", - " batch_size=32,)\n", - " return ds" - ] - }, - { - "cell_type": "markdown", - "id": "8625cae5-9e25-4629-9288-e8843df3e1b7", - "metadata": {}, - "source": [ - "Typically, data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features." - ] - }, - { - "cell_type": "markdown", - "id": "43da64a5-1660-4fbd-b9ca-7905059c47a4", - "metadata": {}, - "source": [ - "All shapes are: (batch, time, features)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "54d03b85-a8f1-4c3c-b53d-41c241c7a5bd", - "metadata": {}, - "outputs": [], - "source": [ - "ds = make_dataset(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "0a84a5fb-4cda-461e-a237-e3a2eea28318", - "metadata": {}, - "outputs": [], - "source": [ - "l = ds.take(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4de6ecde-7e3d-42eb-b65e-349f38e58b68", - "metadata": {}, - "outputs": [], - "source": [ - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e3c20b74-2dd6-4632-8865-6f38ebb21d11", - "metadata": {}, - "outputs": [], - "source": [ - "X_train_ds = make_dataset(X_train)\n", - "y_train_ds = make_dataset(y_train)\n", - "X_test_ds = make_dataset(X_test)\n", - "y_test_ds = make_dataset(y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "0e56ce44-b648-4195-ab6c-88a3dc31e757", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(\n", - "[[[[0.18770352]\n", - " [0.17028235]\n", - " [0.14567316]\n", - " [0.12912521]]\n", - "\n", - " [[0.00774153]\n", - " [0. ]\n", - " [0.0388972 ]\n", - " [0.1035383 ]]\n", - "\n", - " [[0.14808738]\n", - " [0.17584604]\n", - " [0.2224158 ]\n", - " [0.22889872]]\n", - "\n", - " [[0.30970636]\n", - " [0.2859744 ]\n", - " [0.26491043]\n", - " [0.2591856 ]]\n", - "\n", - " [[0.33711928]\n", - " [0.310189 ]\n", - " [0.24795601]\n", - " [0.24181467]]\n", - "\n", - " [[0.29296172]\n", - " [0.3365885 ]\n", - " [0.37652487]\n", - " [0.40132314]]]\n", - "\n", - "\n", - " [[[0.00774153]\n", - " [0. ]\n", - " [0.0388972 ]\n", - " [0.1035383 ]]\n", - "\n", - " [[0.14808738]\n", - " [0.17584604]\n", - " [0.2224158 ]\n", - " [0.22889872]]\n", - 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"id": "58cf0f06-a12e-4e60-905c-eca5f7b734f9", - "metadata": {}, - "source": [ - "- [x] Darts\n", - "- [x] Scikit-learn API compatible regressor\n", - "- [ ] GluonTS\n", - "- [ ] Kats\n", - "- [ ] Custom PyTorch\n", - "- [ ] Custom TensorFlow" - ] - }, - { - "cell_type": "markdown", - "id": "8e991124-59fd-4bde-84d0-c1622e37173a", - "metadata": {}, - "source": [ - "### Darts models" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a1b679c1-4334-4d10-9ef1-019e81a36b90", - "metadata": {}, - "outputs": [], - "source": [ - "from darts.models import BlockRNNModel" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "eaec176b-c27c-4f8b-a4b1-967c258bd944", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "darts.models.forecasting.torch_forecasting_model INFO Train dataset contains 348 samples.\n", - "darts.models.forecasting.torch_forecasting_model INFO Time series values are 32-bits; casting model to float32.\n", - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "---------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | rnn | RNN | 2.0 K \n", - "4 | fc | Sequential | 156 \n", - "---------------------------------------------------\n", - "2.2 K Trainable params\n", - "0 Non-trainable params\n", - "2.2 K Total params\n", - "0.009 Total estimated model params size (MB)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.29it/s, train_loss=4.480]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "`Trainer.fit` stopped: `max_epochs=50` reached.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.16it/s, train_loss=4.480]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: True (mps), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicting DataLoader 0: 100%|██████████████████████████████████| 1/1 [00:00<00:00, 124.78it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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+       "    hierarchy:          None
" + ], + "text/plain": [ + "\n", + "array([[[0.40508181]],\n", + "\n", + " [[1.43521041]],\n", + "\n", + " [[1.17597071]],\n", + "\n", + " [[0.97419216]],\n", + "\n", + " [[0.1523104 ]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n", + " * component (component) object 'random_walk'\n", + "Dimensions without coordinates: sample\n", + "Attributes:\n", + " static_covariates: None\n", + " hierarchy: None" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts[0:5]" + ] + }, + { + "cell_type": "markdown", + "id": "0cbd8da5-81fd-4b2d-8b7d-8394ad87348b", + "metadata": {}, + "source": [ + "---\n", + "## Use `TimeSeries` object" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1cbbd4f4-035d-43fa-93a7-6801b944835f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ts.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "06a8a724-7142-4077-b8b6-afafa8950d7b", + "metadata": {}, + "source": [ + "---\n", + "## Custom Class Creation" + ] + }, + { + "cell_type": "markdown", + "id": "cd126529-3b5e-4a22-a871-60d221b6df6d", + "metadata": {}, + "source": [ + "### Create custom detector" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "40a0dd53-0fe9-40a7-86d9-ee551e4c5e6e", + "metadata": {}, + "outputs": [], + "source": [ + "from ontime.core.detector.abstract_detector import AbstractDetector\n", + "\n", + "class MyDetector(AbstractDetector):\n", + "\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " def detect(self, ts):\n", + " print('I detected')\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "55bc256f-0ca3-4087-b7a9-bf563f26ffe7", + "metadata": {}, + "source": [ + "Load custom detector in OnTime" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6a8dd074-6350-4c3a-a8a7-7d901b790f95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['threshold', 'quantile']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "on.detectors.get_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "91bdf719-f451-4d07-aaed-c2f5f7b4fa1b", + "metadata": {}, + "outputs": [], + "source": [ + "on.detectors.load('my_detector', MyDetector)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bb636aa5-f155-46e4-8038-027d5f5db78a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['threshold', 'quantile', 'my_detector']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "on.detectors.get_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f16ac090-142d-4d1b-8351-40b443275c72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I detected\n" + ] + } + ], + "source": [ + "on.detectors.my_detector().detect(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74a24176-7c9b-4790-9e5c-ac71e8517872", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/docs/0_core/0.2-detectors-generators.ipynb b/notebooks/docs/0_core/0.2-detectors-generators.ipynb new file mode 100644 index 0000000..80c641f --- /dev/null +++ b/notebooks/docs/0_core/0.2-detectors-generators.ipynb @@ -0,0 +1,720 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", + "metadata": {}, + "source": [ + "# Detectors, Generators" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9286e0b8-3c78-4b0f-943c-d219e9840dfe", + "metadata": {}, + "outputs": [], + "source": [ + "# Import to be able to import python package from src\n", + "import sys\n", + "sys.path.insert(0, '../src')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", + "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from tqdm.autonotebook import tqdm\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import ontime as on" + ] + }, + { + "cell_type": "markdown", + "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", + "metadata": {}, + "source": [ + "---\n", + "## Generation of random time series" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e9a96d79-0423-4d79-b01d-726193216238", + "metadata": {}, + "outputs": [], + "source": [ + "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d463df9c-4f02-4c1e-b1a5-7162b9ea8c63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
+       "array([[[-0.15813833]],\n",
+       "\n",
+       "       [[ 0.430772  ]],\n",
+       "\n",
+       "       [[ 0.86925141]],\n",
+       "\n",
+       "       [[-0.93593666]],\n",
+       "\n",
+       "       [[-2.10009435]]])\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n",
+       "  * component  (component) object 'random_walk'\n",
+       "Dimensions without coordinates: sample\n",
+       "Attributes:\n",
+       "    static_covariates:  None\n",
+       "    hierarchy:          None
" + ], + "text/plain": [ + "\n", + "array([[[-0.15813833]],\n", + "\n", + " [[ 0.430772 ]],\n", + "\n", + " [[ 0.86925141]],\n", + "\n", + " [[-0.93593666]],\n", + "\n", + " [[-2.10009435]]])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-01-05\n", + " * component (component) object 'random_walk'\n", + "Dimensions without coordinates: sample\n", + "Attributes:\n", + " static_covariates: None\n", + " hierarchy: None" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ts[0:5]" + ] + }, + { + "cell_type": "markdown", + "id": "2e4f348e-e7f7-4ed6-9f5a-25504e729529", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "851d573e-f47d-4055-9021-f9ef1002694d", + "metadata": {}, + "source": [ + "## Detectors" + ] + }, + { + "cell_type": "markdown", + "id": "5af625dd-ba6b-4f3b-9f42-462fe8918c5a", + "metadata": {}, + "source": [ + "### Threshold" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8310ade1-a382-4d2a-b139-0331b3b8ebed", + "metadata": {}, + "outputs": [], + "source": [ + "td = on.detectors.threshold(low_threshold=-2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5b3d020e-18cc-47f2-a553-eb00ff972ef3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "td.detect(ts).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "ffbed9d6-d331-4708-8d50-25882c85e60d", + "metadata": {}, + "source": [ + "### Quantile" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "04f2a0c4-5744-46bf-b622-9abaaaf6b35c", + "metadata": {}, + "outputs": [], + "source": [ + "td = on.detectors.quantile(low_quantile=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "02f12ec0-d1cc-41db-ba53-c53a98f6d8f3", + "metadata": {}, + "outputs": [], + "source": [ + "td.fit(ts)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d640d149-f0eb-4d19-9e2b-10926d6fa26f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "td.detect(ts).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "047ee4b8-3f9c-4cca-8fe3-53200301a013", + "metadata": {}, + "source": [ + "---\n", + "## Generators\n", + "\n", + "### Constant" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b6723710-34ae-4d60-b120-5dce3df07989", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "on.generators.constant().generate(1, pd.Timestamp('2022-01-01'), pd.Timestamp('2022-12-31')).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "a389170d-8cce-4cd2-8bd2-a6bf2403213c", + "metadata": {}, + "source": [ + "### Gaussian Noise" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ae82840b-2bf5-4d9b-936c-7355cd7da95d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "on.generators.gaussian().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "bb922dbb-03eb-4888-ae64-e7b297dcb9ba", + "metadata": {}, + "source": [ + "### Random Walk" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9802955b-6791-43bb-80d4-74ccb4119d70", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31')).plot();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb55754b-215a-457e-9577-2571c6d81c6d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/docs/0.3-processing.ipynb b/notebooks/docs/0_core/0.3-processing.ipynb similarity index 56% rename from notebooks/docs/0.3-processing.ipynb rename to notebooks/docs/0_core/0.3-processing.ipynb index 2dafe10..b7e4cf6 100644 --- a/notebooks/docs/0.3-processing.ipynb +++ b/notebooks/docs/0_core/0.3-processing.ipynb @@ -499,7 +499,7 @@ "Dimensions without coordinates: sample\n", "Attributes:\n", " static_covariates: None\n", - " hierarchy: None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", @@ -1042,7 +1042,7 @@ "Dimensions without coordinates: sample\n", "Attributes:\n", " static_covariates: None\n", - " hierarchy: None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", @@ -1211,7 +1211,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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", "text/plain": [ "
    " ] @@ -1743,148 +1743,148 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 365, component: 1, sample: 1)>\n",
-       "array([[[ 0.22856328]],\n",
+       "array([[[-0.28446992]],\n",
        "\n",
-       "       [[ 0.37364648]],\n",
+       "       [[ 0.40206202]],\n",
        "\n",
-       "       [[ 1.53911985]],\n",
+       "       [[-0.08163155]],\n",
        "\n",
-       "       [[ 2.95379204]],\n",
+       "       [[ 1.408035  ]],\n",
        "\n",
-       "       [[ 3.28662792]],\n",
+       "       [[ 2.67956629]],\n",
        "\n",
-       "       [[ 3.79643527]],\n",
+       "       [[ 3.28924893]],\n",
        "\n",
-       "       [[ 5.97261116]],\n",
+       "       [[ 4.32680234]],\n",
        "\n",
-       "       [[ 7.0681775 ]],\n",
+       "       [[ 3.39678829]],\n",
        "\n",
-       "       [[ 7.0533794 ]],\n",
+       "       [[ 4.72305147]],\n",
        "\n",
-       "       [[ 7.21773104]],\n",
+       "       [[ 3.25746454]],\n",
        "\n",
        "...\n",
        "\n",
-       "       [[17.86891974]],\n",
+       "       [[ 9.68434639]],\n",
        "\n",
-       "       [[15.91575202]],\n",
+       "       [[11.07561827]],\n",
        "\n",
-       "       [[16.09807771]],\n",
+       "       [[10.03762309]],\n",
        "\n",
-       "       [[16.59950989]],\n",
+       "       [[10.2329436 ]],\n",
        "\n",
-       "       [[16.4253566 ]],\n",
+       "       [[10.42700157]],\n",
        "\n",
-       "       [[17.34655627]],\n",
+       "       [[10.7945289 ]],\n",
        "\n",
-       "       [[17.56370215]],\n",
+       "       [[ 9.36551048]],\n",
        "\n",
-       "       [[18.88084012]],\n",
+       "       [[ 8.11774274]],\n",
        "\n",
-       "       [[17.02758259]],\n",
+       "       [[ 8.65613076]],\n",
        "\n",
-       "       [[16.20917557]]])\n",
+       "       [[10.548976  ]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n",
        "  * component  (component) object 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[ 0.22856328]],\n", + "array([[[-0.28446992]],\n", "\n", - " [[ 0.37364648]],\n", + " [[ 0.40206202]],\n", "\n", - " [[ 1.53911985]],\n", + " [[-0.08163155]],\n", "\n", - " [[ 2.95379204]],\n", + " [[ 1.408035 ]],\n", "\n", - " [[ 3.28662792]],\n", + " [[ 2.67956629]],\n", "\n", - " [[ 3.79643527]],\n", + " [[ 3.28924893]],\n", "\n", - " [[ 5.97261116]],\n", + " [[ 4.32680234]],\n", "\n", - " [[ 7.0681775 ]],\n", + " [[ 3.39678829]],\n", "\n", - " [[ 7.0533794 ]],\n", + " [[ 4.72305147]],\n", "\n", - " [[ 7.21773104]],\n", + " [[ 3.25746454]],\n", "\n", "...\n", "\n", - " [[17.86891974]],\n", + " [[ 9.68434639]],\n", "\n", - " [[15.91575202]],\n", + " [[11.07561827]],\n", "\n", - " [[16.09807771]],\n", + " [[10.03762309]],\n", "\n", - " [[16.59950989]],\n", + " [[10.2329436 ]],\n", "\n", - " [[16.4253566 ]],\n", + " [[10.42700157]],\n", "\n", - " [[17.34655627]],\n", + " [[10.7945289 ]],\n", "\n", - " [[17.56370215]],\n", + " [[ 9.36551048]],\n", "\n", - " [[18.88084012]],\n", + " [[ 8.11774274]],\n", "\n", - " [[17.02758259]],\n", + " [[ 8.65613076]],\n", "\n", - " [[16.20917557]]])\n", + " [[10.548976 ]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2022-01-01 2022-01-02 ... 2022-12-31\n", " * component (component) object 'random_walk'\n", @@ -1941,7 +1941,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "id": "f5244eb1-4811-4c6d-baf3-33c52da1c824", "metadata": {}, "outputs": [], @@ -1960,30 +1960,30 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "99fa151a-1fe4-4aea-95c0-8a6029e34c51", "metadata": {}, "outputs": [], "source": [ - "ts_corr = on.processors.correlation.process(ts, '1D')" + "correlation = on.processors.correlation('1D')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "943287fb-a54d-4c3c-a797-ce44057241da", + "metadata": {}, + "outputs": [], + "source": [ + "ts_corr = correlation.process(ts)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "dea59b2d-2ce0-4916-a7a6-b6778bdbedfd", "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { "image/png": 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", diff --git a/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb b/notebooks/docs/0_core/0.4-modelling.ipynb similarity index 56% rename from notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb rename to notebooks/docs/0_core/0.4-modelling.ipynb index c17e8cd..95d13e1 100644 --- a/notebooks/docs/0.4.2-modelling-libraries_tensorflow.ipynb +++ b/notebooks/docs/0_core/0.4-modelling.ipynb @@ -5,7 +5,7 @@ "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", "metadata": {}, "source": [ - "# Modelling Libraries - Tensorflow" + "# Modelling Libraries" ] }, { @@ -40,8 +40,7 @@ "source": [ "import pandas as pd\n", "import numpy as np\n", - "import ontime as on\n", - "from darts.datasets import EnergyDataset" + "import ontime as on" ] }, { @@ -50,7 +49,7 @@ "metadata": {}, "source": [ "---\n", - "## Load data" + "## Generation of random time series" ] }, { @@ -60,658 +59,10 @@ "metadata": {}, "outputs": [], "source": [ - "ts = EnergyDataset().load()\n", + "ts = on.generators.random_walk().generate(start=pd.Timestamp('2022-01-01'), end=pd.Timestamp('2022-12-31'))\n", "ts = ts.astype(np.float32)" ] }, - { - "cell_type": "markdown", - "id": "1d4bec6b-eedb-4a88-ba68-dbeae5f0644e", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", - "metadata": {}, - "source": [ - "## Preprocessing" - ] - }, - { - "cell_type": "markdown", - "id": "b7ab9b51-6c63-4068-ac53-98790bf55fde", - "metadata": {}, - "source": [ - "- [x] Normalize\n", - "- [x] Split train, test, val\n", - "- [ ] Feature engineering\n", - " - add weather for location\n", - " - add day of the week, month, year, etc.\n", - " - add whatever\n", - "- [x] Windowing\n", - "- [x] Windowing - Split (parts to train as X, parts to predict as y)\n", - "- [ ] Windowing - to tf.data.Dataset\n", - "- [ ] Windowing - to Pytorch DataLoaders" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1daa085b-81ad-4569-9f78-3c0884bf93a3", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n", - "from darts.dataprocessing.transformers import Scaler\n", - "\n", - "def normalize(ts: on.TimeSeries, type='minmax', return_transformer=False):\n", - " match type:\n", - " case 'minmax':\n", - " scaler = MinMaxScaler()\n", - " case 'zscore':\n", - " scaler = StandardScaler()\n", - " transformer = Scaler(scaler)\n", - " ts_transformed = transformer.fit_transform(ts)\n", - " if return_transformer:\n", - " return ts_transformed, transformer\n", - " else:\n", - " return ts_transformed" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "de144fa1-d419-46ae-9da1-102db4da92bb", - "metadata": {}, - "outputs": [], - "source": [ - "def train_test_split(ts: on.TimeSeries, test_split=None, train_split=None) -> tuple:\n", - " \"\"\"\n", - " Description\n", - " \n", - " :param ts: TimeSeries to split\n", - " :param test_split: float, int or pd.TimeStamp\n", - " :param train_split: float, int or pd.TimeStamp\n", - " \"\"\"\n", - " \n", - " if train_split is not None and test_split is not None:\n", - " raise Exception('Only one of those two parameters can be set : train_split, test_split.')\n", - "\n", - " if train_split is None and test_split is None:\n", - " test_split = 0.25\n", - " \n", - " # split ts in subts : train, test\n", - " if test_split is not None: \n", - " train_set, test_set = ts.split_after(1-test_split)\n", - " \n", - " if train_split is not None:\n", - " train_set, test_set = ts.split_after(train_split)\n", - "\n", - " return train_set, test_set" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9a297972-1588-4539-8168-05ec379c794d", - "metadata": {}, - "outputs": [], - "source": [ - "def split_by_n(ts, n, drop_last=True):\n", - "\n", - " # Get DataFrame\n", - " df = ts.pd_dataframe()\n", - " \n", - " # Calculate the total number of splits needed\n", - " total_splits = -(-len(df) // n) # Ceiling division to get the number of parts\n", - " \n", - " # Initialize a list to hold the DataFrame splits\n", - " splits_df = []\n", - " \n", - " # Loop through the DataFrame and split it\n", - " for split in range(total_splits):\n", - " start_index = split * n\n", - " end_index = start_index + n\n", - " # Append the part to the list, using slicing with .iloc\n", - " splits_df.append(df.iloc[start_index:end_index])\n", - "\n", - " # If the last dataframe has a different length, then drop it.\n", - " if drop_last:\n", - " last_df = splits_df[-1]\n", - " second_last = splits_df[-2] \n", - " if len(last_df) != len(second_last):\n", - " splits_df = splits_df[:-1]\n", - "\n", - " # Change the data sctructure from DataFrame to TimeSeries\n", - " return list(map(on.TimeSeries.from_dataframe, splits_df))\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9614843a-70c2-4213-8d03-e2df030236c1", - "metadata": {}, - "outputs": [], - "source": [ - "def split_inputs_from_targets(ts_list, input_len, target_len):\n", - "\n", - " # Change inner data structure to DataFrame\n", - " dfs = [ts.pd_dataframe() for ts in ts_list]\n", - "\n", - " # Create initial arrays\n", - " input_series_list = []\n", - " target_series_list = []\n", - " \n", - " # Iterate over each DataFrame in the list\n", - " for df in dfs:\n", - " # Check if the DataFrame is large enough to accommodate input_len and label_len\n", - " if len(df) >= input_len + target_len:\n", - " # Get the first input_len items\n", - " input_series = df.iloc[:input_len]\n", - " input_series_list.append(input_series)\n", - " \n", - " # Get the last label_len items\n", - " target_series = df.iloc[-target_len:]\n", - " target_series_list.append(target_series)\n", - " else:\n", - " raise Exception('input_len + label_len is longer that the total length of the DataFrame')\n", - "\n", - " input_ts_list = list(map(on.TimeSeries.from_dataframe, input_series_list))\n", - " target_ts_list = list(map(on.TimeSeries.from_dataframe, target_series_list))\n", - " \n", - " return input_ts_list, target_ts_list" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "074f7abd-8d8c-4a9e-a0a7-4215f6a88f68", - "metadata": {}, - "outputs": [], - "source": [ - "def to_numpy(ts_list):\n", - " return np.array([ts.pd_dataframe().to_numpy() for ts in ts_list]) " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "312a3eb7-162f-4d7e-a68e-78b6d6842493", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tensorflow as tf\n", - "\n", - "\n", - "class WindowGenerator:\n", - " def __init__(self, input_width, target_width, offset, ts, target_columns=None):\n", - " # Store the raw data.\n", - " self.ts = ts\n", - " self.df = ts.pd_dataframe()\n", - "\n", - " # Work out the target column indices.\n", - " self.target_columns = target_columns\n", - " if target_columns is not None:\n", - " self.target_columns_indices = {name: i for i, name in\n", - " enumerate(target_columns)}\n", - " self.column_indices = {name: i for i, name in\n", - " enumerate(self.df.columns)}\n", - "\n", - " # Work out the window parameters.\n", - " self.input_width = input_width\n", - " self.target_width = target_width\n", - " self.offset = offset\n", - "\n", - " self.total_window_size = input_width + offset\n", - "\n", - " self.input_slice = slice(0, input_width)\n", - " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", - "\n", - " self.target_start = self.total_window_size - self.target_width\n", - " self.targets_slice = slice(self.target_start, None)\n", - " self.target_indices = np.arange(self.total_window_size)[self.targets_slice]\n", - "\n", - " def __repr__(self):\n", - " return '\\n'.join([\n", - " f'Total window size: {self.total_window_size}',\n", - " f'Input indices: {self.input_indices}',\n", - " f'Target indices: {self.target_indices}',\n", - " f'Target column name(s): {self.target_columns}'])\n", - "\n", - " def split_window(self, features):\n", - " inputs = features[:, self.input_slice, :]\n", - " targets = features[:, self.targets_slice, :]\n", - " if self.target_columns is not None:\n", - " targets = tf.stack(\n", - " [targets[:, :, self.column_indices[name]] for name in self.target_columns],\n", - " axis=-1)\n", - "\n", - " # Slicing doesn't preserve static shape information, so set the shapes\n", - " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", - " inputs.set_shape([None, self.input_width, None])\n", - " targets.set_shape([None, self.target_width, None])\n", - "\n", - " return inputs, targets\n", - "\n", - " def make_dataset(self, data):\n", - " data = np.array(data, dtype=np.float32)\n", - " ds = tf.keras.utils.timeseries_dataset_from_array(\n", - " data=data,\n", - " targets=None,\n", - " sequence_length=self.total_window_size,\n", - " sequence_stride=1,\n", - " shuffle=True,\n", - " batch_size=32,)\n", - " return ds.map(self.split_window)\n", - "\n", - " @property\n", - " def dataset(self):\n", - " return self.make_dataset(self.df)\n", - "\n", - " @property\n", - " def example(self):\n", - " \"\"\"Get and cache an example batch of `inputs, targets` for plotting.\"\"\"\n", - " result = getattr(self, '_example', None)\n", - " if result is None:\n", - " # No example batch was found, so get one from the dataset\n", - " result = next(iter(self.dataset))\n", - " # And cache it for next time\n", - " self._example = result\n", - " return result\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "68e883a6-a762-4a81-bf1c-6bb20a4c157c", - "metadata": {}, - "source": [ - "### Test with WindowGenerator" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "dde4ea44-58ad-4f5f-8d0b-773f431d232f", - "metadata": {}, - "outputs": [], - "source": [ - "df = ts.pd_dataframe()\n", - "df = df.interpolate()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c9a69edc-e303-4eec-bf1f-f069adf117ac", - "metadata": {}, - "outputs": [], - "source": [ - "to_drop = []\n", - "for k, v in df.isna().sum().items():\n", - " if v != 0:\n", - " to_drop.append(k)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "65c2c0f7-2138-487d-b676-67e5cb075b34", - "metadata": {}, - "outputs": [], - "source": [ - "df = df.drop(to_drop, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "08a228d2-bae2-4a32-b5d8-2478c0957ad5", - "metadata": {}, - "outputs": [], - "source": [ - "ts = on.TimeSeries.from_dataframe(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "19717f00-b1d5-4ba2-8b07-6feed1a30659", - "metadata": {}, - "outputs": [], - "source": [ - "ts_t = normalize(ts)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3b376cac-1262-485b-9c58-d8971c81bd13", - "metadata": {}, - "outputs": [], - "source": [ - "train, test = train_test_split(ts_t, train_split=0.8)\n", - "train, val = train_test_split(train, train_split=0.8)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a88057c4-033b-4bb5-81bc-edd7b6781e1a", - "metadata": {}, - "outputs": [], - "source": [ - "target_columns = ['generation solar']\n", - "input_width=24\n", - "target_width=12\n", - "\n", - "train_window = WindowGenerator(\n", - " input_width=input_width, \n", - " target_width=target_width, \n", - " offset=1, \n", - " target_columns=target_columns,\n", - " ts=train)\n", - "\n", - "val_window = WindowGenerator(\n", - " input_width=input_width, \n", - " target_width=target_width, \n", - " offset=1, \n", - " target_columns=target_columns,\n", - " ts=val)\n", - "\n", - "test_window = WindowGenerator(\n", - " input_width=input_width, \n", - " target_width=target_width, \n", - " offset=1, \n", - " target_columns=target_columns,\n", - " ts=test)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "1f8d9be9-5c31-4676-8682-74c482b6b592", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 25\n", - "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", - "Target indices: [13 14 15 16 17 18 19 20 21 22 23 24]\n", - "Target column name(s): ['generation solar']" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_window" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0d28062b-709f-4bfd-8b6e-1259df0608e2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(TensorSpec(shape=(None, 24, 26), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 12, 1), dtype=tf.float32, name=None))" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_window.dataset.element_spec" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "30d1b98e-bee2-427b-9dc7-29381b15a740", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(TensorSpec(shape=(None, 24, 26), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 12, 1), dtype=tf.float32, name=None))" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "test_window.dataset.element_spec" - ] - }, - { - "cell_type": "markdown", - "id": "85351a17-2601-4265-b397-817d0c8c02cd", - "metadata": {}, - "source": [ - "## TensorFlow Modelling" - ] - }, - { - "cell_type": "markdown", - "id": "6444e27d-c2c8-4d96-abd9-056675c2a829", - "metadata": {}, - "source": [ - "### Define data" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0789d98b-1a85-4e6d-852e-92b83967f78e", - "metadata": {}, - "outputs": [], - "source": [ - "dataset = {\n", - " 'train': train_window.dataset,\n", - " 'val': val_window.dataset,\n", - " 'test': test_window.dataset,\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "b82bd723-29b8-422f-ab05-444417125b74", - "metadata": {}, - "source": [ - "### Define model" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "45c410da-7ea6-4f07-a18f-6539854904fc", - "metadata": {}, - "outputs": [], - "source": [ - "model = tf.keras.Sequential([\n", - " tf.keras.layers.Conv1D(filters=32,\n", - " kernel_size=(6,),\n", - " activation='relu'),\n", - " tf.keras.layers.Dense(units=32, activation='relu'),\n", - " tf.keras.layers.Dense(units=32, activation='relu'),\n", - " tf.keras.layers.Dense(units=1)\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "3af2167c-04ce-4161-a93b-3f5587d94bd2", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'OUT_STEPS' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[23], line 6\u001b[0m\n\u001b[1;32m 1\u001b[0m model \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mSequential([\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Shape [batch, time, features] => [batch, lstm_units].\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Adding more `lstm_units` just overfits more quickly.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mLSTM(\u001b[38;5;241m32\u001b[39m, return_sequences\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Shape => [batch, out_steps*features].\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mDense(\u001b[43mOUT_STEPS\u001b[49m\u001b[38;5;241m*\u001b[39mnum_features,\n\u001b[1;32m 7\u001b[0m kernel_initializer\u001b[38;5;241m=\u001b[39mtf\u001b[38;5;241m.\u001b[39minitializers\u001b[38;5;241m.\u001b[39mzeros()),\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Shape => [batch, out_steps, features].\u001b[39;00m\n\u001b[1;32m 9\u001b[0m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlayers\u001b[38;5;241m.\u001b[39mReshape([OUT_STEPS, num_features])\n\u001b[1;32m 10\u001b[0m ])\n", - "\u001b[0;31mNameError\u001b[0m: name 'OUT_STEPS' is not defined" - ] - } - ], - "source": [ - "model = tf.keras.Sequential([\n", - " # Shape [batch, time, features] => [batch, lstm_units].\n", - " # Adding more `lstm_units` just overfits more quickly.\n", - " tf.keras.layers.LSTM(32, return_sequences=False),\n", - " # Shape => [batch, out_steps*features].\n", - " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", - " kernel_initializer=tf.initializers.zeros()),\n", - " # Shape => [batch, out_steps, features].\n", - " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", - "])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "d6705888-c015-4247-a43d-60f88441c736", - "metadata": {}, - "source": [ - "### Training" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "5bd01720-b468-453a-8769-0080d787a336", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "absl WARNING At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0822 - mean_absolute_error: 0.2424 - val_loss: 0.0813 - val_mean_absolute_error: 0.2346\n", - "Epoch 2/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0796 - mean_absolute_error: 0.2395 - val_loss: 0.0808 - val_mean_absolute_error: 0.2464\n", - "Epoch 3/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0786 - mean_absolute_error: 0.2382 - val_loss: 0.0801 - val_mean_absolute_error: 0.2416\n", - "Epoch 4/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0780 - mean_absolute_error: 0.2374 - val_loss: 0.0800 - val_mean_absolute_error: 0.2431\n", - "Epoch 5/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0774 - mean_absolute_error: 0.2367 - val_loss: 0.0801 - val_mean_absolute_error: 0.2313\n", - "Epoch 6/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0773 - mean_absolute_error: 0.2365 - val_loss: 0.0798 - val_mean_absolute_error: 0.2433\n", - "Epoch 7/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0770 - mean_absolute_error: 0.2361 - val_loss: 0.0798 - val_mean_absolute_error: 0.2444\n", - "Epoch 8/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0767 - mean_absolute_error: 0.2357 - val_loss: 0.0798 - val_mean_absolute_error: 0.2420\n", - "Epoch 9/20\n", - "701/701 [==============================] - 1s 1ms/step - loss: 0.0767 - mean_absolute_error: 0.2356 - val_loss: 0.0802 - val_mean_absolute_error: 0.2317\n" - ] - } - ], - "source": [ - "MAX_EPOCHS = 20\n", - "\n", - "early_stopping = tf.keras.callbacks.EarlyStopping(\n", - " monitor='val_loss',\n", - " patience=2,\n", - " mode='min'\n", - ")\n", - "\n", - "model.compile(\n", - " loss=tf.keras.losses.MeanSquaredError(),\n", - " optimizer=tf.keras.optimizers.Adam(),\n", - " metrics=[tf.keras.metrics.MeanAbsoluteError()]\n", - ")\n", - "\n", - "history = model.fit(\n", - " dataset['train'], \n", - " epochs=MAX_EPOCHS,\n", - " validation_data=dataset['val'],\n", - " callbacks=[early_stopping]\n", - ")\n" - ] - }, - { - "cell_type": "markdown", - "id": "36ffc316-1e9c-468f-97f3-32df5a302f58", - "metadata": {}, - "source": [ - "### Evaluate" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "81b8a1a9-95dc-4266-8213-f4e6b9f61108", - "metadata": {}, - "outputs": [], - "source": [ - "performance = model.evaluate(dataset['test'], verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "id": "af94a073-55af-454e-a8e1-ce919ce37365", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.05250326171517372, 0.15813955664634705]" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "performance" - ] - }, - { - "cell_type": "markdown", - "id": "8f6f79f5-efb5-40dd-9293-a9699923f60d", - "metadata": {}, - "source": [ - "Concepts\n", - "--------\n", - "\n", - "- Single-step models : Predict one value\n", - " - Single-output models : Predict one value of one feature\n", - " - Multi-output models : Predict one value of many features\n", - "\n", - "- Multi-step models : Predict many values\n", - " - Single-output models : Predict many values of one feature\n", - " - Multi-output models : Predict many values of many features" - ] - }, { "cell_type": "markdown", "id": "6bb9090a-bc1c-4a06-9b6d-ddee9ac64a9a", @@ -721,33 +72,15 @@ "## Models" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "e17ddc34-ed99-4946-ba23-c781b1eab631", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "46cda348-38b4-4672-9f99-a0e0757f00a1", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "58cf0f06-a12e-4e60-905c-eca5f7b734f9", + "id": "c91c7618-9742-4a87-91d7-468f91c222ec", "metadata": {}, "source": [ - "- [x] Darts\n", - "- [x] Scikit-learn API compatible regressor\n", - "- [ ] GluonTS\n", - "- [ ] Kats\n", - "- [ ] Custom PyTorch\n", - "- [ ] Custom TensorFlow" + "Here are a few examples to add models to onTime with : \n", + "\n", + "- Darts models\n", + "- Sklearn models\n" ] }, { @@ -770,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "eaec176b-c27c-4f8b-a4b1-967c258bd944", "metadata": {}, "outputs": [ @@ -803,7 +136,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.29it/s, train_loss=4.480]" + "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 43.73it/s, train_loss=16.60]" ] }, { @@ -817,7 +150,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 46.16it/s, train_loss=4.480]\n" + "Epoch 49: 100%|██████████████████████████████| 11/11 [00:00<00:00, 43.60it/s, train_loss=16.60]\n" ] }, { @@ -834,7 +167,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicting DataLoader 0: 100%|██████████████████████████████████| 1/1 [00:00<00:00, 124.78it/s]\n" + "Predicting DataLoader 0: 100%|███████████████████████████████████| 1/1 [00:00<00:00, 2.98it/s]\n" ] }, { @@ -1204,46 +537,46 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-9.234826 ]],\n",
+       "array([[[10.742026]],\n",
        "\n",
-       "       [[-9.625329 ]],\n",
+       "       [[10.184119]],\n",
        "\n",
-       "       [[-8.548808 ]],\n",
+       "       [[10.689295]],\n",
        "\n",
-       "       [[-9.272842 ]],\n",
+       "       [[10.624429]],\n",
        "\n",
-       "       [[-9.6081705]]], dtype=float32)\n",
+       "       [[10.057509]]], dtype=float32)\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
        "  * component  (component) object 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[-9.234826 ]],\n", + "array([[[10.742026]],\n", "\n", - " [[-9.625329 ]],\n", + " [[10.184119]],\n", "\n", - " [[-8.548808 ]],\n", + " [[10.689295]],\n", "\n", - " [[-9.272842 ]],\n", + " [[10.624429]],\n", "\n", - " [[-9.6081705]]], dtype=float32)\n", + " [[10.057509]]], dtype=float32)\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n", " * component (component) object 'random_walk'\n", @@ -1253,7 +586,7 @@ " hierarchy: None" ] }, - "execution_count": 15, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1279,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "73778d5b-e8d1-4df9-b0dd-b877e2670323", "metadata": {}, "outputs": [], @@ -1289,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "f712c297-579a-4ede-88a6-198ed7b17ca0", "metadata": {}, "outputs": [ @@ -1668,46 +1001,46 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-17.22121839]],\n",
+       "array([[[15.82046499]],\n",
        "\n",
-       "       [[-17.54466988]],\n",
+       "       [[16.25745354]],\n",
        "\n",
-       "       [[-18.1406066 ]],\n",
+       "       [[16.78803853]],\n",
        "\n",
-       "       [[-18.56771941]],\n",
+       "       [[17.49676382]],\n",
        "\n",
-       "       [[-18.52810896]]])\n",
+       "       [[18.3747027 ]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
        "  * component  (component) object 'pred'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[-17.22121839]],\n", + "array([[[15.82046499]],\n", "\n", - " [[-17.54466988]],\n", + " [[16.25745354]],\n", "\n", - " [[-18.1406066 ]],\n", + " [[16.78803853]],\n", "\n", - " [[-18.56771941]],\n", + " [[17.49676382]],\n", "\n", - " [[-18.52810896]]])\n", + " [[18.3747027 ]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n", " * component (component) object 'pred'\n", @@ -1717,7 +1050,7 @@ " hierarchy: None" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/docs/0.6-plots.ipynb b/notebooks/docs/0_core/0.5-plots.ipynb similarity index 100% rename from notebooks/docs/0.6-plots.ipynb rename to notebooks/docs/0_core/0.5-plots.ipynb diff --git a/notebooks/docs/0.7-anomaly-frequency.ipynb b/notebooks/docs/0_core/0.6-anomaly-frequency.ipynb similarity index 100% rename from notebooks/docs/0.7-anomaly-frequency.ipynb rename to notebooks/docs/0_core/0.6-anomaly-frequency.ipynb diff --git a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb b/notebooks/docs/1_module/1.0-preprocessing-common.ipynb similarity index 64% rename from notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb rename to notebooks/docs/1_module/1.0-preprocessing-common.ipynb index 88560fd..50e53d6 100644 --- a/notebooks/docs/0.4.1-modelling-libraries_preprocessing.ipynb +++ b/notebooks/docs/1_module/1.0-preprocessing-common.ipynb @@ -5,7 +5,7 @@ "id": "41296cc6-9d84-47c5-8a92-2d292f6f3c4a", "metadata": {}, "source": [ - "# Modelling Libraries - Preprocessing" + "# Module - Preprocessing" ] }, { @@ -22,28 +22,26 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "2028eed7-b1c3-4c9e-b6a0-00433caa7d0f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", - "import ontime as on\n", "from darts.datasets import EnergyDataset" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4733b4e6-71a2-42b2-93fd-a5615b84ac1a", + "metadata": {}, + "outputs": [], + "source": [ + "import ontime as on" + ] + }, { "cell_type": "markdown", "id": "e24da8ab-6a83-4c2f-9ff0-c633d4693a91", @@ -55,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "e9a96d79-0423-4d79-b01d-726193216238", "metadata": {}, "outputs": [], @@ -77,22 +75,17 @@ "id": "c2c873dd-8643-40cd-895b-fddd7a515c6d", "metadata": {}, "source": [ - "## Preprocessing" + "## Common Preprocessing" ] }, { - "cell_type": "markdown", - "id": "b7ab9b51-6c63-4068-ac53-98790bf55fde", + "cell_type": "code", + "execution_count": 10, + "id": "a630af5c-687e-48e2-a6d4-5a8cb1d1ec66", "metadata": {}, + "outputs": [], "source": [ - "- [x] Normalize\n", - "- [x] Split train, test, val\n", - "- [ ] Feature engineering\n", - " - add weather for location\n", - " - add day of the week, month, year, etc.\n", - " - add whatever\n", - "- [x] Windowing\n", - "- [x] Windowing - Split (parts to train as X, parts to predict as y)" + "from ontime.module import preprocessing" ] }, { @@ -100,12 +93,12 @@ "id": "9b508ee5-7c7e-4793-904e-45a40df354db", "metadata": {}, "source": [ - "### Test with common functions" + "### Normalize" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "a4b12f07-8a97-403a-a554-89e166574120", "metadata": {}, "outputs": [ @@ -121,57 +114,89 @@ } ], "source": [ - "ts_t = on.model.preprocessing.common.normalize(ts)" + "ts_t = preprocessing.common.normalize(ts)" + ] + }, + { + "cell_type": "markdown", + "id": "42428ed1-7556-4341-9675-bad6dca0ecac", + "metadata": {}, + "source": [ + "### Train test split (for time series)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8b67892d-db8c-4f12-93b6-147016da4186", "metadata": {}, "outputs": [], "source": [ - "train, test = on.model.preprocessing.common.train_test_split(ts_t, train_split=0.8)" + "train, test = preprocessing.common.train_test_split(ts_t, train_split=0.8)" + ] + }, + { + "cell_type": "markdown", + "id": "498b0e13-04bc-45ee-ab1a-3996fbfd1df2", + "metadata": {}, + "source": [ + "### Split time series in chunks" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "500e954a-82d6-4eff-bbdd-0b889c2a10f8", "metadata": {}, "outputs": [], "source": [ - "train_list = on.model.preprocessing.common.split_by_length(train, 6)\n", - "test_list = on.model.preprocessing.common.split_by_length(test, 6)" + "train_list = preprocessing.common.split_by_length(train, 6)\n", + "test_list = preprocessing.common.split_by_length(test, 6)" + ] + }, + { + "cell_type": "markdown", + "id": "b4a88496-6b33-4bff-abb7-1d5ff4c81597", + "metadata": {}, + "source": [ + "### Split in X and y" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "id": "f7897c44-71ba-4752-86c6-547387245ae4", "metadata": {}, "outputs": [], "source": [ - "X_train, y_train = on.model.preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n", - "X_test, y_test = on.model.preprocessing.common.split_inputs_from_targets(test_list, 4, 2)" + "X_train, y_train = preprocessing.common.split_inputs_from_targets(train_list, 4, 2)\n", + "X_test, y_test = preprocessing.common.split_inputs_from_targets(test_list, 4, 2)" + ] + }, + { + "cell_type": "markdown", + "id": "9626370a-e4ba-4421-b40b-d6e7c5787beb", + "metadata": {}, + "source": [ + "### Transform in generic data type " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "a4ab9cfa-289d-4d8e-be40-d5d4247f5ab5", "metadata": {}, "outputs": [], "source": [ - "X_train = on.model.preprocessing.common.timeseries_list_to_numpy(X_train)\n", - "y_train = on.model.preprocessing.common.timeseries_list_to_numpy(y_train)\n", - "X_test = on.model.preprocessing.common.timeseries_list_to_numpy(X_test)\n", - "y_test = on.model.preprocessing.common.timeseries_list_to_numpy(y_test)" + "X_train = preprocessing.common.timeseries_list_to_numpy(X_train)\n", + "y_train = preprocessing.common.timeseries_list_to_numpy(y_train)\n", + "X_test = preprocessing.common.timeseries_list_to_numpy(X_test)\n", + "y_test = preprocessing.common.timeseries_list_to_numpy(y_test)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "id": "1b0a2843-6d02-4b08-96f8-91712e521bf5", "metadata": {}, "outputs": [ diff --git a/notebooks/docs/0.5-context.ipynb b/notebooks/docs/2_context/2.0-context-common.ipynb similarity index 95% rename from notebooks/docs/0.5-context.ipynb rename to notebooks/docs/2_context/2.0-context-common.ipynb index 334fba8..0b99a4f 100644 --- a/notebooks/docs/0.5-context.ipynb +++ b/notebooks/docs/2_context/2.0-context-common.ipynb @@ -5,12 +5,12 @@ "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", "metadata": {}, "source": [ - "# Context" + "# Context - Common" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "id": "54e70524-472a-49e4-bca9-32e57a1c4313", "metadata": {}, "outputs": [], @@ -22,27 +22,25 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "70a32352-80c9-40b7-8f68-1aeecfc52658", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", - "import ontime as on\n", "from darts.datasets import EnergyDataset" ] }, + { + "cell_type": "code", + "execution_count": 9, + "id": "24fa5881-61b9-4ca0-9987-a6945136a29d", + "metadata": {}, + "outputs": [], + "source": [ + "import ontime as on" + ] + }, { "cell_type": "markdown", "id": "43dac0e6-ae1e-4bbd-8537-2f2ed1262c76", @@ -61,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "e75060cc-c514-4210-b359-585f4f51e873", "metadata": {}, "outputs": [], @@ -79,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "4d355f16-5c6d-477a-802c-9b1dbf3718f0", "metadata": {}, "outputs": [], @@ -92,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "c1cca8db-e15f-4e40-936b-8ef18e7a63c3", "metadata": {}, "outputs": [], @@ -102,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "ebe23c8b-82ca-4ba4-aba7-969ee926e802", "metadata": {}, "outputs": [], @@ -113,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "7732e3a2-753a-4f3c-aa92-766e8fc70adc", "metadata": {}, "outputs": [], @@ -129,22 +127,40 @@ "---" ] }, + { + "cell_type": "markdown", + "id": "df7fffe4-ce41-47be-a304-793e84ff1c9c", + "metadata": {}, + "source": [ + "## Common Context" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "54d07f6a-c1c1-45dc-91f4-8c92f1f89ed8", + "metadata": {}, + "outputs": [], + "source": [ + "from ontime.context import common" + ] + }, { "cell_type": "markdown", "id": "0850a3ae-ce2a-4e8f-98d0-dfad92bc5c72", "metadata": {}, "source": [ - "## Profiler" + "### Profiler" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "id": "38a123fd-37cb-4206-b0dc-fb8ecd597b46", "metadata": {}, "outputs": [], "source": [ - "profiler = on.context.common.Profiler()" + "profiler = common.Profiler()" ] }, { @@ -152,12 +168,12 @@ "id": "7706081d-0a79-410e-ae65-480de017c194", "metadata": {}, "source": [ - "### Daily Aggregation" + "#### Daily Aggregation" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "id": "9e34e371-9d34-41e8-8145-116c6bc463d3", "metadata": {}, "outputs": [], @@ -168,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "id": "fc72abf9-f1d9-4757-80c5-ecffa37d756f", "metadata": {}, "outputs": [ @@ -193,12 +209,12 @@ "id": "628fc1ce-1631-4844-bd51-649fff52a5e8", "metadata": {}, "source": [ - "### Weekly Aggregation" + "#### Weekly Aggregation" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "id": "e26c11bc-e46d-45ab-aab6-4e80c4288ca0", "metadata": {}, "outputs": [], @@ -209,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "id": "4409d4ee-aca9-4bae-b1d1-03283554bbbe", "metadata": {}, "outputs": [ @@ -234,32 +250,32 @@ "id": "813f08f7-51aa-4413-92da-8455bc854aed", "metadata": {}, "source": [ - "## Generic Predictor" + "### Generic Predictor" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "id": "e5df7b11-4c2d-4b26-9b9c-f1bf521b6300", "metadata": {}, "outputs": [], "source": [ - "model = on.context.common.GenericPredictor()" + "model = common.GenericPredictor()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "id": "402c4d6e-dd2c-4920-9736-eee98fa3ab32", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -278,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "id": "33c52d1f-6983-4edb-945b-9578d954f738", "metadata": {}, "outputs": [], @@ -288,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "id": "c10dd2d7-7a48-4343-b18a-152175dcd799", "metadata": {}, "outputs": [ @@ -297,23 +313,23 @@ "text/html": [ "\n", "\n", - "
