DM2020_Lab2_Master.ipynb

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   "source": [
    "# Data Mining Lab 2\n",
    "In this lab session we will focus on the use of Neural Word Embeddings "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. Data preparation\n",
    "2. Feature engineering\n",
    "3. Model\n",
    "4. Results evaluation\n",
    "5. Other things you could try\n",
    "6. Deep Learning\n",
    "7. Word to Vector\n",
    "8. Clustering\n",
    "9. High-dimension Visualization\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Necessary Library Requirements:\n",
    "\n",
    "#### Same as Lab1:\n",
    "- [Jupyter](http://jupyter.org/) (Strongly recommended but not required)\n",
    "    - Install via `pip3 install jupyter` and use `jupyter notebook` in terminal to run\n",
    "- [Scikit Learn](http://scikit-learn.org/stable/index.html)\n",
    "    - Install via `pip3 sklearn` from a terminal\n",
    "- [Pandas](http://pandas.pydata.org/)\n",
    "    - Install via `pip3 install pandas` from a terminal\n",
    "- [Numpy](http://www.numpy.org/)\n",
    "    - Install via `pip3 install numpy` from a terminal\n",
    "- [Matplotlib](https://matplotlib.org/)\n",
    "    - Install via `pip3 maplotlib` from a terminal\n",
    "- [Plotly](https://plot.ly/)\n",
    "    - Install via `pip3 install plotly` from a terminal\n",
    "- [Seaborn](https://seaborn.pydata.org/)\n",
    "    - Install and signup for `seaborn`\n",
    "- [NLTK](http://www.nltk.org/)\n",
    "    - Install via `pip3 install nltk` from a terminal\n",
    "    \n",
    "#### New Libraries to intsall:\n",
    "- [Gensim](https://pypi.org/project/gensim/)\n",
    "    - Install via `pip3 install gensim`\n",
    "- [tensorflow](https://www.tensorflow.org/)\n",
    "    - Install via `pip3 install tensorflow=1.15`\n",
    "    - Also install `pip3 install tensorflow-hub`\n",
    "- [Keras](https://keras.io/)\n",
    "    - Install via `pip3 install keras`\n",
    "    \n",
    "                                                                                            \n",
    "                                                                                           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dataset:** [SemEval 2017 Task](https://competitions.codalab.org/competitions/16380)\n",
    "\n",
    "**Task:** Classify text data into 4 different emotions using word embedding and other deep information retrieval approaches.\n",
    "\n",
    "![pic0](pics/pic0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 1. Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before beggining the lab, please make sure to download the [Google News Dataset](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit) and place it in a folder named \"GoogleNews\" in the same directory as this file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Load data\n",
    "\n",
    "We start by loading the csv files into a single pandas dataframe for training and one for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "### training data\n",
    "anger_train = pd.read_csv(\"data/semeval/train/anger-ratings-0to1.train.txt\",\n",
    "                         sep=\"\\t\", header=None,names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "sadness_train = pd.read_csv(\"data/semeval/train/sadness-ratings-0to1.train.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "fear_train = pd.read_csv(\"data/semeval/train/fear-ratings-0to1.train.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "joy_train = pd.read_csv(\"data/semeval/train/joy-ratings-0to1.train.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine 4 sub-dataset\n",
    "train_df = pd.concat([anger_train, fear_train, joy_train, sadness_train], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>emotion</th>\n",
       "      <th>intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>How the fu*k! Who the heck! moved my fridge!.....</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>So my Indian Uber driver just called someone t...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002</td>\n",
       "      <td>@DPD_UK I asked for my parcel to be delivered ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003</td>\n",
       "      <td>so ef whichever butt wipe pulled the fire alar...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004</td>\n",
       "      <td>Don't join @BTCare they put the phone down on ...</td>\n",
       "      <td>anger</td>\n",
       "      <td>0.896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "      id                                               text emotion  intensity\n",
       "0  10000  How the fu*k! Who the heck! moved my fridge!.....   anger      0.938\n",
       "1  10001  So my Indian Uber driver just called someone t...   anger      0.896\n",
       "2  10002  @DPD_UK I asked for my parcel to be delivered ...   anger      0.896\n",
       "3  10003  so ef whichever butt wipe pulled the fire alar...   anger      0.896\n",
       "4  10004  Don't join @BTCare they put the phone down on ...   anger      0.896"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### testing data\n",
    "anger_test = pd.read_csv(\"data/semeval/dev/anger-ratings-0to1.dev.gold.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "sadness_test = pd.read_csv(\"data/semeval/dev/sadness-ratings-0to1.dev.gold.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "fear_test = pd.read_csv(\"data/semeval/dev/fear-ratings-0to1.dev.gold.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "joy_test = pd.read_csv(\"data/semeval/dev/joy-ratings-0to1.dev.gold.txt\",\n",
    "                         sep=\"\\t\", header=None, names=[\"id\", \"text\", \"emotion\", \"intensity\"])\n",
    "\n",
    "# combine 4 sub-dataset\n",
    "test_df = pd.concat([anger_test, fear_test, joy_test, sadness_test], ignore_index=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle dataset\n",
    "train_df = train_df.sample(frac=1)\n",
    "test_df = test_df.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Training df:  (3613, 4)\n",
      "Shape of Testing df:  (347, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Training df: \", train_df.shape)\n",
    "print(\"Shape of Testing df: \", test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 1 (Take home): **  \n",
    "Plot word frequency for Top 30 words in both train and test dataset. (Hint: refer to DM lab 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([2, 1, 1, ..., 2, 2, 1])"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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    {
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       "array([1, 1, 2, ..., 1, 1, 1])"
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     "execution_count": 7,
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   "source": [
    "# Answer here\n",
    "\n",
    "# Calculate word frequencies\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect1 = CountVectorizer()\n",
    "train_counts = count_vect1.fit_transform(train_df.text)\n",
    "count_vect2 = CountVectorizer()\n",
    "test_counts = count_vect2.fit_transform(test_df.text)\n",
    "\n",
    "train_term_frequencies = []\n",
    "test_term_frequencies = []\n",
    "for j in range(0, train_counts.shape[1]):\n",
    "    train_term_frequencies.append(sum(train_counts[:, j].toarray()))\n",
    "for j in range(0, test_counts.shape[1]):\n",
    "    test_term_frequencies.append(sum(test_counts[:, j].toarray()))\n",
    "\n",
    "train_term_frequencies = np.asarray(train_counts.sum(axis=0))[0]\n",
    "test_term_frequencies = np.asarray(test_counts.sum(axis=0))[0]\n",
    "\n",
    "train_term_frequencies\n",
    "test_term_frequencies"
   ]
  },
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Frequencies of Test Text (Top 30)\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"term_freq\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"vect\"}, \"type\": \"category\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('adef6eba-e8b4-40f8-b5a1-1a7ba99d8fe2');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot top 30 frequent vocabularies\n",
    "\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "\n",
    "train_xy = {'term_freq': train_term_frequencies, 'vect': count_vect1.get_feature_names()}\n",
    "train_xy_df = pd.DataFrame(train_xy)\n",
    "train_xy_df = train_xy_df.sort_values(by=['term_freq'], ascending=False)[0:30]       # get the top 30 frequent vocabularies in train text\n",
    "\n",
    "test_xy = {'term_freq': test_term_frequencies, 'vect': count_vect2.get_feature_names()}\n",
    "test_xy_df = pd.DataFrame(test_xy)\n",
    "test_xy_df = test_xy_df.sort_values(by=['term_freq'], ascending=False)[0:30]       # get the top 30 frequent vocabularies in test text\n",
    "\n",
    "#plotly.offline.init_notebook_mode()\n",
    "fig1 = px.bar(train_xy_df, x='term_freq', y='vect', text='term_freq', title=\"Word Frequencies of Train Text (Top 30)\")\n",
    "fig1 = fig1.update_yaxes(type='category')\n",
    "fig1 = fig1.update_layout(height=1000)\n",
    "plotly.offline.iplot(fig1)\n",
    "\n",
    "fig2 = px.bar(test_xy_df, x='term_freq', y='vect', text='term_freq', title=\"Word Frequencies of Test Text (Top 30)\")\n",
    "fig2 = fig2.update_yaxes(type='category')\n",
    "fig2 = fig2.update_layout(height=1000)\n",
    "plotly.offline.iplot(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 1.2 Save data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will save our data in Pickle format. The pickle module implements binary protocols for serializing and de-serializing a Python object structure.   \n",
    "  \n",
    "Some advantages for using pickle structure:  \n",
    "* Because it stores the attribute type, it's more convenient for cross-platform use.  \n",
    "* When your data is huge, it could use less space to store also consume less loading time.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "## save to pickle file\n",
    "train_df.to_pickle(\"train_df.pkl\") \n",
    "test_df.to_pickle(\"test_df.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "## load a pickle file\n",
    "train_df = pd.read_pickle(\"train_df.pkl\")\n",
    "test_df = pd.read_pickle(\"test_df.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information: https://reurl.cc/0Dzqx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### 1.3 Exploratory data analysis (EDA)\n",
    "\n",
    "Again, before getting our hands dirty, we need to explore a little bit and understand the data we're dealing with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emotion\n",
       "anger       857\n",
       "fear       1147\n",
       "joy         823\n",
       "sadness     786\n",
       "Name: text, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group to find distribution\n",
    "train_df.groupby(['emotion']).count()['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '% of instances')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Emotion')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Emotion distribution')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUcAAADgCAYAAACQJ6SJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYi0lEQVR4nO3deZxddXnH8c+XpSyJJEpgGsMyAqlIXSKMLC40QYqIKEEoEkEJWCltARVqxeISK5EgWFwrDQUBESMWERqUxdSAWEAmECBAKTSLEEMwSCABjFme/nF+w1zGc+/cuZlzzp3J9/163dfce7bfM7+589yz3PN7FBGYmdnLbVZ1AGZm7cjJ0cwsh5OjmVkOJ0czsxxOjmZmOZwczcxyODlaYSRdJOmzJbQzUdITNa8flDRxkLZ9nKSba16HpD0GY9tpe6sl7TZY27PBI3/PcXiTtBjoANbXTL4sIk4d5HamAn8dEW8fzO022fZE4MqI2GkA63QCi4AtI2LdANYLYHxEPDbAMJE0N8X57wNd18q3RdUBWCneGxE/qzqIoUbSFgNJnDa8+LB6EyZpqqRfSrpQ0kpJCyW9NU1/XNJTkk6oWX6UpCsk/VbSEkmfkbSZpNcBFwEHpMPElWn5yySdU7P+RyU9Jul3kq6X9OqaeSHpFEmPpli+JUl14t4mbfsZSQ8Bb+kzf7Gkg9PzfSV1S3pO0nJJ/5IWuy39XJliPqBPfzwNTEvTbu8TwmGpr1ZIOl/SZqmtaZKurImjM/1eW0iaDrwD+GZq75s1v/cejfq35m91u6QL0u+9SNK7m/k7W2ucHG0/4H5ge+AqYBZZstkDOJ7sn3lkWvYbwChgN+AvgA8DJ0bEw8ApwB0RMTIiRvdtRNJBwLnAMcBYYElqq9bhqe03puXeVSfmzwO7p8e7gBPqLAfwNeBrEbFdWv7qNP3A9HN0ivmOmv5YSHYqYnqdbR4JdAF7A0cAJzVoH4CIOBv4BXBqai/vtEZu/9bM3w94BBgDfBm4pN4HiG08J8dNw4/T3ljP46M18xZFxHciYj3wA2Bn4J8jYk1E3Az8AdhD0ubAscCnI2JVRCwGvgJ8qMkYjgMujYh7ImIN8GmyPc3OmmVmRMTKiPg18HNgQp1tHQNMj4jfRcTjwNcbtLs2xT8mIlZHxJ39xPmbiPhGRKyLiBfrLHNeavvXwFeBKf1ss19N9u+SiLg4/a0uJ/uQ6djYti2fk+OmYXJEjK55XFwzb3nN8xcBIqLvtJFkeytbku3x9VgCjGsyhlfXrhsRq4Gn+6z/ZM3zF1K79bb1eJ846vkI8GfA/0i6W9Lh/cT5eD/z+y6zJMWzsZrp35f6JyJeSE/r9ZFtJCdHa9YKsr2wXWum7QIsTc/7+9rDb2rXlTSC7FB+ad016ltGtodbG0euiHg0IqYAOwLnAf+R2q4XbzNf3+jb9m/S8+eBbWvm/ekAtt1f/1rJnBytKelQ7mpguqRXSNoVOAPouQCxHNhJ0p/U2cT3gRMlTZC0FfAl4K50+DhQVwOflvRKSTsBp9VbUNLxknaIiA3AyjR5A/Db9LOV7xh+MrW9M/AxstMRAPOBAyXtImkU2amDWsvrtddE/1rJnBw3Df+ZrpD2PK5tcTunke0dLQRuJ7uAc2ma91/Ag8CTklb0XTF9leizwDVke367k51ja8UXyA45FwE3A99tsOyhwIOSVpNdnDk2Il5Mh6XTgV+m87D7D6D964B5ZMnwBuASgIi4hSxR3p/mz+6z3teAo9PV5rzzpI3610rmL4GbmeXwnqOZWQ4nRzOzHE6OZmY5nBzNzHI4OZqZ5RgSo/KMGTMmOjs7qw4j1/PPP8+IESOqDqMtuC96uS96tXNfzJs3b0VE7JA3b0gkx87OTrq7u6sOI9fcuXOZOHFi1WG0BfdFL/dFr3buC0l1bz31YbWZWQ4nRzOzHE6OZmY5nBzNzHI4OZqZ5RgSV6utdZ1n3VBaW2e+YR1TS2pv8Yz3lNKObbq852hmlsPJ0cwsh5OjmVkOJ0czsxxOjmZmOQpLjpK2lvQrSfdJelDSF9L010i6S9Jjkn7QoCCTmVllitxzXAMcFBFvIivOfmgqYnQecGFE7AE8Q1ZX2MysrRSWHCOzOr3cMj0COAj4jzT9cmByUTGYmbWq0OqDkjYnK1G5B/At4HzgzrTXSKr7+9OIeH3OuicDJwN0dHTsM2vWrMLi3BirV69m5MiRVYdR1wNLny2trY5tYPmL5bT1hnGjymmoRe3+vihTO/fFpEmT5kVEV968Qu+QSYXKJ0gaDVwL7DmAdWcCMwG6urqiXceDa+ex6oDS7liB7A6ZrzxQzk1Xi4+bWEo7rWr390WZhmpflHK1OiJWAj8HDgBGS+r5D9oJWFpGDGZmA1Hk1eod0h4jkrYB/hJ4mCxJHp0WOwG4rqgYzMxaVeQx0Fjg8nTecTPg6oiYLekhYJakc4B7gUsKjMHMrCWFJceIuB94c870hcC+RbVrZjYYfIeMmVkOJ0czsxxOjmZmOZwczcxyODmameVwcjQzy+HkaGaWw8nRzCyHk6OZWQ4nRzOzHE6OZmY5nBzNzHI4OZqZ5XByNDPL4eRoZpajyJHAd5b0c0kPpbrVH0vTp0laKml+ehxWVAxmZq0qciTwdcCZEXGPpFcA8yTdkuZdGBEXFNi2mdlGKXIk8GXAsvR8laSHgXFFtWdmNpgKrVv9UiNSJ3Ab8HrgDGAq8BzQTbZ3+UzOOq5bPQhct7oa7f6+KFM790WjutWFJ0dJI4FbgekR8SNJHcAKIIAvAmMj4qRG2+jq6oru7u5C42xVu9fk7RyudatnvKeUdlrV7u+LMrVzX0iqmxwLvVotaUvgGuB7EfEjgIhYHhHrI2IDcDEutmVmbajIq9UiK7v6cET8S830sTWLHQksKCoGM7NWFXkM9DbgQ8ADkuanaf8ETJE0geywejHwNwXGYGbWkiKvVt8OKGfWT4pq08xssPR7WC1pd0lbpecTJZ0uaXThkZmZVaiZPcdrgC5JewAzgeuAq4C2vrOlrKu0Z75hHVNLaqvdr9CaDSfNXJDZEBHryC6efCMiPgmM7WcdM7MhrZnkuFbSFOAEYHaatmVxIZmZVa+Z5HgicADZl7gXSXoN8N1iwzIzq1a/5xwj4iFJnwJ2Sa8XAecVHZjZYCv7biGfix7amrla/V5gPnBjej1B0vUFx2VmVqlmDqunkd3itxIgIuYDuxUWkZlZG2jqgkxE9B3aZUMRwZiZtYtmvuf4oKQPAptLGg+cDvx3sWGZmVWrmeR4GnA2sIbsy983AecUGZSZFWs4Xpwa7AtTzVytfoEsOZ49qC2bmbWxZq5W31J7L7WkV0q6qdCozMwq1swFmTERsbLnRSppsGNhEZmZtYGm7q2WtEvPC0m7ko3FaGY2bDVzQeZs4HZJt5KNz/gOUuGrRiTtDFwBdJAl05kR8TVJrwJ+AHSSDXZ7TF6BLTOzKvW75xgRNwJ7kyW0WcA+EdHMOceeutV7AfsDfy9pL+AsYE5EjAfmpNdmZm2l2RoyWwG/IyunupekA/tbISKWRcQ96fkqoKdu9RHA5Wmxy4HJA4zZzKxw/R5WSzoP+ADwIL13xgRZHeqmpLrVbwbuAjoiYlma9STZYbeZWVvpt261pEeAN0bEmpYa+OO61SsjYnTN/Gci4pU5651MOrfZ0dGxz6xZswbUblnF7Nu9kH1Z/QDui1rui15l9UUr/TBp0qS6daubuSCzkGxw2wEnx7y61cBySWMjYlkq0/pU3roRMZOsLANdXV0x0KLgZQ0XVWoh++MmDnidsvoB3Be13Be9yuqLVvqhkWYifgGYL2kONQkyIk5vtFK9utXA9WSjis9IP68baNBmZkVrJjlenx4DVa9u9QzgakkfAZYAx7SwbTOzQjVzb/Xl/S1TZ716dasB3tnKNs3MytLM1erxwLnAXsDWPdMjwgPemtmw1cz3HL8DfJvsS92TyO56ubLIoMzMqtZMctwmIuaQfe1nSURMA1zRx8yGtWYuyKyRtBnwqKRTgaXAyGLDMjOrVjN7jh8DtiUrj7APcDzw4SKDMjOrWjPJsTMiVkfEExFxYkQcRaphbWY2XDWTHD/d5DQzs2Gj7jlHSe8GDgPGSfp6zaztyK5cm5kNW40uyPwG6AbeB8yrmb4K+ESRQZmZVa1ucoyI+4D7JF0VEWshK64F7OyRu81suGvmnOMtkrZL5Q3uAS6WdGHBcZmZVaqZ5DgqIp4D3g9cERH74XujzWyYayY5bpHGXTwGmF1wPGZmbaGZ5PjPwE3AYxFxt6TdgEeLDcvMrFrNDFn2Q+CHNa8XAkcVGZSZWdWaGbJsB+CjZHWmX1o+Ik4qLiwzs2o1c1h9HTAK+BlwQ82jIUmXSnpK0oKaadMkLZU0Pz0OazVwM7MiNTMqz7YR8akWtn0Z8E2y8R9rXRgRF7SwPTOz0jSz5zi7lT28iLgN+N3AQzIzq14zdatXASPIKg+uJasLExGxXb8blzqB2RHx+vR6GjAVeI7s1sQz691t47rVf8z1iXu5L3q5LzKDXbe63+S4MXKSYwewAgjgi8DYZi7sdHV1RXd394Da7hyOdatnDHwA9rL6AdwXtdwXvUqrW91CP0iqmxwbjcqzZ0T8j6S98+ZHxD0DDSQiltds/2L8pXIza1ON0vkZZIe1X8mZF8BBA21M0tiIWJZeHgksaLS8mVlVGo3Kc3L6OamVDUv6PjARGCPpCeDzwERJE8iS62Lgb1rZtplZ0Qo7ERARU3ImX1JUe2Zmg6mZr/KYmW1y6iZHSW9LP7cqLxwzs/bQaM+xp27MHWUEYmbWThqdc1wraSZ/XGALgIg4vbiwzMyq1Sg5Hg4cDLyLlxfYMjMb9hp9lWcFMEvSw6nYlpnZJqOZq9VPS7o2DT/2lKRrJO1UeGRmZhVqJjl+B7geeHV6/GeaZmY2bDWTHHeMiO9ExLr0uAzYoeC4zMwq1UxyXCHpeEmbp8fxwNNFB2ZmVqVmkuNJZGVZnwSWAUcDJxYZlJlZ1ZqpPrgEeF8JsZiZtQ3fW21mlsPJ0cwsh5OjmVmOppOjpP0l3ShprqTJTSyfV7f6VZJukfRo+vnKFuM2MytUoyHL/rTPpDPIShscRlYcqz+XAYf2mXYWMCcixgNz0mszs7bTaM/xIkmfk7R1er2S7Gs8R5KVVm2oTt3qI4DL0/PLgckDCdbMrCx1k2NETAbuBWZL+jDwcWArYHtaT2odNQW2ngQ6WtyOmVmh+q1bLWlz4O/IhjCbnvYIm9v4H9etXhkRo2vmPxMRuecdJZ1MVv2Qjo6OfWbNmtVss0B5RctdvL2X+6KX+6JXWX3RSj9MmjSpbt3quslR0vuATwDrgC+R7UV+FhgHnB0R/9dfwznJ8RFgYkQskzQWmBsRr+1vO11dXdHd3d3fYi9TVtFyF2/v5b7o5b7oVVZftNIPkuomx0bnHM8B3k126+B5EbEyIs4kS5DTBxxF5nrghPT8BOC6FrdjZlaoRun8WeD9wLbAUz0TI+JR4Nj+NlynbvUM4GpJHwGWkCVeM7O20yg5HglMAdYCHxzohuvUrQZ450C3ZWZWtv7KJHyjxFjMzNqGbx80M8vh5GhmlsPJ0cwsh5OjmVkOJ0czsxxOjmZmOZwczcxyODmameVwcjQzy+HkaGaWw8nRzCyHk6OZWQ4nRzOzHE6OZmY5nBzNzHKUU+SiD0mLgVXAemBdvRoOZmZVqSQ5JpPSgLpmZm3Hh9VmZjn6rVtdSKPSIuAZIIB/i4iZOcu4bnUfrk/cy33Ry32RKa1udZEkjYuIpZJ2BG4BTouI2+ot77rVGdcn7uW+6OW+yJRZt7owEbE0/XwKuBbYt4o4zMzqKT05Shoh6RU9z4FDgAVlx2Fm1kgVV6s7gGsl9bR/VUTcWEEcZmZ1lZ4cI2Ih8Kay2zUzGwh/lcfMLIeTo5lZDidHM7McTo5mZjmcHM3Mcjg5mpnlcHI0M8vh5GhmlsPJ0cwsh5OjmVkOJ0czsxxOjmZmOZwczcxyODmameVwcjQzy1FJcpR0qKRHJD0m6awqYjAza6SKMgmbA98C3g3sBUyRtFfZcZiZNVLFnuO+wGMRsTAi/gDMAo6oIA4zs7qqSI7jgMdrXj+RppmZtY3S61ZLOho4NCL+Or3+ELBfRJzaZ7mTgZPTy9cCj5QaaPPGACuqDqJNuC96uS96tXNf7BoRO+TNqKL64FJg55rXO6VpLxMRM4GZZQXVKknd9YqCb2rcF73cF72Gal9UcVh9NzBe0msk/QlwLHB9BXGYmdVVRWnWdZJOBW4CNgcujYgHy47DzKyRKg6riYifAD+pou0CtP2hf4ncF73cF72GZF+UfkHGzGwo8O2DZmY5nBytKZJOl/SwpO9VHUs7kfTfVcfQLiR1SlpQdRyDpZJzjps6SSI7pbGh6lgG4O+AgyPiiVY3IGmLiFg3iDFVLiLeWnUMVgzvOdaQ9GNJ8yQ9mL6EjqTVkqZLuk/SnZI60vTd0+sHJJ0jaXXNdj4p6W5J90v6QprWmQbbuAJYwMu/69nWJF0E7Ab8VNLZki6V9CtJ90o6Ii3TKekXku5Jj7em6RPT9OuBhyr8NQqR3h+SdL6kBen98IE07wpJk2uW/V5Pf7UzSSMk3ZDe8wskfUDS59J7eoGkmekDHkn7pOXuA/6+ZhtTJf1I0o2SHpX05Zp5h0i6I71PfihpZJo+Q9JD6f/mgjTtr1Kb90m6rdSOiAg/0gN4Vfq5DVkC2x4I4L1p+peBz6Tns4Ep6fkpwOr0/BCyq3Mi+/CZDRwIdAIbgP2r/j1b7JvFZHc6fAk4Pk0bDfwvMALYFtg6TR8PdKfnE4HngddU/TsU1C+rgaOAW8i+mtYB/BoYC/wF8OO03ChgEbBF1TE38TsdBVxc83pUz/9Gev3dmv+J+4ED0/PzgQXp+VRgYVp3a2AJ2Q7BGOA2YERa7lPA59L/2iP0XiQenX4+AIyrnVbWw3uOL3d6+gS8k+wPOR74A1mCA5hHluQADgB+mJ5fVbONQ9LjXuAeYM+0HYAlEXFnUcGX5BDgLEnzgblkb/xdgC2BiyU9QNYvtSMt/SoiFpUcZ5neDnw/ItZHxHLgVuAtEXEr2Q0POwBTgGtiaJxWeAD4S0nnSXpHRDwLTJJ0V/r7HgT8uaTRZAmrZ4/uu322Mycino2I35MdNewK7E/23vhleg+dkKY/C/weuETS+4EX0jZ+CVwm6aNkHz6l8TnHRNJE4GDggIh4QdJcsn/8tZE+toD19N9nAs6NiH/rs/1Osj2ooU7AURHxsnvdJU0DlgNvIttj/n3N7OHwe7fqCuB4sjvBTqw4lqZExP9K2hs4DDhH0hyyQ+auiHg8/a23bmJTa2qe9/zvCLglIqb0XVjSvsA7gaOBU4GDIuIUSfsB7wHmSdonIp7eiF+vad5z7DUKeCYlxj3JPuEauZPs8AOyN36Pm4CTas6jjJO046BHW52bgNNqzjm9OU0fBSyL7CLThyj5U75ivwA+IGnztJd4IPCrNO8y4OMAETEkzrlKejXwQkRcSXaovHeatSK9r48GiIiVwEpJb0/zj2ti83cCb5O0R2prhKQ/S9sdFdkNIp8g+5BF0u4RcVdEfA74LSWeq/eeY68bgVMkPUx27qO/w9+PA1dKOjut+yxARNws6XXAHSl/rCbbc1hfUNxl+yLwVeB+SZuRnUc7HPhX4BpJHybrj01lbzGAa8lOs9yXXv9jRDwJEBHL03vqx5VFOHBvAM6XtAFYC/wtMJnsPPyTZOMj9DgRuFRSADf3t+GI+K2kqcD3JW2VJn8GWAVcJ2lrsr3LM9K88yWNT9PmkPVxKXyHTIskbQu8GBEh6ViyizNtfyXSBo+k7YF7ImLXBstsS3YOb+907s6GCO85tm4f4Jvp8HIlcFK14ViZ0qHnXOCCBsscDFwCXOjEOPR4z9HMLIcvyJiZ5XByNDPL4eRoZpbDydEqJ2m9pPk1j7MGYZudkj5Y87pL0tc3dru26fAFGaucpNURMXKQtzkR+IeIOHwwt2ubDu85WtuStFjSuWlvslvS3pJukvR/kk5Jy0g5I+IAM4B3pHU/oWx0oNlpnVcpG4HpfmUjK70xTZ+mbMShuZIWSjq9mt/c2oG/52jtYJs0CEGPcyPiB+n5ryNigqQLyW7FexvZfb0LgIuA9wMTyG43GwPcnYa2OouaPce0J9njC8C9ETFZ0kFk9z9PSPP2BCYBrwAekfTtiFg7mL+sDQ1OjtYOXoyICXXm9ZTtfQAYGRGrgFWS1qRRYV4aEQdYLulW4C3Acw3aezvpvviI+C9J20vaLs27ISLWAGskPUU2BFnLA/za0OXDamt3PSO7bODlo7xsoJgP97yRZGwT5ORoQ129EXFWkR0a11vnOHjpcHtFRDTa07RNkD8VrR30Ped4Y0Q0+3We3BFxJD0NrE+DF19GNvhwj2lkI8ncTzao6gkbF74NR/4qj5lZDh9Wm5nlcHI0M8vh5GhmlsPJ0cwsh5OjmVkOJ0czsxxOjmZmOZwczcxy/D+8zUbt6zsu0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# the histogram of the data\n",
    "labels = train_df['emotion'].unique()\n",
    "post_total = len(train_df)\n",
    "df1 = train_df.groupby(['emotion']).count()['text']\n",
    "df1 = df1.apply(lambda x: round(x*100/post_total,3))\n",
    "\n",
    "#plot\n",
    "fig, ax = plt.subplots(figsize=(5,3))\n",
    "plt.bar(df1.index,df1.values)\n",
    "\n",
    "#arrange\n",
    "plt.ylabel('% of instances')\n",
    "plt.xlabel('Emotion')\n",
    "plt.title('Emotion distribution')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Feature engineering\n",
    "### Using Bag of Words\n",
    "Using scikit-learn ```CountVectorizer``` perform word frequency and use these as features to train a model.  \n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build analyzers (bag-of-words)\n",
    "BOW_vectorizer = CountVectorizer() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Learn a vocabulary dictionary of all tokens in the raw documents.\n",
    "BOW_vectorizer.fit(train_df['text'])\n",
    "\n",
    "# 2. Transform documents to document-term matrix.\n",
    "train_data_BOW_features = BOW_vectorizer.transform(train_df['text'])\n",
    "test_data_BOW_features = BOW_vectorizer.transform(test_df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3613x10115 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 51467 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the result\n",
    "train_data_BOW_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_data_BOW_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add .toarray() to show\n",
    "train_data_BOW_features.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3613, 10115)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the dimension\n",
    "train_data_BOW_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2k17', '2much', '2nd', '30', '300', '301', '30am', '30pm', '30s', '31']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observe some feature names\n",
    "feature_names = BOW_vectorizer.get_feature_names()\n",
    "feature_names[100:110]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The embedding is done. We can technically feed this into our model. However, depending on the embedding technique you use and your model, your accuracy might not be as high, because:\n",
    "\n",
    "* curse of dimensionality  (we have 10,115 dimension now)\n",
    "* some important features are ignored (for example, some models using emoticons yeld better performance than counterparts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"😂\" in feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try using another tokenizer below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tingchun/anaconda3/envs/dm/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:484: UserWarning:\n",
      "\n",
      "The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CountVectorizer(max_features=500,\n",
       "                tokenizer=<function word_tokenize at 0x7f8fc4b8ea70>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(3613, 500)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "\n",
    "# build analyzers (bag-of-words)\n",
    "BOW_500 = CountVectorizer(max_features=500, tokenizer=nltk.word_tokenize) \n",
    "\n",
    "# apply analyzer to training data\n",
    "BOW_500.fit(train_df['text'])\n",
    "\n",
    "train_data_BOW_features_500 = BOW_500.transform(train_df['text'])\n",
    "\n",
    "## check dimension\n",
    "train_data_BOW_features_500.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_BOW_features_500.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['change',\n",
       " 'cheer',\n",
       " 'cheerful',\n",
       " 'cheering',\n",
       " 'cheery',\n",
       " 'class',\n",
       " 'come',\n",
       " 'comes',\n",
       " 'could',\n",
       " 'country']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observe some feature names\n",
    "feature_names_500 = BOW_500.get_feature_names()\n",
    "feature_names_500[100:110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"😂\" in feature_names_500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 2 (Take home): **  \n",
    "Generate an embedding using the TF-IDF vectorizer instead of th BOW one with 1000 features and show the feature names for features [100:110]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(max_features=1000)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(3613, 1000)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "['bitter',\n",
       " 'black',\n",
       " 'blacks',\n",
       " 'blame',\n",
       " 'blessed',\n",
       " 'blood',\n",
       " 'bloody',\n",
       " 'blues',\n",
       " 'body',\n",
       " 'boiling']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Answer here\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "TFIDF_1000 = TfidfVectorizer(max_features=1000)\n",
    "TFIDF_1000.fit(train_df[\"text\"])\n",
    "\n",
    "train_data_TFIDF_features_1000 = TFIDF_1000.transform(train_df[\"text\"])\n",
    "\n",
    "train_data_TFIDF_features_1000.shape\n",
    "\n",
    "feature_names_1000 = TFIDF_1000.get_feature_names()\n",
    "feature_names_1000[100:110]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 3. Model\n",
    "### 3.1 Decision Trees\n",
    "Using scikit-learn ```DecisionTreeClassifier``` performs word frequency and uses these as features to train a model.  \n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape:  (3613, 500)\n",
      "y_train.shape:  (3613,)\n",
      "X_test.shape:  (347, 500)\n",
      "y_test.shape:  (347,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# for a classificaiton problem, you need to provide both training & testing data\n",
    "X_train = BOW_500.transform(train_df['text'])\n",
    "y_train = train_df['emotion']\n",
    "\n",
    "X_test = BOW_500.transform(test_df['text'])\n",
    "y_test = test_df['emotion']\n",
    "\n",
    "## take a look at data dimension is a good habbit  :)\n",
    "print('X_train.shape: ', X_train.shape)\n",
    "print('y_train.shape: ', y_train.shape)\n",
    "print('X_test.shape: ', X_test.shape)\n",
    "print('y_test.shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['joy', 'fear', 'sadness', 'sadness', 'sadness', 'anger', 'joy',\n",
       "       'anger', 'fear', 'fear'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## build DecisionTree model\n",
    "DT_model = DecisionTreeClassifier(random_state=0)\n",
    "\n",
    "## training!\n",
    "DT_model = DT_model.fit(X_train, y_train)\n",
    "\n",
    "## predict!\n",
    "y_train_pred = DT_model.predict(X_train)\n",
    "y_test_pred = DT_model.predict(X_test)\n",
    "\n",
    "## so we get the pred result\n",
    "y_test_pred[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 4. Results Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will check the results of our model's performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.99\n",
      "testing accuracy: 0.66\n"
     ]
    }
   ],
   "source": [
    "## accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "acc_train = accuracy_score(y_true=y_train, y_pred=y_train_pred)\n",
    "acc_test = accuracy_score(y_true=y_test, y_pred=y_test_pred)\n",
    "\n",
    "print('training accuracy: {}'.format(round(acc_train, 2)))\n",
    "print('testing accuracy: {}'.format(round(acc_test, 2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       anger       0.67      0.70      0.69        84\n",
      "        fear       0.67      0.65      0.66       110\n",
      "         joy       0.65      0.65      0.65        79\n",
      "     sadness       0.66      0.65      0.65        74\n",
      "\n",
      "    accuracy                           0.66       347\n",
      "   macro avg       0.66      0.66      0.66       347\n",
      "weighted avg       0.66      0.66      0.66       347\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## precision, recall, f1-score,\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_true=y_test, y_pred=y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[59 12 10  3]\n",
      " [15 72 10 13]\n",
      " [ 6 13 51  9]\n",
      " [ 8 10  8 48]]\n"
     ]
    }
   ],
   "source": [
    "## check by confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_true=y_test, y_pred=y_test_pred) \n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funciton for visualizing confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes, title='Confusion matrix',\n",
    "                          cmap=sns.cubehelix_palette(as_cmap=True)):\n",
    "    \"\"\"\n",
    "    This function is modified from: \n",
    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "    \"\"\"\n",
    "    classes.sort()\n",
    "    tick_marks = np.arange(len(classes))    \n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(5,5))\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           xticklabels = classes,\n",
    "           yticklabels = classes,\n",
    "           title = title,\n",
    "           xlabel = 'True label',\n",
    "           ylabel = 'Predicted label')\n",
    "\n",
    "    fmt = 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    ylim_top = len(classes) - 0.5\n",
    "    plt.ylim([ylim_top, -.5])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot your confusion matrix\n",
    "my_tags = ['anger', 'fear', 'joy', 'sadness']\n",
    "plot_confusion_matrix(cm, classes=my_tags, title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 3 (Take home): **  \n",
    "Can you interpret the results above? What do they mean?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer here\n",
    "- Over 60% of the prediction is correct.\n",
    "- False prediction is more likely to happen on Fear (other emotion is likely to be false predicted to fear). \n",
    "- Joy and Sadness are less likely to be predicted to each another (but still exist, although the two emotions are almost opposite)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 4 (Take home): **  \n",
    "Build a model using a ```Naive Bayes``` model and train it. What are the testing results? \n",
    "\n",
    "*Reference*: https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['fear', 'fear', 'sadness', 'sadness', 'sadness', 'fear', 'joy',\n",
       "       'anger', 'fear', 'fear'], dtype='<U7')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NB training accuracy: 0.79\n",
      "NB testing accuracy: 0.69\n"
     ]
    }
   ],
   "source": [
    "# Answer here\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "NB_model = MultinomialNB()\n",
    "NB_model.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred_NB = NB_model.predict(X_train)\n",
    "y_test_pred_NB = NB_model.predict(X_test)\n",
    "\n",
    "y_test_pred_NB[:10]\n",
    "\n",
    "acc_train_NB = accuracy_score(y_true=y_train, y_pred=y_train_pred_NB)\n",
    "acc_test_NB = accuracy_score(y_true=y_test, y_pred=y_test_pred_NB)\n",
    "\n",
    "print('NB training accuracy: {}'.format(round(acc_train_NB, 2)))\n",
    "print('NB testing accuracy: {}'.format(round(acc_test_NB, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 5 (Take home): **  \n",
    "\n",
    "How do the results from the Naive Bayes model and the Decision Tree model compare? How do you interpret these differences? Use the theoretical background covered in class to try and explain these differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer here\n",
    "- For the training accuracy, the Decision Tree model performs much better than the Naive Bayes model, but for the testing accuracy, the Naive Bayes model performs slightly better than the Decision Tree model.\n",
