From d5df4b16287b920893b8f7fb4371ec530f68bf18 Mon Sep 17 00:00:00 2001 From: Ricky Macharm Date: Wed, 19 Apr 2023 18:00:01 +0200 Subject: [PATCH] some alterations to my_image_export --- .../EffectiveXGBoost-checkpoint.py | 61 +- .ipynb_checkpoints/Untitled-checkpoint.ipynb | 6 + EffectiveXGBoost.py | 61 +- Source.gv | 16 + Source.gv.pdf | Bin 0 -> 15951 bytes Untitled.ipynb | 396 ++++++++++ __pycache__/EffectiveXGBoost.cpython-39.pyc | Bin 7484 -> 9455 bytes chp11_XGBoostHyperparameters.ipynb | 71 +- chp4_TreeCreation.ipynb | 613 +-------------- chp5_StumpsOnRealData.ipynb | 203 +++-- chp8_RandomForest.ipynb | 712 +++++++++--------- chp9_XGBoost.ipynb | 300 ++++---- img/xg_depth004_tree0.dot | 28 + img/xg_depth004_tree0.png | Bin 0 -> 151476 bytes img/xg_depth4_tree0.dot | 28 + img/xg_depth4_tree0.png | Bin 0 -> 151476 bytes 16 files changed, 1245 insertions(+), 1250 deletions(-) create mode 100644 .ipynb_checkpoints/Untitled-checkpoint.ipynb create mode 100644 Source.gv create mode 100644 Source.gv.pdf create mode 100644 Untitled.ipynb create mode 100644 img/xg_depth004_tree0.dot create mode 100644 img/xg_depth004_tree0.png create mode 100644 img/xg_depth4_tree0.dot create mode 100644 img/xg_depth4_tree0.png diff --git a/.ipynb_checkpoints/EffectiveXGBoost-checkpoint.py b/.ipynb_checkpoints/EffectiveXGBoost-checkpoint.py index 3f40ed1..69b311c 100644 --- a/.ipynb_checkpoints/EffectiveXGBoost-checkpoint.py +++ b/.ipynb_checkpoints/EffectiveXGBoost-checkpoint.py @@ -11,6 +11,8 @@ import urllib.request import zipfile +from IPython.display import Image, display + from feature_engine import encoding, imputation from sklearn import base, pipeline @@ -245,4 +247,61 @@ def inv_logit(p: float) -> float: float The output of the inverse logit function. """ - return np.exp(p) / (1 + np.exp(p)) \ No newline at end of file + return np.exp(p) / (1 + np.exp(p)) + +def my_image_export(model, n_trees, filename, title='', direction='TB'): + """ + Export a specified number of trees from an XGBoost model as a graph + visualization in dot and png formats. + + Parameters: + ----------- + model : xgboost.core.Booster + The XGBoost model to visualize. + n_trees : int + The number of trees to export. + filename : str + The name of the file to save the exported visualization. + title : str, optional + The title to display on the graph visualization. + direction : str, optional + The direction to lay out the graph. Valid values are 'TB' (top to bottom) + and 'LR' (left to right). + + Returns: + -------- + None + + Notes: + ------ + This function generates a dot file containing the graph visualization of the + specified number of trees from the model. It then modifies the dot file to add + a title and set the direction of the graph layout. The modified dot file is saved + to disk as a dot file and a png file. The png file has the same name as the dot file + but with the '.png' extension. + + Example: + -------- + >>> import xgboost as xgb + >>> model = xgb.train(params, dtrain) + >>> my_dot_export(model, n_trees=2, filename='mytree', title='My Tree Visualization', direction='LR') + + This example exports the first two trees from the specified XGBoost model as a + graph visualization with the title 'My Tree Visualization' and a left-to-right + layout. The visualization is saved to disk as 'mytree.dot' and 'mytree.png'. + """ + res = xgb.to_graphviz(model, num_trees=n_trees) + content = f''' node [fontname = "Roboto Condensed"]; + edge [fontname = "Roboto Thin"]; + label = "{title}" + fontname = "Roboto Condensed" + ''' + out = res.source.replace('graph [ rankdir=TB ]', + f'graph [ rankdir={direction} ];\n {content}') + # dot -Gdpi=300 -Tpng -ocourseflow.png courseflow.dot + dot_filename = filename + with open(dot_filename, 'w') as f: + f.write(out) + png_filename = dot_filename.replace('.dot', '.png') + subprocess.run(f'dot -Gdpi=300 -Tpng -o{png_filename} {dot_filename}'.split()) + display(Image(filename=png_filename)) diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/EffectiveXGBoost.py b/EffectiveXGBoost.py index 