    \n", + "
    \n", "" ], "text/plain": [ "alt.Chart(...)" ] }, - "execution_count": 11, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -380,32 +396,32 @@ "id": "1eacbd84-bb31-48b1-bd5b-6ab7db392204", "metadata": {}, "source": [ - "## Generic Detector" + "### Generic Detector" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "id": "9715fd4d-24d8-4d95-a77a-3c347916f2aa", "metadata": {}, "outputs": [], "source": [ - "model = on.context.common.GenericDetector()" + "model = common.GenericDetector()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "id": "56eae67d-ae1a-438c-bbd4-e1b52c93c2c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -424,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "id": "3fcf48b4-6d60-411d-88e5-191d575967a7", "metadata": {}, "outputs": [], @@ -434,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 30, "id": "317ef652-cfa1-4d85-b873-a1944c13fefc", "metadata": {}, "outputs": [ @@ -443,23 +459,23 @@ "text/html": [ "\n", "\n", - "
    \n", + "
    \n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, - "execution_count": 38, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -531,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 31, "id": "0e31b255-cf60-4470-910e-31fe826b9782", "metadata": {}, "outputs": [], @@ -541,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 32, "id": "fe406b99-9025-4553-8c3d-0237a670e513", "metadata": {}, "outputs": [ @@ -550,23 +566,23 @@ "text/html": [ "\n", "\n", - "
    \n", + "
    \n", "" ], "text/plain": [ "alt.LayerChart(...)" ] }, - "execution_count": 40, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -627,6 +643,14 @@ "source": [ "on.plots.anomalies(test[:72], predetected[:72])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23994cad-3e1f-49f2-90c5-08080409c2a6", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git "a/notebooks/docs/code block\nTime series.ipynb" "b/notebooks/docs/code block\nTime series.ipynb" deleted file mode 100644 index 1b64f85..0000000 --- "a/notebooks/docs/code block\nTime series.ipynb" +++ /dev/null @@ -1,17422 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "2Pmxv2ioyCRw" - }, - "source": [ - "##### Copyright 2019 The TensorFlow Authors." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "cellView": "form", - "id": "b-2ShX25yNWf" - }, - "outputs": [], - "source": [ - "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "# you may not use this file except in compliance with the License.\n", - "# You may obtain a copy of the License at\n", - "#\n", - "# https://www.apache.org/licenses/LICENSE-2.0\n", - "#\n", - "# Unless required by applicable law or agreed to in writing, software\n", - "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "# See the License for the specific language governing permissions and\n", - "# limitations under the License." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pa49bUnKyRgF" - }, - "source": [ - "# Time series forecasting" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "11Ilg92myRcw" - }, - "source": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
    \n", - " View on TensorFlow.org\n", - " \n", - " Run in Google Colab\n", - " \n", - " View source on GitHub\n", - " \n", - " Download notebook\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GU8C5qm_4vZb" - }, - "source": [ - "This tutorial is an introduction to time series forecasting using TensorFlow. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs).\n", - "\n", - "This is covered in two main parts, with subsections: \n", - "\n", - "* Forecast for a single time step:\n", - " * A single feature.\n", - " * All features.\n", - "* Forecast multiple steps:\n", - " * Single-shot: Make the predictions all at once.\n", - " * Autoregressive: Make one prediction at a time and feed the output back to the model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XVhK72Pu1cJL" - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "7rZnJaGTWQw0" - }, - "outputs": [], - "source": [ - "import os\n", - "import datetime\n", - "\n", - "import IPython\n", - "import IPython.display\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import tensorflow as tf\n", - "\n", - "mpl.rcParams['figure.figsize'] = (8, 6)\n", - "mpl.rcParams['axes.grid'] = False" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TokBlnUhWFw9" - }, - "source": [ - "## The weather dataset\n", - "\n", - "This tutorial uses a weather time series dataset recorded by the Max Planck Institute for Biogeochemistry.\n", - "\n", - "This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in 2003. For efficiency, you will use only the data collected between 2009 and 2016. This section of the dataset was prepared by François Chollet for his book Deep Learning with Python." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "xyv_i85IWInT" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip\n", - "13568290/13568290 [==============================] - 1s 0us/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 5103616/13568290 [==========>...................] - ETA: 0s" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 8396800/13568290 [=================>............] - ETA: 0s" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "13568290/13568290 [==============================] - 0s 0us/step\n" - ] - } - ], - "source": [ - "zip_path = tf.keras.utils.get_file(\n", - " origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n", - " fname='jena_climate_2009_2016.csv.zip',\n", - " extract=True)\n", - "csv_path, _ = os.path.splitext(zip_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "R81Wx8WP4c3G" - }, - "source": [ - "This tutorial will just deal with **hourly predictions**, so start by sub-sampling the data from 10-minute intervals to one-hour intervals:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "TX6uGeeeWIkG" - }, - "outputs": [], - "source": [ - "df = pd.read_csv(csv_path)\n", - "# Slice [start:stop:step], starting from index 5 take every 6th record.\n", - "df = df[5::6]\n", - "\n", - "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VdbOWXiTWM2T" - }, - "source": [ - "Let's take a glance at the data. Here are the first few rows:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "ojHE-iCCWIhz" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    p (mbar)T (degC)Tpot (K)Tdew (degC)rh (%)VPmax (mbar)VPact (mbar)VPdef (mbar)sh (g/kg)H2OC (mmol/mol)rho (g/m**3)wv (m/s)max. wv (m/s)wd (deg)
    5996.50-8.05265.38-8.7894.43.333.140.191.963.151307.860.210.63192.7
    11996.62-8.88264.54-9.7793.23.122.900.211.812.911312.250.250.63190.3
    17996.84-8.81264.59-9.6693.53.132.930.201.832.941312.180.180.63167.2
    23996.99-9.05264.34-10.0292.63.072.850.231.782.851313.610.100.38240.0
    29997.46-9.63263.72-10.6592.22.942.710.231.692.711317.190.400.88157.0
    \n", - "
    " - ], - "text/plain": [ - " p (mbar) T (degC) Tpot (K) Tdew (degC) rh (%) VPmax (mbar) \\\n", - "5 996.50 -8.05 265.38 -8.78 94.4 3.33 \n", - "11 996.62 -8.88 264.54 -9.77 93.2 3.12 \n", - "17 996.84 -8.81 264.59 -9.66 93.5 3.13 \n", - "23 996.99 -9.05 264.34 -10.02 92.6 3.07 \n", - "29 997.46 -9.63 263.72 -10.65 92.2 2.94 \n", - "\n", - " VPact (mbar) VPdef (mbar) sh (g/kg) H2OC (mmol/mol) rho (g/m**3) \\\n", - "5 3.14 0.19 1.96 3.15 1307.86 \n", - "11 2.90 0.21 1.81 2.91 1312.25 \n", - "17 2.93 0.20 1.83 2.94 1312.18 \n", - "23 2.85 0.23 1.78 2.85 1313.61 \n", - "29 2.71 0.23 1.69 2.71 1317.19 \n", - "\n", - " wv (m/s) max. wv (m/s) wd (deg) \n", - "5 0.21 0.63 192.7 \n", - "11 0.25 0.63 190.3 \n", - "17 0.18 0.63 167.2 \n", - "23 0.10 0.38 240.0 \n", - "29 0.40 0.88 157.0 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WRzj1inMfgcO" - }, - "source": [ - "Here is the evolution of a few features over time:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "Vg5XIc5tfNlG" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_cols = ['T (degC)', 'p (mbar)', 'rho (g/m**3)']\n", - "plot_features = df[plot_cols]\n", - "plot_features.index = date_time\n", - "_ = plot_features.plot(subplots=True)\n", - "\n", - "plot_features = df[plot_cols][:480]\n", - "plot_features.index = date_time[:480]\n", - "_ = plot_features.plot(subplots=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wXWLG0_WBhZS" - }, - "source": [ - "### Inspect and cleanup" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yhmZXJew6GlS" - }, - "source": [ - "Next, look at the statistics of the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "h510pgKVrrai" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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    countmeanstdmin25%50%75%max
    p (mbar)70091.0989.2128428.358886913.60984.20989.57994.7201015.29
    T (degC)70091.09.4504828.423384-22.763.359.4115.48037.28
    Tpot (K)70091.0283.4930868.504424250.85277.44283.46289.530311.21
    Tdew (degC)70091.04.9564716.730081-24.800.245.2110.08023.06
    rh (%)70091.076.00978816.47492013.8865.2179.3089.400100.00
    VPmax (mbar)70091.013.5765767.7398830.977.7711.8217.61063.77
    VPact (mbar)70091.09.5339684.1836580.816.228.8612.36028.25
    VPdef (mbar)70091.04.0425364.8985490.000.872.195.30046.01
    sh (g/kg)70091.06.0225602.6558120.513.925.597.80018.07
    H2OC (mmol/mol)70091.09.6404374.2348620.816.298.9612.49028.74
    rho (g/m**3)70091.01216.06123239.9742631059.451187.471213.801242.7651393.54
    wv (m/s)70091.01.70256765.447512-9999.000.991.762.86014.01
    max. wv (m/s)70091.02.96304175.597657-9999.001.762.984.74023.50
    wd (deg)70091.0174.78909586.6194310.00125.30198.10234.000360.00
    \n", - "
    " - ], - "text/plain": [ - " count mean std min 25% 50% \\\n", - "p (mbar) 70091.0 989.212842 8.358886 913.60 984.20 989.57 \n", - "T (degC) 70091.0 9.450482 8.423384 -22.76 3.35 9.41 \n", - "Tpot (K) 70091.0 283.493086 8.504424 250.85 277.44 283.46 \n", - "Tdew (degC) 70091.0 4.956471 6.730081 -24.80 0.24 5.21 \n", - "rh (%) 70091.0 76.009788 16.474920 13.88 65.21 79.30 \n", - "VPmax (mbar) 70091.0 13.576576 7.739883 0.97 7.77 11.82 \n", - "VPact (mbar) 70091.0 9.533968 4.183658 0.81 6.22 8.86 \n", - "VPdef (mbar) 70091.0 4.042536 4.898549 0.00 0.87 2.19 \n", - "sh (g/kg) 70091.0 6.022560 2.655812 0.51 3.92 5.59 \n", - "H2OC (mmol/mol) 70091.0 9.640437 4.234862 0.81 6.29 8.96 \n", - "rho (g/m**3) 70091.0 1216.061232 39.974263 1059.45 1187.47 1213.80 \n", - "wv (m/s) 70091.0 1.702567 65.447512 -9999.00 0.99 1.76 \n", - "max. wv (m/s) 70091.0 2.963041 75.597657 -9999.00 1.76 2.98 \n", - "wd (deg) 70091.0 174.789095 86.619431 0.00 125.30 198.10 \n", - "\n", - " 75% max \n", - "p (mbar) 994.720 1015.29 \n", - "T (degC) 15.480 37.28 \n", - "Tpot (K) 289.530 311.21 \n", - "Tdew (degC) 10.080 23.06 \n", - "rh (%) 89.400 100.00 \n", - "VPmax (mbar) 17.610 63.77 \n", - "VPact (mbar) 12.360 28.25 \n", - "VPdef (mbar) 5.300 46.01 \n", - "sh (g/kg) 7.800 18.07 \n", - "H2OC (mmol/mol) 12.490 28.74 \n", - "rho (g/m**3) 1242.765 1393.54 \n", - "wv (m/s) 2.860 14.01 \n", - "max. wv (m/s) 4.740 23.50 \n", - "wd (deg) 234.000 360.00 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe().transpose()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TzOTnWOoWMGK" - }, - "source": [ - "#### Wind velocity" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "i47LiW5DCVsP" - }, - "source": [ - "One thing that should stand out is the `min` value of the wind velocity (`wv (m/s)`) and the maximum value (`max. wv (m/s)`) columns. This `-9999` is likely erroneous.\n", - "\n", - "There's a separate wind direction column, so the velocity should be greater than zero (`>=0`). Replace it with zeros:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "qFOq0_80vF4d" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.0" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wv = df['wv (m/s)']\n", - "bad_wv = wv == -9999.0\n", - "wv[bad_wv] = 0.0\n", - "\n", - "max_wv = df['max. wv (m/s)']\n", - "bad_max_wv = max_wv == -9999.0\n", - "max_wv[bad_max_wv] = 0.0\n", - "\n", - "# The above inplace edits are reflected in the DataFrame.\n", - "df['wv (m/s)'].min()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vtmu2IBPgPG8" - }, - "source": [ - "### Feature engineering\n", - "\n", - "Before diving in to build a model, it's important to understand your data and be sure that you're passing the model appropriately formatted data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FYyEaqiD6j4s" - }, - "source": [ - "#### Wind\n", - "The last column of the data, `wd (deg)`—gives the wind direction in units of degrees. Angles do not make good model inputs: 360° and 0° should be close to each other and wrap around smoothly. Direction shouldn't matter if the wind is not blowing.\n", - "\n", - "Right now the distribution of wind data looks like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "YO7JGTcWQG2z" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Wind Velocity [m/s]')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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AAAAIiJFGAAAAP5g97YuRRgAAAATESCMAAIAfxrjkMaEdXzMhrq8+ETQCAAD44ZYjt0I8ESbE9dWnxhvuAgAAoN4w0ggAAOCHx4R+4orHhLS6esVIIwAAAAJipBEAAMAPTx1MhAl1ffWp8bYcAAAA9YaRRgAAAD88cuQJ8WznUNdXnxhpBIBj4ERGWW3G7bba5LgCbwCanXnz5qlHjx5KTk5WcnKycnNz9frrr3ufr6ioUF5enlq2bKnExESNGjVKe/bs8alj586dGj58uOLj45Wenq6pU6eqpqYm6LbwKwQAAOCH2zh1sgWjTZs2euCBB7Rx40Zt2LBBF1xwgUaMGKEtW7ZIkiZPnqzXXntNL7zwglatWqVdu3bpsssu+7/34HZr+PDhqqqq0tq1a7Vw4UItWLBAM2bMCLo/HGNMI578HVhpaalSUlLUXyMU6UQ1dHMANBFOpN3viXG7Q3dQ4wldXUCYqzHVWqlXVFJSouTk5Ho9dm3scOXyqxWdGB3SuqvKqvTcwL8f1/tKS0vT73//e/385z9X69attWjRIv385z+XJH322Wc65ZRTVFBQoLPOOkuvv/66Lr74Yu3atUsZGRmSpPnz52vatGn69ttvFR1t//4YaQQAAKhnpaWlPltlZWXA17jdbj333HMqLy9Xbm6uNm7cqOrqag0aNMhbpkuXLmrbtq0KCgokSQUFBerevbs3YJSkIUOGqLS01DtaaYugEQAAwA+PHHlMiLf/ToTJyclRSkqKd8vPzz9iOzZv3qzExETFxMToxhtv1OLFi9W1a1cVFRUpOjpaqampPuUzMjJUVFQkSSoqKvIJGGufr30uGMyeBgAAqGeFhYU+l6djYmKOWLZz587atGmTSkpK9OKLL2rs2LFatWpVfTTTB0EjAACAH6YOUu6Y/9ZXOxvaRnR0tDp27ChJ6t27t9avX6/HHntMV1xxhaqqqlRcXOwz2rhnzx5lZmZKkjIzM/Xee+/51Fc7u7q2jC0uTwMAADQiHo9HlZWV6t27t6KiorR8+XLvc1u3btXOnTuVm5srScrNzdXmzZu1d+9eb5lly5YpOTlZXbt2Deq4jDQCAAD4UXsfYqjrDMb06dM1bNgwtW3bVvv379eiRYu0cuVKvfHGG0pJSdH48eM1ZcoUpaWlKTk5WTfffLNyc3N11llnSZIGDx6srl27asyYMZo9e7aKiop05513Ki8v76iXxP0haASAY+C4bH/4I6xKmZrqY28MgCZr7969uuaaa7R7926lpKSoR48eeuONN3ThhRdKkh599FG5XC6NGjVKlZWVGjJkiP70pz95Xx8REaElS5bopptuUm5urhISEjR27FjNmjUr6LaQpxEAjoHLMreZ8dj9xBI0Ar7CIU/jz5aNU1RCaPM0VpdXafGFTzfI+zpejDQCAAD4EQ6Xp8MJE2EAAAAQECONAAAAfnjqIOVOqOurT4w0AgAAICBGGgEAAPzgnkZfjDQCAAAgoAYNGlevXq1LLrlE2dnZchxHL7/8ss/zxhjNmDFDWVlZiouL06BBg/TFF180TGMBAECzUjvSGOqtsWrQoLG8vFw9e/bU3Llz/T4/e/ZsPf7445o/f77WrVunhIQEDRkyRBUVFfXcUgDwZdxuq80VG2O1yXEF3gCgATXoPY3Dhg3TsGHD/D5njNGcOXN05513asSIEZKkv/71r8rIyNDLL7+sK6+8sj6bCgAAmhnuafQVtn+67tixQ0VFRRo0aJB3X0pKivr27auCgoIjvq6yslKlpaU+GwAAQLC4PO0rbIPGoqIiSVJGRobP/oyMDO9z/uTn5yslJcW75eTk1Gk7AQAAmoOwDRqP1fTp01VSUuLdCgsLG7pJAACgETL6vwTfodrsVqMPT2EbNGZmZkqS9uzZ47N/z5493uf8iYmJUXJyss8GAACA4xO2QWP79u2VmZmp5cuXe/eVlpZq3bp1ys3NbcCWAQCA5oB7Gn016OzpsrIybdu2zft4x44d2rRpk9LS0tS2bVtNmjRJ9913nzp16qT27dvrrrvuUnZ2tkaOHNlwjQYAAGiGGjRo3LBhgwYMGOB9PGXKFEnS2LFjtWDBAt1xxx0qLy/X9ddfr+LiYp177rlaunSpYmNjG6rJAACgmSDljq8GDRr79+8vY458S6jjOJo1a5ZmzZpVj60CAAuWybadhHi7+srKjqMxAFD3GjRoBAAACFeMNPoiaAQAAPCDoNFX2M6eBgAAQPhgpBEAAMAPYxyZEI8Mhrq++sRIIwAAAAJipBEAAMCP2qX/Ql1nY8VIIwAAAAJipBEAAMAPZk/7YqQRAAAAATHSCADHwHHZjRaYyqo6bgmAusLsaV+MNAIAACAgRhoBAAD84J5GXwSNAAAAfnB52heXpwEAABAQI40AAAB+mDq4PM1IIwAAAJo0RhoBAAD8MJKMCX2djRUjjQAAAAiIkUYAOBaRdj+fTkREHTcEQF3xyJGjEKfcCXF99YmRRgAAAATESCMAAIAf5Gn0RdAIAADgh8c4clgRxovL0wAAAAiIkUYAAAA/jKmDlDuNOOcOI40AAAAIiJFGAAAAP5gI44uRRgAAAARkNdL46quvBl3xhRdeqLi4uKBfBwCNgXXS7ugoq2Ku6OiAZTxVVXbHBBASjDT6sgoaR44cGVSljuPoiy++0EknnXQsbQIAAECYsb6nsaioSOnp6VZlk5KSjrlBAAAA4YA8jb6s7mkcO3ZsUJear776aiUnJx9zowAAABpabcqdUG/ByM/P1xlnnKGkpCSlp6dr5MiR2rp1q0+Z/v37y3Ecn+3GG2/0KbNz504NHz5c8fHxSk9P19SpU1VTUxNUW6xGGp9++umgKp03b15Q5QEAAHC4VatWKS8vT2eccYZqamr0m9/8RoMHD9Ynn3yihIQEb7kJEyZo1qxZ3sfx8fHe/3e73Ro+fLgyMzO1du1a7d69W9dcc42ioqL0u9/9zrotx51yp7S0VG+99ZY6d+6sU0455XirAwAACAuHRgZDPREmuPJLly71ebxgwQKlp6dr48aN6tevn3d/fHy8MjMz/dbx73//W5988onefPNNZWRk6LTTTtO9996radOm6e6771a0xUQ86RhS7lx++eX64x//KEk6ePCg+vTpo8svv1w9evTQP//5z2CrAwAAaHZKS0t9tsrKSqvXlZSUSJLS0tJ89j/zzDNq1aqVunXrpunTp+vAgQPe5woKCtS9e3dlZGR49w0ZMkSlpaXasmWLdZuDDhpXr16t8847T5K0ePFiGWNUXFysxx9/XPfdd1+w1QEAAISl2pQ7od4kKScnRykpKd4tPz8/YHs8Ho8mTZqkc845R926dfPu/+Uvf6m///3vWrFihaZPn66//e1vuvrqq73PFxUV+QSMkryPi4qKrPsj6MvTJSUl3uh26dKlGjVqlOLj4zV8+HBNnTo12OoAAACancLCQp9JwzExMQFfk5eXp48//lhr1qzx2X/99dd7/7979+7KysrSwIEDtX37dnXo0CFkbQ46aMzJyVFBQYHS0tK0dOlSPffcc5KkH374QbGxsSFrGACEM+N2W5VzkhPt6tv7rUVllheHjMeuHICjMv/dQl2nJCUnJweVaWbixIlasmSJVq9erTZt2hy1bN++fSVJ27ZtU4cOHZSZman33nvPp8yePXsk6Yj3QfoT9OXpSZMmafTo0WrTpo2ys7PVv39/SYcuW3fv3j3Y6gAAAHAExhhNnDhRixcv1ltvvaX27dsHfM2mTZskSVlZWZKk3Nxcbd68WXv37vWWWbZsmZKTk9W1a1frtgQ90virX/1KZ555pgoLC3XhhRfK5ToUd5500knc0wgAAJqMcFhGMC8vT4sWLdIrr7yipKQk7z2IKSkpiouL0/bt27Vo0SJddNFFatmypT766CNNnjxZ/fr1U48ePSRJgwcPVteuXTVmzBjNnj1bRUVFuvPOO5WXl2d1WbyWddB43nnnacSIERoxYoT69OmjPn36+Dw/fPhw64MCAACEvbq8Pm2pNvd17ZXdWk8//bSuvfZaRUdH680339ScOXNUXl6unJwcjRo1Snfeeae3bEREhJYsWaKbbrpJubm5SkhI0NixY33yOtqwDhonTJigV155Rffcc4/atGmjSy+9VJdeeqnOPvtsOU7jXRIHAAAgXJkAiR1zcnK0atWqgPW0a9dO//rXv46rLdb3NF5zzTX65z//qe+++04PP/ywiouL9Ytf/EKZmZm67rrr9PLLL+vgwYPH1RgAAICwURfpdpr62tM/FhMTo4suukhPPPGEdu3apVdffVVZWVm666671LJlS1188cV655136qKtAAAAaCDHvYxg37591bdvX91///3avn27Xn31Ve3evTsUbQMAAGgwh5YRDH2djdVxBY1lZWXyeP4vH1jr1q01efLk424UAAAAwkvQQeOOHTs0ceJErVy5UhUVFd79xhg5jiO3ZcJbAACAcBYOKXfCSdBB49VXXy1jjJ566illZGQwcxpAw2qgVVKc6Gi7gm674xqPxTUrVnoB0ICCDho//PBDbdy4UZ07d66L9gAAAISHupjt3IhHGoOePX3GGWeosLCwLtoCAAAQNmonwoR6a6yCHmn8y1/+ohtvvFHffPONunXrpqioKJ/na5esAQAAQNMRdND47bffavv27Ro3bpx3n+M4TIQBAABNSxgsIxhOgg4ar7vuOvXq1UvPPvssE2EAAACaiaCDxq+//lqvvvqqOnbsWBftAQAACAuk3PEV9ESYCy64QB9++GFdtAUAAABhKuiRxksuuUSTJ0/W5s2b1b1798Mmwlx66aUhaxwAAECDasT3IIZa0EHjjTfeKEmaNWvWYc+FeiKM2+3W3Xffrb///e8qKipSdna2rr32Wt15553N815KmyTGJP9FcxPqpN0REXblYuySe5v4GKtyrqjAP8eeqiqrugCgLgQdNP54rem69uCDD2revHlauHChTj31VG3YsEHjxo1TSkqKbrnllnprBwAAaH64p9FX0EFjfVq7dq1GjBih4cOHS5JOPPFEPfvss3rvvfcauGUAAKDJI+WOD6uJMI8//rgqKiqsK50/f772799/zI2qdfbZZ2v58uX6/PPPJR1awnDNmjUaNmzYEV9TWVmp0tJSnw0AAADHxyponDx5clBB4B133KFvv/32mBtV69e//rWuvPJKdenSRVFRUerVq5cmTZqk0aNHH/E1+fn5SklJ8W45OTnH3Q4AANAcOXW0NU5Wl6eNMRo4cKAiI+2uZh88ePC4GlXr+eef1zPPPKNFixbp1FNP1aZNmzRp0iRlZ2dr7Nixfl8zffp0TZkyxfu4tLSUwBEAAOA4WUWBM2fODKrSESNGKC0t7Zga9GNTp071jjZKUvfu3fX1118rPz//iEFjTEyMYmLsZisCAAAcEfc0+qiToDFUDhw4IJfL9wp6REREvc7gBgAAQJjPnr7kkkt0//33q23btjr11FP1wQcf6JFHHtF1113X0E0DAABNHSONPsI6aPzDH/6gu+66S7/61a+0d+9eZWdn64YbbtCMGTMaumlWnMiowIUkmRAmRAeaDJtk9pJ9cm/L+ozH8hfd8h5vY7kQgfVxAaCBhHXQmJSUpDlz5mjOnDkN3RQAANDcGOfQFuo6G6mwDhoBAAAaijGHtlDX2VhZXv/5PytWrKiLdgAAACCMBR00Dh06VB06dNB9992nwsLCumgTAABAwzN1tDVSQQeN33zzjSZOnKgXX3xRJ510koYMGaLnn39eVVVVddE+AAAAhIGgg8ZWrVpp8uTJ2rRpk9atW6eTTz5Zv/rVr5Sdna1bbrlFH374YV20EwAAoH7VToQJ9dZIBR00/tjpp5+u6dOna+LEiSorK9NTTz2l3r1767zzztOWLVtC1UYAAAA0sGMKGqurq/Xiiy/qoosuUrt27fTGG2/oj3/8o/bs2aNt27apXbt2+sUvfhHqtgIAANQbx9TN1lgFnXLn5ptv1rPPPitjjMaMGaPZs2erW7du3ucTEhL00EMPKTs7O6QNDSu2SYItk3Y7rjBO/hvqBMtAA3EiIuwK2p7LcbF25SLsvt+uhPiAZdwlJXbHBIA6EHTQ+Mknn+gPf/iDLrvsMsXExPgt06pVK1LzAACAxo1lBH0EfXl65syZ+sUvfnFYwFhTU6PVq1dLkiIjI3X++eeHpoUAAAANgYkwPoIOGgcMGKB9+/Ydtr+kpEQDBgwISaMAAAAQXoK+PG2MkeMcHiV///33SkhICEmjAAAAGhyXp31YB42XXXaZJMlxHF177bU+l6fdbrc++ugjnX322aFvIQAAABqcddCYkpIi6dBIY1JSkuLi4rzPRUdH66yzztKECRNC30IAAICGwEijD+ug8emnn5YknXjiibr99tu5FA0AANCMBH1P48yZM+uiHQAAAOGFkUYfVkHj6aefruXLl6tFixbq1auX34kwtd5///2QNa6xs04mbMtUWxz0uFaGBBqfECfbd0VH29UXG2VVriotLnAhSdGfWXy/AaABWQWNI0aM8E58GTlyZF22BwAAIDzURV7FRpyn0Spo/PElaS5PAwAAND9B39O4fv16eTwe9e3b12f/unXrFBERoT59+oSscQAAAA3FMYe2UNfZWAV9A1xeXp4KCwsP2//NN98oLy8vJI0CAABocKaOtkYq6KDxk08+0emnn37Y/l69eumTTz4JSaMAAAAQXoIOGmNiYrRnz57D9u/evVuRkUFf7QYAAMAR5Ofn64wzzlBSUpLS09M1cuRIbd261adMRUWF8vLy1LJlSyUmJmrUqFGHxWo7d+7U8OHDFR8fr/T0dE2dOlU1NTVBtSXooHHw4MGaPn26SkpKvPuKi4v1m9/8RhdeeGGw1QEAAOAIVq1apby8PL377rtatmyZqqurNXjwYJWXl3vLTJ48Wa+99ppeeOEFrVq1Srt27fIu/ywdWu55+PDhqqqq0tq1a7Vw4UItWLBAM2bMCKotjjEmqKvr33zzjfr166fvv/9evXr1kiRt2rRJGRkZWrZsmXJycoJqQF0rLS1VSkqK+muEIh27vGoBWeaFC3WeRlMTxnkajadhjoumq4HOZds8jU6ndlblKjOTrMpFrw18e4/nwAGruoCmoMZUa6VeUUlJiZKTk+v12LWxQ7sH75MrNjakdXsqKvT1tDuP+X19++23Sk9P16pVq9SvXz+VlJSodevWWrRokX7+859Lkj777DOdcsopKigo0FlnnaXXX39dF198sXbt2qWMjAxJ0vz58zVt2jR9++23irb83Qv6V/mEE07QRx99pNmzZ6tr167q3bu3HnvsMW3evDnsAkYAAIBwVFpa6rNVVlZava72Sm9aWpokaePGjaqurtagQYO8Zbp06aK2bduqoKBAklRQUKDu3bt7A0ZJGjJkiEpLS7VlyxbrNh/TTYgJCQm6/vrrj+WlTYPlqJoTYRe5y2WX6NNmRYsGGd0E6oLt9yzS7gqC7Yow1o6yMpbPcSPsyjlRIboSUhdsR3254oCmpg6Te/90oG3mzJm6++67j/pSj8ejSZMm6ZxzzlG3bt0kSUVFRYqOjlZqaqpP2YyMDBUVFXnL/DhgrH2+9jlbxxQ0bt++XXPmzNGnn34qSeratatuvfVWdejQ4ViqAwAAaFYKCwt9Lk/Xrrx3NHl5efr444+1Zs2aumzaEQV9efqNN95Q165d9d5776lHjx7q0aOH1q1bp1NPPVXLli2rizYCAADUvzrM05icnOyzBQoaJ06cqCVLlmjFihVq06aNd39mZqaqqqpUXFzsU37Pnj3KzMz0lvnpbOrax7VlbAQdNP7617/W5MmTtW7dOj3yyCN65JFHtG7dOk2aNEnTpk0LtjoAAIDwFAbJvY0xmjhxohYvXqy33npL7du393m+d+/eioqK0vLly737tm7dqp07dyo3N1eSlJubq82bN2vv3r3eMsuWLVNycrK6du1q3ZagL09/+umnev755w/bf91112nOnDnBVgcAAIAjyMvL06JFi/TKK68oKSnJew9iSkqK4uLilJKSovHjx2vKlClKS0tTcnKybr75ZuXm5uqss86SdChdYteuXTVmzBjNnj1bRUVFuvPOO5WXl2d1WbxW0CONrVu31qZNmw7bv2nTJqWnpwdbHQAAQFiqXXs61Fsw5s2bp5KSEvXv319ZWVne7R//+Ie3zKOPPqqLL75Yo0aNUr9+/ZSZmamXXnrJ+3xERISWLFmiiIgI5ebm6uqrr9Y111yjWbNmBdWWoEcaJ0yYoOuvv15ffvmlzj77bEnSO++8owcffFBTpkwJtjoAAAAcgU067djYWM2dO1dz5849Ypl27drpX//613G1Jeig8a677lJSUpIefvhhTZ8+XZKUnZ2tu+++W7fccstxNQYAACBsHMM9iFZ1NlJBB42O42jy5MmaPHmy9u/fL0lKSrJb8QAAAACN0zHlaazVbINF22UEo+y613hsk4WHLnG3baJj22Maj+WfTiT/ha0QJ5R2LJPoK8LuuO4Eu5vHK1PtvkNx0RbJvRsqyTbfWzRXjDT6sIpqevXqJcdy9YP333//uBoEAACA8GMVNI4cObKOmwEAABBejmW2s02djZVV0Dhz5sy6bgcAAEB4qcO1pxujoPM0SlJxcbH+8pe/aPr06dq3b5+kQ5elv/nmm5A2DgAAAOEh6IkwH330kQYNGqSUlBR99dVXmjBhgtLS0vTSSy9p586d+utf/1oX7QQAAKhfTITxEfRI45QpU3Tttdfqiy++UGxsrHf/RRddpNWrV4e0cQAAAAgPQY80rl+/Xk888cRh+0844QTveogAAACNHRNhfAU90hgTE6PS0tLD9n/++edq3bp1SBoFAACA8BL0SOOll16qWbNm6fnnn5d0aIWYnTt3atq0aRo1alTIG9io2STrleRUVYesPs/BCquqIhLircq5yw9YlSP5L0LNNhm3E2n3PQv5OWrZPneM5UzJyMA/x9bJ9u1y94ecdftqLH/zgIbGPY0+gh5pfPjhh1VWVqb09HQdPHhQ559/vjp27KikpCTdf//9ddFGAAAANDDrkcbbb79d//M//6MuXbpo2bJlWrNmjT766COVlZXp9NNP16BBg+qynQAAAPWrDu5pbMwjjdZB4yuvvKJHH31Uffv21f/8z//oiiuu0LnnnluXbQMAAGg4XJ72YX15+osvvtCKFSt08skn69Zbb1VmZqbGjx+vtWvX1mX7AAAAEAaCuqexX79+WrBggYqKivTYY4/p888/17nnnqtTTjlFDz30kPbs2VNX7QQAAKhfpo62RuqYlhFMSEjQddddp7fffluff/65LrvsMuXn56tt27ahbh8AAADCQNApd36svLxcb7/9tlatWqUffvhBnTt3DlW7AAAAGhTJvX0d00jjmjVrdN111ykrK0u33HKLTj75ZL399tv69NNPQ90+AAAAhAHrkcbdu3dr4cKFWrBggT7//HOdddZZeuSRR3TllVcqMTGxLtsYdqyTDkfZJR021TV2B3YHTk7siosNWEaSjNsu+68rOjqk9ZHUF3Ls/lY1Hrs/x22/j/LYHdeJjbEqZyLsjludYNk+i+T9oU6e7Yqx+73wVFXZHdfydwBA42QdNObk5Khly5YaM2aMxo8fr1NOOaUu2wUAAIAwYn15+vnnn9c333yjhx56qF4Dxm+++UZXX321WrZsqbi4OHXv3l0bNmyot+MDAIBmitnTPqxHGi+77LK6bIdfP/zwg8455xwNGDBAr7/+ulq3bq0vvvhCLVq0qPe2AACA5oWJML6Oa/Z0XXvwwQeVk5Ojp59+2ruvffv2DdgiAACA5umYZk/Xl1dffVV9+vTRL37xC6Wnp6tXr1568sknj/qayspKlZaW+mwAAADHhEvTXmEdNH755ZeaN2+eOnXqpDfeeEM33XSTbrnlFi1cuPCIr8nPz1dKSop3y8nJqccWAwAANE1hHTR6PB6dfvrp+t3vfqdevXrp+uuv14QJEzR//vwjvmb69OkqKSnxboWFhfXYYgAA0GQwEcaH1T2NwUyCeemll465MT+VlZWlrl27+uw75ZRT9M9//vOIr4mJiVFMjF2eNQAAANixChpTUlK8/2+M0eLFi5WSkqI+ffpIkjZu3Kji4uKQz7A+55xztHXrVp99n3/+udq1axfS4wAAAPwUs6d9WQWNP569PG3aNF1++eWaP3++Iv67OoHb7davfvUrJScnh7RxkydP1tlnn63f/e53uvzyy/Xee+/pz3/+s/785z+H9DjBsl2VQZF2k9OdFLt+M/t+CFyoxnJ1GVu277Um8Go1kqxXA7FmLI+LsGG9opLluWe9SorH7rvhxNqtklKdbLfiU0yx5Tlq8Xthu9KLdZ/YrgwVZfdb5rFd3cr2d4DvNxBWgv4X/KmnntLtt9/uDRglKSIiQlOmTNFTTz0V0sadccYZWrx4sZ599ll169ZN9957r+bMmaPRo0eH9DgAAACH4Z5GH0HnaaypqdFnn32mzp07++z/7LPP5PGE/q/Ciy++WBdffHHI6wUAADgaLk/7CjpoHDdunMaPH6/t27frzDPPlCStW7dODzzwgMaNGxfyBgIAAKDhBR00PvTQQ8rMzNTDDz+s3bt3Szo0y3nq1Km67bbbQt5AAACABlEXl5Ob00ijy+XSHXfcoTvuuMO72kqoJ8AAAAAgvBzX2tMEiwAAoMlipNFH0LOn9+zZozFjxig7O1uRkZGKiIjw2QAAAND0BD3SeO2112rnzp266667lJWVJcexy7kGAADQmDB72lfQQeOaNWv09ttv67TTTquD5jQw24SztiOqEZb1VVRaFXOltQhc6GCFVV2egwetytmyTyZcFdLjIozYfn9CnODdiY+zK1hpee5ZJsj3RNr9wXywpd37TY2LDljGsVwi1VSFNgm4LBOy236y1knAAYSVoIPGnJwcGdOIw2QAAAAb3NPoI+g/+efMmaNf//rX+uqrr+qgOQAAAGGCFWF8BD3SeMUVV+jAgQPq0KGD4uPjFRXlu/7qvn37QtY4AAAAhIegg8Y5c+bUQTMAAADCCxNhfAUdNI4dO7Yu2gEAAIAwZhU0lpaWehN5164CcyQk/AYAAE0CE2F8WAWNLVq00O7du5Wenq7U1FS/uRmNMXIcR263O+SNBAAAQMOymj391ltvKS0tzfv//rYVK1borbfeqtPGAgAA1JfaexpDvQVj9erVuuSSS5SdnS3HcfTyyy/7PH/ttdfKcRyfbejQoT5l9u3bp9GjRys5OVmpqakaP368ysrKgu4Pq5HG888/Xzt27FD79u3Vv3//oA/S1NgmxPW0sLtU7yqzTLTt9gQuY5lQ3BVnlxDZVNklRPZU2iUobyj2yccZKa9rtp+FE2V5y7XlqlROquWtMzV250BVkt13zfYfCE984OTeLpfdMUN+9ctjVyNJu4HQKy8vV8+ePXXdddfpsssu81tm6NChevrpp72PY36yEMDo0aO1e/duLVu2TNXV1Ro3bpyuv/56LVq0KKi2WE+E6dChg9q1a6cBAwboggsuUP/+/dWmTZugDgYAANBo1OE9jT+dIxITE3NYsCdJw4YN07Bhw45aZUxMjDIzM/0+9+mnn2rp0qVav369+vTpI0n6wx/+oIsuukgPPfSQsrOzrZtundz7rbfe0tixY/Xll19qwoQJateunTp16qQbbrhBzz33nPbs2WN9UAAAgLBXh8m9c3JylJKS4t3y8/OPuZkrV65Uenq6OnfurJtuuknff/+997mCggKlpqZ6A0ZJGjRokFwul9atWxfUcaxHGvv37++9NF1RUaG1a9dq5cqVWrlypRYuXKjq6mp16dJFW7ZsCaoBAAAAzU1hYaFPxhl/o4w2hg4dqssuu0zt27fX9u3b9Zvf/EbDhg1TQUGBIiIiVFRUpPT0dJ/XREZGKi0tTUVFRUEdK+g8jZIUGxurCy64QOeee64GDBig119/XU888YQ+++yzY6kOAAAg7Dj/3UJdp3QoRWEo0hReeeWV3v/v3r27evTooQ4dOmjlypUaOHDgcdf/Y0GtPV1VVaXVq1frnnvu0YABA5Samqobb7xRP/zwg/74xz9qx44dIW0cAAAA7J100klq1aqVtm3bJknKzMzU3r17fcrU1NRo3759R7wP8kisRxovuOACrVu3Tu3bt9f555+vG264QYsWLVJWVlZQBwQAAGgUGmFy7//85z/6/vvvvfFZbm6uiouLtXHjRvXu3VvSoXkqHo9Hffv2Dapu66Dx7bffVlZWlnfm9Pnnn6+WLVsGdTAAAADYKysr844aStKOHTu0adMmpaWlKS0tTffcc49GjRqlzMxMbd++XXfccYc6duyoIUOGSJJOOeUUDR06VBMmTND8+fNVXV2tiRMn6sorrwxq5rQUxOXp4uJi/fnPf1Z8fLwefPBBZWdnq3v37po4caJefPFFffvtt0EdGAAAIJyFQ3LvDRs2qFevXurVq5ckacqUKerVq5dmzJihiIgIffTRR7r00kt18skna/z48erdu7fefvttn4k1zzzzjLp06aKBAwfqoosu0rnnnqs///nPQfeH9UhjQkKChg4d6s0yvn//fq1Zs0YrVqzQ7NmzNXr0aHXq1Ekff/xx0I1oqiqyEqzKxf3HLpmwc8AigbZl4mTbhMjmgF2yXmOZ/Nc2sbMt22TcTSJptxPULcihYyySyiuIz9YyAb0tU11tVc7x2L0PJSVaFatKskwqbnlYp9LiHLXsY1dcrN0xLcuZA3YLEERYJmT3HLRc0MCJsipmauzOAaAx6t+/v4w58r+xb7zxRsA60tLSgk7k7c8xzZ6WDgWRtUOjLVq0UGRkpD799NPjbhAAAEBYaIT3NNYl66DR4/Fow4YNWrlypVasWKF33nlH5eXlOuGEEzRgwADNnTtXAwYMqMu2AgAA1K9GHOSFmnXQmJqaqvLycmVmZmrAgAF69NFH1b9/f3Xo0KEu2wcAAIAwYB00/v73v9eAAQN08skn12V7AAAAwsKxTFyxqbOxsg4ab7jhhrpsBwAAAMLYMU+EAQAAaNKYCOOjgXJ4AAAAoDFhpBEAAMAP7mn0xUgjAAAAAmKk8Udc0dFW5RzLFSNs1bSItyoXER3443LZrsphucKDK9FuVRtPhcVqNUGwXWGmwdiuzmK5mkpI6wrxyjG2K71Yl4uyW+XDiY0JXCgYlt9bE293XMdusSTV2H295UkI/PsTYbmajnXfHWWVCZ/6kpPs6rNcecmptus8T2WF3XEb4vuI5oF7Gn0w0ggAAICAGGkEAADwg3safRE0AgAA+MPlaR9cngYAAEBAjDQCAAD4w0ijD0YaAQAAEBAjjQAAAH4wEcYXI40AAAAIiJHGHzGWiWkVY5c41x1nG5PbfQzu2MDJk2NL7ZJ2K8TJfyMS7DIYm6oqq3JOZGj/nrH9bK3PAVuWSYcdlxOwjG3Cc9sk9R7Lz8L6PURZ/pw4gd+rJPtz1DahveW57E60O25MqW2iaLv+q04M3H+Rlsn2rbkt34NlQnZZngOuGsvvmWUy7lCfyyQBhxf3NPpgpBEAAAABMdIIAADgh2OMHMurFMHU2VgRNAIAAPjD5WkfXJ4GAABAQIw0AgAA+EHKHV+MNAIAACAgRhoBAAD84Z5GH4w0AgAAIKDmM9LouAImdnVskwRbqrFM7u2qtvuzwxMROCmyO80u+a+rosaqnFNRaVXOeOyS4domqJbskv/afmamptrusLbJf21ZJgk2Nm/Xsm3W79WybU50rF05ywTvtkm7TXycVTl3il0510G7fvFE2fVzZWpoz5XIgxYnQbXd99akJluVcyotk2JbMjF2/6Q4CXafmQ7aLVbghDgpv/E0zO8Awg/3NPpipBEAAAABNZ+RRgAAgGBwT6OPRjXS+MADD8hxHE2aNKmhmwIAAJq42svTod4aq0YTNK5fv15PPPGEevTo0dBNAQAAaHYaRdBYVlam0aNH68knn1SLFi0aujkAAKA5MHW0NVKNImjMy8vT8OHDNWjQoIBlKysrVVpa6rMBAADg+IT9RJjnnntO77//vtavX29VPj8/X/fcc08dtwoAADQHjfkexFAL65HGwsJC3XrrrXrmmWcUG2uXK2769OkqKSnxboWFhXXcSgAAgKYvrEcaN27cqL179+r000/37nO73Vq9erX++Mc/qrKyUhE/Se4cExOjmBg/yYONR9LRE6w6UXbdUdGxtVW5A60DJ+OWJFe1XezuiQpcX2R5lN0xy+2SdivKsr44u2S9HrdlkluXXd8Zy2THtomxHdvjWiYpd0VHh6w+Y5nA2Im0+8wcy7Y5lsm4ZSz/HLf8LGyTdodaTaLd74Cryu79umPtzqmq1MCfW2TLFKu6nBrL75ljeb7H2Z0rFSfYJRWPdVl+H/cVW5XzWC8aYMk2GXeoFwNA+DHG/rctmDobqbAOGgcOHKjNmzf77Bs3bpy6dOmiadOmHRYwAgAAoG6EddCYlJSkbt26+exLSEhQy5YtD9sPAAAQSiwj6Cusg0YAAIAGw4owPhpd0Lhy5cqGbgIAAECz0+iCRgAAgPrgeA5toa6zsWLqFwAAAAJipBEAAMAf7mn0wUgjAAAAAmKkEQAAwA9S7vhqNkGjExEhxzl6MnAnzm6pQtktoqCaeMtycXYVuqMDl6tOsvtII0stVw2xXVliv10x65UWPJYrRliu4hNqAU4lL9uVY2z6xWW5MovtKjmuKLv6HMtVgZRgd8J7WiTalYux62THso9rYuy+3ybE11/clgvqeCIsvt8tE6zqiiivtjuo5Xstb2P52Vr8RkmS47F7H7H77FbAifS3CpgfpqrKqpy7rNyqXEhXjrGtC2hAXJ4GAADwp3YZwVBvQVi9erUuueQSZWdny3Ecvfzyyz9potGMGTOUlZWluLg4DRo0SF988YVPmX379mn06NFKTk5Wamqqxo8fr7KysqC7g6ARAADAj9rL06HeglFeXq6ePXtq7ty5fp+fPXu2Hn/8cc2fP1/r1q1TQkKChgwZooqKCm+Z0aNHa8uWLVq2bJmWLFmi1atX6/rrrw+6P5rN5WkAAIBwUVpa6vM4JiZGMX5utRg2bJiGDRvmtw5jjObMmaM777xTI0aMkCT99a9/VUZGhl5++WVdeeWV+vTTT7V06VKtX79effr0kST94Q9/0EUXXaSHHnpI2dnZ1m1mpBEAAMAfU0ebpJycHKWkpHi3/Pz8oJu3Y8cOFRUVadCgQd59KSkp6tu3rwoKCiRJBQUFSk1N9QaMkjRo0CC5XC6tW7cuqOMx0ggAAFDPCgsLlZyc7H3sb5QxkKKiIklSRkaGz/6MjAzvc0VFRUpPT/d5PjIyUmlpad4ytggaAQAA/KjLlDvJyck+QWNjwOVpAACARigzM1OStGfPHp/9e/bs8T6XmZmpvXv3+jxfU1Ojffv2ecvYImgEAADwJwxS7hxN+/btlZmZqeXLl3v3lZaWat26dcrNzZUk5ebmqri4WBs3bvSWeeutt+TxeNS3b9+gjtdsLk+74mLlcqKPXsgyuXdVsl23OXY5llUdb5cQ1ybpcESFXYJYT6xlcu8qyzdRY5lQukWqVTnPfsv8UZZfPsdlmZHdJgmvJFNjmTzZsj4nOsC5KUmWSaxdyXbJs61FWv5MhDjRenm2RZ9IivnBbVXORNmdAzUxdp+Zy+6wiim2+9w8kYHbZyzPY3eCZUJ2S9WJdn3ybR+795pRYHeuxCbEWZWzZfkrIMd1wLKgXT9b/140BMvfqJAmMpfdb7JjPJLl96wpKysr07Zt27yPd+zYoU2bNiktLU1t27bVpEmTdN9996lTp05q37697rrrLmVnZ2vkyJGSpFNOOUVDhw7VhAkTNH/+fFVXV2vixIm68sorg5o5LTWjoBEAACAY4bCM4IYNGzRgwADv4ylTpkiSxo4dqwULFuiOO+5QeXm5rr/+ehUXF+vcc8/V0qVLFRv7fwNhzzzzjCZOnKiBAwfK5XJp1KhRevzxx4NuO0EjAACAPz9KkRPSOoPQv39/maNcVXMcR7NmzdKsWbOOWCYtLU2LFi0K7sB+cE8jAAAAAmKkEQAAwI9wuDwdThhpBAAAQECMNAIAAPjjMdaZK4Kqs5FipBEAAAABMdIIAADgTxjMng4nzSZoNB6PjHP05KSmVYpVXRWpdgO01UlWxazHexN2h+5Mc9x2iVqdsgq7CpPsEkob26TdoWabtNttmUnWNoGtZcJrJyIicKFoizKSHNtk3JbJ7G2Te3vi7ZJxV7ayS9jsjrZLxVyeZdc+22TcEZV23zO3bbJwy/zUNvW5Y+36OPY7u2T7Hss+LjnJrlzsCfutyhV3sltvN36v3W9yzNeWiactk6M7Bw9alTNVdkm7ncjAScCtf3tsk2yHmM17kOzfh81vHsm9w0+zCRoBAACC4agOZk+Htrp6RdAIAADgT4jXivbW2UgxEQYAAAABMdIIAADgB8m9fTHSCAAAgIAYaQQAAPCHlDs+GGkEAABAQIw0AgAA+OEYIyfEs51DXV99ajZBoysxUS7X0RPjViXaJc61TdYbecCunGOZvLQ6IXB2p+oEuwTQkfstM0VF2tWnykq7cpac2Bi7gpbJdWX5NmyT/8qxTBLsaoDBfNuk3ZYJymta2yVirkmyS/7rsUyKXdnCrpzt98xteUpVx9sd12P56xlh+dVwuQP/Q1KVZNl3yXafRWULq2IyXe2S8l/f5R2rco9/M8SqnDvW7otrYu3er1Nql3zc9vfHhPB3z7H87TGeEP+m2CYLN3btc1n2nRMd+N9bx0RIVVbVoZ40m6ARAAAgKJ7/bqGus5EiaAQAAPCDy9O+mAgDAACAgBhpBAAA8IeUOz4YaQQAAEBAjDQCAAD4Y8yhLdR1NlKMNAIAACAgRhoBAAD8cMyhLdR1NlaMNAIAACCg5jPSmJggRRw9U31VC7sVYapS7Q5pLEPyKMsVLUJ5TNs8USbK7hRxPHbZSq1XWqiwW2nBY1nOlRBvVc6JsFw6xm25jI8lJ95imSHbzyzJ7r164u3Od1el3ao7HsvvjywX3bF1sLXlYW0T6lqOAkQetKzPkk37PHYLn6jGclGg6mS7N5scb/c9i3fZlYvKtPvRO9gywapc9Pd2bziy1Ha1kiSrchExlr9n+wOvqGMsf1Mcj+UJGmH5j4Hl74rtb61ju1pWdOCT2fq91iXuafTBSCMAAAACaj4jjQAAAEFwPEFcpQiizsaKoBEAAMAfLk/74PI0AAAAAmKkEQAAwB+WEfTBSCMAAAACYqQRAADAD8cY6xR1wdTZWDHSCAAAgICazUhjVXaKPJFHTwBb2s4usXNkud0xbZcKsq3PJnF3TVxoE7oq0rK+GMvEzm7LJOCRdoluI1q3tCpnk1z3UIV254CxTWYeZZmN2aacZbJep9qu79yxdl9/l8vuuLbnXnWibTmrYtblHNt87JbJx6vt8j8r7lu7clWRgQ9s+x7cdjmnVZNkdx6Paf+eVbnWkaVW5bJblFiVK42x+3ArW9u9YVdlslU5p6LGrpxVKclJtjhZbH+jLFknC4+zTIweb7dogCItf0PTUgOXcVdK39kdts4we9oHI40AAAAIqNmMNAIAAATFSAp1Mu7GO9BI0AgAAOAPE2F8hfXl6fz8fJ1xxhlKSkpSenq6Ro4cqa1btzZ0swAAAJqdsA4aV61apby8PL377rtatmyZqqurNXjwYJWXW84cAQAAOFZG/zcZJmRbQ7+pYxfWl6eXLl3q83jBggVKT0/Xxo0b1a9fvwZqFQAAQPMT1kHjT5WUHErTkJaWdsQylZWVqqys9D4uLbVLAQEAAOCDlDs+wvry9I95PB5NmjRJ55xzjrp163bEcvn5+UpJSfFuOTk59dhKAACApqnRjDTm5eXp448/1po1a45abvr06ZoyZYr3cWlpqXJyclSdFCkTFeDtWgb/Val25aJ/sCsXWWl3YFd14HIxxZaJnePskk679lcGLiRJkZankrvKrpxlwllVhrh9lpxYu2TCjmWycFUF7heT1dqqqupUu76rSbDrE5Nil8I4wuL8lKSDsXb1VSdYFVNNgl0+DBNrmRnbMmOz47J7v27Lfo7cH/hveJddzmlVZNi916yT7DInRzl2B36vvINVucLvWliVi0uxKqaqMrvxj8jUOLty5Xa/UxHloftdcZLsEpmbigqrcq5ouwUXjLHMJxNtuVBBgl0S8JpWgb/gNTWWv591ySP7LO7B1NlINYqgceLEiVqyZIlWr16tNm3aHLVsTEyMYmIsl0MAAACAlbAOGo0xuvnmm7V48WKtXLlS7du3b+gmAQCAZoI8jb7C+p7GvLw8/f3vf9eiRYuUlJSkoqIiFRUV6eDBgw3dNAAA0NSFPN1O8BNr7r77bjmO47N16dLF+3xFRYXy8vLUsmVLJSYmatSoUdqzZ0+oe0JSmAeN8+bNU0lJifr376+srCzv9o9//KOhmwYAAFAvTj31VO3evdu7/Xh+x+TJk/Xaa6/phRde0KpVq7Rr1y5ddtllddKOsL88DQAA0CDCJOVOZGSkMjMzD9tfUlKi//3f/9WiRYt0wQUXSJKefvppnXLKKXr33Xd11llnHXdzfyysRxoBAACaotLSUp+t8ijZQL744gtlZ2frpJNO0ujRo7Vz505J0saNG1VdXa1BgwZ5y3bp0kVt27ZVQUFByNtM0AgAAOBPHd7TmJOT45NXOj8/328T+vbtqwULFmjp0qWaN2+eduzYofPOO0/79+9XUVGRoqOjlZqa6vOajIwMFRUVhbw7wvryNAAAQFNUWFio5ORk7+MjpQscNmyY9/979Oihvn37ql27dnr++ecVF2eXezRUmk3QWJXokifq6AOrkZaTsmO/tSsXdcDuvoUay2THERbFHI/dMaN+sHuzTqVdklsTY5dI1nFZDm5bJLuWJFnW51gmpjWW79dJSrIqpwrL5OMWycI9sXZfV9uk3WXZdn1im7S7Js7uPK448iqgPtzxdhlwnVS7zyw52e6cb5lQblUuO8FuiVKX5aoBCZGBz5VVOzta1aX9dgne02IPWJV78ZvT7Y5ryRTZ/UMXVWZXn+2iBhGVduUct91nZiwTWTvlFv1suaCB41hmmvZYfn8syyk+tMFJTXzg36mamjAIUeowuXdycrJP0GgrNTVVJ598srZt26YLL7xQVVVVKi4u9hlt3LNnj997II8Xl6cBAAAaibKyMm3fvl1ZWVnq3bu3oqKitHz5cu/zW7du1c6dO5WbmxvyY4dBGA8AABB+wiG59+23365LLrlE7dq1065duzRz5kxFREToqquuUkpKisaPH68pU6YoLS1NycnJuvnmm5WbmxvymdMSQSMAAIB/YZBy5z//+Y+uuuoqff/992rdurXOPfdcvfvuu2rdurUk6dFHH5XL5dKoUaNUWVmpIUOG6E9/+lNo2/xfBI0AAABh6rnnnjvq87GxsZo7d67mzp1b520haAQAAPDHYyQnxCONlhNWwxETYQAAABAQI40AAAD+hME9jeGEkUYAAAAExEgjAACAX3Uw0miZ8D8cNZugMaJKigj0OVne7GosM/K7aqyKKfKAXUZ+m5U5ovdVWNVlIi1XUomIsCpntVyNJHksB7fdlqsU2K4wY8lJTLAraLvSi+VKOSYlMWCZqhZ2K0aUnWC30kt5tlUxxfxg99lWB34LkqQau0U05LSy6+Pck3ZYleuZ9B+rct3iCq3KRcjuHO0U9YNVuSiLbs6MsVuF5l//OdWq3LcH7D607/bZlTPfB17ZSJLi9tidU7arakVU2X0WnmjL370qu/Y5tj8/Nr9nrhCvzOK2W/1GUXZhgCfB7rN1J9j95lUlBf63paba8t8f1JtmEzQCAAAEhXsafRA0AgAA+OMxCvnlZFLuAAAAoCljpBEAAMAf4zm0hbrORoqRRgAAAATESCMAAIA/TITxwUgjAAAAAmKkEQAAwB9mT/toNkGj4zFyAnxQ7mi7hK5Ju+yydlfH2Q3keuxyMSuqLPDNs8YyybZTaZkMN9Euoaur5IBVORNnl/jVsU2KbVnOKbdrn1yWyWRj7fqlOivFqpwnJvBxS9taJu1uY1VMVS3szoHqRLtzyh1vV19Mpt1ncWrrb63K3Zb1hlW5CMsf/niXXVLkDpGW2cyVZFkusB+q7TKj92i1y6rc6q86WJWL3GGXUDrCbm0BJRTZfRZR5XblnBrLJODl1VblXOV2ieVtE15H1Fh8N8osf6NiLP/BsHwPtr+hxnIhBafa7ncgfk/g9tXUWC6igHrTbIJGAACAoHBPow+CRgAAAH+M6iBoDG119YmJMAAAAAiIkUYAAAB/uDztg5FGAAAABMRIIwAAgD8ej6QQL/vnYRlBAAAANGGMNAIAAPjDPY0+mk3QGFXmVmTk0ZP2RpXZJfWtTLXrNpdlwtmKFnYJpR2L6jwxdsl/476xSyTrTrBLJOuyTDgb+d1+q3KKCu2paRLs+sXE2h23JiXWrlyCXX1VSYEH/V1uu/PJE2mXjNtEWP5wuezqk2Ve9OgA38NaNcbuQshdX420KvfayUutym2qssxQbekHj913bb8ncL/sPJBmVddne9OtymmrXYLyxJ121UVbJuOOPGiZALrQru9clXZJu02U5W9tqC8f1lic83F2vymqtltcwnYBAlkuCOE6aJdo251klwjeJgm4Y5MUHfWq2QSNAAAAQWGk0QdBIwAAgD+sPe2DiTAAAAAIiJFGAAAAP4zxyJjQ3lsZ6vrqEyONAAAACIiRRgAAAH+MCf09iI14IgwjjQAAAAiIkUYAAAB/TB3Mnm7EI43NJmiMPFCjyMijJ0WtbBltVVfUfrvkxAcy7bo3uszuptiIysAnWk2cXaLW73smWZWL/87uvTpuu/fqtkx2XRNnl4Q3ar9doltPtN2gek2cXTmbROuS5FgmeLf5bB27j0KpX9iVq0q262OP5a9EZZpdfWZ7qlW5bS3sylW3sUs6fNL2/7EqZ3uPuuug5ftNsUs8HfVN4GTM0SVWVSm63K5c4m7L354qu3KuKrvzPaqkyq6+ECeUjii3q8+47H4HIsrsEsGb+MCfrbP/oFVd1iwXXDARduexq9zuvboi7c73mpTAfeKx/BxQf5pN0AgAABAUj0dyQjzbuRHPniZoBAAA8IfL0z4Y+wUAAEBAjDQCAAD4YTwemRBfnia5NwAAAJo0RhoBAAD84Z5GH4w0AgAAICBGGgEAAPzxGPvEvLYa8Uhjswkaf+gSr4jo2KOWafnRfqu6XAftkpfG77BLPC233U2xJjZwslan2jIDtO1amm7L+mzLVdn1XYxlOdsbih3HclDdZZccXdF2ieBVZZfEWJEWX0WP5c3TUXZJfRVj+R4q7BIiW3Ms+9j2s7A9l6vtzimrz0KSKbPMoB1hmTDeMsmyDRPi76NC2LagWJ4rETWWv7W2bM8B2+NGB/5O2oYRju33+6BdsvBQX250vv3eqly0RfJ+lyfEvz04bs0maAQAAAiKMZJCndy78Y40ck8jAAAAAmKkEQAAwA/jMTIhvqfRNOKRRoJGAAAAf4xHob88TXJvAAAANGGNImicO3euTjzxRMXGxqpv37567733GrpJAACgiTMeUyfbsQiHWCjsg8Z//OMfmjJlimbOnKn3339fPXv21JAhQ7R3796GbhoAAECdC5dYKOyDxkceeUQTJkzQuHHj1LVrV82fP1/x8fF66qmnGrppAACgKTOeutmCFC6xUFhPhKmqqtLGjRs1ffp07z6Xy6VBgwapoKDA72sqKytVWfl/CUFLSkokSe6qioDHq6kJXEaSXG67hK6OZTnr5N4W5RzbpL62w+Me2/psy1n2iaeBknsrxAmlLd+HVeJu2+Te1uVs30MDJfcO+Wdhe+7ZncvGWCZu91gm93ZCmNzb9vtoLM9P00DJvW3PARPi5N62/6jbHvcYL0f644Swrrpg+71wLH5XajxV/62z4d5zjapDvvR0jQ5970pLS332x8TEKCYm5rDyxxIL1ZWwDhq/++47ud1uZWRk+OzPyMjQZ5995vc1+fn5uueeew7b//Gzs+qkjQAAIEgl9kX379+vlJSUumuLH9HR0crMzNSaon/VSf2JiYnKycnx2Tdz5kzdfffdh5U9llioroR10Hgspk+frilTpngfFxcXq127dtq5c2e9n3ThqrS0VDk5OSosLFRycnJDNycs0CeHo0980R+Ho08OR58c7lj7xBij/fv3Kzs7uw5b519sbKx27NihKtulYINkjJHzk6su/kYZw01YB42tWrVSRESE9uzZ47N/z549yszM9PuaIw3vpqSk8AX+ieTkZPrkJ+iTw9EnvuiPw9Enh6NPDncsfdKQgz2xsbGKjY1tsOPXOpZYqK6E9USY6Oho9e7dW8uXL/fu83g8Wr58uXJzcxuwZQAAAHUvnGKhsB5plKQpU6Zo7Nix6tOnj84880zNmTNH5eXlGjduXEM3DQAAoM6FSywU9kHjFVdcoW+//VYzZsxQUVGRTjvtNC1duvSwG0KPJCYmRjNnzmwU9wrUF/rkcPTJ4egTX/TH4eiTw9Enh6NPjt/xxkKh4pjGvHI2AAAA6kVY39MIAACA8EDQCAAAgIAIGgEAABAQQSMAAAACatJB49y5c3XiiScqNjZWffv21XvvvdfQTao3d999txzH8dm6dOnifb6iokJ5eXlq2bKlEhMTNWrUqMMShzZ2q1ev1iWXXKLs7Gw5jqOXX37Z53ljjGbMmKGsrCzFxcVp0KBB+uKLL3zK7Nu3T6NHj1ZycrJSU1M1fvx4lZWV1eO7CK1AfXLttdcedt4MHTrUp0xT6pP8/HydccYZSkpKUnp6ukaOHKmtW7f6lLH5ruzcuVPDhw9XfHy80tPTNXXqVNXUhHg95Hpi0yf9+/c/7Dy58cYbfco0pT6ZN2+eevTo4U1OnZubq9dff937fHM7R6TAfdLczpHmoskGjf/4xz80ZcoUzZw5U++//7569uypIUOGaO/evQ3dtHpz6qmnavfu3d5tzZo13ucmT56s1157TS+88IJWrVqlXbt26bLLLmvA1oZeeXm5evbsqblz5/p9fvbs2Xr88cc1f/58rVu3TgkJCRoyZIgqKiq8ZUaPHq0tW7Zo2bJlWrJkiVavXq3rr7++vt5CyAXqE0kaOnSoz3nz7LPP+jzflPpk1apVysvL07vvvqtly5apurpagwcPVnl5ubdMoO+K2+3W8OHDVVVVpbVr12rhwoVasGCBZsyY0RBv6bjZ9IkkTZgwwec8mT17tve5ptYnbdq00QMPPKCNGzdqw4YNuuCCCzRixAht2bJFUvM7R6TAfSI1r3Ok2TBN1Jlnnmny8vK8j91ut8nOzjb5+fkN2Kr6M3PmTNOzZ0+/zxUXF5uoqCjzwgsvePd9+umnRpIpKCiopxbWL0lm8eLF3scej8dkZmaa3//+9959xcXFJiYmxjz77LPGGGM++eQTI8msX7/eW+b11183juOYb775pt7aXld+2ifGGDN27FgzYsSII76mqffJ3r17jSSzatUqY4zdd+Vf//qXcblcpqioyFtm3rx5Jjk52VRWVtbvG6gDP+0TY4w5//zzza233nrE1zT1PjHGmBYtWpi//OUvnCM/UtsnxnCONFVNcqSxqqpKGzdu1KBBg7z7XC6XBg0apIKCggZsWf364osvlJ2drZNOOkmjR4/Wzp07JUkbN25UdXW1T/906dJFbdu2bTb9s2PHDhUVFfn0QUpKivr27evtg4KCAqWmpqpPnz7eMoMGDZLL5dK6devqvc31ZeXKlUpPT1fnzp1100036fvvv/c+19T7pKSkRJKUlpYmye67UlBQoO7du/sk2R0yZIhKS0t9Rl0aq5/2Sa1nnnlGrVq1Urdu3TR9+nQdOHDA+1xT7hO3263nnntO5eXlys3N5RzR4X1Sq7meI01Z2K8Icyy+++47ud3uwzKlZ2Rk6LPPPmugVtWvvn37asGCBercubN2796te+65R+edd54+/vhjFRUVKTo6WqmpqT6vycjIUFFRUcM0uJ7Vvk9/50jtc0VFRUpPT/d5PjIyUmlpaU22n4YOHarLLrtM7du31/bt2/Wb3/xGw4YNU0FBgSIiIpp0n3g8Hk2aNEnnnHOOunXrJklW35WioiK/51Htc42Zvz6RpF/+8pdq166dsrOz9dFHH2natGnaunWrXnrpJUlNs082b96s3NxcVVRUKDExUYsXL1bXrl21adOmZnuOHKlPpOZ5jjQHTTJohDRs2DDv//fo0UN9+/ZVu3bt9PzzzysuLq4BW4ZwduWVV3r/v3v37urRo4c6dOiglStXauDAgQ3YsrqXl5enjz/+2Ofe3+buSH3y43tYu3fvrqysLA0cOFDbt29Xhw4d6ruZ9aJz587atGmTSkpK9OKLL2rs2LFatWpVQzerQR2pT7p27dosz5HmoElenm7VqpUiIiIOm722Z88eZWZmNlCrGlZqaqpOPvlkbdu2TZmZmaqqqlJxcbFPmebUP7Xv82jnSGZm5mETp2pqarRv375m008nnXSSWrVqpW3btklqun0yceJELVmyRCtWrFCbNm28+22+K5mZmX7Po9rnGqsj9Yk/ffv2lSSf86Sp9Ul0dLQ6duyo3r17Kz8/Xz179tRjjz3WrM+RI/WJP83hHGkOmmTQGB0drd69e2v58uXefR6PR8uXL/e536I5KSsr0/bt25WVlaXevXsrKirKp3+2bt2qnTt3Npv+ad++vTIzM336oLS0VOvWrfP2QW5uroqLi7Vx40Zvmbfeeksej8f7A9jU/ec//9H333+vrKwsSU2vT4wxmjhxohYvXqy33npL7du393ne5ruSm5urzZs3+wTTy5YtU3JysvdSXWMSqE/82bRpkyT5nCdNqU/88Xg8qqysbJbnyJHU9ok/zfEcaZIaeiZOXXnuuedMTEyMWbBggfnkk0/M9ddfb1JTU31majVlt912m1m5cqXZsWOHeeedd8ygQYNMq1atzN69e40xxtx4442mbdu25q233jIbNmwwubm5Jjc3t4FbHVr79+83H3zwgfnggw+MJPPII4+YDz74wHz99dfGGGMeeOABk5qaal555RXz0UcfmREjRpj27dubgwcPeusYOnSo6dWrl1m3bp1Zs2aN6dSpk7nqqqsa6i0dt6P1yf79+83tt99uCgoKzI4dO8ybb75pTj/9dNOpUydTUVHhraMp9clNN91kUlJSzMqVK83u3bu924EDB7xlAn1XampqTLdu3czgwYPNpk2bzNKlS03r1q3N9OnTG+ItHbdAfbJt2zYza9Yss2HDBrNjxw7zyiuvmJNOOsn069fPW0dT65Nf//rXZtWqVWbHjh3mo48+Mr/+9a+N4zjm3//+tzGm+Z0jxhy9T5rjOdJcNNmg0Rhj/vCHP5i2bdua6Ohoc+aZZ5p33323oZtUb6644gqTlZVloqOjzQknnGCuuOIKs23bNu/zBw8eNL/61a9MixYtTHx8vPnZz35mdu/e3YAtDr0VK1YYSYdtY8eONcYcSrtz1113mYyMDBMTE2MGDhxotm7d6lPH999/b6666iqTmJhokpOTzbhx48z+/fsb4N2ExtH65MCBA2bw4MGmdevWJioqyrRr185MmDDhsD+0mlKf+OsLSebpp5/2lrH5rnz11Vdm2LBhJi4uzrRq1crcdtttprq6up7fTWgE6pOdO3eafv36mbS0NBMTE2M6duxopk6dakpKSnzqaUp9ct1115l27dqZ6Oho07p1azNw4EBvwGhM8ztHjDl6nzTHc6S5cIwxpv7GNQEAANAYNcl7GgEAABBaBI0AAAAIiKARAAAAARE0AgAAICCCRgAAAARE0AgAAICACBoBAAAQEEEjAAAAAiJoBJqolStXynEcFRcXH1c91157rUaOHBmSNoWyrnA89ldffSXHceQ4jk477bR6b0/tZ+44ToP1M4Cmi6ARCHPz589XUlKSampqvPvKysoUFRWl/v37+5StDRq2b9+us88+W7t371ZKSkqdtu/HgYrL5VJKSop69eqlO+64Q7t37/Yp+9hjj2nBggV12p7awG3Tpk31fuxab775ppYvX14vx/qx2s/88ssvr/djA2j6CBqBMDdgwACVlZVpw4YN3n1vv/22MjMztW7dOlVUVHj3r1ixQm3btlWHDh0UHR2tzMxMOY5TL+3cunWrdu3apfXr12vatGl688031a1bN23evNlbJiUlRampqUeso6qqqs7aF+jYodSyZUu1bNmyXo71Y7WfeVxcXL0fG0DTR9AIhLnOnTsrKytLK1eu9O5buXKlRowYofbt2+vdd9/12T9gwADv///48vSCBQuUmpqqN954Q6eccooSExM1dOhQn9FAt9utKVOmKDU1VS1bttQdd9wh2+Xp09PTlZmZqZNPPllXXnml3nnnHbVu3Vo33XSTt8xPL8n2799fEydO1KRJk9SqVSsNGTJEkvTxxx9r2LBhSkxMVEZGhsaMGaPvvvvO+zqPx6PZs2erY8eOiomJUdu2bXX//fdLktq3by9J6tWrlxzH8Y7G/vTYlZWVuuWWW5Senq7Y2Fide+65Wr9+vU9fOo6j5cuXq0+fPoqPj9fZZ5+trVu3WvXHj9n0q8fjUX5+vtq3b6+4uDj17NlTL774ok+ZV199VZ06dVJsbKwGDBighQsXhuQWBACwQdAINAIDBgzQihUrvI9XrFih/v376/zzz/fuP3jwoNatW+cNGv05cOCAHnroIf3tb3/T6tWrtXPnTt1+++3e5x9++GEtWLBATz31lNasWaN9+/Zp8eLFx9TmuLg43XjjjXrnnXe0d+/eI5ZbuHChoqOj9c4772j+/PkqLi7WBRdcoF69emnDhg1aunSp9uzZ43PJdfr06XrggQd011136ZNPPtGiRYuUkZEhSXrvvfckHbpEvHv3br300kt+j3vHHXfon//8pxYuXKj3339fHTt21JAhQ7Rv3z6fcr/97W/18MMPa8OGDYqMjNR1110XdF/Y9Gt+fr7++te/av78+dqyZYsmT56sq6++WqtWrZIk7dixQz//+c81cuRIffjhh7rhhhv029/+Nui2AMAxMwDC3pNPPmkSEhJMdXW1KS0tNZGRkWbv3r1m0aJFpl+/fsYYY5YvX24kma+//toYY8yKFSuMJPPDDz8YY4x5+umnjSSzbds2b71z5841GRkZ3sdZWVlm9uzZ3sfV1dWmTZs2ZsSIEUds20+P82Ovv/66kWTWrVtnjDFm7NixPnWdf/75plevXj6vuffee83gwYN99hUWFhpJZuvWraa0tNTExMSYJ5980m97duzYYSSZDz74wGf/j49dVlZmoqKizDPPPON9vqqqymRnZ3vff+37evPNN71l/t//+39Gkjl48GBQxw7UrxUVFSY+Pt6sXbvW53Xjx483V111lTHGmGnTpplu3br5PP/b3/7Wb9//tJ8BIBQiGyhWBRCE/v37q7y8XOvXr9cPP/ygk08+Wa1bt9b555+vcePGqaKiQitXrtRJJ52ktm3bHrGe+Ph4dejQwfs4KyvLOwpYUlKi3bt3q2/fvt7nIyMj1adPH+tL1D9V+7qj3VfZu3dvn8cffvihVqxYocTExMPKbt++XcXFxaqsrNTAgQOPqU219VRXV+ucc87x7ouKitKZZ56pTz/91Kdsjx49vP+flZUlSdq7d+9R+/nHbPp127ZtOnDggC688EKf11ZVValXr16SDt0zesYZZ/g8f+aZZ1q1AQBCgaARaAQ6duyoNm3aaMWKFfrhhx90/vnnS5Kys7OVk5OjtWvXasWKFbrggguOWk9UVJTPY8dxjjkgtFEbgJ144olHLJOQkODzuKysTJdccokefPDBw8pmZWXpyy+/DGkbA/lxn9UGvx6PJ6THKCsrkyT9v//3/3TCCSf4PBcTExPSYwHAseKeRqCRGDBggFauXKmVK1f6pNrp16+fXn/9db333ntHvZ8xkJSUFGVlZWndunXefTU1Ndq4ceMx1Xfw4EH9+c9/Vr9+/dS6dWvr151++unasmWLTjzxRHXs2NFnS0hIUKdOnRQXF3fElDbR0dGSDk0+OZLa2eXvvPOOd191dbXWr1+vrl27WrfVhk2/du3aVTExMdq5c+dh7zknJ0fSoQlRP55BL8ln4g4A1DVGGoFGYsCAAcrLy1N1dbV3pFGSzj//fE2cOFFVVVXHFTRK0q233qoHHnhAnTp1UpcuXfTII49Yz8zdu3evKioqtH//fm3cuFGzZ8/Wd999d8SJKEeSl5enJ598UldddZXuuOMOpaWladu2bXruuef0l7/8RbGxsZo2bZruuOMORUdH65xzztG3336rLVu2aPz48UpPT1dcXJyWLl2qNm3aKDY29rBclQkJCbrppps0depUpaWlqW3btpo9e7YOHDig8ePHB9VeG4H6NSkpSbfffrsmT54sj8ejc889VyUlJXrnnXeUnJyssWPH6oYbbtAjjzyiadOmafz48dq0aZM372R9pVUC0LwRNAKNxIABA3Tw4EF16dLFO1NYOhQ07t+/35ua53jcdttt2r17t8aOHSuXy6XrrrtOP/vZz1RSUhLwtZ07d5bjOEpMTNRJJ52kwYMHa8qUKcrMzAyqDdnZ2XrnnXc0bdo0DR48WJWVlWrXrp2GDh0ql+vQxZG77rpLkZGRmjFjhnbt2qWsrCzdeOONkg7dL/j4449r1qxZmjFjhs477zyfdEW1HnjgAXk8Ho0ZM0b79+9Xnz599MYbb6hFixZBtdeGTb/ee++9at26tfLz8/Xll18qNTVVp59+un7zm99IOpRK6MUXX9Rtt92mxx57TLm5ufrtb3+rm266iUvYAOqFY+ryhiYAaEa++uortW/fXh988EHAZQRD4f7779f8+fNVWFjos//aa69VcXGxXn755TpvA4Dmg3saASDEzj77bJ199tkhr/dPf/qT1q9fry+//FJ/+9vf9Pvf/15jx471Pv/2228rMTFRzzzzTMiPDQCMNAJAiNTU1Oirr76SdGjWc+0kllCZPHmy/vGPf2jfvn1q27atxowZo+nTpysy8tCdRgcPHtQ333wjSUpMTAz61gAAOBqCRgAAAATE5WkAAAAERNAIAACAgAgaAQAAEBBBIwAAAAIiaAQAAEBABI0AAAAIiKARAAAAARE0AgAAIKD/D5/Ie/1Q1qm3AAAAAElFTkSuQmCC", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist2d(df['wd (deg)'], df['wv (m/s)'], bins=(50, 50), vmax=400)\n", - "plt.colorbar()\n", - "plt.xlabel('Wind Direction [deg]')\n", - "plt.ylabel('Wind Velocity [m/s]')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yWnf5dwMU1_g" - }, - "source": [ - "But this will be easier for the model to interpret if you convert the wind direction and velocity columns to a wind **vector**:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "6GmSTHXw6lI1" - }, - "outputs": [], - "source": [ - "wv = df.pop('wv (m/s)')\n", - "max_wv = df.pop('max. wv (m/s)')\n", - "\n", - "# Convert to radians.