    "- The reason might be that, the Decision Tree model is likely to overfit the training data, so the training accuracy can be so high (up to 99%); but an overfitting model would not perform well on the data it did not seen before (ex: the testing data).\n",
    "- The Naive Bayes model does not overfit during the training process as much as the Decision Tree model, so the accuracy gap between training and testing is much closer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Other things you can try"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus, there are several things you can try that will affect your results. In order to yield better results, you can experiment by: \n",
    "    * Trying different features (Feature engineering)\n",
    "        -Eg. Word2Vec,PCA,LDA,FastText, Clustering......\n",
    "    * Trying different models\n",
    "    * Analyzing your results and interpret them to improve your feature engineering/model building process\n",
    "    * Iterate through the steps above until finding a satisfying result\n",
    "Remember that you should also consider the task at hand and the model you'll feed the data to. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 6. Deep Learning\n",
    "\n",
    "We use [Keras](https://keras.io/) to be our deep learning framwork, and follow the [Model (functional API)](https://keras.io/models/model/) to build a Deep Neural Network (DNN) model. Keras runs with Tensorflow in the backend. It's a nice abstraction to start working with NN models. \n",
    "\n",
    "Because Deep Learning is a 1-semester course, we can't talk about each detail about it in the lab session. Here, we only provide a simple template about how to build & run a DL model successfully. You can follow this template to design your model.\n",
    "\n",
    "We will begin by building a fully connected network, which looks like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Fully Connected Network](pics/pic1.png)\n",
    "\n",
    "(source: https://github.com/drewnoff/spark-notebook-ml-labs/tree/master/labs/DLFramework)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Prepare data (X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape:  (3613, 500)\n",
      "y_train.shape:  (3613,)\n",
      "X_test.shape:  (347, 500)\n",
      "y_test.shape:  (347,)\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "\n",
    "# standardize name (X, y) \n",
    "X_train = BOW_500.transform(train_df['text'])\n",
    "y_train = train_df['emotion']\n",
    "\n",
    "X_test = BOW_500.transform(test_df['text'])\n",
    "y_test = test_df['emotion']\n",
    "\n",
    "## check dimension is a good habbit \n",
    "print('X_train.shape: ', X_train.shape)\n",
    "print('y_train.shape: ', y_train.shape)\n",
    "print('X_test.shape: ', X_test.shape)\n",
    "print('y_test.shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Deal with categorical label (y)\n",
    "\n",
    "Rather than put your label `train_df['emotion']` directly into a model, we have to process these categorical (or say nominal) label by ourselves. \n",
    "\n",
    "Here, we use the basic method [one-hot encoding](https://en.wikipedia.org/wiki/One-hot) to transform our categorical  labels to numerical ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check label:  ['anger' 'fear' 'joy' 'sadness']\n",
      "\n",
      "## Before convert\n",
      "y_train[0:4]:\n",
      " 614       anger\n",
      "2353        joy\n",
      "1522       fear\n",
      "2954    sadness\n",
      "Name: emotion, dtype: object\n",
      "\n",
      "y_train.shape:  (3613,)\n",
      "y_test.shape:  (347,)\n",
      "\n",
      "\n",
      "## After convert\n",
      "y_train[0:4]:\n",
      " [[1. 0. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 0. 1.]]\n",
      "\n",
      "y_train.shape:  (3613, 4)\n",
      "y_test.shape:  (347, 4)\n"
     ]
    }
   ],
   "source": [
    "## deal with label (string -> one-hot)\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "print('check label: ', label_encoder.classes_)\n",
    "print('\\n## Before convert')\n",
    "print('y_train[0:4]:\\n', y_train[0:4])\n",
    "print('\\ny_train.shape: ', y_train.shape)\n",
    "print('y_test.shape: ', y_test.shape)\n",
    "\n",
    "def label_encode(le, labels):\n",
    "    enc = le.transform(labels)\n",
    "    return keras.utils.to_categorical(enc)\n",
    "\n",
    "def label_decode(le, one_hot_label):\n",
    "    dec = np.argmax(one_hot_label, axis=1)\n",
    "    return le.inverse_transform(dec)\n",
    "\n",
    "y_train = label_encode(label_encoder, y_train)\n",
    "y_test = label_encode(label_encoder, y_test)\n",
    "\n",
    "print('\\n\\n## After convert')\n",
    "print('y_train[0:4]:\\n', y_train[0:4])\n",
    "print('\\ny_train.shape: ', y_train.shape)\n",
    "print('y_test.shape: ', y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_shape:  500\n",
      "output_shape:  4\n"
     ]
    }
   ],
   "source": [
    "# I/O check\n",
    "input_shape = X_train.shape[1]\n",
    "print('input_shape: ', input_shape)\n",
    "\n",
    "output_shape = len(label_encoder.classes_)\n",
    "print('output_shape: ', output_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Imgur](pics/pic2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/tingchun/anaconda3/envs/dm/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                32064     \n",
      "_________________________________________________________________\n",
      "re_lu_1 (ReLU)               (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "re_lu_2 (ReLU)               (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 4)                 260       \n",
      "_________________________________________________________________\n",
      "softmax_1 (Softmax)          (None, 4)                 0         \n",
      "=================================================================\n",
      "Total params: 36,484\n",
      "Trainable params: 36,484\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers import ReLU, Softmax\n",
    "\n",
    "# input layer\n",
    "model_input = Input(shape=(input_shape, ))  # 500\n",
    "X = model_input\n",
    "\n",
    "# 1st hidden layer\n",
    "X_W1 = Dense(units=64)(X)  # 64\n",
    "H1 = ReLU()(X_W1)\n",
    "\n",
    "# 2nd hidden layer\n",
    "H1_W2 = Dense(units=64)(H1)  # 64\n",
    "H2 = ReLU()(H1_W2)\n",
    "\n",
    "# output layer\n",
    "H2_W3 = Dense(units=output_shape)(H2)  # 4\n",
    "H3 = Softmax()(H2_W3)\n",
    "\n",
    "model_output = H3\n",
    "\n",
    "# create model\n",
    "model = Model(inputs=[model_input], outputs=[model_output])\n",
    "\n",
    "# loss function & optimizer\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# show model construction\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/tingchun/anaconda3/envs/dm/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 3613 samples, validate on 347 samples\n",
      "Epoch 1/25\n",
      "3613/3613 [==============================] - 1s 181us/step - loss: 1.3228 - accuracy: 0.3737 - val_loss: 1.2661 - val_accuracy: 0.4438\n",
      "Epoch 2/25\n",
      "3613/3613 [==============================] - 0s 114us/step - loss: 1.0081 - accuracy: 0.6266 - val_loss: 0.9533 - val_accuracy: 0.6225\n",
      "Epoch 3/25\n",
      "3613/3613 [==============================] - 0s 133us/step - loss: 0.6099 - accuracy: 0.7869 - val_loss: 0.7862 - val_accuracy: 0.7205\n",
      "Epoch 4/25\n",
      "3613/3613 [==============================] - 0s 135us/step - loss: 0.4182 - accuracy: 0.8464 - val_loss: 0.8092 - val_accuracy: 0.6974\n",
      "Epoch 5/25\n",
      "3613/3613 [==============================] - 0s 127us/step - loss: 0.3238 - accuracy: 0.8876 - val_loss: 0.8133 - val_accuracy: 0.6974\n",
      "Epoch 6/25\n",
      "3613/3613 [==============================] - 0s 104us/step - loss: 0.2564 - accuracy: 0.9142 - val_loss: 0.8866 - val_accuracy: 0.7118\n",
      "Epoch 7/25\n",
      "3613/3613 [==============================] - 0s 134us/step - loss: 0.2132 - accuracy: 0.9319 - val_loss: 0.9372 - val_accuracy: 0.7032\n",
      "Epoch 8/25\n",
      "3613/3613 [==============================] - 0s 122us/step - loss: 0.1802 - accuracy: 0.9422 - val_loss: 0.9782 - val_accuracy: 0.6715\n",
      "Epoch 9/25\n",
      "3613/3613 [==============================] - 0s 104us/step - loss: 0.1507 - accuracy: 0.9549 - val_loss: 1.0857 - val_accuracy: 0.6859\n",
      "Epoch 10/25\n",
      "3613/3613 [==============================] - 0s 109us/step - loss: 0.1352 - accuracy: 0.9601 - val_loss: 1.1286 - val_accuracy: 0.6801\n",
      "Epoch 11/25\n",
      "3613/3613 [==============================] - 0s 120us/step - loss: 0.1196 - accuracy: 0.9646 - val_loss: 1.1499 - val_accuracy: 0.6715\n",
      "Epoch 12/25\n",
      "3613/3613 [==============================] - 1s 139us/step - loss: 0.1139 - accuracy: 0.9648 - val_loss: 1.1945 - val_accuracy: 0.6715\n",
      "Epoch 13/25\n",
      "3613/3613 [==============================] - 0s 103us/step - loss: 0.1001 - accuracy: 0.9704 - val_loss: 1.2787 - val_accuracy: 0.6916\n",
      "Epoch 14/25\n",
      "3613/3613 [==============================] - 0s 100us/step - loss: 0.0977 - accuracy: 0.9704 - val_loss: 1.2874 - val_accuracy: 0.6801\n",
      "Epoch 15/25\n",
      "3613/3613 [==============================] - 0s 100us/step - loss: 0.0888 - accuracy: 0.9726 - val_loss: 1.3352 - val_accuracy: 0.6715\n",
      "Epoch 16/25\n",
      "3613/3613 [==============================] - 1s 143us/step - loss: 0.0868 - accuracy: 0.9729 - val_loss: 1.3733 - val_accuracy: 0.6715\n",
      "Epoch 17/25\n",
      "3613/3613 [==============================] - 0s 114us/step - loss: 0.0858 - accuracy: 0.9732 - val_loss: 1.4236 - val_accuracy: 0.6744\n",
      "Epoch 18/25\n",
      "3613/3613 [==============================] - 0s 99us/step - loss: 0.0815 - accuracy: 0.9740 - val_loss: 1.4313 - val_accuracy: 0.6628\n",
      "Epoch 19/25\n",
      "3613/3613 [==============================] - 0s 94us/step - loss: 0.0744 - accuracy: 0.9743 - val_loss: 1.4563 - val_accuracy: 0.6888\n",
      "Epoch 20/25\n",
      "3613/3613 [==============================] - 0s 99us/step - loss: 0.0654 - accuracy: 0.9762 - val_loss: 1.4651 - val_accuracy: 0.6772\n",
      "Epoch 21/25\n",
      "3613/3613 [==============================] - 0s 97us/step - loss: 0.0766 - accuracy: 0.9737 - val_loss: 1.5167 - val_accuracy: 0.6888\n",
      "Epoch 22/25\n",
      "3613/3613 [==============================] - 1s 161us/step - loss: 0.0656 - accuracy: 0.9787 - val_loss: 1.5457 - val_accuracy: 0.6571\n",
      "Epoch 23/25\n",
      "3613/3613 [==============================] - 1s 153us/step - loss: 0.0631 - accuracy: 0.9792 - val_loss: 1.5785 - val_accuracy: 0.6859\n",
      "Epoch 24/25\n",
      "3613/3613 [==============================] - 1s 150us/step - loss: 0.0666 - accuracy: 0.9765 - val_loss: 1.5968 - val_accuracy: 0.6427\n",
      "Epoch 25/25\n",
      "3613/3613 [==============================] - 0s 120us/step - loss: 0.0643 - accuracy: 0.9762 - val_loss: 1.5934 - val_accuracy: 0.6715\n",
      "training finish\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import CSVLogger\n",
    "\n",
    "csv_logger = CSVLogger('logs/training_log.csv')\n",
    "\n",
    "# training setting\n",
    "epochs = 25\n",
    "batch_size = 32\n",
    "\n",
    "# training!\n",
    "history = model.fit(X_train, y_train, \n",
    "                    epochs=epochs, \n",
    "                    batch_size=batch_size, \n",
    "                    callbacks=[csv_logger],\n",
    "                    validation_data = (X_test, y_test))\n",
    "print('training finish')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5 Predict on testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.4217623e-01, 1.7711633e-01, 6.0421489e-02, 2.0285904e-02],\n",
       "       [3.9193552e-07, 9.9999535e-01, 2.5353144e-12, 4.2841516e-06],\n",
       "       [2.4480896e-05, 1.1768584e-03, 4.3542846e-07, 9.9879825e-01],\n",
       "       [6.9604113e-08, 2.4503587e-01, 5.0228386e-06, 7.5495905e-01],\n",
       "       [9.9619567e-01, 1.8719451e-06, 6.3596563e-05, 3.7387880e-03]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## predict\n",
    "pred_result = model.predict(X_test, batch_size=128)\n",
    "pred_result[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['anger', 'fear', 'sadness', 'sadness', 'anger'], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_result = label_decode(label_encoder, pred_result)\n",
    "pred_result[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing accuracy: 0.67\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('testing accuracy: {}'.format(round(accuracy_score(label_decode(label_encoder, y_test), pred_result), 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.373651</td>\n",
       "      <td>1.322828</td>\n",
       "      <td>0.443804</td>\n",
       "      <td>1.266089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.626626</td>\n",
       "      <td>1.008122</td>\n",
       "      <td>0.622478</td>\n",
       "      <td>0.953278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.786881</td>\n",
       "      <td>0.609878</td>\n",
       "      <td>0.720461</td>\n",
       "      <td>0.786233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.846388</td>\n",
       "      <td>0.418153</td>\n",
       "      <td>0.697406</td>\n",
       "      <td>0.809235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.887628</td>\n",
       "      <td>0.323820</td>\n",
       "      <td>0.697406</td>\n",
       "      <td>0.813262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.914199</td>\n",
       "      <td>0.256447</td>\n",
       "      <td>0.711816</td>\n",
       "      <td>0.886609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.931913</td>\n",
       "      <td>0.213193</td>\n",
       "      <td>0.703170</td>\n",
       "      <td>0.937213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.942153</td>\n",
       "      <td>0.180205</td>\n",
       "      <td>0.671470</td>\n",
       "      <td>0.978199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.954885</td>\n",
       "      <td>0.150677</td>\n",
       "      <td>0.685879</td>\n",
       "      <td>1.085678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.960144</td>\n",
       "      <td>0.135164</td>\n",
       "      <td>0.680115</td>\n",
       "      <td>1.128564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.964572</td>\n",
       "      <td>0.119582</td>\n",
       "      <td>0.671470</td>\n",
       "      <td>1.149907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.964849</td>\n",
       "      <td>0.113869</td>\n",
       "      <td>0.671470</td>\n",
       "      <td>1.194496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.970385</td>\n",
       "      <td>0.100101</td>\n",
       "      <td>0.691643</td>\n",
       "      <td>1.278680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>0.970385</td>\n",
       "      <td>0.097652</td>\n",
       "      <td>0.680115</td>\n",
       "      <td>1.287408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.972599</td>\n",
       "      <td>0.088798</td>\n",
       "      <td>0.671470</td>\n",
       "      <td>1.335229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.972876</td>\n",
       "      <td>0.086823</td>\n",
       "      <td>0.671470</td>\n",
       "      <td>1.373298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.973152</td>\n",