3f40ed1..69b311c 100644 --- a/EffectiveXGBoost.py +++ b/EffectiveXGBoost.py @@ -11,6 +11,8 @@ import urllib.request import zipfile +from IPython.display import Image, display + from feature_engine import encoding, imputation from sklearn import base, pipeline @@ -245,4 +247,61 @@ def inv_logit(p: float) -> float: float The output of the inverse logit function. """ - return np.exp(p) / (1 + np.exp(p)) \ No newline at end of file + return np.exp(p) / (1 + np.exp(p)) + +def my_image_export(model, n_trees, filename, title='', direction='TB'): + """ + Export a specified number of trees from an XGBoost model as a graph + visualization in dot and png formats. + + Parameters: + ----------- + model : xgboost.core.Booster + The XGBoost model to visualize. + n_trees : int + The number of trees to export. + filename : str + The name of the file to save the exported visualization. + title : str, optional + The title to display on the graph visualization. + direction : str, optional + The direction to lay out the graph. Valid values are 'TB' (top to bottom) + and 'LR' (left to right). + + Returns: + -------- + None + + Notes: + ------ + This function generates a dot file containing the graph visualization of the + specified number of trees from the model. It then modifies the dot file to add + a title and set the direction of the graph layout. The modified dot file is saved + to disk as a dot file and a png file. The png file has the same name as the dot file + but with the '.png' extension. + + Example: + -------- + >>> import xgboost as xgb + >>> model = xgb.train(params, dtrain) + >>> my_dot_export(model, n_trees=2, filename='mytree', title='My Tree Visualization', direction='LR') + + This example exports the first two trees from the specified XGBoost model as a + graph visualization with the title 'My Tree Visualization' and a left-to-right + layout. The visualization is saved to disk as 'mytree.dot' and 'mytree.png'. + """ + res = xgb.to_graphviz(model, num_trees=n_trees) + content = f''' node [fontname = "Roboto Condensed"]; + edge [fontname = "Roboto Thin"]; + label = "{title}" + fontname = "Roboto Condensed" + ''' + out = res.source.replace('graph [ rankdir=TB ]', + f'graph [ rankdir={direction} ];\n {content}') + # dot -Gdpi=300 -Tpng -ocourseflow.png courseflow.dot + dot_filename = filename + with open(dot_filename, 'w') as f: + f.write(out) + png_filename = dot_filename.replace('.dot', '.png') + subprocess.run(f'dot -Gdpi=300 -Tpng -o{png_filename} {dot_filename}'.split()) + display(Image(filename=png_filename)) diff --git a/Source.gv b/Source.gv new file mode 100644 index 0000000..e92f5e2 --- /dev/null +++ b/Source.gv @@ -0,0 +1,16 @@ +digraph { + graph [ rankdir=TB ]; + node [fontname = "Roboto Condensed"]; + edge [fontname = "Roboto Thin"]; + label = "XGBoost Stump" + fontname = "Roboto Condensed" + + + 0 [ label="r<0.5" 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+ "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "d349e04c-b737-432f-ad40-fbe1d97c3d4e", + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "from IPython.display import Image, display\n", + "\n", + "def my_image_export(model, n_trees, filename, title='', direction='TB'):\n", + " \"\"\"Exports a specified number of trees from an XGBoost model as a graph\n", + " visualization in dot and png formats.\n", + " Parameters:\n", + " -----------\n", + " model: \n", + " An XGBoost model.\n", + " n_trees: \n", + " The number of tree to export.\n", + " filename: \n", + " The name of the file to save the exported visualization.\n", + " title: \n", + " The title to display on the graph visualization (optional).\n", + " direction: \n", + " The direction to lay out the graph, either 'TB' (top to\n", + " bottom) or 'LR' (left to right) (optional).