\n", - "wd_rad = df.pop('wd (deg)')*np.pi / 180\n", - "\n", - "# Calculate the wind x and y components.\n", - "df['Wx'] = wv*np.cos(wd_rad)\n", - "df['Wy'] = wv*np.sin(wd_rad)\n", - "\n", - "# Calculate the max wind x and y components.\n", - "df['max Wx'] = max_wv*np.cos(wd_rad)\n", - "df['max Wy'] = max_wv*np.sin(wd_rad)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7iI0zDoxWDyB" - }, - "source": [ - "The distribution of wind vectors is much simpler for the model to correctly interpret:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "bMgCG5o2SYKD" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-11.305513973134667, 8.24469928549079, -8.27438540335515, 7.7338312955467785)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.hist2d(df['Wx'], df['Wy'], bins=(50, 50), vmax=400)\n", - "plt.colorbar()\n", - "plt.xlabel('Wind X [m/s]')\n", - "plt.ylabel('Wind Y [m/s]')\n", - "ax = plt.gca()\n", - "ax.axis('tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_8im1ttOWlRB" - }, - "source": [ - "#### Time" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7YE21HKK40zQ" - }, - "source": [ - "Similarly, the `Date Time` column is very useful, but not in this string form. Start by converting it to seconds:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "LIFf-VjMfnh3" - }, - "outputs": [], - "source": [ - "timestamp_s = date_time.map(pd.Timestamp.timestamp)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EC_pnM1D5Sgc" - }, - "source": [ - "Similar to the wind direction, the time in seconds is not a useful model input. Being weather data, it has clear daily and yearly periodicity. There are many ways you could deal with periodicity.\n", - "\n", - "You can get usable signals by using sine and cosine transforms to clear \"Time of day\" and \"Time of year\" signals:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "MBfX6CDwax73" - }, - "outputs": [], - "source": [ - "day = 24*60*60\n", - "year = (365.2425)*day\n", - "\n", - "df['Day sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n", - "df['Day cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n", - "df['Year sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n", - "df['Year cos'] = np.cos(timestamp_s * (2 * np.pi / year))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "mXBbTJZfuuTC" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Time of day signal')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(np.array(df['Day sin'])[:25])\n", - "plt.plot(np.array(df['Day cos'])[:25])\n", - "plt.xlabel('Time [h]')\n", - "plt.title('Time of day signal')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HiurzTGQgf_D" - }, - "source": [ - "This gives the model access to the most important frequency features. In this case you knew ahead of time which frequencies were important. \n", - "\n", - "If you don't have that information, you can determine which frequencies are important by extracting features with Fast Fourier Transform. To check the assumptions, here is the `tf.signal.rfft` of the temperature over time. Note the obvious peaks at frequencies near `1/year` and `1/day`:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "EN4U1fcMiTYs" - }, - "outputs": [ - { - "data": { - "image/png": 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+ffooISFB48eP1+jRo7V69Wqz5rPPPlN0dLSmTZumHTt2qEuXLgoLC9PJkyfNmrL6AgAAAIsxrlHt2rWN9957z0hNTTVcXFyMJUuWmOv27dtnSDLi4uIMwzCMlStXGo6OjkZycrJZM3fuXMNmsxlZWVmGYRjGpEmTjA4dOtjtY9iwYUZYWJh5Pzg42IiKijLv5+XlGYGBgcb06dMNwzDK1ZfySEtLMyQZaWlp5d4G1cfj728xmj633FiyPanIuhW7jhtNn1tuPDhvUxX0DAAAlFdF8tpVj/HNy8vTokWLlJGRoZCQEMXHxysnJ0ehoaFmTdu2bdWkSRPFxcVJkuLi4tSpUyfVr1/frAkLC1N6erp51DguLs6ujYKagjays7MVHx9vV+Po6KjQ0FCzpjx9KU5WVpbS09PtbgAAAKgZKhx8d+/eLS8vL7m5uWnMmDFaunSp2rdvr+TkZLm6usrX19euvn79+kpOTpYkJScn24XegvUF60qrSU9P16VLl3T69Gnl5eUVW1O4jbL6Upzp06fLx8fHvDVu3Lh8TwoAAABuehUOvm3atFFCQoK2bNmip556ShEREfrxxx+vR99uuMmTJystLc28JSUlVXWXAAAAUEmcK7qBq6urWrZsKUkKCgrStm3bNGvWLA0bNkzZ2dlKTU21O9KakpKigIAASVJAQECR2RcKZlooXHPl7AspKSmy2Wzy8PCQk5OTnJyciq0p3EZZfSmOm5ub3NzcKvBsAAAAoLq45nl88/PzlZWVpaCgILm4uCg2NtZcd+DAASUmJiokJESSFBISot27d9vNvhATEyObzab27dubNYXbKKgpaMPV1VVBQUF2Nfn5+YqNjTVrytMXAAAAWEuFjvhOnjxZAwYMUJMmTXT+/HktXLhQ69at0+rVq+Xj46PIyEhFR0erTp06stlsGjdunEJCQnTbbbdJkvr166f27dtr+PDhmjFjhpKTkzVlyhRFRUWZR1rHjBmj2bNna9KkSRo1apTWrFmjxYsXa8WKFWY/oqOjFRERoR49eig4OFgzZ85URkaGRo4cKUnl6gsAAACspULB9+TJk3r88cd14sQJ+fj4qHPnzlq9erXuvfdeSdJbb70lR0dHDR06VFlZWQoLC9O7775rbu/k5KTly5frqaeeUkhIiDw9PRUREaGXX37ZrGnevLlWrFihCRMmaNasWWrUqJHee+89hYWFmTXDhg3TqVOnNHXqVCUnJ6tr165atWqV3QlvZfUFAAAA1uJgGIZR1Z24WaWnp8vHx0dpaWmy2WxV3R1UsogPtmr9T6f0+oNd9EBQI7t1K3ef0NOf7FBw8zpa/CeGxwAAcLOqSF675jG+AAAAQHVA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCRUKvtOnT9ett94qb29v1atXT4MHD9aBAwfsanr37i0HBwe725gxY+xqEhMTFR4erlq1aqlevXqaOHGicnNz7WrWrVun7t27y83NTS1bttT8+fOL9GfOnDlq1qyZ3N3d1bNnT23dutVufWZmpqKiouTn5ycvLy8NHTpUKSkpFXnIAAAAqCEqFHzXr1+vqKgobd68WTExMcrJyVG/fv2UkZFhV/fEE0/oxIkT5m3GjBnmury8PIWHhys7O1ubNm3SggULNH/+fE2dOtWsOXLkiMLDw9WnTx8lJCRo/PjxGj16tFavXm3WfPbZZ4qOjta0adO0Y8cOdenSRWFhYTp58qRZM2HCBH311VdasmSJ1q9fr+PHj2vIkCEVfpIAAABQAxjX4OTJk4YkY/369eayu+++23jmmWdK3GblypWGo6OjkZycbC6bO3euYbPZjKysLMMwDGPSpElGhw4d7LYbNmyYERYWZt4PDg42oqKizPt5eXlGYGCgMX36dMMwDCM1NdVwcXExlixZYtbs27fPkGTExcWV6/GlpaUZkoy0tLRy1aN6efz9LUbT55YbS7YnFVm3Ytdxo+lzy40H522qgp4BAIDyqkheu6YxvmlpaZKkOnXq2C3/5JNP5O/vr44dO2ry5Mm6ePGiuS4uLk6dOnVS/fr1zWVhYWFKT0/X3r17zZrQ0FC7NsPCwhQXFydJys7OVnx8vF2No6OjQkNDzZr4+Hjl5OTY1bRt21ZNmjQxa66UlZWl9PR0uxsAAABqBuer3TA/P1/jx4/XHXfcoY4dO5rLH330UTVt2lSBgYHatWuXnnvuOR04cED//e9/JUnJycl2oVeSeT85ObnUmvT0dF26dEnnzp1TXl5esTX79+8323B1dZWvr2+RmoL9XGn69Ol66aWXKvhMAAAAoDq46uAbFRWlPXv26Pvvv7db/uSTT5o/d+rUSQ0aNFDfvn116NAhtWjR4up7egNMnjxZ0dHR5v309HQ1bty4CnsEAACAynJVQx3Gjh2r5cuXa+3atWrUqFGptT179pQkHTx4UJIUEBBQZGaFgvsBAQGl1thsNnl4eMjf319OTk7F1hRuIzs7W6mpqSXWXMnNzU02m83uBgAAgJqhQsHXMAyNHTtWS5cu1Zo1a9S8efMyt0lISJAkNWjQQJIUEhKi3bt3282+EBMTI5vNpvbt25s1sbGxdu3ExMQoJCREkuTq6qqgoCC7mvz8fMXGxpo1QUFBcnFxsas5cOCAEhMTzRoAAABYR4WGOkRFRWnhwoX64osv5O3tbY6V9fHxkYeHhw4dOqSFCxfqvvvuk5+fn3bt2qUJEyborrvuUufOnSVJ/fr1U/v27TV8+HDNmDFDycnJmjJliqKiouTm5iZJGjNmjGbPnq1JkyZp1KhRWrNmjRYvXqwVK1aYfYmOjlZERIR69Oih4OBgzZw5UxkZGRo5cqTZp8jISEVHR6tOnTqy2WwaN26cQkJCdNttt1XKkwcAAIDqo0LBd+7cuZIuX6SisA8//FAjRoyQq6urvv32WzOENm7cWEOHDtWUKVPMWicnJy1fvlxPPfWUQkJC5OnpqYiICL388stmTfPmzbVixQpNmDBBs2bNUqNGjfTee+8pLCzMrBk2bJhOnTqlqVOnKjk5WV27dtWqVavsTnh766235OjoqKFDhyorK0thYWF69913K/QEAQAAoGZwMAzDqOpO3KzS09Pl4+OjtLQ0xvvWQBEfbNX6n07p9Qe76IEg+7HqK3ef0NOf7FBw8zpa/CeGxgAAcLOqSF67pnl8AQAAgOqC4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEioUfKdPn65bb71V3t7eqlevngYPHqwDBw7Y1WRmZioqKkp+fn7y8vLS0KFDlZKSYleTmJio8PBw1apVS/Xq1dPEiROVm5trV7Nu3Tp1795dbm5uatmypebPn1+kP3PmzFGzZs3k7u6unj17auvWrRXuCwAAAKyhQsF3/fr1ioqK0ubNmxUTE6OcnBz169dPGRkZZs2ECRP01VdfacmSJVq/fr2OHz+uIUOGmOvz8vIUHh6u7Oxsbdq0SQsWLND8+fM1depUs+bIkSMKDw9Xnz59lJCQoPHjx2v06NFavXq1WfPZZ58pOjpa06ZN044dO9SlSxeFhYXp5MmT5e4LAAAALMS4BidPnjQkGevXrzcMwzBSU1MNFxcXY8mSJWbNvn37DElGXFycYRiGsXLlSsPR0dFITk42a+bOnWvYbDYjKyvLMAzDmDRpktGhQwe7fQ0bNswICwsz7wcHBxtRUVHm/by8PCMwMNCYPn16uftSlrS0NEOSkZaWVq56VC+Pv7/FaPrccmPJ9qQi61bsOm40fW658eC8TVXQMwAAUF4VyWvXNMY3LS1NklSnTh1JUnx8vHJychQaGmrWtG3bVk2aNFFcXJwkKS4uTp06dVL9+vXNmrCwMKWnp2vv3r1mTeE2CmoK2sjOzlZ8fLxdjaOjo0JDQ82a8vTlSllZWUpPT7e7AQAAoGa46uCbn5+v8ePH64477lDHjh0lScnJyXJ1dZWvr69dbf369ZWcnGzWFA69BesL1pVWk56erkuXLun06dPKy8srtqZwG2X15UrTp0+Xj4+PeWvcuHE5nw0AAADc7K46+EZFRWnPnj1atGhRZfanSk2ePFlpaWnmLSkpqaq7BAAAgErifDUbjR07VsuXL9eGDRvUqFEjc3lAQICys7OVmppqd6Q1JSVFAQEBZs2Vsy8UzLRQuObK2RdSUlJks9nk4eEhJycnOTk5FVtTuI2y+nIlNzc3ubm5VeCZAAAAQHVRoSO+hmFo7NixWrp0qdasWaPmzZvbrQ8KCpKLi4tiY2PNZQcOHFBiYqJCQkIkSSEhIdq9e7fd7AsxMTGy2Wxq3769WVO4jYKagjZcXV0VFBRkV5Ofn6/Y2Fizpjx9AQAAgHVU6IhvVFSUFi5cqC+++ELe3t7mWFkfHx95eHjIx8dHkZGRio6OVp06dWSz2TRu3DiFhITotttukyT169dP7du31/DhwzVjxgwlJydrypQpioqKMo+2jhkzRrNnz9akSZM0atQorVmzRosXL9aKFSvMvkRHRysiIkI9evRQcHCwZs6cqYyMDI0cOdLsU1l9AQAAgHVUKPjOnTtXktS7d2+75R9++KFGjBghSXrrrbfk6OiooUOHKisrS2FhYXr33XfNWicnJy1fvlxPPfWUQkJC5OnpqYiICL388stmTfPmzbVixQpNmDBBs2bNUqNGjfTee+8pLCzMrBk2bJhOnTqlqVOnKjk5WV27dtWqVavsTngrqy8AAACwDgfDMIyq7sTNKj09XT4+PkpLS5PNZqvq7qCSRXywVet/OqXXH+yiB4Ia2a1bufuEnv5kh4Kb19HiPzE0BgCAm1VF8to1zeMLAAAAVBcEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWQPAFAACAJRB8AQAAYAkEXwAAAFgCwRcAAACWUOHgu2HDBg0cOFCBgYFycHDQsmXL7NaPGDFCDg4Odrf+/fvb1Zw9e1aPPfaYbDabfH19FRkZqQsXLtjV7Nq1S3feeafc3d3VuHFjzZgxo0hflixZorZt28rd3V2dOnXSypUr7dYbhqGpU6eqQYMG8vDwUGhoqH7++eeKPmQAAADUABUOvhkZGerSpYvmzJlTYk3//v114sQJ8/bpp5/arX/ssce0d+9excTEaPny5dqwYYOefPJJc316err69eunpk2bKj4+Xq+99ppefPFF/etf/zJrNm3apEceeUSRkZH64YcfNHjwYA0ePFh79uwxa2bMmKG3335b8+bN05YtW+Tp6amwsDBlZmZW9GEDAACgmnOu6AYDBgzQgAEDSq1xc3NTQEBAsev27dunVatWadu2berRo4ck6Z133tF9992n119/XYGBgfrkk0+UnZ2tDz74QK6ururQoYMSEhL05ptvmgF51qxZ6t+/vyZOnChJ+utf/6qYmBjNnj1b8+bNk2EYmjlzpqZMmaJBgwZJkj766CPVr19fy5Yt08MPP1ykb1lZWcrKyjLvp6enV/TpAQAAwE3quozxXbdunerVq6c2bdroqaee0pkzZ8x1cXFx8vX1NUOvJIWGhsrR0VFbtmwxa+666y65urqaNWFhYTpw4IDOnTtn1oSGhtrtNywsTHFxcZKkI0eOKDk52a7Gx8dHPXv2NGuuNH36dPn4+Ji3xo0bX+MzAQAAgJtFpQff/v3766OPPlJsbKz+8Y9/aP369RowYIDy8vIkScnJyapXr57dNs7OzqpTp46Sk5PNmvr169vVFNwvq6bw+sLbFVdzpcmTJystLc28JSUlVfjxAwAA4OZU4aEOZSk8hKBTp07q3LmzWrRooXXr1qlv376VvbtK5ebmJjc3t6ruBgAAAK6D6z6d2S233CJ/f38dPHhQkhQQEKCTJ0/a1eTm5urs2bPmuOCAgAClpKTY1RTcL6um8PrC2xVXAwAAAOu47sH3119/1ZkzZ9SgQQNJUkhIiFJTUxUfH2/WrFmzRvn5+erZs6dZs2HDBuXk5Jg1MTExatOmjWrXrm3WxMbG2u0rJiZGISEhkqTmzZsrICDAriY9PV1btmwxawAAAGAdFQ6+Fy5cUEJCghISEiRdPoksISFBiYmJunDhgiZOnKjNmzfr6NGjio2N1aBBg9SyZUuFhYVJktq1a6f+/fvriSee0NatW7Vx40aNHTtWDz/8sAIDAyVJjz76qFxdXRUZGam9e/fqs88+06xZsxQdHW3245lnntGqVav0xhtvaP/+/XrxxRe1fft2jR07VpLk4OCg8ePH65VXXtGXX36p3bt36/HHH1dgYKAGDx58jU8bAAAAqpsKj/Hdvn27+vTpY94vCKMRERGaO3eudu3apQULFig1NVWBgYHq16+f/vrXv9qNnf3kk080duxY9e3bV46Ojho6dKjefvttc72Pj4+++eYbRUVFKSgoSP7+/po6dardXL+33367Fi5cqClTpuiFF15Qq1attGzZMnXs2NGsmTRpkjIyMvTkk08qNTVVvXr10qpVq+Tu7l7Rhw0AAIBqzsEwDKOqO3GzSk9Pl4+Pj9LS0mSz2aq6O6hkER9s1fqfTun1B7vogaBGdutW7j6hpz/ZoeDmdbT4TwyNAQDgZlWRvHbdx/gCAAAANwOCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLIPgCAADAEgi+AAAAsASCLwAAACyB4AsAAABLqHDw3bBhgwYOHKjAwEA5ODho2bJldusNw9DUqVPVoEEDeXh4KDQ0VD///LNdzdmzZ/XYY4/JZrPJ19dXkZGRunDhgl3Nrl27dOedd8rd3V2NGzfWjBkzivRlyZIlatu2rdzd3dWpUyetXLmywn0BAACANVQ4+GZkZKhLly6aM2dOsetnzJiht99+W/PmzdOWLVvk6empsLAwZWZmmjWPPfaY9u7dq5iYGC1fvlwbNmzQk08+aa5PT09Xv3791LRpU8XHx+u1117Tiy++qH/9619mzaZNm/TII48oMjJSP/zwgwYPHqzBgwdrz549FeoLAAAALMK4BpKMpUuXmvfz8/ONgIAA47XXXjOXpaamGm5ubsann35qGIZh/Pjjj4YkY9u2bWbN119/bTg4OBjHjh0zDMMw3n33XaN27dpGVlaWWfPcc88Zbdq0Me8/9NBDRnh4uF1/evbsafzpT38qd1+ulJmZaaSlpZm3pKQkQ5KRlpZW0acG1cDj728xmj633FiyPanIuhW7jhtNn1tuPDhvUxX0DAAAlFdaWlq581qljvE9cuSIkpOTFRoaai7z8fFRz549FRcXJ0mKi4uTr6+vevToYdaEhobK0dFRW7ZsMWvuuusuubq6mjVhYWE6cOCAzp07Z9YU3k9BTcF+ytOXK02fPl0+Pj7mrXHjxtfydAAAAOAmUqnBNzk5WZJUv359u+X169c31yUnJ6tevXp2652dnVWnTh27muLaKLyPkmoKry+rL1eaPHmy0tLSzFtSUlI5HjUAAACqA+eq7sDNxM3NTW5ublXdDQAAAFwHlXrENyAgQJKUkpJitzwlJcVcFxAQoJMnT9qtz83N1dmzZ+1qimuj8D5Kqim8vqy+AAAAwDoqNfg2b95cAQEBio2NNZelp6dry5YtCgkJkSSFhIQoNTVV8fHxZs2aNWuUn5+vnj17mjUbNmxQTk6OWRMTE6M2bdqodu3aZk3h/RTUFOynPH0BAACAdVQ4+F64cEEJCQlKSEiQdPkksoSEBCUmJsrBwUHjx4/XK6+8oi+//FK7d+/W448/rsDAQA0ePFiS1K5dO/Xv319PPPGEtm7dqo0bN2rs2LF6+OGHFRgYKEl69NFH5erqqsjISO3du1efffaZZs2apejoaLMfzzzzjFatWqU33nhD+/fv14svvqjt27dr7NixklSuvgAAAMA6KjzGd/v27erTp495vyCMRkREaP78+Zo0aZIyMjL05JNPKjU1Vb169dKqVavk7u5ubvPJJ59o7Nix6tu3rxwdHTV06FC9/fbb5nofHx998803ioqKUlBQkPz9/TV16lS7uX5vv/12LVy4UFOmTNELL7ygVq1aadmyZerYsaNZU56+AAAAwBocDMMwqroTN6v09HT5+PgoLS1NNputqruDShbxwVat/+mUXn+wix4IamS3buXuE3r6kx0Kbl5Hi//E0BgAAG5WFclrlTrGFwAAALhZEXwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAIAlEHwBAABgCQRfAAAAWALBFwAAAJZA8AUAAOWyI/GcEpJSq7obwFVzruoOAACAm19GVq6GvLtJkvTTKwPk6syxM1Q//NYCAIAypWfmmD9n5eZVYU+Aq0fwBQAAgCUQfAEAAGAJBF8AAABYAsEXAAAAlkDwBQAAFWJUdQeAq0TwBQAAZXKQQ1V3AbhmBF8AAABYAsEXAAAAlkDwBQAAgCUQfAEAQIUYnN2GaorgCwAAAEsg+AIAgDI5MKkDagCCLwAAACyB4AsAAABLIPgCAADAEgi+AACgYpjVAdUUwRcAAACWQPAFAABlysnLN392JD2gmuJXFwAAVIiTI3OboXoi+AIAgDJxtTbUBARfAABQIQ7iiC+qJ4IvAACoEINpHVBNEXwBAABgCZUefF988UU5ODjY3dq2bWuuz8zMVFRUlPz8/OTl5aWhQ4cqJSXFro3ExESFh4erVq1aqlevniZOnKjc3Fy7mnXr1ql79+5yc3NTy5YtNX/+/CJ9mTNnjpo1ayZ3d3f17NlTW7dureyHCwAAgGriuhzx7dChg06cOGHevv/+e3PdhAkT9NVXX2nJkiVav369jh8/riFDhpjr8/LyFB4eruzsbG3atEkLFizQ/PnzNXXqVLPmyJEjCg8PV58+fZSQkKDx48dr9OjRWr16tVnz2WefKTo6WtOmTdOOHTvUpUsXhYWF6eTJk9fjIQMAAOAmd12Cr7OzswICAsybv7+/JCktLU3vv/++3nzzTd1zzz0KCgrShx9+qE2bNmnz5s2SpG+++UY//vijPv74Y3Xt2lUDBgzQX//6V82ZM0fZ2dmSpHnz5ql58+Z644031K5dO40dO1YPPPCA3nrrLbMPb775pp544gmNHDlS7du317x581SrVi198MEH1+MhAwAA4CZ3XYLvzz//rMDAQN1yyy167LHHlJiYKEmKj49XTk6OQkNDzdq2bduqSZMmiouLkyTFxcWpU6dOql+/vlkTFham9PR07d2716wp3EZBTUEb2dnZio+Pt6txdHRUaGioWVOcrKwspaen290AAIA9pjZDdVXpwbdnz56aP3++Vq1apblz5+rIkSO68847df78eSUnJ8vV1VW+vr5229SvX1/JycmSpOTkZLvQW7C+YF1pNenp6bp06ZJOnz6tvLy8YmsK2ijO9OnT5ePjY94aN258Vc8BAAA1DWEXNYFzZTc4YMAA8+fOnTurZ8+eatq0qRYvXiwPD4/K3l2lmjx5sqKjo8376enphF8AAIAa4rpPZ+br66vWrVvr4MGDCggIUHZ2tlJTU+1qUlJSFBAQIEkKCAgoMstDwf2yamw2mzw8POTv7y8nJ6diawraKI6bm5tsNpvdDQAAADXDdQ++Fy5c0KFDh9SgQQMFBQXJxcVFsbGx5voDBw4oMTFRISEhkqSQkBDt3r3bbvaFmJgY2Ww2tW/f3qwp3EZBTUEbrq6uCgoKsqvJz89XbGysWQMAAABrqfTg++yzz2r9+vU6evSoNm3apPvvv19OTk565JFH5OPjo8jISEVHR2vt2rWKj4/XyJEjFRISottuu02S1K9fP7Vv317Dhw/Xzp07tXr1ak2ZMkVRUVFyc3OTJI0ZM0aHDx/WpEmTtH//fr377rtavHixJkyYYPYjOjpa//73v7VgwQLt27dPTz31lDIyMjRy5MjKfsgAAACoBip9jO+vv/6qRx55RGfOnFHdunXVq1cvbd68WXXr1pUkvfXWW3J0dNTQoUOVlZWlsLAwvfvuu+b2Tk5OWr58uZ566imFhITI09NTERERevnll82a5s2ba8WKFZowYYJmzZqlRo0a6b333lNYWJhZM2zYMJ06dUpTp05VcnKyunbtqlWrVhU54Q0AAJSt8GWKOc8N1VWlB99FixaVut7d3V1z5szRnDlzSqxp2rSpVq5cWWo7vXv31g8//FBqzdixYzV27NhSawAAAGAN132MLwAAAHAzIPgCAADAEgi+AAAAsASCLwAAKBNXbkNNQPAFAAAVYpCCUU0RfAEAAGAJlT6dWU10z+vr5OzuWa5aJ0cHPRPaSg/1aHydewUAwI3DMV7UBATfcjh5PkuO2U7lrv88/leCLwAAwE2G4FsOi/90m7y8bWXWbTx4WtO/3s/HYgAAgJsQwbcc2gf6yGYrO/gmnb14A3oDAEDV4vgOqitObgMAAIAlEHwBAECZmMIMNQHBFwAAAJZA8AUAAIAlEHwBAABgCczqAKBMH2/+RVuOnJUk3dHCTw8HN6niHgGoSgz3RXVF8AVQqovZuZr6xR7l//Yf3crdJzSkeyO5OvOFEWAlZF3UBARfAKXKzTfM0CtJefmG8ivhcE9+vqGvdh1XSnqm2jfwUa9W/tfcJgAApSH4AjfY1C/2aOGWRLtlHi5Oeu3BLurfMaCKenXjbT5yRs8sSpAkOTk6KH5KqHxruVZtp24SmTl5Wr7rhNo18FaHQJ+q7g4gieENqBn4rhK4wVbuTlZuvmF3O5+Vq3UHTlZ1126otIs55s95+YbOZ+aWWHsi7ZLifzmrC1kl19Qk/4n7Rc8u2alBszdWdVcAoEbhiC9QRT4Z3VMt63npg41H9M/1h6u6OzetY6mXdMff10iSWtT1VOyfe1dth26AI2cyJF0eZgLcLBwcqroHwLXjiC9QRfy8XFXf5i5vNz5/lubXsxfNn385c7GUSgDXk91QBz6ToZoi+AIAAMASONQElGLb0bNq+5eviyxv6Ouh/3vq9hpxMlbimYuKO3xakuTl5qI7WvrplRX7lJKeKUm6u3XdquxeuRxIPq+FW35RbU9Xjbm7hdxdnK6qnU2HTuun5PO6r3MD1fN2r+ReXp0Vu07IwUG6r1ODqu4KLI/DvKj+CL5AMVrX95abs6OycvOVmZNfZP2hUxnafSxNd7a6+UNhWUbO36pDpzLM+3e3rqv1P50y728+fKYqulUhb8Yc0Oq9KZKkDoE+urd9/Qq3kZ6Zo0f/vUWStOd4ul5/sEul9vFqRS3cIUn6NvputaznVcW9AYDqjeALFKNlPS9tnxKqtEs5RdZFfGAfFKu7MxnZkqQ6nq46m5Gt5LTLR3q93Jx1IStXOXk3/1GeS4U+nFzKybuqNi5m/b5d6sWir3tVK+53EQBQMQRfoATe7i7ydncpstzFqWYOjW/fwKbvD54277s5O+pC1tW1lZmTp7diftKJtEz5e7kpul9reZVxEt+LX+5VgI+7Hr61iTo1qj5z1xqGoXnrD+vcxWxF9W4pn1ouOp+Zo7djf5ZvLVc93buFHDgdHjWMwbAHVFMEX9RYe46lKelsybMAnDp/lakOpfpy53FNWbpb6YXm5e3axFf/0yWw1O1i91+exzjp3CV9NCr4qvf/8lc/6mR6piJ7Nb/ugTM/39Dba37WzG9/liQ19/fUI8FNFPNjiv793RFJUliH+mpZz/u69gO40X48nq7bW3K1RVQ/BF9cs7SLOcrMvbqvlyWplqtTsUdWr8XR0xn6wzvfl6vW2ZGjcZXpf5fuLnIxiqwKDD/IzL763yVJOn0hS6+s2Kcezeqoa2Nfc/m3P6Zo6hd75Oflpo8je8qn1rX/zsUdPmOGXunykW5JysrNL7Ss6BhxoDoqPJ1Z0jmmFkT1RPDFNVn6w6+KXrzzmi5l6erkqIVP9FSPZnUqrV8nfzua6+bsqC6NfEus8/d2velmLcjJy9eyH47p9IVsc5mXu7Pu79awzOECN4Ps30Lfn+9trRW7T2h/8vkq6cfFbPvwvXL3CR1Py9TxtEztSDqnPm3qXXXb3/98WhnZuWbQBQBUDzf//6K4qe1MSpNhXL6ij9NVfK2cm28oOy9fe4+nV2rwLdCwtocWjwmp9Havp9h9JzXx811Fll/KztWTd7Wogh5dnfu7N9SOxHNVFnyvxZbDZ/RWzE+6r1MDuTg5qLm/pzlsYn9yuv74/uXZH+7rFFCp+83Nu/yh4cuE45XaLlAZLl7jtzHAzYDgi0oxtk9L/blfmwpvF7Vwh1bsOnEdelR9pf929n6AzV29WvkrISlVB09euOFn9Xu5O+vsxexrOpp/I6zak6wNhaZfK4+4Q2fUo2ntEofYnM/K1azYnzUr9vIwhgeDGum136Y3O1PoSHzho/LX6lxGtsJmbtCZjGzlcali3IQ+255U1V0ArlnNPD0dqAHaB9r0+oNd1KuKTiCp6+Wm9x7voYlhbTQxrI0+HHGr3frcvHxNWbZbw9/fotELtumHxHNXva9RdzRXuwa2IssNw9De4+m/7zPf0I+F7kvSZ9sSS2z3m73Jen31Ae36NdVu+b82HNYj/95s3j+Xka1/f3e4xHaWxP+qQ6culPUwrslPKed18nwWoRc3rexCY9dv9g/EQEk44gtcpWcWJcjN2f6zY/emtTX7kW41Zvqqvu3qq2+7yxeDuJBlP2Z297E0fbz599Dp6easbk1qV3gfwc3qaOrA9vp69wk99ckOu3Xrfjqll5f/aLdsyNyNej/iVq3em6x63m52J5IVdiEzV0/+J16SFPNjijoE2gfrPccuB+is3Dx9uOmo3v/+SKn97PvGeo26o7lcnEt+bbcfPaeRdzQvtZ3rISs3Tyt2nVDHhj5qXb/kGSQMw9DPJy/oFn9POf82Ld+5jGx5uTvX2Gn6AKAwgq9FJJ29qB3XcESuJJV1FOy7n0+X+8IDzo4O6t8xQI1q16qUfVdUi3pe2p98Xmczin7NvWLXCb04sIPqervd8H7l5xvadvSsOY2Yh4uTet5Sxy7QrD1wUmt/mzbMxclRjwQ3uer95V5xZDK3nBe6OJB8XodOXdD2o2X/Ph47d8n8uaGvh46lXlJmTr4mfb5Lx1IvryvpM0ZmoUB8ZWgvMH3lPv1zQ8lHeq/0wcbSw/GK3Sc0pxzt7P41Tc989oO83V305kNd1KJu2Vdkizt0WnW93NTEz/73/lxGtiIXbNOOxFTVcnXSjy/3L7GNf393WK+u3K/7uzXUW8O6as+xNA2es1EdAm36YmyvcvQcVsZRXtQEBN/rYOvRs2r5wsqq7oadK0NKZXN2vLqjRe7OTpKkb/el6Nt9KeXebvPhs3ovosdV7fNazRrWVU/3blHkP4GBs7+XYUi3/z1WDvo9jfl7uWrRkyFFAktl+78dvxY5Ke7Zfq019p5Wv99fvNO8UpskpaRnXtc+XelsRrbC3/6uwr+P/TsEaN7wILV4YaXy8g272RSu5T/j5ZU8vrysqfEuZefp9IUsPbPoBx0+ffnqf39d/qPmjwzWyt2l9+X1b37SnLWHtPelMDkW2s/z/92lHYmpki6ffHQ2I1s/JJ7Tt/tS9Ezf1grwcTdrP/j+qCRp6Q/H9Nawrvru59PKzTe089c0s+anlPNqVNtDtVxL/+9h7/E0Ld1xTKN6NVegr0eptagZCl+04mbLwIZh6EJWbqVPjYmah+BbidoEeMvDxUmXcvKue9C8WsHN61T6vLXe7s4a3K30ixOU5Knet8jNxdFu7Fhpfj13UZsPn1Xapco7qaiinJ0c1SGw6JXFujX21Y7E1N8u8fv76388LVPbjp697sG34FLDfp6ucnFyVHJ6pk6k2QfbgrOyg5vV0dajZxV36IwuZBZ/NPR6OJuRdVP9bVzl5zXT1iNny12bn2+o38z1Sjp7yW75kdMZyszJ04K4X8pso+C9JTsnT8dTL6l1fW+t3mv/gfHnlPOKXLBdkuTk6KBXBnf6vQ+lfEpIu5SjdQdO6plFCerexFf/ffqOUvsybuHl8H487ZLefSyozL5fC8MwNPm/u2UY0t+HdqoxQ4mqnUK/PqdvsgsAPfd/u7R4+6/6fEzIdZkhCDUHwbcS3VLXS/F/CS0yef/Nos5vgehm0rKet169v1PZhb9ZteeENh8uf9i4kZaMuV0nz9sHzfGLErSlAuGoMvTrUF8BNg+99e1PJdaEd26grUfPmkd/nRwd5FuBCzos2Z4kV+eb63fpaniWcVSzonLzDfV4JabIWOeT5zO145dzRUJvgZy8il3kYtDs73XoVIZmP9qtyLpTha4znVjC/ooT/LdvzfHSBUeQS1NwxDp230lz2fajZ3Xw5AU91KOx3VHpwl5bvV+5eYYm39euyLozF7IUtXCH7m0foMhev4+VPpORrUXbLs8o8Oew1qrn7V5kW9xYb8T8pHF9W5VdeIMs3v6rJGn22oOaP/Lqr/yImo/gW8lquTqX+RUhaiYnRwc18LH/ytfNxamKelO63m3q6oMRPczpuFrV89KyH46Vuo2Tg4McHC4PLfjLF3srtL+Xv/pR3/yYopG3N7vaLts5U8z46qtx+reQ+NawLprw2c5KajNbMT/+fhQ2/VKOohbu0C9nKu9KV4dOXQ6dW4r5EBhdwuPIycs3L+wiSSfSLmn5rt/nC77yJMH8fMMMrxezc5WTZ2j0gm3KyTO0pNDc2Fm5+erz+jrFRt+tB+bFSZICfNzVu5gLhJy+kKU5aw9Jkkbc0UwBNnftTz6vFnW95OrsqE+2JGrz4bPafPisXfAtfKSacaZVqBocaK8GXUQVI6EBN8D3B0+bJ1hdyr62bwQWb0/SugO/H2Xz8XDVs/1aV6gNRwcH3dO2vt2yguCbV0Ky8HB10pTw9lq7/6S+P3i6XPtpVd9baw+c0vmsXMX8mGIXCK+Gs6PDVU33dSz1UrFjEguCf10vd9ncnc0TAyvT3PWHKhx6Ozfy0a5C424L6/jiavPnT7YUHR6RXejoceH5jV9ffcCu7s5/rC112Mn73x/RE3fdosycPN01Y63dnMWj5m+zqz1yOkOHT/9+omvSueKPNBd+7XLzDC3enqTn/m+3BnYJ1Ijbm+pMoaPVh09d0C3lOOkPN1A1+NDBMBiUheCLaul4aqbe+e3iAsX5tYT/eG8019+Gliz94ZiWXnFE1fUqh51MWbanyJjo9oFF58C9WgdPljxTR2Sv5rqvU4BCpq8pV1uTB7TV/d0aauSH23QmI+u38c+l23r0rLYdPaseTWtr3wn7OXv/8of2mrJsT7n2faUrn//Crub/ynva1tOa32bIuMXf0/zq/0pO5RhTn5L+e+Cb98cg9WtfXxEfbtV3Pxf9gFH4tS/PZ4BPtybqf7oEFpm9oqyx1v/Z/It6NKuteesPFblQR3H9KtzeX5bt0fDbmurLncfl7+WqOp6uenftIQ3q+vu5AFuPnNW/v7s8S8ZXO4/rq532V6vr++Z67X0pTO7OTtpz7PcPAUu2J2nYrU2qZOYUq6sGuVeVfAoLaiCCL6oV99+GDhxLvaQ3Ykoew1rAo4qHGoy9p6W83JyUc0XIaF3PW839PSvU1mfbftW6A6fM4PNc/7b6es8J7fo1rcyTA3Pz8pVv2J+VfaWQFn76dFuSsnPz5eAg3d26brF1JY0TP552SZsOnrb7KtrBwUHtGti0+YW+mvBZQqnh073Qa/XgvDjzRNHL7Vxe/sfbmmr2moNKrsTZKEJu8VPXxr7Fruvdpq5ub+Gn6V/vL/IVu7d7+d4+1x0o/apyp85nKfTN9eb9e9vXl6OjQ4XH/ZZk8n93a+mO0oexFCfx7EXd/+6mq95vs+dXmD/7eLgo7VKOviwUbv+8ZKduqVvy34BhSD+lXNCS7Un6ZMvv80W//s1P+u+OY1rzbO+r7huuzpXnMNyMbqJzZ3GTIviiWrm9hb8mhLYuV/BxdJAGdW14A3pVsq6NfTXz4aInIBUn6dxFrdpzQruP2X/F3fC3qaJOX8gyx6R6uztr5B3NtD85vcSvxAss2pqoKcv2lHmEr3/HBvrxpfrKMww5yKHEk9f8vdw0eUBbJSSlytHBQVm5efp230n9kJiqR9/bUq7HWpzbW/pp5B3N9OHGo5JkN6/z0O6Nrrrd0vRpU1cflnIizGsPdFFdbzdN/3q/uaxDoE1P3HmLEs/+PnzhWsZyF8y0IV3+nS04QvyPoZ1192vrrrrdwrYevf4nWPaf+V2J60q63PbhU8UfJS8weM7G4rc7nWEG66Z+tbT4TyGa/N/d6t2mrobf1lQODg6X57S+lGNegKWwz+N/1YJNR/Wvx4OKjMsvyaXsPHm43pxj9m+UjQfPVHUXypR6sepm/EH1QPBFteLq7KhnQm+eM4krg9Nvc2ptPHjG7j+WggA0qldzdW3iq4xCF2FoE+Btd4T0m73JJR4h/P7gabvQe0tdT9W3FX9WvLOTY7neFP50dwvz55PpmXJ13qvNh88We1GPAne19teKXSfsxqBKv08p5ubspOh7W5vBV7o81nXByGDV9nQ1l9X2dK3UI74VFdmruQZ3a6i3Cw21eX5AW0V8sPWa2y581bWmfp7609236J/ry3+BDSv65cxF9Xw1VpK0Zv9JTb3ixMtvo+9Wy3peSjxzUa+s+FF3tvI3T84Mmb5GXz9zp34+eUEDOzcocXxoQciePqSTfjlzUZG9mltuqMXFazw34Ubx86rc1yU7N18Lt/yiXq3qqmU9xpzXBARfoIqFd2qg7UfPKj3z96NiLk6O5gwITo4OurWEeSk93S7/CReeMs3L7fc/673H082LPTw/oK0e7dlEnq7O5Rp3Wl71bO5697EgRX2yQytKuQjD/d0aaVCXhjIkrf/ppGavOShDKvUSv8/2a2MXeiVp3h+7a8PPp/WXMsb6/nlxQgUexbW5XsMK/3xvG93Rwl/N/Dx112trr9NearbCw0gk6ZsrTrAcMOvykeqP437R4jEhit2XosgF2/VU7xbq0sjH7oTHyf/dLUk6cvqC/jn89wvoZOfm69t9KUq/lKOHr+FqiFUhPTNHuXmG6lzxd3Y+M0enL2SrUW0PuTg5qv3U1UW2LTzzx82isnszd90hc2rIo38Pr+TWURUIvkAVa+JXS++PuPWqth13T0vV83Yzp6LycHHSw7c2NsfSJiSlmrW+Hi6yXcerGjk72f+XU9xY4IL/JO9pW7/IrBLl1dTPU8P9PNWotodGfritxLrynEhX1hngBcM9qmoKLVdnR93Vuq7dh6IujX01vm8rjZxf8mNHxW09etZuXPLcdYdKrF29N0XNnl+hZ/q20qZDp7Wt0OW3uzetrbx8Qy3reWnb0bM6evqiwjrU16WcPKVfyrU7EfVSdp7cXRyrZCYCwzDk4OCgzi9+I0na+1LY7x+kD5/RsH9tlnT5JNw7WvoV28YtL6zUmj/freb+nnaP4VxGthJ+TdVdrepW6ofs8nCs5OeytPnQUT0RfIFqrIGPh8aHFp3KbEj3Rjp5Pkvpv42trOPpqgGdGlzXvoy4vZkysnKVlZuvro19SxxOUVn6tKmnlwd10Ksr98nXw1VOjg46llr+2Ty83Zz1YNDvY4f/p2ugPt6cKH8vV93bvr46NvSRj8flDwov/U8HTfuyYnMXlya8U4NSj46Xxt3ZUX3a1tOsh7vqmUUJldYnVNysYmaW6ffWhiLLXli6u9R2XJwclJNnaFnUHRry7kb9T5dATRvYQZsPn9GZjGx1b1Jbfl6uGvNxvH5ITFXILX6KO3xGw29rqvDODdSpoY883ZyVm5evE2mXr9i4MylV6346qcycfB0+dUEzH+6mhr7u+vPineYlqgtftKbDtNVq5ldLp85nKaPQuPPsvHytLeUEzXvesD+i3qWRj9l+ZK/mmtS/jZwcHOR8gy6etGpv8g3ZD6ovSwTfOXPm6LXXXlNycrK6dOmid955R8HBXNkFNVddbzf95Q/tb+g+uzWprfciru7I9dV6PKSZHg9pJkma9sWeEi/76+/lpvTMHNWu5aKU9Cy1qOupVePvsjsq/crgTnq2Xxv5eLgUOQLXqPbvJ0B5F3PUvHYtV/l7uZknH0qXA8BzA9rqn+sPa39yumrXcpWnm7P+8of2Sr2YfdXBt2As96CuDc3gOyG09TUfmfpoVLAer4Rxyqi4gm8nCk7mW5ZwXMsSjpdYH3f48rkA/9n8i/6zuexLXUsqdgx66kX7kw6PVsJFVnYWOtn2/e+P6P3vL09Z90hwE8UdOq2jZy7q/m4NFeDjrm9/TNHTfVrojpb+quvlVqEj3wVHrKuD6tRXK6jxwfezzz5TdHS05s2bp549e2rmzJkKCwvTgQMHVK9e0SsLAaiepg3soEd7NlVtTxf96T+Xj4xJ0sO3Ntar93eSocvjpdMu5sjLvfhxzr61XIssk6S7WtfVXwd3lAzDnObNVmg6Mz8vV22Y1FupF3P05H+2a8+xdPVuU0+3t/DX7S38i7SXmZOnJ++6RR4uThof2krNJ68s9bG5OP4e0AtfTnjM3S209cgZDeneUO0aeOvJ/8SX2s7YPi31bFgbTf7vbn26NdFuXYdAm5aP66UH58XZzahxJTdnR816uKvGfLyj1H0BhRX+fSs8rWFlXTGxsMJDVgZ3DZS7i5Ma16mlXi395VvLRT+nXFBtTxd5uDirns1NTg4OupSTZw71cHSQ8vMlTzf7WTx2JJ5Tx0AfuTg5KDMnX3mGYZ5TUVK43ZmUqkFzNmpgl0C980j5Zvg5cyFLzo6O8qnAZeRRfg6GUbMvANmzZ0/deuutmj17tiQpPz9fjRs31rhx4/T888+Xum16erp8fHyUlpYmm63yLhAAoHiGYejRf29R3OEzCvRx11fjel3VWdrnM3O069c0tazndd2GXGRk5erz+F9V3+au/h0DzOVZuXlKOntJLep6lvsoz8P/itPmw2f1bL/WGntP8bOW9J+5QfuTz+vp3i00qX/bYmuSzl7U0Lmb1Kq+l7o29tWuX9PsLjYxfUgnPRLcRBsPntZjhaae++Ev99qdRJh09qKycvOVejFbb685aF4BbszdLTSoa6DaNbDplskrisyZ+nhIU31UwlH34rz0Px0UcXszu6BSYHSv5vp4yy/KzKmc+YwB3Firxt+ptgE3JjtVJK/V6OCbnZ2tWrVq6fPPP9fgwYPN5REREUpNTdUXX3xhV5+VlaWsrN+/qkxLS1OTJk2UlJRE8AVukMJvSVb5ejArN0+/nr2oW+p6lfqYz17IUp0yPggUPH8F7fxrwyG9HXtQkrT1f/uqlquzcvLyNe7THfr+5zN6eVAHDSljnuT8fEPns3LNMc+StDPpnP74/lbzxL8ZD3TWgI4Bupidp1quTjqXka2P4n7R/E1H1amhj3Ly8rXneLr+NrijUs5nqnHtWurfMUAODg5aufuEYn9MURO/Wvp4yy965+Huuq2FnzYfOqPRH2039xlgc9PSqDvk4eIkZydHu8cmSQ183HUi7fJUd/e0ras1+0u/eAiA62d4SFM9V8KH9MqWnp6uxo0bKzU1VT4+PqUXGzXYsWPHDEnGpk2b7JZPnDjRCA4OLlI/bdo0Q5evysiNGzdu3Lhx48atGt2SkpLKzIY1foxvRUyePFnR0dHm/fz8fJ09e1Z+fn435ZGnW2+9Vdu2Vf6URtfabkW3L299eepKq6nouoJPkDfjEf+a8tpXZJuy6q52/fV6Lq+X69lf/vZvflX9t1/S+yKv/fVX1a99ZW5f2e/7hmHo/PnzCgwMLLPNGh18/f395eTkpJQU+wnLU1JSFBAQUKTezc1Nbm72XyP6+vpezy5eEycnp+sSyK613YpuX9768tSVVnO162w2200XfGvKa1+Rbcqqu9r11+u5vF6uZ3/527/53Sx/+1e+L/LaX383y2tfGdtfj/f9Moc4/ObGTKxXRVxdXRUUFKTY2FhzWX5+vmJjYxUSElKFPascUVFRN2W7Fd2+vPXlqSut5mrX3YxqymtfkW3Kqrva9bz2ldc2f/vXX0352+e1r7ia8tpXZJtrfd8vTo0+uU26PJ1ZRESE/vnPfyo4OFgzZ87U4sWLtX//ftWvf3VXjoI1MKsHANjjfRHVXY0e6iBJw4YN06lTpzR16lQlJyera9euWrVqFaEXZXJzc9O0adOKDH8BAKvifRHVXY0/4gsAAABINXyMLwAAAFCA4AsAAABLIPgCAADAEgi+AACgXNatWycHBwelpqZWdVeAq0LwBQDAAjZs2KCBAwcqMDBQDg4OWrZsWbF1ffr00XvvvXdjOwfcIARf1CgVeWN/6aWX5OLiou+//95uXUZGhm655RY9++yzN6DHAHBjZGRkqEuXLpozZ06JNWfPntXGjRs1cODAG9gz4MYh+KJGqcgb+5gxYzRu3DiNGDFCGRkZ5vpJkybJw8NDr7zySqX3Lzs7u9LbBIDyGDBggF555RXdf//9JdasWLFC3bt3N+e6X7lypVq3bi0PDw/16dNHR48etas/c+aMHnnkETVs2FC1atVSp06d9Omnn5rrP/roI/n5+SkrK8tuu8GDB2v48OGV9+CAciL4okap6Bv7q6++KldXVz333HOSpLVr1+q9997TRx99JFdXV02fPl3NmzeXh4eHunTpos8//9xsJy8vT5GRkeb6Nm3aaNasWXb7GjFihAYPHqy//e1vCgwMVJs2ba7PAweASvDll19q0KBBkqSkpCQNGTJEAwcOVEJCgkaPHq3nn3/erj4zM1NBQUFasWKF9uzZoyeffFLDhw/X1q1bJUkPPvig8vLy9OWXX5rbnDx5UitWrNCoUaNu3AMDflPjr9wGXKnwG7u7u7s++ugj3X777br33ns1fvx4vfDCCwoKCtLf/vY3ffzxx5o3b55atWqlDRs26I9//KPq1q2ru+++W/n5+WrUqJGWLFkiPz8/bdq0SU8++aQaNGighx56yNxfbGysbDabYmJiquohA0CZsrKytGrVKr344ouSpLlz56pFixZ64403JElt2rTR7t279Y9//MPcpmHDhnbDwsaNG6fVq1dr8eLFCg4OloeHhx599FF9+OGHevDBByVJH3/8sZo0aaLevXvfsMcGFCD4wlKufGOXpB49emjy5MkaMmSIunXrpv/93/9VVlaWXn31VX377bcKCQmRJN1yyy36/vvv9c9//lN33323XFxc9NJLL5ntNG/eXHFxcVq8eLFd8PX09NR7770nV1fXG/Y4AaCi1qxZo3r16qlDhw6SpH379qlnz552NQXvhwXy8vL06quvavHixTp27Jiys7OVlZWlWrVqmTVPPPGEbr31Vh07dkwNGzbU/PnzNWLECDk4OFz/BwVcgeALS7nyjb3AX/7yF7388st6/vnn5ezsrAMHDujixYu699577eqys7PVrVs38/6cOXP0wQcfKDExUZcuXVJ2dra6du1qt02nTp0IvQBuel9++aX+53/+p0LbvPbaa5o1a5ZmzpypTp06ydPTU+PHj7c7n6Fbt27q0qWLPvroI/Xr10979+7VihUrKrv7QLkQfGEpJb2xOzs72/174cIFSZfHAzds2NCu1s3NTZK0aNEiPfvss3rjjTcUEhIib29vvfbaa9qyZYtdvaenZ6U/DgCoTIZh6KuvvtLHH39sLmvXrp3d2FxJ2rx5s939jRs3atCgQfrjH/8oScrPz9dPP/2k9u3b29WNHj1aM2fO1LFjxxQaGqrGjRtfp0cClI7gC8so7o29JO3bt5ebm5sSExN19913F1uzceNG3X777Xr66afNZYcOHaq0/gJAZbpw4YIOHjxo3j9y5IgSEhJUp04dnTx5UhcvXlSvXr3M9WPGjNEbb7yhiRMnavTo0YqPj9f8+fPt2mzVqpU+//xzbdq0SbVr19abb76plJSUIsH30Ucf1bPPPqt///vf+uijj67r4wRKQ/BFjVLRN/aSeHt769lnn9WECROUn5+vXr16KS0tTRs3bpTNZlNERIRatWqljz76SKtXr1bz5s31n//8R9u2bVPz5s2v50MEgKuyfft29enTx7wfHR0tSYqIiFDjxo113333md96SVKTJk30f//3f5owYYLeeecdBQcH69VXX7WbjWHKlCk6fPiwwsLCVKtWLT355JMaPHiw0tLS7Pbt4+OjoUOHasWKFRo8ePD1faBAKRwMwzCquhNAZVm3bp3dG3uBgjf2I0eOlHjE18HBQUuXLjXflA3D0Ntvv625c+fq8OHD8vX1Vffu3fXCCy/orrvuUlZWlsaMGaOlS5fKwcFBjzzyiHx8fPT1118rISFB0uXpzFJTU0u8kAYA3Aw6d+6sKVOm2J2YW9n69u2rDh066O23375u+wDKQvCFZdyIN3YAqG6ys7M1ffp0RUdHy9vbu9LbP3funNatW6cHHnhAP/74I/OZo0ox1AGWkJ2draFDh2rAgAFV3RUAuKm4urpq2rRp1639bt266dy5c/rHP/5B6EWV44gvAAAALIFLFgMAAMASCL4AAACwBIIvAAAALIHgCwAAAEsg+AIAAMASCL4AYHHZ2dlq2bKlNm3aJEk6evSoHBwczAuxVEdX8xgefvhhvfHGG9evUwCqHMEXQI0yYsQIOTg4FLkVvpQ17M2bN0/NmzfX7bffXtVdqVJTpkzR3/72tyKX2wVQcxB8AdQ4/fv314kTJ+xuzZs3L1KXnZ1dBb27uRiGodmzZysyMrKqu1LlOnbsqBYtWpR4WXMA1R/BF0CN4+bmpoCAALubk5OTevfurbFjx2r8+PHy9/dXWFiYJGnPnj0aMGCAvLy8VL9+fQ0fPlynT58228vIyNDjjz8uLy8vNWjQQG+88YZ69+6t8ePHmzUODg5atmyZXT98fX01f/58835SUpIeeugh+fr6qk6dOho0aJCOHj1qrh8xYoQGDx6s119/XQ0aNJCfn5+ioqKUk5Nj1mRlZem5555T48aN5ebmppYtW+r999+XYRhq2bKlXn/9dbs+JCQklHrEOz4+XocOHVJ4eHipz+n69esVHBwsNzc3NWjQQM8//7xyc3PN9efPn9djjz0mT09PNWjQQG+99VaR5+hKO3fuVJ8+feTt7S2bzaagoCBt377dXL9x40b17t1btWrVUu3atRUWFqZz585JklatWqVevXrJ19dXfn5++sMf/qBDhw6V+hjKep0laeDAgVq0aFGp7QCovgi+ACxlwYIFcnV11caNGzVv3jylpqbqnnvuUbdu3bR9+3atWrVKKSkpeuihh8xtJk6cqPXr1+uLL77QN998o3Xr1mnHjh0V2m9OTo7CwsLk7e2t7777Ths3bpSXl5f69+9vd+R57dq1OnTokNauXasFCxZo/vz5duH58ccf16effqq3335b+/bt0z//+U95eXnJwcFBo0aN0ocffmi33w8//FB33XWXWrZsWWy/vvvuO7Vu3Vre3t4l9v3YsWO67777dOutt2rnzp2aO3eu3n//fb3yyitmTXR0tDZu3Kgvv/xSMTEx+u6778p8jh577DE1atRI27ZtU3x8vJ5//nm5uLhIuhzY+/btq/bt2ysuLk7ff/+9Bg4cqLy8PEmXP4xER0dr+/btio2NlaOjo+6//37l5+cXu6/yvM6SFBwcrK1btyorK6vUvgOopgwAqEEiIiIMJycnw9PT07w98MADhmEYxt13321069bNrv6vf/2r0a9fP7tlSUlJhiTjwIEDxvnz5w1XV1dj8eLF5vozZ84YHh4exjPPPGMuk2QsXbrUrh0fHx/jww8/NAzDMP7zn/8Ybdq0MfLz8831WVlZhoeHh7F69Wqz702bNjVyc3PNmgcffNAYNmyYYRiGceDAAUOSERMTU+xjP3bsmOHk5GRs2bLFMAzDyM7ONvz9/Y358+eX+Hw988wzxj333GO37MiRI4Yk44cffjAMwzBeeOGFIn2fM2eO4eXlZeTl5Rnp6emGi4uLsWTJEnN9amqqUatWLbvn6Ere3t4l9u2RRx4x7rjjjhK3vdKpU6cMScbu3buLfQxlvc4Fdu7caUgyjh49Wu59A6g+nKsucgPA9dGnTx/NnTvXvO/p6Wn+HBQUZFe7c+dOrV27Vl5eXkXaOXTokC5duqTs7Gz17NnTXF6nTh21adOmQn3auXOnDh48WOTIamZmpt1X9B06dJCTk5N5v0GDBtq9e7eky0dBnZycdPfddxe7j8DAQIWHh+uDDz5QcHCwvvrqK2VlZenBBx8ssV+XLl2Su7t7qX3ft2+fQkJC5ODgYC674447dOHCBf366686d+6ccnJyFBwcbK738fEp8zmKjo7W6NGj9Z///EehoaF68MEH1aJFC/Oxltbvn3/+WVOnTtWWLVt0+vRp80hvYmKiOnbsWKS+rNe5devWkiQPDw9J0sWLF0vtO4DqieALoMbx9PQs8av9wiFYki5cuKCBAwfqH//4R5HaBg0alHs2CAcHBxmGYbes8NjcCxcuKCgoSJ988kmRbevWrWv+XPBVf+F2C0JdQSgrzejRozV8+HC99dZb+vDDDzVs2DDVqlWrxHp/f38zWN9oL774oh599FGtWLFCX3/9taZNm6ZFixbp/vvvL/OxDhw4UE2bNtW///1vBQYGKj8/Xx07dizxhMWyXucCZ8+elWT/mgCoORjjC8DSunfvrr1796pZs2Zq2bKl3c3T01MtWrSQi4uLtmzZYm5z7tw5/fTTT3bt1K1bVydOnDDv//zzz3ZHDbt3766ff/5Z9erVK7IfHx+fcvW1U6dOys/P1/r160usue++++Tp6am5c+dq1apVGjVqVKltduvWTfv37y8S2gtr166d4uLi7Go2btwob29vNWrUSLfccotcXFy0bds2c31aWlqR56g4rVu31oQJE/TNN99oyJAh5hjlzp07KzY2tthtzpw5owMHDmjKlCnq27ev2rVrZ570VpKyXucCe/bsUaNGjeTv719m3wFUPwRfAJYWFRWls2fP6pFHHtG2bdt06NAhrV69WiNHjlReXp68vLwUGRmpiRMnas2aNdqzZ49GjBghR0f7t8977rlHs2fP1g8//KDt27drzJgxdkdvH3vsMfn7+2vQoEH67rvvdOTIEa1bt07/7//9P/3666/l6muzZs0UERGhUaNGadmyZWYbixcvNmucnJw0YsQITZ48Wa1atVJISEipbfbp00cXLlzQ3r17S6x5+umnlZSUpHHjxmn//v364osvNG3aNEVHR8vR0VHe3t6KiIjQxIkTtXbtWu3du1eRkZFydHS0Gx5R2KVLlzR27FitW7dOv/zyizZu3Kht27apXbt2kqTJkydr27Ztevrpp7Vr1y7t379fc+fO1enTp1W7dm35+fnpX//6lw4ePKg1a9YoOjq61MdZ1utc4LvvvlO/fv1KbQtA9UXwBWBpgYGB2rhxo/Ly8tSvXz916tRJ48ePl6+vrxluX3vtNd15550aOHCgQkND1atXryJjhd944w01btxYd955px599FE9++yzdkMMatWqpQ0bNqhJkyYaMmSI2rVrp8jISGVmZspms5W7v3PnztUDDzygp59+Wm3bttUTTzyhjIwMu5rIyEhlZ2dr5MiRZbbn5+en+++/v9ghGAUaNmyolStXauvWrerSpYvGjBmjyMhITZkyxax58803FRISoj/84Q8KDQ3VHXfcoXbt2pU4ftjJyUlnzpzR448/rtatW+uhhx7SgAED9NJLL0m6fCT4m2++0c6dOxUcHKyQkBB98cUXcnZ2lqOjoxYtWqT4+Hh17NhREyZM0GuvvVbq4yzP65yZmally5bpiSeeKPN5A1A9ORilfb8FAChW79691bVrV82cObOqu1LEd999p759+yopKUn169cvs37Xrl269957dejQoWJP/roaGRkZatiwod54441qc3GMuXPnaunSpfrmm2+quisArhOO+AJADZGVlaVff/1VL774oh588MFyhV7p8njaf/zjHzpy5MhV7/uHH37Qp59+qkOHDmnHjh167LHHJEmDBg266jZvNBcXF73zzjtV3Q0A1xGzOgBADfHpp58qMjJSXbt21UcffVShbUeMGHHN+3/99dd14MABubq6KigoSN999121Okls9OjRVd0FANcZQx0AAABgCQx1AAAAgCUQfAEAAGAJBF8AAABYAsEXAAAAlkDwBQAAgCUQfAEAAGAJBF8AAABYAsEXAAAAlvD/AdeQaDT+3frYAAAAAElFTkSuQmCC", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fft = tf.signal.rfft(df['T (degC)'])\n", - "f_per_dataset = np.arange(0, len(fft))\n", - "\n", - "n_samples_h = len(df['T (degC)'])\n", - "hours_per_year = 24*365.2524\n", - "years_per_dataset = n_samples_h/(hours_per_year)\n", - "\n", - "f_per_year = f_per_dataset/years_per_dataset\n", - "plt.step(f_per_year, np.abs(fft))\n", - "plt.xscale('log')\n", - "plt.ylim(0, 400000)\n", - "plt.xlim([0.1, max(plt.xlim())])\n", - "plt.xticks([1, 365.2524], labels=['1/Year', '1/day'])\n", - "_ = plt.xlabel('Frequency (log scale)')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2rbL8bSGDHy3" - }, - "source": [ - "### Split the data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qoFJZmXBaxCc" - }, - "source": [ - "You'll use a `(70%, 20%, 10%)` split for the training, validation, and test sets. Note the data is **not** being randomly shuffled before splitting. This is for two reasons:\n", - "\n", - "1. It ensures that chopping the data into windows of consecutive samples is still possible.\n", - "2. It ensures that the validation/test results are more realistic, being evaluated on the data collected after the model was trained." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "ia-MPAHxbInX" - }, - "outputs": [], - "source": [ - "column_indices = {name: i for i, name in enumerate(df.columns)}\n", - "\n", - "n = len(df)\n", - "train_df = df[0:int(n*0.7)]\n", - "val_df = df[int(n*0.7):int(n*0.9)]\n", - "test_df = df[int(n*0.9):]\n", - "\n", - "num_features = df.shape[1]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-eFckdUUHWmT" - }, - "source": [ - "### Normalize the data\n", - "\n", - "It is important to scale features before training a neural network. Normalization is a common way of doing this scaling: subtract the mean and divide by the standard deviation of each feature." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mxbIic5TMlxx" - }, - "source": [ - "The mean and standard deviation should only be computed using the training data so that the models have no access to the values in the validation and test sets.\n", - "\n", - "It's also arguable that the model shouldn't have access to future values in the training set when training, and that this normalization should be done using moving averages. That's not the focus of this tutorial, and the validation and test sets ensure that you get (somewhat) honest metrics. So, in the interest of simplicity this tutorial uses a simple average." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "Eji6njXvHusN" - }, - "outputs": [], - "source": [ - "train_mean = train_df.mean()\n", - "train_std = train_df.std()\n", - "\n", - "train_df = (train_df - train_mean) / train_std\n", - "val_df = (val_df - train_mean) / train_std\n", - "test_df = (test_df - train_mean) / train_std" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G6ufs8kk9JQw" - }, - "source": [ - "Now, peek at the distribution of the features. Some features do have long tails, but there are no obvious errors like the `-9999` wind velocity value." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "T0UYEnkwm8Fe" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/ly/qvll8cvj47d6jl_123hgbpdr0000gp/T/ipykernel_81691/3214313372.py:5: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " _ = ax.set_xticklabels(df.keys(), rotation=90)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_std = (df - train_mean) / train_std\n", - "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n", - "plt.figure(figsize=(12, 6))\n", - "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n", - "_ = ax.set_xticklabels(df.keys(), rotation=90)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZBBmdxZ2HgfJ" - }, - "source": [ - "## Data windowing\n", - "\n", - "The models in this tutorial will make a set of predictions based on a window of consecutive samples from the data. \n", - "\n", - "The main features of the input windows are:\n", - "\n", - "- The width (number of time steps) of the input and label windows.\n", - "- The time offset between them.\n", - "- Which features are used as inputs, labels, or both. \n", - "\n", - "This tutorial builds a variety of models (including Linear, DNN, CNN and RNN models), and uses them for both:\n", - "\n", - "- *Single-output*, and *multi-output* predictions.\n", - "- *Single-time-step* and *multi-time-step* predictions.\n", - "\n", - "This section focuses on implementing the data windowing so that it can be reused for all of those models.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YAhGUVx1jtOy" - }, - "source": [ - "Depending on the task and type of model you may want to generate a variety of data windows. Here are some examples:\n", - "\n", - "1. For example, to make a single prediction 24 hours into the future, given 24 hours of history, you might define a window like this:\n", - "\n", - " ![One prediction 24 hours into the future.](images/raw_window_24h.png)\n", - "\n", - "2. A model that makes a prediction one hour into the future, given six hours of history, would need a window like this:\n", - "\n", - " ![One prediction one hour into the future.](images/raw_window_1h.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sa2BbfNZt8wy" - }, - "source": [ - "The rest of this section defines a `WindowGenerator` class. This class can:\n", - "\n", - "1. Handle the indexes and offsets as shown in the diagrams above.\n", - "1. Split windows of features into `(features, labels)` pairs.\n", - "2. Plot the content of the resulting windows.\n", - "3. Efficiently generate batches of these windows from the training, evaluation, and test data, using `tf.data.Dataset`s." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rfx3jGjyziUF" - }, - "source": [ - "### 1. Indexes and offsets\n", - "\n", - "Start by creating the `WindowGenerator` class. The `__init__` method includes all the necessary logic for the input and label indices.\n", - "\n", - "It also takes the training, evaluation, and test DataFrames as input. These will be converted to `tf.data.Dataset`s of windows later." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "Kem30j8QHxyW" - }, - "outputs": [], - "source": [ - "class WindowGenerator():\n", - " def __init__(self, input_width, label_width, shift,\n", - " train_df=train_df, val_df=val_df, test_df=test_df,\n", - " label_columns=None):\n", - " # Store the raw data.\n", - " self.train_df = train_df\n", - " self.val_df = val_df\n", - " self.test_df = test_df\n", - "\n", - " # Work out the label column indices.\n", - " self.label_columns = label_columns\n", - " if label_columns is not None:\n", - " self.label_columns_indices = {name: i for i, name in\n", - " enumerate(label_columns)}\n", - " self.column_indices = {name: i for i, name in\n", - " enumerate(train_df.columns)}\n", - "\n", - " # Work out the window parameters.\n", - " self.input_width = input_width\n", - " self.label_width = label_width\n", - " self.shift = shift\n", - "\n", - " self.total_window_size = input_width + shift\n", - "\n", - " self.input_slice = slice(0, input_width)\n", - " self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n", - "\n", - " self.label_start = self.total_window_size - self.label_width\n", - " self.labels_slice = slice(self.label_start, None)\n", - " self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n", - "\n", - " def __repr__(self):\n", - " return '\\n'.join([\n", - " f'Total window size: {self.total_window_size}',\n", - " f'Input indices: {self.input_indices}',\n", - " f'Label indices: {self.label_indices}',\n", - " f'Label column name(s): {self.label_columns}'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yVJgblsYzL1g" - }, - "source": [ - "Here is code to create the 2 windows shown in the diagrams at the start of this section:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "IsM5kRkz0UwK" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 48\n", - "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", - "Label indices: [47]\n", - "Label column name(s): ['T (degC)']" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w1 = WindowGenerator(input_width=24, label_width=1, shift=24,\n", - " label_columns=['T (degC)'])\n", - "w1" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "viwKsYeAKFUn" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 7\n", - "Input indices: [0 1 2 3 4 5]\n", - "Label indices: [6]\n", - "Label column name(s): ['T (degC)']" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "w2 = WindowGenerator(input_width=6, label_width=1, shift=1,\n", - " label_columns=['T (degC)'])\n", - "w2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kJaUyTWQJd-L" - }, - "source": [ - "### 2. Split\n", - "\n", - "Given a list of consecutive inputs, the `split_window` method will convert them to a window of inputs and a window of labels.\n", - "\n", - "The example `w2` you define earlier will be split like this:\n", - "\n", - "![The initial window is all consecutive samples, this splits it into an (inputs, labels) pairs](images/split_window.png)\n", - "\n", - "This diagram doesn't show the `features` axis of the data, but this `split_window` function also handles the `label_columns` so it can be used for both the single output and multi-output examples." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "W4KbxfzqkXPW" - }, - "outputs": [], - "source": [ - "def split_window(self, features):\n", - " inputs = features[:, self.input_slice, :]\n", - " labels = features[:, self.labels_slice, :]\n", - " if self.label_columns is not None:\n", - " labels = tf.stack(\n", - " [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n", - " axis=-1)\n", - "\n", - " # Slicing doesn't preserve static shape information, so set the shapes\n", - " # manually. This way the `tf.data.Datasets` are easier to inspect.\n", - " inputs.set_shape([None, self.input_width, None])\n", - " labels.set_shape([None, self.label_width, None])\n", - "\n", - " return inputs, labels\n", - "\n", - "WindowGenerator.split_window = split_window" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G6U6VtVuM15s" - }, - "source": [ - "Try it out:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "YeCWbq6KLmL7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All shapes are: (batch, time, features)\n", - "Window shape: (3, 7, 19)\n", - "Inputs shape: (3, 6, 19)\n", - "Labels shape: (3, 1, 1)\n" - ] - } - ], - "source": [ - "# Stack three slices, the length of the total window.\n", - "example_window = tf.stack([np.array(train_df[:w2.total_window_size]),\n", - " np.array(train_df[100:100+w2.total_window_size]),\n", - " np.array(train_df[200:200+w2.total_window_size])])\n", - "\n", - "example_inputs, example_labels = w2.split_window(example_window)\n", - "\n", - "print('All shapes are: (batch, time, features)')\n", - "print(f'Window shape: {example_window.shape}')\n", - "print(f'Inputs shape: {example_inputs.shape}')\n", - "print(f'Labels shape: {example_labels.shape}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xtMk1ffk2Mmd" - }, - "source": [ - "Typically, data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features.\n", - "\n", - "The code above took a batch of three 7-time step windows with 19 features at each time step. It splits them into a batch of 6-time step 19-feature inputs, and a 1-time step 1-feature label. The label only has one feature because the `WindowGenerator` was initialized with `label_columns=['T (degC)']`. Initially, this tutorial will build models that predict single output labels." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tFZukGXrJoGo" - }, - "source": [ - "### 3. Plot\n", - "\n", - "Here is a plot method that allows a simple visualization of the split window:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "fmgd1qkYUWT7" - }, - "outputs": [], - "source": [ - "w2.example = example_inputs, example_labels" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "jIrYccI-Hm3B" - }, - "outputs": [], - "source": [ - "def plot(self, model=None, plot_col='T (degC)', max_subplots=3):\n", - " inputs, labels = self.example\n", - " plt.figure(figsize=(12, 8))\n", - " plot_col_index = self.column_indices[plot_col]\n", - " max_n = min(max_subplots, len(inputs))\n", - " for n in range(max_n):\n", - " plt.subplot(max_n, 1, n+1)\n", - " plt.ylabel(f'{plot_col} [normed]')\n", - " plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n", - " label='Inputs', marker='.', zorder=-10)\n", - "\n", - " if self.label_columns:\n", - " label_col_index = self.label_columns_indices.get(plot_col, None)\n", - " else:\n", - " label_col_index = plot_col_index\n", - "\n", - " if label_col_index is None:\n", - " continue\n", - "\n", - " plt.scatter(self.label_indices, labels[n, :, label_col_index],\n", - " edgecolors='k', label='Labels', c='#2ca02c', s=64)\n", - " if model is not None:\n", - " predictions = model(inputs)\n", - " plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n", - " marker='X', edgecolors='k', label='Predictions',\n", - " c='#ff7f0e', s=64)\n", - "\n", - " if n == 0:\n", - " plt.legend()\n", - "\n", - " plt.xlabel('Time [h]')\n", - "\n", - "WindowGenerator.plot = plot" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HXvctEuK68vX" - }, - "source": [ - "This plot aligns inputs, labels, and (later) predictions based on the time that the item refers to:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "XjTqUnglOOni" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w2.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UqiqcPOldPG6" - }, - "source": [ - "You can plot the other columns, but the example window `w2` configuration only has labels for the `T (degC)` column." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "EBRe4wnlfCH8" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "w2.plot(plot_col='p (mbar)')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xCvD-UaUzYMw" - }, - "source": [ - "### 4. Create `tf.data.Dataset`s" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kLO3SFR9Osdf" - }, - "source": [ - "Finally, this `make_dataset` method will take a time series DataFrame and convert it to a `tf.data.Dataset` of `(input_window, label_window)` pairs using the `tf.keras.utils.timeseries_dataset_from_array` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "35qoSQeRVfJg" - }, - "outputs": [], - "source": [ - "def make_dataset(self, data):\n", - " data = np.array(data, dtype=np.float32)\n", - " ds = tf.keras.utils.timeseries_dataset_from_array(\n", - " data=data,\n", - " targets=None,\n", - " sequence_length=self.total_window_size,\n", - " sequence_stride=1,\n", - " shuffle=True,\n", - " batch_size=32,)\n", - "\n", - " ds = ds.map(self.split_window)\n", - "\n", - " return ds\n", - "\n", - "WindowGenerator.make_dataset = make_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LvsxQwJaCift" - }, - "source": [ - "The `WindowGenerator` object holds training, validation, and test data.