       "      <td>0.085822</td>\n",
       "      <td>0.674352</td>\n",
       "      <td>1.423627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.973983</td>\n",
       "      <td>0.081545</td>\n",
       "      <td>0.662824</td>\n",
       "      <td>1.431262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>0.974260</td>\n",
       "      <td>0.074383</td>\n",
       "      <td>0.688761</td>\n",
       "      <td>1.456257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>0.976197</td>\n",
       "      <td>0.065417</td>\n",
       "      <td>0.677233</td>\n",
       "      <td>1.465126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>0.973706</td>\n",
       "      <td>0.076602</td>\n",
       "      <td>0.688761</td>\n",
       "      <td>1.516722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>0.978688</td>\n",
       "      <td>0.065572</td>\n",
       "      <td>0.657061</td>\n",
       "      <td>1.545725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>0.979242</td>\n",
       "      <td>0.063121</td>\n",
       "      <td>0.685879</td>\n",
       "      <td>1.578488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>0.976474</td>\n",
       "      <td>0.066574</td>\n",
       "      <td>0.642651</td>\n",
       "      <td>1.596790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>0.976197</td>\n",
       "      <td>0.064314</td>\n",
       "      <td>0.671470</td>\n",
       "      <td>1.593365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch  accuracy      loss  val_accuracy  val_loss\n",
       "0       0  0.373651  1.322828      0.443804  1.266089\n",
       "1       1  0.626626  1.008122      0.622478  0.953278\n",
       "2       2  0.786881  0.609878      0.720461  0.786233\n",
       "3       3  0.846388  0.418153      0.697406  0.809235\n",
       "4       4  0.887628  0.323820      0.697406  0.813262\n",
       "5       5  0.914199  0.256447      0.711816  0.886609\n",
       "6       6  0.931913  0.213193      0.703170  0.937213\n",
       "7       7  0.942153  0.180205      0.671470  0.978199\n",
       "8       8  0.954885  0.150677      0.685879  1.085678\n",
       "9       9  0.960144  0.135164      0.680115  1.128564\n",
       "10     10  0.964572  0.119582      0.671470  1.149907\n",
       "11     11  0.964849  0.113869      0.671470  1.194496\n",
       "12     12  0.970385  0.100101      0.691643  1.278680\n",
       "13     13  0.970385  0.097652      0.680115  1.287408\n",
       "14     14  0.972599  0.088798      0.671470  1.335229\n",
       "15     15  0.972876  0.086823      0.671470  1.373298\n",
       "16     16  0.973152  0.085822      0.674352  1.423627\n",
       "17     17  0.973983  0.081545      0.662824  1.431262\n",
       "18     18  0.974260  0.074383      0.688761  1.456257\n",
       "19     19  0.976197  0.065417      0.677233  1.465126\n",
       "20     20  0.973706  0.076602      0.688761  1.516722\n",
       "21     21  0.978688  0.065572      0.657061  1.545725\n",
       "22     22  0.979242  0.063121      0.685879  1.578488\n",
       "23     23  0.976474  0.066574      0.642651  1.596790\n",
       "24     24  0.976197  0.064314      0.671470  1.593365"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's take a look at the training log\n",
    "training_log = pd.DataFrame()\n",
    "training_log = pd.read_csv(\"logs/training_log.csv\")\n",
    "training_log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 6 (Take home): **  \n",
    "\n",
    "Plot the Training and Validation Accuracy and Loss (different plots), just like the images below (Note: the pictures below are an example from a different model). How to interpret the graphs you got? How are they related to the concept of overfitting/underfitting covered in class?\n",
    "<table><tr>\n",
    "    <td><img src=\"pics/pic3.png\" style=\"width: 300px;\"/> </td>\n",
    "    <td><img src=\"pics/pic4.png\" style=\"width: 300px;\"/> </td>\n",
    "</tr></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8fcfc66310>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8fcfc66650>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.03, 'Training Accuracy per epoch')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([-5.,  0.,  5., 10., 15., 20., 25., 30.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Epochs')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fcf18c090>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8fcfb01c90>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8fcfb01fd0>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.03, 'Training Loss per epoch')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([-5.,  0.,  5., 10., 15., 20., 25., 30.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(array([-0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Epochs')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Loss')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8fcf9f6650>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer here\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plt.subplot(121)\n",
    "plt.plot(training_log[\"epoch\"], training_log[\"accuracy\"], 's-', color = 'r', label=\"train accuracy\")\n",
    "plt.plot(training_log[\"epoch\"], training_log[\"val_accuracy\"], 'o-', color = 'g', label=\"val accuracy\")\n",
    "plt.title(\"Training Accuracy per epoch\", x=0.5, y=1.03)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel(\"Epochs\", fontsize=20)\n",
    "plt.ylabel(\"Accuracy\", fontsize=20)\n",
    "plt.legend(loc = \"best\", fontsize=20)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(training_log[\"epoch\"], training_log[\"loss\"], 's-', color = 'r', label=\"train loss\")\n",
    "plt.plot(training_log[\"epoch\"], training_log[\"val_loss\"], 'o-', color = 'g', label=\"val loss\")\n",
    "plt.title(\"Training Loss per epoch\", x=0.5, y=1.03)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlabel(\"Epochs\", fontsize=20)\n",
    "plt.ylabel(\"Loss\", fontsize=20)\n",
    "plt.legend(loc = \"best\", fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note\n",
    "\n",
    "If you don't have a GPU (level is higher than GTX 1060) or you are not good at setting lots of things about computer, we recommend you to use the [kaggle kernel](https://www.kaggle.com/kernels) to do deep learning model training. They have already installed all the librarys and provided free GPU for you to use.\n",
    "\n",
    "Note however that you will only be able to run a kernel for 6 hours. After 6 hours of inactivity, your Kaggle kernel will shut down (meaning if your model takes more than 6 hours to train, you can't train it at once).\n",
    "\n",
    "\n",
    "### More Information for your reference\n",
    "\n",
    "* Keras document: https://keras.io/\n",
    "* Keras GitHub example: https://github.com/keras-team/keras/tree/master/examples\n",
    "* CS229: Machine Learning: http://cs229.stanford.edu/syllabus.html\n",
    "* Deep Learning cheatsheet: https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-deep-learning\n",
    "* If you want to try TensorFlow or PyTorch: https://pytorch.org/tutorials/\n",
    "https://www.tensorflow.org/tutorials/quickstart/beginner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## 7. Word2Vector\n",
    "\n",
    "We will introduce how to use `gensim` to train your word2vec model and how to load a pre-trained model.\n",
    "\n",
    "https://radimrehurek.com/gensim/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.1 Prepare training corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>text_tokenized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>10614</td>\n",
       "      <td>What terrible thing to see and have no vision-...</td>\n",
       "      <td>[What, terrible, thing, to, see, and, have, no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>30349</td>\n",
       "      <td>Vacation wise, Egypt offers great places, amaz...</td>\n",
       "      <td>[Vacation, wise, ,, Egypt, offers, great, plac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>20665</td>\n",
       "      <td>No @Chrishilly1987 for MOS 😡  #shocking Leads ...</td>\n",
       "      <td>[No, @, Chrishilly1987, for, MOS, 😡, #, shocki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>40127</td>\n",
       "      <td>Smokeys dad is sad :/</td>\n",
       "      <td>[Smokeys, dad, is, sad, :, /]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>21140</td>\n",
       "      <td>Don't #worry if you're not the best , if you a...</td>\n",
       "      <td>[Do, n't, #, worry, if, you, 're, not, the, be...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                               text  \\\n",
       "614   10614  What terrible thing to see and have no vision-...   \n",
       "2353  30349  Vacation wise, Egypt offers great places, amaz...   \n",
       "1522  20665  No @Chrishilly1987 for MOS 😡  #shocking Leads ...   \n",
       "2954  40127                              Smokeys dad is sad :/   \n",
       "1997  21140  Don't #worry if you're not the best , if you a...   \n",
       "\n",
       "                                         text_tokenized  \n",
       "614   [What, terrible, thing, to, see, and, have, no...  \n",
       "2353  [Vacation, wise, ,, Egypt, offers, great, plac...  \n",
       "1522  [No, @, Chrishilly1987, for, MOS, 😡, #, shocki...  \n",
       "2954                      [Smokeys, dad, is, sad, :, /]  \n",
       "1997  [Do, n't, #, worry, if, you, 're, not, the, be...  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check library\n",
    "import gensim\n",
    "\n",
    "## ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# # if you want to see the training messages, you can use it\n",
    "# import logging\n",
    "# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n",
    "\n",
    "## the input type\n",
    "train_df['text_tokenized'] = train_df['text'].apply(lambda x: nltk.word_tokenize(x))\n",
    "train_df[['id', 'text', 'text_tokenized']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([list(['What', 'terrible', 'thing', 'to', 'see', 'and', 'have', 'no', 'vision-\\\\n\\\\nHelen', 'Keller-\\\\n\\\\n-Begin', 'with', 'the', 'end', 'in', 'mind-\\\\n\\\\nStephen', 'Covey-\\\\n\\\\n', '#', 'whereareugoing']),\n",
       "       list(['Vacation', 'wise', ',', 'Egypt', 'offers', 'great', 'places', ',', 'amazing', 'scenery', ',', 'bright', 'skies', 'and', 'good', 'weather', 'at', 'a', 'very', 'good', 'and', 'affordable', 'price', 'for', 'students', '.']),\n",
       "       list(['No', '@', 'Chrishilly1987', 'for', 'MOS', '😡', '#', 'shocking', 'Leads', 'from', 'the', 'front', '💪🏻'])],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## create the training corpus\n",
    "training_corpus = train_df['text_tokenized'].values\n",
    "training_corpus[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Training our model\n",
    "\n",
    "You can try to train your own model. More details: https://radimrehurek.com/gensim/models/word2vec.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>text_tokenized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>10614</td>\n",
       "      <td>What terrible thing to see and have no vision-...</td>\n",
       "      <td>[What, terrible, thing, to, see, and, have, no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>30349</td>\n",
       "      <td>Vacation wise, Egypt offers great places, amaz...</td>\n",
       "      <td>[Vacation, wise, ,, Egypt, offers, great, plac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>20665</td>\n",
       "      <td>No @Chrishilly1987 for MOS 😡  #shocking Leads ...</td>\n",
       "      <td>[No, @, Chrishilly1987, for, MOS, 😡, #, shocki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2954</th>\n",
       "      <td>40127</td>\n",
       "      <td>Smokeys dad is sad :/</td>\n",
       "      <td>[Smokeys, dad, is, sad, :, /]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>21140</td>\n",
       "      <td>Don't #worry if you're not the best , if you a...</td>\n",
       "      <td>[Do, n't, #, worry, if, you, 're, not, the, be...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                               text  \\\n",
       "614   10614  What terrible thing to see and have no vision-...   \n",
       "2353  30349  Vacation wise, Egypt offers great places, amaz...   \n",
       "1522  20665  No @Chrishilly1987 for MOS 😡  #shocking Leads ...   \n",
       "2954  40127                              Smokeys dad is sad :/   \n",
       "1997  21140  Don't #worry if you're not the best , if you a...   \n",
       "\n",
       "                                         text_tokenized  \n",
       "614   [What, terrible, thing, to, see, and, have, no...  \n",
       "2353  [Vacation, wise, ,, Egypt, offers, great, plac...  \n",
       "1522  [No, @, Chrishilly1987, for, MOS, 😡, #, shocki...  \n",
       "2954                      [Smokeys, dad, is, sad, :, /]  \n",
       "1997  [Do, n't, #, worry, if, you, 're, not, the, be...  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## the input type\n",
    "train_df['text_tokenized'] = train_df['text'].apply(lambda x: nltk.word_tokenize(x))\n",
    "train_df[['id', 'text', 'text_tokenized']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "## setting\n",
    "vector_dim = 100\n",
    "window_size = 5\n",
    "min_count = 1\n",
    "training_iter = 20\n",
    "\n",
    "## model\n",
    "word2vec_model = Word2Vec(sentences=training_corpus, \n",
    "                          size=vector_dim, window=window_size, \n",
    "                          min_count=min_count, iter=training_iter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Imgur](https://i.imgur.com/Fca3MCs.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3 Generating word vector (embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.25475168,  0.77963465,  0.7247676 ,  0.14983922, -0.284533  ,\n",
       "       -0.5570314 , -0.12271586, -0.43448082,  0.53554064,  0.4511307 ,\n",
       "        0.16957802,  0.26085538, -0.31323826, -1.2209098 ,  0.4103406 ,\n",
       "       -0.20331277,  0.01151454,  0.09091204, -0.03478828, -0.12413713,\n",
       "        0.13272251,  0.1369641 ,  0.37679935, -0.7748548 , -0.50548345,\n",
       "       -0.02903457,  0.3950992 , -0.3534181 ,  0.10046913,  0.3423763 ,\n",
       "       -0.5802897 , -0.11152413,  0.3992158 ,  0.2771776 , -0.08417739,\n",
       "       -0.27321747,  0.09761987,  0.3244943 ,  0.35299015,  0.01962478,\n",
       "       -0.17747083,  0.08096018,  0.16502015, -0.08463431,  0.71507686,\n",
       "       -0.1905256 ,  0.78570646, -0.3808626 , -0.8878437 ,  0.42457074,\n",
       "        0.00843012, -0.50989246,  0.30210575,  0.59980035, -0.45552874,\n",
       "        0.3881596 ,  0.33375788,  0.93176544,  0.78889966,  0.21732312,\n",
       "        0.7265938 ,  0.38992995,  0.11753018, -0.54702544, -0.04325525,\n",
       "        0.26919463, -0.38044265, -0.18675306, -0.7462693 ,  0.64913523,\n",
       "       -1.2330691 , -0.80827224,  0.3238134 , -0.04707748,  0.3213934 ,\n",
       "       -1.2425408 ,  0.9655974 , -0.7374651 , -0.29054278,  0.34387273,\n",
       "       -0.17631637, -0.67434114, -1.0110224 ,  0.06780822, -0.28849465,\n",
       "       -1.1632317 , -0.75952286, -0.9623379 ,  0.22564383,  0.36569974,\n",
       "       -0.47612733, -0.84247583,  0.13835204, -0.27938157,  0.7024308 ,\n",
       "        0.17477196, -0.40754515,  0.22236544, -0.15232505,  0.38866267],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the corresponding vector of a word\n",
    "word_vec = word2vec_model.wv['happy']\n",
    "word_vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('🕋', 0.9563794136047363),\n",
       " ('PUSSY\\\\nP', 0.9357126951217651),\n",
       " ('👌', 0.9348244667053223),\n",
       " ('MHchat', 0.9335946440696716),\n",
       " ('cant', 0.9294830560684204),\n",
       " ('Be', 0.9257979989051819),\n",
       " ('blessing', 0.9254446029663086),\n",
       " ('kinda', 0.9235854148864746),\n",
       " ('RiceFODays', 0.9225559830665588),\n",
       " ('iamTinaDatta', 0.9225308299064636)]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the most similar words\n",
    "word = 'happy'\n",
    "topn = 10\n",
    "word2vec_model.most_similar(word, topn=topn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.4 Using a pre-trained w2v model\n",
    "\n",
    "Instead of training your own model ,you can use a model that has already been trained. Here, we see 2 ways of doing that:\n",
    "\n",
    "\n",
    "#### (1) Download model by yourself\n",
    "\n",
    "source: [GoogleNews-vectors-negative300](https://code.google.com/archive/p/word2vec/)\n",
    "\n",
    "more details: https://radimrehurek.com/gensim/models/keyedvectors.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load ok\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('glad', 0.7408890724182129),\n",
       " ('pleased', 0.6632170677185059),\n",
       " ('ecstatic', 0.6626912355422974),\n",
       " ('overjoyed', 0.6599286794662476),\n",
       " ('thrilled', 0.6514049768447876),\n",
       " ('satisfied', 0.6437948942184448),\n",
       " ('proud', 0.636042058467865),\n",
       " ('delighted', 0.6272379159927368),\n",
       " ('disappointed', 0.6269949674606323),\n",
       " ('excited', 0.6247665882110596)]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "\n",
    "## Note: this model is very huge, this will take some time ...\n",
    "model_path = \"GoogleNews/GoogleNews-vectors-negative300.bin.gz\"\n",
    "w2v_google_model = KeyedVectors.load_word2vec_format(model_path, binary=True)\n",
    "print('load ok')\n",
    "\n",
    "w2v_google_model.most_similar('happy', topn=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2) Using gensim api\n",
    "\n",
    "Other pretrained models are available here: https://github.com/RaRe-Technologies/gensim-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 104.8/104.8MB downloaded\n",
      "load ok\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('birthday', 0.9577817916870117),\n",
       " ('thank', 0.9376667141914368),\n",
       " ('welcome', 0.93361496925354),\n",
       " ('love', 0.9176183938980103),\n",
       " ('miss', 0.916450023651123),\n",
       " ('hello', 0.9158351421356201),\n",
       " ('thanks', 0.9150084853172302),\n",
       " ('merry', 0.9053248167037964),\n",
       " ('bless', 0.902732253074646),\n",
       " ('wish', 0.9013164639472961)]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gensim.downloader as api\n",
    "\n",
    "## If you see `SSL: CERTIFICATE_VERIFY_FAILED` error, use this:\n",
    "import ssl\n",
    "import urllib.request\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "glove_twitter_25_model = api.load(\"glove-twitter-25\")\n",
    "print('load ok')\n",
    "\n",
    "glove_twitter_25_model.most_similar('happy', topn=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5 king + woman - man = ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run one of the most famous examples for Word2Vec and compute the similarity between these 3 words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('queen', 0.7118192911148071),\n",
       " ('monarch', 0.6189674139022827),\n",
       " ('princess', 0.5902431607246399),\n",
       " ('crown_prince', 0.5499460697174072),\n",
       " ('prince', 0.5377321243286133),\n",
       " ('kings', 0.5236844420433044),\n",
       " ('Queen_Consort', 0.5235945582389832),\n",
       " ('queens', 0.5181134343147278),\n",
       " ('sultan', 0.5098593235015869),\n",
       " ('monarchy', 0.5087411999702454)]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2v_google_model.most_similar(positive=['king', 'woman'], negative=['man'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 7 (Take home): **  \n",
    "\n",
    "Now, we have the word vectors, but our input data is a sequence of words (or say sentence). \n",
    "How can we utilize these \"word\" vectors to represent the sentence data and train our model?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Answer here\n",
    "We can take the average of the word vectors in a sentence, or even multiply the word vectors with TFIDF scores and then take the average to represent the sentence data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Clustering: k-means\n",
    "\n",
    "Here we introduce how to use `sklearn` to do the basic **unsupervised learning** approach, k-means.    \n",
    "\n",
    "more details: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic concept\n",
    "\n",
    "![Image](https://i.imgur.com/PEdUf54.png)\n",
    "\n",
    "(img source: https://towardsdatascience.com/k-means-clustering-identifying-f-r-i-e-n-d-s-in-the-world-of-strangers-695537505d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target words:  ['happy', 'fear', 'angry', 'car', 'teacher', 'computer']\n"
     ]
    }
   ],
   "source": [
    "# clustering target\n",
    "target_list = ['happy', 'fear', 'angry', 'car', 'teacher', 'computer']\n",
    "print('target words: ', target_list)\n",
    "\n",
    "# convert to word vector\n",
    "X = [word2vec_model.wv[word] for word in target_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=2)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word: happy \t cluster: 0\n",
      "word: fear \t cluster: 0\n",
      "word: angry \t cluster: 0\n",
      "word: car \t cluster: 1\n",
      "word: teacher \t cluster: 1\n",
      "word: computer \t cluster: 1\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# we have to decide how many cluster (k) we want\n",
    "k = 2\n",
    "\n",
    "# k-means model\n",
    "kmeans_model = KMeans(n_clusters=k)\n",
    "kmeans_model.fit(X)\n",
    "\n",
    "# cluster result\n",
    "cluster_result = kmeans_model.labels_\n",
    "\n",
    "# show\n",
    "for i in range(len(target_list)):\n",
    "    print('word: {} \\t cluster: {}'.format(target_list[i], cluster_result[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Imgur](pics/pic6.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int32)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check cluster membership\n",
    "word = 'student'\n",
    "word_vec = word2vec_model.wv[word]\n",
    "kmeans_model.predict([word_vec])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int32)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check cluster membership\n",
    "word = 'sad'\n",
    "word_vec = word2vec_model.wv[word]\n",
    "kmeans_model.predict([word_vec])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 9. High-dimension Visualization: t-SNE\n",
    "\n",
    "No matter if you use the Bag-of-words, tf-idf, or word2vec, it's very hard to see the embedding result, because the dimension is larger than 3.  \n",
    "\n",
    "In Lab 1, we already talked about PCA. We can use PCA to reduce the dimension of our data, then visualize it. However, if you dig deeper into the result, you'd find it is insufficient...\n",
    "\n",
    "Our aim will be to create a visualization similar to the one below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Image](pics/pic7.png)\n",
    "source: https://www.fabian-keller.de/research/high-dimensional-data-visualization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we would like to introduce another visualization method called t-SNE.  \n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1 Prepare visualizing target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's repare data lists like:\n",
    "    - happpy words\n",
    "    - angry words\n",
    "    - data words\n",
    "    - mining words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "happy_words:  ['happy', 'glad', 'pleased', 'ecstatic', 'overjoyed', 'thrilled']\n",
      "angry_words:  ['angry', 'irate', 'enraged', 'indignant', 'incensed', 'annoyed']\n",
      "data_words:  ['data', 'Data', 'datasets', 'dataset', 'databases', 'statistics']\n",
      "mining_words:  ['mining', 'Mining', 'mines', 'coal_mining', 'mine', 'miner']\n",
      "\n",
      "target words: \n",
      "['happy', 'glad', 'pleased', 'ecstatic', 'overjoyed', 'thrilled', 'angry', 'irate', 'enraged', 'indignant', 'incensed', 'annoyed', 'data', 'Data', 'datasets', 'dataset', 'databases', 'statistics', 'mining', 'Mining', 'mines', 'coal_mining', 'mine', 'miner']\n",
      "\n",
      "color list:\n",
      "['b', 'b', 'b', 'b', 'b', 'b', 'g', 'g', 'g', 'g', 'g', 'g', 'r', 'r', 'r', 'r', 'r', 'r', 'y', 'y', 'y', 'y', 'y', 'y']\n"
     ]
    }
   ],
   "source": [
    "word_list = ['happy', 'angry', 'data', 'mining']\n",
    "\n",
    "topn = 5\n",
    "happy_words = ['happy'] + [word_ for word_, sim_ in w2v_google_model.most_similar('happy', topn=topn)]\n",
    "angry_words = ['angry'] + [word_ for word_, sim_ in w2v_google_model.most_similar('angry', topn=topn)]        \n",
    "data_words = ['data'] + [word_ for word_, sim_ in w2v_google_model.most_similar('data', topn=topn)]        \n",
    "mining_words = ['mining'] + [word_ for word_, sim_ in w2v_google_model.most_similar('mining', topn=topn)]        \n",
    "\n",
    "print('happy_words: ', happy_words)\n",
    "print('angry_words: ', angry_words)\n",
    "print('data_words: ', data_words)\n",
    "print('mining_words: ', mining_words)\n",
    "\n",
    "target_words = happy_words + angry_words + data_words + mining_words\n",
    "print('\\ntarget words: ')\n",
    "print(target_words)\n",
    "\n",
    "print('\\ncolor list:')\n",
    "cn = topn + 1\n",
    "color = ['b'] * cn + ['g'] * cn + ['r'] * cn + ['y'] * cn\n",
    "print(color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9.1 Plot using t-SNE (2-dimension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 862.5x862.5 with 0 Axes>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8cc9d70e50>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'happy')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'glad')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'pleased')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'ecstatic')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'overjoyed')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'thrilled')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'angry')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'irate')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'enraged')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'indignant')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'incensed')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'annoyed')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'data')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Data')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'datasets')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'dataset')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'databases')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'statistics')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'mining')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Mining')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'mines')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'coal_mining')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'mine')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'miner')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SvfrAHI7teZdffnm8SFvz5s2ZNGkSI0aMYP78+ZQsWRKApUuXcvHFF7Nw4UJq1qy53Wv16NGDoUOHAtChQwfeeecdnnrqqXhfhrfffjvT67Vr13LYYYexevVq1q5dy2WXXcbHH39M6dKlmTBhAkA87uGVV16hWLFEkDriiCOoXbs2lStnX4xtwoQJcf0l6e/MFgRJymeKFduXxo3HU6BAkRyPR1ES++33JCVKHLzH6pCWtpnly5/kww+P5K23KjNrVg1++mkikPMiba1bt870cN2wYUMAvv/++x3ep0uXLpleN2nShEWLFmUrN2vWrEzdnooWLcpbb73FySefDCTWgdh33335+eefadasGQCzZ8/m2GOPjcMBJAY9H3rooTnWpVmzZg6GliQMCJKUL5Ur15kWLT6nevXLKVSocvp4gwrss8+FtGjxCRUrdt9j9968eQUffHAIX3zRk19+mcGWLctZuPB77rvvIwBWr74z08N6xiJt28oYuLzteIKc7GiRtssuuyzev3LlSmbOnBm/LlKkCGlpaXFAyDj3o48+olq1akRRxPfff0/hwoWJoogRI0YwaNAgqlSpwsyZM/nxxx9ZvHhxpnv/0XESAN99910cRipWrMjAgQN56KGHiKKIBQsW7PAzkKT8xoAgSflU0aK1qFv3Pxx66FLatt3CYYf9SP3691CsWIM9ds8Q0vj00+NZt+6DjD3Zynz//TB++OGBPVaHDBdeeGH8+7777hsPhgZYv349J510EikpKdnOK1iwILNmzaJq1arxKs/Dhg3jm2++4dFHH6Vu3bps2rSJXr167VI9djROAmDz5s0cffTRzJ07lwceeIBRo0Yxf/58brzxxj/61iUpTxkQJEmx1atfY82a2Tstt3DhDaSlpe72+yfGXcDmzcszjV049NBDGTduHBs3bmT9+vVs2rQpHgeRVRRFtG7dmsMOO4xp06YBidaBJ598kgMPPJDvvvuOkiVLMmPGDH744Yed1qlHjx5cd911dOjQgWuvvZZjjjmGp556Kj4+atQovvvuO8aPH0+fPn049thjefbZZylduvSf+CQkKe8YECTpL+zjjz/mpJNOoly5chQtWpQGDRrE326HELjzzjtp0KABhQoVokqVKlxwwQWsWbMm0zXuvfdeDjnkEMqWLUutWl047zyYNWvH9928eSmrV0/daf2OPvpovvzyS5YvX87o0aOpUaMGjz32WHy8U6dOpKSk0KbNwbz0UicWLfoPAG+/XZlLLjk7LteiRQt+/fVXihYtyty5cwE4+eST6datW3p9NsdlU1NTiaKIxo0bs3btWgAWL15MuXLlOOKIIwD4+eefAbjrrruy1TljatSzzz47vt62U6NmHScxe/ZsatSoQcuWLeN9URRl6v4kSX8lBgRJ+ot69913OeSQQ/j222+58847efHFF7n00kvjvvVXX301l156KUcffTSTJk3i3//+N6NGjaJLly6kpaXF11mwYAH9+vVj3LhxDBvWgAYN4Kqr4N13d3z/TZsW77hAuu7du1O0aFGOPPLIeFrUZ555BkiMM7jzzn58/vkHXHrpq2zbpSmE31ooihQpwkEHHQTAvHnzSE5OZvz48Rx//PEAmb7Rz7DPPvvw6KOPAokxAqtXr+bXX39lv/32o0qVKgDceuutcSsDJIJDxtSo5557LgDPPPNMpqlRtx0nAYkZmypWrJjt/pUqVdqlz0eS8hunOZWkv4gQAhs3LiAtbQOFClXlsssuo1y5csyePTueqad9+/YArFq1ittvv50+ffpw7733Aolv6ytUqEDv3r154YUX4ofr2267Lb5HuXLVaNjwUxYvhokTYZsvxQG44orEIm0ABQsWB8hxEG67du0YPHgwQ4cOzTQt6urVq5k0aRKzZs3il19+ISlpGXPmXMRpp8G998LgwYnF4Lb1xBNw1FFJfPrpYbzzzjukpaUxd+5cGjduTFJSEo899hjTpk1j8eLF9O3bl6lTf2vZyGgxaN26NbNmzeKRRx7hzDPP5KCDDmLp0qWULFmSp556iiOPPBKAuXPnxlOjjh07FoDHHnuM9u3b5zg1KiRmRvr888+z7V++fHmO5SUpv7MFQZLyuRC2snjxvbz7bkPeeacOc+Y05rXXyvHWWzPp3r1Dpmk8M8yePZvNmzdnG4h76qmnkpSUxIwZM+J977//Pl27dqVSpUocdNArdOgA770HO5qhNIoKUabM0btU/x1Ni7pkyX2EkEqNGonjK1bkeDcWL76DRo0aAXDSSSfRuHHjTCV+/PHHHO89ePBgILGwWuHChbnuuutYvXp13P2nWrVqmboLrVixItvUqBUrVtzu1KiQCB+LFi3i3W2aXEIIPPvss9s9R5LyMwOCJOVjaWmpfPZZd7755kI2bJgX71+7No20tMDWrU+wcuXL2c5btWoVQNyVJkNSUhLlypWLj3///fccddRRrFq1iuHDh/PGG1N46KHitGwJ23Trz6ZSpR4UKlR+l95DTlOgZuxbsSLxLX1ycuJYzvcMrFv3EVu3JmYk+s9//pOtxJYtW3K8d0ZXoM8//5zNmzdTvHhxpk6dGq93kJycnGnK0g0bNvzu7kJ9+/alTp06/OMf/+C///0vkydP5uSTT2b16tUAFCjgP7WS/lr8/1qSlI99//0t/PTThPRXv/XPL1ECChSAn35K5bPPurF5c+Zv0DPWF1i2bFmm/ampqaxcuTI+/vLLL/PLL78wduxYTjnlFA4//ChOPvk5Nm2Ktlun4sUPoF69u/78mwNSU3/Z5bJpaet/9/VvvvlmIPEtf9WqVXnttdfibkc5KVq0aI6tETvqLlSoUCFeffVVmjZtyjnnnEOfPn2oXr06559/PgClSpX63fWWpLxkQJCkfCotbQuLF98DZH9YL1IE9t8fpkyBDRt+ZenSRzMdb926NYUKFWLMmDGZ9j/99NOkpqbSrl07ILGeACS+Sc/w0081+PTTAhQsWDTbvcuW7UyzZm+QlLR7HnqTkytmu0eGokWhUyfYZ5/E64IFS2YrkzHWIcOoUaMyLahWq1YtQgjsu+++2c4LIWSainTBggUcd9xxTJ48mfXr19O3b19CCBQvXpy33norLjdkyJBMC8UB1K1bl8mTJ7NhwwZWrFjB3XffzZtvvkmdOnUMCJL+chykLEn51Jo1s9myJee+9QDnnguXXALnnw+nnz6CI45oxXfffcdHH33E8OHDGThwIMOGDaN48eIce+yxfPHFF1xzzTUcfvjhdOnSBYAOHTqQlJTE6aefzsCBA1m6dCmDBw+mRo0apKWlccghb7Fhw7csWrQS+AcVK/6TpKTsD+p/VOXKvVm48IadlIooVaotyclld1Luz7vmmmt45pln6NSpE5dddhmbNm3i+uuvp1KlSjvsKnTHHXeQkpJC/fr1Wbt2LePGjePFF1/kgQf2/IJykrS72YIgSflUaurPOzzesCEMHw4VK8Ktty7i2GOP5dZbb6VatWoA3Hjjjdxxxx289NJLdO3alZtvvpnTTz+dF198MX7Ybdy4MaNHj2bhwoUcf/zx/Oc//+Hmm2+mTZs2ABQuvA+lS7ehZMlme+Q9Vq16DgULprC9VoSEQI0al++R+2fVqFEjXnzxRdauXcspp5zClVdeyQUXXMDBBx+8w5aAwoULc+edd9K1a1dOOeUU5s6dy8iRIznnnHNypd6StDtFWZtJ9yZRFNUD5s2bN4969erldXUk6XdZs+Y9PvigxS6UjChV6nCaNXtjj9dpT/j55xl88klXtm5dl+PxevXuolq1i3O5Vr9Zt24d9erVo0uXLjzyyCN5Vg9J+dc333xD/fr1AeqHEL7J6/