\n", - "\n", - "Add properties for accessing them as `tf.data.Dataset`s using the `make_dataset` method you defined earlier. Also, add a standard example batch for easy access and plotting:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "id": "2jZ2KkqGCfzu" - }, - "outputs": [], - "source": [ - "@property\n", - "def train(self):\n", - " return self.make_dataset(self.train_df)\n", - "\n", - "@property\n", - "def val(self):\n", - " return self.make_dataset(self.val_df)\n", - "\n", - "@property\n", - "def test(self):\n", - " return self.make_dataset(self.test_df)\n", - "\n", - "@property\n", - "def example(self):\n", - " \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n", - " result = getattr(self, '_example', None)\n", - " if result is None:\n", - " # No example batch was found, so get one from the `.train` dataset\n", - " result = next(iter(self.train))\n", - " # And cache it for next time\n", - " self._example = result\n", - " return result\n", - "\n", - "WindowGenerator.train = train\n", - "WindowGenerator.val = val\n", - "WindowGenerator.test = test\n", - "WindowGenerator.example = example" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fF_Vj6Iw3Y2w" - }, - "source": [ - "Now, the `WindowGenerator` object gives you access to the `tf.data.Dataset` objects, so you can easily iterate over the data.\n", - "\n", - "The `Dataset.element_spec` property tells you the structure, data types, and shapes of the dataset elements." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "id": "daJ0-U383YVs" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(TensorSpec(shape=(None, 6, 19), dtype=tf.float32, name=None),\n", - " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Each element is an (inputs, label) pair.\n", - "w2.train.element_spec" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XKTx3_Z7ua-n" - }, - "source": [ - "Iterating over a `Dataset` yields concrete batches:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:08.954732Z", - "iopub.status.busy": "2023-10-27T05:28:08.954485Z", - "iopub.status.idle": "2023-10-27T05:28:09.094541Z", - "shell.execute_reply": "2023-10-27T05:28:09.093757Z" - }, - "id": "6gtKXEgf4Iml" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inputs shape (batch, time, features): (32, 6, 19)\n", - "Labels shape (batch, time, features): (32, 1, 1)\n" - ] - } - ], - "source": [ - "for example_inputs, example_labels in w2.train.take(1):\n", - " print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n", - " print(f'Labels shape (batch, time, features): {example_labels.shape}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LyuGuJUgjUK3" - }, - "source": [ - "## Single step models\n", - "\n", - "The simplest model you can build on this sort of data is one that predicts a single feature's value—1 time step (one hour) into the future based only on the current conditions.\n", - "\n", - "So, start by building models to predict the `T (degC)` value one hour into the future.\n", - "\n", - "![Predict the next time step](images/narrow_window.png)\n", - "\n", - "Configure a `WindowGenerator` object to produce these single-step `(input, label)` pairs:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:09.098464Z", - "iopub.status.busy": "2023-10-27T05:28:09.097851Z", - "iopub.status.idle": "2023-10-27T05:28:09.102956Z", - "shell.execute_reply": "2023-10-27T05:28:09.102372Z" - }, - "id": "G5QX1G1JTPCr" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 2\n", - "Input indices: [0]\n", - "Label indices: [1]\n", - "Label column name(s): ['T (degC)']" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "single_step_window = WindowGenerator(\n", - " input_width=1, label_width=1, shift=1,\n", - " label_columns=['T (degC)'])\n", - "single_step_window" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RKTm8ajVGw4N" - }, - "source": [ - "The `window` object creates `tf.data.Dataset`s from the training, validation, and test sets, allowing you to easily iterate over batches of data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:09.106468Z", - "iopub.status.busy": "2023-10-27T05:28:09.105880Z", - "iopub.status.idle": "2023-10-27T05:28:09.242161Z", - "shell.execute_reply": "2023-10-27T05:28:09.241552Z" - }, - "id": "Do4ILUaBF8oc" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inputs shape (batch, time, features): (32, 1, 19)\n", - "Labels shape (batch, time, features): (32, 1, 1)\n" - ] - } - ], - "source": [ - "for example_inputs, example_labels in single_step_window.train.take(1):\n", - " print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n", - " print(f'Labels shape (batch, time, features): {example_labels.shape}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "D1bbPiR3VAm_" - }, - "source": [ - "### Baseline\n", - "\n", - "Before building a trainable model it would be good to have a performance baseline as a point for comparison with the later more complicated models.\n", - "\n", - "This first task is to predict temperature one hour into the future, given the current value of all features. The current values include the current temperature. \n", - "\n", - "So, start with a model that just returns the current temperature as the prediction, predicting \"No change\". This is a reasonable baseline since temperature changes slowly. Of course, this baseline will work less well if you make a prediction further in the future.\n", - "\n", - "![Send the input to the output](images/baseline.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:09.245879Z", - "iopub.status.busy": "2023-10-27T05:28:09.245206Z", - "iopub.status.idle": "2023-10-27T05:28:09.249843Z", - "shell.execute_reply": "2023-10-27T05:28:09.249208Z" - }, - "id": "9TybQaIsi3yg" - }, - "outputs": [], - "source": [ - "class Baseline(tf.keras.Model):\n", - " def __init__(self, label_index=None):\n", - " super().__init__()\n", - " self.label_index = label_index\n", - "\n", - " def call(self, inputs):\n", - " if self.label_index is None:\n", - " return inputs\n", - " result = inputs[:, :, self.label_index]\n", - " return result[:, :, tf.newaxis]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0vb3f948i8p8" - }, - "source": [ - "Instantiate and evaluate this model:" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:09.253150Z", - "iopub.status.busy": "2023-10-27T05:28:09.252653Z", - "iopub.status.idle": "2023-10-27T05:28:11.066664Z", - "shell.execute_reply": "2023-10-27T05:28:11.065941Z" - }, - "id": "IS3-QKc4sX0D" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/439 [..............................] - ETA: 3:55 - loss: 0.0140 - mean_absolute_error: 0.0822" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 29/439 [>.............................] - ETA: 0s - loss: 0.0125 - mean_absolute_error: 0.0768 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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}, - "source": [ - "That printed some performance metrics, but those don't give you a feeling for how well the model is doing.\n", - "\n", - "The `WindowGenerator` has a plot method, but the plots won't be very interesting with only a single sample.\n", - "\n", - "So, create a wider `WindowGenerator` that generates windows 24 hours of consecutive inputs and labels at a time. The new `wide_window` variable doesn't change the way the model operates. The model still makes predictions one hour into the future based on a single input time step. Here, the `time` axis acts like the `batch` axis: each prediction is made independently with no interaction between time steps:" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.071282Z", - "iopub.status.busy": "2023-10-27T05:28:11.070474Z", - "iopub.status.idle": "2023-10-27T05:28:11.076109Z", - "shell.execute_reply": "2023-10-27T05:28:11.075407Z" - }, - "id": "C8jNR5uuJ5Zp" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 25\n", - "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", - "Label indices: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24]\n", - "Label column name(s): ['T (degC)']" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wide_window = WindowGenerator(\n", - " input_width=24, label_width=24, shift=1,\n", - " label_columns=['T (degC)'])\n", - "\n", - "wide_window" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZAnj7CFZkuYv" - }, - "source": [ - "This expanded window can be passed directly to the same `baseline` model without any code changes. This is possible because the inputs and labels have the same number of time steps, and the baseline just forwards the input to the output:\n", - "\n", - "![One prediction 1h into the future, ever hour.](images/last_window.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.079555Z", - "iopub.status.busy": "2023-10-27T05:28:11.078996Z", - "iopub.status.idle": "2023-10-27T05:28:11.208572Z", - "shell.execute_reply": "2023-10-27T05:28:11.207768Z" - }, - "id": "sGKdvdg087qs" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 24, 19)\n", - "Output shape: (32, 24, 1)\n" - ] - } - ], - "source": [ - "print('Input shape:', wide_window.example[0].shape)\n", - "print('Output shape:', baseline(wide_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SKqQHX1K0JW-" - }, - "source": [ - "By plotting the baseline model's predictions, notice that it is simply the labels shifted right by one hour:" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.212395Z", - "iopub.status.busy": "2023-10-27T05:28:11.211685Z", - "iopub.status.idle": "2023-10-27T05:28:11.648458Z", - "shell.execute_reply": "2023-10-27T05:28:11.647749Z" - }, - "id": "jQyAPVLgWTOZ" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wide_window.plot(baseline)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "e93TLUhfAVg2" - }, - "source": [ - "In the above plots of three examples the single step model is run over the course of 24 hours. This deserves some explanation:\n", - "\n", - "- The blue `Inputs` line shows the input temperature at each time step. The model receives all features, this plot only shows the temperature.\n", - "- The green `Labels` dots show the target prediction value. These dots are shown at the prediction time, not the input time. That is why the range of labels is shifted 1 step relative to the inputs.\n", - "- The orange `Predictions` crosses are the model's prediction's for each output time step. If the model were predicting perfectly the predictions would land directly on the `Labels`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E4aOJScj52Yu" - }, - "source": [ - "### Linear model\n", - "\n", - "The simplest **trainable** model you can apply to this task is to insert linear transformation between the input and output. In this case the output from a time step only depends on that step:\n", - "\n", - "![A single step prediction](images/narrow_window.png)\n", - "\n", - "A `tf.keras.layers.Dense` layer with no `activation` set is a linear model. The layer only transforms the last axis of the data from `(batch, time, inputs)` to `(batch, time, units)`; it is applied independently to every item across the `batch` and `time` axes." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.652522Z", - "iopub.status.busy": "2023-10-27T05:28:11.652259Z", - "iopub.status.idle": "2023-10-27T05:28:11.661267Z", - "shell.execute_reply": "2023-10-27T05:28:11.660553Z" - }, - "id": "6341OXuQ5xA9" - }, - "outputs": [], - "source": [ - "linear = tf.keras.Sequential([\n", - " tf.keras.layers.Dense(units=1)\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.664721Z", - "iopub.status.busy": "2023-10-27T05:28:11.664202Z", - "iopub.status.idle": "2023-10-27T05:28:11.845036Z", - "shell.execute_reply": "2023-10-27T05:28:11.844350Z" - }, - "id": "KwaOM8RucUSn" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 1, 19)\n", - "Output shape: (32, 1, 1)\n" - ] - } - ], - "source": [ - "print('Input shape:', single_step_window.example[0].shape)\n", - "print('Output shape:', linear(single_step_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OMZTYIj3bYLg" - }, - "source": [ - "This tutorial trains many models, so package the training procedure into a function:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.848956Z", - "iopub.status.busy": "2023-10-27T05:28:11.848338Z", - "iopub.status.idle": "2023-10-27T05:28:11.853315Z", - "shell.execute_reply": "2023-10-27T05:28:11.852692Z" - }, - "id": "CbCL6VIrk-Gt" - }, - "outputs": [], - "source": [ - "MAX_EPOCHS = 20\n", - "\n", - "def compile_and_fit(model, window, patience=2):\n", - " early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n", - " patience=patience,\n", - " mode='min')\n", - "\n", - " model.compile(loss=tf.keras.losses.MeanSquaredError(),\n", - " optimizer=tf.keras.optimizers.Adam(),\n", - " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", - "\n", - " history = model.fit(window.train, epochs=MAX_EPOCHS,\n", - " validation_data=window.val,\n", - " callbacks=[early_stopping])\n", - " return history" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OobVjM-schwj" - }, - "source": [ - "Train the model and evaluate its performance:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:11.856499Z", - "iopub.status.busy": "2023-10-27T05:28:11.856003Z", - "iopub.status.idle": "2023-10-27T05:28:40.532673Z", - "shell.execute_reply": "2023-10-27T05:28:40.531745Z" - }, - "id": "9agbz2qB9bLS" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 17:22 - loss: 2.0953 - mean_absolute_error: 1.2495" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 21/1534 [..............................] - ETA: 3s - loss: 2.6312 - mean_absolute_error: 1.3650 " - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", - "I0000 00:00:1698384492.579925 449804 device_compiler.h:186] Compiled cluster using XLA! 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"1534/1534 [==============================] - 5s 3ms/step - loss: 0.2011 - mean_absolute_error: 0.2786 - val_loss: 0.0245 - val_mean_absolute_error: 0.1169\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 2/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 58s - loss: 0.0272 - mean_absolute_error: 0.1218" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 23/1534 [..............................] - ETA: 3s - loss: 0.0246 - mean_absolute_error: 0.1201 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"1534/1534 [==============================] - 4s 3ms/step - loss: 0.0092 - mean_absolute_error: 0.0702 - val_loss: 0.0090 - val_mean_absolute_error: 0.0706\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 55s - loss: 0.0051 - mean_absolute_error: 0.0605" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 21/1534 [..............................] - ETA: 3s - loss: 0.0118 - mean_absolute_error: 0.0781 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"performance['Linear'] = linear.evaluate(single_step_window.test, verbose=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7U9XukYh8beN" - }, - "source": [ - "Like the `baseline` model, the linear model can be called on batches of wide windows. Used this way the model makes a set of independent predictions on consecutive time steps. The `time` axis acts like another `batch` axis. There are no interactions between the predictions at each time step.\n", - "\n", - "![A single step prediction](images/wide_window.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:40.537236Z", - "iopub.status.busy": "2023-10-27T05:28:40.536479Z", - "iopub.status.idle": "2023-10-27T05:28:40.561691Z", - "shell.execute_reply": "2023-10-27T05:28:40.560926Z" - }, - "id": "K9UVM5Sw9KQN" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 24, 19)\n", - "Output shape: (32, 24, 1)\n" - ] - } - ], - "source": [ - "print('Input shape:', wide_window.example[0].shape)\n", - "print('Output shape:', linear(wide_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "X-CGj85oKaOG" - }, - "source": [ - "Here is the plot of its example predictions on the `wide_window`, note how in many cases the prediction is clearly better than just returning the input temperature, but in a few cases it's worse:" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:40.565380Z", - "iopub.status.busy": "2023-10-27T05:28:40.564918Z", - "iopub.status.idle": "2023-10-27T05:28:41.009006Z", - "shell.execute_reply": "2023-10-27T05:28:41.007992Z" - }, - "id": "bCC8VVo-OvwV" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wide_window.plot(linear)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Is51vU8EMl6c" - }, - "source": [ - "One advantage to linear models is that they're relatively simple to interpret.\n", - "You can pull out the layer's weights and visualize the weight assigned to each input:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:41.013850Z", - "iopub.status.busy": "2023-10-27T05:28:41.013143Z", - "iopub.status.idle": "2023-10-27T05:28:41.211704Z", - "shell.execute_reply": "2023-10-27T05:28:41.210787Z" - }, - "id": "d4uCTbsmK8VI" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(x = range(len(train_df.columns)),\n", - " height=linear.layers[0].kernel[:,0].numpy())\n", - "axis = plt.gca()\n", - "axis.set_xticks(range(len(train_df.columns)))\n", - "_ = axis.set_xticklabels(train_df.columns, rotation=90)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ylng7215boIY" - }, - "source": [ - "Sometimes the model doesn't even place the most weight on the input `T (degC)`. This is one of the risks of random initialization. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W18e6da1cNbw" - }, - "source": [ - "### Dense\n", - "\n", - "Before applying models that actually operate on multiple time-steps, it's worth checking the performance of deeper, more powerful, single input step models.\n", - "\n", - "Here's a model similar to the `linear` model, except it stacks several a few `Dense` layers between the input and the output: " - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:28:41.216061Z", - "iopub.status.busy": "2023-10-27T05:28:41.215349Z", - "iopub.status.idle": "2023-10-27T05:29:33.717496Z", - "shell.execute_reply": "2023-10-27T05:29:33.716656Z" - }, - "id": "Z86WkYp7cNAD" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 33:19 - loss: 2.2005 - mean_absolute_error: 1.1882" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 16/1534 [..............................] - ETA: 5s - loss: 0.7024 - mean_absolute_error: 0.5700 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 31/1534 [..............................] - ETA: 5s - loss: 0.4030 - mean_absolute_error: 0.4007" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"1534/1534 [==============================] - 6s 4ms/step - loss: 0.0079 - mean_absolute_error: 0.0647 - val_loss: 0.0073 - val_mean_absolute_error: 0.0623\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 3/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 59s - loss: 0.0081 - mean_absolute_error: 0.0647" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 15/1534 [..............................] - ETA: 5s - loss: 0.0075 - mean_absolute_error: 0.0642 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"1521/1534 [============================>.] - ETA: 0s - loss: 0.0075 - mean_absolute_error: 0.0627" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0075 - mean_absolute_error: 0.0627 - val_loss: 0.0073 - val_mean_absolute_error: 0.0616\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 4/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 58s - loss: 0.0103 - mean_absolute_error: 0.0713" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"1513/1534 [============================>.] - ETA: 0s - loss: 0.0073 - mean_absolute_error: 0.0611" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "1529/1534 [============================>.] - ETA: 0s - loss: 0.0073 - mean_absolute_error: 0.0611" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0073 - mean_absolute_error: 0.0611 - val_loss: 0.0071 - val_mean_absolute_error: 0.0618\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 5/20\n" - 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"1534/1534 [==============================] - 6s 4ms/step - loss: 0.0071 - mean_absolute_error: 0.0603 - val_loss: 0.0072 - val_mean_absolute_error: 0.0613\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 59s - loss: 0.0075 - mean_absolute_error: 0.0616" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 16/1534 [..............................] - ETA: 5s - loss: 0.0057 - mean_absolute_error: 0.0568 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"1534/1534 [==============================] - 6s 4ms/step - loss: 0.0069 - mean_absolute_error: 0.0593 - val_loss: 0.0066 - val_mean_absolute_error: 0.0571\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 7/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 58s - loss: 0.0056 - mean_absolute_error: 0.0615" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 15/1534 [..............................] - ETA: 5s - loss: 0.0053 - mean_absolute_error: 0.0551 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"1521/1534 [============================>.] - ETA: 0s - loss: 0.0068 - mean_absolute_error: 0.0589" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "1534/1534 [==============================] - 6s 4ms/step - loss: 0.0068 - mean_absolute_error: 0.0589 - val_loss: 0.0066 - val_mean_absolute_error: 0.0585\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 8/20\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/1534 [..............................] - ETA: 59s - loss: 0.0028 - mean_absolute_error: 0.0413" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"439/439 [==============================] - 1s 2ms/step - loss: 0.0068 - mean_absolute_error: 0.0575\n" - ] - } - ], - "source": [ - "dense = tf.keras.Sequential([\n", - " tf.keras.layers.Dense(units=64, activation='relu'),\n", - " tf.keras.layers.Dense(units=64, activation='relu'),\n", - " tf.keras.layers.Dense(units=1)\n", - "])\n", - "\n", - "history = compile_and_fit(dense, single_step_window)\n", - "\n", - "val_performance['Dense'] = dense.evaluate(single_step_window.val)\n", - "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j5dv_whJdswH" - }, - "source": [ - "### Multi-step dense\n", - "\n", - "A single-time-step model has no context for the current values of its inputs. It can't see how the input features are changing over time. To address this issue the model needs access to multiple time steps when making predictions:\n", - "\n", - "![Three time steps are used for each prediction.](images/conv_window.png)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Zac-ti8agbJ7" - }, - "source": [ - "The `baseline`, `linear` and `dense` models handled each time step independently. Here the model will take multiple time steps as input to produce a single output.\n", - "\n", - "Create a `WindowGenerator` that will produce batches of three-hour inputs and one-hour labels:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gtN4BwZ37niR" - }, - "source": [ - "Note that the `Window`'s `shift` parameter is relative to the end of the two windows.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:29:33.721895Z", - "iopub.status.busy": "2023-10-27T05:29:33.721637Z", - "iopub.status.idle": "2023-10-27T05:29:33.727158Z", - "shell.execute_reply": "2023-10-27T05:29:33.726511Z" - }, - "id": "lBh0j5djUKY2" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 4\n", - "Input indices: [0 1 2]\n", - "Label indices: [3]\n", - "Label column name(s): ['T (degC)']" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "CONV_WIDTH = 3\n", - "conv_window = WindowGenerator(\n", - " input_width=CONV_WIDTH,\n", - " label_width=1,\n", - " shift=1,\n", - " label_columns=['T (degC)'])\n", - "\n", - "conv_window" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:29:33.730290Z", - "iopub.status.busy": "2023-10-27T05:29:33.730057Z", - "iopub.status.idle": "2023-10-27T05:29:34.316259Z", - "shell.execute_reply": "2023-10-27T05:29:34.315487Z" - }, - "id": "dCQ5gvs68Xkd" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Given 3 hours of inputs, predict 1 hour into the future.')" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "conv_window.plot()\n", - "plt.title(\"Given 3 hours of inputs, predict 1 hour into the future.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "We0HdMxKeqB_" - }, - "source": [ - "You could train a `dense` model on a multiple-input-step window by adding a `tf.keras.layers.Flatten` as the first layer of the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:29:34.320517Z", - "iopub.status.busy": "2023-10-27T05:29:34.320269Z", - "iopub.status.idle": "2023-10-27T05:29:34.332247Z", - "shell.execute_reply": "2023-10-27T05:29:34.331632Z" - }, - "id": "oNQnUOkOnC1G" - }, - "outputs": [], - "source": [ - "multi_step_dense = tf.keras.Sequential([\n", - " # Shape: (time, features) => (time*features)\n", - " tf.keras.layers.Flatten(),\n", - " tf.keras.layers.Dense(units=32, activation='relu'),\n", - " tf.keras.layers.Dense(units=32, activation='relu'),\n", - " tf.keras.layers.Dense(units=1),\n", - " # Add back the time dimension.\n", - " # Shape: (outputs) => (1, outputs)\n", - " tf.keras.layers.Reshape([1, -1]),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:29:34.335199Z", - "iopub.status.busy": "2023-10-27T05:29:34.334970Z", - "iopub.status.idle": "2023-10-27T05:29:34.390377Z", - "shell.execute_reply": "2023-10-27T05:29:34.389704Z" - }, - "id": "cayD74luo4Vq" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 3, 19)\n", - "Output shape: (32, 1, 1)\n" - ] - } - ], - "source": [ - "print('Input shape:', conv_window.example[0].shape)\n", - "print('Output shape:', multi_step_dense(conv_window.example[0]).shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:29:34.393844Z", - "iopub.status.busy": "2023-10-27T05:29:34.393261Z", - "iopub.status.idle": "2023-10-27T05:30:06.869401Z", - "shell.execute_reply": "2023-10-27T05:30:06.868569Z" - }, - "id": "fu91yEbRo9-J" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/438 [..............................] - ETA: 35s - loss: 0.0052 - mean_absolute_error: 0.0573" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 26/438 [>.............................] - ETA: 0s - loss: 0.0066 - mean_absolute_error: 0.0539 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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0.0066 - mean_absolute_error: 0.0565" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "411/438 [===========================>..] - ETA: 0s - loss: 0.0066 - mean_absolute_error: 0.0565" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "438/438 [==============================] - 1s 2ms/step - loss: 0.0066 - mean_absolute_error: 0.0568\n" - ] - } - ], - "source": [ - "history = compile_and_fit(multi_step_dense, conv_window)\n", - "\n", - "IPython.display.clear_output()\n", - "val_performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.val)\n", - "performance['Multi step dense'] = multi_step_dense.evaluate(conv_window.test, verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:06.873786Z", - "iopub.status.busy": "2023-10-27T05:30:06.873215Z", - "iopub.status.idle": "2023-10-27T05:30:07.348247Z", - "shell.execute_reply": "2023-10-27T05:30:07.347568Z" - }, - "id": "tnqdXYT6pkEh" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "conv_window.plot(multi_step_dense)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gWfrsP8mq8lV" - }, - "source": [ - "The main down-side of this approach is that the resulting model can only be executed on input windows of exactly this shape. " - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:07.352337Z", - "iopub.status.busy": "2023-10-27T05:30:07.351690Z", - "iopub.status.idle": "2023-10-27T05:30:07.384287Z", - "shell.execute_reply": "2023-10-27T05:30:07.383653Z" - }, - "id": "j-q6tz5Yq8Jk" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 24, 19)\n", - "\n", - "ValueError:Exception encountered when calling layer 'sequential_2' (type Sequential).\n", - "\n", - "Input 0 of layer \"dense_4\" is incompatible with the layer: expected axis -1 of input shape to have value 57, but received input with shape (32, 456)\n", - "\n", - "Call arguments received by layer 'sequential_2' (type Sequential):\n", - " • inputs=tf.Tensor(shape=(32, 24, 19), dtype=float32)\n", - " • training=None\n", - " • mask=None\n" - ] - } - ], - "source": [ - "print('Input shape:', wide_window.example[0].shape)\n", - "try:\n", - " print('Output shape:', multi_step_dense(wide_window.example[0]).shape)\n", - "except Exception as e:\n", - " print(f'\\n{type(e).__name__}:{e}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bvvajm3ip_8V" - }, - "source": [ - "The convolutional models in the next section fix this problem." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CrpU6gwSJome" - }, - "source": [ - "### Convolution neural network\n", - " \n", - "A convolution layer (`tf.keras.layers.Conv1D`) also takes multiple time steps as input to each prediction." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cdLBwoaHmsWb" - }, - "source": [ - "Below is the **same** model as `multi_step_dense`, re-written with a convolution. \n", - "\n", - "Note the changes:\n", - "* The `tf.keras.layers.Flatten` and the first `tf.keras.layers.Dense` are replaced by a `tf.keras.layers.Conv1D`.\n", - "* The `tf.keras.layers.Reshape` is no longer necessary since the convolution keeps the time axis in its output." - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:07.387646Z", - "iopub.status.busy": "2023-10-27T05:30:07.387379Z", - "iopub.status.idle": "2023-10-27T05:30:07.397169Z", - "shell.execute_reply": "2023-10-27T05:30:07.396589Z" - }, - "id": "5azaMBj4ac9t" - }, - "outputs": [], - "source": [ - "conv_model = tf.keras.Sequential([\n", - " tf.keras.layers.Conv1D(filters=32,\n", - " kernel_size=(CONV_WIDTH,),\n", - " activation='relu'),\n", - " tf.keras.layers.Dense(units=32, activation='relu'),\n", - " tf.keras.layers.Dense(units=1),\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ftaH6B5ECRiK" - }, - "source": [ - "Run it on an example batch to check that the model produces outputs with the expected shape:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:07.400740Z", - "iopub.status.busy": "2023-10-27T05:30:07.400166Z", - "iopub.status.idle": "2023-10-27T05:30:07.445983Z", - "shell.execute_reply": "2023-10-27T05:30:07.445389Z" - }, - "id": "5YNgt1-e98lH" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Conv model on `conv_window`\n", - "Input shape: (32, 3, 19)\n", - "Output shape: (32, 1, 1)\n" - ] - } - ], - "source": [ - "print(\"Conv model on `conv_window`\")\n", - "print('Input shape:', conv_window.example[0].shape)\n", - "print('Output shape:', conv_model(conv_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5m4kC-jGCY3x" - }, - "source": [ - "Train and evaluate it on the ` conv_window` and it should give performance similar to the `multi_step_dense` model." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:07.449331Z", - "iopub.status.busy": "2023-10-27T05:30:07.448798Z", - "iopub.status.idle": "2023-10-27T05:30:42.379916Z", - "shell.execute_reply": "2023-10-27T05:30:42.379026Z" - 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The convolutional layer is applied to a sliding window of inputs:\n", - "\n", - "![Executing a convolutional model on a sequence](images/wide_conv_window.png)\n", - "\n", - "If you run it on wider input, it produces wider output:" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:42.384632Z", - "iopub.status.busy": "2023-10-27T05:30:42.383907Z", - "iopub.status.idle": "2023-10-27T05:30:42.501246Z", - "shell.execute_reply": "2023-10-27T05:30:42.500484Z" - }, - "id": "hoqccxx9r5jF" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wide window\n", - "Input shape: (32, 24, 19)\n", - "Labels shape: (32, 24, 1)\n", - "Output shape: (32, 22, 1)\n" - ] - } - ], - "source": [ - "print(\"Wide window\")\n", - "print('Input shape:', wide_window.example[0].shape)\n", - "print('Labels shape:', wide_window.example[1].shape)\n", - "print('Output shape:', conv_model(wide_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h_WGxtLIHhRF" - }, - "source": [ - "Note that the output is shorter than the input. To make training or plotting work, you need the labels, and prediction to have the same length. So build a `WindowGenerator` to produce wide windows with a few extra input time steps so the label and prediction lengths match: " - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:42.505045Z", - "iopub.status.busy": "2023-10-27T05:30:42.504414Z", - "iopub.status.idle": "2023-10-27T05:30:42.510238Z", - "shell.execute_reply": "2023-10-27T05:30:42.509583Z" - }, - "id": "_VPvJ_VwTc0f" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 27\n", - "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "Label indices: [ 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]\n", - "Label column name(s): ['T (degC)']" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LABEL_WIDTH = 24\n", - "INPUT_WIDTH = LABEL_WIDTH + (CONV_WIDTH - 1)\n", - "wide_conv_window = WindowGenerator(\n", - " input_width=INPUT_WIDTH,\n", - " label_width=LABEL_WIDTH,\n", - " shift=1,\n", - " label_columns=['T (degC)'])\n", - "\n", - "wide_conv_window" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:42.513238Z", - "iopub.status.busy": "2023-10-27T05:30:42.512977Z", - "iopub.status.idle": "2023-10-27T05:30:42.705644Z", - "shell.execute_reply": "2023-10-27T05:30:42.704880Z" - }, - "id": "gtqlWYXeKXej" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wide conv window\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 26, 19)\n", - "Labels shape: (32, 24, 1)\n", - "Output shape: (32, 24, 1)\n" - ] - } - ], - "source": [ - "print(\"Wide conv window\")\n", - "print('Input shape:', wide_conv_window.example[0].shape)\n", - "print('Labels shape:', wide_conv_window.example[1].shape)\n", - "print('Output shape:', conv_model(wide_conv_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yzxbbS56cSBV" - }, - "source": [ - "Now, you can plot the model's predictions on a wider window. Note the 3 input time steps before the first prediction. Every prediction here is based on the 3 preceding time steps:" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:42.709185Z", - "iopub.status.busy": "2023-10-27T05:30:42.708889Z", - "iopub.status.idle": "2023-10-27T05:30:43.224828Z", - "shell.execute_reply": "2023-10-27T05:30:43.224036Z" - }, - "id": "gR7VyL45UuEe" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wide_conv_window.plot(conv_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H4crpOcoMlSe" - }, - "source": [ - "### Recurrent neural network\n", - "\n", - "A Recurrent Neural Network (RNN) is a type of neural network well-suited to time series data. RNNs process a time series step-by-step, maintaining an internal state from time-step to time-step.\n", - "\n", - "You can learn more in the [Text generation with an RNN](https://www.tensorflow.org/text/tutorials/text_generation) tutorial and the [Recurrent Neural Networks (RNN) with Keras](https://www.tensorflow.org/guide/keras/rnn) guide.\n", - "\n", - "In this tutorial, you will use an RNN layer called Long Short-Term Memory (`tf.keras.layers.LSTM`)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vfQbHSMb1ATa" - }, - "source": [ - "An important constructor argument for all Keras RNN layers, such as `tf.keras.layers.LSTM`, is the `return_sequences` argument. This setting can configure the layer in one of two ways:\n", - "\n", - "1. If `False`, the default, the layer only returns the output of the final time step, giving the model time to warm up its internal state before making a single prediction: \n", - "\n", - "![An LSTM warming up and making a single prediction](images/lstm_1_window.png)\n", - "\n", - "2. If `True`, the layer returns an output for each input. This is useful for:\n", - " * Stacking RNN layers. \n", - " * Training a model on multiple time steps simultaneously.\n", - "\n", - "![An LSTM making a prediction after every time step](images/lstm_many_window.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:43.229414Z", - "iopub.status.busy": "2023-10-27T05:30:43.229121Z", - "iopub.status.idle": "2023-10-27T05:30:43.243949Z", - "shell.execute_reply": "2023-10-27T05:30:43.242875Z" - }, - "id": "DXKLCJy8nWNU" - }, - "outputs": [], - "source": [ - "lstm_model = tf.keras.models.Sequential([\n", - " # Shape [batch, time, features] => [batch, time, lstm_units]\n", - " tf.keras.layers.LSTM(32, return_sequences=True),\n", - " # Shape => [batch, time, features]\n", - " tf.keras.layers.Dense(units=1)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "F124B00KZcLC" - }, - "source": [ - "With `return_sequences=True`, the model can be trained on 24 hours of data at a time.\n", - "\n", - "Note: This will give a pessimistic view of the model's performance. On the first time step, the model has no access to previous steps and, therefore, can't do any better than the simple `linear` and `dense` models shown earlier." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:43.247234Z", - "iopub.status.busy": "2023-10-27T05:30:43.246965Z", - "iopub.status.idle": "2023-10-27T05:30:43.780907Z", - "shell.execute_reply": "2023-10-27T05:30:43.779886Z" - }, - "id": "eZEROCQVYV6q" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input shape: (32, 24, 19)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Output shape: (32, 24, 1)\n" - ] - } - ], - "source": [ - "print('Input shape:', wide_window.example[0].shape)\n", - "print('Output shape:', lstm_model(wide_window.example[0]).shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:30:43.784848Z", - "iopub.status.busy": "2023-10-27T05:30:43.784120Z", - "iopub.status.idle": "2023-10-27T05:32:12.903783Z", - "shell.execute_reply": "2023-10-27T05:32:12.902995Z" - }, - "id": "uvdWRl1e9WJl" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/438 [..............................] - ETA: 37s - loss: 0.0045 - mean_absolute_error: 0.0469" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 18/438 [>.............................] - ETA: 1s - loss: 0.0055 - mean_absolute_error: 0.0512 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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0.0056 - mean_absolute_error: 0.0517" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "438/438 [==============================] - 1s 3ms/step - loss: 0.0056 - mean_absolute_error: 0.0517\n" - ] - } - ], - "source": [ - "history = compile_and_fit(lstm_model, wide_window)\n", - "\n", - "IPython.display.clear_output()\n", - "val_performance['LSTM'] = lstm_model.evaluate(wide_window.val)\n", - "performance['LSTM'] = lstm_model.evaluate(wide_window.test, verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:32:12.907984Z", - "iopub.status.busy": "2023-10-27T05:32:12.907330Z", - "iopub.status.idle": "2023-10-27T05:32:13.360680Z", - "shell.execute_reply": "2023-10-27T05:32:13.360021Z" - }, - "id": "NwAOWCVgB26e" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "wide_window.plot(lstm_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pYglOCKehi8F" - }, - "source": [ - "### Performance" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2pCk0_rwhi8H" - }, - "source": [ - "With this dataset typically each of the models does slightly better than the one before it:" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:32:13.364669Z", - "iopub.status.busy": "2023-10-27T05:32:13.364422Z", - "iopub.status.idle": "2023-10-27T05:32:13.545906Z", - "shell.execute_reply": "2023-10-27T05:32:13.545288Z" - }, - "id": "JjEkt488hi8I" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(len(performance))\n", - "width = 0.3\n", - "metric_name = 'mean_absolute_error'\n", - "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", - "val_mae = [v[metric_index] for v in val_performance.values()]\n", - "test_mae = [v[metric_index] for v in performance.values()]\n", - "\n", - "plt.ylabel('mean_absolute_error [T (degC), normalized]')\n", - "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", - "plt.bar(x + 0.17, test_mae, width, label='Test')\n", - "plt.xticks(ticks=x, labels=performance.keys(),\n", - " rotation=45)\n", - "_ = plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:32:13.549415Z", - "iopub.status.busy": "2023-10-27T05:32:13.548913Z", - "iopub.status.idle": "2023-10-27T05:32:13.552614Z", - "shell.execute_reply": "2023-10-27T05:32:13.552030Z" - }, - "id": "cBMCpsdphi8L" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Baseline : 0.0852\n", - "Linear : 0.0686\n", - "Dense : 0.0595\n", - "Multi step dense: 0.0589\n", - "Conv : 0.0661\n", - "LSTM : 0.0521\n" - ] - } - ], - "source": [ - "for name, value in performance.items():\n", - " print(f'{name:12s}: {value[1]:0.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b5rUJ_2YMWzG" - }, - "source": [ - "### Multi-output models\n", - "\n", - "The models so far all predicted a single output feature, `T (degC)`, for a single time step.\n", - "\n", - "All of these models can be converted to predict multiple features just by changing the number of units in the output layer and adjusting the training windows to include all features in the `labels` (`example_labels`):" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:32:13.556270Z", - "iopub.status.busy": "2023-10-27T05:32:13.555679Z", - "iopub.status.idle": "2023-10-27T05:32:13.691337Z", - "shell.execute_reply": "2023-10-27T05:32:13.690576Z" - }, - "id": "9Gk0Z91xjOwv" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Inputs shape (batch, time, features): (32, 24, 19)\n", - "Labels shape (batch, time, features): (32, 24, 19)\n" - ] - } - ], - "source": [ - "single_step_window = WindowGenerator(\n", - " # `WindowGenerator` returns all features as labels if you \n", - " # don't set the `label_columns` argument.\n", - " input_width=1, label_width=1, shift=1)\n", - "\n", - "wide_window = WindowGenerator(\n", - " input_width=24, label_width=24, shift=1)\n", - "\n", - "for example_inputs, example_labels in wide_window.train.take(1):\n", - " print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n", - " print(f'Labels shape (batch, time, features): {example_labels.shape}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XmcjHfDskX1N" - }, - "source": [ - "Note above that the `features` axis of the labels now has the same depth as the inputs, instead of `1`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9k7S5IHNhSNF" - }, - "source": [ - "#### Baseline\n", - "\n", - "The same baseline model (`Baseline`) can be used here, but this time repeating all features instead of selecting a specific `label_index`:" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:32:13.695235Z", - "iopub.status.busy": "2023-10-27T05:32:13.694902Z", - "iopub.status.idle": "2023-10-27T05:32:13.709220Z", - "shell.execute_reply": "2023-10-27T05:32:13.708464Z" - }, - "id": "sqqB9W-pjr5i" - }, - "outputs": [], - "source": [ - "baseline = Baseline()\n", - "baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", - " metrics=[tf.keras.metrics.MeanAbsoluteError()])" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:32:13.712327Z", - "iopub.status.busy": "2023-10-27T05:32:13.712010Z", - "iopub.status.idle": "2023-10-27T05:32:15.096415Z", - 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"439/439 [==============================] - 1s 3ms/step - loss: 0.0693 - mean_absolute_error: 0.1321\n" - ] - } - ], - "source": [ - "history = compile_and_fit(dense, single_step_window)\n", - "\n", - "IPython.display.clear_output()\n", - "val_performance['Dense'] = dense.evaluate(single_step_window.val)\n", - "performance['Dense'] = dense.evaluate(single_step_window.test, verbose=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dsc9pur_mHsx" - }, - "source": [ - "#### RNN\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:33:07.587842Z", - "iopub.status.busy": "2023-10-27T05:33:07.587224Z", - "iopub.status.idle": "2023-10-27T05:35:45.745510Z", - "shell.execute_reply": "2023-10-27T05:35:45.744480Z" - }, - "id": "4QbGLMyomXaz" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/438 [..............................] - ETA: 36s - loss: 0.0625 - 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"])\n", - "\n", - "history = compile_and_fit(lstm_model, wide_window)\n", - "\n", - "IPython.display.clear_output()\n", - "val_performance['LSTM'] = lstm_model.evaluate( wide_window.val)\n", - "performance['LSTM'] = lstm_model.evaluate( wide_window.test, verbose=0)\n", - "\n", - "print()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UwhY2f_Nn0_K" - }, - "source": [ - "\n", - "\n", - "#### Advanced: Residual connections\n", - "\n", - "The `Baseline` model from earlier took advantage of the fact that the sequence doesn't change drastically from time step to time step. Every model trained in this tutorial so far was randomly initialized, and then had to learn that the output is a a small change from the previous time step.