r8WXYxkqR8qkSJgylatAEbNnzNtgOUswtUqnR6blVrtytdui0tWnzKkiX3sWzZY2zZ8hMFChSlfPl/UK3aRZQs2XLnF9mNLrzwQg499FCqVq3KDz/8wN13383q1au5+OK8CymSlJsMCJKUT0VRRM2a/8eXX/bdUSkKFapKpUqn5Va1CCGwdevWHZZJSvp9/7wUKVKTunX/Q926/yGErURRwT9TxT9l48aNXHHFFSxfvpxChQrRsmVLpk6dStOmTfOsTpKUmwwIkpSPVa7chw0bvmPhwutI9NPPaElI/F6oUGUOOODVeFXj3DBjxox45eHtmT9/PrVq1fpD18/LcADw8MMP5+n9JSmvGRAkKZ+rXXsoZcp0YMmS+1i58kVC2EjhwjWoUqUfVaqcvcsLlu0uBx98MHPmzNlhmapVq+ZSbSRJu5sBQZL+AkqXPoLSpbe/wFduKlGiBM2bN8/rakiS9hCnOZUkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiuRoQoiiqFkXR8CiKZkVRtD6KohBFUa0cyoXtbAfmZn0lSZKkv5ukXL5fPeAU4H3gTaDjDsqOAkZk2ff1nqmWJEmSJMj9gPBGCKESQBRF/dhxQFgSQpidO9WSJEmSBLncxSiEkJab95MkSZL0++TnQcrnRlG0KX2swutRFB2R1xWSJEmS9na53cVoVz0BvAD8ANQELgdej6Lo6BDC9JxOiKKoLFA2y+4ae7KSkiRJ0t4mXwaEEELvbV6+GUXRROBT4Abg8O2cdhEweE/XTZIkSdqb5ecuRrEQwlrgRaDFDordA9TPsh2152snSZIk7T3yZQvCDoTtHghhFbBq231RFO3xCkmSJEl7k79EC0IURSWBrsC7eV0XSZIkaW+W6y0IURR1S//14PSfx0RRtAJYEUKYEUXRZUADYBq/DVK+DKgM9Mzt+kqSJEl/J3nRxWhcltf3p/+cAbQDvgJOSt9KAWuAt4CzQgi2IEiSJEl7UK4HhBDCDgcGhBAmAZNyqTqSJEmStvGXGIMgSZIkKXcYECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgKF8aNWoUURTFW/HixalVqxYnnXQSY8eOJYTwu6/50UcfMWTIEFatWrUHaixJkrR3MCAoXxs3bhyzZs1i8uTJXH/99RQuXJjTTjuNo48+mg0bNvyua3300UcMHTrUgCBJkrQDBoS/gHbt2tGuXbvfdc706dMZMmQIaWlpf+ie06dPJ4oipk6d+ofO310OPPBAWrduTdu2benduzdjxoxh7NixvP766/z73//O07pJkiTtjQwIe6np06czdOjQPxwQ8rOTTz6ZE044gYcffpj169cDMHjwYA466CBKlixJ+fLlad++PbNnz47PGTVqFGeccQYA9evXj7suLViwAIB7772XQw45hLJly1K6dGlat27Niy++mOvvTZIkKa8ZEPSXdOyxx7Jp0ybee+89AJYsWcKAAQOYOHEio0aNomLFirRp04ZPPvkEgC5dunDNNdcAv3VbmjVrFlWqVAFgwYIF9OvXj3HjxvH000/TvHlzunbtyssvv5w3b1CSJCmvhBD22g2oB4R58+aFv4qnnnoqNGjQIBQqVCg0atQojB8/PrRt2za0bds2hBDChg0bwiWXXBIaN24cihcvHipVqhS6du0avvjii/gagwcPDkC2LcOgQYNCs2bNQokSJUK5cuXCkUceGWbNmpWpHtOmTQtAeOaZZ0KfPn1C6dKlQ4kSJUKPHj3CTz/9lKns8OHDQ+vWrUOZMmVCqVKlQqtWrcILL7yQqcyWLVvCNddcE+rUqRMKFy4cypUrFw477LDw5ptvZio3YsSI0LRp05CUlBSA0K1bt7By5cpMZe66665QvXr1AITixYuHgw8+OIwfPz4+npqaGrZs2RL23XffcNFFF8X7H3vssbAr/z1s3bo1bNmyJRx99NHh+OOP32FZSZKkefPmZTxv1Qv54Bn4z262IOQjU6dOpUePHtSvX5/x48dz+eWXc/HFF/PVV1/FZTZt2sTatWu55pprePHFF3nggQfYuHEjhxxyCMuWLQOgX79+nHXWWQDMnDkz/rY8w86+bd/WJZdcQhRFPPXUU9x44408//zzdOvWLVOZXfn2/ZZbbuHOO+/koosu4pVXXuGxxx7jqKOOYtXKlTB9OnTvzpVFinB+//50WLqUi9u0AeDNN9/kmGOOYevWrQCMHj2agQMH0rZtWwAuvPBCunXrxsyZMznyyCMpV64cSUlJJCcn8/XXX2f67Hbk/fffp2vXrlSqVCk+f8qUKbt8viRJ0l4jrxPKntz4i7UgHHrooWG//fYLW7dujffNmjUrAHELQlapqanh119/DSkpKeGOO+6I92e0ImzZsmWH99zet+0ZLQidOnXKVP6JJ54IQJg6dWqO19vet+9dunQJJ510UubCaWkhXHJJCBDmQygAYSiEEEXhsfRWjzH33ReAMGHChBBCCOeff35o1qxZeOihhwIQ3njjjfD++++H5OTk0LVr1zBhwoQwe/bsMGfOnHDAAQdk+ty214KwaNGiUKpUqXDIIYeEp59+OsyaNSvMmTMndO7cOdSsWXOHn58kSZItCPrztm6F8eOhY0coXx4qVmTrcccx59136XbyyRQo8NufpXXr1tSqVSvT6WPHjqVVq1aULl2apKQkihcvzrp163b52+6pU6fu8rftp5xySqbX3bt3p0CBAplaJHbl2/cWLVowefJkrr76ambOnMnmzZvhrrsSGzAFSAN6AqkhsDX9vAPuuosSJUrwxhtvxNf56KOPGDZsGMnJyey33348++yzJCUlMX78eE488URatWpF8+bNWb169S59Hi+//DK//PILY8eO5ZRTTqF169Y0b948HgD9Z/zZtRcWLFjAkCFD+O6777Idq1WrFn379s2Ta0mSpL1XUl5X4G9n/Xo46SR49VWIIki0dPDTCy+wBag0eTIMGgTJyfEplSpVin+fNGkS//znP+nTpw+DBw+mfPnyFChQgGOPPZaNGzfu9PYffPABxx57LJ06deKRRx6hSpUqFCxYkH79+uV4/rb3BihUqBBlypRhyZIlAHz//fccddRRNGrUiOHDh1OjRg2SkpK49tpr+eKLL+LzrrrqKooUKcITTzzBTTfdREpKCt1SU7kVKA/8mF6uXpb77zdvHgBPPvkkH3zwAdOmTePtt9/moYceIooiqlWrRrVq1eJZiTK8/vrrLFq0iCiKSEtLo0CBAhQuXBgg2/oJGUEgeZvP/Ouvv+att96iWrVqO/1MdyRj7YVevXpRtmzZ333+ggULGDp0KIcffjh16tTJdGzChAmULFkyT64lSZL2XgaE3HbWWYlwAHE4gMRDcjKw/IMP4LLL4O6742PLly+nZs2aAIwZM4Z69eoxatSo+PiWLVt2+Rvqbb9t3/aBePXq1ZQuXTpb+eXLl2d6vXnzZlavXs0+++wDZP72fduH6azfvicnJ3PFFVdwxRVXsGzZMl4YNoxL77mH9cDTQLn0cq8CZYBJwHXAzUDF5s35z7p1LF++nNNOO41x48bRsWNHRo0axRtvvMEFF1zA+vXr6du3L2eccQZff/01119/PSVKlGDhwoVxQGjUqBEA9913H3369CE5OZmmTZvSoUMHkpKSOP300xk4cCBLly5l8ODB1KhRI19PE9usWbN8eS1JkvTXZhej3DRvHowZk+OhgkAL4Bkg7f774aefAHjnnXfiufoh8eCdlJQ51z3++OPxIN4MO/q2vGDBgjl+256TsWPHZno9btw40tLSOOSQQ+LrQc7fvm9P5cqV6de0KR2AT9P3HU3iP8ZFQHOgdvr+K4Ez33uPr7/+mvnz57Np0ybGjBnDyy+/TJUqVfjnP/9J7969KVSoEG+99RZdu3bl0Ucf5X//+1+2b+wPOOAAhgwZwqRJkzj88MNp0aIFP/zwA40bN2b06NEsXLiQ448/nv/85z/cfPPNtEkfKL0zX3/9NSeddBIVK1akSJEi1KhRg+7duzNy5Mg/tfbC9OnTOfLIIxOfz9FHx+dPnz4dyN4taNmyZfTp04eqVatSuHBhqlSpQteuXfnxxx9/97UA5s+fT+/evalcuTKFCxemTp06XHzxxfHxOXPmcPTRR1OuXDmKFi1KnTp1OO+883bpM5MkSfmXLQi56fHHd3h4KNARODE1lf7XXMOK1q0ZPHgwlStXjst07tyZ5557jgEDBtC1a1fee+89hg8fnu3b/4xvy2+//XaOOeYYChYsSPPmzencuTN33XVXtm/bM1oEsvrss88444wzOPXUU/n666+5+uqradeuHUcddRTALn/7fsIJJ3DAAQdw0EEHUaZMGT58801eBvqnH68LXAFcADwHfEjiP86KQN0yZfilenXKlClDhQoVmDlzJv/973/57LPP+OGHH9iyZQsVK1bkpZdeomHDhgAMGTKEhQsXApnDSwiBwYMHM3jwYCZNmkTTpk0pVKgQTZs2ZdSoUbRu3Toue+qpp+7w75WhS5culClThgceeIDy5cuzZMkSJk+ezHHHHcc111zDDTfcwLhx4+IWlqxrL9SqVYvU1FQmTZpE165deemll+jcuTMHHXQQ9913H+effz733HMPLVq0yPS3zap3794sXLiQW2+9lerVq7N8+XJee+011q9f/7uvNX/+fFq2bEmxYsW47rrrqF+/PosWLeLV9NavdevW0alTJ1q2bMmoUaMoUaIECxYs4O23396lz0ySJOVjeT1Kek9u5LdZjPr2DSHRsSjT1jZ9CxCehLAvhEIFC+a4DsLWrVvD1VdfHcqVKxeSkpJCmzZtwgcffBBq1qwZ+vTpE98qNTU1nHfeeaFChQohiqJM6yDcc889oVatWqFIkSKhefPmYcqUKZnuEcJvsxg9++yzoU+fPqFUqVIhJSUlnHbaaWHFihVxufnz54du3brF6xs0atQoPPXUU6FPnz6ZZgC67bbbQqtWrULZsmVDkSJFwr716oXBSUlhc5bP4t/psxcVhFAEQhUIKYULhwoVKoS2bduGUaNGhcMOOywUKVIkJCUlhcqVK4euXbuGdu3ahdKlS4elS5eGEEL4/vvvw1lnnRWAMHPmzDBr1qxMaz2cddZZ4X//+194/fXXw6RJk8I///nPkJycHObOnbtrf8u0tBA2bw4rVqwIQJg4cWKOxf7s2gsZf4cpU6ZkOyfr37x48eLh7rvv3u49fs+1evfuHYoXLx6WLFmS47XmzJkTgPDxxx/v8H1JkvR3sLfNYpTnFdijby6/BYQLL9xpQIi366/f4aV2dRrTPW1HD507dd552T6LQyHsB2ErhFCgQAhly4ZZr7+eJ1O95ujll0Po2jWEpKQQIKRVrhzqlCkT9qtfPzz00EPh66+/zlR8RwHhvffeC126dAkVK1aMQxwQGjRoEJf5PQ/17dq1C9WqVQt33XVXmDt3bkhLS8tU/vdcq1KlSuGf//zndj+Gn3/+OZQuXTq0bt06PP7442HRokXbLStJ0t5ubwsIjkHITSecsGfK/hUtXAgrVmTatRWYA3QjfXBMkSIwcSKtjzwyT6d6BRLxZeBA6NwZXnwRUlMBiJYvZ8rq1TRftIj/+/e/2XfffalTpw4PPPDADu+fMfvTqlWrGD58OG+//TZz5syhc+fOuzQbVU6efvrpeAxF06ZN2Weffbjuuuv+0EDrlStX7nAGp1KlSjFt2jSqVq3KeeedR40aNdh///159tln/1DdJUlS/mFAyE3t2zOmalUaAoWBxsCELEU2AgP22Yf9TzuNlJQUKleuzHHHHceXX34ZlxkyZAhDhw4FEv3rs07xOXjwYA466CBKlixJ+fLlad++PbNnz850n3Xr1nHhhRdSo0YNChcuTMWKFenQoUOm+6SmpjJs2DAaNmxI4cKFqVq1KgMHDowfYHc28PXJJ5+kWbNmpKSkULJkSZo0acKIESPgm2+gZUsYNy5TnX6CxFSvAJ06wbvvwuGHAzlP9brffvvx5JNP8s477zBnzhwqVKjwu6Z6TUlJ4ZFHHmH27NnMmTOHAw44YPvnP/QQ3HFH4vfw2+xThEAd4H+bN7OicGE+fOst2rdvz3nnncdLL7203TrsibUXKlasyH333ceSJUv48ssv6du3L4MHD0585r9TxliKHTnwwAN59tlnWbVqFbNmzaJu3bqccsopfPrppzs8T5Ik5W8OUs5FU197jR5Ll9IlOZnbt2xhBXAxiYfiBullNlWrxtrDD+eaE06gSpUqrFq1ivvvv59DDjmEL774IjEDUL9+LF68mEceeYSZM2dSsGDBTPdZsmQJAwYMoFq1avz666888cQTtGnThvfff58mTZoAMGDAAJ5//nluuukm6tevz8qVK3nrrbf4+eef4+v06tWLSZMmccUVV3DooYfyxRdfcO2117JgwQKeffbZHQ58nTlzJr169eKiiy7i1ltvJS0tjS+//DJx/d69s7UewDZTvQJ89RWkDziGvJ3qlbQ0uOWWTOtWZBMC0fLlHPjpp9xxxx088sgjfPrpp/G38H907YXtzUa1Mw0aNOCmm27iwQcfjB/Yf8+1OnbsyPjx41m6dGk8qHp7kpKSaN26Nddffz3PP/88X3zxBfvvv//vqq8kSco/DAh70po1sGBBYtGzevUYPHgwDRs2ZOILL1DghhtoN2oU5UPgQ6BBgQLQvz+lBg9m5Dbflm/dupVOnTpRqVIlnnrqKZo1a8b06dPjWYdatWqVbdrTkSNHZjq/c+fONG7cmJEjR3J3+voKs2bNomfPnpx11llx2ZNOOin+/c033+Tpp5/mv//9L6effjqQmLGobNmy9OrVi8mTJ/Puu++y7777ArDffvtlmgFo9uzZlC5dmrvSV0qGxEMn778PV16Z48e17VSvQxYsoMBLL0HXrvFUrxkB4Y9M9VqiRIl4/46meq1duzbZvPMOzJ+fY53nkgh5/yQx4GXr8OGMmjaNpKQk2rdvH9fzj669sO+++5KUlMSjjz5K2bJlKVy4MA0aNMj0fgB++eUXOnToQM+ePWnYsCHJyclMnDiR1atXJz7333EtgKFDhzJ58mQOPfRQrrrqKurVq8eSJUt4+eWXeeKJJ3jhhRd46KGHOPHEE6lduza//vor99xzDyVKlIinwJUkSX9ReT0IYk9u5NUg5a+/TsxYVLhwPPg2tUKFkFygQLj2iiviYm0POyy0PeigUKtKldD28MPj/U8//XRo2bJlKFWqVDxwFQj9+/ePB95ee+212x2AO2XKlNCuXbtQtmzZTOd36tQpLtO3b99QpkyZcOONN4Y5c+aE1NTUTNe46qqrQqFChcKvv/4atmzZEm8//vhjAMIFF1wQgHDbbbflOPB1+vTpAQg9e/YMkyZNCqtXr04cuP76bAOTt92mQIggHAfhhWOPDY899lioUaNGqFy5cjxI+cEHHwxAuOSSS8LUqVPDzTffHPbZZ59QunTpTANtn3vuuQCEwYMHh9mzZ4c5c+aEEEJ4+eWX47pNnTo13H///aFKlSphn332yXkg9HPPbbe+yyGcDqE+hKIQyhQoENq0aRNefvnl+PQhQ4aEqlWrhgIFCgQgzJ8/P/47N2jQYIezP2W839q1a4eCBQsGIEybNi2EkHlg8caNG8O//vWv0KhRo1C8ePFQokSJ0Lx58zB69Ojffa0M33zzTTj11FNDuXLlQuHChUOdOnXCgAEDQgghfPnll+GUU04JtWrVCoULFw7ly5cPxxxzTJg9e3b2z0+SpL3c3jZIOc8rsEffXF4EhPfeC6FkyWwPksvSH9LvrV49hJ9/DiGEeGrRVq1axQ+mzz//fABCnz59wosvvhjeeeedMGfOnFChQoXQp0+fnQaE999/PyQnJ4euXbuGCRMmxA/GBxxwQKaH37Vr14Yrr7wy1K1bNwChbNmy4ZJLLgm//vprCCGEfv36ZQoXWbfTTz99hwEhhBDGjRsX2rRpE5KSkkJSUlI46qijwsdnnbXDgJBpqtcCBXY41WuVKlVC0aJF99hUr7Hp03da5wAhRFEIrVv//v9mJEnSX5oB4S+05XpA2LQphKpVE9NzZnl4HM1v8/s3KlUq00NvrVq1whFHHBEuueSSUKpUqRBFUahUqVLo2rVr+OKLL8LmzZtDwYIFwwEHHJDjw3qGQYMGhUqVKgUglCtXLhx55JHx3P81atQIbdu2DWvXrg0XXHBBqF69eihUqFCoUKFCOOyww8K///3vkJSUFP7973+HLVu2hDZt2oQoikJycnIoX7586NGjR5g5c2aYM2dO/A1+1i3j2+is1q5dGyZNmhQaNWoUqpQqlZjCdFe2QYNy46+2c5s3h1CpUiIA7KzOt9+e17WVJEm5bG8LCM5itDuNHw8//JAY1LqNqUAvoAxQBRi4Zg0XX3ghX331FWvWrGHBggWkpaWxdu1a6tevT/Xq1XnggQfYuHEjhxxyCPfeey9bt26lfv368ZiB/v0TaxBPnTo1vs+SJUs48MADKVKkCI888ggVK1akTZs2jBw5kkWLFgGJwcljx45l8ODBTJkyhREjRtC6dWtOOukkmjRpwqeffkqvXr149913CSFwww03MGjQIF588UXuuOMOmjdvzmmnncZ9990XXw/gP//5DwcddFCOH0tKSgpdu3alf//+LP3lF1YWKbLzzzKKoG/fXf7o96jkZLjkku0PUAYoUABKlsw/dZYkSfqDDAi704QJiQfbLAYDDYExwBLguRC4+NBDWbZsGZ9//jmVK1cmKSmJkSNH0q9fPxYtWsT06dO55JJLWL9+PUOHDqV06dIUL148nuEmY+Dpm2++yTvvvMN7773HyJEjGTBgABs3buTpp5/mzDPPpEyZMlx88cXxoOaMwckjR47krbfeIjk5ma5du/LKK6/w8ccfU7duXZ5++mlGjBjBaaedxrBhw/j555/p378/48ePZ9CgQfTp0yce2Hr44YeTlJTE22+/zSeffMJ7773H2rVrGTRoEP3792fMmDG88cYbPPnkk9xzzz0ceOCBVLjssp1/lmecATkNGM4rl18Op56a+L1ADv9nk75mA2XL5m69JEmSdrMo7Ohb0b+4KIrqAfPmzZtHvXr19vwNO3eGV1/N9E3zVqAocCVwHfAUMARYULAgoUAB9t13X8qXLw/Aeeedx+23387HH3/Mpk2b4mucfPLJvPfee7Rr145atWoxdOhQNm7cyKWXXsq4ceP46aefCCEwZcoUbrzxRt59991M8+k3b96c4sWLA1C7dm0mTpzI/vvvz/Lly1m6dClbt26lTp06nH322SxfvpzbbruN1atXk5SUxL333suoUaP46quv2Lx5M1WrVqVnz560adOG4447jilTpvDtt99yyy23sGjRIrZu3cq0adPiWW3mzp3LqlWrqFixIh07duT666+nauXKcP758OCDmT+/jGlETz4ZRo+G9FmI8o20NHjsMbjnHpg7N7GvcGHo0SMRIPbbL2/rJ0mS8sQ333xD/fr1AeqHEL7J6/r8aXndx2lPbuTmGITU1BD23z9bn/R4cHLWvupPP/27BidnyBiknFeDkweljwuYNm3adgcn77LZs0Po0yeEBg1CqF8/hFNOCeH110NIS/vj18wNaWkhLF0awnffhZD+uUmSpL+vvW0Mgusg7C6XXAI5rCCbafGvDCVLQteuLL/iilxf/CslJYVhw4YxbNgwFi5cyDPPPMOVV15JoUKFuOWWWyhXrhxFihThzTffzPE+VatW3aX67JJWrRLbX00UQeXKeV0LSZKkPcIxCLvDF1/AvffmeGjbxb/iocsDB/LOJ5+wYMGCuNwfWfxrWzta/Gt7atasycCBA+PByQCdO3dm48aN/PLLLzRv3jzblhEQ/ugKv5IkScrfbEHYHUaM2OHhoUBH4ESgf6tWrKhWjcGnnELlbb6F7ty5M8899xwDBgyga9euvPfeewwfPjzTt/8AjRo1AuD222/nmGOOoWDBgjRv3pzOnTtz11130bdvX8444wy+/vprrr/++nhwcoZDDjmE448/niZNmpCSksKMGTP4+OOP6dOnDwDt2rXjtNNOo1u3blx66aW0bNmSAgUKsGDBAiZPnswtt9zCvvvu+7tW5ZUkSdJfSF73cdqTG7k1BqFt253OkR8v/pWUlKeLf/373/8OBx54YChZsmQoVqxY2H///cPdd9+d6e1s3bo13HXXXaFp06ahcOHCoWTJkqFp06bh8ssvDz+nL/IWwvZX5ZUkSfo72dvGIDiL0e7Qvj1Mn77jefIzjBoF6d/WS5Ik6a9vb5vFyDEIu0PLlrsWDjLKSpIkSfmUAWF36N8/xwXSMokiaNfOufIlSZKUrxkQdofatWHw4O0fjyIoVgzuvjv36iRJkiT9AQaE3WXQILj9dshpFp/GjeHNN6Fp09yvlyRJkvQ7OM3p7hJFcOmlie5GY8fCvHlQqFBiAPMRR+y8C5IkSZKUDxgQdrfixeGMM/K6FpIkSdIfYhcjSZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKZarASGKompRFA2PomhWFEXroygKURTVyqFckSiKbo2iaGkURRvSy7fJzbpKkiRJf0e53YJQDzgFWA28uYNyjwBnA4OArsBS4JUoig7c0xWUJEmS/s5ye5rTN0IIlQCiKOoHdMxaIIqiA4AewJkhhMfS980APgOuA47PvepKkiRJfy+52oIQQkjbhWLHA1uAp7c5LxUYA3SKoqjwHqqeJEmS9LeXHwcpNwbmhxDWZ9n/GVCIRDclSZIkSXtAflxJuSyJMQpZrdrmeDZRFJXN4ViN3VgvSZIkaa+XHwPCH3URMDivKyFJkiT9leXHgLAaqJnD/ozWgVU5HAO4B3giy74awGu7qV6SJEnSXi8/BoTPgJOiKCqWZRxCI2Az8E1OJ4UQVpElPERRtMcqKUmSJO2N8uMg5UlAMtA9Y0cURUnAP4FXQwib8qpikiRJ0t4u11sQoijqlv7rwek/j4miaAWwIoQwI4TwYRRFTwN3RVGUDMwHzgVqAz1zu76SJEnS30ledDEal+X1/ek/ZwDt0n8/A7gRuAEoDXwMdA4hfJAL9ZMkSZL+tnI9IIQQdjowIISwAbg0fZMkSZKUS/LjGARJkiRJecSAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqRYvgsIURS1i6Io5LD9nNd1kyRJkvZ2SXldgR24CJizzevUvKqIJEmS9HeRnwPCFyGE2XldCUmSJOnvJN91MZIkSZKUd/JzQBgdRdHWKIpWRlH0ZBRFNfK6QpIkSdLeLj92MfoFuB2YAawBmgFXAbOiKGoWQvgxp5OiKCoLlM2y21AhSZIk/Q75LiCEED4EPtxm14woit4A3iUxcPma7Zx6ETB4D1dPkiRJ2qvlu4CQkxDCB1EUfQ202EGxe4AnsuyrAby2xyomSZIk7WX+EgFhG2G7B0JYBazadl8URXu8QpIkSdLeJD8PUo5FUdQcaECim5EkSZKkPSTftSBEUTQamA98APxMYpDy/wFLSHQjkiRJkrSH5LuAAHwKnAZcCBQDlgHjgcEhhJ/ysmKSJEnS3i7fBYQQwjBgWF7XQ5IkSfo7+kuMQZAkSZKUOwwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTF8mVAiKKoehRFz0RR9EsURWuiKBofRVGNvK6XJEmStLfLdwEhiqJiwOtAQ6AP0BuoD0yLoqh4XtZNkiRJ2tsl5XUFcnA2UAdoEEL4BiCKornAPKA/cEce1k2SJEnaq+W7FgTgeGB2RjgACCHMB94CTsizWkmSJEl/A/kxIDQGPs1h/2dAo1yuiyRJkvS3kh+7GJUFVuewfxVQZnsnRVFUNv3cbTmwWZIkSfod8mNA+KMuAgbndSUkSZKkv7L8GBBWk3NLwfZaFjLcAzyRZV8N4LXdVC9JkiRpr5cfA8JnJMYhZNUI+Hx7J4UQVpHohhSLomj31kySJEnay+XHQcrPA62jKKqTsSOKolrAYenHJEmSJO0h+TEgPAwsACZGUXRCFEXHAxOB74EReVkxSZIkaW+X7wJCCOFXoD3wNfA4MBqYD7QPIazLy7pJkiRJe7v8OAaBEMIi4OS8rockSZL0d5PvWhAkSZIk5R0DgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJsXwXEKIoWhBFUchhOzGv6yZJkiTt7ZLyugLb8QowJMu+r/KgHpIkSdLfSn4NCD+FEGbndSUkSZKkv5t818VIkiRJUt7Jry0Ix0VRtB4oCHwI3BxCeG5HJ0RRVBYom2V3jT1TPUmSJGnvlB8DwiRgDjAfqARcAEyIoqh3COGJHZx3ETA4F+onSZIk7bX2aECIoqgDMGUXis4IIbQDCCFcmOUaE4DZwDBgRwHhnhyO1wBe29X6SpIkSX93e7oF4W1gv10ot357B0IIW6MoGgfcEkVRlRDC0u2UWwWs2nZfFEW/p66SJEnS394eDQghhPXAl7vzkrvxWpIkSZKyyPezGEVRlAT8E1gUQliW1/WRJEmS9mb5apByFEWnAScAk4HvSQxSPh84CDgtD6smSZIk/S3kq4BAYuaiisCtJKYs/RV4D+gcQnglLysmSZIk/R3kq4CQvnpy+7yuhyRJkvR3le/HIEiSJEnKPQYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMVyLSBEUXRpFEWToihaGkVRiKJoyA7KnhhF0YdRFG2MomhhFEXXRFFUMLfqKkmSJP1d5WYLwtlAReC5HRWKoqgT8CwwBzgGuBu4BrhpD9dPkiRJ+ttLysV7NQ4hpEVRlAScs4NyNwMzQwj/Sn89LYqiFOCaKIruDCEs2+M1lSRJkv6mcq0FIYSQtrMyURRVBw4Enshy6HEgmUSLgiRJkqQ9JL8NUm6c/vPTbXeGEOYD64FGuV4jSZIk6W8kN7sY7Yqy6T9X53Bs9TbHs4miqGwOx2vspnpJkiRJfwt/qAUhiqIO6TMR7WybvpvruyMXAfOybK/l4v0lSZKkv7w/2oLwNrDfLpRb/zuvm9FyUCaHY2WAVTs49x6yj12ogSFBkiRJ2mV/KCCEENYDX+7mugB8lv6zMTArY2cURbWAYsDnO6jTKrIEiCiKdn8NJUmSpL1YvhqkHEJYBHwM9MxyqBewBXgp1yslSZIk/Y3k2iDlKIqaA7X4LZQ0iqKoW/rvk9NbJQCuAl6IomgE8BTQjMRCaXe7BoIkSZK0Z+XmLEYXAH22ed09fQOoDSwACCFMTg8Og4G+wHISqyjfmFsVlSRJkv6uci0ghBD6knjg35Wy44Hxe7I+kiRJkrLLV2MQJEmSJOUtA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIkSTEDgiRJkqSYAUGSJElSzIAgSZIkKWZAkCRJkhQzIEiSJEmKGRAkSZIkxQwIkiRJkmIGBEmSJEkxA4IkSZKkmAFBkiRJUsyAIEmSJClmQJAkSZIUMyBIkiRJihkQJEmSJMUMCJIkSZJiBgRJkiRJMQOCJEmSpJgBQZIkSVLMgCBJkiQpZkCQJEmSFDMgSJIkSYoZECRJkiTFDAiSJEmSYgYESZIk5VvTp08niiKmT5++W663YMECoihi1KhRu+V6eyMDgiRJkqSYAUGSJElSzIAgSZKkPPPUU0/RsGFDihQpQpMmTXj++edp164d7dq12+45r776KsceeyxVqlShWLFi7L///tx+++1s3bo1U7n169dz3nnnUa5cOVJSUjj++ONZvHjxHn5Hf30GBEmSJOWJKVOm0LNnTxo2bMj48eO57LLLuOSSS/j66693eN53333HUUcdxaOPPsqLL75Inz59GDJkCFdffTUAQ4YMIYoizj77bEaOHMmll17K+PHjadCgAT169MiNt/aXlpTXFZAkSdLf0+DBg2nUqBETJkwgiiIA9t9/f5o3b86+++673fPOOeec+PcQAkcccQSbN2/mtttu46abboqPjRkzhhtvvJErr7wSgI4dO7Ju3ToefPDBPfSO9g62IEiSJGmP27ABRo2C446DNm2ge/etzJnzHieddHIcDgAOPvhgateuvcNrLV26lP79+1OzZk0KFSpEcnIy11xzDT///DM//vhjXC4tLY1TTjkl07mnnnrqbn1feyMDgiRJkvaod9+FWrXgjDPgxRdh5kx45pmfSE3dwsiRFVm4MHP5SpUqbfdaaWlpHH/88bzwwgtcc801vP7668yZMyfuXrRx48ZM5c8991xSUlKoWbMm1113HRUqVABgy5YtDBgwgP3335+UlBQqV67Mcccdx5dffpnp/FGjRhFFEW+88QYnnngiKSkplCtXjvPPP58NGzZkrd68KIrOi6LojiiKfoyiaH0URS9EUVQro0AURZOiKPow64lRFNWOoigtiqJzsh7LbQYESZIk7THz5kGHDvDTT4nXISQ2KA8ks2zZjxx1FKxZ89s5y5cv3+71vv32W9577z1uueUWzj77bI444giaN29OwYIFcyx/8MEH89xzz3HiiScyePBg/ve//wGJgLB27VquueYaXnzxRR544AE2btzIIYccwrJly7Jdp1evXtSrV4/x48czYMAAHn74Yc4999ycbvl/QH3gDOB84GDg1SiKktOPPwAcGEVRyyzn/Qv4FRi93TefSxyDIEmSpD3m5pth7dqcjhQEmgPP8u23Q3j00YhLLoH333+f+fPnU6NGjRyvt379egCSk5PjfVu2bGH06OzP1VEUUbJkSTp06ECHDh14/fXX43LFihVj5MiRcdmtW7fSqVMnKlWqxFNPPcWAAQMyXevYY4/ltttuAxJjGaIoYtCgQVx11VUUKJDpO/e1wAkhhLT0OnwNzAROBx4BXga+A/oD76aXSSYRKEaHEHL8tHKTLQiSJEnaI9atgyef3FGJocBnwEncdttk/ve//9G9e3cqV66c9aE7tt9++1GzZk2uvvpqnnnmGR55ZCL16h0dd1Nq2xamTUv8fvLJJzNo0CBuuukmpkyZQmpqaqbWgbFjx9KqVStKly5NUlISxYsXZ926dXz11VfZ7pvTWIa0tDTefffdrEWfyQgHACGEt4DFwCHpr9OAEcCpURSVSi92IlApfX+eMyBIkiRpj1i8GLIMCcjiaBI9ar5gyZKTuOWWW7j99tupXLkypUqVyvGMQoUK8dxzz1G5cmV69Tqdfv3OZ9GiNqSmJmYqWrQI3ngjUbZ370c466yzuO222zjppJNYt24d5cqVA+Cjjz7in//8J/vttx9PPvkk77zzDnPmzKFChQrZxjFA9nERGa+XLFmStWhO/aOWA/ts8/oREk0ovdNfnwO8G0LINjYhL9jFSJIkSXtEoUK7UqoH0IMiReCzz2Dx4sV88cUX/OMf/wCgXbt2hMSghdiBBx7IiBEzad4cChSAtPj7+n6Zyp16ajE+/vgBHnjgAQD69u3L9OnTCSHQs2dP6tWrx6hRo+LyW7ZsYdWqVTnWcvny5TRu3DjTa4B99tkna9GcRlhXAj7KeBFCWBlF0VigfxRFrwBHZqt8HrIFQZIkSTmaPn06URQxffr0P3R+rVpQsyZsM4tpFhuAc4FnOfDAGTz22GMcffTRFCtWjH79dvy8fMcdidaJ38JBDlffsIB9940yhYAM69evJykp83fljz/+eLbVmDOMHTs20+sxY8ZQoEABWrVqlbVotyiK4mfsKIoOA6oBs7KUux/YHxgJ/AKM2f47yV22IEiSJGmPKFAAzj8f/v3v7ZUoCCwDLuC991by5ZfFOeKIIxg3bhxVqlTZ7nU3b97Z2IbMcgoRnTt35rnnnmPAgAF07dqV9957j+HDh1O6dOkcrzF58mQuv/xyOnbsyLvvvsvQoUM5/fTTqV+/Pt988822RUsAz0VRNAKoAAwD5gH/27ZQCGF2+nSnbYDhIYT1u/6O9iwDgiRJkvaYiy5KrH0wY0ZORwsBE/jXv+DBB3fU0pDZqlU7G9uQWU5lzz77bL7//nseffRRRowYQYsWLZg0aRInnXRSjtd44oknuP3223nggQcoVKgQZ599djyrURbDgHrAKKA4MA24IISwJYey44Bm5JPByRnsYiRJkvQ3NGTIEKIo4pNPPuHII4+kWLFiVKlShUGDBpG2o347wPjx42ndujXFihWjdOnSdO/enUWLFmUqM2bMGNq3b0+1ahV4770UypdvRqFC/81Uplw56Nr1bt54Yz+KFStKmTJlaN68ORMmTNjh/c49tzuQ+X6wHjgPKAfcBhxHonXit7EQo0aNYsGCBQAUKFCAG264gR9++IH169czY8YMmjVrxoIFC3LsklS1alUmTpzIunXrWLVqFffddx9FixbN6ePZHEK4NIRQIYRQLITQJYQwfzsfZVdgZgjhs+0czxMGBEmSpL+xE088kQ4dOvDcc8/Ro0cPrr/+eq677rrtln/wwQc5+eSTadSoEc888wwjRozg008/pW3btqzdZsGD7777jm7dujF69Giee+45zj33ONLS+tGv34M89BA8/zzcdttoXnppIKeddhqTJ09m9OjRdOvWLdNA4Zzu9+WXn1KkSFsSSw5k6E+iO/+lwHigAYkB0JCUj/rMRFFUOIqiQ6IouhY4FLg1r+uUVT76uCRJkrQnzZ0LX30FycmJNQog0dXmyisTU4R27NiRNWvWcPvtt3PJJZdkO3/dunVcccUVnHHGGTz66KPx/pYtW9KgQQMeeeSR+LyrrroqPp6Wlka7du1YunQp7777AA8/fA4AF1wwi6ZNmzJo0KC47LHHHrtL99t33wYkZgu9BPgKeBK4EbgyvVRHYB3w4B/4pPaoKsDbwM/ATSGE5/O2OtnZgiBJkrSXe/11aNUKDjgATjkFTjopMQsQQNu22RcAW7duHZ9++mm268yaNYs1a9bQs2dPUlNT46169eo0bNiQNzIWIADmzZvHaaedxj777ENycjLJycmMHDky0yJkLVq04KOPPuLCCy9k6tSp8SrJu3K//fZrSP36iftF0TtAGpB4LxlrrJ1++ql/8pNLTI0aQqBevXq7Urx+CGHkjgqEEBaEEKIQQpkQwtV/uoJ7gAFBkiRpLzZhAnTsCHPmZN6fsbRA9+6V2Hatrx0sAMaPP/4IQIcOHeKH/oztk08+4dtvvyWKIiZNmsTRRx/Nxx9/zM0338ybb77JnDlzOPPMM9m0aVN8vdNPP50HHniAd955h06dOlG2bFmOOOKIeGrVnd2vaNFvefxxqFRpaUbtgUQQeuop+L//y2lJguwWLFhAFOU8HerfkV2MJEmS9lK//AK9eyem+cyy1lhsyZLlnH9+HZ57LvF62wXAUlNTM5XNWIV41KhRjBs3jjlz5vDiiy/Gxz///HP69OnD559/zsKFC3nzzTc5/PDD4+NZrxdFEf3796d///6sXr2aV199lQEDBtCoUSMOOuigeEXjBg0aULx4cUaMSEz28/7773POOecwcOBAevWCrVur0LcvjBu3nAMOqEO9eokZkWbMyGlRY+2MLQiSJEl7qf/9D379dfvhIGEskybBwoWJV2PGjCElJYUmTZpkK3nooYdSokQJvvnmG8qXL09ycjLNmzePtxo1agDErQTJycnxuatXr2bixInbrUXJkiU5+eSTOfXUU1mwYAElS5aM77dhwwZKlCgR36dBgwYA8f1at25FgQIF+OabsdSv/9t0qWPG5Ju1x/5SbEGQJEnaS736auJheccB4WHS0tIYPrwF8AojR45kyJAhlCpVKlvJkiVLcuutt3LuuecS0i8apT+Np6SkcPHFFwNQu3ZtkpOTOeywwyhevDhNmzZl1apVlC9fnl9++SU+78ADDyQlJYWvvvqKlStXcvXVV/PII4+wfv16pk+fTrt27bj11ls555xz2LJlCxMnTqRUqVJMmTIFgKlTp9KuXTsaNGhAjx49uOaaa7j//vvjrkkFCmT/Lnz9+vVcdtllPP3002zatIn27dvz7+2v5Pa3ZAuCJEnSXmrjxl1ZfGwiMIV77jmeJ554gmuuuYZrr712u6X79+/Pww8/TNmyZYmiiMKFC1OtWjXat28fD+QdNGgQXbp0oWbNmqxfv5633nqL4sWL06tXr0zXWrBgAXPnzmXTpk0UKFCAUaNGcfTRR2e7X5MmTVi/fj29e/fm2GOPjccKbDtwuEWLFmzdujXuItW4cWMqVqwIwIYNGzJdb+TIkVx66aWMHz8+Dhf6Ta4FhCiKLo2iaFIURUujKApRFA3ZTrlR6cezbnflVl0lSZL2BnXqJMYf7FhDYBqTJm1g2bJlXH/99fE37+3atSOEQLt27TKdcdZZZ3HcccdRtWpVNm7cyPfff8/EiROpVasWAG3atGHChAl8++23bNmyhfPOO4+5c+cyePDguOUBoGjRoixbtoxffvmFLVu2sGjRIi644IJsNSxbtiwHHngga9asYf369YwePRogvt+6deu49tprOeOMM9i0aRMbN27k/fffZ9q0aSQnJ8ddnr766iuefPJJrrvuOq6++mo6duzIrbfeSpcuXX7vR7tXy80WhLOBisBzu1B2BXBIlu3OPVYzSZKkvVC/frtWrlo16NBh+8c3b4axY+Hcc+HMM+Hmm2GbL+WzyfrA3aRJEzZt2hR/u5+hc+fO21uN+HfZ1elX33nnHdLS0jjllOxTu+o3uTkGoXEIIS2KoiTgnJ2U3RxCmJ0blZIkSdpbtWgBxx+fWLV4R264AQoWzPnY669Djx6Q5dmeKILixWHr1uznli1bNtPrwoULA8SzEmWoUqXKTt/Drth2OtSclClTBoClSxPToWZM5Zoh6+u/u1wLCCGEnTZwSZIkafd68snE4miTJ2cdsDyEAgWGcOut0KdPzue+/TYccwxkmZ0USFxn3ToYMADuueeP1S3a+QCJXbLt9KuNGzfOdrxEiRLAb4Fk+fLl1KlTJz6etWXj7y6/zmJUMYqin4DSwHck1tG+LYSwdXsnRFFUFiibZXeNPVZDSZKkv4DixeGFF2DGDHjoIfjyS0hOhnbt4JxzoHbt7Z87YABs2bK9WZAKAxsYPjzR9Wi//fZM/XfFttOv9tle2gFatUpMhzp27FiuvPLKeL/ToWaWHwPCR8D7wGdAEeAkYBhQH9hRT7qLgMF7unKSJEl/NVGUCARZxhrv0Ecfwbvv7qhEI2AV8ABDhzbn6quL/Ika/jkZ06+ef/75rFixgmOOOYZSpUqxZMkSZsyYQbt27ejRo0c8Y9GgQYNIS0ujRYsWvPrqq0yePDnP6p4f/aGAEEVRB2DKLhSdEUJo93uuHUK4K8uuyVEUrQMuiaLolhDCvO2ceg/wRJZ9NYDXfs/9JUmSlAgIO9YPmA1cxdNP/8zs2TXj6UfzQv/+/alevTq33norTz75JKmpqeyzzz4cccQRHHjggXG5ESNGkJKSwm233cbmzZtp3749Tz75ZKYVn3dV3759mT59OlOnTt2N72T3iKKoHTANODKEMP13nRt2vHLG9m5YjF3rvrM+hLAoy7lJwBZgaAhhyC7eryXwDtAjhPDU76hnPWDevHnzMs2TK0mSpB373/+2PzZhW1EEbdvCtGl7vk75zbfffsuaNWsoUaIE9evXB6gfQvgmr+sFfy4g/KEWhBDCeuDLP3Lun/T704wkSZJ+txYtdq1cCNCq1Z6tS36zadMmChcuTN26dQH45pt8kQl2m7/KSso9SYSDOXldEUmSpL+D/fZLjFnY2URDUQT9++dKlWIvv/wyhxxyCEWLFqVUqVKceOKJfPXVVwCcf/75VKpUidQsUy9t2rSJMmXKcPHFF8f7VqxYwTnnnMM+++xD4cKFadiwIQ899FCm80aNGkUURbzxxht0796d0qVL0yo9EfXt2zderC1DFEVVoij6XxRFP0VRtCmKorlRFPXa5vjB6YsAn5D1faUvGLw4iqKC2+z7VxRFH0dRtDH9mo+kT86z7XkVoih6MoqiNVEU/RxF0f9ITPbzh+TaIOUoipoDtfgtlDSKoqhb+u+TQwjroyiqCTwOjAG+ITE8/iSgLzAihPBtbtVXkiTp7+7uu+HQQxOLom1vRebBg3c8E9Lu9vLLL9OlSxfat2/P008/zbp16xg0aBCHH344H330Eb179+b+++/n1Vdf5dhjj2XUqFGkpaVRqlQpfv75Z04//XQA1qxZw+GHH86GDRsYMmQItWvX5pVXXuHcc89l06ZNXHjhhQB88MEHAPTs2ZPTTjuNZ555htTUVKZPn85///vfrGsoFAUmA2WAq4DvgV7A41EUFQshPBRCeD+KojlAf2BixolRFJUGTgH+kzFzZxRFNwMDSYy1vRzYB7gB2D+KokO3meFzPHBA+j3nAf8Ehv/hDzmEkCsbMIpEK0BOW630MmVJrLS8ENgIrAc+AC4ACvyBe9YDwrx584IkSZJ+v/ffD6Fx4xASnYl+20qUCOG220JIS8vd+hx88MGhXr16YcuWLfG+7777LiQlJYUBAwaEEEKoX79+OPXUU0MIIbRt2zYcdthh4YQTTgj77bdffM51110XChcuHL7++utM1+/Xr18oV65cfP3DDjssAOGSSy7JVO6XX34Jxx57bKhRo0aYN29exjPtkPSf7ULmZ9KpwI9AwfTXfYGtQM1tylwEpALV0l/XSi8zKMu1Dku/x4npr49Of31qlnIv5VSXXdlyrYtRCKFvCCHazrYgvcyqEMKJIYSaIYQiIYRiIYSDQgj3BhdakyRJynUHHQSffJJYR2HwYPi//4NHH4WlS2HgwJ13Qfoz3n8fzjoL9t030UrRteuvfPDBB5xyyj9JSvqtI0zt2rU57LDDmDFjBgC9e/dm4sSJrF27FoAtW7YwefJkevfuHZ/z8ssv06pVK2rXrk1qamq8derUiZUrV/L5559nqstJJ52U6XXJkiWpUKFC1sXeWgJLQvZBwU8AFUjMDQuJ3jI/A2dvU6Y/8GIIYXH666NJ9LwZHUVRUsZGYuKetUCb9HKHkAgSz2a55x9e3OGvMgZBkiRJeSSKoE0bGDIEbroJzjgjsQDbn/Xxxx9z/PHHU6ZMGYoWLcphhx3Gm2++SQiJ8NG8OTz66AzmzTuaBQtK8eKL5Qkh8Nhj8/n118Q1nnzySZo1a8bMmTP58MMPadKkCQUKFGDjxo20aNGCGTNm8O6777Jlyxauuuoq2qUvBrF06VLeeOMNkpOTM23du3cHYOXKlfTt25e33noLgLZt2xJFUTzmIKOL0caNG7d9S6WADVEUvRVF0br0MQHvkmgNgPRFfUMIG4HHgDPTH/yPIBEeHtzmWhXTf35DYgbQbbcSQLn041WA1SGELVk+3j+8PHR+XChNkiRJe7kPPviAI444gmbNmvHwww9TrFgxHnzwQTp06MC//vU29957MIku+ieT6FUzAigOnMDSpYvo0wcuuWQmvXr14qKLLiItLY3169dz9tlns2HDBg477DC2bNlCs2bN+Oqrr9hvv/249957KVmyJJBoAahSpQoXX3wxZcqUYcWKFYwePZrVq1fzzDPP0LRpU2rUqMGHH37I3LlzGTt2LNWrV6dw4cI7elulSXRx/xToA6wDDgIapx9ftU3ZB4BLgRNIjLldALyyzfGV6T87AqtzuFfG8aVAmSiKkrOEhEo5nLNLDAiSJEnKFRs3ws8/Q4kScPnll1OjRg1ef/11ChUqBECnTp1o3Hh/HnzwemACcDFwIInp/DM6vhwMrODZZ7dSq9ZsSpcuzYABA7j//vu58MILueiiiwAoW7Ys55xzDk2aNGH9+vWcf/75tG7dOq7LiSeeyPDhwznjjDOoWLEiW7du5fTTT6dGjRr8+OOPlChRIt4AmjVrtivramUUuC2E8Fb6769EUfQqiTEIcb+lEMK36fsvT3+T12XpUj8FSANqhBB2tEDxLKAgiSS1bbeiU3dW2e2xi5EkSZL2qPfegx49EsGgShVISdnAtGkzOPjg7hQoUCDu/x9CoGbNDqSmvgF8RWLemn5kfmS9nsREPV2ZPXsLq1evpkmTJhQtWpR+/frFpbp3705ycjJffPEFBQoUoFu3bttWiQEDBpCcnBy3CiQlJVGjRmId4KuvvvqPvtVCwBJgfBRF/aIo6hxF0eMkxhNcG36bdSjD/UCr9Df4yLYHQmL2zluAe6Mo+k8URV2iKDoqiqK+URSNjqLoyPRyU4CZwIgoii6IoqhTFEWPAvv/0TdhQJAkSdIe89RT0Lo1jBkDvy1NsIoQtjJ69PXZxgC8+uq9JHrUZPSgqZblip2BF4GfmTXrOooWLUqhQoX49ddfadq0KR06dGDu3LmULl2a4447js2bN1O2bNm4JSDD//73P5YvX87+++9PmTJlSEpKokyZMgBxUNgVUfZR2r2AV4GbSfSROgDoHUJ4KGvB9DeyAZgYQsg2ZiCEcBXwLxIDksemX+8KEh/QvG2K/oPE9KrDgKdJ9BK6YJffRBZ2MZIkSdIe8emn0Lt3Yg2FxMybGUqT+J76fOD0eL0FSASJ22+HxDhcSHwhn1VnoqgzPXrA448n9qxbt47p06dzxRVX0LlzZxYvXsy4ceNo165dtkXTEvcZw1FHHcXUqVPjffPnz6dOnTqZuiLVq1ePBQsWbLd7UcaYhm0UDiH0zqlsDtqTWDvhwe0VCCE8TmKdsO0KIawATsvh0B+aY8oWBEmSJO0Rw4fD1q1ZwwEkBhsfAXwMHMSkSc1p3jyx/etfzYHmwL4kJv8ZSWI6/8xCgK5df3udkpJC165d6d+/P0uXLmXlykQLROHChdmwYUO289evX09ycnKmfY899li2cjmd/8MPPzB9+nQAGjVqtO2hX0l8479DURTVjaLoaOBO4IMQwms7Oyc32YIgSZKkPWLMDmfiv4NEz5lOTJ16FhMnViE19Sc++OADatfeyvz5NwN3keg90x44h8RSAl8AP1Kp0lDmzh3E668v58gjj6Rq1aosXryYe+65hwMPPJAKFSoAiQf4+++/n6effpq6detSokQJGjRoQOfOnbnlllu46aabaNmyJa+//jrPPPNMtlo2atSIVatW8cADD9C8eXOKFCnCa6+9xm233QYQr8yc7jZgcBRFzwKjSaxXcCCwMYSw7crG15LoivQxkOkC+cLvXVntr7ThSsqSJEl5IjU1++rL2bfPA/wzQIWQnFwo7LPPPuG4444L//3vi6F69YwyrwVoF6B4+tY0FCr0aJg5M4QXXnghdOzYMVSuXDkUKlQoVKtWLZx55plhyZIlcT2WLl0ajjnmmJCSkhKA0LZt2xBCCOvXrw/nnHNOKF++fEhJSQldunQJ3333XQDC4MGD4/PXrVsXTj311FC6dOkAhJo1a4YQQpg2bVoAwrRp07ZdSbke0I3EYmYbgDXpv3cN+eDZeFe3KGRv89lrRFFUD5g3b968XZmWSpIkSbtR6dLwyy+7Vvann6Bcud9eL1sGV18No0fDpk2JfVGU6FZ0/fVwwAG7vbp/2DfffEP9+vUB6ocQvsnr+vxZjkGQJEnSHtGz587LRBF07pw5HABUrgyPPAJLl8Jrr8Err8DChfD88/krHOyNDAiSJEnaIy68EJKTEyFge0KAgQO3f7xMGWjfHjp2hOrVd38dlZ0BQZIkSXtEw4aJgcpJOUyLkxEa7r4bOnTI3XppxwwIkiRJ2mP+8Q94/3046ywoUiSxLykJunWDN9+Eiy7K2/opO6c5lSRJ0h7VpAmMHAkjRsC6dVC8eM6tCsof/NNIkiQpVxQsCKVK5XUttDN2MZIkSZIUMyBIkiRJihkQJEmSJMUMCJIkSfnA9OnTiaKI6dOn53VV9DdnQJAkSZIUMyBIkiRJihkQJElSnvvmm2/o3bs3tWvXpmjRotSpU4dzzz2X1atXZyrXt29fqlWrxocffsgRRxxBsWLFqF+/Pg8++GCmcqNGjSKKImbPnk3Pnj0pWbIkVatW5aKLLmLjxo2Zyi5dupTTTz+d8uXLU7hwYZo2bcoTTzwRH3///feJooiJEydmq3dGfbZu3Rrve+ihhzjggAMoUqQI5cuX56yzzmLVqlWZzluxYgU9evSgZMmSlC5dmtNPP52ff/75j3580m5lQJAkSXnuhx9+oHr16tx111288sorDBo0iNdee41jjz02W9k1a9bQo0cPevXqxcSJE2nRogXnnnsu06ZNy1a2d+/e1K1bl/Hjx3Puuedy3333MWzYsPj4r7/+Stu2bXnppZe46aabeO6552jSpAm9e/fmoYceAuDggw+mRYsWjBgxItO1f/75Z8aOHUu/fv0oWLAgAFdeeSXnn38+HTp04Pnnn+fWW2/l5Zdf5phjjskUIv7xj3/wwgsvcNNNN/H000+TlJTEhRdeuFs+S+lPCyHstRtQDwjz5s0LkiTpr2PLli3hzTffDED44IMP4v19+vQJQHj99dfjfRs3bgxly5YNZ599drzvscceC0AYNGhQput26dIl1K9fP349fPjwAIRp06ZlKnfUUUeFChUqhNTU1Ph6BQoUCAsWLIjL3H333aFgwYLh+++/DyGEMH/+/FCgQIEwdOjQTNeaOXNmAMKECRNCCCG8+uqrAQhPPfVUpnKdO3fOsS7K/+bNmxeAANQL+eAZ+M9utiBIkqRct3L9Sh5+/2FueOMG7n33Xr776TtuuukmGjZsSNGiRUlOTuaII44A4Kuvvsp0brFixTjyyCPj14ULF2bfffdl0aJF2e7TpUuXTK+bNGmSqdwbb7zBPvvsQ7t27TKV69WrFytWrODzzz8H4NRTT6V06dI8/PDDcZkRI0bQpUsXqlWrBsCUKVNIS0ujZ8+epKamxlurVq0oUaIEb7zxBgCzZs2iYMGCnHzyyZnueeqpp+7SZyftaUl5XQFJkvT3sXnrZi5/9XJGvD+CTVs3xfsveuUiojkR/3f1/9G+TXtKlCjB4sWL+cc//pFtzECZMmWyXbdw4cLZygGULVs2W7lNm36776pVq6hSpUq28ypXrhwfByhSpAhnnHEGjz76KEOGDGHWrFl8/vnn3HbbbfE5P/74IwD16tXL8b2vXLkSSIx5KFOmDMnJyZmOV6pUKcfzpNxmC4IkSX9RQ4YMIYqi3Xa9KIoYMmTIHrv+1rStnDLuFO55955M4QAgfBpIa5LGy1VfptXhrWjRogWlS5f+3feYPn06Q4YMIS0tbZfKly1blmXLlmXbn7Fv24Bx7rnnsmzZMiZOnMiIESOoVasWnTp1io+XK1cOgFdffZU5c+Zk2zI+2ypVqrB69Wq2bNmS6Z7Lly//Xe9V2lMMCJIk/UX169ePWbNm/WWuP+7zcUz8KvtMQABsAQrC+0vf567ZdwHw2GOP/e57TJ8+naFDh2aMRdyptm3bsnjxYt56661M+5988kkqVqxIo0aN4n1169alY8eO3HrrrTzzzDOcffbZFCjw26PU0UcfTYECBVi0aBHNmzfPttWuXRuAQw45hK1bt/Lss89muueYMWN+9/uV9gS7GEmS9BdVrVq1uP/7X+H69825j4iIQA4P7/WAj4CKcOcPd7Jo9CLefvvt3Xbv7enbty933303//jHP7jxxhupVq0ao0ePZsqUKYwYMSKenSjDeeedxwknnEBycjJnnXVWpmN169bliiuu4IILLuCrr76ibdu2FClShO+//54pU6bQr18/jjzySI4++mgOP/xw+vfvz08//UT9+vV5+umn+fTTT/f4+5V2hS0IkiT9RWXtAhRFEddccw333HMPtWvXpkSJErRt25bPPvss03lbt27lmmuuoUqVKhQrVox27dplK5PT9SExf/9pp51GyZIlKVOmDGeccQbPP/88URQxffr0uFy7du04/PDDmTp1KgcddBDFihVj5pUzCV9kCQcrgfHAImAr8BKsfGQli5cv5qmnnoqLZaw3sGrVKlasWJHj+gcLFixg6NChQKL1A6B+/fo7/AyLFy/OjBkz6NixI1deeSUnnHACH3/8MY8//jj/+te/spXv0qULRYsW5YQTTshxzMBNN93EQw89xBtvvMEpp5zCCSecwC233EKZMmUy1WX8+PEce+yx/N///R///Oc/SU1N5d57791hXX+vrF3GpF1lC4IkSXuRJ554ggYNGnD33XezefNmLr/8ck444QS+/PJLkpIS/+wPGTKEm266iUsvvZSOHTvy3nvvcfzxx+/S9f/xj3/wySefMGzYMOrVq8ezzz673fn7v/32Wy6++GL+7//+j7LlytLlgi4wFrgAKJdeaC1QEjgWKAKsBt6EH5b8QIsWLeKuQtOnT2fNmjXMmzePe+65hzp16vDYY49x7rnn0qBBA6ZPn87ixYu5/PLLGTNmDDNnzsz27X/Ge8/60FylShUef/zxXXr/r7/+Ohs2bOCcc87ZbpnevXvTu3fvHV6nQoUKmQJQhl3tGiXtSQYESZL2IsnJybzwwguZZsjp3r077777LoceeiirV6/mzjvv5F//+lc8A0/Hjh0pWLAgV1555Q6v/eqrrzJz5kyefvppTjnlFAA6derE8ccfn+MUoz/99BNvvPFG/M15g3814Kv/+wo+A9qkF6qVvmWoDsUqF+OjER/x4Ycf0qxZs/jQ2rVruf/+++MpTtu0acMrr7zCU089xZFHHkm1atVo0KABAK1atYoD0e7w7bff8t133zFgwAAOOuggjjrqqN12bSm/sYuRJEl/AWkhjVe/fZVe43vR/r/tOXHMiXy49MNs5Y4++uhM4aBJkyYA8QP8J598wq+//ho/4GfYlTn4Z8+eTcGCBTnppJMy7e/WrVuO5evXr5+pW80FR14AxYFftimUCrwBDAduAK6H9SPWA39s/YNtuznBrne7ApgwYQKHHXYYKSkplCxZkpYtW/L8888DcP3113PMMcewevVqVq1aReHChalatSoDBw7MNL3qggULiKKIESNGMGjQIKpUqULp0qU57rjjWLx4cab7PfnkkzRr1iy+X5MmTbKt1jxjxgyOOuooSpQoQfHixenUqVO2sQq72mVM2lW2IEiSlM8tW7eM4548jveWvkeU/r9AIHyV6I7y9vdvc2j1Q4Gc5/0H4ofYpUuXAtnn3N+VOfh/7/z9WetyxoFncGmhS9mSus30nq8B7wBtgepQrnQ5bml1C/169vtT6x9sa1e6XQ0fPpyLLrqIE088kf/+97+kpKTwwQcfsGDBAgBGjRrFxo0bmTRpEldccQWHHnooX3zxBddeey0LFizINiPRsGHDOPTQQ3n00Uf58ccfGThwIL169YoDzMyZM+nVqxcXXXQRt956K2lpaXz55Zf8/PPP8TVefPFFTjjhBLp06cITTzwBwC233MIRRxzB3LlzqV69OvDnuoxJOTEgSJKUj63fsp6j/3c0n65IfGsc0v+3rY6Pd+Tds9/dpetlLAq2fPlyGjduHO/flTn4t52/f9uQsKvz9xcvVJzKKZXZlLKJH0ksKsanwAFAW2hYviETT53IkrlLdul6GVLTUnn/h/dJLphMWsi+/sHOul2tWbOGq666ipNOOonx48fHZbZd4+DNN9/k6aef5r///S+nn346AB06dKBs2bL06tWLjz76iAMPPDAuX6tWLZ588sn49YoVK7j88sv54YcfqFq1KrNnz6Z06dLcddddcZmOHTtmqvfFF19M27ZtmTjxt6lhjzzySOrUqcPtt9/OXXfd9ae6jEnbYxcjSZLysdFzR8fhYHt+3fIrN7554y5dr2nTphQvXpyxY8dm2r8rc/C3bt2arVu3MmHChEz7x40bt0v3BigQFeCYescw66xZnN/ifJLTkmlYqSEv93yZz877jH3L7bvL6x98seILvvjpC976/i2aP9ycAx48gFlLEus2/Pjzj3G5nXW7evvtt1m3bl2OsxZlePnllylUqBDdunUjNTU13jIe6t94441M5Y899thMr7Pes0WLFqxevZpevXrxwgsvZGo5AJg3bx7ffvstPXv2zHS/YsWKccghh8T3+zNdxqTtsQVBkqR87KEPHqIABUhjxysDj/1s7A6PZyhdujQDBgzgxhtvpESJEnTs2JE5c+bwyCOP7PTcjh07cthhh/Gvf/2Ln376iXr16vHMM8/w8ccfA2RaNGxnWldrTetqrVl53EomTpzIt69+S1QvYvz48bu0/sE7i9+hw+MdWLduXab9qUVSATi478E88e9Et5yddbtauXIlwA7XfPjxxx/ZvHkzxYsXz/F4xjUy7Oyebdu2Zdy4cQwfPjwe09G2bVvuuOMOmjZtyo8/JgLOWWedlW29BYAaNWoAf67LmLQ9BgRJkvKxL3/6cqfhABLdbHbVkCFDCCEwcuRI7r33Xlq1asWkSZMydTnangkTJnDhhRdyxRVXULBgQY4//niuv/56+vbtS6lSpXa5DhmGDx9OCIGrr74aSHzz/tRTT9GyZcscy2/ZsoVUUjlhzAms37I+e4H06VOXTVtGh8kddqkO5cuXB2DJkiXsv//+OZYpV64cRYoU4c0338zxeNWqVXfpXtvq1q0b3bp1Y926dUyfPp0rrriCzp07s3jxYsqVS7yRYcOG0aFD9vdRqFAh4M91GZO2x4AgSVI+llwgefsHj0zf0n2y/BP2r5j5AbdWrVrZ5tYvWLAgN9xwAzfccEOm/VnLZawZ8M033zB06FBmzpzJsmXLqFKlCr169eKmm26iTJkyXHDBBRQrVozbbruNadOmMWnSJLZu3coHH3xA/fr1GThwYLxuQMag36lTp3L55ZfzxRdfUK1aNW699VZmzpzJ9OnTGT16NCGEeEag++67j4oVK5KWlkbhwoUZ+uRQll++HE4FzsjymWRMfHQZJCUlkTpo58Hp0EMPJSUlhYceeijTuINtde7cmVtuuYVffvllt09xmpKSQteuXfnuu++4+OKLWblyJQ0aNKBWrVp89tlnOxxLsG2Xsfbt28f7d6XLmLQ9BgRJkvKx9rXbM/6L8dkGJm8rIqJC8Qo0KNdgj9Thhx9+oHr16tx1113Mnj2bhQsXMmnSJKZNm0bHjh154IEHuPzyy1m2bBlr1qyhR48eXHLJJQwaNCjTYmYZU5R+/vnndOnShZYtWzJmzBg2b97M9ddfzy+//JJjN6Ubb7yRFi1a8NBDD7F161ZGrR0FVYH3gIbbFNwAGWOfKbDrrSolSpRg2LBhXHjhhZx88sn07NmTEiVK8NFHH1GkSBEuvPBC2rVrx2mnnUa3bt34//buPLqq+tz/+PsJhEDCLGCAqEEDMqjMg1JAIwIi1VoEtKBBwV8VvAJVrldvhSAGaWm4BQoVkMGivyjDTylX1NYBWV4HUJCrqDRZCBqkVIwGBBJC8v39sU+OOZk8CSc5Cfm81sraZO/v2XnykDM8e3+H3/zmN/Tr14+IiAgOHDjA1q1b+d3vfkenTp2CzumsWbM4cuQI11xzDe3atSMzM5PFixfTo0cPWrduDcDSpUu56aabOH36NGPHjqVVq1YcOXKEyZMnc+mll7Jz586z6jImUhYVCCIiIjXY1L5T2fTZpnLbOBy/7v1rIuuVc7fhLAwePJjBg72VzU6fPs3WrVvJysri0KFD5OTkMG/ePGbOnMldd931k4uZATz++OM0bdqUV199lejoaAAGDRpEhw4diI2NLfHzzz//fF544QXMDIBlzyzD+hlus4Pvgea+hnugnDqqXPfddx+xsbEsWLCA8ePHExkZSZcuXXj00Uf9bZ555hmWLFnC6tWrSUlJISoqivj4eIYPH17hPv/9+/dn8eLFzJgxg6ysLNq0acOwYcOYO3euv83IkSPZvn07KSkpTJ48mVOnThEbG0t+fr6/iICz6zImUho7l5f0NrMEID09PZ2EhIRwhyMiIlJhzjnuf/l+/rTzT2W26duuL28mvUlMg9IH0FZU5rFM1n60lvSsdKLqRTEkbgj7X9rPs888y8GDBwPWHUhLS/PPmDNx4kQ2bNjAiRMnAs535ZVX0qxZM1555RUALr74YgYNGsTTTz8d0C4xMZH9+/f7uyEdOHCADh06MGfOHGbNmuVvd+fmO1m7cy0sBPoAhT1+lgItgdt+POfGMRsZ3XV0pfKQm5vrH1xck8THx3P11Vezdu3acIciPhkZGYWLAnZ0zmWEO56zpWlORUREajAzY/H1i/n90N9zXqPzAo41qNeAST0n8fodr4ekODhTcIZpL08j/o/xPPrmo6zbs46Vu1YyYeoEZiXP4qobruKll15ix44d/vUCKrOY2eHDh2nTpk2JdmVdhS8ciFsoqXsSRAI98LoZPQvMA74BvgUOeu2aN2zOi/NfJC4ujt27dzNo0CCio6Pp2LEjTz75ZMA5165di5mxfft2xowZQ/Pmzenfvz8AO3fu5JZbbiEuLo5GjRpx6aWX8sgjj3Dq1KmAcxRf0TgxMZHPP/8cMyM5OTmg7Z49e7jxxhtp0aIFjRo1YuDAgaUOgF60aBHx8fE0bNiQPn36lDlIWiSU1MVIRESkhjMzZg6cyf397+eVjFf4+vjXNI1qyvCE4bSKbhWynzP1pams2LXC/71708FbQBOgOzzb4lkmd5xM3wv6lpheNBjbtm1j27ZtxMbG+qfxLKqsmXcKuxYVGnLREK6Mu5J3L3oX3gW+A2LxioOWwF+ASfDvE/6dfZ/tC2pcRKHx48dz2223sXHjRs6c8cYwfPnll/To0YOJEyfSpEkT9u7dy2OPPcb+/fsDBgPPnj3b391q6NChfPjhh6WuaLxr1y4GDRpEz549WblyJdHR0Tz55JMMHTqUd955h969ewOwatUqpk+fzsSJExk3bhwZGRncdtttHD9+PNiUi1SOc+6c/QISAJeenu5ERESkbHv+uceRTODXDByTcDTE0QdnyeauWnWVc86522+/3QFuzZo1/nMkJSW59u3blzj3kCFD3JAhQ9zs2bMd4MaNG+datWrlTpw44W/z9ddfuwYNGriLLrrIv++LL75wgFu5cmWJcx754YhrfGljRySOdjjq4UjEMQtHK1x8/3iXX5DvkpKSHODeeOMN/2NzcnJcy5Yt3d133+3ft2bNGge46dOnl5ungoICl5eX59atW+fMzB09etQ551xWVpaLiYlx9957b0D71NRUB7jZs2f79yUmJrrOnTu73Nxc/74zZ864zp07u5tuusk551x+fr6Li4tzw4cPDzjfc8895wCXlJRUbpxSvdLT0x3eCJgEVwM+A5/tl7oYiYiICMs/WF5yZzPgArzLbR+B2+F45813GJs01r+YWX5+foV/1iOPPEJ2djbDhw9n8+bNrF+/nmHDhnH++eeXudjaybyTpH2cxvy357PovUUczjrMqYxT9L2mL3wNOGg2oBk3d76ZX4z8Bdn/yCbCvHNFR0cH3CmIioqiU6dO/lWNiypctKyoY8eO8dBDD3HJJZcQFRVFZGQkt99+O8450tPTgR9XNB4zZkzAY2+55ZaA70+dOsVbb73FmDFjiIiI8K+Q7Jxj6NCh/hWSMzMzyczMLLFC8ujRo6lfXx1ApGrpL0xERET46MhHGBY4neqbeF2MZuJdG90K1IP3E94n72Qe4H2QzcnJ4eGHH2bz5s1kZ2cTGxtL3759WbBgAZ07e/OQHjhwgDlz5gDQvXt3AN5++2127NhB+/btmTFjBkuWLOHgwYM0aNCA9u3bM3q0N7j4lYxXeDD1QbJzs3+M7RiQDzv/ttP7vgCy52XzAi/4mxQUeAvMBTMuolDx8Q4Ad955J6+99hqPPfYYPXr0ICYmhh07djB16lT/OQpXNC4+tqL4uIqsrCzy8/OZO3duwIxFRRUUFJS5QnL9+vX9i6iJVBUVCCIiIuK/2l6qGGAMsBeIhkbRjViYspCYmBh69uxJbm4ux48f589//jNt27YlKyuLZcuWceWVV/LZZ5+xbds2MjMzSU5OZtWqVbz99tvUq1cPgAEDBnDmzBkGDx5MRkYG/fr1IyUlhffee4+5c+fSZ3QfNjXaBLnFYmoIGDTs1pCcT3JYunRpidWXy7obUZ7i4x1ycnLYvHkzycnJTJs2zb//448/DmhXWFj861//KndF4+bNmxMREcHUqVO54447So0hIiIiYIXkos6cOcO3335bwd9KpGJUIIiIiAgD2g/g7S/f/umGDjZu2chl7QNXbH7qqaf8/87Pz/evDZCWlsaMGTOIi4sjLi4O8NYAmDFjBldddRW5ubk8//zzvPvuu9SvX58VK1ZwxRVXcO2113L4+GGWLlgKHYDGxeL4AWgDOftyaNGhBVOmTDm7BJQhNzeX/Px8IiMD15goPsXo5ZdfTkxMDBs2bAjozrRhw4aAdjExMQwaNIg9e/bQq1evMouYuLg4LrjgAtavX89dd93l379p0yb/4GmRqqICQURERBjXbRx/ePcPP9muXc92JYoDgPXr15Oamsq+ffvIzv6xK9C+fftKPU9OTg4PPfQQR44coaCggIYNG7J161a6du3q/wB8qM0hKAAyCVwxGbyuT0cAg+/PfM/Kp1fSKb4TR48eZdeuXeTn5zN//vzgfvlyNGvWjAEDBpCamkrbtm1p1aoVq1ev5tChQwHtWrRowfTp05k3bx5NmjRh6NCh7Nq1y7+icdFCYOHChQwePJjhw4czadIk2rZtWyLuiIgIZs+ezeTJk7nzzju59dZbycjIYP78+TRt2vSsfy+R8qhAEBERqeO2HdjGjWklp+MsqnB8wqjeo0oc27JlC+PGjSMpKYnZs2fTqlUrIiIiGDlyZKn9/AFWrlzp//d1113Ha6+9RmJiYuk//FQp+272fX0Dbptj5m9mcuqHU7Ru3ZpevXpxzz33lPv7VERaWhr33nsvU6dOpVGjRowdO5ZFixYxalRgLubMmYNzjlWrVrF48WL69+/P2rVrGThwIM2aNfO369WrFzt37mTOnDncf//9ZGdnlxr3pEmT+OGHH1i4cCFpaWlcdtllpKWlMWHChJD9biKlUYEgIiJSh+07uo8b/u8N5Jwp/YN8oS6tu/Apn9I6pnWJY8899xwJCQkB3W7y8vLIysoKKobzzjuPDh06sH79+oD9v3z+l3yV/RU0L+fBrYExkParNK7veH2Jw2WtNrxt27aA7ydOnMjEiRNLbRsfH8/LL79cYr9zLuD7evXqkZKSQkpKin/fxo0bAa8oKKpLly4BayiUZdq0aQFjHwD/StMiVUUFgoiISB224J0FnMw7+ZPtbux0I5/yaanHTp48WWLqzXXr1pWYAjUqKgrwpvps0qSJf/+IESPYtGkTjRs39s96BNB3f18yP8sMnFmpFIbRpXWXn/wdqtr777/PSy+9RP/+/WnYsCEffvgh8+fPZ8CAAfzsZz8Ld3giQVOBICIiUkflnMnh2Y+f/cl2hrFq96oyj48YMYIXX3yRGTNmMGrUKD744AOWLFlC8+bNA9p17doVgNTUVK6//nrq1atHnz59GD9+PGvWrOHaa6/lgQceoHv37pw+fZrYvbG4ZxzcCjQoO7YRCSOIbx4f5G9ddRo3bsz27dtZunQpx44do02bNowdO5YnnniixOxIIjWZCgQREZE66psT3/xk1yIAh+Obk9+Uefzuu+/mq6++YvXq1Sxfvpy+ffuyZcuWEouOjRo1iilTprBs2TIee+wx/6qtkZGRvPrqq8yfP58VK1bwxRdfEBMTwyWXXEJC9wQy6mWU+nMjLIIG9RrweOLjFfvFq0i3bt1KdF0SqY2seP+5c4mZJQDp6enpJCQkhDscERGRGuXoyaO0XlByTEFpIiyCvEfzyl8voQqczDtJ0otJbPx0oz8O5xwOR+vo1mwcu5HBFw2u1phEisvIyKBjx44AHZ1zpVe0tYjuIIiIiNRRraJb0bttb3b/czcFrqDMdoXdeKq7OACIjoxmw5gNfPKvT1izew0Hsg/QqH4jRnYcyeguo4mqH1XtMYmc61QgiIiI1GHT+k/jjhdLX9G3kMNxX9/7qimi0l3W5jJSh6eGNQaRuqL6LwWIiIhIjTH+ivHc0d0rEIzAgbSF388YMIMRCSOqPTYRCQ8VCCIiInVYhEWw5qY1/OG6P9CuSbuAYxe3uJjlo5aTOixVs/CI1CHqYiQiIlLHRVgED1z1ANMGTOP9zPf5Luc72sS0oU+7PmEZdyAi4aUCQURERACoH1GfgRcODHcYIhJmuiwgIiIiIiJ+1VIgmFknM1tkZv9rZj+Y2WEz+6uZdS+j/d1m9rmZ5ZrZPjO7pzriFBERERGp66rrDsIw4BrgaeDnwBSgNfCemfUu2tDM7gaWA5uAEcAGYJmZ3VtNsYqIiIiI1FnVNQbhOWCpK7Jss5m9ARwApgF3+PbVB1KAdc65//Q1fdPM2gFzzewp51xeNcUsIiIiIlLnVMsdBOfc0aLFgW9fNvAPoH2R3Vfi3Vl4ptgp1gHnAT+ryjhFREREROq6sA1SNrOWwGXAZ0V2d/NtPynWfK9v27Wq4xIRERERqcvCOc3pEsCAPxbZ19K3/a5Y26xix0vwFRzFj194FvGJiIiIiNQ5lSoQzGwo8Pcgmr7lnLu6lMc/DPwKmOScy6hMDKW4H5gdonOJiIiIiNRJlb2D8A7QJYh2J4vv8E1ZOg/4rXNudbHDhXcOWgCHi+wvvDOQRdkWU3LswoXA60HEKSIiIiIiVLJAcM6dBD6v6OPM7HZgGZDqnEsppUnhWINuBBYIhWMPPi0npiyKFRBmVtEQRURERETqtGobpGxmNwNrgKeccw+W0exd4Cgwvtj+CXgf/v+n6iIUEREREZFqGaRsZoOBNGAPsNbMBhQ5nOuc2w3gnMszs0fxFkY7BLwGJAJ3Af/mnDtdHfGKiIiIiNRV1TWLUSIQBfSi5F2Ag0B84TfOuSfNzAEPADOBL4H7nHPLqidUEREREZG6q1oKBOdcMpBcgfbLgeVVFY+IiIiIiJQubAuliYiIiIhIzaMCQURERERE/FQgiIiIiIiInwoEERERERHxU4EgIiIiIiJ+KhBERERERMRPBYKIiIiIiPhV10Jp4RIJcPDgwXDHISIiIiLnqCKfNSPDGUeomHMu3DFUGTNLBF4PdxwiIiIiUidc65x7I9xBnK1zvUCIAfoDh4G8EJzyQryC41rgyxCcry5TLkNHuQwd5TJ0lMvQUS5DS/kMHeXyR5FAW+B959yJcAdzts7pLka+/6CQVXFmVvjPL51zGaE6b12kXIaOchk6ymXoKJeho1yGlvIZOsplCZ+FO4BQ0SBlERERERHxU4EgIiIiIiJ+KhBERERERMRPBULFZAFzfFs5O8pl6CiXoaNcho5yGTrKZWgpn6GjXJ6jzulZjEREREREpGJ0B0FERERERPxUIIiIiIiIiJ8KBBERERER8VOBICIiIiIifioQgmBmvzGzLWZ22MycmSWX07aRmSWbWbqZ5ZrZETP7bzNrUI0h12gVyWeRx1xsZid97ROqIcxaIZhcmllTM5tlZu+Y2bdm9r3v37+o/ohrrgo+z39hZrvNLMfMDprZb82sXjWGW+uY2XlmtsjM9pvZKTP7wsz+ZGatwx1bbWRm7c1stZn90/de84WZPRHuuGo7M7vV9/zPDHcstY2ZdfI9x//XzH7wvZb+1cy6hzs2qTgVCMG5G2gDvFheIzOLBF4G7gRSgeuAKUAmoA8PPwoqn8UsA7KrJJraLZhcXoj3d/gWMAEYB/wDeMHMplZ1gLVIsM/z4cAmYCdwPbAI+C0wr4rjq7XMzIC/Ar8CFuDlbQFwK7DFd1yCZGbxwA6gE3A/MAxIBs6EL6raz8yaA38E/hneSGqtYcA1wNPAz/Hed1oD75lZ73AGJhWnaU6DYGYRzrkCM6sP5AFznHPJpbT7D+ARoJtz7qtqDrPWCDafRdr/Cvgv4AnftqNzLqNagq3hgsmlmcUAzjl3stj+1/FyeWG1BVyDVeB5vhs45pwbUmTfLLwi4ULnnD5cFGNmnYB9wK+dcyuK7L8H+DPQ2Tm3L1zx1TZm9grQEhjonMsLdzznCjNbAVwEHAaGOufiwhxSrWJmrYBvXZEPlmbWDDgAbHHO3RGu2KTidAchCM65giCbTgE2qDgoXwXyiZm1ABYCDwLfV1VMtVUwuXTOnSheHPh8ALQLfVS1UzC5NLMLgB7AM8UOrQMi8a6MS0mFXSyPFdv/vW+r96IgmdklwHBgiYqD0DGzgXh3WHVXtZKcc0eLFge+fdl4d6zbhycqqSy9KIeImV0IXADsN7OVZnbM1z/5dTPrEebwarPfA58759aFO5Bz0GDg83AHUct0820/KbrTOfcFcBLoWu0R1Q57ge3Ao2bWx8wam1k/YBbwsnPus/CGV6sM9G1PmdnffeMPvjOzv5jZeWGNrJbydQ9eASzQ3enQMrOWwGWAnuO1jAqE0Cm8EvsQcDFe39rb8PrfbfMVEFIBZjYIuAPvzoyEkJn9H2AAXrctCV5L3/a7Uo59V+S4FOG7qjgSr5vRTuA48D6wHxgdxtBqo8L3mtV4V2avx3vfuQF41cz0vl5xDwFR6PWwKiwBDG9sh9Qide6FxMyG+mYo+KmvbRU8dWEuTwI/d85tdc69gPei3Yhz9LZlVeXTvFmflgP/5Zz7tEqCr2Gq8G+z+M+5GlgM/MU592wIQq9xqiuXdVUl87sSryi9Bxji2/YBNtblD7WVyGVhrrY556Y6597wjeuYAvTG635UZ1U0n+bNivefwH3OuZywBl/DnO3rqJk9jDcxwX26M1P71A93AGHwDtAliHal9dkuz7e+7f8U7e/tnPvKzD4HelbwfLVFVeVzOtACWGzezBIA0b5tEzNr4pw7XsFz1nRVlUs/M+uLN5vMG8Dkyp6nFqiqXBbeOWhRyrEWQFYFz1dbVSi/ZnYD3h3Voc65133HtpvZfuBveDOebK6KQGuBiv6tFr7X/L3Y8b/5tj3xZtOrqyqaz8V4r4fvFXmvaYA3+VZzINc5dyrUQdYSlX4dNW8CgnnAb51zq0MdmFS9Olcg+D68V0W/6/1AeS8iQQ/MrU2qMJ9dgVjgUCnHdgF78AaLnjOqMJcAmNnlwKvAR8Doc3mAYxXmcq9v2w14t3CnedNORgN14m5XJfJ7uW+7s9j+Hb5tF+pogVCJXO79iePn5HtNsCqRz654MxeV1W1wEd4Fqzqnsq+jZnY73tTkqc65lJAHJtWizhUIVcU5l2dmLwGDzSzGOXcC/IOXO+NdtZXgzQfWFts3Aq+v6AS8vswSJDPriHfFcT8wqg5fETsrzrkvzWwPMB54qsihCXhTo9blK7flKZz6tR/wWpH9/X3b0i4ESOnew8vncLz+3YVG+LbFizAp361Aw2L7/gOvu9YYvHWMJEhmdjOwBnjKOfdguOORytM6CEEwsz5APF7fz+eBDcB63+GthV2KzKwr3hWxD/AWSmsIzMYbqHyFc+5I9UZeMwWbz1IeNxHvhUfrIPgEk0sza4P3d9kUuJ0fuygU2u2cy62eiGuuCjzPRwL/jdenPg2vS8cTeNNOzqzmsGsFM2uKN4uJAXPxrkp2xnt9PA10dc79EL4IaxczS8K7gLIc+H9AApCCd3cwsfhUk1IxZrYWrYNQYWY2GK+r217g3wi8m5XrnNsdlsCkUlQgBMH3YpFUxuEOzrkDRdr2A36Hd2UsD3gTeFAfaH9UkXwWe9xEVCAECCaXvkHJb5ZzmjJzXpdU8Hn+S7wPt52BI3h3E1Kcc/lVHGatZd4aEslAItAWbzGq14Bk55zuIFSQrxvHQ0BHvLEvG4GHVWidPRUIlWNmyXivi6U56JyLr75o5GypQBAREREREb86O7WciIiIiIiUpAJBRERERET8VCCIiIiIiIifCgQREREREfFTgSAiIiIiIn4qEERERERExE8FgoiIiIiI+KlAEBERERERPxUIIiIiIiLipwJBRERERET8VCCIiIiIiIifCgQREREREfFTgSAiIiIiIn7/H8TLzLwlwrYHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 862.5x862.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "## w2v model\n",
    "model = w2v_google_model\n",
    "\n",
    "## prepare training word vectors\n",
    "size = 200\n",
    "target_size = len(target_words)\n",
    "all_word = list(model.vocab.keys())\n",
    "word_train = target_words + all_word[:size]\n",
    "X_train = model[word_train]\n",
    "\n",
    "## t-SNE model\n",
    "tsne = TSNE(n_components=2, metric='cosine', random_state=28)\n",
    "\n",
    "## training\n",
    "X_tsne = tsne.fit_transform(X_train)\n",
    "\n",
    "## plot the result\n",
    "plt.figure(figsize=(7.5, 7.5), dpi=115)\n",
    "plt.scatter(X_tsne[:target_size, 0], X_tsne[:target_size, 1], c=color)\n",
    "for label, x, y in zip(target_words, X_tsne[:target_size, 0], X_tsne[:target_size, 1]):\n",
    "    plt.annotate(label, xy=(x,y), xytext=(0,0),  textcoords='offset points')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### ** >>> Exercise 8 (Take home): **  \n",
    "\n",
    "Generate a t-SNE visualization to show the 15 words most related to the words \"angry\", \"happy\", \"sad\", \"fear\" (60 words total)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angry_words:  ['glad', 'pleased', 'ecstatic', 'overjoyed', 'thrilled', 'satisfied', 'proud', 'delighted', 'disappointed', 'excited', 'happier', 'Said_Hirschbeck', 'elated', 'thankful', 'unhappy']\n",
      "happy_words:  ['irate', 'enraged', 'indignant', 'incensed', 'annoyed', 'agitated', 'frustrated', 'furious', 'unhappy', 'outraged', 'livid', 'irritated', 'exasperated', 'angrier', 'Angry']\n",
      "sad_words:  ['saddening', 'Sad', 'saddened', 'heartbreaking', 'disheartening', 'Meny_Friedman', 'parishioner_Pat_Patello', 'saddens_me', 'distressing', 'reminders_bobbing', 'Turkoman_Shiites', 'saddest', 'unfortunate', 'sorry', 'bittersweet']\n",
      "fear_words:  ['fearful', 'fears', 'Fear', 'fearing', 'worry', 'dread', 'feared', 'anxiety', 'apprehension', 'afraid', 'worries', 'concern', 'distrust', 'worried', 'Fearful']\n",
      "\n",
      "target words: \n",
      "['glad', 'pleased', 'ecstatic', 'overjoyed', 'thrilled', 'satisfied', 'proud', 'delighted', 'disappointed', 'excited', 'happier', 'Said_Hirschbeck', 'elated', 'thankful', 'unhappy', 'irate', 'enraged', 'indignant', 'incensed', 'annoyed', 'agitated', 'frustrated', 'furious', 'unhappy', 'outraged', 'livid', 'irritated', 'exasperated', 'angrier', 'Angry', 'saddening', 'Sad', 'saddened', 'heartbreaking', 'disheartening', 'Meny_Friedman', 'parishioner_Pat_Patello', 'saddens_me', 'distressing', 'reminders_bobbing', 'Turkoman_Shiites', 'saddest', 'unfortunate', 'sorry', 'bittersweet', 'fearful', 'fears', 'Fear', 'fearing', 'worry', 'dread', 'feared', 'anxiety', 'apprehension', 'afraid', 'worries', 'concern', 'distrust', 'worried', 'Fearful']\n",
      "\n",
      "color list:\n",
      "['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 862.5x862.5 with 0 Axes>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f8ccadff550>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'glad')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'pleased')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'ecstatic')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'overjoyed')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'thrilled')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'satisfied')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'proud')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'delighted')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'disappointed')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'excited')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'happier')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Said_Hirschbeck')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'elated')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'thankful')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'unhappy')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'irate')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'enraged')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'indignant')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'incensed')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'annoyed')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'agitated')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'frustrated')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'furious')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'unhappy')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'outraged')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'livid')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'irritated')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'exasperated')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'angrier')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Angry')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'saddening')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Sad')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'saddened')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'heartbreaking')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'disheartening')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Meny_Friedman')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'parishioner_Pat_Patello')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'saddens_me')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'distressing')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'reminders_bobbing')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Turkoman_Shiites')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'saddest')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'unfortunate')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'sorry')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'bittersweet')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'fearful')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'fears')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Fear')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'fearing')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'worry')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'dread')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'feared')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'anxiety')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'apprehension')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'afraid')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'worries')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'concern')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'distrust')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'worried')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Fearful')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 862.5x862.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Answer here\n",
    "word_list = ['angry', 'happy', 'sad', 'fear']\n",
    "\n",
    "topn = 15\n",
    "angry_words = [word_ for word_, sim_ in w2v_google_model.most_similar('happy', topn=topn)]\n",
    "happy_words = [word_ for word_, sim_ in w2v_google_model.most_similar('angry', topn=topn)]        \n",
    "sad_words = [word_ for word_, sim_ in w2v_google_model.most_similar('sad', topn=topn)]        \n",
    "fear_words = [word_ for word_, sim_ in w2v_google_model.most_similar('fear', topn=topn)]        \n",
    "\n",
    "print('angry_words: ', angry_words)\n",
    "print('happy_words: ', happy_words)\n",
    "print('sad_words: ', sad_words)\n",
    "print('fear_words: ', fear_words)\n",
    "\n",
    "target_words = angry_words + happy_words + sad_words + fear_words\n",
    "print('\\ntarget words: ')\n",
    "print(target_words)\n",
    "\n",
    "print('\\ncolor list:')\n",
    "cn = topn\n",
    "color = ['b'] * cn + ['g'] * cn + ['r'] * cn + ['y'] * cn\n",
    "print(color)\n",
    "\n",
    "\n",
    "## w2v model\n",
    "model = w2v_google_model\n",
    "\n",
    "## prepare training word vectors\n",
    "size = 200\n",
    "target_size = len(target_words)\n",
    "all_word = list(model.vocab.keys())\n",
    "word_train = target_words + all_word[:size]\n",
    "X_train = model[word_train]\n",
    "\n",
    "## t-SNE model\n",
    "tsne = TSNE(n_components=2, metric='cosine', random_state=28)\n",
    "\n",
    "## training\n",
    "X_tsne = tsne.fit_transform(X_train)\n",
    "\n",
    "## plot the result\n",
    "plt.figure(figsize=(7.5, 7.5), dpi=115)\n",
    "plt.scatter(X_tsne[:target_size, 0], X_tsne[:target_size, 1], c=color)\n",
    "for label, x, y in zip(target_words, X_tsne[:target_size, 0], X_tsne[:target_size, 1]):\n",
    "    plt.annotate(label, xy=(x,y), xytext=(0,0),  textcoords='offset points')\n",
    "plt.show()"
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