\n", - "\n", - "While you can get around this issue with careful initialization, it's simpler to build this into the model structure.\n", - "\n", - "It's common in time series analysis to build models that instead of predicting the next value, predict how the value will change in the next time step. Similarly, residual networks—or ResNets—in deep learning refer to architectures where each layer adds to the model's accumulating result.\n", - "\n", - "That is how you take advantage of the knowledge that the change should be small.\n", - "\n", - "![A model with a residual connection](images/residual.png)\n", - "\n", - "Essentially, this initializes the model to match the `Baseline`. For this task it helps models converge faster, with slightly better performance." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yP58A_ORx0kM" - }, - "source": [ - "This approach can be used in conjunction with any model discussed in this tutorial. \n", - "\n", - "Here, it is being applied to the LSTM model, note the use of the `tf.initializers.zeros` to ensure that the initial predicted changes are small, and don't overpower the residual connection. There are no symmetry-breaking concerns for the gradients here, since the `zeros` are only used on the last layer." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:35:45.749796Z", - "iopub.status.busy": "2023-10-27T05:35:45.749063Z", - "iopub.status.idle": "2023-10-27T05:35:45.754041Z", - "shell.execute_reply": "2023-10-27T05:35:45.753181Z" - }, - "id": "7YlfnDQC22TQ" - }, - "outputs": [], - "source": [ - "class ResidualWrapper(tf.keras.Model):\n", - " def __init__(self, model):\n", - " super().__init__()\n", - " self.model = model\n", - "\n", - " def call(self, inputs, *args, **kwargs):\n", - " delta = self.model(inputs, *args, **kwargs)\n", - "\n", - " # The prediction for each time step is the input\n", - " # from the previous time step plus the delta\n", - " # calculated by the model.\n", - " return inputs + delta" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - 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0.0617 - mean_absolute_error: 0.1178" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "438/438 [==============================] - 1s 3ms/step - loss: 0.0617 - mean_absolute_error: 0.1178\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "CPU times: user 2min 8s, sys: 26.9 s, total: 2min 35s\n", - "Wall time: 58.8 s\n" - ] - } - ], - "source": [ - "%%time\n", - "residual_lstm = ResidualWrapper(\n", - " tf.keras.Sequential([\n", - " tf.keras.layers.LSTM(32, return_sequences=True),\n", - " tf.keras.layers.Dense(\n", - " num_features,\n", - " # The predicted deltas should start small.\n", - " # Therefore, initialize the output layer with zeros.\n", - " kernel_initializer=tf.initializers.zeros())\n", - "]))\n", - "\n", - "history = compile_and_fit(residual_lstm, wide_window)\n", - "\n", - "IPython.display.clear_output()\n", - "val_performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.val)\n", - "performance['Residual LSTM'] = residual_lstm.evaluate(wide_window.test, verbose=0)\n", - "print()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I42Er9Du6co1" - }, - "source": [ - "#### Performance" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LZxR38P_6pUi" - }, - "source": [ - "Here is the overall performance for these multi-output models." - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:36:44.547056Z", - "iopub.status.busy": "2023-10-27T05:36:44.546788Z", - "iopub.status.idle": "2023-10-27T05:36:44.717796Z", - "shell.execute_reply": "2023-10-27T05:36:44.717147Z" - }, - "id": "6XgTK9tnr7rc" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(len(performance))\n", - "width = 0.3\n", - "\n", - "metric_name = 'mean_absolute_error'\n", - "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", - "val_mae = [v[metric_index] for v in val_performance.values()]\n", - "test_mae = [v[metric_index] for v in performance.values()]\n", - "\n", - "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", - "plt.bar(x + 0.17, test_mae, width, label='Test')\n", - "plt.xticks(ticks=x, labels=performance.keys(),\n", - " rotation=45)\n", - "plt.ylabel('MAE (average over all outputs)')\n", - "_ = plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:36:44.720860Z", - "iopub.status.busy": "2023-10-27T05:36:44.720629Z", - "iopub.status.idle": "2023-10-27T05:36:44.724382Z", - "shell.execute_reply": "2023-10-27T05:36:44.723777Z" - }, - "id": "URz3ajCc6kBj" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Baseline : 0.1638\n", - "Dense : 0.1333\n", - "LSTM : 0.1206\n", - "Residual LSTM : 0.1193\n" - ] - } - ], - "source": [ - "for name, value in performance.items():\n", - " print(f'{name:15s}: {value[1]:0.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_Vt2MJhNxwPU" - }, - "source": [ - "The above performances are averaged across all model outputs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eYokb7Om2YbK" - }, - "source": [ - "## Multi-step models\n", - "\n", - "Both the single-output and multiple-output models in the previous sections made **single time step predictions**, one hour into the future.\n", - "\n", - "This section looks at how to expand these models to make **multiple time step predictions**.\n", - "\n", - "In a multi-step prediction, the model needs to learn to predict a range of future values. Thus, unlike a single step model, where only a single future point is predicted, a multi-step model predicts a sequence of the future values.\n", - "\n", - "There are two rough approaches to this:\n", - "\n", - "1. Single shot predictions where the entire time series is predicted at once.\n", - "2. Autoregressive predictions where the model only makes single step predictions and its output is fed back as its input.\n", - "\n", - "In this section all the models will predict **all the features across all output time steps**.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WFsDAwVt4_rq" - }, - "source": [ - "For the multi-step model, the training data again consists of hourly samples. However, here, the models will learn to predict 24 hours into the future, given 24 hours of the past.\n", - "\n", - "Here is a `Window` object that generates these slices from the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:36:44.727613Z", - "iopub.status.busy": "2023-10-27T05:36:44.727206Z", - "iopub.status.idle": "2023-10-27T05:36:45.241450Z", - "shell.execute_reply": "2023-10-27T05:36:45.240709Z" - }, - "id": "1cFYtsz6XiGw" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Total window size: 48\n", - "Input indices: [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n", - "Label indices: [24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47]\n", - "Label column name(s): None" - ] - }, - "execution_count": 77, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "OUT_STEPS = 24\n", - "multi_window = WindowGenerator(input_width=24,\n", - " label_width=OUT_STEPS,\n", - " shift=OUT_STEPS)\n", - "\n", - "multi_window.plot()\n", - "multi_window" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5lg8SInh9Jzd" - }, - "source": [ - "### Baselines" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "axwpoWYOApJL" - }, - "source": [ - "A simple baseline for this task is to repeat the last input time step for the required number of output time steps:\n", - "\n", - "![Repeat the last input, for each output step](images/multistep_last.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:36:45.244898Z", - "iopub.status.busy": "2023-10-27T05:36:45.244651Z", - "iopub.status.idle": "2023-10-27T05:36:47.157824Z", - "shell.execute_reply": "2023-10-27T05:36:47.157089Z" - }, - "id": "_5iaHSaJ9Rxv" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 1:13 - loss: 0.6018 - mean_absolute_error: 0.5035" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 29/437 [>.............................] - ETA: 0s - loss: 0.6245 - mean_absolute_error: 0.4990 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 57/437 [==>...........................] - ETA: 0s - loss: 0.6219 - mean_absolute_error: 0.4985" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 85/437 [====>.........................] - ETA: 0s - loss: 0.6225 - mean_absolute_error: 0.4995" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "112/437 [======>.......................] - ETA: 0s - loss: 0.6267 - mean_absolute_error: 0.5003" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class MultiStepLastBaseline(tf.keras.Model):\n", - " def call(self, inputs):\n", - " return tf.tile(inputs[:, -1:, :], [1, OUT_STEPS, 1])\n", - "\n", - "last_baseline = MultiStepLastBaseline()\n", - "last_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", - " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", - "\n", - "multi_val_performance = {}\n", - "multi_performance = {}\n", - "\n", - "multi_val_performance['Last'] = last_baseline.evaluate(multi_window.val)\n", - "multi_performance['Last'] = last_baseline.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(last_baseline)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AvHZ93ObAfMA" - }, - "source": [ - "Since this task is to predict 24 hours into the future, given 24 hours of the past, another simple approach is to repeat the previous day, assuming tomorrow will be similar:\n", - "\n", - "![Repeat the previous day](images/multistep_repeat.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:36:47.162098Z", - "iopub.status.busy": "2023-10-27T05:36:47.161536Z", - "iopub.status.idle": "2023-10-27T05:36:48.984400Z", - "shell.execute_reply": "2023-10-27T05:36:48.983674Z" - }, - "id": "L8Y1uMhGwIRs" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 1:08 - loss: 0.4046 - mean_absolute_error: 0.3896" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 28/437 [>.............................] - ETA: 0s - loss: 0.4342 - mean_absolute_error: 0.3962 " - ] - }, - { - "name": "stdout", - 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0.4263 - mean_absolute_error: 0.3955" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "437/437 [==============================] - 1s 2ms/step - loss: 0.4270 - mean_absolute_error: 0.3959\n" - ] - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class RepeatBaseline(tf.keras.Model):\n", - " def call(self, inputs):\n", - " return inputs\n", - "\n", - "repeat_baseline = RepeatBaseline()\n", - "repeat_baseline.compile(loss=tf.keras.losses.MeanSquaredError(),\n", - " metrics=[tf.keras.metrics.MeanAbsoluteError()])\n", - "\n", - "multi_val_performance['Repeat'] = repeat_baseline.evaluate(multi_window.val)\n", - "multi_performance['Repeat'] = repeat_baseline.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(repeat_baseline)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "tbndS-ct9C2Q" - }, - "source": [ - "### Single-shot models\n", - "\n", - "One high-level approach to this problem is to use a \"single-shot\" model, where the model makes the entire sequence prediction in a single step.\n", - "\n", - "This can be implemented efficiently as a `tf.keras.layers.Dense` with `OUT_STEPS*features` output units. The model just needs to reshape that output to the required `(OUTPUT_STEPS, features)`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "NCKS4m1VKrDQ" - }, - "source": [ - "#### Linear\n", - "\n", - "A simple linear model based on the last input time step does better than either baseline, but is underpowered. The model needs to predict `OUTPUT_STEPS` time steps, from a single input time step with a linear projection. It can only capture a low-dimensional slice of the behavior, likely based mainly on the time of day and time of year.\n", - "\n", - "![Predict all timesteps from the last time-step](images/multistep_dense.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:36:48.989133Z", - "iopub.status.busy": "2023-10-27T05:36:48.988843Z", - "iopub.status.idle": "2023-10-27T05:37:19.728629Z", - "shell.execute_reply": "2023-10-27T05:37:19.727949Z" - }, - "id": "kfRz_WVhIQcd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 34s - loss: 0.3062 - mean_absolute_error: 0.3227" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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loss: 0.2559 - mean_absolute_error: 0.3049\n" - ] - }, - { - "data": { - "image/png": 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_linear_model = tf.keras.Sequential([\n", - " # Take the last time-step.\n", - " # Shape [batch, time, features] => [batch, 1, features]\n", - " tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n", - " # Shape => [batch, 1, out_steps*features]\n", - " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", - " kernel_initializer=tf.initializers.zeros()),\n", - " # Shape => [batch, out_steps, features]\n", - " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", - "])\n", - "\n", - "history = compile_and_fit(multi_linear_model, multi_window)\n", - "\n", - "IPython.display.clear_output()\n", - "multi_val_performance['Linear'] = multi_linear_model.evaluate(multi_window.val)\n", - "multi_performance['Linear'] = multi_linear_model.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(multi_linear_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zi2TMHk2IRrh" - }, - "source": [ - "#### Dense\n", - "\n", - "Adding a `tf.keras.layers.Dense` between the input and output gives the linear model more power, but is still only based on a single input time step." - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:37:19.733015Z", - "iopub.status.busy": "2023-10-27T05:37:19.732769Z", - "iopub.status.idle": "2023-10-27T05:38:07.271768Z", - "shell.execute_reply": "2023-10-27T05:38:07.271056Z" - }, - "id": "jezm-BKaGj91" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 34s - loss: 0.2548 - mean_absolute_error: 0.3072" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - " 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_dense_model = tf.keras.Sequential([\n", - " # Take the last time step.\n", - " # Shape [batch, time, features] => [batch, 1, features]\n", - " tf.keras.layers.Lambda(lambda x: x[:, -1:, :]),\n", - " # Shape => [batch, 1, dense_units]\n", - " tf.keras.layers.Dense(512, activation='relu'),\n", - " # Shape => [batch, out_steps*features]\n", - " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", - " kernel_initializer=tf.initializers.zeros()),\n", - " # Shape => [batch, out_steps, features]\n", - " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", - "])\n", - "\n", - "history = compile_and_fit(multi_dense_model, multi_window)\n", - "\n", - "IPython.display.clear_output()\n", - "multi_val_performance['Dense'] = multi_dense_model.evaluate(multi_window.val)\n", - "multi_performance['Dense'] = multi_dense_model.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(multi_dense_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "icsBAjCzMaMl" - }, - "source": [ - "#### CNN" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "34lCZrWYNBwd" - }, - "source": [ - "A convolutional model makes predictions based on a fixed-width history, which may lead to better performance than the dense model since it can see how things are changing over time:\n", - "\n", - "![A convolutional model sees how things change over time](images/multistep_conv.png)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:38:07.276523Z", - "iopub.status.busy": "2023-10-27T05:38:07.276266Z", - "iopub.status.idle": "2023-10-27T05:38:54.043730Z", - "shell.execute_reply": "2023-10-27T05:38:54.042918Z" - }, - "id": "0xJoIP6PMWMI" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 34s - loss: 0.2273 - 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", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "CONV_WIDTH = 3\n", - "multi_conv_model = tf.keras.Sequential([\n", - " # Shape [batch, time, features] => [batch, CONV_WIDTH, features]\n", - " tf.keras.layers.Lambda(lambda x: x[:, -CONV_WIDTH:, :]),\n", - " # Shape => [batch, 1, conv_units]\n", - " tf.keras.layers.Conv1D(256, activation='relu', kernel_size=(CONV_WIDTH)),\n", - " # Shape => [batch, 1, out_steps*features]\n", - " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", - " kernel_initializer=tf.initializers.zeros()),\n", - " # Shape => [batch, out_steps, features]\n", - " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", - "])\n", - "\n", - "history = compile_and_fit(multi_conv_model, multi_window)\n", - "\n", - "IPython.display.clear_output()\n", - "\n", - "multi_val_performance['Conv'] = multi_conv_model.evaluate(multi_window.val)\n", - "multi_performance['Conv'] = multi_conv_model.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(multi_conv_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "weBjeZAFJOP4" - }, - "source": [ - "#### RNN" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8022xOKxOO92" - }, - "source": [ - "A recurrent model can learn to use a long history of inputs, if it's relevant to the predictions the model is making. Here the model will accumulate internal state for 24 hours, before making a single prediction for the next 24 hours.\n", - "\n", - "In this single-shot format, the LSTM only needs to produce an output at the last time step, so set `return_sequences=False` in `tf.keras.layers.LSTM`.\n", - "\n", - "![The LSTM accumulates state over the input window, and makes a single prediction for the next 24 hours](images/multistep_lstm.png)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:38:54.048251Z", - "iopub.status.busy": "2023-10-27T05:38:54.047656Z", - "iopub.status.idle": "2023-10-27T05:40:01.913933Z", - "shell.execute_reply": "2023-10-27T05:40:01.913256Z" - }, - "id": "Bf1ks6RTzF64" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 35s - loss: 0.2475 - mean_absolute_error: 0.2971" - ] - }, - { - "name": "stdout", - 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "multi_lstm_model = tf.keras.Sequential([\n", - " # Shape [batch, time, features] => [batch, lstm_units].\n", - " # Adding more `lstm_units` just overfits more quickly.\n", - " tf.keras.layers.LSTM(32, return_sequences=False),\n", - " # Shape => [batch, out_steps*features].\n", - " tf.keras.layers.Dense(OUT_STEPS*num_features,\n", - " kernel_initializer=tf.initializers.zeros()),\n", - " # Shape => [batch, out_steps, features].\n", - " tf.keras.layers.Reshape([OUT_STEPS, num_features])\n", - "])\n", - "\n", - "history = compile_and_fit(multi_lstm_model, multi_window)\n", - "\n", - "IPython.display.clear_output()\n", - "\n", - "multi_val_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.val)\n", - "multi_performance['LSTM'] = multi_lstm_model.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(multi_lstm_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d5n-1cDW12Vo" - }, - "source": [ - "### Advanced: Autoregressive model\n", - "\n", - "The above models all predict the entire output sequence in a single step.\n", - "\n", - "In some cases it may be helpful for the model to decompose this prediction into individual time steps. Then, each model's output can be fed back into itself at each step and predictions can be made conditioned on the previous one, like in the classic Generating Sequences With Recurrent Neural Networks.\n", - "\n", - "One clear advantage to this style of model is that it can be set up to produce output with a varying length.\n", - "\n", - "You could take any of the single-step multi-output models trained in the first half of this tutorial and run in an autoregressive feedback loop, but here you'll focus on building a model that's been explicitly trained to do that.\n", - "\n", - "![Feedback a model's output to its input](images/multistep_autoregressive.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PKRreBbULRXY" - }, - "source": [ - "#### RNN\n", - "\n", - "This tutorial only builds an autoregressive RNN model, but this pattern could be applied to any model that was designed to output a single time step.\n", - "\n", - "The model will have the same basic form as the single-step LSTM models from earlier: a `tf.keras.layers.LSTM` layer followed by a `tf.keras.layers.Dense` layer that converts the `LSTM` layer's outputs to model predictions.\n", - "\n", - "A `tf.keras.layers.LSTM` is a `tf.keras.layers.LSTMCell` wrapped in the higher level `tf.keras.layers.RNN` that manages the state and sequence results for you (Check out the [Recurrent Neural Networks (RNN) with Keras](https://www.tensorflow.org/guide/keras/rnn) guide for details).\n", - "\n", - "In this case, the model has to manually manage the inputs for each step, so it uses `tf.keras.layers.LSTMCell` directly for the lower level, single time step interface." - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:01.918165Z", - "iopub.status.busy": "2023-10-27T05:40:01.917903Z", - "iopub.status.idle": "2023-10-27T05:40:01.922575Z", - "shell.execute_reply": "2023-10-27T05:40:01.921871Z" - }, - "id": "s5tz3Nu0R5JG" - }, - "outputs": [], - "source": [ - "class FeedBack(tf.keras.Model):\n", - " def __init__(self, units, out_steps):\n", - " super().__init__()\n", - " self.out_steps = out_steps\n", - " self.units = units\n", - " self.lstm_cell = tf.keras.layers.LSTMCell(units)\n", - " # Also wrap the LSTMCell in an RNN to simplify the `warmup` method.\n", - " self.lstm_rnn = tf.keras.layers.RNN(self.lstm_cell, return_state=True)\n", - " self.dense = tf.keras.layers.Dense(num_features)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:01.925600Z", - "iopub.status.busy": "2023-10-27T05:40:01.925379Z", - "iopub.status.idle": "2023-10-27T05:40:01.937413Z", - "shell.execute_reply": "2023-10-27T05:40:01.936820Z" - }, - "id": "2OXVM9G1U7xR" - }, - "outputs": [], - "source": [ - "feedback_model = FeedBack(units=32, out_steps=OUT_STEPS)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ph5uFSfTUNho" - }, - "source": [ - "The first method this model needs is a `warmup` method to initialize its internal state based on the inputs. Once trained, this state will capture the relevant parts of the input history. This is equivalent to the single-step `LSTM` model from earlier:" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:01.940554Z", - "iopub.status.busy": "2023-10-27T05:40:01.940336Z", - "iopub.status.idle": "2023-10-27T05:40:01.944047Z", - "shell.execute_reply": "2023-10-27T05:40:01.943486Z" - }, - "id": "vM2K_LLdRjDZ" - }, - "outputs": [], - "source": [ - "def warmup(self, inputs):\n", - " # inputs.shape => (batch, time, features)\n", - " # x.shape => (batch, lstm_units)\n", - " x, *state = self.lstm_rnn(inputs)\n", - "\n", - " # predictions.shape => (batch, features)\n", - " prediction = self.dense(x)\n", - " return prediction, state\n", - "\n", - "FeedBack.warmup = warmup" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6JkaSYaZ9eB7" - }, - "source": [ - "This method returns a single time-step prediction and the internal state of the `LSTM`:" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:01.947452Z", - "iopub.status.busy": "2023-10-27T05:40:01.946910Z", - "iopub.status.idle": "2023-10-27T05:40:02.113871Z", - "shell.execute_reply": "2023-10-27T05:40:02.113239Z" - }, - "id": "w9Fz6NTKXXwU" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "TensorShape([32, 19])" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prediction, state = feedback_model.warmup(multi_window.example[0])\n", - "prediction.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "S_ZdvPjdX3y3" - }, - "source": [ - "With the `RNN`'s state, and an initial prediction you can now continue iterating the model feeding the predictions at each step back as the input.\n", - "\n", - "The simplest approach for collecting the output predictions is to use a Python list and a `tf.stack` after the loop." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yotTad3nZXQU" - }, - "source": [ - "Note: Stacking a Python list like this only works with eager-execution, using `Model.compile(..., run_eagerly=True)` for training, or with a fixed length output. For a dynamic output length, you would need to use a `tf.TensorArray` instead of a Python list, and `tf.range` instead of the Python `range`." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:02.117766Z", - "iopub.status.busy": "2023-10-27T05:40:02.117346Z", - "iopub.status.idle": "2023-10-27T05:40:02.122428Z", - "shell.execute_reply": "2023-10-27T05:40:02.121847Z" - }, - "id": "g1GRDu3mZtr9" - }, - "outputs": [], - "source": [ - "def call(self, inputs, training=None):\n", - " # Use a TensorArray to capture dynamically unrolled outputs.\n", - " predictions = []\n", - " # Initialize the LSTM state.\n", - " prediction, state = self.warmup(inputs)\n", - "\n", - " # Insert the first prediction.\n", - " predictions.append(prediction)\n", - "\n", - " # Run the rest of the prediction steps.\n", - " for n in range(1, self.out_steps):\n", - " # Use the last prediction as input.\n", - " x = prediction\n", - " # Execute one lstm step.\n", - " x, state = self.lstm_cell(x, states=state,\n", - " training=training)\n", - " # Convert the lstm output to a prediction.\n", - " prediction = self.dense(x)\n", - " # Add the prediction to the output.\n", - " predictions.append(prediction)\n", - "\n", - " # predictions.shape => (time, batch, features)\n", - " predictions = tf.stack(predictions)\n", - " # predictions.shape => (batch, time, features)\n", - " predictions = tf.transpose(predictions, [1, 0, 2])\n", - " return predictions\n", - "\n", - "FeedBack.call = call" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ubop-YWp15XW" - }, - "source": [ - "Test run this model on the example inputs:" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:02.125416Z", - "iopub.status.busy": "2023-10-27T05:40:02.125192Z", - "iopub.status.idle": "2023-10-27T05:40:02.229792Z", - "shell.execute_reply": "2023-10-27T05:40:02.229200Z" - }, - "id": "Xja83zEYaM2D" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Output shape (batch, time, features): (32, 24, 19)\n" - ] - } - ], - "source": [ - "print('Output shape (batch, time, features): ', feedback_model(multi_window.example[0]).shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qMs0rYB8be9M" - }, - "source": [ - "Now, train the model:" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:40:02.232874Z", - "iopub.status.busy": "2023-10-27T05:40:02.232620Z", - "iopub.status.idle": "2023-10-27T05:47:05.859323Z", - "shell.execute_reply": "2023-10-27T05:47:05.858639Z" - }, - "id": "VBRVG2hnNyrO" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - " 1/437 [..............................] - ETA: 36s - loss: 0.2431 - mean_absolute_error: 0.3142" - ] - }, - { - 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loss: 0.2280 - mean_absolute_error: 0.3019\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "history = compile_and_fit(feedback_model, multi_window)\n", - "\n", - "IPython.display.clear_output()\n", - "\n", - "multi_val_performance['AR LSTM'] = feedback_model.evaluate(multi_window.val)\n", - "multi_performance['AR LSTM'] = feedback_model.evaluate(multi_window.test, verbose=0)\n", - "multi_window.plot(feedback_model)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hGjcJsAQJUkI" - }, - "source": [ - "### Performance" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sODAwr2ndtDB" - }, - "source": [ - "There are clearly diminishing returns as a function of model complexity on this problem:" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:47:05.863879Z", - "iopub.status.busy": "2023-10-27T05:47:05.863606Z", - "iopub.status.idle": "2023-10-27T05:47:06.038056Z", - "shell.execute_reply": "2023-10-27T05:47:06.037421Z" - }, - "id": "WZwWBA8S6B3L" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.arange(len(multi_performance))\n", - "width = 0.3\n", - "\n", - "metric_name = 'mean_absolute_error'\n", - "metric_index = lstm_model.metrics_names.index('mean_absolute_error')\n", - "val_mae = [v[metric_index] for v in multi_val_performance.values()]\n", - "test_mae = [v[metric_index] for v in multi_performance.values()]\n", - "\n", - "plt.bar(x - 0.17, val_mae, width, label='Validation')\n", - "plt.bar(x + 0.17, test_mae, width, label='Test')\n", - "plt.xticks(ticks=x, labels=multi_performance.keys(),\n", - " rotation=45)\n", - "plt.ylabel(f'MAE (average over all times and outputs)')\n", - "_ = plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Zq3hUsedCEmJ" - }, - "source": [ - "The metrics for the multi-output models in the first half of this tutorial show the performance averaged across all output features. These performances are similar but also averaged across output time steps. " - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": { - "execution": { - "iopub.execute_input": "2023-10-27T05:47:06.041402Z", - "iopub.status.busy": "2023-10-27T05:47:06.041000Z", - "iopub.status.idle": "2023-10-27T05:47:06.044860Z", - "shell.execute_reply": "2023-10-27T05:47:06.044275Z" - }, - "id": "jKq3eAIvH4Db" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last : 0.5157\n", - "Repeat : 0.3774\n", - "Linear : 0.2990\n", - "Dense : 0.2776\n", - "Conv : 0.2739\n", - "LSTM : 0.2763\n", - "AR LSTM : 0.2944\n" - ] - } - ], - "source": [ - "for name, value in multi_performance.items():\n", - " print(f'{name:8s}: {value[1]:0.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MpBFwfnaHP23" - }, - "source": [ - "The gains achieved going from a dense model to convolutional and recurrent models are only a few percent (if any), and the autoregressive model performed clearly worse. So these more complex approaches may not be worth while on **this** problem, but there was no way to know without trying, and these models could be helpful for **your** problem." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pOzaIRYBhqwg" - }, - "source": [ - "## Next steps\n", - "\n", - "This tutorial was a quick introduction to time series forecasting using TensorFlow.\n", - "\n", - "To learn more, refer to:\n", - "\n", - "- Chapter 15 of Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition.\n", - "- Chapter 6 of Deep Learning with Python.\n", - "- Lesson 8 of Udacity's intro to TensorFlow for deep learning, including the exercise notebooks.\n", - "\n", - "Also, remember that you can implement any classical time series model in TensorFlow—this tutorial just focuses on TensorFlow's built-in functionality.\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "name": "time_series.ipynb", - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/getting-started.ipynb b/notebooks/getting-started.ipynb index 10e19c3..2f54b7b 100644 --- a/notebooks/getting-started.ipynb +++ b/notebooks/getting-started.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 41, "id": "70a32352-80c9-40b7-8f68-1aeecfc52658", "metadata": {}, "outputs": [], @@ -13,95 +13,68 @@ ] }, { - "cell_type": "markdown", - "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", + "cell_type": "code", + "execution_count": 51, + "id": "f8a26d78-229f-47f7-9f66-d0c245dbc096", "metadata": {}, + "outputs": [], "source": [ - "# **onTime Demo**" + "import ontime as on" ] }, { "cell_type": "markdown", - "id": "9f48dcdc-2c09-48bf-8e0e-a9e8f6cd84bf", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "id": "9f94ac2b-bc8a-4757-affb-6e570a024804", + "metadata": {}, "source": [ - "---\n", - "## Scenario\n", - "\n", - "1. Creation of the model\n", - "2. Packaging of the model\n", - "3. Add the model to the library\n", - "4. Use the model with other tools from the library\n", - "\n", - "## Structure\n", - "\n", - " .\n", - " └── ontime\n", - " ├── abstract <- Used today\n", - " ├── config\n", - " ├── detectors <- Used today\n", - " ├── generators <- Used today\n", - " ├── metrics\n", - " ├── models <- Used today\n", - " ├── plots \n", - " ├── processors\n", - " ├── time_series <- Used today\n", - " └── utils\n" + "# **onTime** — Getting Started" ] }, { "cell_type": "markdown", - "id": "8a308508-779c-432f-a610-9b2d9984357e", + "id": "19665f45-64ac-47bc-a2d8-951a282764c0", "metadata": {}, "source": [ "---\n", - "## Creation of the model" + "## Structure of the Library" ] }, { "cell_type": "markdown", - "id": "bde92f83-48bb-4f8b-a4fa-cc68985b1c0c", - "metadata": {}, - "source": [ - "Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "379906f3-4248-4d1d-92c1-c3906d79f72b", + "id": "56de274f-bce1-4252-bd44-f639c22eac6e", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The `LightGBM` module could not be imported. To enable LightGBM support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "The `Prophet` module could not be imported. To enable Prophet support in Darts, follow the detailed instructions in the installation guide: https://github.com/unit8co/darts/blob/master/INSTALL.md\n", - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - } - ], "source": [ - "import pandas as pd\n", - "import ontime as on" + "The library is divided in three parts : \n", + "\n", + "1. `core` for all basic features\n", + "2. `module` for all features using core features. E.g. benchmarking, ml preprocessing, etc.\n", + "3. `context` for all features related to the usage of onTime in an applied scenario" ] }, { "cell_type": "markdown", - "id": "021b7dd0-d8a7-49d3-bee5-521e2963bc94", + "id": "c0271c7d-d9b4-414e-b7be-83adeafcc741", "metadata": {}, "source": [ - "Generate some fake data" + "## `core` Features\n", + "\n", + "This is a low level API. Most objects and functions are accessible in the base object : \n", + " \n", + " ontime\n", + " ├── detectors\n", + " ├── generators\n", + " ├── Model\n", + " ├── plots\n", + " ├── processors\n", + " └── TimeSeries\n", + "\n", + "For instance : " ] }, { "cell_type": "code", - "execution_count": 67, - "id": "2c459a9e-4747-454c-8bbd-2c9bfaad1c19", + "execution_count": 52, + "id": "bcbdae2b-2833-43d6-9bf7-16caef87cf75", "metadata": {}, "outputs": [ { @@ -471,46 +444,46 @@ " fill: currentColor;\n", "}\n", "
    <TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
-       "array([[[-0.58126402]],\n",
+       "array([[[-0.07710256]],\n",
        "\n",
-       "       [[-1.36677965]],\n",
+       "       [[-0.30611734]],\n",
        "\n",
-       "       [[-1.98731443]],\n",
+       "       [[-0.42833724]],\n",
        "\n",
-       "       [[-3.63736368]],\n",
+       "       [[-0.49277018]],\n",
        "\n",
-       "       [[-4.18556985]]])\n",
+       "       [[ 1.13635256]]])\n",
        "Coordinates:\n",
        "  * time       (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n",
        "  * component  (component) object 'random_walk'\n",
        "Dimensions without coordinates: sample\n",
        "Attributes:\n",
        "    static_covariates:  None\n",
-       "    hierarchy:          None
  • static_covariates :
    None
    hierarchy :
    None
    • " ], "text/plain": [ "\n", - "array([[[-0.58126402]],\n", + "array([[[-0.07710256]],\n", "\n", - " [[-1.36677965]],\n", + " [[-0.30611734]],\n", "\n", - " [[-1.98731443]],\n", + " [[-0.42833724]],\n", "\n", - " [[-3.63736368]],\n", + " [[-0.49277018]],\n", "\n", - " [[-4.18556985]]])\n", + " [[ 1.13635256]]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 2023-01-01 2023-01-02 ... 2023-01-05\n", " * component (component) object 'random_walk'\n", @@ -520,27 +493,27 @@ " hierarchy: None" ] }, - "execution_count": 67, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ts = on.generators.random().generate(start=pd.Timestamp('01-01-2023'), end=pd.Timestamp('12-31-2023'))\n", + "ts = on.generators.random_walk().generate(start=pd.Timestamp('01-01-2023'), end=pd.Timestamp('12-31-2023'))\n", "ts[0:5]" ] }, { "cell_type": "code", - "execution_count": 68, - "id": "cffaf9a3-1a28-4e04-b22e-57e6b10a6d1c", + "execution_count": 54, + "id": "6b9959a6-489a-4c80-a53e-e65cad57fe62", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -548,47 +521,160 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 2))\n", - "ts.plot(ax=ax);" + "ts.plot();" + ] + }, + { + "cell_type": "markdown", + "id": "2487b267-ec4a-49bc-9bda-33e4bc39194c", + "metadata": {}, + "source": [ + "## `module` and `context` features" ] }, { "cell_type": "markdown", - "id": "d61a953b-aceb-4145-9526-b784b73946d8", + "id": "0904039a-4cb4-4795-9656-8c48a919dfee", "metadata": {}, "source": [ - "Split in train and test" + "High level API with various features. Let's load some data for an example :" ] }, { "cell_type": "code", - "execution_count": 69, - "id": "b1b9e04b-fcc8-402c-8e8c-6feb67d2fd08", + "execution_count": 61, + "id": "4c906526-20c4-47b5-8023-8216c6af18d1", + "metadata": {}, + "outputs": [], + "source": [ + "from darts.datasets import EnergyDataset\n", + "ts = EnergyDataset().load()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "15724cb7-d0d6-40ab-a240-57df7e8f3c21", + "metadata": {}, + "outputs": [], + "source": [ + "df = ts.pd_dataframe()\n", + "df = df.interpolate()\n", + "cols = ['generation biomass', 'generation solar', 'generation nuclear']\n", + "df = df[cols]" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "401cf66e-f0eb-48e0-ac7c-adf2bef92a3c", + "metadata": {}, + "outputs": [], + "source": [ + "ts = on.TimeSeries.from_dataframe(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "3013d9e8-d7cd-4caf-be3f-607eab84647e", + "metadata": {}, + "outputs": [], + "source": [ + "ts_uni = ts['generation solar'].slice(pd.Timestamp('2015'), pd.Timestamp('2016'))\n", + "ts_multi = ts.slice(pd.Timestamp('2015'), pd.Timestamp('2016'))" + ] + }, + { + "cell_type": "markdown", + "id": "a1a81ffb-dca7-4ee5-93e9-836223ae6956", + "metadata": {}, + "source": [ + "### `module` Features" + ] + }, + { + "cell_type": "markdown", + "id": "adb031b1-74bc-4ed2-ae54-bc16681f0363", + "metadata": {}, + "source": [ + "High level API with features related to data processing, ML/AI, etc." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "79fd0990-dac4-461e-98b0-29f1e579b11a", + "metadata": {}, + "outputs": [], + "source": [ + "train, test = on.module.preprocessing.common.train_test_split(ts_uni, test_split=0.3)" + ] + }, + { + "cell_type": "markdown", + "id": "0641d9e6-c5b8-49a1-a3db-9df1927d62be", + "metadata": {}, + "source": [ + "### `context` Features" + ] + }, + { + "cell_type": "markdown", + "id": "2982c551-1607-40c8-911d-154a3493e4b1", + "metadata": {}, + "source": [ + "High level API with features related to a physical machine or process." + ] + }, + { + "cell_type": "markdown", + "id": "f7fe172e-588a-4f7e-aa0e-1cdc07dd8aca", + "metadata": {}, + "source": [ + "#### Profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "ad40696c-c5fa-4b5f-8f49-92c4fc0a9941", "metadata": {}, "outputs": [], "source": [ - "train, test = ts.split_after(pd.Timestamp('09-30-2023'))" + "profiler = on.context.common.Profiler()" ] }, { "cell_type": "markdown", - "id": "58a033a7-fa9d-417a-9796-5ea4c587c981", + "id": "0a304607-7ccb-4d2b-9796-dd80bce76b6c", + "metadata": {}, + "source": [ + "What does the common week looks like ?" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "bac9e224-c12a-4d7a-b7ca-db2a9acff278", "metadata": {}, + "outputs": [], "source": [ - "Make a plot" + "week_mean = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEAN)\n", + "week_median = profiler.profile(ts_uni, profiler.Period.WEEKLY, profiler.Aggregation.MEDIAN)" ] }, { "cell_type": "code", "execution_count": 70, - "id": "a3c4a5e4-6b63-4b15-b101-73a83dc16802", + "id": "e8474db3-5e1e-4d08-bdc1-30f88bfd5f28", "metadata": {}, "outputs": [ { "data": { - "image/png": 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wO9fwFW/zdi9aFnkOBDEHusQBADwoRpvLPQb8Kps/3gAgcSCCe0MvblhB6DnQTO86ZswY7N27F7VqqSNahg0bJtpes2bNEvfLz8+Pn/Zo7KqRRPlC1xz28uY54MSBj48Phg4dCjs7O52eA+HwXEUSB/oOKwDSQwtZ2SyiNC6NB/FAWkbF8R6kZ7K49HKaYoMgoLKH+IEsHFZIkvEcNK+n+yH+oJiRLrmZD+Q5sEA4cZCSkiL7NgaIPQeenp5wdHTk17mCSRz9+vUTrTs4OJS4XzY2NnxZ6YiIiHKb3IYwPLpiUO7evVtuBEJBQQHvFRMmJPPy8oKzs7No39q1awNQPxi52Qzh4eEV5neh77ACUJT5FQBOnToFALgTIz2F70aUQbpXLjhxDVCp1Mtdm2pvF8UcpBcZixsGcLADOr0ifvtvEARsmVPUEFOMk1fokRBCMQcWiDDuQFcmQqHnwMPDg0+XrFQqMXbsWNG+NjY2WLx4Mb+uOcygL1zth/z8fERFVaBfMSFLZmYmxo0bx6/PmzcPN27cQKdOnfi2xo0b4/lzA5WNMyIJCQlQvbxb+/n58e0Mw2h5D15//XV+mfMe5OTkaHnxrBWht6g4z0Hnzp355WPHjgEA/hGUZKgXULT8bwWaEXpEUHGxazNtD4DUsALLskhMUy+7uwAeroxoaMHRXjzU8CBetydGrpojeQ4sELnAP5VKhVOnTvEVFTnPgUKhgKurKwYNGoS9e/fi/PnzksmNPv30U8yaNQuffPIJ3nrrrVL1TThnWTNnAlE+YVkWUVFRpa6XsWrVKkRGFg2cNm3aFI0aNRJNq01KStJK521KIiMj8eWXXxZbr0QYbyAUB4DaO8fh7OzMew4AoG3btvzyyZMny9pdi2f//v2iwm7FeQ5q1qzJF3g7c+YMcnNz8U900UOri+CtWZ8EP9bCuZdpHxQKoEMT7e1iz4H6/wfxQMLLd8aQGur/6whq591/Io5dKG5YIeW5tHjwqyzZbHJIHAiQm7GwevVqdOjQAU2bNkVKSgrvOXB3d4dCoQDDMOjZs6eo1LIQpVKJb775BsuWLSvVsAJQ5DkA1OmYifLPzJkzUatWLfTt27dUnxdOj504cSKfE6BXr16YNm0av81cufXT09PRrl07fPvttxgxYoTOfXWJA+FvUSiSAaBjx478slQSJWvin3/+EeV9sLOzg5ubm45PqD0vnPcgOzsbFy5cEHkO2jYqemtOz6o4MQepL8sse7sBbs7angNP16IhA85zcOxq0fbOr6o3vi7wOrRqAFRyB5xfjjI/iFfHNjx+Jm1XqVLPbs7S/TEHJA4ECD0HwhvqF198AUB9k1q/fj3vORC+0Rgb8hxYH9999x0AYN++faWaWSAMnP3yyy9Fs2CE14u5ZrjMmzeP/x2Fh4cjK0s6vRzLsnxBMkBbHEydOpVfXrBggWhb3bp1+QJD+/btwxtvvCGqwWBNCG0EqIcU9Jn5JBxmOnv2LG6+HJWs4qnOCshRkTwHmS9LTTjLvKsplQw81ZPXBOKg6CHf6RX1/5P6Ac3rqUXBwvEMGIZBjZf19CIfA8FvsQh+S53oSJNkiamObUNL8WWMBIkDAUJ35eXLl/llYdGSQ4cO8Z4DzVzmxqRWrVr8jAXyHJR/cnNzReuleesVVgMV1gYBxGPR5vAc5ObmYs2aNaK2KlWqYMqUKXxymLi4OGzcuBE//vgjfvtNXevW0dFRqz7J8OHDsWDBAqxbt05rG8MwIu/BwYMHsXz5ciN8I/MjLPAGQO/ET8KgxGvXb+HtLuqHW4cmgLsg1lOfokLWQlaO+n9nR/l9uKGFpHS1gD3+0nPg4gg0q6dedrRncOkXBeJ3MHiljlqoCeMOUp4D+QXAsK9ZLN/KovEoFVZtU1//Up6D7i0sw2sAkDgQIRy/5CJ7c3JyROr8yJEj/BixKcWBra0tH89w7949nbMpCMtHM6iUK6RTEjjPgYuLi9ZwlbnFQXJyspYAevHiBVavXs3HQAwcOBCjR4/Ghx9+yO+zdu1aLc+Bvb09ZsyYgTFjxkieS/hmDFivZ034wgJA73tA3bp1YWdnBwD45+ZVrPxIgWMrFdg6TwF3l6L99KkbYA2oVCxecOJAxygvF5SYnqku0sTFG7zWELC1ET/EbQTrDYKkj/fpjyxuRgEfrmRRUMBKBiR2kx6ZNgskDgRUrlyZd8deuXIFmZmZiIiIkE2DaUpxABS5ivPy8mjGQjlHGEgIlM5zwIkDTa8BYH5xkJ4unx7u8ePHKCwsxIULF0Tt9vb2pQrYfffdd9GjRw9+XRiLYS08f/5cK8fJ3Llz9fqsjY0NH7MUGRmJFy9e8NtEngMziIPTN1h88ZMKj02YFT5boFld9PAcAID/oKJngHAoRopBHYt/+49Plp6tUDdAu81ckDjQoEOHDgCAwsJCnD9/Hv/++6/svlwyFlMhHEemoYXyjaY4iIqKwurVq7F+/Xq9Zi+oVCp+uq0wkJZDLn7GVOiaPpmVlSWZn6FevXqyFQZ14eTkhH379uHVV18FoC7EZG05D65eLYqG69KlC7Zt24aZM2fq/fnGjRsDUF83wgqNdrYMHO3Vy2kSbm5jUljIYsCXLBb/AfSbZbrzZhaNEuv0HLg5S7cHVdP98G9at/g+xD4V5zlwcwbWf8GUOHuuMSFxoIGwTvzZs2d1PoS5H5ypMMaMBZZlzRbNXpHRHD8GgClTpmDs2LH4888/i/18eno6LyKkPAe2trZ8u6WJg5SUFMnxcuH1XRq4hEiFhYV4+LCUdXMtFOF01LfffhsDBgzgY5D0QXivatmyJT7//HN+nfMemNpzkJZZlH3wWiQQFWdjkvNy8QaA7pgDf5kphUE+uo/PMAxmDte9z50YdSwCoI7/SNvHYHQvyxEGAIkDLYTBO/PmzcO8efNk9zW1ODDGjIVevXrBx8cHP/zwg0GOR+iHpudAyPvvv1/s54XBiFKeAwB8FH9CQoLJSxoXJw6kMjdqTlMsKcLU5NY27MblWAHAp2YvCZr3qvDwohrNXNxBehaQm8fiZhSL/IKyXy/HrrD4+wQLlUr6WKkanootJ13LfE59yBJ4DnQNK4zqyaCSu3Z7ceIAAKa/w2BoZ2B0T+kYhAlLi2zi5QaL8hhwkDjQoEaNGpK5CJycnNCkSRNRW/369U3UKzW1a9eGjY1aXf/xxx9a07pKSnJyMvbv3w8A+Oijj8rcP0J/dIkDLlOgLoTTGOXEARd3kJOTg4wM0/qMheJg/Pjxovn4qampRvUcANYXdyCsWKkZsKkPbdq0QdOmRRmPhH8fj5fi4HkWMGIBi8ajWHT9hMWQOSp8van40sNS3LjPouunLAZ/xWKbTG4qTXGw54KT9I4GRt9hhXqBDJ6FMVpJiYKKKbgEAK5ODLbMVWD9dEWx6ZC9daeqMBskDjRQKpWSD/1ffvlFVCIWUAdQmRI7OzvRdMtZs2ZplWLVJDk5GZcvX5b8gZe3qn3lmZycHEybNg3ffPMNnj17ptP2+sQcCMWB1LACYN6gRGFAYseOHXHu3Dl+Xc5zUFZxYM2eA11JovTBzs4O4eHhiIuLQ3p6umjmgzAo8U91hmWcugH8dRyYs4EVJf8RkpvHYt5GFu8vU+G5RgKlpVtYvn7Dx6v08xw8TbPh0xMbE32HFQD1G319jdAyKW+CLip76N6uy3thTkgcSKApAn755Re88847JRrjMxaTJ08WreuKcn/x4gVCQkLQvHlzrFu3Tmu75g1anzdWonSsWrUKS5YswezZs0WBZK1atdLaV5+/gz7DCuYUB8I3Uzc3N1HCMKmYAx8fH9Gbf2kQioP79++X6ViWBicONAu9lQSGYVCtWjW4ublBoSi69QunM0rxv8PaD/f8ArVnYO5GFj/tBDbtF28XPvgzXkASTXEAAAt/A65FGHcITDysULw7v57GDIKSDgFULkZMNK5leUMKgBnEQbNmzdC2bVu0a9cO7dq1w4YNG0zdhWLRfIPhirtMnDiRb/vpp59M2ieOyZMni3LIc/kYpDh69CifHW/8+PFa2zXfXoUPHMKwLFmyhF9ev349v9ynTx+tfQsKCop15ZbUc2DqLIm6xEFqaqpImL7//vvYs2dPqWYqCPH39+crOJ49e9ZqxC7Lsry9SuM1KA53mah8jm0ngZxc8fW4/SRw5mbRenSceLuwroCKheT1nCqRdGnl30CLCSzikownEPQdVuCo5F62h7dmOWgh301g8HZX2c1mxSyeg23btuH06dM4ffo0Ro8ebY4u6ERTHHCBUi1atMDmzZuxaNEis/a7TZs2/Bju6dOnZR8kwsyOUmiKg379+iEsLMwwnSREyL1tyIkDYVlwKfTxHAhLH5ckej8tLQ1Dhw7FlClTRA/YO3fuoFevXlixYkWxxxCKA3d3dzg6OvJvvELPgUKhwA8//CAaDy8tSqWSr9iYmJgoCrorzwgTShlDHHgU4zl4ngUcuSJuuxMrvucIUy+/yGHxr+Byy8pWz+vXRMpzAAAFhcAFI+axEg0r6CEOGgviP9uUIr2x3LDCdxMYfPEOo5VQyVIwzdyRUpKXl4e8vDxRm42NDZ/tyxBwNz/hTVAYc8BFfHPb3333Xa3PmhqGYdC6dWscOHAAT58+xb179ySrQQoL1gDa/RUGOQHAuXPn0L9/f/4t01revAyJ1PVSWrikWw4ODsjJyRFtq1KlCsLCwrRKfLMsi4yMDNHf1svLS7I/NWrU4JcjIyP17vO0adOwdetWAOqpvYMGDQIATJo0CadOncK+ffvQq1cvkRsfENtGGHPg4uIClUoFT09PZGdnIzU1lRe0Pj4+YBjGYNdajx49sHPnTgDAnj17ZIuhmYPSXjvCmQp+fn4G/126acQBTnwTWPEB8NthYOwidVtMvHjWgWZsQEpG0fe6GQVodvFmFAsfL7GgkCtZDAC5+fKzHMqK0HPg5FD8eXq/BgzsANx+APz0acn/ft4ywwq9Wxd/bkPebziEQ0q6MIs4GDFiBBiGQcuWLfHxxx/LZhrcuHEj1q5dK2obPHgwhgwZYvA+CX+ACoUCEyZMwIkTJ/Dtt98iNtbyCp2HhITgwIEDANRlXKWCIzUTOEVERIj2kxqXValUCAsLQ+fOnUU2IcSU1DZSP+6GDRvi4cOHsLW11RIHBQUFGDx4MMLDw2Fvb4/NmzcjLy8P4eHhOHbsmMhblJ2dLXmNCkX07du39b6Ohb+5/fv38w9Y4RDW/v37Jb0egNo2whiH9PR0xMbGwsVF/YqamJjIB116eXkZ9PfVqFEjfnnnzp0W6Zks6bVz7do1ftnZ2dng96PCPFcAXvx67apJiHuSBTbPEYC6JHT0ozTExhYJvodxlQAUjUckJOYgNlb9Nz960QWAeKjr9NUU1K0idhU8ivcCID19MfphMmJjy1bsYec5Z3z7hyfe7piBTwcW9f1JvDsADwBAVsYzxMbq9rACwBLBZVRS8xfm2AMomv/YsfELdG+aDSdk6n0sQ96LhS8NujC5OFi7di1CQ0ORkZGBRYsWYd68ebKFUkaNGoV33nlH1GYMz8GjR4/g7+8vUlSaRWMsjXbt2mHp0qUA1G5HqWyNwjSpgNrtKtxPznV9//59dO7cWcsmhPz1UhxSMxCaN2+OwMBA2WGhrKwsHD16FPXr18fXX38tuY9SqUTTpk0lg2UDAgLg6uqKjIwMxMXFlSqjZ7Vq1SQ/l5qaqtUutI0wQ2GDBg1ga2uLqlWrIiIiQuQNDAoKMmim0cDAQNSqVQv379/Hw4cPTZ7FVBeluXZyc3N5Lw6gtqWhv1OQv3i9d/tKCKxeCfUEz/JCxgOBgR78erZGWYfsfAe+X8kSAYibD3vhZqwXRvUA+rVTt+ULLvuWDVhcvFPkXlfaeSMwUDqWRl8+fVkl/McwD+w874FK7sDc0YCN4D2qRkAVGPsS0ZQe+5c6wc7WCZoCSorS3m8MgUHFwZgxY0SZvISMHj0a77//Pl55RV3r0tPTE1OnTkXPnj2Rm5sr+eZrZ2dnUCGgC4VCUa4ehMKEMdeuXUNsbKyWItSMCH/y5IloKqTcdLpz585h/Pjx5c4mpqQktsnLy+MreQqpU6cOFAqFTpfhjz/+KKoboEmNGjV0TqmtWbMmrl+/jpiYGBQWFpZ4xo1KpZL8njdu3JD9/gqFgo85cHR05Pvn5eWltW9oaKjBr7FKlSrh/v37eP78OViWLXOgo6EpybXz8ccf4+DBg/y6MR4Sni4sAEHtAH91Gt/KHkXtqZlid3RSuviaFW6Pide+np8kqf+F/wsM6KDeLy2zaL/VnzDoPa0ACanqR1J6lv7ubynUAY1F3+lxovpfv5niJEZuTgwUCuOO+Vf1EvfFwb7k38sc92KDnm39+vW4fPmy5D+prG/clzV19jZrICgoiBdO+/btQ3BwsNbMD01xIAxKy8vLk8xvDwCXLl3SivUgSo/cNEJOqOm6/iMjIyUfqhxSsSZCuLgAfVMKa3o4uMBHlUolCqq8fv26zuNw4kCY/Eg4Y4HDEIGImgiHKXUVgCoPnD9/XrRuDE9IocaznPs7ewmS82jGByRpmFUYXPjg5cQYGyUwtrd4v6cpQPbLmQ/cZ5RKoEktYO0nRfejtDKWjz6rI/1LjGDiTnF5DgyBp2kSPxock0qRqKgoREREoLCwEM+fP8f333+Pli1bSmYkJHRjY2Mj8gIA6rcMIZriQDhupasWfE5Ojs6CU0TJkJtGyD3Y/f39JbcD6oeyrmx/xYkDYapducRALMti4cKFaNiwodZwGjdlMi0tTSRiHjx4oHNGBfdQFooDKZFjDHGgOW2yPCO8dkaOHFnmRFFSdH4VcH0ZlPjfWUUC0FMwi0FYJIhlWdE6ALzIAfJejhNw0xgDqgLvddd+K3/28k/CiQNPF4BhADenouurrOLgzE39Xjj1ma1QVhQKBgPV9fwwteRFR82GScVBSkoKpk+fjg4dOmDw4MFQKBQ6axcQuqlXr55oXZgit6CgQMszIBQHxaWXpeyJhkNOiHEzYTZs2ACGYeDs7IyHDx8iISEBo0aN4vfTLNUrRFMgaqJPYqDvv/8eM2fOxJ07d/Dhhx+KtnHiQJhXgUPOe8CyLO85cHcvCtXWFAfe3t5GeRMWeg6KmxJqyRQUFPB2b9GiBTZu3GiUHPwergzu/JfBuTUM3u1W1G5jw/AJkoSeg6xsIFfCsZiaAaRmsEh/+WCvUU099a+FRsJZXhy83I97s3ZzKnJhlFUcnL5Z/D6A6bITbp3HIOJ/DBZPssxpi1KYNCCxefPm2L59uylPadVoTiUDisaInz59quWuFrqVhWmXq1SpgmfPnsHT05N/0zJ10hxrRk4ccDf6Nm3aICYmBi4uLvwDVJjASJcXpzjPgVBA/vrrr5g0aZLoAZOamopZs+Tr5eoSB+Hh4ejYsSNYlkV0dDQUCgVYlkVWVhZ/7Qk9B5wY4mjatKlxHnYCcVCePQeJiYm8HYXXgzGoXoVB9Sra7V6uQHqmWBxoDilwpGaIcwjUqKZ+az64FBgyh8Xhlxmbn6aqyzWna4gDF0fDiAOWZXHrgX77msJzAKjtUFveQWiRULRZOUYqIQqXSU3qgXTjxg3+ZiMUB2FhYUhLS8O2bdv4NhIHhkMfWwYEBIjerKtUKbpTCx9wV6+KE90XJw5ee+01Pnj1woULWuWgIyMjdcaXFCcOAOCTTz5BrVq1EBwcjFGjRone1oXiYPDgwaKqp61bt9bZ99IiHFYoz54D4XWjKaxMBRd3oM5joL536BIHwsyINaqphZ+HK4MBHYpE4LNUcXloThwoFYDby9mRaWWoE5byvKgcspDOr4rXFQrA3jTx7uUSEgflmGHDhmmlzuXGlaUK28TFxSEmJgYAcPNmkd+tYcOGcHd3F2XU0xWTQJQMqb9Fz549dX5G6k1RqVRqVQYV/s2ksLGx4ae8AsDChQtFHqXi5k+npKRApVJpJdQC1OKAZVls3LiRbzt16hTmzJnDrwvFgZubG06fPo05c+Zg7NixRqsEai3DCsJAVnOJA65ioEqlzpQI6BYHt6KLrq0aguqFVQWxqE9TxQGMwoA9LltjWTwHTyWcRT5ewKzhYi+Vi6Nllkq2FEgclGMqVaqEmJgY0c343Llz+Oijj7B48WK+TTj8cObMGahUKty6dYvfxiWnEXoiTF2ox5oRxgy0atUKTZo0wc8//6zzM0LPAYeHhwcYhuEDT8eMGaPX9KYePXqgZcuWANSiUBgBr5klUxOVSoW0tDRJz0FMTAyuX78uSpUMAJs2beKXhTEHAODg4IC5c+di7dq1ssnPyoq1BCRakucAKApKFIqDmgLn5W+HWczbVLQuFAdVBOLgWSqLpylF68IqhyURB7cfsJi9ToVDl8SZBhMEqZqnvgWcWsXg6joG7RqLz0UJYHVD4qCc4+LiIkoRO3PmTPzwww84e/Ys3zZixAh++b333sPmzZv5BEmhoUXJwp2cnPgbKw0rGI47d+4AUN/gz58/j2vXrumcoQBIew64v83y5cvx5MkTreyhuhBOJRbOSNAn81pSUpJIHAiHBoReA00cHByMks20OKzFcyD8DRo75kAOb4npjElpRW21BY6rP44ULXu6AiGCtCuanoNIgSat6Vv09s6Jg5w87WJPmgz+isU3/wW6T2Ux+KuifYWeg2reDNo1ZlCtkrqGwZBORdsyi0+MWKEhcWAFSAUmChk2bBhsbIpiT4UpZYXiAChyUz99+pRqKxiAxMRE3iVfkmlocp4DDl9f3xK5RIcMGcLHNOzYsYMXh8V5DgBtcdC7d9Hk9VWrVkl+pkmTJoiPj0fbtm317qOhsBbPgSUMK4hyHbwcCkhKFyRMkhjVcrQHLv7MwMWp6PoUew6AyMfCpEtF24Tlo4VxCZqkZ7K4K0g9vP0UkPyyXwkCr4SPxuzZd16nYQR9IXFgBQQFBck+KJydnREcHKxVwIejffv2onVOHOTl5Um6komScffuXX5ZmNWyOCpVqqT1N5VKIqQvDg4OGDhwIAB1Wm2uLoec50AoRI4dOyaKORgxYoRkX5YsWQKlUomaNWti7969Rhs2KA5r9ByYTRy4Fl2DyS+HEx4Lwk9qV9e+73w7jkFtf3G7qxPg8DL472mK2HMgFBjCCpFyQwvRcSzW79VuvxurnqmQkFwkPDTFwWshQPOXE3imDJA+PqGGxIEV4ODgIBu1XrNmTTAMgw0bNmD69OmibQEBAejcubOoTRjgps9bJaGb27eLas+WRBwolUqtUsxlEQcA+OqKAPD3338DKPoba+Yg4EofA8Ds2bOxd2/R3bh69eoYO3as1vEnTpyI69ev48aNG/D19S1TX8uCtXgOLGJYQTBGn/JcPQVx/0X1uqM90DpE+zN922i3MQzDew+epRWJA6VSHJvgXlTLiRcHuXks9p1XP/QTklmEjGDx2WrtIYcr94Dm41ks/qOoraqGOGAYBoeXMTj9I4MVH5AXQRckDqwEuWlhXIa8KlWqYOHChaKH/9ChQ7UC2oTb9Um3S+jm8uXL/HJJxAGg/UAoqzjo1KkTf4zdu3cjPz+fT3almYxIrp6Dp6cnbGxsMHnyZFFm0+DgYDg5OcHZ2RmOjibKLCODcIbEgQMH8N///teMvSkdp0+fxokTJwCovX9c0LCp0QxIvHinKIlR9xZA/cCi6YcA0LgWUNNP+qHLiYPENODfl7eWIB/A1kY75gAoEgczfmHR6wsW7aaw2H0OyM6V7uvXm1lc0cgXpuk5AAB3FwZtGzFQKkkc6ILEgZUgJw40cyGsW7cOSqUS1apVk5xKJoxfWLhwIdVYKAP79u0T1bsoaepbQ4sDW1tbdOigzuOamZmJc+fO8bUUqlevjpkzZwIAevXqhSFDhvBF0oRUrlwZgFpM7Nq1i38Q6yoOZWpsbGzg6lo0P27EiBGIiIgwY49KRnJyMt544w1+vbjpqsbEVzBT+sAl9UOao19bBk4ODM78yOCzocDIHsDmmfIPXC4okWWLHvCaMQtCcXD/MXDgIovlL4tS3n8CXLgtH6SoWf/BRll+6xpYAiQOrAR9E8p0794dDx48wD///COZRKlfv358St7Lly9j/fr1Bu1nRYFlWZHrvVevXlo5KYojODhYtG6IMXxhxsSOHTvyy/7+/vjmm28QFRWFXbt2wdnZGVeuXNGqiioc6ujWrRvu3buHXbt2YcmSJWXumyHRtFVxhaIsia1bt4rKrQunKpuaugHqugcAcOF20du8Ugn0fnnLCa3JYOlkBTbOUKBxLXlxUF8iU7aWOBA8zKesYNHjc7EY2HlG+xi6BICxKy5aMyQOrATNOgscUmPD/v7+sg8qZ2dnrF69ml+XK8FN6ObBgwd8Iil/f39s2bKlxMcYMEAcMVVWzwEgf52EhoaCYRgEBwfzJY4ZhtGKe9Bc9/HxQd++fc0+lKCJZqBlTk6OzJ6Wx6+//sovX758GW+//bbZ+uLkwKCmRPjIyg8YeLuX7MHb6RXt/esHitsquWvtIkLTOwAADWtotwFAQaF0O6EfJA6sBIVCgcmTJwMAOnTogHXr1mH37t1o1KhRiY8lrOSXkpKiY09CjitXrvDLo0ePLtWYcZcuXUTrtra2Ze6XlDiYOHGiKBeGkOLEQXnBkNdxYWEhfv31V+zfv99gx+SIjo7mk1SFhoYapWplSdF8+H4xDJg8oORv5O0aF3khAHX64jc1ZrpW8SjZMWePABoYvnYXARIHVsXKlStx5coVHDx4EGPGjBHNRy8Jwsh1EgelQxiIWNobvK2tLdq0KQr9Li6fhT7UrVtXtF6zZk389NNPsm/+mmKAizmwdCZNmiRaT05Oltmz5GzduhXvvfceevbsyWcaNRQ7duzgl4cNG2bQY5eWEA1x0FHCA6APrk4MhLXgmtQCfCuJj1VFT+eYmzNw6HsGX41gEBos3Z8l5agCoiVC4sCKUCqVePXVV2Fvb1+m47i4uPBJk0gclAyWZfHtt9+K0leX5e3v77//RteuXTFixAiDJBTSHIsv7pjl1XPwzTffYOjQofx6Wa7jvXv3YsaMGXwJdGE57ZUrV5a+kwKePXuGsWPHYurUqXyb5rCSuWhYQ/yQfa1kcbUiZg0vWv5sqPbDW1/PQesQ4PXmDGxsGIRqaOZK7sCZ1Qw+e6v0/SRMXLKZKB8wDAN3d3ckJyeTOCgh165dw5dffsmv+/j4lGnOv4+PDw4fPmyIrkkSEiIxUV1AeRUHXl5eWLBgAV+FUp/reOfOnVizZg0aNWqETz75BH5+fnjy5AnvgcvOzsaKFSv4GR4A9KptoQ+rV68WBf/Wr1+/2IqbpqJBkHjd3aX0b+SfDGFw7xEL/yrAW120tzs7Ak4OwAuNEJFm9YDLgsrlwtwIoeK4XbxaB2gTSl6DskKeA0IS7g2TxEHJ4Kpicrz22mtm6ok8n332Gb/cp08fnfuWV3EAQBR0W9ywQnZ2Nt577z0cPnwY33//PZo3b45nz55hz549/D6cl0AoDrjgzbKiOdWyX79+BjmuIWgYBNQLUC9/N6FsD11vdwZ/fa3AsikK2ZkElT3E6yd+YLBrgXjf6pWL1r3cxNv8ysfIl8VDngNCEk4cZGRkID8/3yDBcBUBzvXM8fXXX5upJ/J88cUXyMvLQ7NmzbRiEDQprzEHgDoZklKpRGFhYbEi99ChQ8jIKKojHB8fj3HjxmklhwIgKnmdn59vkL5mZopzBUvNMjIXNjYMwn9R5xloXKv4/ctKFQ8gNkG87luJwS+fA+OXqG3fVcdIHc1eNAwkDghJhKV2U1NTJQsBEdoIaxDs3bu3WLe9OahcuTJ++OEHvfYtz54DhmHg5eWFxMREJCcnQ6VSITExUTIV8bZt27TawsLCRNNHNctPA+K/d1kQ1oBISUkxyLRVQ+LixKBJbdOcSzMokUuBPK4PgyAfQKkAWjQQK4A6/kDEy9mrDuLUHEQpoWEFQhJh4BoNLeiP8GFRnt6y5dD8DuVJHABFM2+Sk5PRtWtX+Pj4iEpWA+oHc1hYGAC1t+Hjjz/mtwlrM6hUKhQUFIg+ayhxwJ1HWDa9oqIpDoRJjl5vzqBzU23XwPov1G1KJfDRIHIdGAISB4QkwrckEgf6IxxWsAZxoCkGhHULygOcOMjIyMDx48cBgM8HwjFt2jSkp6tLDvbv319yKIE7BleLgsPQ4qCiCwNAe8aCPqXJ2zZi8M8mBv/+ql0RkigdJA4ISYztObh//z5mzZpVrtLa6oO1eQ40xYE+N2pLoriU1VeuXMHatWsBqKfwfv311zrLIwurbAIkDoxBpRJmXuQICWZQS6KENFE6SBwQkhjbczBp0iQsWLAAbdu2FQV4lRVDHqs0cA8LrkJheaek9SAsDc1S1BzcNb106VK+7ZtvvkFAQECJxEFaWlqZgxJzc3ORnZ0NwDD1M8o7NoaZAEKUERIHhCTG9hwcOXIEAJCVlYW4uDiDHHPp0qXw8fExa7EoThxYg9cAABwdHdGuXTsA4imQ5QU5cRMZGYmHDx/ir7/+AqD+e40fPx6AdjVMIZriACh79kVhMCJ5DghLgcQBIYkpAxKvXr1qkON89913ePbsGRYtWmSQ45WUwsJCJCUlAbAecQCop/ldunRJlPWxvCDnOYiMjMT+/fv5nAWTJk3iU0iXxHMAqOMUyjK8IAx6JHEA9G9ftLxgPA0TmAsSB4QkxhxW0HTDGkIcsCzL32QfP35sluGFlJQU/rzWNPXTwcEBzZs3N1g2QFMi5zmIiIjghRwgTnHt4eGhVaqaQ1gzg+PChQu816E0kDgQE+jD4OBSBj98xODjwebuTcWl/P3aCZNgTHEgvBkChhEHL168gEqlAqDOdvf8uURtVyNjbcGI1sDrr78u2R4ZGSn7UGYYRqf3QIqdO3dqZcfUF+GwAsUcqOnWgsEHAxk42pPnwFyQOCAkMeawgqY4OHjwIP9gLy2aGebi4+PLdLzSYG3TGK2BWrVq4e+//9bK8KlLHAAoNpi0Ro0aWm0rVqwAoL72SiJOyXNAWCIkDghJXFxc+GVDv4VrioPc3Fx07doVOTk5Mp8oHmHqW8D44mDu3LkYPHiwKJhS6DmwpmGF8s7AgQMRERGBkydPIjhYXaXn7t27okBCzYeycOjLz89PtG3SpEl80iQh//nPf7Bs2TIEBQWhbt26ePr0qV79I3FAWCIkDghJbGxs+LcnLkGModAUBwBw/PhxrFu3rtTH1BQHCQkJMnuWnTNnzmD+/Pn4+++/MXPmTL5d+DAgz4FlERQUhPbt26NRo0YA1MNQV65c4bfrEgfVqlUTbevVqxdCQkJw/PhxhIWF8VU48/Pz8dlnnyEvLw8JCQn4/fff9eobiQPCEiFxQMjCZcMztDgQjrG2bduWX16yZEmp54ybcljh1KlT/PLmzZv55QcPHvDLcln2CPPCiQNAHbgKALa2tvxMBQ5hQKvm1MYOHToAADp27Ig+ffpg+vTpqF69uta5hBUddSEUBxRzQFgKJA4IWbigRGMOK4wdOxY9evQAADx8+BC7du0q1TFNOawgHD4QDr9ER0fzyzVr1jTa+YnSIxQHHJ6enlqZH1etWsUvz5s3Dy1atAAADBkyRPQ3B9TxCdu2bYOvr6+o/dSpU3oJa8pzQFgiJA4IWThxkJGRIaphX1Y03ajvv/8+v37x4sVSHdOU4kA4BU74VslFq9va2mqNUxOWgZw40KR3797YsmUL9u7di6ZNm2Lfvn3Ytm0bn2pZkxYtWuDmzZtYvnw5WrZsCQAoKCjAwYMHi+0TDSsQlgiVbCZkERbZycjIMJjLU9ONKnzL/ueff0p1TFMOKwjFATePnmVZ3nMQFBQEpZJywFoiwcHBcHJywosXL/g2qQeyQqHA0KFD+XVvb28MGDBA57G9vb3x8ccfo3bt2ujduzcA9TTdIUOG6PyccDYQiQPCUjC452DBggXo168fmjVrppUwRKVS4fvvv0fHjh3RrVs3/O9//zP06QkDIhQHhow70HxT8vX15W+KpRUHpvIcsCwrqsyXm5sLQD3UkJWVBYCGFCwZpVKJhg0bitoM/UAOCQnhl8+cOYPjx4/rjKUR1uNwcnIyaF8IorQYXBzUqVMHX375paRbddu2bbhy5Qq2b9+OdevW4bfffsOlS5cM3QXCQAgTIekbd/Ds2TOsWbMGc+fOlc05rznGyjAMQkNDAQBxcXEICAjAsWPHStRXU4mDxMREkZeCO68wAQ43XY6wTIwtDvz9/fmZPmfPnkXnzp3x+eefIzU1FZ9//jm+/fZb0f5cfgya/kpYEgYfVhg0aJD6wDbah963bx/effddeHl5wcvLC/369cPevXv5YB9N8vLykJeXJ+6wjY1satPSwCXfKWsSHmuCs4XQc5CamlqsjZ48eYLQ0FDey/Ds2TP8+OOPWvsJPQfu7u5QqVQICQnhZwE8evQI8+bNQ8eOHfXus6Z4SUtLQ1ZWllYUelmJiIgQrWdkZEClUuH+/ft8W40aNSrk9VRefkt16tQRrXPXoCGpX7++yHO6cuVKREVFYc+ePdi2bRt69+6Nxo0bo7CwkBfRlStXtnjbGYvycu2YGmPYRd806CaNOYiOjkbt2rX59Vq1auHMmTOy+2/cuFErAGjw4MHFjuGVhkePHhn8mNZEZGQkWJaFl5eXVrQ2x4YNG0TDDwcOHEBsbKzWflw+ABsbGyQmJiIpKUky0jsmJkYrilwOKU/B1atXJaeYlQXNobLnz58jNjZWNGfe1dVV8ntXFCz9t6RZjEmhUBj87xUQEKB1rQinNp49exYeHh5ISkrip026uLhU6OsGsPxrx1wY0i5S2T2lMKk4yM7OFqUldXZ2FgUGaTJq1Ci88847ojZjeA4ePXoEf3//cllYxhhwNhE+WMPCwhAWFgZfX1/cuXMHrq6uWp8LDw8XrT948ACOjo68u3TTpk1IS0vjXfGenp4ICgoCAHTu3FnreI6OjjrL5xaHQqEweL4BLsZAuO7t7Y2dO3fybe3bt6+QeQ7Ky2+JK0HNERQUZPC/V/PmzbF9+3bZ7Q4ODggMDBQNUQUEBFTI6wYoP9eOqTGnXUokDsaMGYMbN25Ibhs9erRoSpoUjo6OfNAWAGRlZekMwLGzszOoENCFQqGgi1ID4ewELl1sXFwcDh48qOW9ycnJESUH4njttdfwv//9DwkJCRgzZoxom6enJ2/zVq1aYfz48fjll1/47ffu3dPKTieH5mwFQO2hMPTfVFg/gWPJkiX8G1+3bt1Qv359g56zvGHpvyWh9xJQexIM3d9atWrp3J6SkgKFQqE1LdaS7WYKLP3aMRfmsEuJxMH69evLdLLg4GDcv3+f/3FGRUVR8JYFIwxIFHLt2jUMGTIEx44dw+bNmzFlyhSkpaUhOzsbgHpKFzeOGhMTgyFDhqBSpUpaxxEGYDEMg//85z945ZVXMGnSJADq/Pf6xh1IiQNjBCVKpWUWDn0tXrzY4OckDItmESZjZCVs3749HB0d+d+EJtzvgyp5EpaKwaVIfn4+cnNzwbIsCgoK+GUA6NGjB3799Vekpqbi0aNH2LlzJ3r16mXoLhAGQk4cnD9/HgDQpUsX/Pe//0W/fv3w66+/8tu//vpr0f5PnjyR9DiNGjVKq0341n337l29+6o5WwEwjjiQ8hxwMRR16tRB48aNDX5OwrjoGtosLVWqVMHp06fx66+/Sl4TnDgQXk80W4GwJAwuDiZPnow2bdrg4cOHmDJlCtq0acPfpAcNGoSmTZuif//+GD16NIYNGyY7U4EwP8LZCkLCw8NFRYbi4uLw559/AlC7aEeNGlVs0GjHjh0lxUGDBg345Tt37ujdV1OJA12V9po2bWrw8xHGgZtOaGtriy5duhjlHE2bNsW7776LLVu2YPr06Thw4AC/jRtOIM8BYakYPCBROGasiUKhwGeffYbPPvvM0KcljICc5+DFixfYtm2bqI2bcjp69Gg4Ojpiy5YtqF69OpYtWybab/bs2UhNTcXs2bMlZyJUrlyZH5YoiTjghhXs7Oz4vpA4IOT49NNP4eXlhXr16ukd11Ja6tWrh4ULF4qGGMhzQFg6lD6ZkEVOHABqD5EUI0eOBKCOIWjVqpVom729PebMmVNsauGQkBCcPHkS8fHxSEpKkoxX0ITzHPj7+yM2NhYFBQUGL9ucn58vCiDThMRB+cHBwQETJ0406Tnt7e3h7OyMrKws8hwQFg+FhRKyaIqDuXPn6swmxzCMKBJccz5tnTp19Ko5IByjvXnzZrH7syzLiwN3d3d++qOhPQfCG7kUr7zyikHPR1gf3O9HynNA4oCwJEgcELLY29uL1t98803MnTtXdv9q1aqJpp5qzkTRd4qfsHKePuIgJyeHrxrp6urKu4mfPXtm0GqSwiEFYb4OQD09TpenhSCAopkRycnJUKlUvOB0cXExeDZPgigLJA6sjKCgIKxYscIgx9KMCQgNDcWECRNkvQcBAQGidc399BUHJfUcCIMRheJApVJJzi4oLcJhCs258jSkQOgDl51RpVIhLS0Njx8/BgD4+PiYs1sEoQWJAwugY8eO+Pjjjw1yrPDwcIwfP94gxwLUQwm2traYP38+lEol7O3tZWciaIoDTXGhmdNejoYNG/IJP+SSbgkRvtFXrlxZFGBmyKEF4Xk0Ky+++uqrBjsPYb0IcyrcuXOHD6QtLmkSQZgaEgflAC5nhD5UrlzZoGVf58yZg6ysLHz55Zd821tvvSW5r6Y4ACCqw6BvTm9HR0deSNy+fRs5OTk69xeWUPb19RW9hRlLHJDngCgNwroOFy5c4JdJHBCWBokDMzNy5EicPHkSK1euBMMwYBgGmzZtAsMw2L9/P5o2bQp7e3ucOXMGUVFRePPNN1G1alW4uLigefPmOHLkiOh4msMKDMNg3bp16N+/P5ycnFC7dm0+FbK+aGaU69Chg2SOAilxsG3bNri5uaFPnz5asxd00bJlSwDq2gW///67zn2F4sDPz89ongPhschzQJQGoefg888/55c1xSZBmBurn8rYrFmzYqe0FRYW6hVFXxJ8fHy0qrJJsXLlSkRERCAkJITPLHj79m0AwPTp07F06VIEBwfD09MTjx49Qs+ePfHtt9/C3t4e//3vf9GnTx/cu3dP8sHMMW/ePCxevBhLlizBqlWr8M477yA2NlarOp2+MAyDDRs24OeffxYFLUr1oVu3bkhOTpYs4a2LSZMmYfPmzQDUNT0qV66M3r17S+ZGiIuL45f9/Pz4jJyAYcUBNz4MiJM11axZ0ygpeAnrQy5ehzwHhKVh9eIgISFB9GZpabi7u8POzg5OTk68O/zff/8FoE5D/Prrr/P7enl5iYL15s+fjx07diAsLAxTpkyRPcfIkSPx9ttvAwAWLFiAH374AZcuXcIbb7xRpr5rFsWSEyglFQaA2nPQtm1bvqR33759cfToUcnqjZrDCsLa54bMdcCVTWUYBo0bN4afnx+ePHmCnj17GuwchHXTvn17yXYSB4SlYfXiQJ8oYGN5DspKs2bNROuZmZmYO3cu9u7di/j4eBQUFCA7OxsPHz7UeRzh1EBnZ2e4ubkZNIqfQ5f3ojQsXLhQVF73xIkTxYoDPz8/UXyGIT0HnDioVKkSHB0dcfLkSZw/fx79+vUz2DkI6yYwMBAJCQkIDAwUlf/mSpcThKVg9eKgONe+SqVCbGwsAgMDLa5UqOZc+qlTp+Lw4cNYunQpatWqBUdHRwwaNIhPFyyHZswAwzCit+uyMHHiRPz8888IDQ0t9TCFHG3btsX169fRpEkTAEXDLZpwwwpKpRJVqlQR5TYwhDi4fv06fvrpJ/5YXExDzZo1tWIPCKI4KleujGXLlomyjJqqND1B6ItlPQ0rKHZ2dnol6zl79ixGjhyJ/v37IzQ0FD4+PoiJiTF+B3WwbNkyhIWF4ciRI5LxAGUlJCQEDg4OAOTFAec58PHxgVKphJ2dHZ9y2RDiYNy4caKaIcbOxU9YP2+++Sa/3LdvXzP2hCCkIXFgAQQFBeHixYuIiYlBUlKS7Ft97dq1sX37dly/fh03btzAsGHDDOYBKC2Ojo7o06eP0YrGKJVK1KtXDwAQGRmpNa0xPz+fHyLx8/Pj27lhnfj4eFGAYmnQ9D6ROCDKip+fHzZu3IgBAwbgm2++MXd3CEILEgcWwNSpU6FUKtGgQQNUrlxZNoZg2bJl8PT0ROvWrdGnTx907969Qkyha9iwIQD1ENC9e/dE24QPf19fX76de4Dn5eUhNTXVoP0hcUAYgpEjR2Lbtm0IDQ01d1cIQgurjzkoD9SpUwfnz58XtXHVDYUEBQXh2LFjojbN6oiawwxSb81paWml6qe54MQBoB5aaNy4MfLz82Fra6sVjMihmeugtPEQwqAxqWMTBEFYI+Q5ICweTXHwv//9D25ubnjvvfdw//59fltgYCC/LHyAl2U64/Pnz7XaSBwQBGHtkDggLJ6QkBB++fbt23j33XeRk5ODX3/9Ffv27eO3CQs7+fv788sRERGlPreUOKAiOQRBWDskDgiLJygoiK8XoTljYcuWLfyyUBy88sor/HJ4eHipz52eni5ab9eunSi2gSAIwhohcUBYPAqFgn/wC4cRhNjb24sSyTRp0oRPbFUWcSD0HIwYMQJHjx41ypRNgiAIS4LEAVEuEMYdSFGnTh1RlksnJyd+OOLOnTvIysoq1XmF4kDzHARBENYKiQOiXFCcOOByIQhp0aIFAPUUyKtXr5bqvMJhBTc3t1IdgyAIorxB4oAoF5RGHDRv3pxfvnjxYqnOK/QckDggCKKiQOKAKBcIZyxwCLMyCoUAR+vWrfnlo0ePluq8QnHg7u5eqmMQBEGUNygJElEuCAgIQGhoKP755x++LTw8HGvXroWDgwN69+6t9ZkGDRrA19cXcXFxOHnyJHJycvg6DfpCwwoEQVREyHNAlAsYhsG3334ravP398f8+fMxa9YsyRkEDMOgW7duAIDs7GycO3euxOelYQWCICoiJA4sgI4dO+Ljjz822PFGjhyJfv36Gex4lkLv3r3RvXt3AMDQoUP1mlLIiQMAOHToUInPScMKBEFURGhYgSg3MAyDHTt24MaNG2jWrJlen+nQoQO/fOPGjRKfk4YVCIKoiJDnwMyMHDkSJ0+exMqVK8EwDBiGQUxMDG7duoUePXrAxcUFVatWxfDhw5GUlMR/7u+//0ZoaCgcHR3h7e2Nrl27IisrC3PnzsXmzZuxa9cu/ngnTpww3xc0MI6OjmjVqhVsbPTTtdWqVeOzK0ZHR5f4fDSsQBBERcTqPQfNxqmQkKJjBxYoLPSDUgmAURnsvD5ewOW1xWuvlStXIiIiAiEhIfj6668BALa2tmjRogXGjh2L5cuXIzs7G1988QWGDBmCY8eOIT4+Hm+//TYWL16M/v37IyMjA6dPnwbLspg6dSru3r2L58+fY+PGjQBQ6oqE1gDDMAgODsatW7cQExODwsLCEiUy4sSBnZ0dHBwcoFIZ7hohCIKwVKxeHCSkAE8Si9vLfGZwd3eHnZ0dnJyc+II+33zzDV555RUsWLCA32/Dhg3w9/dHREQEMjMzUVBQgAEDBvCVCIU14R0dHZGbm0sFgl7CiYO8vDzExcWJijJxnDlzBsuWLcOYMWPQq1cvvp0bViCvAUEQFQmrFwc+xb00s0BhYQGUShvAgCnziz2vDm7cuIHjx4/DxcVFa1tUVBS6deuGLl26IDQ0FN27d0e3bt0waNAgeHp6lqHH1ktwcDC/HB0dLSkOhgwZgvj4eOzYsQOFhYVQKBSIjY3FgwcPAJA4IAiiYmH14qA4175KpUJs7BMEBgZCobCMEIzMzEz06dMHixYt0tpWrVo1KJVKHD58GOfOncOhQ4ewatUqzJo1CxcvXkSNGjXM0GPLRlMcCIMUAaCgoADx8fH8+v379+Ht7Y1GjRrxbTRTgSCIioTVi4PygJ2dHQoLC/n1V199Fdu2bUNQUJBs4B3DMGjTpg3atGmDr776CoGBgdixYwc+/fRTreNVdDTFAQDcunULrq6uCAwM5L0DHFeuXIFSqRQFI1aqVMk0nSUIgrAALONVuYITFBSEixcvIiYmBklJSZg8eTJSUlLw9ttvIzw8HFFRUTh48CBGjRqFwsJCXLx4EQsWLMDly5fx8OFDbN++HYmJiXxZ46CgINy8eRP37t1DUlIS8vPzzfwNzYumODh06BBCQ0PRoEED3Lt3D//++69o/8uXL+PMmTOitmnTppmkrwRBEJaAwT0HCxYswKVLl/D48WP8/PPPovno//nPf7BhwwbY2dnxbadPnzZ0F8odU6dOxYgRI9CgQQNkZ2fjwYMHOHv2LL744gt069YNubm5CAwMxBtvvAGFQgE3NzecOnUKK1aswPPnzxEYGIjvv/8ePXr0AACMGzcOJ06cQLNmzZCZmYnjx4+jY8eO5v2SZiQoKIhfPn78OC8GXrx4gQ8//BCvv/66aP/Lly8jMzMTgNpDk5KSAg8PD1N1lyAIwuwYXBzUqVMH3bp1w/z58yW39+7dG7Nnzzb0acs1derUwfnz57Xat2/fLrl//fr1ceDAAdnjVa5cuVTZAK0VR0dHdO3aFUeOHEF8fLwovuDQoUNaUz0vX76M3NxcAOpqkCQMCIKoaBhcHAwaNEh9YD2T1OgiLy8PeXl5ojYbGxuR56GscPPWaf56EdZok02bNqFVq1Z4/Pix1rYtW7aI1l+8eMEvt27dWmQHa7SNoSDb6IbsIw/ZRhpj2EXfwHuTByQePXoUJ06cQNWqVTF27Fh07txZdt+NGzdi7dq1orbBgwdjyJAhBu/Xo0ePDH7M8o612aR///5YtWpViT5Tt25dxMbGarVbm20MCdlGN2Qfecg20hjSLvrOaGNYlmUNdlYBAwcOxIwZM0QxB9HR0XBzc4OHhwfCw8Mxffp0rF69GiEhIZLHMJXn4NGjR/D397eYqYzmxlptcuLECXTp0oVfr1atGhISEsD9BLy8vJCSIk6nGRkZKQpotFbbGAKyjW7IPvKQbaQxhl2M4jkYM2aMbPGa0aNH4/3339f5eeFN9rXXXkP37t1x8uRJWXFgZ2dnUCGgC4VCQRelBtZmk9atW4vWFy1ahDp16iAsLAzu7u548803Ua9ePX67j48PatasKVn90dpsY0jINroh+8hDtpHGHHYpkThYv369QU9OFwFhShwcHKBQKPjxu/r166NZs2Zo2bIlv09wcDCfC6Ft27Z6lYUmCIKwNgz+dM7Pz0dubi5YlkVBQQG/DAAnT55EZmYmVCoVwsPDsX//frRt29bQXSAIWfbs2QNnZ2e88cYbePXVV7W2C4fB2rRpY8quEQRBWAwGD0icPHkyrl69CgCYMmUKACAsLAy+vr44cOAA5s6di8LCQvj6+mLWrFlo3LixobtAELL06NEDqampsLW1ldz+xhtvYOvWrVAqlXzeCIIgiIqGwcXBL7/8Irtt4cKFhj4dQZQYOWEAAO+99x6cnJzg5+eHunXrmrBXBEEQlgPVViAIAUqlEkOHDjV3NwiCIMwKRQQSBEEQBCGCxAFBEARBECJIHBAEQRAEIYLEAUEQBEEQIoyWPpkgCIIgiPIJeQ4IgiAIghBB4oAgCIIgCBEkDgiCIAiCEEHigCAIgiAIESQOCIIgCIIQQeKAIAiCIAgRJA4IgiAIghBB4oAgCIIgCBEkDgiCIAiCEEHigCAIgiAIESQOCIIgCIIQQeKAICSgkiPSFBQUmLsLBEGYABIHFZCUlBRzd8Fi+fvvvwEADMOYuSeWx2+//YYVK1YgNzfX3F2xSDIzM83dBYIwGFYtDo4cOYIZM2bg1q1bAACVSmXmHpmXffv2YcCAAViwYAGWLVuG58+fm7tLFsPevXvRs2dP7N+/H5mZmRX+WhGyb98+9OjRAytXrsS9e/dgb29P9hFw4MAB9O3bF7Nnz8by5cuRlJRk7i5ZDEeOHMG4ceNw4cIFAHQPFmLpzycbc3fAGOTn52Pr1q3YvHkzAgICcPjwYYSEhEChsGotJEtmZiaWL1+Oy5cv45NPPkFwcDBGjhyJevXqoWfPnmBZtsK+KWdkZGDBggU4e/YsFi5ciDZt2pi7SxZDQkICPv30U2RlZWH+/PmoWbMm3nrrLaSlpcHDw8Pc3bMILl26hHXr1mHGjBnw8PDAmjVrsGbNGowYMQKBgYHm7p7ZKCwsxO7du7Fu3Tr4+/tj27ZtaNWqFRQKRYW+3wDl5/lkWb0xECzLwtvbG19//TUGDx6MhIQEnDhxgt9W0WAYBk2bNsXOnTvRsWNHeHh4wM3NDXFxcfz2iopKpUJubi6GDx+ONm3aoKCgAGfPnsXjx4/N3TWzo1Qq0bdvX+zatQvNmjVDWloaatSogbt375q7a2ansLAQAHDz5k20bNkSr732GurXr49x48YhNjYW27dvN3MPzY+Pjw+mTZuGCRMmIDc3F9u2bQNQMe/BQsrL88lqxMHJkyeRkJCAnJwc2NnZoUWLFmjVqhVatWoFf39/nDx5EhkZGWAYxqL+AMZCaA9nZ2d06tQJDMPg8OHD6N69O7y9vcGyLM6dO4f4+Hhzd9ekcLbJzs6Gu7s7unXrhqioKHz66afo1asX/vrrL4wYMQKbNm1CYmKiubtrUoS2qVy5Mt566y1+m7e3N549e8Y/GC3NDWoKOPvk5+cDANLS0hAVFcVvb9CgAZKSknD16lVcuXLFXN00C6mpqfyyUqlEaGgo2rdvj5CQELRp0waHDh1CamoqFApFhbt2yuPziWEtpSel5M6dO/j888/h7OyMSpUqwd7eHsuXLxftc+HCBezevRtNmjTB4MGDoVKpLM6FYyiKs8eFCxfg6+uLgIAA3L17F3/++SeqVKmCSZMmWb0HQdM2dnZ2WLFiBVQqFRYvXoy4uDh88MEHqF27No4ePYq9e/eiU6dO6NOnj7m7bnSKu24KCwuhVCoxc+ZMODo6Yvbs2WbsrenRtI+trS1WrlyJtLQ0dO/eHZ9//jm6d++O69evY/v27QgICICfnx+GDBli7q4bncuXL+Orr77CK6+8gunTp8PV1VVrn+joaKxfvx6+vr6YPHmyVd+DhZTn55P5e1BGTp8+jW7dumHr1q2YM2cOYmJisHr1aqSlpfH7NGnSBLVr18bVq1eRkJAAhUKBrKws83XaiMjZg5uh0KpVKwQEBKCgoAD169dHtWrVcP/+feTk5Ji558ZH0zaxsbFYuXIlCgsLMXbsWMyYMQO1a9dGYWEhunTpAjc3N9y5cweAZbn7jEFxvyNurLhmzZpgWRbZ2dnm7bCJ0bTPw4cPsXLlSnh4eGDOnDk4dOgQpkyZgu+//x4jRoxAYWEhH/BrzdfO/fv3sWHDBrz22muIjIzEzZs3Jb9vQEAAOnTogKtXr+LBgwdQKBQVIiC6PD+fyr04OHHiBHx9fQEAVatWxZdffonw8HBcu3aNd105ODigVatWqFSpErZu3Yp58+Zh8+bNvGvQmpCzx40bN0SuPBsbdSyqk5MTlEolHB0dzdJfUyJlm6tXr+LMmTPw9vZGtWrVAKhdogDg6enJe1Os3atS3O+IYRgwDAMXFxfcv38fjo6OVv3Q00Tu2jlx4gR69uyJNWvWYMaMGdi5cyeaNGkCW1tb2NnZAbDua6dWrVro06cPZs+ejTZt2uDvv/9GcnKy1n42NjZo0qQJmjZtil9++QVz587FkiVLrP6lpDw/n8qtOODGPVu3bi0a22vatCkaNmyIY8eOid5u6tWrh+joaPz6669ITk7GO++8A1tbW5P321joY48XL14AAD+O/vvvv+PPP/9Et27dTN9hE6LLNiEhITh27Biv1Lm3mT/++APHjx9Hly5dTN9hE6Lv74gTAp07d0ZsbCwiIyOt+qHHUdy1c+TIEWRmZsLGxga1a9cGAGzcuBFnzpxB69atzdJnU8FdE6+//joAYPz48YiPj8epU6ckk2VVqVIFjx8/xpEjR5Ceno7PPvsMDg4OJu2zqbCG51O5FQfc212DBg2Qn5+PS5cu8duGDx+OU6dO4dmzZwCA9PR0zJ49GzExMdi8eTN++OEHuLu7m6XfxkIfe3Ci4Ny5cxg4cCD27NmDBQsW8D9ua6Uktjl79ix69+6N3bt3Y/78+WjatKlZ+mwq9P0dcUIgOTkZQ4YMgZeXl1n6a2qKs8/p06f5ayc6Ohqff/459u7di6+++gq1atUyS59NBXdN2NjYoKCgAI6Ojhg8eDDCwsLw6NEjkacyLy8PixYtwpUrV7Bp0yYsX77caqbDckJI+H2t4flk0eIgISEBmzZtwokTJ0RZ2ViW5V0u9evXR9WqVXHw4EH+j+Tj44PatWsjPDwcAODs7IyxY8di7969aNCggem/iIEoqz24C7RLly6YMWMGfv/9dzRq1Mj0X8QIGMo2bdu25W0TGhpq+i9iBMpqm8uXL/OfqVevHiZPngxvb2/TfgkjYqj7TGBgICZOnIi///67Qlw7Qu8AN0w5cOBA2NnZ4fDhw1AoFPwQg62tLcaMGYODBw+iYcOGpv0SRoBlWWRlZeHLL7/ks6oKgwit4flkseJg5cqVeOutt5CQkICff/4ZS5YsQXp6OgC1YuVcLnZ2dujUqRMSExOxevVqAOqkPwqFAs2aNQOgvnDLe0ISQ9ijefPmAAAXFxfeNtaAIW3j6upqVYmQDGEba/aeGPI+Y2dnh5o1a5rnixiB4mzDCQIuJwj38Js6dSoOHz6MKVOm4I033kBERAQYhkGlSpXM80WMAMMwSEpKwvHjxxEeHs7n/uCGE6zh+WSRGRL37NmDxMRE/Pbbb6hevTpOnDiB1atXi8Y4t23bhu+++w5jx47FmDFjYGdnh2nTpuHhw4e4evUqWrZsyQeClHfIHvKQbeQxpG2sMasdXTvy6GubxYsXY/jw4ZgyZQovFu7evYuYmBgEBwdj586d8PPzM9fXMCoxMTGoWbMmAgMDcfDgQdSvX58fTti+fTsWLlxYvq8b1kLIz8/nl1NSUtiMjAyWZVn2ypUrbJ8+fdg333yTvXr1KsuyLBsfH88OHz6cPX/+vOgY8fHxbHh4OHvt2jWT9dtYkD3kIdvIQ7bRDdlHHkPY5tKlS+y7776r1V7eEdqGW46MjGQXL17M7tixg506dSp74sQJlmVZNjo6mn3vvffK/XVj9iRIqampvCKtVasW+vfvz08Bio2NxapVq1C7dm20bdsWp06dAsMweOutt/hgFpZloVKpeMVW3iF7yEO2kYdsoxuyjzxkG3k0bTNgwAB+yGD37t24desWPv74Y/z0008AgKFDh8Ld3R0uLi4AyrdtzBpzsGfPHrz11lv8NKA9e/Zg0aJF/PaAgAAsXrwYEyZMQMOGDdG8eXNER0fzAVKFhYVgGKZcGl4Ksoc8ZBt5yDa6IfvIQ7aRR8o23333Hb89KCgICoUCjo6OaNGiBS5evIjhw4fj+PHjAKzANuZyWWRkZLCrVq1id+3axbfdvXuXHTBgAJuSksKyLMuqVCqWZVk2NzeX/79Hjx7svn37TN9hI0P2kIdsIw/ZRjdkH3nINvLosk1ycjLLsiy7e/dudtGiRezNmzfZ/v37s3379mWnTJnCxsfHsyxbZLvyikkDEp8+fQqGYVClShU4OjqiU6dOqF69Or89PT0d7u7ufLY+LviFc3HduXMH1atX55ONlHfIHvKQbeQh2+iG7CMP2UYefW3j5OQEAKhduzbmzZuH06dPY/LkyfDx8cFvv/2Gs2fPYuDAgeU+gNck4iA/Px9z5szB9evXUblyZbRr1w69e/fm57uyLyOh7e3t4eTkxEe9AkBKSgpOnjzJp7mdOHFiuU8uQvaQh2wjD9lGN2Qfecg28pTUNtwwgbe3N5YtW4YmTZrwxaacnJxQp04ds30XQ2KSmIMDBw4gPT0dYWFhGD58OB4/fowFCxZo7Xf06FH4+vqKLkwvLy9ER0fDxcUFu3fvxtChQ03RZaNC9pCHbCMP2UY3ZB95yDbylNQ2XEBipUqV0K5dO7i6uvLZEa1FGABGFAc5OTl87u379+/Dzc0NNjY26NKlC8aMGYOYmBj89ddfANTKjWVZ3L59m8/zf+DAAWzfvh0A8OGHH+KLL77gI0DLI2QPecg28pBtdEP2kYdsI48hbLNz507+eJZQYtnQGHxY4eHDh1i6dCmcnJzg6OiIadOmwdXVFUqlEhkZGXB1dYW/vz/GjBmDNWvW8Ok2X7x4AQ8PD6SlpeGjjz7CP//8g2nTpgGA2QtQlAWyhzxkG3nINroh+8hDtpHHGLaxVgwqd3bu3ImJEyeiTp06ePfdd3Hv3j2sX78etWrVQnh4OJ4+fcrv27FjRwQHB2Pbtm0A1EVLTp8+jfnz56NWrVo4duwY3njjDUN2z+SQPeQh28hDttEN2Uceso08ZJuSYVBxEBcXh/Hjx2PKlCkICQnBd999hy1btqBNmzZwc3PD3r17kZaWBkCtRH18fJCXl6fuiEKBcePGYdeuXfjggw8M2S2zQfaQh2wjD9lGN2Qfecg28pBtSoZBhxU4FwygHqdRKpWoUaMGCgoKMHbsWCxfvhyBgYHo0aMHnJyckJaWxpemrFevnkVVpDIEZA95yDbykG10Q/aRh2wjD9mmZBhUHFStWhWAeuqHra0tkpKSwDAM7Ozs8Morr6Bv3744ePAgjh07hoKCAsTFxfHTRawxoIPsIQ/ZRh6yjW7IPvKQbeQh25QMo+Q54JI/XLp0CTVq1ODnhQ4cOBBt27bF2bNnkZGRgZEjRxrj9BYH2UMeso08ZBvdkH3kIdvIQ7bRD6OIg8LCQiiVSkREROD1118HAGzduhWZmZkYPXo0Bg4caIzTWixkD3nINvKQbXRD9pGHbCMP2UY/jOIrUSqVKCgoQE5ODp4+fYpx48Zh8+bNCAkJMcbpLB6yhzxkG3nINroh+8hDtpGHbKMfRkufHB0djQsXLiAyMhLDhg3De++9Z6xTlQvIHvKQbeQh2+iG7CMP2UYesk3xMCyXJsrAFBQU4M8//8SgQYNgb29vjFOUK8ge8pBt5CHb6IbsIw/ZRh6yTfEYTRwQBEEQBFE+qXjzMwiCIAiC0AmJA4IgCIIgRJA4IAiCIAhCBIkDgiAIgiBEkDggCIIgCEIEiQOCIAiCIESQOCAIgiAIQgSJA4IgdHL58mU0a9YMzZo1Q1xcnLm7QxCECSBxQBAEz9y5c9GsWTOMHz+eb3NxcUFISAhCQkJgZ2dnxt4RBGEqjFZbgSAI66BevXrYtGmTubtBEIQJofTJBEEAAPr06YP4+Hit9p9//hkTJ04EAISFhcHX1xdz587Fnj17UK1aNUyYMAE//fQTMjMz0bdvX0yePBmrV69GWFgYXFxcMGrUKAwaNIg/XmJiItasWYPz588jLS0NVatWRZ8+fTBy5EjY2ND7CkFYAvRLJAgCAFC3bl1kZ2cjLS0Nzs7OqFGjBgDg33//lf1MUlISvvvuO1SqVAlZWVn4448/cOHCBTx79gwuLi54+vQpFi9ejKZNm6JGjRpIS0vDyJEj8fTpU/4c0dHR+Pnnn/HkyRPMmTPHVF+XIAgdUMwBQRAAgKVLl6Jt27YA1EJh06ZN2LRpE+rVqyf7mfz8fPz444/Yvn07qlatCgB49OgR/vjjD/z111+wt7eHSqXClStXAABbt27F06dP4e3tjZ07d+KPP/7AokWLAAB79uzBo0ePjPwtCYLQB/IcEARRatzc3NCkSRMAgI+PD54+fYqaNWvC19cXAODp6YmEhASkpKQAAG7fvg0ASE5Oxuuvvy46FsuyuHXrFvz9/U33BQiCkITEAUEQpcbZ2ZlfViqVWm0MwwBQP/g1P8cNWwhxcHAwRjcJgighJA4IguDhHs45OTlGOX6DBg1w9uxZKJVKLFiwgPcwZGVl4fjx4+jUqZNRzksQRMkgcUAQBE9QUBAA4M6dOxg6dCgcHR0xbtw4gx1/yJAh2LVrF549e4aBAweiRo0ayMrKwtOnT1FQUIDevXsb7FwEQZQeCkgkCIKnb9++6Ny5M1xcXBAVFYVbt25BpVIZ7Pienp7YuHEj+vTpA3d3d0RFRSE3NxevvPIKPv30U4OdhyCIskF5DgiCIAiCEEGeA4IgCIIgRJA4IAiCIAhCBIkDgiAIgiBEkDggCIIgCEIEiQOCIAiCIESQOCAIgiAIQgSJA4IgCIIgRJA4IAiCIAhCBIkDgiAIgiBEkDggCIIgCEIEiQOCIAiCIET8H7XcSjYowAq9AAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -596,1247 +682,407 @@ } ], "source": [ - "from matplotlib import pyplot as plt\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 2))\n", - "train.plot(ax=ax, label='train')\n", - "test.plot(ax=ax, label='test')\n", - "ax.legend();" + "week_mean.plot();\n", + "week_median.plot();" ] }, { "cell_type": "markdown", - "id": "3e3abf2b-3966-47d0-a83a-9a29771ee643", + "id": "3abbc2e7-a631-40b0-878c-36a552f95dc8", + "metadata": {}, + "source": [ + "#### Generic Predictor" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "c775b740-fe78-4c0c-87fe-a51af3d42993", "metadata": {}, + "outputs": [], "source": [ - "Create the model" + "model = on.context.common.GenericPredictor()" ] }, { "cell_type": "code", - "execution_count": 71, - "id": "e58247b7-6a10-428b-a0ab-aebb39b02124", + "execution_count": 80, + "id": "a633dce3-27d8-4deb-bb0f-d40e3af96f8a", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from skforecast.ForecasterAutoreg import ForecasterAutoreg \n", - "from sklearn.neural_network import MLPRegressor\n", - "\n", - "model = ForecasterAutoreg(\n", - " regressor = MLPRegressor(),\n", - " lags = 30\n", - " )\n", - "model.fit(y=train.pd_series())" + "model.fit(train)" ] }, { - "cell_type": "code", - "execution_count": 72, - "id": "c33947fa-700a-4a0c-9486-5e7bc55b83bc", + "cell_type": "markdown", + "id": "6943aa60-c757-4f54-bf63-854beecfab80", "metadata": {}, - "outputs": [], "source": [ - "preds = model.predict(steps=7)" + "What does the future looks like ?" ] }, { - "cell_type": "markdown", - "id": "221ebcea-6cda-4560-98fa-cc553cb439ad", + "cell_type": "code", + "execution_count": 82, + "id": "04dfe189-f736-4fe3-b27c-467c154b2c42", "metadata": {}, + "outputs": [], "source": [ - "Plot the prediction" + "pred = model.predict(48)" ] }, { "cell_type": "code", - "execution_count": 73, - "id": "d3e70c93-e129-4f4f-874e-e7c7f60cd704", + "execution_count": 83, + "id": "67da9001-1e2a-4578-9436-8db8151190dc", "metadata": {}, "outputs": [ { "data": { - "image/png": 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0STAcQGBgIDt27GDChAlomv4lfeXKFU6ePEmNGjXw8PAgU6ZMtGzZknv37pnet3TpUgoVKoSbmxvp06fn008/JTIykmHDhjF//nxWrVpl2t727dutV8B4unbtGq1ataJPnz7cunXrlevMnj3bNCx4q1atSJs2bVKGaLM0TaNfv35s2bLFdLvutWvXKFeuHFOmTDFrB8rIJ4q6X8O1ML2XZuGcsHSERjKXhCcXsZp9+vy9a3dLC4YQtkBmU42HgPZGQu6CwRCDs7MLJP57MN6808HBmfHL/x48eECNGjUoWLAgw4cPByBZsmTkz5+fdu3a0bJlS548eUL//v2JjIxk165d3L59m2zZsjF69Gjq16/Po0eP+Pvvv2nZsiUAbdu25eHDh8ydOxeAdOnS4erqapnCJtCrZgi8cuUKlSpV4sqVKwCkTJmSPn360K9fP1KlSgXot2TmypWLq1evAvoMqnnz5rWZMtiKmzdv0rRpU3bt2mVa1r17d1MC+y4MBn2ci9X/6M+zZoC90zSyZHi37cbEKDLWVdx/BGk8IGz1uyUs8WHLdRhfUgbHZu1945Lkn2iHQu7BjTtgq7srTZo0uLq6kjJlSry9vQH47rvvKFq0KCNHjjStN3v2bPz8/Dh//jyPHz8mJiaGBg0a4OfnB0ChQoVM67q5ufHs2TPT9mzZ5cuXqVSpkilxAHj8+DEjRoxg+vTpDB06lPbt27NmzRrTOtWrV7dacmHrfHx82Lp1KwMHDuTnn38GYOLEiXh5efHNN9+807bPXMU0iJaHm5G1o5zeObkAcHHRqFFK8b/N8CBC7zxa8f2581gIm2SbZ0wb450OUEnfgvEujh07xrZt20wTXL3o0qVLVK9encqVK1OoUCGqVatG1apVadSokd1dMggKCqJSpUoEBwcDkDdvXj799FOmT59OTEwMoaGhdO3alfHjx8dpgTFnh1hHlCxZMsaOHUvBggVp06YNAEOHDiVjxox06tQp0dstmENj50SoP0jxXaswCuUw3+iptT7W+N9mvUF27W5FxaJJcKAKIV5LEox4ODjT6d+mpht20wwXERFB7dq1GTVqlGmZ0Wjkxo0bBAQE4OzszKZNm9i9ezcbN25k4sSJDBo0iH379pE9e3YrRh5/Fy9epHLlyly/fh2AfPnysXXrVjJnzkyvXr0YNGgQS5YsAYgziFS+fPmoWrWqVWK2N61bt+b+/fv06dMH0CeE8/LyolGjRoneZtE8GmcWKEJuPTVXmABUKwnOzmAwwNo9MKarWTcvhEgg2z9TinhxdXWNM3ZBsWLFOHXqFP7+/uTKlcv0z9/f3zRjqKZplClThm+//ZYjR47g6urKihUrXrk9W3P58mU++eQTU3JRoEABtm/fTubMmQHIlSsXv//+O3v37jUNJhWrR48e79yX4H3Su3dvBgwYAOhDejdv3pytW7e+0zaTW6A7T7rUGmX0IT04FwwXrkn3MiGsSRIMB+Hv78++ffu4cuUKd+7coWvXrty7d48vvviCAwcOcOnSJTZs2EC/fv0wGAzs27ePkSNHcvDgQYKDg1m+fDlhYWHkz5/ftL3jx49z7tw57ty5Q3R0tJVL+Ny5c+f44osvuHHjBgAFCxZk27Ztr5ysrFSpUuzYsYPVq1dTrlw5vvjiC1OTv4i/H374gdatWwMQFRVF3bp1OXz48FvelfQ+K/08cfxzjxUDESIJ3H2gaDTESOBIIyeDbDChViJeDAaDCgoKUgaDwdqhvNK5c+fURx99pNzc3BSgLl++rM6fP6/q16+vPD09lZubm8qXL59q3bq1iomJUadPn1bVqlVTGTJkUMmTJ1d58uRREydONG0vNDRUValSRXl4eChAbdu2zXqFe8HFixeVt7e3AhSgChcurEJDQ60dVoLZ+t/Tq0RHR6s6deqY9n2GDBnU+fPnE7UtS5X/1GWjopxBUc6gKvey7L61xzr8LymDfRsw1WD6e9fKG1TToQZ1+rLR9Lq1943cphpP1r7dxxwcoQw1a9Y0DfNdpEgRNm3ahJeXl5WjSjh7rYsnT55QtWpV0y2s/v7+plaxV/1LnTo1PXr0eGk7liq/Uoqcnysu39InTbu7ViO1u2Uuh9lrHb5IymC/Xvxbf5GmQbNP4ZtAjVxZlNymKkR87Ny505Rc+Pj4sHHjRrtMLuyZm5sba9asoXz58pw4cYIrV67Qtevre1P6+fm9MsGwFE3TqPWxYuIyiDHAxgPQqGKSfbwQSebIeUzJRQ4fiHgCofdBKfhtEyzaomheBVpXduHfkQiS3PuT7gm7ppTiq6++Mj3v1asX6dOnt2JE7y9PT0/Wr1+Pv7//W9e1RkfhWqXNM6rnkfOKZsON9J1s5PLNhG3n7gNF5BNpHLa2Ub8pMtQ2Mnim0ayj0dqCpTuel6f/FxpBizVGd9ZIn0ZfZjTCgg1QZaAP3/1qnRilBUPYhbVr17J7925Av2Okfv36Vo7o/ebj48OBAwfYsGEDBoMBZ2fnV/5LmTJlksdWoQi4u0HkE1i3Vx891Nk5/pdJIh4rvpmjmLBU/5IGGPeHonFFRd/PNQLyvXpbkU8Uy3fC/PWKrYfBww0Gt4SejSC5q9y1lNR+XKj4aoZ+Ev5+AXilgV5NrByUmSil+GOb/tjJCeqXB3c3jX5fQKe6MGk5jFmsuPcQDEaNfNmsE6ckGMLmGY1GBg0aZHo+fPjw93oGVFvh5eVF8+bNrR3GS5K7alQNUKz4G8LC4cBZ+OiD+L135d+K7uMV18PiLjca4fet8PtWRcWieqJRrYS+fPsRWLDRyNLtejN1rEePYcA0xfTVMKYL1CuH3d4ebTAoLlyHw+fh8HnF4fNgMMLUPhoF/G2vTJOWPU8uYvWerMiZBWqXsb14E+r4Jbio30RHhQ8hY9rnZUqVUuOrFtC1Pkz4Q7Fu91MalE9hlTglwRA2b9GiRZw4cQKAkiVLUq9ePdPInUK8Sq2PNVb8/XxUz48+ePNJ5dptRfcJilXPp18hhSsM+lJDAROXKcLC9eXbj8D2I4p82SDicRauv2JWe9+McD1Mvx4edBMaDFZUKALjuukDjdmD3ScUi7fqycTRi3qL0H/1nqRYP8a2yjPvL70uY5UtDLuO63XxxXDF3xPtpw5e549tz8vXqOKry5LaXWNQS0Xz8rdxcrJOJwy764MREBBA2bJlKVeuHOXKlWPOnDnWDklYUFRUFEOGDDE9/+GHH+z2V6BIOjU/ev547RvGwzAYFOOWKPK3jJtcVC0BJ+drDG6lMaSVxtU/NGb008j7QlPz2WC4fuf5b7TU7tC+NuyarK9/ZLZGpRfmQ9lxFIq3V7QbZSTkrm33B7gaoqjUS+8s+8+JVycXAFsOwb2HtlOWP7Yp2o56Hs+gL2HnRI3PK+vPI59ArYGKG2G2E3NCKaX4Y7v+WNOgQfk3r2/Nr0u7bMFYtmzZKwdVEo5n1qxZXL58GYBPP/2UTz75BGPshXEhXsM7vUaJfIoDZ+HYRb2FwjdT3G/aJ88UzYYrVv79fFmmdDC+u0bTT+JeznBLrtG+NrT9DNbu1q9v/30cnDRFlRIagTU06pbV14v1YS7YMh5W74I+UxSXbui/omf/CWv3KA7NxCwTvVnC7D8VUS+MrefvDcXyQLE8GsXywKpd+qWfGAOs/BvafGa9WGOt2wvNhitTv5keDWFEOw1N05g7UE+a9pyCm3eg9kDFzongkdI29/+bnAyC89f0x+UK63/rtsouE4yEiIqKIioqKs4yFxeXBE89HntSs+eTm72VITIy0jT9POgzxBqNRrsrx6s4QhneRVKU/7PSev8LgDW7FZ3qPv/VGv4I6g2Cv4/rzzUNOtaB79uBZyqFUrz2roNaH+v/rt02cifsBh8WyGIaY8BofPk9tcvoLSKTV8CIX+FhJNy+B1NXKoa3te4v6VfVg8EAc9fpj52c4MyvkCtr7Kt6vJ4eMH21vmTJNkVgDeuVw2g0sudMctr+rIgx6Cfb1jVhbFe9DpVSuCaD5d/Bx130WzuPXIBmIxTLhivsrTtXbOsFQKMKbz6GLHWcxXdMDbsbaCsgIAAvLy80TaNUqVL06tULT0/P164/ffp0Zs6cGWdZ48aNadLEQboTO7ApU6YwZswYQJ9efcqUKVaOSNiTk1dcqTNUn5um0oePmd1b77l5+74zgWMycu66/iPDPYWRKd3CKFfIvJOvvcr1MGcq9suCUWn4Zohm+083rdqE/So7jqeg9Vi9hfjF/fYipaBcnyzcvOuCi7Ni/y/X8fSwTrJ89JIrLUZl4vEz/aRXq1Qk4zrdwfkV58ALN5LR6DtvHj3WX2xT7SGDm91PynDfWdWvMnPxpv63u2f8dTKlTfpbweM7IabdJRhHjhyhUKFCPHr0iFGjRvHs2TPGjRv32vXN2YJx7do1fH197Xa0OHOVIUeOHPTs2ZOePXuaMbq47t+/T65cuQgPD8fJyYkTJ06QL18+QOrCESRF+ZWCbI31JvEUrhC2Wu94Wb0vXL2tr5PBE/4cBcXzJnz7iS1Dtb6w+aD++O9J8HHBhH+2ubyqDE2GwrId+utLR0D9cq9+b98pME6frJhZA6B1jSQI+D8eREDBVoqbd/Us7bPSsGwEJHtD2/zmg1Czv34XDMB37aDf5+BiB+35p69AoUD9cZmCsHPSm9e31HEW323Z1C5t27Ytx44de+Vrbdq0oUuXLhQtqveaSps2LX379qVmzZo8e/aM5MmTv/J9rq6uCU4m3sTJycnuTwjmKIOmae+8jfDwcGJiYkiXLt1L2/rpp58IDw8HIDAwkAIFCrz0fqkL+xX7u8bS5f+stJGZa+BpFIxZrF+muPNAf83fGzaO1cjt+25NCAktw5dVFZsP6uVftBnKFrZ+/ceWISxcsfofPbaMaaFOGQ0np1fvnyaV9A6yAMu2Q9vPkr4cA6cbTclF+Q9h6XDtrWOOVC0JU/soOvykxz54FizaAmO7alQr+eb3KqXYdRy2HYEqAVC6YNI2Py3fGTsVEDSu9Pq6+S9rfc/YVIIxe/bsBK0fu8PsrBHGJkRFRZk18Uqo+fPnExgYCECyZMnIlCkT3t7eeHt7kzlzZhYuXAjoCeLQoUOtFqcwvyfXnnCk/THS9fcEC989V6u0xsw1+vfDt/OeLy+cE9b/pJHZK+mvT9QvD53GwpNn8Ps2GN9DkczFNq6TLNwI0TH645bVeGNcpQrot+NeC4XNh+D+I0XaVElXjh1HFTPW6I/dUxiZ95UTKZLH7/Pb19a4FqoYMV9/fuoyVO+rqFZSMaaLRsEccbdz/5FiwQaYvlpx+oq+bNT/IGgxZEqXdGV+8fbUhhWS7GMTzfqpcwJcunSJ8+fPYzAYePjwIWPHjqVUqVKkSGGdQURsScWKFenWrRvdunUjTZo0eHl5MWTIEFPy5e/vz3fffUefPn3w9PSkQ4cOAOzatYty5crh5uaGr68vPXr0IDIy0rTd0NBQateujZubG9mzZ+e3336L87lKKYYNG0a2bNlInjw5Pj4+b5174unTpwwcOND0PDo6muvXr3Pw4EHWrl3LzJkzefJEvy+ua9euZMtmpWHohEWErAsl/MADrna4zqNTjyz6WZWLQ/L/5NEViui3LlojuQB9IKS6ZfXHdx/Ahv1WCeMlSilm//n8BNb2szfvH03TTPO8RMcQ5zZfS3vyTNHuhdtR+zUKx887YdsY3taJfyZrlMz/fNmG/fBhG0WnMUZu31PsPaVo/YORLA0UPX95nlwAPH4K01a9WzkS4uxVxUn9hjpKfwBZM9pGUvomNtWC8Tb37t3jhx9+IDQ0FHd3d0qWLMm3335r8c/9u/w/RIVFEWMwEOR8BSxcr65erpTbWSbB75s/fz5t27Zl//79HDx4kA4dOpAtWzbat28PwNixY+nWrRujRo3CycmJS5cuUb16db777jvmzJlDWFiYKUmZO3cuoF+euHnzJtu2bSNZsmT06NGD0NBQ02cuW7aMcePGsXjxYj744ANCQkJee5kr1oIFCwgJCQH0xMfT05Nbt24RGhoapzUqU6ZMceYfEY4he0c/YiJiuDDyIvvrH6Lk0uKkKZLGIp/l7qZRuZhi3V79eYPy8NsQLd6/dC2leRWNxVv0v/WFGxW1Prb+yWLfaf2XPECZQpDP7+0xNa6omS6TLN2uCKyRNOUYNkeZRrL8uCC0qPwISJfg7XxcSGPPVH2U1oHTFcG39dFZp6/Wb9WNeUX/yY8Lwr4z+t02U1YqBjZPmqHgl25//vh1g2vZGrtKMEqUKMHy5cutHYbN8vX1Zdy4cWiaRt68eTlx4gTjxo0zJRiVKlWiXbt2pql727VrR/PmzenVqxcAuXPn5pdffqFChQpMnTqV4OBg/vrrL/bv30+JEiUA/TJW/vzPU/7g4GC8vb359NNPSZYsGdmyZaNkyZKvjdFgMJjuDAFYsmSJadsxMTGEhYUREhLCnTt3KFy4MBkyZDD3bhI2IOf/ZefB4weEjg9jX/2DlFhSnLQlPC3yWT910YgxKD4uqDG4JQmal8RSqpWE9Gn0FoxVu+BhpLLYtPLxlZDWi1ilCkCWDHAjTJ+5NvyRwtPCl0kOn1OM/bdzqWsymNHv3ZrinZw0vvhUH8p9/B8wcoEi4glxkos0Hvolo451ND7IrtF0qJEl2/TZSxdvgVZJ0MH1xcnN7OHyCNhZgmEt5XaWwWg0cvXqVdPJ2RZ99NFHcQYHKl26NGPHjjXNaBkQEBBn/WPHjnH8+PE4lz2UUhiNRi5fvsz58+dxcXGhePHiptfz5csX57bgxo0bM378eHLkyEH16tWpWbMmtWvXxuU1XbJXr17N+fPnAf2yTmxyAfrdPZkzZyZz5syJ3wnCbqRvkZb03uk5M/As+xsepMTvxUlXOq3ZP6eAv8aGsdZPKl6UzEWjaSXFlJV6B9QVO5PmJPU6EY/1EyXok7Q1rhi/9zk5aTSqoE8MFx0Dq/+BltUtFibRMYq2oxWxk/QObqmR309x9eq7b9stuT6HR5ua8M1sxYKNel+djnX0gddSpnj+N9SrscaSf/tDjF+qaFndsvPMXLimOHZRf1wyP/h529bf8+vY5plSWIS7u3uc5xEREXTs2JGjR4+a/h07dowLFy6QM2fOeG3T19eXc+fOMWXKFNzc3OjSpQvly5cnOjr6pXWVUowaNcr0fMCAAe9WIGH3/Nr6UmjCBxgeGzjQ5BB3dty1dkhJpkXV5yeJ3zZZt6P6HzueT9T2eeWEjXDZuNLzdf/YbtlyjF0MRy/ojwtmhwHNzP8ZmdJpTO/nRORGjb3TnGhdU4uTXIA+eV5s342jF2Dnm68Kv7OlO54/tpfLIyAJhkPZt29fnOd79+4ld+7cr515tFixYpw+fZpcuXK99M/V1ZV8+fIRExPDoUOHTO85d+6c6fbRWG5ubtSuXZtffvmF7du3s2fPHtPkZC/atWuXKcZChQpRrVq1dyyxcAS+LbLy4ZRCGJ4aOPjFYUI3vTywkyP66API4aM/3nIYbt6xXpIx98/nj9vVStgJrPQH+mUS0C+TPIiwTDnOX1MMmxd7ezPMHqDhmsxyJ9s3tUhomkavxs9fH/+HZesuzuRmdnJ5BCTBcCjBwcH07t2bc+fOsWjRIiZOnPjGwbAGDBjA7t276datG0ePHuXChQusWrWKbt26AZA3b16qV69Ox44d2bdvH4cOHaJdu3a4ubmZtjFv3jxmz57NyZMnCQoKYuHChbi5ueHn9/L9h6NHjzY97t+/v0xaJkyyNPGh6KwPUQbFoS+PcHPFLWuHZHGaptG8iv7YaHx+iSKpXbrpwj8n9ccfZCfOXRXx4eSk0fDfCbeiovXLJOZmNCraj1Y8+3fMxJ6NoGQB635/NKoIPl7641W7IOimZZKMSzcUR/5ttSmeF7L72M/3piQYDqRly5Y8efKEkiVL0rVrV3r27Gm6HfVVChcuzI4dOzh//jzlypWjaNGifPPNN/j4+JjWmTt3Lj4+PlSoUIEGDRrQoUMHMmbMaHrd09OTmTNnUqZMGQoXLszmzZtZs2YN6dOnj/NZJ0+eZO3atYB+WaVp06ZmLr2wd5nrelNsbhE0J42j7Y5zccwlhx/jpnkV618m+eNvD9Pjtp9piUr841wm2Zb4ciiluPtAcTJIH4xswQbF6P8pvvxOmS5DZM8MI9pa/ySbzEWjWwM9DqVg4jLL1N/kFS+2Xli/3Alhd0OFW4utd/KsWLEiRYoUYfz48a9dx5plCAwMZP58fVSbcePGme5cSQxbr4v4cIQyvIs3lf/+/vsc+vIoUXei8GmcmUITCuKc3Pb2kbnqsEQHIwf/nZTt9K8a+f2T7iTyLMpIlgYG7j50JpkL3Fyu4eWZ8M83GhW+jRQ37+h3doSu0kjjEf/t3AlX9J2i+H2r3un1TTb9rPFpwPNtW/NYuvtAkbWh4mkUpEoJ15dpZr0baNMBRdU++ik6mQuc/03DP3P8t2/t7xnbO2qFw7l+/brpTpW0adPSrl07K0ckbFnakmn5eNNHeOTz4OYft9hf/wDP7rzlrGPHWlixFWPtHrj7UO+jVbcsiUou4N/LJP/2DYiKhjW74/c+pRRLtioKtFTMX//25KJ7Q+IkF9aWPo1Gy3+7kj16/HwWWnMIC1e0/P7538MPHRKWXNgCSTCExY0fP56YGH384S5duuDh4fGWd4j3XcpsbpT+qxQZKntxf184u6vu5dHZCGuHZRFNP9E7LQL8tilppz6Y80LnzviOffE6jSsm7DLJrTuKhoMVTYcpwsL1ZZ4eUL0UBNaAgc1hQg+N34dp7Jyocfl3jV962t4pq+cLnT1/WaYwGN69/pRStP5BEXJPf161BPyfHU4ALuNgOIjt27dbO4RXCg8PZ/r06QAkT578rcOICxErWWoXiv+vKGcGnePqrGD2VN9H0bkfkqGSl7VDMyvv9BpVAhQb9sOVENh9Uh9J05KiohWj/gfr/x2m3DejPnnXuyhTCDKnh1t3YcOB1w8eppQ+r0eviYr7L4wUX78cTOmt4Z3evn6lF/DXqFpCsfEABN2Etbuh7mtmoI2vicvgzz3644xpYf7X8Z/YzJZIgiEsatq0aURE6L88W7duHaeDqBBv4+TixAej8uOeKyWnvz7LwaaHKTThA7J+kcXaoZlV8yoaG/Y/Hzq8TCHLnUz2nlK0G61Mw4IDdK777iOc6pdJFJOWw7MoaPm9IndWRcoUkDK5hnsKSJlCH1L8rxfuqM/gCZP/T5/XxF7vLOvVWGPjgecDb9Utl/hyHL2g6Df1eSvI/K/tL+mKJQmGsJinT5+aOp1qmkafPn2sG5CwW/7t/XDPkZLDrY9xvPtJNBeNLI193v5GO1G/nH7yffwUlmyDCT2U2cd4iHisGDxL8csy/a4HAGcnaFfjAX0/N89cMI0rakxarm887uRnr75s0LwKjO+euI6ltqRaScibDc4Fw/YjepJQJHfCyxT5RPHFt4qof8cp/L8mUL2U/e4bSTDEO7lx4waLFy/m9u3bPHjwgIcPH/Lw4UMePHhAaGgot2/fBqBhw4bkypXLytEKe5ahcgYCFhXjQNNDHOtyAidXJzLXTeAUmjbKI6VG3bKKRZvh3kOYtBx6m/FO7r/2KjqN1SfzilUsjz6PRzrXcJydzZNglCkE5QrD38ffvJ6PF0zro1G7jP2ePF/k5KTRsxF0+VlPpCYsVcz9KuFl+79JirPB+uOiufWOnfZMEgyRKBEREYwePZoxY8aYplZ/k/79+ydBVMLRpS+TjoCFxTjY7DBHOxxHc9Hw/iyTtcMyi/a1NBZt1k9Q/aYq8maDz0rH7wQTel+x7zTcfagnKHcfKu4+0J/fvKP364jllhyGt9Ho1RicnMwzj0csZ2eN7b/ApRv6XRWPn0HkU71lJvZ/t+RQpwwJuo3VHrSsBl/PgPAImL8e8voqBjSP/2WfpdsVM9foj1OmgEVDtSSZpdWSJMEQCWIwGJg7dy5DhgwxTbn+OilSpMDT05M2bdrEmdRMiHfhVTE9xX4twuEvj3Ck7TGK/1qUjFXtf9bdSsU0vv5SMXKBPrLn58MU/0yBwjnffJJZvEUR+MPzUS7fpHJxmN5XI2cWfZtGo/nvWHFy0sjta/bN2jx3N40+TWHIbIVS8NUMxYGzMO9rSPWWuV0OndNHKo01sadG3mz2nVyA3KYqEmDDhg0UKVKE9u3bm5ILFxcXevbsybZt2zh8+DCXLl0iLCyMp0+f8uTJE27dusX3339v5ciFo8n4aQaKzikCCg63OkLY1jvWDsksRrTVOzuCPvlYrQGKkLuvTgKUUvy4UL9m/7bkwscL5gzU2PTz8+RCmN/XX8K3bTRiGy2W74SSHRRnr766Di9eV3w+zEhAe0X4v3dhN6kErWsmUcAWJi0Y4q3Onj1Lr1692LBhQ5zlDRo04McffyR37txWiky8zzLVyEiRmYU52u44h748QsCiYniVT//2N9owJyeN+V/D1RD91++1UKj7tWL7L/p04rFiYhRdxylmrHn+3qafQLnCGunTQPrU//7797G7m/3eoWFPnJw0vgnU5wxpPkLxIALOBkPJjopfB0G9f+8uCbmrGDFfr78Yw/P3F8yutzA5Sl1JgiFeKyoqilGjRvHdd98RFfX8J1KJEiUYO3Ys5cq9483eQryjzHW8UdMURzse51DzIxT4MR8+DTPjnOLVMwjbg5QpNFaNhFKdFNdCYf8ZCBypWDRUP4E9eqxoOjTurZ7ft9f4qoUkEbbis9IaB2dA/UGKk5f1/ij1Bym+aqFwdoKfl+j9UWJl8ITBLTU61sHu+128SBIM8Ur79++nXbt2caZd9/Pz44cffqBp06bv5fwZwjb5NMiMilYc63aCEz1OcW74BbK1ykq2QF9S+KSwdniJktlLY82PULarIuKJfutqHl9F53r6ZZPY2TWTucC8rzSaVXGck5KjyJVVY+80aDdamWbK/WFh3HU83KBPU+jzufbWfhr2SM4S75GwsDBWrVrF1q1bCQ8Pf+U6kZGR9O7dm9KlS5uSC2dnZwYMGMCZM2f44osvJLkQNidLUx/K/V0G31ZZiYmM4eLYILYV3cmRdse4v/++Xc7K+mEujUVDn1/P/+5X+LD18+TC0wM2jpXkwpa5u2n87xuNn7tpOL/QqJbMRZ9X5dJijWFtnBwyuQBpwXivNGnShJ07d5qe58yZk+LFi5v+PX36lO7du3P58vMh/ooWLcqsWbMoVqyYNUIWIt5S5fOg0M8fkHdIbq7/doOrs4K5tSKEWytCSP1havza+JK5njcuHvH72nsc/ITri26Q0teNrM2sM3JorY81xnaF3pP0BOnOA325vzesG520M6+KxNE0jf9roo9rMWyuIoePfjkkh4/j150kGO+Jc+fOxUkuAC5dusSlS5dYsmTJS+unSJGCb7/9lt69e+PiIn8mwn64pnUlR7fsZO/sz+31oVydEczdXfc40fMUZwadJXOjzGT7Mitpirw8uJThqYHb60K5tvAGd3feBQWpC6WyWoIB0KuxPkLk9NX684B8sOYH+x0++n1VsajG9qLvV53JmeM9sWzZMtPj8uXLExUVxdGjR3n69OlL61asWJEZM2bI3SHCrmnO+iBc3p9l4tHZCK79eo0bv9/k2rzrXJt3ndQfpsb3y6z4NMzMk6uPubbwBjeX3iQ6XJ/51yO/B74tsuDTyLpDkmuaxsRekN8PnjzTm9bd3d6vE5WwT5JgvCdeTDDmzp1Ljhw5iImJ4cyZMxw6dIhDhw4REhJCzZo1adWqlfSzEA4lVT4PCozMT95v8hCy9jbXfr3OvX/uc+rYaU5/dQYVrV+CcEnlgm9gVnybZyVN0dQ2c1dGMheNno2tHYUQCSMJxnsgKCiIw4cPA1CwYEH8/f0BfZCsQoUKUahQIQIDA60XoBBJxDmFM1ka+ZClkQ8RFyO5vvA6t1bdxs03Bb7Ns+JdOxPOKe33FlchbIkkGO+B5cuXmx7XqFHDipEIYTs8crmTb1he8g3La+1QhHBI0g7+Hli6dKnpcfXq1a0YiRBCiPeFJBgO7tq1a+zbpw/5V7hwYbJnz27liIQQQrwPJMFwcC9eHmnYsKEVIxFCCPE+kQTDwb14eaRBgwZWjEQIIcT7RBIMB3br1i3++ecfAPLnz0+BAgWsHJEQQoj3hSQYDmzFihWmORjk8ogQQoikJAmGA3txcK1GjRpZMRIhhBDvG0kwHFRYWBjbt28HIFeuXBQuXNi6AQkhhHivSILhoFatWoXRaAT0yyO2MuSxEEKI94MkGA7qxbtH5PKIEEKIpCYJhgO6f/8+W7ZsAcDPz4/ixYtbOSIhhBDvG0kwHNDq1auJidGnnJbLI0IIIaxBEgwHJJdHhBBCWJskGA7m4cOHbNy4EQAfHx9KlSpl5YiEEEK8jyTBcDBr164lKioK0C+PODlJFQshhEh6cvZxMC8OriWjdwohhLAWSTAcyJ07d/jzzz8ByJgxI2XLlrVyREIIId5XkmA4kDlz5vDs2TMAWrRogbOzs5UjEkII8b6yyQRj5MiR1KtXj4CAAA4ePBjnNaPRyNixY6lYsSJVq1blt99+s1KUcYWEhJj6PliDwWBg6tSppuedO3e2WixCCCGETSYYefLkYfDgwWTJkuWl15YtW8ahQ4dYvnw5s2bNYuHChezfv98KUer27dtHnTp1yJo1q+nyhDX89ddfXLlyBYDq1auTK1cuq8UihBBC2GSC0ahRIwICAnBxcXnptXXr1tGiRQvSpUtHtmzZqFevnlVP7Pfv32fNmjUYDAbmzp1rtTgmT55sety1a1erxSGEEEIAvHwGt3FBQUHkzp3b9DxXrlzs2rXrtetHRUW9dOnCxcUFV1fXBH1u7MRhsf/Hqly5MlmyZOHGjRusW7eOmzdv4u3tnaBtv6uLFy+yfv16APz9/alWrdpLccLry2BvHKEcjlCGd+EI5Zcy2AZHKIOlWGrfxHf4A7tLMJ48eYK7u7vpubu7O48fP37t+nPnzmXmzJlxljVu3JgmTZok6vOvXbv20rK6desyZcoUDAYDkyZNon379onadmKNHj3a9Lhp06Zcv379jeu/qgz2yBHK4QhleBeOUH4pg21whDJYirn3Tfbs2eO1XpInGG3btuXYsWOvfK1NmzZ06dLlje93c3MjMjLS9DwyMpKUKVO+dv3WrVvTvHnzOMsS24Jx7do1fH19X8reevTowZQpUwB9mvQRI0Yk2fwfjx8/No19kTx5cnr37o2Xl9cr131TGeyJI5TDEcrwLhyh/FIG2+AIZbAUa++bJE8wZs+e/U7vz5EjBxcvXjRdJrl06RI5cuR47fqurq4JTibexMnJ6aWKyps3L2XLlmXXrl2cPn2agwcPJtkQ3b///jvh4eGA3nqRMWPGt77nVWWwR45QDkcow7twhPJLGWyDI5TBUqy1b2yyNqKjo3n27BlKKWJiYkyPAWrUqMGCBQu4f/8+165dY+XKlXz22WdWjlhvKYmVVJ09lVLSuVMIIYRNsskEo2vXrpQpU4bg4GC6detGmTJluHXrFqDfYVK8eHHq169PmzZtaNasGSVLlrRyxHq/jthLNYsXL+bJkycW/8y9e/dy5MgRAAICAmxiPwghhBBgo508Z8yY8drXnJyc6NOnD3369EnCiN4uVapUNGnShHnz5vHgwQNWrFhBs2bNLPqZsf0+QFovhBBC2BabbMGwV0l5mSQ0NJQlS5YAkC5dOpo2bWrRzxNCCCESQhIMMypXrhw5c+YEYMuWLVy9etVinzV79mzT+B5t27bFzc3NYp8lhBBCJJQkGGakaRqBgYGA3gFz/vz5Fvkcg8HAtGnTTJ/ZqVMni3yOEEIIkViSYJhZq1atTGNgzJs3zyKjy61du5bg4GBAv6vmTbfpCiGEENYgCYaZ+fr6UqVKFQAuX77Mzp07zbp9pRQTJ040PZfOnUIIIWyRJBgWYMnOngsWLGDLli2APuhY9erVzbp9IYQQwhwkwbCAevXq4enpCcDSpUt59OiRWbYbFBREt27dTM9//PFHGblOCCGETZKzkwWkSJGCL774AtDnCom9nfRdxMTE0KJFC1OyEhgYSOPGjd95u0IIIYQlSIJhIS9eJpkzZ847b+/7779nz549gH5p5JdffnnnbQohhBCWYpMjeTqCgIAAChYsyMmTJ9m9ezczZ87E3d2d6OhooqOjiYmJITo6mjRp0lCvXj1Sp0792m3t2bOHESNGAODs7MzChQtJlSpVUhVFCCGESDBJMCxE0zRat25tGtK8Q4cOr13Xx8eHiRMnUr9+/ZemeX/06BEtWrTAYDAA8M0331C6dGnLBS6EEEKYgVwisaAWLVrEq6Xh5s2bNGzYkLp16740+mePHj0ICgoC4OOPP+brr7+2SKxCCCGEOUkLhgVlzJiRXbt2sXHjRpycnHBxcSFZsmSmf87OzixatIh169YBsGbNGrZs2cLw4cPp2bMny5cvZ968eYA+mdqCBQtwcZEqE0IIYfvkbGVhhQsXpnDhwq99vXnz5ixbtowePXpw69YtHj9+TN++fVmwYEGc1oxJkybJiJ1CCCHshlwisTJN02jUqBFnzpyha9eupj4Yx44dIzw8HICmTZvy5ZdfWjFKIYQQImEkwbARadKkYdKkSezdu5cPP/zQtNzX15epU6e+1PlTCCGEsGWSYNiYkiVLcvDgQSZNmkTLli3ZsGEDadOmtXZYQgghRIJIHwwb5OLiQteuXWUiMyGEEHZLWjCEEEIIYXaSYAghhBDC7CTBEEIIIYTZSYIhhBBCCLPTlFLK2kEIIYQQwrFIC4YQQgghzE4SDCGEEEKYnSQYQgghhDA7STCEEEIIYXaSYAghhBDC7CTBEEIIIYTZSYIhhBBCCLOTBEMIIYQQZicJhhBCCCHMThIMIYQQQpidJBhCCCGEMDsXawcgzOPixYssW7aMLFmyULZsWfz9/a0d0jt58OABadKksXYY4j3lSMeTHEuOTymFpmnWDuMl0oIBXL9+nUOHDgFgNBqtHE3CLVy4kA4dOuDm5sauXbuYPn06J0+etHZYiRIUFESTJk0YM2aMtUNJtKCgIBYuXMiOHTt4+vSptcNJUvZ+LIHjHE9yLDm2q1evsnPnTsB2j7X3ugXDaDQyY8YMFixYgK+vL1OmTCFdunQYjUacnOwj91JKcfjwYfr370/16tW5dOkSkydP5tGjR9YOLUFiYmL44Ycf2LZtGw0bNqRr167WDinBYmJiGD9+PGvWrKF27dr88ccflC9fnsaNG5MtWzZrh2dRjnAsgWMcT3IsOTaj0cicOXOYO3cuTk5OLF26lEyZMtnksWZb0SSxoKAgwsLC6NKlC/nz5+e3334DsLlK+q9nz56ZHgcHB8f58suZMycRERG4ublZI7RE27dvHzt27KB///6mL8Tw8HDrBpVA69ev5/bt2/z+++/07duX4cOHExQURGhoqLVDszh7PZbA8Y4nOZYcW1hYGGFhYQwcOJAqVaowadIkAJu8RPLetWA8e/aM5MmTA5A+fXqaN29OpkyZ2LNnD4sXL+bMmTPkz58fg8GAs7OzlaON69atW4wcOZIUKVLg7e1Nz5498fPz48MPP2T37t1cvnyZgwcPEhwczJw5c8iXLx9NmjTBy8vL2qG/0ovXDfPmzUujRo04cOAA7u7uzJs3j7Rp0+Lr68tnn31Grly5rBztq0VHR5MsWTIA/P398fDwwNvbm5iYGD788EMiIyO5fv06AQEBVo7U/Oz5WALHOp7kWHJskZGRpEyZEk3TSJ06Nc2aNSNTpkzkzJmTr776ioMHDxIQEGBzx5rt/7wwk1u3btG9e3cGDx7M2LFjiYmJIW3atOTIkQN3d3cKFSpEvnz5+N///gdgU5UEEBERQf/+/cmaNStNmjTh6NGjDB48mJCQELp160arVq04fvw4OXLkYNOmTTRs2JCbN2+ydu1aa4f+kpCQECZNmsSJEydMy7y8vChRogRnzpxh2LBh1KtXj1q1anH9+nWmT59uc79cbty4Qf/+/fn+++9NvyAKFixIxYoVAXBxcSE6Oho3Nze77iD4KvZ+LIHjHE9yLDm2kJAQOnXqRL9+/Rg0aBD379/Hzc0NPz8/UqRIQe7cualatSrTpk0D9GNNKWXlqJ97LxKMV32ZfPPNN1y+fNm0TqZMmShfvjx37txh8+bNVoz21a5cuYKbmxv/93//R4kSJZg4cSKRkZGsWrWK+/fvkzJlSqKiomjVqhUAFSpU4OnTp6RIkcLKkce1fft2unTpwv/+9z/2798fp+k2f/78dOrUid9++43atWtTsWJFunbtiqurK8ePH7de0P+xfPly2rRpQ5YsWahQoQJbt27l+++/B8BgMJg6XN2/f587d+6QLl06a4ZrVo5wLIFjHE9yLDm2p0+f8u233+Lv70///v0JDw9n9OjR7N27F9BbrZIlS0bdunV58uQJS5cuBWzrUsl7kWC86svk0aNHbNy4Mc5BmSdPHooXL86GDRsA/QAODg62UtRxpUiRgjNnzpgOOE9PTz777DMuXLjAuXPncHd35/z581y6dAmAc+fOERwcTJYsWawZ9ksiIyNp27Yt33zzDcePH+fUqVOm11KmTEnJkiXx9vYmKioKgIwZM3Lq1ClTU7y1xcTEcPPmTbp3707Pnj2pVKkSP/30E1u2bOHx48dxfq2fOHECV1dXU6e0Q4cOcefOHWuFbhaOcCyBYxxPcizZ97H0Nnfu3EEpRYsWLfD392f06NGkTp2aTZs2ERISYkokvL29adCgAcuWLQPgr7/+4ty5c9YM3eS9SDBe92Vy8eJFjh07ZlovdvnNmzcpWbIkU6dOtZlOan5+fhQpUoT58+ebllWpUoVkyZJx6NAhUqdOTatWrfj1118JDAyke/fuNGnShHLlylkx6udim+0qVapEhQoVqF69Ol5eXuzZs4ebN2+a1ov9hejq6groXyyZMmXCz88v6YN+BWdnZ2rVqmXarzExMURERJAjRw6io6OB5x0br1+/Tt26dTl//jx169Zl5syZVovbXBzhWAL7Pp7kWHKMY+ltNE3j7Nmzpnr08PCgSpUqPHv2jO3bt5vWc3FxoWHDhhgMBkqUKMG0adNMfVmsTr0HoqKiVLdu3dS0adNMy2JiYtTAgQPVtGnTlMFgUEajUUVGRqrmzZurihUrqpUrVyZ5nI8ePXrta9HR0WrFihWqXbt26urVq6blf/75p6pfv77peVhYmNq9e7eKiYmxaKzmcOrUKdW5c2e1bt06FR0dbVp+//59tXXrVtWjRw/1ySefqDVr1lgxytczGo1KKaUOHDigOnfubNrnRqNRGQwG1aNHDxUQEKA++eQTtWjRImuGajb2ciwppVRISMhrX3O040mOJcdiMBiUUkp169ZNjRgxIs7yiRMnqh9//FGFh4crpZSKjIxULVq0UBUqVLC5+rWdnxTvKCIi4rWvaZpG5cqVTT3CQc+ey5Urx6ZNm3ByckLTNEJCQvj444/ZtGkTdevWTarQWb9+PXXq1GHIkCH8/PPPpqa/F69Buri48OGHH+Ln58fEiRNN7/X19SVz5sxERERgNBrx8vKidOnSVu1Y97q6+O9gMAUKFKBgwYL8888/ca7he3p6EhwcTN68edmwYQO1atWyaLyvEp8yxDZR7tixg2zZspn2uaZpODk5ERERQcuWLdmwYQOff/655YM2k9u3b7/2NVs/lgA2bdpEtWrVGDJkCN9++y1nz54F7PN4el1d2NOxFJ8yOOqxlFjq31aq5s2bs2/fPi5evAjoLTpFihTh4MGDpE6d2rR++fLl2bx5s1Xq9000pWyoy2kirF+/nilTppAzZ058fX1p2bIlXl5eGAwG0x8nwOXLl/ntt9948OABP/30E6A3GU6bNo1Ro0bh4eFhlfj379/P6NGj6dOnD56enkyZMgUvLy8CAwNNTZknT55kzpw5dOrUCVdXV7p27UrJkiUJCAhg/vz5lClThp49e1ol/hfFty5eHBAmNDSUUaNGUbZsWR48eMDjx4/p3LkzRqPRKl/qCSlD7Jdip06daN26NR999BHLli3DaDTSuHFjm+sU+DabNm1izJgx+Pn5kSVLFpo2bUq+fPns5lgCfRyLr776is6dO+Pn58eMGTN4+PAhnTt3Jl++fIB9HE/xrQtbPpYSUgZHO5beZvPmzfz++++0bduWjz76yFSPSimUUqZ9c//+fWbNmsWJEyf49ddfAf3Okv79+/Pzzz/b5C3TL7LrFoz9+/cza9YsvvrqKzp06MDly5eZPHkyV69exdnZGScnJ06ePEnv3r2Jjo6mRYsWnD59mm+//ZY///yTESNGkCdPHqt8IRoMBgCOHz9OyZIlKV26NPnz56d9+/YEBwebOuzMmzePnj17kjNnTvLkyYO/vz9jx44lS5YsrF69murVq1v9yxDiXxe9evUydZwDveNZ5syZGTlyJAsXLqRQoUJommaVL8TElCH2iy8kJISOHTsyY8YMU0dAe/pCDA4OZt68eQwZMoQhQ4YQExPD1KlTOXv2rM0fS/D8F9/9+/dRSlG4cGF8fX3p3LkzWbNmZcqUKYB9HE/xrQtbPpYSUwZHOZbexGAwsHLlSsaPH4+Li4vpez42oYhNvE6ePEn37t05c+YM3bt3JyIigr59+/Lbb7/Rq1cv8ubNS/r06a1ZlPix4uWZRIu9Pjdz5kw1atQo0/Jjx46pNm3aqLFjxyqllJo7d6765JNP1KRJk0zrnDlzRs2cOVN16NBBzZ49O2kDV0pt375d3bp1Sz158kQppdRPP/2kOnXqZHo9Ojpa1alTRzVr1kxduHBBBQcHq4iICNPrsdcqlVI2cV04oXUxefJk0zrR0dFq5syZqmTJkmrhwoVJG/gL3qUMx44dUwEBAapy5cpq1qxZSRu4GcT+PR09elR98cUX6sGDB0oppW7cuKFGjx6tunfvrpSyzWNJKaXu3bsX5/nOnTtV165dVWhoqGnZ2bNnVfPmzdWePXvUrVu34vR1sqXjKaF1YYvH0ruUwd6PpfiIiYlRe/bsUTt27FBHjhxR3bt3V0uXLjW9ppRSK1asUNWqVYuzb65cuaJWrFih+vTpo+bOnWuN0BPFrhIMez45nzp1StWsWVM1btxYde7cWfXo0UMppXfCKlmypPrjjz/UgwcP1M6dO1WvXr3U2LFj1ZIlS+LE+2L81mauujhw4IB6/Phx0gb/r3ctg1JKXb9+Xc2bN89qZUgsez8xHzhwQNWoUUN9/fXX6uHDh6bl0dHRqmbNmnE6u0VERKjp06erIUOGmDrPRUdH28zxZK66sOax9K5lUMp+j6W3+e++if0OiY6OVosXL1YdO3aMs87ly5dN30lKxT3WYv9+7YVdXCI5ffo0n332GZMnT2b48OEMGDAAgHbt2nH48GGWLl3Kw4cP2bNnDzly5KB48eIcOXIEX19f3N3dMRgML01nm9TNhn///TdVq1ZlyZIlDB06lODgYCZMmICnpydDhw5l48aNdO/enbFjx9KqVSuMRiMPHjwAMF1DtYUBVMxVF7ECAgKSfJ4Hc5QhtoNalixZaNWqld3MVXHw4EFq1qzJmDFj4sy5Ubp0aS5fvsy+fftMy7JmzUr58uVZt24dGTNmxMPDg5iYGKsfSxcvXmTOnDmULl2aCxcucPz4cdO1axcXF5o1a8bUqVN5+PAhAO7u7qROnZro6GiMRqNpPWsfT+aqi1jWOJbMUQZ7PZbe5nX7xt3dHdA7GpcsWZL06dObRr01Go34+/uTIkWKVx5rtnSrd3zYRbSOcHLevn07Pj4+gD7S4eDBgzl8+DDbt2+nZs2aTJkyha+++oqVK1dSpEgRkiVLZrqX2Zb+qMxVF9asD3OUwZbqJL4c5cScK1cuateuzZAhQyhTpgxLly7l7t27priaN29OmjRpmDt3LtevXwf0uymio6NtIn4wb11YqzzmKoM9Hktv87p981++vr5UqFCBw4cPc/nyZZycnEz7yxbOW+/KLmrWnk/OsZ05P/74Yw4dOmRaXrx4cQoWLMjmzZuJiIjAxcWF3LlzAzB37lx27dpFmTJlrBLzm9hzXcRyhDIkhiOcmGO/pKtUqQJAhw4duHXrFjt37iQmJsa03uDBg7ly5QqDBw9mwoQJ/PDDDzZ1PDlCXThCGSzldfvmv1xcXChSpAjFihVjxowZDBs2jJ9++omnT586xP6x6W9LRzg5xzYfFyhQgOjoaPbv32967csvv+Tvv/8mLCwM0Ke87tevH3/++SfffPONTc166Ah14QhlSCxHOTHHfum6uLgQExODm5sbjRs3ZvXq1Vy7ds3U3F6gQAEGDBhA48aNiYqKYsKECdSvX9+aoZs4Ql04QhksJb77JlbGjBm5ceMGmzdv5sGDB/Tp08dh7pqxi06emzdvVr1791b79u0zLbt165YqX768CgoKUkopdf78edW3b1/VsGFDdfz48SSN79atW2ru3Llq27Zt6unTp6blRqNRRUVFKaX0XtSjRo1Sw4cPjzPSXtu2bdXvv/+ulFLq2bNn6uLFi0kae0LZel3EhyOU4V3E/v0tXbpUtWrVSgUFBcXpPHbr1i21du1aNXr0aHXkyJEkj+9Nx9OLx86L2rdvr6ZPn66U0kfftJUOnG9j63URH45QBkt527559uyZ+vHHH1XVqlXVyZMnrRWmxVg9wbD3k/P48eNVhQoV1A8//KCaNm2qRowYYRrC9UVhYWFq//79qnv37mr8+PFKKX1o8Pbt26tLly4lddivZO91oZRjlCGxHOHEHN/j6dq1a0qp51/g586dU40aNVJdu3ZVAQEB6vz580ka9385Ql04Qhks5V33zZ07d0zrh4WFWT5gK7HqJZIJEybw+eefExISwrRp0/jpp59Mnek0TTNd93Z1daVSpUqEhYUxefJkQB/K2cnJiYCAANM6OXPmTNL4165dS1hYGAsXLmTgwIF06tSJY8eOxbl2tmzZMkqUKMGyZcsoWrQobdu2Zd26dfTp04fatWuTLl06U38Aa7L3unCUMiTW28ru4uICYLoWHttU27dvXzZt2kS3bt2oUaOGaUhia4jv8VSqVClWrlwJYCrXmTNnuHLlCu7u7qxcudJ0icsaHKEuHKEMlmKOfVO9enXOnz+Ppmk2PxrnO7FWZrNmzRo1aNAg0y+Rbdu2qUaNGpkGZlFKb1YKCAhQ06ZNU9HR0ero0aOqatWqqnfv3qpixYpqwIABce4XTgovZqf37t0z3ct96NAhVbt2bVW3bl11+PBhpZSe5X755Zdqz549cbZx69YtdeDAAZtpLrTXuniRI5QhseJb9pIlS6qJEyfGee/KlStVQECA6t+/v7p+/XqSxq2UeY6n/fv3qxYtWry03BrsuS5iOUIZLEX2TcIkaYJhzyfne/fuqREjRqjvvvtOLV68WD179sz02pUrV1SfPn3UtGnT1MmTJ9WUKVPU1KlT1f37903rGI1Gq48U+CJ7rotYjlCGxLL3E7MjHU/2XhdKOUYZLEX2TeIlyWRn9+/fZ/LkyWiaRq5cuahfvz6urq4AXL16lYkTJ5I7d27Kli3Lzp070TSNzz//HE9Pz9hWFqtN2AN60+3EiROpVKkSOXLkYM2aNeTJk4chQ4aY4lMvTFBz8OBBlixZQtWqVfn0008xGAxWnd30RfZeF45ShsRyhLI7yvHkCHXhCGWwFNk3787ifTDWrl3L559/brr1b+3atYwaNcr0erZs2Rg9ejQdO3bkgw8+oESJEgQFBXHw4EEA08x71qqkiIgIrly5QteuXRk4cCBNmjRh0KBBHD16lPv375vWc3JyIioqCoDChQtz8uRJoqOjgaQf6fB17L0uwDHKkFiOUHZHOZ4coS4coQyWIvvGTCzZPPLo0SM1ceJEtWrVKtOyM2fOqAYNGpjGXo/tZRzbRPrs2TNVo0YNtW7dOkuG9kYhISHq9u3bSil9noWTJ0/G6cm+d+9e1bp169derz9y5Ihq3769unDhQpLEGx/2WhcvcoQyJJY9l93Rjid7rotYjlAGS5F9Yz4u5k5Ybt++jaZpZMyYETc3NypVqkTWrFlNrz948IA0adKYxpuP7SEe2/R0+vRpsmbNapVe4NHR0QwdOpSjR4+SIUMGypUrR61atfjggw8ATOPCJ0+enJQpU5p6CwPcu3ePHTt2cPjwYXbt2kWnTp2sPlCWPddFLEcoQ2LZe9kd6Xiy97oAxyiDpci+sQyzJRiO8GWyfv16Hjx4wOrVq9m+fTu7du1i5MiR/PLLL3HW27JlCz4+PnHKkC5dOoKCgvDw8GDNmjV4eHgkdfgmjlAXjlCGxHKUsjvC8eQIdeEIZbAU2TeWZbY+GC9+mXz55Zdcv36dkSNHvrRefL5MmjZtaq6w3urp06emoV0vXrxI6tSpcXFxoXLlyrRt25YrV67wxx9/APofo1KKU6dOUbVqVUAv9/LlywHo0aMHAwYMsGpyERuTPdbFixyhDIllz2V3tOPJnusiliOUwVJk31jWOyUY9vxlEhwcTI8ePRg2bBjDhw/nyZMnpEqVisyZM/Po0SM0TcPX15e2bdsya9YsjEYjrq6uPHnyBE9PT8LDw+nZsyejR48mZcqUAKaBnKzBnuvCkcqQWPZedkc6nuy9LhylDJYi+ybpJCrBsPcvk5UrV9KpUyfy5MlDixYtOHfuHLNnzyZXrlwcOHCA27dvm9atWLEiOXLkYNmyZYA+Idnff//NiBEjyJUrF1u3bqV69epJFvt/2XtdOEoZEssRyu4ox5Mj1IUjlMFSZN8kvQQnGI7wZXLz5k06dOhAt27dKFiwID/++COLFy+mTJkypE6dmj///JPw8HBA/wPy9vY23TLn5ORE+/btWbVqFd27d0/y2F/kCHXhCGVILEcpuyMcT45QF45QBkuRfWMdCU4wHOHLpGHDhlSoUAHQm8CcnZ3Jnj07MTExtGvXjkOHDrF9+3aePXtGypQpCQ8PJ02aNADky5ePDh06mAZTsSZHqAtHKENiOUrZHeF4coS6cIQyWIrsG+tI8F0kDRs2NN2a86ovk3HjxuHn50eNGjVe+WVSoEAB85YgETJlygToPYSTJUvGnTt30DQNV1dXihYtSp06ddiwYQNbt24lJiaGmzdvmnoVx44uaAscoS4coQyJ5Shld4TjyRHqwhHKYCmyb6wjwQmGI3yZxIq9l3n//v1kz57dNOpaw4YNKVu2LP/88w+PHj0iMDDQilG+niPUhSOUIbEcrez2fDw5Ql04QhksRfaNdSR6HAx7/jKJFTunwfnz56lSpQoAS5YsISIigjZt2tCwYUMrRxg/jlAXjlCGxHKUsjvC8eQIdeEIZbAU2TdJK9EJhiN8mTg7OxMTE8PTp0+5ffs27du35+bNmwwdOtTaoSWII9SFI5QhsRyl7I5wPDlCXThCGSxF9k3SSnSC4QhfJqD3EN67dy8XLlygWbNmtGzZ0tohJZgj1IUjlCGxHKns9n48OUJdOEIZLEX2TdJ6p6HC7f3LBCBHjhz06tWLRo0akTx5cmuHk2iOUBeOUIbEcpSyO8Lx5Ah14QhlsBTZN0lHU7FDmiVCTEwMv//+u11/mTgKR6gLRyhDYr3PZbc1jlAXjlAGS5F9k3TeKcEQQgghhHgVuf9GCCGEEGYnCYYQQgghzE4SDCGEEEKYnSQYQgghhDA7STCEEEIIYXaSYAghhBDC7CTBEEIIIYTZSYIhhLC4gwcPEhAQQEBAADdv3rR2OEKIJCAJhhDCrIYNG0ZAQAAdOnQwLfPw8KBgwYIULFgQV1dXK0YnhEgq7zQXiRBCxEe+fPmYN2+etcMQQiQhGSpcCGE2tWvX5tatWy8tnzZtGp06dQJg9erV+Pj4MGzYMNauXUvmzJnp2LEjU6dOJSIigjp16tC1a1cmT57M6tWr8fDwoHXr1jRq1Mi0vbCwMKZMmcKePXsIDw8nU6ZM1K5dm8DAQFxc5HeTELZAjkQhhNnkzZuXJ0+eEB4ejru7O9mzZwfg7Nmzr33PnTt3+PHHH/Hy8iIyMpJFixaxd+9eQkND8fDw4Pbt24wePZrixYuTPXt2wsPDCQwM5Pbt26bPCAoKYtq0ady4cUOm3hbCRkgfDCGE2YwZM4ayZcsCerIxb9485s2bR758+V77nujoaCZNmsTy5cvJlCkTANeuXWPRokX88ccfJE+eHKPRyKFDhwBYsmQJt2/fJn369KxcuZJFixYxatQoANauXcu1a9csXEohRHxIC4YQwqpSp05NkSJFAPD29ub27dvkzJkTHx8fANKmTUtISAj37t0D4NSpUwDcvXuXKlWqxNmWUoqTJ0/i6+ubdAUQQrySJBhCCKtyd3c3PXZ2dn5pmaZpgJ48/Pd9sZdgXpQiRQpLhCmESCBJMIQQZhV7gn/69KlFtl+gQAH++ecfnJ2dGTlypKmlIzIykm3btlGpUiWLfK4QImEkwRBCmJW/vz8Ap0+fpmnTpri5udG+fXuzbb9JkyasWrWK0NBQGjZsSPbs2YmMjOT27dvExMRQq1Yts32WECLxpJOnEMKs6tSpwyeffIKHhweXLl3i5MmTGI1Gs20/bdq0zJ07l9q1a5MmTRouXbrEs2fPKFq0KL179zbb5wgh3o2MgyGEEEIIs5MWDCGEEEKYnSQYQgghhDA7STCEEEIIYXaSYAghhBDC7CTBEEIIIYTZSYIhhBBCCLOTBEMIIYQQZicJhhBCCCHMThIMIYQQQpidJBhCCCGEMDtJMIQQQghhdv8P7Qfsrlur+JUAAAAASUVORK5CYII=", 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    " + "alt.Chart(...)" ] }, + "execution_count": 83, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "fig, ax = plt.subplots(figsize=(6, 2))\n", - "train.split_after(pd.Timestamp('09-01-2023'))[1].plot(ax=ax, label='train')\n", - "test.split_after(pd.Timestamp('11-01-2023'))[0].plot(ax=ax, label='test')\n", - "preds.plot(ax=ax, label='preds')\n", - "ax.legend();" + "on.plots.prediction(train[-96:], pred, test[:48])" ] }, { "cell_type": "markdown", - "id": "ab33b163-4161-4fe7-b674-986a5f2a580d", + "id": "dc965c95-1b5c-43e4-9c4b-57ff73c275c1", "metadata": {}, "source": [ - "---\n", - "## Packaging of the model" + "## Generic Detector" ] }, { "cell_type": "code", - "execution_count": 74, - "id": "29975ac3-7189-4399-a20f-4ccdde603ef0", + "execution_count": 84, + "id": "9751b373-97d3-45e9-9969-2b4ba224f815", "metadata": {}, "outputs": [], "source": [ - "from ontime.abstract import AbstractBaseModel\n", - "from skforecast.ForecasterAutoreg import ForecasterAutoreg\n", - "from sklearn.neural_network import MLPRegressor\n", - "\n", - "\n", - "class MyPrivateModel(AbstractBaseModel):\n", - " \"\"\"\n", - " Model to predict 14 days of activity given a training on 7 days.\n", - " \"\"\"\n", - "\n", - " def __init__(self):\n", - " super().__init__()\n", - "\n", - " def fit(self, series):\n", - " super().fit(series)\n", - " self.model = ForecasterAutoreg(\n", - " regressor = MLPRegressor(),\n", - " lags = 30\n", - " )\n", - " self.model.fit(y=series.pd_series())\n", - "\n", - " def predict(self):\n", - " horizon = 7\n", - " super().predict(horizon)\n", - " predictions = self.model.predict(steps=horizon)\n", - " return on.TimeSeries.from_series(predictions)\n", - "\n" + "model = on.context.common.GenericDetector()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "89be0c48-0ab6-42b4-ab64-611b95d2a76f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(train)" ] }, { "cell_type": "markdown", - "id": "27102eef-6797-4679-90c7-2db2a6a5e157", + "id": "06be738b-8bfd-4c95-8dec-ed52803e5ff9", "metadata": {}, "source": [ - "Try the model for fun" + "Does the current signal has problem ? " ] }, { "cell_type": "code", - "execution_count": 75, - "id": "11837b8a-0667-4271-9b66-c4fef3d19c86", + "execution_count": 87, + "id": "4650f34a-9cdb-4ea6-9dfe-2c66109b2627", "metadata": {}, "outputs": [], "source": [ - "model = MyPrivateModel()" + "detected_test = model.detect(test)" ] }, { "cell_type": "code", - "execution_count": 76, - "id": "b5e1ec63-aa13-4659-a3a1-f3c6d00f33b2", + "execution_count": 90, + "id": "49e09caa-37f1-4201-b79a-4d1d65d86a8c", "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/fred.montet/Library/Caches/pypoetry/virtualenvs/ontime-FpQu8-YN-py3.10/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] + "data": { + "text/html": [ + "\n", + "\n", + "
    \n", + "" + ], + "text/plain": [ + "alt.LayerChart(...)" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model.fit(train)" + "on.plots.anomalies(test[:72], detected_test[:72])" + ] + }, + { + "cell_type": "markdown", + "id": "2af5c7ba-1617-46f0-9551-67cc51ac1884", + "metadata": {}, + "source": [ + "What if we want to have an idea about the future problems ?" ] }, { "cell_type": "code", - "execution_count": 77, - "id": "42e58973-1697-4e5c-b07b-0c8a3cb700a2", + "execution_count": 91, + "id": "866533ed-bdab-4b29-bc91-50ed0043e84a", "metadata": {}, "outputs": [], "source": [ - "ts = model.predict()" + "predetected = model.predetect(72)" ] }, { "cell_type": "code", - "execution_count": 78, - "id": "7052b1b8-3a72-47ad-90bf-472d7247172e", + "execution_count": 92, + "id": "44bff6ea-f249-48b6-816f-c49e0d264520", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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"Use another tool from onTime" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "id": "762ad088-bb06-4919-91d1-7671cf17aa40", - "metadata": {}, - "outputs": [], - "source": [ - "det = on.detectors.quantile(low_quantile=0.4, high_quantile=0.6)\n", - "det.fit(train);" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "id": "fedb2166-276b-4af9-b867-b4a08218f4e4", - "metadata": {}, - "outputs": [], - "source": [ - "preds = model.predict()" - ] - }, - { - "cell_type": "markdown", - "id": "8a6e3e8f-aab6-40ae-a92a-bd3dae828caf", - "metadata": {}, - "source": [ - "Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "id": "097fdea6-5ae3-4622-abc2-1bc33fc190bb", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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++/eIw72Xb86hs4fOzWBAe+MvX2kcREmh0yl0agq/fqzS/p96rifqOH3J2Ejs+rhga00IIaxHmogioKrGWez//VVlza/mGwd7e+jc9G7j4O0hv0xFyRXsB6umxjJkXhWi4+Bs5N1GolJ5ee0LUVxIE1FIVFXl6AX4725j4xBxLe8+9vbGFf8GdFDo0xrKy3FhUYoE+Oj5dSF0mmicA3QuCtr/3UhUqSA/C0IUB9JEWJCqqhy7eLdxCDdzsTl7e+jY2Ng49G0jjYMo3YIqwe6FCh1CVS7HwIVoaP9PlV8Xgl9F+dkQwtZJE/GYVFXleNjfjcMuCDPTONjZQccmxkMVfdsiM9GFuEdgJYU9i4yHMyKuGX+G2oeq/Pox+PvIz4oQtkyaiAJQVZUT4XfnOFy8kncfOzvo0Phu41CxnPwyFEJLVR+F3QuhwwSV8KvGUbx2f49IBPjKz44oPiy97PXQoUNJSkpi3bp1Fnk8S5MmIp9UFc5GOfDlDuOow4XovPvY2UH7RsZDFf2kcRDikfj7KOz5xDgicfGKcQJyzqGNwErysySELbLtK5nYiI37VeoNgZ7TKvPvb8jVQOSMOHw2SeHaTwo7P7ZjTG9FGgghCqBKBYXdnyjUqmq8fTnGOCIRcc2mrhMohFlDhw5lz549LFy4EEVRUBSFy5cvc+rUKbp3746bmxs+Pj68/PLLJCQkmO73448/0qBBA5ydnfH29qZz586kpKQwffp0vv76a9avX296PFtbEVOaiHxwcYLz9zQOigLtG8OnExWurlXYtdCOsX0UWSxHCAuoXF7h148Vav/dSETFGkckwq9KIyFs28KFC2nZsiWjRo3i+vXrXL9+HXd3dzp27Ejjxo05dOgQW7duJTY2loEDBwJw/fp1Bg0axPDhwzl79iy7d++mX79+qKrK5MmTGThwIN26dTM93lNPPWXlZ5mbHM7Ih3ZPgE85CPRJ56WuTvRvr8iiOEIUokrlFXZ/Ah0nqJy5bLzwXLt/Gidb1vCXn73SqnkIxNw0FPnX9fWCQ8sf/je3h4cHjo6OuLi44OvrC8D7779P48aNmT17tmm/FStW4O/vz4ULF0hOTkav19OvXz8CAoxXpWvQoIFpX2dnZzIyMkyPZ2ukicgHnU7h4iqVhLjYv6/ZLr/EhChsPl6KcR2JCSqnLsHV+LvrSNSqKj+DpVFMIlxNePh+tuT48eP8+uuvuLm55flceHg4Tz/9NJ06daJBgwZ07dqVp59+mv79+1OuXDkrVPvopInIJ1dnKGavXSGKvYrlFHYthM4TjWdEXUu4O9mydoA0EqWNrxdghW+772NcaTY5OZlevXoxZ86cPJ+rVKkS9vb2bN++nd9//51ffvmFRYsW8fbbb/Pnn38SFBT0GFUXDWkihBA2rYKnws4F0OV142JuMYl3RyTqBkojUZocXAZ2drY9lc/R0ZHs7GzT7SZNmrB27VoCAwPR6cy/5SqKQqtWrWjVqhXvvvsuAQEB/O9//2PSpEl5Hs/W2PZ3QwghMK7sunOBQuMaxtuxicYRiVMRMtlS2JbAwED+/PNPLl++TEJCAq+99hqJiYkMGjSIv/76i/DwcLZt28awYcPIzs7mzz//ZPbs2Rw6dIioqCh++ukn4uPjqVOnjunxTpw4wfnz50lISCArK8vKzzA3aSKEEMWCV1mFnR8rNK1lvB2fZFxT4kS4NBLCdkyePBl7e3vq1q1LhQoVyMzMZP/+/WRnZ/P000/ToEEDJkyYgKenJ3Z2dpQtW5bffvuNHj16ULNmTd555x3mz59P9+7dARg1ahS1atWiWbNmVKhQgf3791v5GeamqKoqP4H5YDAYiIyM/HtipfRe95JszJNctD1ONkl3VJ5+XeWvc8bb3h6w4yOFRjWK/6ENec2YJ7los3Y28t0QQhQrnu4K2z9SaFHXePvGLeg0UeXIefl7SIiiJk2EEKLY8XBT+GW+Qst6xtuJt42NxGFpJIQoUjbdRKxcuZJmzZpx7Ngxa5cihLAxZV0Vts1XaPX3ujxJycZG4q+z0kgIUVRstomIi4tj27ZtlC9f3tqlCCFslLuLwpa5Cm0aGm/fSobOk1T+PCONhBBFwWabiAULFhASEoKDg4O1SxFC2DB3F4XN/1Fo18h4+3YKdJmkcuCUNBJCFDabXGzq0KFD3Lp1iw4dOvDRRx89dP/MzEwyMzNzbdPpdDg6OlqsJoPBkOtfcZdkY57kos3S2bg4wcYPoPe/4NejcCcVnn5dZfN/VNPhjuJAXjPmSS7aCiub/J7pYXOneOr1eoYMGcLMmTMJDg6mV69ezJo1i0aNGmneZ+nSpSxfvjzXtgEDBpiukiaEKB3SMhRCFlZg/2lnAFzKGFjxehzNa2VYuTIhipf8Lrld5CMRI0aM4Pjx42Y/N3z4cMqVK0ejRo0IDg7O92MOGzaMwYMH59pWGCMR0dHR+Pv7y3nK95FszJNctBVmNts+gr7vwPa/IDXDjhEf+bLxQ0yHO2yZvGbMk1y0WTubIm8ivvzyywd+/vXXX+fo0aPs3LkTgJs3b/L6668zbtw4+vbta/Y+jo6OFm0YHsTOzk5exBokG/MkF22FkY2rM2yYrdL3HZWtf0JKOjwzFX7+UKFDk+KxIJW8ZsyTXLRZKxubmxMxffp0MjLuDj2+8sorTJkyhRYtWlixKiFEceJURuF/78Nz01Q2/wGp6dDzTZWNH0KnpsWjkRCiOLC5ls7d3Z3y5cubPuzs7PDw8MDJycnapQkhihGnMgo/va/Q6ynj7bQMeOZNlV8O2tQ0MCGKNZtrIu63cePGB06qFEIILWUcFX6cpdC7tfF2eiY8+y+VrX9KIyGEJdh8EyGEEI/D0UFhzQyFvm2MtzMyofe/VDYfkEZCiMclTYQQosRzdFD4YYZC//bG25lZ0PcdlZ9/l0ZCiMchTYQQolRw0CmseldhYAfj7cws6PeOyvq90kgIUVDSRAghSg0HncJ30xQGdTbeztJD/3dV/vebNBJCFIQ0EUKIUkWnU/i/fykM7mK8rc+Gge+prN0tjYQQj0qaCCFEqaPTKXz9L4UhXY239dnw/AyVNbukkRDiUUgTIYQoleztFVZMVRja3Xg7OxtenKWyeqc0EqJ4SElT+eM0rPrVjXQrXR7G5lasFEKIomJvr/Dlm2Bvp/LlJmMjMXiWavz3aVnZUtgGVVWJjIHjYXAiAo6HqRwPg/BrYLyEpjdPt4Qn6xR9bdJECCFKNTs7hWVvGBuJZRvBYIAhs1UMKrzcVRoJUbRS0lROX4Lj4cZm4US4sXG4lfzg+52IkCZCCCGsws5O4fPXwd5e5fN1xkbildkq2QYY2l0aCWF5qqoSHWccXTCOMBhHFy5eyRldeDDnMlA/CBpWB/9yibRu4FX4RZshTYQQQmBsJD6daByRWPyT8Rf58A9VDAYY3lMaCVFwaRkqpyLgRDgcDzc2CyfCIekhows5/CvCE8HwRHVoWF3hiWAIrmI8HGcwGIiMvEOAnzQRQghhVYqi8EmosZFY+KOxkRgxxzgiMaqXNBLiwVRV5UpczryFu4cjLlwxjm49jJMj1K+Wu1loWB3Kudvua0+aCCGEuIeiKCwYb2wkPlpj3BYy19hIjOltu7/MRdFKy1A5c/meZuHvxuHmnfzd36+CsUEwjjAoNKwONfyMpx8XJ9JECCHEfRRFYd5rxjkSc783bhs733jWxmv9itcvefF4VFXlWgKmQxA5hyMuXDGezfMwZRyhXmDuZqFhdfD2KBmvI2kihBDCDEVRmDPGOCLx4XfGbeM+Np61Mf65kvEGIHJL/3t04d7TKE9EwI1b+bt/5fI5hyLgiWDj4YiaxXB04VFIEyGEEBoURWF2iLGR+Pc3xm3/XGgckZgwsOS+MZR0qqoScyPnNErj6MKJcDgXlb/RBUcH4+iCqVn4u3Eo71n6XhPSRAghxAMoisKskcZDGzNXGrdNXGycI/H6C6XvTaO4ychUORuZczhCNTUOCfkcXfD1yntmRK2qxou5CWkihBDioRRFYcZwBTtFZfpXxpP4J39mbCSmvChvJrYi5oaxSTgRfvdwxLko47VRHsZBB3UD854ZUbGcfH8fRJoIIYTIp/eGKdjbwbQvjY3Em0uMjcRbL8kbTVHKzFI5F3X3zIicxiHuZv7u7+MFDav9PcLw9+GIWlXB0UG+j49KmgghhHgE77yiYG8P/1pmbCT+tcw4R+KdV+QNqDDo9Sp/nYOff3MnKhFOhBs4GwlZ+offV2cPdQJynxnxRDD4eMn3ylKkiRBCiEf01kvGEYk3lxgbiWlfGkck3hsmb06PS1VVLl6BHYdg+yGVX4/mXDfiwSsyVvA0NgjGEQbj4Yg6ATK6UNhssolo1qwZTk5OKIrxmz9s2DCGDx9u5aqEEOKuKS8aG4nJnxkbielfqRhUlenDFNPvLpE/sYkqOw/DjsMqOw5BdJz2vvb2UKdq7tMoG1YDX28kdyuwySYCYO3atfj4+Fi7DCGE0PT6Cwp2djBpsbGRmLkSsrNVZo2UN7QHSUlT+e047DiksuOwcT6DFm8P6NgEGgXcoGsrb+oHKZRxlGxthc02EUIIURxMHGgckQj9xNhI/Psb0GerfDBaGokcer3KofPGQxQ7Dqv8fkp7ToOTI7RpCF2eVOjc1HiIAlQiI5MJCPDGzk4ytSU220S88sorKIpCixYtmDBhAp6enpr7ZmZmkpmZmWubTqfD0dHRYvUY/r56iiE/V1EpZSQb8yQXbSUtm3H9wE6B8QuNt+esMjYSc8aoPEofUVJyUVW4EM3fhyhg91G4lWJ+X0WBpjWhczPo1BSeqgdOZQDuXg+7pORSGAorGzs7u3ztp6hqfq5cXrSOHj1KgwYNuHPnDnPmzCEjI4MFCxZo7r906VKWL1+ea9uAAQMYOHCgRepJSkrim2++wdPTk3Llypk+cm47Oztb5OsIIYq373a5Me1rb9Pt4V1v8/agm4/USBRX8bfs+P2ME/tOOfP7GSeuJ2r/jRrgk0Wruum0qpdOyzrpeLpJc2BrgoKC8rVfkTcRI0aM4Pjx42Y/N3z4cF599dVc2xISEujRowd79+6lTJkyZu9X2CMRR48epVmzZpqfd3Z2xtvbG29vb8qXL4+Xl9cDb3t7e1O2bNkSM9RpMBiIjo7G398/391raSC5aCvJ2SzbCGPn3739z+fgo3Hkq5EoTrkkp8JvJ4yjDTsPw8kI7X3L/z2voVNT6NwUAis92tcqTrkUtcLKJr+PVeSHM7788stH2j/niTyo13F0dLTooYv73bz54BVM0tLSuHLlCleuXMn3Y+p0Ory8vExNxb0NhtZtLy8v7O3tH/fpFBo7Ozv5ATdDctFWErMZ0xsc7FVGzVVRVfhkLRhU+CQ0/2dt2GIuOes17DhsnBB54PSD5zW0fQI6N7s7r8EScxlsMRdbYa1sbG5ORHh4ONnZ2VSvXp2UlBTmz59PixYtcHJyslpNjRo1Yt26dVy4cAFFUUhMTCQhIYEbN25w48aNXP/PysrK12Pq9Xri4uKIi3vAuUz3URQFT09Ps03GgxoQrREcIUThGPGMcUGq4R8aG4nFP0G2QWXxBMu8mRYFVVU5H3V3MuSvR+H2A+Y1NKtlnNfQpZlCy3rgVKZ4PE/xeGyuiUhMTOSDDz4gLi4OV1dXmjdvzowZM6xaU/ny5enVqxeRkZEEBARodnuqqpKcnJyrqbi/ybj/dkJCAqmpqfmqQ1VVbt68yc2bNwkLC8t3/a6urg8d5bi/IXF1dS0xh1uEsIah3RXsFBj6gbGR+Hyd8fTPz1+33UYi5kbu9RquxGvvG1zlbtPQvjF4lbXN5yQKl801EU8++SQ//fSTtcsoEEVRcHd3x93dPd+TUgDS09Pz3XDk/D8pKSnfj5+SkkJKSgqRkZH5vo+jo+MjHWYRQuQ1pJvx9M8hs1UMBuN8CYOqsnSybTQSyanG9Rq2HzI2Dacuae9b3sPYNHRuqtCpKQRWsn79wvpsrokojZycnKhSpQpVqlTJ9330ej2JiYn5ajhybicmJpKdnY/L2WGcrHr9+nWuX7+er/0dHByoXbs2DRo0oGHDhqZ/q1SpIiMaolQb/LRxQaqX3jc2El/8bDy0sfwNsLcv2p+NnHkN2w/dndegdYVL5zJ/z2toqtC5mXGFSFtofIRtkSaimNLpdFSsWJGKFSvm+z4Gg4Hbt28/0ohHQkICGRkZD33srKwsTp48ycmTJ1m1apVpu6enZ67GokGDBtSvX5+yZcsW6HkLURwN6mwckXhxlvFiXV9tNh7aWDG1cBsJVVU5F3l3MuTuY9rzGuzs7s5r6NxU4an6yMqQ4qGkiShF7Ozs8PT0xNPTk+rVq+frPqqqkpqa+sCGIyYmhuPHjxMREYFen3u6dlJSEnv37mXv3r25tgcGBuZpLmrWrIlOJy9JUTIN7GgckRg0Q0WfDf+3zXhoY+Vblm0krieo7Dxyd0npqw+Y11DD727T0KEJlHOXpkE8GvmNLR5IURRcXV1xdXWlatWqZvcxGAxERkZSqVIlLly4wMmTJzlx4oRpZMLcqa+XL1/m8uXLbNy40bStTJky1KlTJ88hEV9fXzkkIkqE/u2NIxID3zM2Et/+Yjy08X//Ap2uYK/xO6l3r0Ox/RCcfsC8hgqeOWs1GA9RBPjKz5V4PNJECItxdHSkYcOGNGzYkMGDB5u2JyYmcurUqVyNxcmTJ0lOTs51/4yMDI4dO8axY8dybff29s4zalGvXj3c3NyK4mkJYVF92yr8OBMGvKeSpYfvd4DBoPLtO8ZDCg+TpVf56+zf8xoOq/yRj3kNXZoZm4YG1WReg7AsaSJEofPy8qJt27a0bdvWtC1n9OL+UYvz58/nWQP+xo0b7N69m927d5u2KYpCtWrV8jQXwcHBNr0glxAAvdsorJ0F/d9VycyCH3YZRyS+fSfvvjnzGnImQ+4+Bnc0zgq3s4MnaxtXhez893oNMq9BFCZpIoRV2NnZERQURFBQEM8++6xpe3p6OmfPns3TXNx/loiqqoSHhxMeHs66detM252cnKhXr16e5kIuKy9sTa9WCv97H/q+Y2wkftwN2Qb44BW4lgC/HlVN8xquJWg/Tg0/6NLM2DS0byzzGkTRkiZC2BQnJycaN25M48aNc21PSEgwNRQ5zcWpU6fyLNSVnp7O4cOHOXz4cK7tFStWNDUUOc1F3bp1cXFxKfTnJISWHi0V1s+GPm+rZGTC/36D3475ceM23HsVy3tV8My9XoPMaxDWJE2EKBbKly9Phw4d6NChg2mbwWAgIiIiT3Nx8eLFPNdaiYuLY+fOnezcudO0zc7OjuDg4DyjFtWqVZP1+UWR6dZCYcNs6P0vlfRMuHE79+E4F6e76zV0eRLqB8m8BmE7pIkQxVZOExAcHEzfvn1N21NTUzlz5kyuxuLEiRPEx+c+181gMHDhwgUuXLjA2rVrTdtdXV3NHhIpX758kT03Ubo83Vxh44fQf5rKnVSVJ2sbJ0J2eVLhH3VlXoOwXdJEiBLHxcWFZs2a5bl8e2xsbJ5Ri9OnT5Oenp5rv5SUFA4ePMjBgwdzba9UqVKeQyJ16tSx6sXhRMnRuZlC9I8qkVHR1K1VVUbDRLEgTYQoNXx8fPDx8aFz586mbdnZ2YSFheWZyBkeHp7n/jnLgP/yyy+mbfb29tSsWTNPcxEQEFAkz0mULK7O4Opkfi6EELZImghRqtnb21OrVi1q1apF//79TduTk5M5ffp0nkMiiYmJue6fnZ3N2bNnOXv2LGvWrDFtd3Nzo0GDBlSuXJmgoCAqVKhA+fLlTR85tz08POQvTiFEsSVNhBBmuLm50aJFC1q0aGHapqoq169fz3NI5MyZM2RmZua6f3JyMgcOHHjo17G3tzddGVWr0bh/m5xRIoSwFdJECJFPiqJQuXJlKleuTNeuXU3bs7KyuHjxYp5DIpcvX37oY2ZnZxMXF0dcXFy+63B2dn5gs3H/bW9vbxwcHArylIUQ4oGkiRDiMTk4OFC3bl3q1q3L888/b9qelJTEwYMHcXBwIDExkYSEBNNHfHx8ntv3r3mhJS0tjejoaKKjo/Ndo6enp9lRDa3bcphFCJEf0kQIUUjKli1LjRo1CAgIyNcbcs7VUs01GVrb7r9qqpakpCSSkpIICwvL1/45h1m0DqnIYRYhBEgTIYTNcHFxwcXFBX9//3ztr6oqt27deugIx723b968ma/HftzDLA9rPry8vPLdAAkhbJc0EUIUU4qi4OnpiaenJ8HBwfm6T1ZWVr4Ordx7u7AOsyiKQvny5U2n3vr6+pr+f//tChUqoNPJryshbI38VApRijg4OJjemPMrNTX1oYdW7r+dna1xbep7qKpKfHw88fHxnDp16oH73t9wmGs6crZJwyFE0ZGfNCHEA7m4uFC1alWqVq2ar/0NBkOewyz3NxpxcXFER0eTlJRETExMnlNk7/eoDYe3t/dDRzd8fHyoWLGiNBw2JDMzkzt37nDnzh1u375t+vfWrVtcvXqV2rVr4+fnR5UqVfD29kZRZDlwa5OfHiGERdnZ2VGuXDnKlStHjRo1zO5jMBiIjIwkICAARVG4desWsbGxpo+YmBjN2xkZGQ/8+qqqmpqV06dPP3DfexuOB41u5BxSkVNl88rIyDD7xn/vv/nd9rDv7b0cHR2pVKmS6bRrcx9VqlShbNmy0mwUIptsIlJSUvjoo4/49ddfMRgMtG3blpkzZ1q7LCFEIbh3bketWrUeuK+qqty+ffuhjUbOx/3XRTH3ePltOAC8vb0fOrqRc0jFVhsOVVVNb/yP+iZv7nMPG0UqLJmZmURGRhIZGfnA/VxcXB7YaOR8uLq6FlHlJYtNNhEzZ87E19eXDRs24OTklO/T0oQQJZuiKHh4eODh4UHNmjUfuO+9DUd+Rjke1nAA3Lhxgxs3buS74cjPpNGKFSs+tOFQVZX09PQCvcmb25aVlfXQ+otCmTJlcHd3p2zZsri7u+f6/73/urm5kZycjF6v5/r161y7ds30cf/Vee+XmppKWFjYQ99HPDw8HtpoVKpUiTJlylgygmLP5pqIiIgIzp07x+zZs7G3twegdu3aD7xPZmZmnm5Yp9Ph6OhosboMBkOuf8Vdko15kou2osom543pYWevqKrKnTt3cjUWcXFxmrfT0tIe+rVzGo4zZ848dN+chqNixYoYDAYyMzNJTk7O9cZvK6fEOjk5mX2TN9cAuLm5mW0Kcr4v+f0dbTAYiI6Oxt/fP8+aKxkZGcTExJiaivubjJzbSUlJD/wat27d4tatW5w9e/aB+5UvX97UUNzbXNx7CKUo59oU1s9SfhebU1RVtalLxv38889s3bqVcuXK8fvvv+Pv78/EiRN54oknNO+zdOlSli9fnmvbgAEDGDhwYGGXK4QoZVRVJSUlxeziX+YmkuZnhKOwOTk54erqipubm+nD1dXV7Lb7/+/q6oq7u7tpf1s9TPMwqampprVPcprBnP8/aoP4MHZ2dpQvX56KFSuamsN7R55y/l+uXDmbXRk2KCgoX/vZXBOxYsUKPvvsM9555x2eeeYZdu7cyX/+8x/WrVuHu7u72fsU1UiEVidc2kk25kku2kpLNqqqkpycnO8RjnvX5HBxccn3X/z3fpj7XHE/A6WoXi85h8DMjWTc/39LzAVxcHAwjWI8aGTDw8NDc3JoYWWT38cq8lfWiBEjOH78uNnPDR8+3HRcqk+fPgB07dqVFStWcOrUKVq2bGn2fo6OjhZtGB7Ezs6uRP/SexySjXmSi7bSkE1+53AA3L59m0uXLlGnTp0i+51WnBTF6yXnzKJ69epp7qOqKomJiVy9ejVXw3H/R0xMzAPXTMnKyiIqKoqoqKgH1uTs7Kw5T8PX1xdVVfHx8bHK0vNF3kR8+eWXD/z8H3/8kafjktNzhBClQc4cguI+clDS5Zwa7O3tTcOGDTX3y87OJj4+/qHNxsOWl09LSyM8PJzw8HDNfVauXMkrr7xS4OdUUDb3Sm3WrBmqqvLzzz/TvXt3fv31VxISEqhfv761SxNCCCHyzd7eHl9fX3x9fWnatKnmfpmZmcTGxj602XjQtW8qV65cGE/hoWyuidDpdMyfP59Zs2YxZ84cAgICmDdvnuZ8CCGEEKI4c3R0xN/f/6EX30tLSzPNy8hpOK5evcrFixepXr16EVWbm801EQA1atTg//7v/6xdhhBCCGEznJ2dqVatGtWqVTNtu3f1V2so2TOahBBCCFFopIkQQgghRIFIEyGEEEKIArG5xaaEEEIIUTzISIQQQgghCkSaCCGEEEIUiDQRQgghhCgQaSKEEEIIUSDSRAghhBCiQKSJEEIIIUSBSBMhhBBCiAKRJkIIIYQQBSJNhBBCCCEKRJoIIYQQQhSINBFCCIvT6/WA8TLFIreMjAwA5IoDuSUkJJCdnW3tMmzSsWPHuHLlirXLMEuaCODKlSscPnwYkF9694qIiODbb79lz549pKenW7scmxIeHs7ixYvZsmULN27csHY5NkFVVZKSkpg4cSIrV64EwM5OfsXkiIiI4LnnnuOjjz4CQFEUK1dkGyIiIhg2bBiffPKJ/CzdJywsjJCQEEaNGsXBgwetXY5Zpfon3GAwsGTJEp5//nnmzp1LYmIidnZ2pb6R0Ov1zJs3j2HDhhETE8NHH33Ep59+SlRUlLVLszq9Xs/s2bMZMWIE2dnZ/PDDDyxevJiYmBhrl2Z1iqKQlJTEkSNH2L9/P+fPnwekMdfr9cyaNYuRI0fSsWNH3nrrLWuXZDMuXLjAxIkTadKkCVOmTMHb2xuQUZqMjAymT5/OyJEjad68OU888YQpE1v7eSrVTURERATx8fG8+uqr1KlTh++++w6Qv562bt1KbGwsP/zwA5MnT2bmzJlEREQQFxdn7dKsbvfu3aiqyo8//khoaChDhgwhLCyMMmXKWLs0m3DlyhUaNWrEP/7xD/l5+tuGDRvYt28fM2fO5LXXXgOQkb2/HThwgDZt2jB+/Hjc3NxMzXhpH6X54IMPSE9PZ+3atYwcOZInn3ySX375BbC9nyedtQsoahkZGaZf+N7e3gwePBgfHx8OHDjA6tWrOXv2LHXq1CE7Oxt7e3srV1t07s0lMDAQNzc3fH190ev1PPHEE6SkpHDlyhWaNWtm5UqL3r3ZNG3alNatW+Pk5MRvv/3GnDlzcHR0JDIyEicnJ5ydna1cbdG5Nxe9Xo9Op6Ny5co4OjpSq1Ytjh49ys6dO+nUqZPp86XFvdk0aNCATp06ERYWhqqqfPfdd/j4+BAQEECfPn3w8vKycrVF595cALKysvDz8+Po0aN8+OGHuLm54e/vT/v27Wnfvr31CrWCe7OZNGkSZcuWBYw/WxUqVMDLy4ukpCQ8PT2tWGVepean+vr168yePRsnJyd8fX0JDQ2lXLlylCtXDjD+oB87doxVq1Yxa9asUtNAmMulfv36ps/rdDqysrJwdnYmMDDQeoVagdZrBuDq1avs2LGDoUOH0qxZMw4cOMCmTZsYNWoUFStWtHLlhctcLjkNwrlz56hSpQotW7bk/Pnz7Nixg1q1auHm5mZzv/wKg7lsatSoQb169VixYgXJycmMHj0aOzs7NmzYwM2bNxkxYkSJz0brNXPjxg1u3brFhQsXGDp0KPXq1WP9+vV8+eWXBAcH4+fnZ+3SC525bHIaiKysLBwcHPD19eX48eO4urpaudq8bGtcpJAkJyczZcoU/Pz8GDhwIMeOHePdd9/l0qVLpn18fHxo27YtCQkJ7Nixw4rVFp2H5ZIzU/rmzZskJCSUqr+YHpaNr68vM2fOZNCgQdSoUYPmzZtz+/Ztfv/9dytXXrjM5fLee+8RHh4OgJ+fH/Hx8ZQpU4YWLVpw/vx5+vbty59//mnlygufuWymTZtGTEwMHTp0YMiQIaxevZp+/frRp08fXn31VeLj42121r2lmMvlnXfe4ebNm/Tr148ff/yRixcv0qVLF6pWrcrQoUOpXr16qfg9rPV75vLlywCm5rxJkyY4OTnxxx9/WLFa80pFE3H58mWcnZ2ZOHEiTz75JIsWLeLOnTv88ssvJCUlmfarWbMmTZs2Zdu2bYDx+HdJnkz4sFxyRmNOnjyJo6MjVatWBeDw4cMkJCRYs/RCl59sVFUlMzMTgICAAC5evIiTk5OVKy9c5nK5ffs2O3bsIDk5mcTERLy8vNi3bx/Tpk1Dp9NRv359WrRoAZTsCXPmsklOTmbdunUYDAZ69uyJp6enaT5EUFAQBw8eLPGnNZrLJSUlhdWrV1OjRg169uxJeno6iYmJALi7u3Pr1i08PDysXHnh0/o9s23bNpKSkkxzQ5KTk6lWrRqpqalWrjivUtFEODk5cfbsWdOsVk9PT3r27ElYWBjHjx837Zez/dq1azRv3pzPP//c5iaxWFJ+c7ly5Qq9e/fmwoUL9O7dm+XLl1ur5CKTn2wURcHR0RGA48eP4+npSfXq1a1Wc1HQyuXChQucOXOGqlWrsnr1ahYuXMjrr7/OnDlz8PLyYsuWLUDJnjCnlU14eDiHDx9Gp9OhKIqp0Tx9+jQNGjTA39/fmmUXuge9Zg4fPsy4ceOws7Nj1apVhIeHc+HCBeLj40t8LpD/38Hly5cnJSWFc+fOAbZ1hkbJfYe8R0BAAI0aNeLrr782bevSpQs6nY5z585hMBhQVZXU1FTeeOMNrl27xttvv80PP/xQoo/JPSwXvV6PXq/nyJEjzJ07l7FjxzJo0CCWLFlC+fLlrVh54XtYNqqqcv36ddatW8fEiRN5++236devHzVq1LBi1YVPKxdHR0eOHDmCr68vixcv5ptvvqF9+/ZUrVqV119/nUGDBlmx6qKRn9dMQkIC27dvZ+LEicyaNYvu3buX+MOEWrnkDM97enryxhtvkJWVxQcffMC4cePo06dPqZjEnZ/3ppyF25o0aWI6nGpLf9zaTiWPKTY2VvNziqLQqVMnDh06ZDo8YW9vT5s2bdi+fTt2dnYoikJMTAxPPfUU27dvp3fv3kVVeqF6nFx0Oh06nY7k5GSGDBnCtm3beOGFF4qq9EL3ONkoikKlSpWIjY2lRo0abNu2jWeeeaaoSi9UBc1l586duLi40KJFC5ycnFBVFXt7eypXrlxUpRe6x33NlC9fnhMnThAQEMCWLVvo1q1bUZVeqAqay65duwBo1qwZb7zxBtOnT2fLli3079+/SOouCo/73pQzL6Jr166mhcpsSbFvIrZv307Xrl2ZNm0aM2bMMA33ZGdnm4Z8dDodTzzxBAEBASxatMh0X39/f3x8fEhOTgagWrVqvPrqqyXiVDRL5HLr1i0APv30U8aPH18icgHLZHPnzh0ARowYIa8ZjLlUrFiRlJQU07aSdOjCEq+Z27dvAxAaGsqECRPkNePvj6+vLykpKaZ5IX5+fiXmzDhLvGZSUlJM+1atWtWmRiBy2F5FjyAqKoqVK1cybdo0pk2bhl6v5/PPP+fcuXPY29tjZ2fHqVOnmDRpEllZWbz00kucOXOGGTNmsGnTJmbNmkXNmjVxc3Oz9lOxKEvlkjOxqSRNFrRUNu7u7gAl4o0ALJeLLZ6C9rgslU3OaXvymsn7mikpjUMOS2Zji43DvWy7Og05M7xv3ryJqqo0bNgQf39/xo4di5+fH5999hkAK1euJDQ0lOrVq1OzZk0CAwOZP38+VapUYcOGDXTr1o3Q0FBrPhWLkly0STbmSS7aJBvzJBdtpTIbtRhJTEzMdfu3335TX3vtNTUuLs607dy5c+rgwYPVAwcOqNevX1fv3Llj+pzBYDD9X6/XF37BRURy0SbZmCe5aJNszJNctJXmbIrFSMShQ4fo0aMH8+bNMx2LBmjZsiWXLl3KtZCNn58fbdu2ZfPmzVSsWBE3Nzf0ej2qquY6RlsShs8kF22SjXmSizbJxjzJRZtkUwwOZ4SFhbFixQpatmzJxYsXOXHiBKqqoqoqOp2OF198kc8//9w0acnV1ZWyZcuSlZVlOnUz5/zskkRy0SbZmCe5aJNszJNctEk2RjbfRAQHB9OrVy+mTZtGq1at+PHHH7lx44Yp+MGDB+Ph4cFXX31lWj7W09OTrKysEvEN0iK5aJNszJNctEk25kku2iQbI0VVbXcd2pxhnpwrAKalpTFs2DAGDhzIs88+a5rlfObMGZYvX87Nmzdp3LgxP/30ExMmTKBv375WfgaFQ3LRJtmYJ7lok2zMk1y0STZ32XQTca+cb9batWvZuHEj7733HgEBAabTX2JiYjh8+DBnzpyhS5cuNGrUyLoFFxHJRZtkY57kok2yMU9y0Vbas7F6ExETE8PWrVsJDAykZcuWpuupq6pKdna22XOqQ0JCaNasGSEhISQkJODt7V1ihoZySC7aJBvzJBdtko15kos2ySZ/rDonYuHChbzwwgvExMSwZMkS5s6da1olUVEU0zcp53hSzhrikydPZvv27YwbN47u3bsTFhZmnSdQSCQXbZKNeZKLNsnGPMlFm2STf1ZrIn7++Wfi4+P59ttvmTp1KmPGjOH48eO5ura1a9fSokUL1q1bB9xd6e3s2bNcvnwZV1dX1q1bV6IueiS5aJNszJNctEk25kku2iSbR2T5pSe0ZWVlmf6fmJhoWmzj8OHDaq9evdTevXurR44cUVVVVa9fv66+/PLL6oEDB3I9xsGDB9WXXnopz/biTHLRJtmYJ7lok2zMk1y0STYFVyRzIm7evMmnn36KoigEBwfTt29fHB0dAYiMjGTRokXUqFGD1q1b89tvv6EoCi+88AKenp45jQ4Gg6HYLcLxMJKLNsnGPMlFm2RjnuSiTbJ5fIXeRPz8888sWrSIDh06UK1aNTZu3EjNmjWZNm0agGlxjpyZrIcOHWLNmjU8/fTTdO7cmezs7BL5DZJctEk25kku2iQb8yQXbZKNhRTmMMedO3fURYsWqevXrzdtO3v2rNqvXz/TWuM5a4ZnZGSY/u3evbu6efPmwizNqiQXbZKNeZKLNsnGPMlFm2RjORa/Jm1sbCyKolCxYkWcnZ3p0KEDfn5+ps/funULDw8PnJ2dAUyTVXKGkM6cOYOfn1+Jm5AiuWiTbMyTXLRJNuZJLtokm8JhsSYiKyuL9957j2PHjlGhQgXatGnDM888Q7169YC7K3yVKVMGFxeXXOfYJiYmsmfPHo4cOcK+ffsYM2YMwcHBlirNqiQXbZKNeZKLNsnGPMlFm2RTuCx2iufWrVu5desWGzZs4OWXX+bKlSvMnj07z347d+6kcuXKub5RXl5eRERE4ObmxsaNG3n++ectVZbVSS7aJBvzJBdtko15kos2yaaQPc6xkLS0NNNxo48++kidOnWqqqrGY0lRUVFqr1691DVr1qiqajyeZDAY1GHDhql//fWXqqqqumXLFnXt2rWqqqpqZmbm45RiUyQXbZKNeZKLNsnGPMlFm2RTdAp0OCMqKop58+bh4uKCs7MzU6ZMwd3dHXt7e+7cuYO7uzv+/v6MGDGCzz77jOeeew5HR0dSU1Px9PQkKSmJ0NBQTp48yZQpUwBwcHCwaHNkDZKLNsnGPMlFm2RjnuSiTbIpeo98OGPdunWMGTOGmjVr8tJLL3H+/Hm+/PJLgoOD+euvv4iNjTXt2759e6pVq8batWsBiIiIYO/evcyaNYvg4GB27dpFt27dLPdsrEhy0SbZmCe5aJNszJNctEk21vHITcS1a9cICQlh3Lhx1K9fnw8//JDVq1fTqlUrypYty6ZNm0hKSgKMHZyvry+ZmZnGL2Znx6hRo1i/fj3jx4+36BOxNslFm2RjnuSiTbIxT3LRJtlYxyMfzsgZ/gHjrFd7e3uCgoLQ6/WMHDmSBQsWEBAQQPfu3XFxcSEpKQkPDw8AateuTd26dS37DGyE5KJNsjFPctEm2ZgnuWiTbKzjkZsIHx8fwHhajIODAwkJCSiKgqOjI40bN+bZZ59l27Zt7Nq1C71ez7Vr10yn0uSs/FUSSS7aJBvzJBdtko15kos2ycY6CrxORM5CHAcPHiQoKMi0/Odzzz1H69at2b9/P3fu3GHo0KEWKbS4kFy0STbmSS7aJBvzJBdtkk3RKnATkbNu+IULF+jSpQsAa9asITk5meHDh/Pcc89ZrMjiRHLRJtmYJ7lok2zMk1y0STZFq8BjOPb29uj1etLT04mNjWXUqFF8/fXX1K9f35L1FTuSizbJxjzJRZtkY57kok2yKVqPtex1REQEf/zxBxcvXuTFF19kyJAhlqqrWJNctEk25kku2iQb8yQXbZJN0XmsS4Hr9Xp++OEH+vfvT5kyZSxZV7EmuWiTbMyTXLRJNuZJLtokm6LzWE2EEEIIIUovOa9FCCGEEAUiTYQQQgghCkSaCCGEEEIUiDQRQgghhCgQaSKEEEIIUSDSRAghhBCiQKSJEEIIIUSBSBMhhBBCiAKRJiKfDAYDly5dwmAwWLsUmyPZmCe5aJNszJNczJNctFk7G2kihBBCCFEgFm8ifvzxRwYPHkyLFi1YunSp5n4Gg4H58+fTvn17nn76ab777rtcn9+/fz99+vShdevWTJo0idu3b1u6VCGEEEI8Bos3EeXLlyckJISOHTs+cL+1a9dy+PBhfvrpJ7744gu+/fZbDh48CEBiYiJvv/02kydPZseOHbi7uzN37lxLlyqEEEKIx/BYlwI3p3379oBxJOFBNm/ezEsvvYSXlxdeXl706dOHTZs20bx5c3799Vfq1q1L69atAQgJCWHAgAG8/fbbODk5Wbrkh4qLi+Onn34iMTERLy8v7OzkKNC97OzsqFu3LgEBAdYuRQghRBGyeBORXxEREdSoUcN0Ozg4mH379gFw6dIlgoODTZ+rUqUKOp2OK1eu5NqeIzMzk8zMzFzbdDodjo6OFqk1PDycsWPHWuSxSipfX19OnjyJl5eXtUuxGTkTnWQyWF6SjXmSi3mSi7bCyia/fyxbrYlIS0vD1dXVdNvV1ZXU1FQAUlNT8fHxybW/q6sraWlpZh/rq6++Yvny5bm2DRgwgIEDB1qk1piYGIs8TkkWExNDaGgo77//vrVLsTnR0dHWLsFmSTbmSS7mSS7aLJ1NUFBQvvazWhPh7OxMSkqK6XZKSgouLi4AuLi45PpczuednZ3NPtawYcMYPHhwrm2WHIlwcXFh+fLlpsMZiqJY5HFLgqysLCZPnkxKSgqrVq1i7NixPPXUU9YuyyYYDAaio6Px9/eXQ2D3kWzMk1zMk1y0WTsbqzUR1apVIywszHRIIzw8nGrVqgHGDmjnzp2mfa9du4Zer8fPz8/sYzk6OlqsYTDHx8eH4cOHExkZSUBAgLyI75OWlsakSZMAGDNmDEeOHCnU70dxY2dnJ68ZDZKNeZKLeZKLNmtlY/GvqNfrycjIwGAwkJ2dTUZGBtnZ2Xn26969O9988w03b94kOjqadevW0bNnTwA6dOjAmTNn+P3330lPT2f58uV06tTJKpMqxcONGzeO+vXrA3D69GnmzZtn5YqEEEIUBYs3EV9++SWtWrVi3bp1rFixglatWrF582aOHj1KmzZtTPv179+fpk2b0rdvX4YPH86LL75I8+bNAfDy8uL9999nzpw5dOrUiaSkJN544w1LlyosxN7entmzZ5u64JkzZxIWFmblqoQQQhQ2RVVV1dpFFAcGg0EOZ2jIyWbRokUsWLAAgE6dOrF9+/ZSPX9EXjPaJBvzJBfzJBdt1s5GvhvCYqZPn07VqlUB2LlzJ99++62VKxJCCFGYpIkQFuPm5sZnn31muj1p0iQSEhKsWJEQQhQf7du3Z8KECabbgYGBfPzxx1arJz+kiRAW1bNnTwYMGABAQkICkydPtnJFQghRPP3111+EhIRYu4wHkiZCWNzChQvx8PAA4Ouvv2bXrl1WrkgIIYqfChUqmNZPslXSRAiLq1SpEh9++KHp9pgxY0hPT7diRUKI0mLr1q20bt0aT09PvL29eeaZZwgPDwfg8uXLKIrCTz/9RIcOHXBxceGJJ57gwIEDuR5j7dq11KtXjzJlyhAYGMj8+fNzfT4wMJD333+fIUOG4ObmRkBAABs2bCA+Pp7evXvj5uZGw4YNOXTokOk+N27cYNCgQVSpUgUXFxcaNGjA999//8Dncv/hjKSkJEaOHEmFChUoW7YsHTt25Pjx46bPHz9+nA4dOuDu7k7ZsmVp2rRprhoKgzQRolCEhISYVq68ePEi//73v61ckRCiNEhJSWHSpEkcOnSInTt3YmdnR9++fXNdWyLnKtHHjh2jZs2aDBo0CL1eD8Dhw4cZOHAgL7zwAidPnmT69OlMmzaNlStX5vo6CxYsoFWrVhw9epSePXvy8ssvM2TIEF566SWOHDlC9erVGTJkCDknQKanp9O0aVM2bdrEqVOnCAkJ4eWXXzZdvTo/BgwYQFxcHFu2bOHw4cM0adKELl26kJSUBMDgwYPx8/Pjr7/+4vDhw0ydOhUHB4fHC/Qh5BTPfLL2aTS2TCubU6dO0bhxY/R6PQ4ODhw7doy6detasdKiJa8ZbZKNebacS7NmzaxyHSFfX18OHjxY4FwSEhKoUKECJ0+exM3NjaCgIL744gtGjBgBwJkzZ6hXrx5nz56ldu3aDB48mPj4eH755RfTY0yZMoVNmzZx+vRpwDhC0KZNG7755hvAeO2gSpUqMW3aNGbOnAnAH3/8QcuWLbl+/Tq+vr5ma3vmmWeoXbu2aYG+9u3b06hRI9PoQ2BgIBMmTGDChAns27ePnj17EhcXR5kyZUyPERwczPDhw5k6dSqenp4sWrSIV1555ZEyehxWW/ZalHz169dnypQpzJ49m6ysLEJCQvjtt99s7pejEOLhYmJiuHr1qrXLeKiLFy/y7rvv8ueff5KQkGAagYiKijL9EdOwYUPT/pUqVQIgLi6O2rVrc/bsWXr37p3rMVu1asXHH39MdnY29vb2eR4j54KRDRo0yLMtLi4OX19fsrOzmT17NmvWrOHq1atkZmaSkZGR7zkPx48fJzk5GW9v71zb09LSiIyMBIxnxI0cOZJvvvmGzp07M2DAAKpXr56vxy8oaSJEoXrnnXdYs2YNYWFh7N+/ny+++MLmZxsLIfLS+mva1r5ur169CAgIYPny5VSuXBmDwUD9+vXJzMw07XPvEH/OgniPeiltc4/xoMedO3cuCxcu5OOPP6ZBgwa4uroyYcKEXHU9SHJyMpUqVWL37t25thsMBu7cuQMY1+p58cUX2bRpE1u2bOG9995j9erV9O3b95Ge26OQJkIUKmdnZ5YsWULnzp0B47Dgs88+a7VfSEKIginsCXoPkt83+Bs3bnD+/HmWL19uuszCvn37Hulr1alTh/379+fatn//fmrWrGkahSiI/fv307t3b1566SXA+JwuXLiQ70O8TZo0ISYmBp1OR2BgoGl7ziGwHDVr1qRmzZpMnDiRQYMG8dVXXxVqEyHjyqLQderUiZdffhmAW7du5VpMRQghLKVcuXJ4e3uzbNkywsLC2LVrl+kKw/n1+uuvs3PnTmbNmsWFCxf4+uuvWbx48WOveVOjRg22b9/O77//ztmzZxk9ejSxsbH5vn/nzp1p2bIlffr04ZdffuHy5cv8/vvvvPPOO5w4cYK0tDTGjRvH7t27iYyMZP/+/fz111/UqVPnsep+GGkiRJGYP3++6VjeDz/8wJYtW6xckRCipLGzs2P16tUcPnyY+vXrM3HiRObOnftIj9GkSRPWrFnD6tWrqV+/Pu+++y4zZ85k6NChj1XbO++8Q5MmTejatSvt27fH19eXPn365Pv+iqKwefNm2rZty7Bhw6hZsyYvvPACkZGRlC9fHnt7e27cuMGQIUOoWbMmAwcOpHv37syYMeOx6n5oXXJ2Rv7Y8qxpa8tvNitXrmTYsGEABAQEcPr0aVxdXYuqzCInrxltko15kot5kos2a2cj3w1RZF555RU6dOgAQGRkJNOnT7duQUIIIR6LNBGiyCiKwpIlS0znOC9YsIBjx45ZtyghhBAFJk2EKFI1a9bk7bffBiA7O5tRo0aRnZ1t5aqEEEIUhDQRosi9+eabphnDhw4d4tNPP7VyRUIIIQpCmghR5BwdHVm2bJnp9ttvv010dLQVKxJCCFEQFm8ibt68SWhoKK1bt6Zfv36aFxcZOHAgbdq0MX00b96c//znPwBcu3aNZs2a5fq8nBJYsrRu3dq0cmVycjLjx4+3ckVCCCEelcVXrJwzZw7e3t7s2LGDP//8k7feeouffvoJDw+PXPutWbPG9P/MzEy6du1Kx44dTdvs7e3Zu3evpcsTNuTDDz9k/fr1xMbGsn79ev73v/8V6spqQgghLMuiIxGpqans3r2b0aNH4+TkRLt27ahevTp79ux54P1+++03XF1dadq0qSXLETauXLlyLFy40HR7/Pjx3L5924oVCSGEeBQWHYmIiorCxcXFdPUyMF6mNCIi4oH327x5M927dzddsASMM/e7deuGTqejQ4cOvPbaazg5OZm9f2ZmZp6LmOh0OhwdHR/j2eSWs3b7o16kpTR4nGz69+9Pt27d2Lp1K1evXuVf//oXn3zyiaVLtAp5zWiTbMyTXMyTXLQVVjb5XbjKoitWHj16lHfffZeNGzeatn366afcunWLf/3rX2bvk5SURLdu3Vi9erXpoiKpqalERUVRo0YN4uLieO+99wgODmbKlClmH2Pp0qUsX74817YBAwYwcOBAyzwxUaiuXLlC165dSUtLQ1EU1q5dS6NGjaxdlhBClFpBQUH52s+iIxHOzs6kpKTk2paSkvLA66X/8ssv1KxZM9dVyVxcXKhduzZgvNb7+PHjmTJlimYTMWzYMAYPHpxrW2GMRERHR+Pv7y/Lrt7ncbMJCAhgxowZTJkyBVVVmT59OgcPHsx1Wd3iSF4z2iQb8yQX8yQXbdbOxqJNRNWqVUlNTSUuLo6KFSsCEB4eTs+ePTXvs3nzZnr06PHAx1UUhQcNmDg6Olq0YXgQOzs7eRFreJxsJk6cyKpVqzh27BgnTpxg4cKFmk1jcSOvGW2SjXmSi3mSizZrZWPRr+ji4kK7du1YunQp6enp7N27l7CwMNq1a2d2/6ioKM6dO0e3bt1ybT916hRRUVGoqkp8fDyffvopbdu2tWSpwsbodDqWLVtm+iGYPn06ly5dsnJVQgghHsTibcvUqVOJj4+nU6dOLFiwgNmzZ+Ph4cGWLVvyzFHYvHkzLVu2xNPTM9f2K1eu8Nprr9GmTRteeeUVgoKCmDBhgqVLFTbmySefZNy4cQCkpaUxduzYB45ACSGEsC65FHg+Wftyq7bMktncuXOHunXrcuXKFQBWrVrFoEGDLFFmkZPXjDbJxjzJxTzJRZu1s5HvhrAp7u7uLF682HR7woQJJCYmWrEiIYQQWqSJEDand+/eppUr4+LiePPNN61ckRBCCHOkiRA2adGiRbi7uwPwxRdfyBLoQghhg6SJEDapSpUqzJ4923Q7JCSEjIwMK1YkhBDiftJECJs1duxYWrRoAcC5c+eYM2eOlSsSQghxL2kihM2yt7dn2bJl2NvbA/Dvf/+b8+fPW7kqIYQQOaSJEDatYcOGvP7664DxQmujR4+WtSOEEMJGSBMhbN57771nuhjMnj17+Oqrr6xckRBCCJAmQhQDLi4ufP7556bbkydPJi4uzooVCSGEAGkiRDHRtWtX08qVN2/eZNKkSVauSAghhDQRothYsGCB6Tor3333Hdu3b7duQUIIUcpJEyGKDR8fH+bNm2e6PWbMGFJTU61YkRBClG7SRIhiZfjw4abLwkdERDBr1iwrVySEEKWXNBGiWFEUhaVLl+Lo6AjAvHnzOHnypJWrEkKI0kmaCFHs1K5dm7feegsAvV5PSEgIBoPBylUJIUTpI02EKJbeeustatWqBcAff/zBkiVLrFyREEKUPtJEiGKpTJkyLF261HT7rbfe4tq1a1asSAghSh9pIkSx1a5dO4YPHw7A7du3+ec//2nlioQQonSxeBNx8+ZNQkNDad26Nf369ePgwYNm95s+fTotW7akTZs2tGnThoEDB+b6/MaNG+nRowft2rVjxowZZGVlWbpUUQLMnTuXChUqALB27Vo2btxo5YqEEKL0sHgTMWfOHLy9vdmxYwehoaG89dZb3Lp1y+y+I0aMYO/evezdu5c1a9aYtoeFhfHRRx8xd+5cNm3aRGxsLF988YWlSxUlgJeXFwsWLDDdfu2110hOTrZiRUIIUXpYtIlITU1l9+7djB49GicnJ9q1a0f16tXZs2fPIz3O1q1b6dixI/Xq1cPNzY3hw4ezadMmS5YqSpAXX3yRp59+GoDo6GimTZtm5YqEEKJ00FnywaKionBxccHHx8e0LTg4mIiICLP7f//993z//fcEBATw2muv0bRpU8C4iFDz5s1zPUZMTAypqam4uLjkeZzMzEwyMzNzbdPpdKa1BCwh5xRCOZUwL1vIZvHixTRs2JD09HQ++eQTXnzxRdPryVpsIRdbJdmYJ7mYJ7loK6xs7OzyN8Zg0SYiLS0NV1fXXNtcXV3NHs544YUXmDRpEs7OzuzYsYNJkyaxevVqKlWqlOdx3NzcADSbiK+++orly5fn2jZgwIA88ywsITo62uKPWVJYMxudTsf48eOZO3cuBoOBYcOG8b///Q+dzqIv8QKR14w2ycY8ycU8yUWbpbMJCgrK134W/Q3r7OxMSkpKrm0pKSlm3/hr165t+n/37t3ZvHkzf/zxB3379s3zODnHuM09DsCwYcMYPHhwrm2FMRIRHR2Nv79/vju00sJWspk1axZbt27l5MmTnD59mg0bNjBx4kSr1WMrudgiycY8ycU8yUWbtbOxaBNRtWpVUlNTiYuLo2LFigCEh4fTs2fPh95XURRUVQWgWrVqhIWFmT4XHh6Or6+vZhPh6Oho0YbhQezs7ORFrMHa2ZQpU4Zly5bx1FNPoaoq7777Lv379ycgIMBqNYH1c7Flko15kot5kos2a2Vj0a/o4uJCu3btWLp0Kenp6ezdu5ewsDDatWuXZ9+dO3eSlpaGXq/nl19+4dixY6Z5EN26dWPXrl2cPXuW5ORkVqxYka9GRIh//OMfjB07FjAe/nrttddMzakQQgjLsnjbMnXqVOLj4+nUqRMLFixg9uzZeHh4sGXLllxzFFatWkW3bt3o1KkT3333HfPmzcPPzw8wTqScOHEikyZNokePHlSoUIERI0ZYulRRQs2ePZtKlSoBsGnTJn788UcrVySEECWTosqfafliMBiIjIwkICBAhtPuY4vZrF27lv79+wPg6+vL2bNn8fT0LNIabDEXWyHZmCe5mCe5aLN2NvLdECVSv3796NWrFwAxMTGmq34KIYSwHGkiRImkKAqLFy82nSq8ZMkSfv/9dytXJYQQJYs0EaLEqlq1Ku+//77pdkhISJ5FyYQQQhScNBGiRBs/frxp5crTp08zb948K1ckhBAlhzQRokSzt7dn2bJlpglHM2fOzLUGiRBCiIKTJkKUeE2aNDGtXJmRkcGYMWNk7QghhLAAaSJEqTBjxgzTypU7d+7k22+/tXJFQghR/EkTIUoFV1dXPvvsM9PtSZMmkZCQYMWKhBCi+JMmQpQaPXr0MK2ampCQwBtvvGHlioQQoniTJkKUKgsXLsTDwwOAlStX8uuvv1q5IiGEKL6kiRCliq+vL3PmzDHdHj16NOnp6VasSAghii9pIkSpM2rUKFq1agXAxYsX+fe//23lioQQoniSJkKUOnZ2dixduhQHBwcA5syZw5kzZ6xclRBCFD/SRIhSqV69ekyZMgWArKwsQkJCMBgMVq5KCCGKF2kiRKn19ttvExwcDMD+/fv54osvrFyREEIUL9JEiFLL2dmZJUuWmG5PmTKFmJgYK1YkhBDFizQRolTr1KkTQ4YMAeDWrVtMmDDBugUJIUQxIk2EKPXmz5+Pt7c3AD/88ANbtmyxckVCCFE8WLyJuHnzJqGhobRu3Zp+/fpx8OBBs/stWLCA3r1707ZtW1544QX27t1r+tyhQ4d48sknadOmjenj6NGjli5VCADKly/P/PnzTbfHjh1LSkqKFSsSQojiweJNxJw5c/D29mbHjh2Ehoby1ltvcevWrTz7ubi48Mknn7B7924mT57MtGnTuHr1qunzVapUYe/evaaPxo0bW7pUIUyGDBlCx44dAYiMjGT69OnWLUgIIYoBizYRqamp7N69m9GjR+Pk5ES7du2oXr06e/bsybPv6NGjCQgIwM7OjmbNmlGtWjXOnTtnyXKEyDdFUViyZAllypQBjCNlx44ds25RQghh43SWfLCoqChcXFzw8fExbQsODiYiIuKB97t9+zbh4eFUq1bNtC02NpYuXbrg5uZGjx49GD58OPb29mbvn5mZSWZmZq5tOp0OR0fHx3g2ueWsISBrCeRVUrKpXr06b7/9Nu+++y7Z2dmMGjWK33//XfN19zAlJZfCINmYJ7mYJ7loK6xs7OzyN8Zg0SYiLS0NV1fXXNtcXV3NHs7IYTAYmDFjBh07diQoKAiAwMBAvv/+e6pWrcrly5eZOnUqzs7OvPTSS2Yf46uvvmL58uW5tg0YMMB0xUZLio6OtvhjlhQlIZuBAwfyzTffcPHiRQ4dOsT777/P0KFDH+sxS0IuhUWyMU9yMU9y0WbpbHLejx9GUVVVtdQXPXfuHK+++iq7du0ybfvPf/6Do6Oj5qlzs2fPJjIykkWLFmmOHGzbto0ffviBFStWmP18UY1EREdH4+/vn+8OrbQoadns27ePdu3aAeDm5sapU6fw9/d/5McpablYkmRjnuRinuSirbCyscpIRNWqVUlNTSUuLo6KFSsCEB4eTs+ePc3uv3DhQs6dO8fnn3/+wDf8hz0ZR0dHizYMD2JnZycvYg0lJZu2bdsSEhLCsmXLSE5OJjQ0lHXr1hX48UpKLoVBsjFPcjFPctFmrWws+hVdXFxo164dS5cuJT09nb179xIWFmb6q+5eX3zxBfv27eOTTz7Jcwjk0KFDppUDo6Ki+PLLL2nbtq0lSxXigT788EPT3J7169fzv//9z8oVCSGE7bF42zJ16lTi4+Pp1KkTCxYsYPbs2Xh4eLBly5ZccxSWLFnClStX6NWrl2ktiJxFfs6dO8ewYcNo3bo148aNo3379przIYQoDOXKlWPhwoWm2+PHj+f27dtWrEgIIWyPRedElGQGg4HIyEjTaanirpKajaqq9OzZ09Tcjhs3jkWLFuX7/iU1F0uQbMyTXMyTXLRZOxv5bgihQVEUPvvsM1xcXAD49NNP+fPPP61clRBC2A5pIoR4gMDAQGbOnAkYRyZCQkLIysqyclVCCGEbpIkQ4iFCQ0NNy66fOHGCBQsWWLkiIYSwDdJECPEQOp2OZcuWmY43Tp8+nUuXLlm5KiGEsD5pIoTIh2bNmjF+/HjAuDLr2LFjkTnJQojSTpoIIfJp1qxZ+Pn5AcZVVFevXm3lioQQwrqkiRAin9zd3fn0009NtydMmEBiYqIVKxJCCOuSJkKIR/Dss8/Sr18/AOLi4njzzTetXJEQQliPNBFCPKJPPvkEd3d3wLh8+969e61ckRBCWIc0EUI8oipVqvDBBx+YboeEhJCRkWHFioQQwjqkiRCiAMaMGUOLFi0A47Ve5syZY+WKhBCi6EkTIUQB2Nvbs2zZMnQ6HQD//ve/OX/+vJWrEkKIoiVNhBAF1LBhQ15//XUAMjMzGTNmjKwdIYQoVaSJEOIxvPvuuwQFBQGwe/duVq5cad2ChBCiCEkTIcRjcHFxYcmSJabbkydPJj4+3ooVCSFE0ZEmQojH9PTTT/Piiy8CkJiYyKRJk6xckRBCFA1pIoSwgAULFlCuXDkAvv32W7Zv327lioQQovBJEyGEBVSsWJG5c+eabo8ZM4bU1FQrViSEEIXP4k3EzZs3CQ0NpXXr1vTr14+DBw+a3S89PZ1p06bRtm1bevbsydatW3N9fuPGjfTo0YN27doxY8YMsrKyLF2qEBY1fPhw2rZtC0BERATvv/++lSsSQojCZfEmYs6cOXh7e7Njxw5CQ0N56623uHXrVp79li5dSlJSEps3b+bDDz9kzpw5XL58GYCwsDA++ugj5s6dy6ZNm4iNjeWLL76wdKlCWJSiKCxduhRHR0cA5s+fz7lz56xclRBCFB6dJR8sNTWV3bt3s379epycnGjXrh3Vq1dnz549PPvss7n23bx5M3PmzMHNzY0GDRrQrl07tm3bxujRo9m6dSsdO3akXr16gPEvvOnTpzN27FhLliuExdWuXZu33nqLGTNmoNfrmTJlCpGRkdjZyZHDexkMBhITE/Hy8pJs7iG5mCe5aMvJ5sUXXyQwMLDIv75Fm4ioqChcXFzw8fExbQsODiYiIiLXfrdv3+bGjRsEBwfn2u/EiROAcSi4efPmuT4XExNDamoqLi4ueb5uZmYmmZmZubbpdDrTX4SWYDAYcv0r7pJscnvzzTdZvXo158+f59SpU9L8CiEKXYMGDahatarFHi+/zZpFm4i0tDRcXV1zbXN1dc1zOCNnwtm9+7q6upKWlmb2cdzc3Ez3M9dEfPXVVyxfvjzXtgEDBjBw4MDHeDbmRUdHW/wxSwrJ5q4ZM2YwePBgsrOzrV2KEKIUiI+PJzIy0mKPl7OI3sNYtIlwdnYmJSUl17aUlJQ8b/w5t1NSUkwNQkpKCs7OzmYfJzk5Odf97jds2DAGDx6ca1thjERER0fj7+8vw2n3kWzyCggIoHr16uzatQsvLy8URbF2STZFVVXT8LRkc5fkYp7koi0nmw4dOhAQEFDkX9+iTUTVqlVJTU0lLi6OihUrAhAeHk7Pnj1z7Ve2bFm8vb0JCwujUaNGpv2qV68OQLVq1QgLCzPtHx4ejq+vr2YT4ejoaNGG4UHs7OzkjVKDZJNbkyZN8Pb2JiAgQHK5j8FgIDIyUrK5j+RinuSizdrZWPQruri40K5dO5YuXUp6ejp79+4lLCyMdu3a5dm3R48erFixgpSUFE6dOsWePXvo2rUrAN26dWPXrl2cPXuW5ORkVqxYkacREUIIIYR1WbxtmTp1KvHx8XTq1IkFCxYwe/ZsPDw82LJlS645CqNHj6Zs2bJ069aNN998kylTpphmlgYHBzNx4kQmTZpEjx49qFChAiNGjLB0qUIIIYR4DIoq1y7OF2sPGdkyycY8yUWbZGOe5GKe5KLN2tlIEyGEEEKIApGWTgghhBAFIk2EEEIIIQpEmgghhBBCFIg0EUIIIYQoEGkihBBCCFEg0kQIIYQQokCkiRBCCCFEgUgTIYQQQogCkSZCCCGEEAUiTYQQQgghCkSaCCGExen1esC4rr/ILSMjAwC54kBuCQkJZGdnW7sMm3Ts2DGuXLli7TLMkiYCuHLlCocPHwbkl969IiIi+Pbbb9mzZw/p6enWLsemhIeHs3jxYrZs2cKNGzesXY5NUFWVpKQkJk6cyMqVKwHkYkn3iIiI4LnnnuOjjz4CQFEUK1dkGyIiIhg2bBiffPKJ/CzdJywsjJCQEEaNGsXBgwetXY5Zpfon3GAwsGTJEp5//nnmzp1LYmIidnZ2pb6R0Ov1zJs3j2HDhhETE8NHH33Ep59+SlRUlLVLszq9Xs/s2bMZMWIE2dnZ/PDDDyxevJiYmBhrl2Z1iqKQlJTEkSNH2L9/P+fPnwekMdfr9cyaNYuRI0fSsWNH3nrrLWuXZDMuXLjAxIkTadKkCVOmTMHb2xuQUZqMjAymT5/OyJEjad68OU888YQpE1v7eSrVTURERATx8fG8+uqr1KlTh++++w6Qv562bt1KbGwsP/zwA5MnT2bmzJlEREQQFxdn7dKsbvfu3aiqyo8//khoaChDhgwhLCyMMmXKWLs0m3DlyhUaNWrEP/7xD/l5+tuGDRvYt28fM2fO5LXXXgOQkb2/HThwgDZt2jB+/Hjc3NxMzXhpH6X54IMPSE9PZ+3atYwcOZInn3ySX375BbC9nyedtQsoahkZGaZf+N7e3gwePBgfHx8OHDjA6tWrOXv2LHXq1CE7Oxt7e3srV1t07s0lMDAQNzc3fH190ev1PPHEE6SkpHDlyhWaNWtm5UqL3r3ZNG3alNatW+Pk5MRvv/3GnDlzcHR0JDIyEicnJ5ydna1cbdG5Nxe9Xo9Op6Ny5co4OjpSq1Ytjh49ys6dO+nUqZPp86XFvdk0aNCATp06ERYWhqqqfPfdd/j4+BAQEECfPn3w8vKycrVF595cALKysvDz8+Po0aN8+OGHuLm54e/vT/v27Wnfvr31CrWCe7OZNGkSZcuWBYw/WxUqVMDLy4ukpCQ8PT2tWGVepean+vr168yePRsnJyd8fX0JDQ2lXLlylCtXDjD+oB87doxVq1Yxa9asUtNAmMulfv36ps/rdDqysrJwdnYmMDDQeoVagdZrBuDq1avs2LGDoUOH0qxZMw4cOMCmTZsYNWoUFStWtHLlhctcLjkNwrlz56hSpQotW7bk/Pnz7Nixg1q1auHm5mZzv/wKg7lsatSoQb169VixYgXJycmMHj0aOzs7NmzYwM2bNxkxYkSJz0brNXPjxg1u3brFhQsXGDp0KPXq1WP9+vV8+eWXBAcH4+fnZ+3SC525bHIaiKysLBwcHPD19eX48eO4urpaudq8bGtcpJAkJyczZcoU/Pz8GDhwIMeOHePdd9/l0qVLpn18fHxo27YtCQkJ7Nixw4rVFp2H5ZIzU/rmzZskJCSUqr+YHpaNr68vM2fOZNCgQdSoUYPmzZtz+/Ztfv/9dytXXrjM5fLee+8RHh4OgJ+fH/Hx8ZQpU4YWLVpw/vx5+vbty59//mnlygufuWymTZtGTEwMHTp0YMiQIaxevZp+/frRp08fXn31VeLj42121r2lmMvlnXfe4ebNm/Tr148ff/yRixcv0qVLF6pWrcrQoUOpXr16qfg9rPV75vLlywCm5rxJkyY4OTnxxx9/WLFa80pFE3H58mWcnZ2ZOHEiTz75JIsWLeLOnTv88ssvJCUlmfarWbMmTZs2Zdu2bYDx+HdJnkz4sFxyRmNOnjyJo6MjVatWBeDw4cMkJCRYs/RCl59sVFUlMzMTgICAAC5evIiTk5OVKy9c5nK5ffs2O3bsIDk5mcTERLy8vNi3bx/Tpk1Dp9NRv359WrRoAZTsCXPmsklOTmbdunUYDAZ69uyJp6enaT5EUFAQBw8eLPGnNZrLJSUlhdWrV1OjRg169uxJeno6iYmJALi7u3Pr1i08PDysXHnh0/o9s23bNpKSkkxzQ5KTk6lWrRqpqalWrjivUtFEODk5cfbsWdOsVk9PT3r27ElYWBjHjx837Zez/dq1azRv3pzPP//c5iaxWFJ+c7ly5Qq9e/fmwoUL9O7dm+XLl1ur5CKTn2wURcHR0RGA48eP4+npSfXq1a1Wc1HQyuXChQucOXOGqlWrsnr1ahYuXMjrr7/OnDlz8PLyYsuWLUDJnjCnlU14eDiHDx9Gp9OhKIqp0Tx9+jQNGjTA39/fmmUXuge9Zg4fPsy4ceOws7Nj1apVhIeHc+HCBeLj40t8LpD/38Hly5cnJSWFc+fOAbZ1hkbJfYe8R0BAAI0aNeLrr782bevSpQs6nY5z585hMBhQVZXU1FTeeOMNrl27xttvv80PP/xQoo/JPSwXvV6PXq/nyJEjzJ07l7FjxzJo0CCWLFlC+fLlrVh54XtYNqqqcv36ddatW8fEiRN5++236devHzVq1LBi1YVPKxdHR0eOHDmCr68vixcv5ptvvqF9+/ZUrVqV119/nUGDBlmx6qKRn9dMQkIC27dvZ+LEicyaNYvu3buX+MOEWrnkDM97enryxhtvkJWVxQcffMC4cePo06dPqZjEnZ/3ppyF25o0aWI6nGpLf9zaTiWPKTY2VvNziqLQqVMnDh06ZDo8YW9vT5s2bdi+fTt2dnYoikJMTAxPPfUU27dvp3fv3kVVeqF6nFx0Oh06nY7k5GSGDBnCtm3beOGFF4qq9EL3ONkoikKlSpWIjY2lRo0abNu2jWeeeaaoSi9UBc1l586duLi40KJFC5ycnFBVFXt7eypXrlxUpRe6x33NlC9fnhMnThAQEMCWLVvo1q1bUZVeqAqay65duwBo1qwZb7zxBtOnT2fLli3079+/SOouCo/73pQzL6Jr166mhcpsSbFvIrZv307Xrl2ZNm0aM2bMMA33ZGdnm4Z8dDodTzzxBAEBASxatMh0X39/f3x8fEhOTgagWrVqvPrqqyXiVDRL5HLr1i0APv30U8aPH18icgHLZHPnzh0ARowYIa8ZjLlUrFiRlJQU07aSdOjCEq+Z27dvAxAaGsqECRPkNePvj6+vLykpKaZ5IX5+fiXmzDhLvGZSUlJM+1atWtWmRiBy2F5FjyAqKoqVK1cybdo0pk2bhl6v5/PPP+fcuXPY29tjZ2fHqVOnmDRpEllZWbz00kucOXOGGTNmsGnTJmbNmkXNmjVxc3Oz9lOxKEvlkjOxqSRNFrRUNu7u7gAl4o0ALJeLLZ6C9rgslU3OaXvymsn7mikpjUMOS2Zji43DvWy7Og05M7xv3ryJqqo0bNgQf39/xo4di5+fH5999hkAK1euJDQ0lOrVq1OzZk0CAwOZP38+VapUYcOGDXTr1o3Q0FBrPhWLkly0STbmSS7aJBvzJBdtpTIbtRhJTEzMdfu3335TX3vtNTUuLs607dy5c+rgwYPVAwcOqNevX1fv3Llj+pzBYDD9X6/XF37BRURy0SbZmCe5aJNszJNctJXmbIrFSMShQ4fo0aMH8+bNMx2LBmjZsiWXLl3KtZCNn58fbdu2ZfPmzVSsWBE3Nzf0ej2qquY6RlsShs8kF22SjXmSizbJxjzJRZtkUwwOZ4SFhbFixQpatmzJxYsXOXHiBKqqoqoqOp2OF198kc8//9w0acnV1ZWyZcuSlZVlOnUz5/zskkRy0SbZmCe5aJNszJNctEk2RjbfRAQHB9OrVy+mTZtGq1at+PHHH7lx44Yp+MGDB+Ph4cFXX31lWj7W09OTrKysEvEN0iK5aJNszJNctEk25kku2iQbI0VVbXcd2pxhnpwrAKalpTFs2DAGDhzIs88+a5rlfObMGZYvX87Nmzdp3LgxP/30ExMmTKBv375WfgaFQ3LRJtmYJ7lok2zMk1y0STZ32XQTca+cb9batWvZuHEj7733HgEBAabTX2JiYjh8+DBnzpyhS5cuNGrUyLoFFxHJRZtkY57kok2yMU9y0Vbas7F6ExETE8PWrVsJDAykZcuWpuupq6pKdna22XOqQ0JCaNasGSEhISQkJODt7V1ihoZySC7aJBvzJBdtko15kos2ySZ/rDonYuHChbzwwgvExMSwZMkS5s6da1olUVEU0zcp53hSzhrikydPZvv27YwbN47u3bsTFhZmnSdQSCQXbZKNeZKLNsnGPMlFm2STf1ZrIn7++Wfi4+P59ttvmTp1KmPGjOH48eO5ura1a9fSokUL1q1bB9xd6e3s2bNcvnwZV1dX1q1bV6IueiS5aJNszJNctEk25kku2iSbR2T5pSe0ZWVlmf6fmJhoWmzj8OHDaq9evdTevXurR44cUVVVVa9fv66+/PLL6oEDB3I9xsGDB9WXXnopz/biTHLRJtmYJ7lok2zMk1y0STYFVyRzIm7evMmnn36KoigEBwfTt29fHB0dAYiMjGTRokXUqFGD1q1b89tvv6EoCi+88AKenp45jQ4Gg6HYLcLxMJKLNsnGPMlFm2RjnuSiTbJ5fIXeRPz8888sWrSIDh06UK1aNTZu3EjNmjWZNm0agGlxjpyZrIcOHWLNmjU8/fTTdO7cmezs7BL5DZJctEk25kku2iQb8yQXbZKNhRTmMMedO3fURYsWqevXrzdtO3v2rNqvXz/TWuM5a4ZnZGSY/u3evbu6efPmwizNqiQXbZKNeZKLNsnGPMlFm2RjORa/Jm1sbCyKolCxYkWcnZ3p0KEDfn5+ps/funULDw8PnJ2dAUyTVXKGkM6cOYOfn1+Jm5AiuWiTbMyTXLRJNuZJLtokm8JhsSYiKyuL9957j2PHjlGhQgXatGnDM888Q7169YC7K3yVKVMGFxeXXOfYJiYmsmfPHo4cOcK+ffsYM2YMwcHBlirNqiQXbZKNeZKLNsnGPMlFm2RTuCx2iufWrVu5desWGzZs4OWXX+bKlSvMnj07z347d+6kcuXKub5RXl5eRERE4ObmxsaNG3n++ectVZbVSS7aJBvzJBdtko15kos2yaaQPc6xkLS0NNNxo48++kidOnWqqqrGY0lRUVFqr1691DVr1qiqajyeZDAY1GHDhql//fWXqqqqumXLFnXt2rWqqqpqZmbm45RiUyQXbZKNeZKLNsnGPMlFm2RTdAp0OCMqKop58+bh4uKCs7MzU6ZMwd3dHXt7e+7cuYO7uzv+/v6MGDGCzz77jOeeew5HR0dSU1Px9PQkKSmJ0NBQTp48yZQpUwBwcHCwaHNkDZKLNsnGPMlFm2RjnuSiTbIpeo98OGPdunWMGTOGmjVr8tJLL3H+/Hm+/PJLgoOD+euvv4iNjTXt2759e6pVq8batWsBiIiIYO/evcyaNYvg4GB27dpFt27dLPdsrEhy0SbZmCe5aJNszJNctEk21vHITcS1a9cICQlh3Lhx1K9fnw8//JDVq1fTqlUrypYty6ZNm0hKSgKMHZyvry+ZmZnGL2Znx6hRo1i/fj3jx4+36BOxNslFm2RjnuSiTbIxT3LRJtlYxyMfzsgZ/gHjrFd7e3uCgoLQ6/WMHDmSBQsWEBAQQPfu3XFxcSEpKQkPDw8AateuTd26dS37DGyE5KJNsjFPctEm2ZgnuWiTbKzjkZsIHx8fwHhajIODAwkJCSiKgqOjI40bN+bZZ59l27Zt7Nq1C71ez7Vr10yn0uSs/FUSSS7aJBvzJBdtko15kos2ycY6CrxORM5CHAcPHiQoKMi0/Odzzz1H69at2b9/P3fu3GHo0KEWKbS4kFy0STbmSS7aJBvzJBdtkk3RKnATkbNu+IULF+jSpQsAa9asITk5meHDh/Pcc89ZrMjiRHLRJtmYJ7lok2zMk1y0STZFq8BjOPb29uj1etLT04mNjWXUqFF8/fXX1K9f35L1FTuSizbJxjzJRZtkY57kok2yKVqPtex1REQEf/zxBxcvXuTFF19kyJAhlqqrWJNctEk25kku2iQb8yQXbZJN0XmsS4Hr9Xp++OEH+vfvT5kyZSxZV7EmuWiTbMyTXLRJNuZJLtokm6LzWE2EEEIIIUovOa9FCCGEEAUiTYQQQgghCkSaCCGEEEIUiDQRQgghhCgQaSKEEEIIUSDSRAghhBCiQKSJEEIIIUSBSBMhhLCIQ4cO0axZM5o1a8a1a9esXY4QoghIEyGEeGTTp0+nWbNmhISEmLa5ublRv3596tevj6OjoxWrE0IUlce6doYQQuSoXbs2K1eutHYZQogiJMteCyEeSa9evbh+/Xqe7UuWLGHMmDEAbNiwgcqVKzN9+nR+/vlnKlWqxOjRo/n8889JTk7m2Wef5bXXXuPTTz9lw4YNuLm5MWzYMPr37296vPj4eD777DMOHDhAUlISPj4+9OrVi6FDh6LTyd8/QtgC+UkUQjySWrVqkZaWRlJSEq6urgQFBQFw7tw5zfskJCTw4YcfUr58eVJSUvj+++/5448/iIuLw83NjdjYWP7zn//QtGlTgoKCSEpKYujQocTGxpq+RkREBEuWLOHq1au89957RfV0hRAPIHMihBCPZN68ebRu3RowNhQrV65k5cqV1K5dW/M+WVlZLF68mJ9++gkfHx8AoqOj+f777/nvf/9LmTJlMBgMHD58GIA1a9YQGxuLt7c369at4/vvv2fOnDkA/Pzzz0RHRxfysxRC5IeMRAghCl3ZsmVp1KgRAL6+vsTGxlK9enUqV64MQLly5YiJiSExMRGA06dPA3Djxg26dOmS67FUVeXUqVP4+/sX3RMQQpglTYQQotC5urqa/m9vb59nm6IogLFBuP9+OYdL7uXk5FQYZQohHpE0EUKIR5bzJp6enl4oj1+3bl3279+Pvb09s2fPNo1YpKSk8Ouvv9KhQ4dC+bpCiEcjTYQQ4pEFBgYCcObMGZ5//nmcnZ0ZNWqUxR5/4MCBrF+/nri4OJ577jmCgoJISUkhNjYWvV7PM888Y7GvJYQoOJlYKYR4ZM8++ywdO3bEzc2N8PBwTp06hcFgsNjjlytXjq+++opevXrh4eFBeHg4GRkZNG7cmEmTJlns6wghHo+sEyGEEEKIApGRCCGEEEIUiDQRQgghhCgQaSKEEEIIUSDSRAghhBCiQKSJEEIIIUSBSBMhhBBCiAKRJkIIIYQQBSJNhBBCCCEKRJoIIYQQQhSINBFCCCGEKBBpIoQQQghRINJECCGEEKJA/h/KRKhpvJM1bgAAAABJRU5ErkJggg==", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(2, figsize=(6, 4))\n", - "preds.plot(ax=ax[0], label='preds')\n", - "test[0:7].plot(ax=ax[0], label='test')\n", - "det.detect(preds).plot(ax=ax[1], label='anomalies')\n", - "fig.subplots_adjust(hspace=.5);" - ] - }, - { - "cell_type": "markdown", - "id": "c9e2831e-592c-4542-b304-df9f0b9e8368", - "metadata": {}, - "source": [ - "---\n", - "## Advantages of this approach\n", - "\n", - "- possibility to share knowledge without complexity\n", - "- respect of the company privacy\n", - "- extendability of the library\n", - "- reusability\n", - "\n", - "For DiagnoBat, some of your model(s) could be added to the main library, depending on your decision.\n", - "\n", - "## Next steps\n", - "\n", - "- First predictor/detectors given our specs\n", - "- Integration of a range of plots about root cause detection\n", - "- Benchmarking of different models\n" + "on.plots.anomalies(test[:72], predetected[:72])" ] }, { diff --git a/src/ontime/__init__.py b/src/ontime/__init__.py index 0d1c19b..aa2abae 100644 --- a/src/ontime/__init__.py +++ b/src/ontime/__init__.py @@ -1,11 +1 @@ -""" OnTime API Definition """ - -from .abstract import * -from .context import * -from .detectors import detectors -from .generators import generators -from .model import Model -from .model import preprocessing -from .plots import plots -from .processors import processors -from .time_series import TimeSeries +from .api.modular import * \ No newline at end of file diff --git a/src/ontime/abstract/__init__.py b/src/ontime/abstract/__init__.py deleted file mode 100644 index cd703ea..0000000 --- a/src/ontime/abstract/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .abstract_base_detector import AbstractBaseDetector -from .abstract_base_generator import AbstractBaseGenerator -from .abstract_base_model import AbstractBaseModel -from .abstract_base_processor import AbstractBaseProcessor diff --git a/src/ontime/context/dhn/__init__.py b/src/ontime/api/__init__.py similarity index 100% rename from src/ontime/context/dhn/__init__.py rename to src/ontime/api/__init__.py diff --git a/src/ontime/api/modular.py b/src/ontime/api/modular.py new file mode 100644 index 0000000..a27457a --- /dev/null +++ b/src/ontime/api/modular.py @@ -0,0 +1,47 @@ +""" +onTime Modular API Definition + +The aim of the Modular API is to give building blocks to the user to build whatever is desired. +`module` and `context` are left as is. + +The core of the API is accessible through with the main object of the library `onTime`. +For instance : + + import ontime as on + on.TimeSeries() + +Features contained in `module` and `context` are accessible through the `onTime` object. +For instance : + + import ontime.module as onm + onm.preprocessing.common.my_function() + +""" + +from ..core import ( + detectors, + generators, + Model, + plots, + processors, + TimeSeries +) + +from .. import module +from .. import context + +__all__ = [ + # core + "detectors", + "generators", + "Model", + "plots", + "processors", + "TimeSeries", + + # module + "module", + + # context + "context" +] diff --git a/src/ontime/config/__init__.py b/src/ontime/config/__init__.py deleted file mode 100644 index b4ca83e..0000000 --- a/src/ontime/config/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .colors import * -from .constants import * diff --git a/src/ontime/config/colors.py b/src/ontime/config/colors.py deleted file mode 100644 index 7bee7ed..0000000 --- a/src/ontime/config/colors.py +++ /dev/null @@ -1,14 +0,0 @@ -from types import SimpleNamespace - -COLORS_DICT = { - "blue_light": "#CADEF7", - "blue": "#29335C", - "blue_dark": "#0A2342", - "green": "#297373", - "yellow": "#E8C547", - "red": "#E4572E", - "red_dark": "#92140C", - "grey": "#CDD1C4", -} - -colors = SimpleNamespace(**COLORS_DICT) diff --git a/src/ontime/config/constants.py b/src/ontime/config/constants.py deleted file mode 100644 index 6c660a8..0000000 --- a/src/ontime/config/constants.py +++ /dev/null @@ -1,25 +0,0 @@ -# Generic -DEFAULT_EXPORT_FILENAME = "export" - -# TimeSeries -TIME_SERIES_FILENAME = "data" -TIME_SERIES_EXT = "csv" - -# Component -COMPONENT_VALUES = "values" -COMPONENT_META_PREFIX = "meta_" -COMPONENT_META_LABEL_PREFIX = "label_" -COMPONENT_META_CI_LOWER = "ci_lower" -COMPONENT_META_CI_UPPER = "ci_upper" - -# Metadata -METADATA_FILENAME = "meta" -METADATA_EXT = "json" -METADATA_CLASS_LABEL = "label" - -# Model -MODEL_TYPE_UNIVARIATE = "univariate" -MODEL_TYPE_MULTIVARIATE = "multivariate" - -# Misc. IO -PICKLE_EXT = "pkl" diff --git a/src/ontime/context/__init__.py b/src/ontime/context/__init__.py index 55e5f84..e4193cf 100644 --- a/src/ontime/context/__init__.py +++ b/src/ontime/context/__init__.py @@ -1 +1 @@ -from .common import * +from . import common diff --git a/src/ontime/context/common/__init__.py b/src/ontime/context/common/__init__.py index 41de82b..3f1b65c 100644 --- a/src/ontime/context/common/__init__.py +++ b/src/ontime/context/common/__init__.py @@ -1,4 +1,11 @@ from .generic_predictor import GenericPredictor from .generic_detector import GenericDetector from .profiler import Profiler -from .anomalies_frequencies import AnomaliesFrequencies +from .anomaly_frequency import AnomalyFrequency + +__all__ = [ + 'GenericPredictor', + 'GenericDetector', + 'Profiler', + 'AnomalyFrequency' +] \ No newline at end of file diff --git a/src/ontime/context/common/anomalies_frequencies.py b/src/ontime/context/common/anomaly_frequency.py similarity index 71% rename from src/ontime/context/common/anomalies_frequencies.py rename to src/ontime/context/common/anomaly_frequency.py index 442d402..1c99da3 100644 --- a/src/ontime/context/common/anomalies_frequencies.py +++ b/src/ontime/context/common/anomaly_frequency.py @@ -1,7 +1,7 @@ -from ...time_series import BinaryTimeSeries, ProbabilisticTimeSeries, TimeSeries +from ...core.time_series import BinaryTimeSeries, ProbabilisticTimeSeries, TimeSeries -class AnomaliesFrequencies: +class AnomalyFrequency: """ Class for computing the frequency of anomalies in a time window. """ @@ -13,21 +13,20 @@ def __init__(self, anomalies_ts: BinaryTimeSeries): def get_number_of_anomaly_in_window(self, window_size: str) -> TimeSeries: """ Compute the number of anomalies in a time window. - - return: TimeSeries with the number of anomalies in the window + :param window_size: str of the size of the time window + :return: TimeSeries with the number of anomalies in the window """ sum_series = self.anomalies_series.rolling(window=window_size).sum() return TimeSeries.from_series(sum_series) - def get_frequency_of_anomaly_in_window( - self, window_size: str - ) -> ProbabilisticTimeSeries: + def get_frequency_of_anomaly_in_window(self, window_size: str) -> ProbabilisticTimeSeries: """ Compute the frequency of anomalies in a time window. The frequency is computed as the number of anomalies in the window divided by the maximum number of anomalies in a window. So 1 mean that all samples in the window are anomalies. - return: ProbabilisticTimeSeries with the frequency of anomalies in the window + :param window_size: str of the size of the time window + :return: ProbabilisticTimeSeries with the frequency of anomalies in the window """ # Compute the maximum number of anomalies in a window max_anomalies = self.anomalies_series.rolling(window=window_size).count().max() diff --git a/src/ontime/context/common/generic_detector.py b/src/ontime/context/common/generic_detector.py index 5dc64f4..e49d80b 100644 --- a/src/ontime/context/common/generic_detector.py +++ b/src/ontime/context/common/generic_detector.py @@ -1,8 +1,9 @@ from darts.models import CatBoostModel from darts.utils.statistics import check_seasonality -from ...time_series import BinaryTimeSeries -from ...detectors import Quantile -from ...model import Model + +from ...core.time_series import BinaryTimeSeries +from ...core.detector import Quantile +from ...core.model import Model class GenericDetector: diff --git a/src/ontime/context/common/generic_predictor.py b/src/ontime/context/common/generic_predictor.py index f590b7e..28c502a 100644 --- a/src/ontime/context/common/generic_predictor.py +++ b/src/ontime/context/common/generic_predictor.py @@ -1,6 +1,7 @@ from darts.models import CatBoostModel from darts.utils.statistics import check_seasonality -import ontime as on + +from ...core.model import Model class GenericPredictor: @@ -22,7 +23,7 @@ def fit(self, ts): lags = 12 if seasonality == 0 else seasonality # Create model - self.model = on.Model( + self.model = Model( CatBoostModel, lags=int(lags), ) diff --git a/src/ontime/context/common/profiler.py b/src/ontime/context/common/profiler.py index 12ed43c..07283d4 100644 --- a/src/ontime/context/common/profiler.py +++ b/src/ontime/context/common/profiler.py @@ -1,11 +1,12 @@ -from ontime.time_series import TimeSeries from enum import Enum + import pandas as pd +from ...core.time_series import TimeSeries + class Profiler: """ - This class should not be instantiated. This class is used to make a profile from a time series. """ @@ -20,7 +21,7 @@ class Aggregation(Enum): SUM = "sum" # Define the all periods possible - # The first element is the offset alias for split_by_period from ontime.time_series + # The first element is the offset alias for split_by_period from ontime.modules.time_series # The second element is the format to make the aggregation (**Also users' format**) # The third element is the format to convert data to match with TimeSeries format and the chosen period class Period(Enum): diff --git a/src/ontime/core/__init__.py b/src/ontime/core/__init__.py new file mode 100644 index 0000000..944744e --- /dev/null +++ b/src/ontime/core/__init__.py @@ -0,0 +1,14 @@ +from .detector import detectors, abstract_detector +from .generator import generators, abstract_generator +from .model import Model, abstract_model +from .plot import * +from .processor import processors, abstract_processor +from .time_series import TimeSeries + +__all__ = [ + "detectors", + "generators", + "Model", + "processors", + "TimeSeries" +] diff --git a/src/ontime/detectors/__init__.py b/src/ontime/core/detector/__init__.py similarity index 89% rename from src/ontime/detectors/__init__.py rename to src/ontime/core/detector/__init__.py index 9602986..1b6a075 100644 --- a/src/ontime/detectors/__init__.py +++ b/src/ontime/core/detector/__init__.py @@ -5,3 +5,5 @@ detectors = Detectors() detectors.load("threshold", Threshold) detectors.load("quantile", Quantile) + +__all__ = ["detectors"] diff --git a/src/ontime/abstract/abstract_base_detector.py b/src/ontime/core/detector/abstract_detector.py similarity index 76% rename from src/ontime/abstract/abstract_base_detector.py rename to src/ontime/core/detector/abstract_detector.py index 3a07fa5..14942e1 100644 --- a/src/ontime/abstract/abstract_base_detector.py +++ b/src/ontime/core/detector/abstract_detector.py @@ -3,13 +3,13 @@ from ..time_series import BinaryTimeSeries, TimeSeries -class AbstractBaseDetector(ABC): +class AbstractDetector(ABC): """Abstract class to define methods to implement for a Detector class. """ # TODO check if this must return a TimeSeries or a BinaryTimeSeries @abstractmethod - def detect(self, ts: TimeSeries) -> BinaryTimeSeries: + def detect(self, ts: TimeSeries, *args, **kwargs) -> BinaryTimeSeries: """Detect features""" raise NotImplementedError diff --git a/src/ontime/detectors/detectors.py b/src/ontime/core/detector/detectors.py similarity index 100% rename from src/ontime/detectors/detectors.py rename to src/ontime/core/detector/detectors.py diff --git a/src/ontime/detectors/registry/__init__.py b/src/ontime/core/detector/registry/__init__.py similarity index 100% rename from src/ontime/detectors/registry/__init__.py rename to src/ontime/core/detector/registry/__init__.py diff --git a/src/ontime/detectors/registry/quantile.py b/src/ontime/core/detector/registry/quantile.py similarity index 88% rename from src/ontime/detectors/registry/quantile.py rename to src/ontime/core/detector/registry/quantile.py index 517a7b3..a518d95 100644 --- a/src/ontime/detectors/registry/quantile.py +++ b/src/ontime/core/detector/registry/quantile.py @@ -1,9 +1,10 @@ from darts.ad.detectors.quantile_detector import QuantileDetector -from ...abstract import AbstractBaseDetector + +from ..abstract_detector import AbstractDetector from ...time_series import BinaryTimeSeries, TimeSeries -class Quantile(QuantileDetector, AbstractBaseDetector): +class Quantile(QuantileDetector, AbstractDetector): """ Wrapper around Darts QuantileDetector. """ diff --git a/src/ontime/detectors/registry/threshold.py b/src/ontime/core/detector/registry/threshold.py similarity index 92% rename from src/ontime/detectors/registry/threshold.py rename to src/ontime/core/detector/registry/threshold.py index 6d76cbe..527e508 100644 --- a/src/ontime/detectors/registry/threshold.py +++ b/src/ontime/core/detector/registry/threshold.py @@ -1,11 +1,12 @@ from typing import Sequence, Union from darts.ad.detectors.threshold_detector import ThresholdDetector -from ...abstract import AbstractBaseDetector + +from ..abstract_detector import AbstractDetector from ...time_series import TimeSeries, BinaryTimeSeries -class Threshold(ThresholdDetector, AbstractBaseDetector): +class Threshold(ThresholdDetector, AbstractDetector): """ Wrapper around Darts ThresholdDetector. """ diff --git a/src/ontime/generators/__init__.py b/src/ontime/core/generator/__init__.py similarity index 95% rename from src/ontime/generators/__init__.py rename to src/ontime/core/generator/__init__.py index e9d4dc5..a937f74 100644 --- a/src/ontime/generators/__init__.py +++ b/src/ontime/core/generator/__init__.py @@ -13,3 +13,5 @@ generators.load("linear", Linear) generators.load("random_walk", RandomWalk) generators.load("sine", Sine) + +__all__ = ["generators"] \ No newline at end of file diff --git a/src/ontime/abstract/abstract_base_generator.py b/src/ontime/core/generator/abstract_generator.py similarity index 74% rename from src/ontime/abstract/abstract_base_generator.py rename to src/ontime/core/generator/abstract_generator.py index 51b8d1d..0e20f46 100644 --- a/src/ontime/abstract/abstract_base_generator.py +++ b/src/ontime/core/generator/abstract_generator.py @@ -2,12 +2,12 @@ from typing import NoReturn -class AbstractBaseGenerator(ABC): +class AbstractGenerator(ABC): """Abstract class to define methods to implement for a Generator class. """ @abstractmethod - def generate(self, **kwargs) -> NoReturn: + def generate(self, *args, **kwargs) -> NoReturn: """Generate features""" raise NotImplementedError diff --git a/src/ontime/generators/generators.py b/src/ontime/core/generator/generators.py similarity index 100% rename from src/ontime/generators/generators.py rename to src/ontime/core/generator/generators.py diff --git a/src/ontime/generators/registry/__init__.py b/src/ontime/core/generator/registry/__init__.py similarity index 100% rename from src/ontime/generators/registry/__init__.py rename to src/ontime/core/generator/registry/__init__.py diff --git a/src/ontime/generators/registry/constant.py b/src/ontime/core/generator/registry/constant.py similarity index 92% rename from src/ontime/generators/registry/constant.py rename to src/ontime/core/generator/registry/constant.py index a050411..aca1559 100644 --- a/src/ontime/generators/registry/constant.py +++ b/src/ontime/core/generator/registry/constant.py @@ -5,10 +5,10 @@ from darts.utils.timeseries_generation import constant_timeseries from ...time_series import TimeSeries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator -class Constant(AbstractBaseGenerator): +class Constant(AbstractGenerator): """ Wrapper around Darts constant time series generator. """ diff --git a/src/ontime/generators/registry/gaussian.py b/src/ontime/core/generator/registry/gaussian.py similarity index 93% rename from src/ontime/generators/registry/gaussian.py rename to src/ontime/core/generator/registry/gaussian.py index b5e4f46..371fa07 100644 --- a/src/ontime/generators/registry/gaussian.py +++ b/src/ontime/core/generator/registry/gaussian.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import gaussian_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Gaussian(AbstractBaseGenerator): +class Gaussian(AbstractGenerator): """ Wrapper around Darts gaussian time series generator. """ diff --git a/src/ontime/generators/registry/holiday.py b/src/ontime/core/generator/registry/holiday.py similarity index 93% rename from src/ontime/generators/registry/holiday.py rename to src/ontime/core/generator/registry/holiday.py index 59663be..1aa2b13 100644 --- a/src/ontime/generators/registry/holiday.py +++ b/src/ontime/core/generator/registry/holiday.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import holidays_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Holiday(AbstractBaseGenerator): +class Holiday(AbstractGenerator): """ Wrapper around Darts holiday time series generator. """ diff --git a/src/ontime/generators/registry/linear.py b/src/ontime/core/generator/registry/linear.py similarity index 93% rename from src/ontime/generators/registry/linear.py rename to src/ontime/core/generator/registry/linear.py index 5327ddb..aceef5e 100644 --- a/src/ontime/generators/registry/linear.py +++ b/src/ontime/core/generator/registry/linear.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import linear_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Linear(AbstractBaseGenerator): +class Linear(AbstractGenerator): """ Wrapper around Darts linear time series generator. """ diff --git a/src/ontime/generators/registry/random_walk.py b/src/ontime/core/generator/registry/random_walk.py similarity index 93% rename from src/ontime/generators/registry/random_walk.py rename to src/ontime/core/generator/registry/random_walk.py index 914dabc..18b26cf 100644 --- a/src/ontime/generators/registry/random_walk.py +++ b/src/ontime/core/generator/registry/random_walk.py @@ -5,10 +5,10 @@ from darts.utils.timeseries_generation import random_walk_timeseries from ...time_series import TimeSeries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator -class RandomWalk(AbstractBaseGenerator): +class RandomWalk(AbstractGenerator): """ Wrapper around Darts random walk time series generator. """ diff --git a/src/ontime/generators/registry/sine.py b/src/ontime/core/generator/registry/sine.py similarity index 94% rename from src/ontime/generators/registry/sine.py rename to src/ontime/core/generator/registry/sine.py index d9a498b..2529e30 100644 --- a/src/ontime/generators/registry/sine.py +++ b/src/ontime/core/generator/registry/sine.py @@ -4,11 +4,11 @@ import pandas as pd from darts.utils.timeseries_generation import sine_timeseries -from ...abstract import AbstractBaseGenerator +from ..abstract_generator import AbstractGenerator from ...time_series import TimeSeries -class Sine(AbstractBaseGenerator): +class Sine(AbstractGenerator): """ Wrapper around Darts sine time series generator. """ diff --git a/src/ontime/model/__init__.py b/src/ontime/core/model/__init__.py similarity index 54% rename from src/ontime/model/__init__.py rename to src/ontime/core/model/__init__.py index 3b4d86e..b7ac7f9 100644 --- a/src/ontime/model/__init__.py +++ b/src/ontime/core/model/__init__.py @@ -1 +1,3 @@ from .model import Model + +__all__ = ["Model"] diff --git a/src/ontime/abstract/abstract_base_model.py b/src/ontime/core/model/abstract_model.py similarity index 68% rename from src/ontime/abstract/abstract_base_model.py rename to src/ontime/core/model/abstract_model.py index 15d991b..37a0727 100644 --- a/src/ontime/abstract/abstract_base_model.py +++ b/src/ontime/core/model/abstract_model.py @@ -5,20 +5,20 @@ from ..time_series import TimeSeries -class AbstractBaseModel(ABC): +class AbstractModel(ABC): """Abstract class to define methods to implement for a Model class inspired by Scikit Learn API. """ - def __init__(self): + def __init__(self, *args, **kwargs): pass @abstractmethod - def fit(self, ts: TimeSeries) -> NoReturn: + def fit(self, ts: TimeSeries, *args, **kwargs) -> NoReturn: """Fit a model""" pass @abstractmethod - def predict(self, horizon: Any) -> Any: + def predict(self, horizon: Any, *args, **kwargs) -> Any: """Usage of the model to predict values""" pass diff --git a/src/ontime/model/libs/darts/__init__.py b/src/ontime/core/model/libs/darts/__init__.py similarity index 100% rename from src/ontime/model/libs/darts/__init__.py rename to src/ontime/core/model/libs/darts/__init__.py diff --git a/src/ontime/core/model/libs/darts/forecasting_model.py b/src/ontime/core/model/libs/darts/forecasting_model.py new file mode 100644 index 0000000..3b32eba --- /dev/null +++ b/src/ontime/core/model/libs/darts/forecasting_model.py @@ -0,0 +1,39 @@ +from ...abstract_model import AbstractModel +from ....time_series import TimeSeries + + +class ForecastingModel(AbstractModel): + """ + Generic wrapper around Darts forecasting models + """ + + def __init__(self, model, **params): + """ Constructor of a ForecastingModel object + + :param model: Dart's forecasting model + :param params: dict of keyword arguments for this model's constructor + """ + super().__init__() + self.model = model(**params) + + def fit(self, ts, **params): + """ + Fit the model to the given time series + + :param ts: TimeSeries + :param params: dict of keyword arguments for this model's fit method + :return: self + """ + self.model.fit(ts, **params) + return self + + def predict(self, n, **params): + """ + Predict n steps into the future + + :param n: int number of steps to predict + :param params: dict of keyword arguments for this model's predict method + :return: TimeSeries + """ + pred = self.model.predict(n, **params) + return TimeSeries.from_darts(pred) diff --git a/src/ontime/model/libs/skforecast/__init__.py b/src/ontime/core/model/libs/skforecast/__init__.py similarity index 100% rename from src/ontime/model/libs/skforecast/__init__.py rename to src/ontime/core/model/libs/skforecast/__init__.py diff --git a/src/ontime/model/libs/skforecast/forecaster_autoreg.py b/src/ontime/core/model/libs/skforecast/forecaster_autoreg.py similarity index 81% rename from src/ontime/model/libs/skforecast/forecaster_autoreg.py rename to src/ontime/core/model/libs/skforecast/forecaster_autoreg.py index 440f779..5d60a1b 100644 --- a/src/ontime/model/libs/skforecast/forecaster_autoreg.py +++ b/src/ontime/core/model/libs/skforecast/forecaster_autoreg.py @@ -1,14 +1,14 @@ from abc import ABCMeta -from ontime.abstract.abstract_base_model import AbstractBaseModel -from ontime.time_series import TimeSeries +from ...abstract_model import AbstractModel +from ....time_series import TimeSeries from skforecast.ForecasterAutoreg import ( ForecasterAutoreg as SkForecastForecasterAutoreg, ) -class ForecasterAutoreg(AbstractBaseModel): +class ForecasterAutoreg(AbstractModel): """ Generic wrapper around SkForecast ForecasterAutoreg models """ diff --git a/src/ontime/model/model.py b/src/ontime/core/model/model.py similarity index 71% rename from src/ontime/model/model.py rename to src/ontime/core/model/model.py index a92421a..bcfa2c1 100644 --- a/src/ontime/model/model.py +++ b/src/ontime/core/model/model.py @@ -1,15 +1,12 @@ from darts.models.forecasting.forecasting_model import ModelMeta -from ontime.abstract.abstract_base_model import AbstractBaseModel -from ontime.time_series import TimeSeries - +from ..time_series import TimeSeries +from .abstract_model import AbstractModel from .libs.darts.forecasting_model import ForecastingModel as DartsForecastingModel -from .libs.skforecast.forecaster_autoreg import ( - ForecasterAutoreg as SkForecastForecasterAutoreg, -) +from .libs.skforecast.forecaster_autoreg import ForecasterAutoreg as SkForecastForecasterAutoreg -class Model(AbstractBaseModel): +class Model(AbstractModel): """ Generic wrapper around all implemented time series libraries """ @@ -23,9 +20,10 @@ def __init__(self, model, **params): # scikit-learn API compatible models self.model = SkForecastForecasterAutoreg(model, **params) - def fit(self, ts, **params): + def fit(self, ts: TimeSeries, **params): """ Fit the model to the given time series + :param ts: TimeSeries :param params: Parameters to pass to the model :return: self @@ -33,11 +31,12 @@ def fit(self, ts, **params): self.model.fit(ts, **params) return self - def predict(self, n, **params): + def predict(self, n: int, **params): """ Predict length steps into the future - :param n: Integer - :param params: Parameters to pass to the predict method + + :param n: int number of steps to predict + :param params: dict to pass to the predict method :return: TimeSeries """ pred = self.model.predict(n, **params) diff --git a/src/ontime/core/plot/__init__.py b/src/ontime/core/plot/__init__.py new file mode 100644 index 0000000..1e5d9ce --- /dev/null +++ b/src/ontime/core/plot/__init__.py @@ -0,0 +1 @@ +from . import plots \ No newline at end of file diff --git a/src/ontime/plots/plots.py b/src/ontime/core/plot/plots.py similarity index 91% rename from src/ontime/plots/plots.py rename to src/ontime/core/plot/plots.py index e1b9d2b..d3eb1ee 100644 --- a/src/ontime/plots/plots.py +++ b/src/ontime/core/plot/plots.py @@ -1,10 +1,13 @@ import pandas as pd import altair as alt +from ..time_series import TimeSeries -def line(ts): + +def line(ts: TimeSeries) -> alt.Chart: """ Standard line plot for TimeSeries + :param ts: TimeSeries :return: Altair Chart """ @@ -30,9 +33,10 @@ def line(ts): return chart -def heatmap(ts): +def heatmap(ts: TimeSeries) -> alt.Chart: """ Plot a Heatmap of a TimeSeries + :param ts: TimeSeries :return: Altair Chart """ @@ -71,9 +75,10 @@ def heatmap(ts): return chart -def prediction(train_ts, pred_ts=None, test_ts=None): +def prediction(train_ts: TimeSeries, pred_ts: TimeSeries = None, test_ts: TimeSeries = None) -> alt.Chart: """ Plot a prediction + :param train_ts: TimeSeries :param pred_ts: TimeSeries :param test_ts: TimeSeries @@ -125,11 +130,12 @@ def prediction(train_ts, pred_ts=None, test_ts=None): return chart -def anomalies(ts, ts_anomaly): +def anomalies(ts: TimeSeries, ts_anomaly: TimeSeries) -> alt.Chart: """ Plot Anomalies - :param ts: normal series - :param ts_anomaly: anomaly series + + :param ts: TimeSeries of the signal + :param ts_anomaly: TimeSeries of the anomalies :return: Altair Chart """ alt.data_transformers.enable("vegafusion") diff --git a/src/ontime/processors/__init__.py b/src/ontime/core/processor/__init__.py similarity index 93% rename from src/ontime/processors/__init__.py rename to src/ontime/core/processor/__init__.py index 288eefa..14a7136 100644 --- a/src/ontime/processors/__init__.py +++ b/src/ontime/core/processor/__init__.py @@ -9,3 +9,5 @@ processors.load("mapper", Mapper) processors.load("windower", Windower) processors.load("correlation", Correlation) + +__all__ = ["processors"] \ No newline at end of file diff --git a/src/ontime/abstract/abstract_base_processor.py b/src/ontime/core/processor/abstract_processor.py similarity index 69% rename from src/ontime/abstract/abstract_base_processor.py rename to src/ontime/core/processor/abstract_processor.py index 8cd9184..1fd3aef 100644 --- a/src/ontime/abstract/abstract_base_processor.py +++ b/src/ontime/core/processor/abstract_processor.py @@ -1,15 +1,14 @@ from abc import ABC, abstractmethod -from typing import NoReturn from ..time_series import TimeSeries -class AbstractBaseProcessor(ABC): +class AbstractProcessor(ABC): """Abstract class to define methods to implement for a Processor class. """ @abstractmethod - def process(self, ts: TimeSeries) -> NoReturn: + def process(self, ts: TimeSeries) -> TimeSeries: """Process time series""" raise NotImplementedError diff --git a/src/ontime/processors/processors.py b/src/ontime/core/processor/processors.py similarity index 100% rename from src/ontime/processors/processors.py rename to src/ontime/core/processor/processors.py diff --git a/src/ontime/processors/registry/correlation.py b/src/ontime/core/processor/registry/correlation.py similarity index 90% rename from src/ontime/processors/registry/correlation.py rename to src/ontime/core/processor/registry/correlation.py index 2d6ab22..2bcc0fe 100644 --- a/src/ontime/processors/registry/correlation.py +++ b/src/ontime/core/processor/registry/correlation.py @@ -7,25 +7,29 @@ import numpy as np from ...time_series import TimeSeries +from ..abstract_processor import AbstractProcessor -class Correlation: +class Correlation(AbstractProcessor): """Correlation class handles correlation computation in a TimeSeries""" - @staticmethod - def process( - ts: TimeSeries, window: Union[int, timedelta, str, BaseOffset, BaseIndexer] - ) -> TimeSeries: - """Compute correlations for a TimeSeries + def __init__(self, window: Union[int, timedelta, str, BaseOffset, BaseIndexer]): + """Constructor of a correlation processor - :param ts: TimeSeries :param window: int, timedelta, str, offset, or BaseIndexer subclass Size of the moving window as in https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html#pandas-dataframe-rolling + """ + self.window = window + + def process(self, ts: TimeSeries) -> TimeSeries: + """Compute correlations for a TimeSeries + + :param ts: TimeSeries :return: TimeSeries Each correlation is a component of the TimeSeries with a name such as 'var_a_var_b' """ df = ts.pd_dataframe() - df = Correlation.compute_correlations(df, window) + df = Correlation.compute_correlations(df, self.window) df = Correlation.pivot(df) df.columns.name = None # Otherwise, the column name is 'pair' and from_dataframe() fails in the next line return TimeSeries.from_dataframe(df) diff --git a/src/ontime/processors/registry/filler.py b/src/ontime/core/processor/registry/filler.py similarity index 95% rename from src/ontime/processors/registry/filler.py rename to src/ontime/core/processor/registry/filler.py index fc91b17..cf07820 100644 --- a/src/ontime/processors/registry/filler.py +++ b/src/ontime/core/processor/registry/filler.py @@ -2,11 +2,11 @@ MissingValuesFiller as DartsMissingValuesFiller, ) -from ...abstract import AbstractBaseProcessor +from ..abstract_processor import AbstractProcessor from ...time_series import TimeSeries -class Filler(AbstractBaseProcessor): +class Filler(AbstractProcessor): """Wrapper around Darts MissingValuesFiller. https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.missing_values_filler.html """ diff --git a/src/ontime/processors/registry/mapper.py b/src/ontime/core/processor/registry/mapper.py similarity index 97% rename from src/ontime/processors/registry/mapper.py rename to src/ontime/core/processor/registry/mapper.py index 8b1f4f7..bf1e29b 100644 --- a/src/ontime/processors/registry/mapper.py +++ b/src/ontime/core/processor/registry/mapper.py @@ -3,11 +3,11 @@ InvertibleMapper as DartsInvertibleMapper, ) -from ...abstract import AbstractBaseProcessor +from ..abstract_processor import AbstractProcessor from ...time_series import TimeSeries -class Mapper(AbstractBaseProcessor): +class Mapper(AbstractProcessor): """ Wrapper around Darts Mapper https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.mappers.html diff --git a/src/ontime/processors/registry/windower.py b/src/ontime/core/processor/registry/windower.py similarity index 97% rename from src/ontime/processors/registry/windower.py rename to src/ontime/core/processor/registry/windower.py index 4346710..3072e90 100644 --- a/src/ontime/processors/registry/windower.py +++ b/src/ontime/core/processor/registry/windower.py @@ -4,11 +4,11 @@ WindowTransformer as DartsWindowTransformer, ) -from ...abstract import AbstractBaseProcessor +from ..abstract_processor import AbstractProcessor from ...time_series import TimeSeries -class Windower(AbstractBaseProcessor): +class Windower(AbstractProcessor): """ Wrapper around Darts WindowTransformer. https://unit8co.github.io/darts/generated_api/darts.dataprocessing.transformers.window_transformer.html#window-transformer diff --git a/src/ontime/time_series/__init__.py b/src/ontime/core/time_series/__init__.py similarity index 63% rename from src/ontime/time_series/__init__.py rename to src/ontime/core/time_series/__init__.py index f073f71..eb92271 100644 --- a/src/ontime/time_series/__init__.py +++ b/src/ontime/core/time_series/__init__.py @@ -2,3 +2,10 @@ from .probabilistic_time_series import ProbabilisticTimeSeries from .binary_time_series import BinaryTimeSeries from .resticted_time_series import RestrictedTimeSeries + +__all__ = [ + 'TimeSeries', + 'ProbabilisticTimeSeries', + 'BinaryTimeSeries', + 'RestrictedTimeSeries' +] diff --git a/src/ontime/time_series/binary_time_series.py b/src/ontime/core/time_series/binary_time_series.py similarity index 97% rename from src/ontime/time_series/binary_time_series.py rename to src/ontime/core/time_series/binary_time_series.py index db20fac..19df797 100644 --- a/src/ontime/time_series/binary_time_series.py +++ b/src/ontime/core/time_series/binary_time_series.py @@ -1,9 +1,8 @@ -import pandas as pd - -from .resticted_time_series import RestrictedTimeSeries import xarray as xr import numpy as np +from .resticted_time_series import RestrictedTimeSeries + class BinaryTimeSeries(RestrictedTimeSeries["BinaryTimeSeries"]): def __init__(self, xa: xr.DataArray): diff --git a/src/ontime/time_series/probabilistic_time_series.py b/src/ontime/core/time_series/probabilistic_time_series.py similarity index 97% rename from src/ontime/time_series/probabilistic_time_series.py rename to src/ontime/core/time_series/probabilistic_time_series.py index 5eb717d..6296837 100644 --- a/src/ontime/time_series/probabilistic_time_series.py +++ b/src/ontime/core/time_series/probabilistic_time_series.py @@ -1,7 +1,7 @@ -from .resticted_time_series import RestrictedTimeSeries import xarray as xr import numpy as np -import pandas as pd + +from .resticted_time_series import RestrictedTimeSeries class ProbabilisticTimeSeries(RestrictedTimeSeries["ProbabilisticTimeSeries"]): diff --git a/src/ontime/time_series/resticted_time_series.py b/src/ontime/core/time_series/resticted_time_series.py similarity index 99% rename from src/ontime/time_series/resticted_time_series.py rename to src/ontime/core/time_series/resticted_time_series.py index 8b585be..66f5860 100644 --- a/src/ontime/time_series/resticted_time_series.py +++ b/src/ontime/core/time_series/resticted_time_series.py @@ -8,14 +8,14 @@ Dict, Sequence, Callable, - Type, ) + +from darts import TimeSeries as DartsTimeSeries import pandas as pd -from .time_series import TimeSeries import xarray as xr import numpy as np -from darts import TimeSeries as DartsTimeSeries +from .time_series import TimeSeries T = TypeVar("T") diff --git a/src/ontime/time_series/time_series.py b/src/ontime/core/time_series/time_series.py similarity index 90% rename from src/ontime/time_series/time_series.py rename to src/ontime/core/time_series/time_series.py index bc57215..abce624 100644 --- a/src/ontime/time_series/time_series.py +++ b/src/ontime/core/time_series/time_series.py @@ -5,10 +5,13 @@ import pandas as pd import xarray as xr -from ..plots import plots - class TimeSeries(DartsTimeSeries): + """ + Main class to handle time series + This is a wrapper around Darts TimeSeries, functions are added to handle various operations + """ + def __init__(self, xa: xr.DataArray): super().__init__(xa) diff --git a/src/ontime/utils/__init__.py b/src/ontime/core/utils/__init__.py similarity index 100% rename from src/ontime/utils/__init__.py rename to src/ontime/core/utils/__init__.py diff --git a/src/ontime/utils/dynamic_class.py b/src/ontime/core/utils/dynamic_class.py similarity index 86% rename from src/ontime/utils/dynamic_class.py rename to src/ontime/core/utils/dynamic_class.py index 658ac20..a6e0dad 100644 --- a/src/ontime/utils/dynamic_class.py +++ b/src/ontime/core/utils/dynamic_class.py @@ -2,6 +2,9 @@ class DynamicClass(Registry): + """ + DynamicClass is a class that can load other classes dynamically + """ def __init__(self): super().__init__() diff --git a/src/ontime/utils/registry.py b/src/ontime/core/utils/registry.py similarity index 78% rename from src/ontime/utils/registry.py rename to src/ontime/core/utils/registry.py index 206efb9..8cb82e3 100644 --- a/src/ontime/utils/registry.py +++ b/src/ontime/core/utils/registry.py @@ -1,4 +1,8 @@ class Registry: + """ + Registry class with the aim to store objects in a dictionary and retrieve them by name + + """ def __init__(self): self.registry = {} diff --git a/src/ontime/utils/utils.py b/src/ontime/core/utils/utils.py similarity index 100% rename from src/ontime/utils/utils.py rename to src/ontime/core/utils/utils.py diff --git a/src/ontime/model/libs/darts/forecasting_model.py b/src/ontime/model/libs/darts/forecasting_model.py deleted file mode 100644 index 215e9a7..0000000 --- a/src/ontime/model/libs/darts/forecasting_model.py +++ /dev/null @@ -1,20 +0,0 @@ -from ontime.abstract.abstract_base_model import AbstractBaseModel -from ontime.time_series import TimeSeries - - -class ForecastingModel(AbstractBaseModel): - """ - Generic wrapper around Darts forecasting models - """ - - def __init__(self, model, **params): - super().__init__() - self.model = model(**params) - - def fit(self, ts, **params): - self.model.fit(ts, **params) - return self - - def predict(self, n, **params): - pred = self.model.predict(n, **params) - return TimeSeries.from_darts(pred) diff --git a/src/ontime/model/preprocessing/__init__.py b/src/ontime/model/preprocessing/__init__.py deleted file mode 100644 index e4193cf..0000000 --- a/src/ontime/model/preprocessing/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import common diff --git a/src/ontime/module/__init__.py b/src/ontime/module/__init__.py new file mode 100644 index 0000000..0fec63c --- /dev/null +++ b/src/ontime/module/__init__.py @@ -0,0 +1 @@ +from . import preprocessing diff --git a/src/ontime/module/preprocessing/__init__.py b/src/ontime/module/preprocessing/__init__.py new file mode 100644 index 0000000..4b0d590 --- /dev/null +++ b/src/ontime/module/preprocessing/__init__.py @@ -0,0 +1 @@ +from . import common \ No newline at end of file diff --git a/src/ontime/model/preprocessing/common.py b/src/ontime/module/preprocessing/common.py similarity index 97% rename from src/ontime/model/preprocessing/common.py rename to src/ontime/module/preprocessing/common.py index 86fb23f..bca7238 100644 --- a/src/ontime/model/preprocessing/common.py +++ b/src/ontime/module/preprocessing/common.py @@ -1,10 +1,9 @@ import numpy as np - -from ontime.time_series import TimeSeries - from sklearn.preprocessing import MinMaxScaler, StandardScaler from darts.dataprocessing.transformers import Scaler +from ...core.time_series import TimeSeries + def normalize( ts: TimeSeries, type="minmax", return_transformer=False @@ -48,7 +47,7 @@ def train_test_split(ts: TimeSeries, test_split=None, train_split=None) -> tuple if train_split is None and test_split is None: test_split = 0.25 - # split ts in subts : train, test + # split time series in sub time series : train, test if test_split is not None: train_set, test_set = ts.split_after(1 - test_split) diff --git a/src/ontime/plots/__init__.py b/src/ontime/plots/__init__.py deleted file mode 100644 index 6757fd4..0000000 --- a/src/ontime/plots/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .plots import * From 1e8f69f146f0ad5dc927f3c2f722a52d9b8bfdf1 Mon Sep 17 00:00:00 2001 From: Fred Montet Date: Fri, 17 Nov 2023 15:27:23 +0100 Subject: [PATCH 8/8] format --- src/ontime/__init__.py | 2 +- src/ontime/api/modular.py | 13 ++----------- src/ontime/context/common/__init__.py | 7 +------ src/ontime/context/common/anomaly_frequency.py | 4 +++- src/ontime/core/__init__.py | 8 +------- src/ontime/core/generator/__init__.py | 2 +- .../core/model/libs/darts/forecasting_model.py | 2 +- src/ontime/core/model/model.py | 4 +++- src/ontime/core/plot/__init__.py | 2 +- src/ontime/core/plot/plots.py | 4 +++- src/ontime/core/processor/__init__.py | 2 +- src/ontime/core/time_series/__init__.py | 8 ++++---- src/ontime/core/utils/dynamic_class.py | 1 + src/ontime/core/utils/registry.py | 1 + src/ontime/module/preprocessing/__init__.py | 2 +- 15 files changed, 25 insertions(+), 37 deletions(-) diff --git a/src/ontime/__init__.py b/src/ontime/__init__.py index aa2abae..b51c3ca 100644 --- a/src/ontime/__init__.py +++ b/src/ontime/__init__.py @@ -1 +1 @@ -from .api.modular import * \ No newline at end of file +from .api.modular import * diff --git a/src/ontime/api/modular.py b/src/ontime/api/modular.py index a27457a..c619725 100644 --- a/src/ontime/api/modular.py +++ b/src/ontime/api/modular.py @@ -18,14 +18,7 @@ """ -from ..core import ( - detectors, - generators, - Model, - plots, - processors, - TimeSeries -) +from ..core import detectors, generators, Model, plots, processors, TimeSeries from .. import module from .. import context @@ -38,10 +31,8 @@ "plots", "processors", "TimeSeries", - # module "module", - # context - "context" + "context", ] diff --git a/src/ontime/context/common/__init__.py b/src/ontime/context/common/__init__.py index 3f1b65c..c5e67c0 100644 --- a/src/ontime/context/common/__init__.py +++ b/src/ontime/context/common/__init__.py @@ -3,9 +3,4 @@ from .profiler import Profiler from .anomaly_frequency import AnomalyFrequency -__all__ = [ - 'GenericPredictor', - 'GenericDetector', - 'Profiler', - 'AnomalyFrequency' -] \ No newline at end of file +__all__ = ["GenericPredictor", "GenericDetector", "Profiler", "AnomalyFrequency"] diff --git a/src/ontime/context/common/anomaly_frequency.py b/src/ontime/context/common/anomaly_frequency.py index 1c99da3..124180b 100644 --- a/src/ontime/context/common/anomaly_frequency.py +++ b/src/ontime/context/common/anomaly_frequency.py @@ -19,7 +19,9 @@ def get_number_of_anomaly_in_window(self, window_size: str) -> TimeSeries: sum_series = self.anomalies_series.rolling(window=window_size).sum() return TimeSeries.from_series(sum_series) - def get_frequency_of_anomaly_in_window(self, window_size: str) -> ProbabilisticTimeSeries: + def get_frequency_of_anomaly_in_window( + self, window_size: str + ) -> ProbabilisticTimeSeries: """ Compute the frequency of anomalies in a time window. The frequency is computed as the number of anomalies in the window divided by the maximum number of anomalies in a window. So 1 mean that all samples in the window are diff --git a/src/ontime/core/__init__.py b/src/ontime/core/__init__.py index 944744e..6948a9c 100644 --- a/src/ontime/core/__init__.py +++ b/src/ontime/core/__init__.py @@ -5,10 +5,4 @@ from .processor import processors, abstract_processor from .time_series import TimeSeries -__all__ = [ - "detectors", - "generators", - "Model", - "processors", - "TimeSeries" -] +__all__ = ["detectors", "generators", "Model", "processors", "TimeSeries"] diff --git a/src/ontime/core/generator/__init__.py b/src/ontime/core/generator/__init__.py index a937f74..67dddd4 100644 --- a/src/ontime/core/generator/__init__.py +++ b/src/ontime/core/generator/__init__.py @@ -14,4 +14,4 @@ generators.load("random_walk", RandomWalk) generators.load("sine", Sine) -__all__ = ["generators"] \ No newline at end of file +__all__ = ["generators"] diff --git a/src/ontime/core/model/libs/darts/forecasting_model.py b/src/ontime/core/model/libs/darts/forecasting_model.py index 3b32eba..57c8e0e 100644 --- a/src/ontime/core/model/libs/darts/forecasting_model.py +++ b/src/ontime/core/model/libs/darts/forecasting_model.py @@ -8,7 +8,7 @@ class ForecastingModel(AbstractModel): """ def __init__(self, model, **params): - """ Constructor of a ForecastingModel object + """Constructor of a ForecastingModel object :param model: Dart's forecasting model :param params: dict of keyword arguments for this model's constructor diff --git a/src/ontime/core/model/model.py b/src/ontime/core/model/model.py index bcfa2c1..02d6846 100644 --- a/src/ontime/core/model/model.py +++ b/src/ontime/core/model/model.py @@ -3,7 +3,9 @@ from ..time_series import TimeSeries from .abstract_model import AbstractModel from .libs.darts.forecasting_model import ForecastingModel as DartsForecastingModel -from .libs.skforecast.forecaster_autoreg import ForecasterAutoreg as SkForecastForecasterAutoreg +from .libs.skforecast.forecaster_autoreg import ( + ForecasterAutoreg as SkForecastForecasterAutoreg, +) class Model(AbstractModel): diff --git a/src/ontime/core/plot/__init__.py b/src/ontime/core/plot/__init__.py index 1e5d9ce..f6f83de 100644 --- a/src/ontime/core/plot/__init__.py +++ b/src/ontime/core/plot/__init__.py @@ -1 +1 @@ -from . import plots \ No newline at end of file +from . import plots diff --git a/src/ontime/core/plot/plots.py b/src/ontime/core/plot/plots.py index d3eb1ee..634ef41 100644 --- a/src/ontime/core/plot/plots.py +++ b/src/ontime/core/plot/plots.py @@ -75,7 +75,9 @@ def heatmap(ts: TimeSeries) -> alt.Chart: return chart -def prediction(train_ts: TimeSeries, pred_ts: TimeSeries = None, test_ts: TimeSeries = None) -> alt.Chart: +def prediction( + train_ts: TimeSeries, pred_ts: TimeSeries = None, test_ts: TimeSeries = None +) -> alt.Chart: """ Plot a prediction diff --git a/src/ontime/core/processor/__init__.py b/src/ontime/core/processor/__init__.py index 14a7136..afbfb04 100644 --- a/src/ontime/core/processor/__init__.py +++ b/src/ontime/core/processor/__init__.py @@ -10,4 +10,4 @@ processors.load("windower", Windower) processors.load("correlation", Correlation) -__all__ = ["processors"] \ No newline at end of file +__all__ = ["processors"] diff --git a/src/ontime/core/time_series/__init__.py b/src/ontime/core/time_series/__init__.py index eb92271..b49a7b5 100644 --- a/src/ontime/core/time_series/__init__.py +++ b/src/ontime/core/time_series/__init__.py @@ -4,8 +4,8 @@ from .resticted_time_series import RestrictedTimeSeries __all__ = [ - 'TimeSeries', - 'ProbabilisticTimeSeries', - 'BinaryTimeSeries', - 'RestrictedTimeSeries' + "TimeSeries", + "ProbabilisticTimeSeries", + "BinaryTimeSeries", + "RestrictedTimeSeries", ] diff --git a/src/ontime/core/utils/dynamic_class.py b/src/ontime/core/utils/dynamic_class.py index a6e0dad..659093c 100644 --- a/src/ontime/core/utils/dynamic_class.py +++ b/src/ontime/core/utils/dynamic_class.py @@ -5,6 +5,7 @@ class DynamicClass(Registry): """ DynamicClass is a class that can load other classes dynamically """ + def __init__(self): super().__init__() diff --git a/src/ontime/core/utils/registry.py b/src/ontime/core/utils/registry.py index 8cb82e3..22014c7 100644 --- a/src/ontime/core/utils/registry.py +++ b/src/ontime/core/utils/registry.py @@ -3,6 +3,7 @@ class Registry: Registry class with the aim to store objects in a dictionary and retrieve them by name """ + def __init__(self): self.registry = {} diff --git a/src/ontime/module/preprocessing/__init__.py b/src/ontime/module/preprocessing/__init__.py index 4b0d590..e4193cf 100644 --- a/src/ontime/module/preprocessing/__init__.py +++ b/src/ontime/module/preprocessing/__init__.py @@ -1 +1 @@ -from . import common \ No newline at end of file +from . import common