\n", + " \"\"\"\n", + " res = xgb.to_graphviz(model, num_trees=n_trees)\n", + " content = f''' node [fontname = \"Roboto Condensed\"];\n", + " edge [fontname = \"Roboto Thin\"];\n", + " label = \"{title}\"\n", + " fontname = \"Roboto Condensed\"\n", + " '''\n", + " out = res.source.replace('graph [ rankdir=TB ]',\n", + " f'graph [ rankdir={direction} ];\\n {content}')\n", + " # dot -Gdpi=300 -Tpng -ocourseflow.png courseflow.dot\n", + " dot_filename = filename\n", + " with open(dot_filename, 'w') as f:\n", + " f.write(out)\n", + " png_filename = dot_filename.replace('.dot', '.png')\n", + " subprocess.run(f'dot -Gdpi=300 -Tpng -o{png_filename} {dot_filename}'.split())\n", + " display(Image(filename=png_filename))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d9bb2434-818d-44e5-9f97-7bfee28c3905", + "metadata": {}, + "outputs": [], + "source": [ + "import dtreeviz\n", + "from feature_engine import encoding, imputation\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import base, compose, datasets, ensemble, metrics, model_selection, pipeline, preprocessing, tree\n", + "import scikitplot\n", + "import xgboost as xgb\n", + "import yellowbrick.model_selection as ms\n", + "from yellowbrick import classifier\n", + "import urllib\n", + "import zipfile\n", + "\n", + "from EffectiveXGBoost import *" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "687b3216-667c-4216-ab60-56b893cb2468", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "G:\\My Drive\\SisengAI\\AlgorithmicTrading\\code_rebuilding\\MattHarrison\\EffectiveXGBoost\\EffectiveXGBoost_MyTake\\EffectiveXGBoost.py:56: DtypeWarning: Columns (0,2,8,10,21,23,24,25,26,27,28,44,56,64,83,85,87,107,109,123,125,150,157,172,174,194,210,218,219,223,246,249,262,264,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,304,306,325,326,329,341,368,371,384,385,389,390,391,393,394) have mixed types. Specify dtype option on import or set low_memory=False.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'objective': 'binary:logistic',\n", + " 'use_label_encoder': False,\n", + " 'base_score': 0.5,\n", + " 'booster': 'gbtree',\n", + " 'callbacks': None,\n", + " 'colsample_bylevel': 1,\n", + " 'colsample_bynode': 1,\n", + " 'colsample_bytree': 1,\n", + " 'early_stopping_rounds': None,\n", + " 'enable_categorical': False,\n", + " 'eval_metric': None,\n", + " 'gamma': 0,\n", + " 'gpu_id': -1,\n", + " 'grow_policy': 'depthwise',\n", + " 'importance_type': None,\n", + " 'interaction_constraints': '',\n", + " 'learning_rate': 0.300000012,\n", + " 'max_bin': 256,\n", + " 'max_cat_to_onehot': 4,\n", + " 'max_delta_step': 0,\n", + " 'max_depth': 6,\n", + " 'max_leaves': 0,\n", + " 'min_child_weight': 1,\n", + " 'missing': nan,\n", + " 'monotone_constraints': '()',\n", + " 'n_estimators': 100,\n", + " 'n_jobs': 0,\n", + " 'num_parallel_tree': 1,\n", + " 'predictor': 'auto',\n", + " 'random_state': 0,\n", + " 'reg_alpha': 0,\n", + " 'reg_lambda': 1,\n", + " 'sampling_method': 'uniform',\n", + " 'scale_pos_weight': 1,\n", + " 'subsample': 1,\n", + " 'tree_method': 'exact',\n", + " 'validate_parameters': 1,\n", + " 'verbosity': None}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path_url = 'https://github.com/mattharrison/datasets/raw/master/data/kaggle-survey-2018.zip'\n", + "file_name = 'kaggle-survey-2018.zip'\n", + "dataset = 'multipleChoiceResponses.csv'\n", + "\n", + "raw = extract_dataset(path_url, file_name, dataset)\n", + "\n", + "# Create raw X and raw y\n", + "kag_X, kag_y = prepX_y(raw, 'Q6')\n", + "\n", + "# Split data\n", + "kag_X_train, kag_X_test, kag_y_train, kag_y_test = (model_selection\n", + " .train_test_split(kag_X, \n", + " kag_y, \n", + " test_size=.3, \n", + " random_state=42, \n", + " stratify=kag_y)\n", + " )\n", + "\n", + "\n", + "# Transform X with pipeline\n", + "pline = pipeline.Pipeline(\n", + " [('tweak', PrepDataTransformer()),\n", + " ('cat', encoding.OneHotEncoder(top_categories=5, drop_last=True,\n", + " variables=['Q1', 'Q3', 'major'])),\n", + " ('num_impute', imputation.MeanMedianImputer(imputation_method='median',\n", + " variables=['education', 'years_exp']))]\n", + " )\n", + "\n", + "X_train = pline.fit_transform(kag_X_train)\n", + "X_test = pline.transform(kag_X_test)\n", + "\n", + "# Transform y with label encoder\n", + "label_encoder = preprocessing.LabelEncoder()\n", + "label_encoder.fit(kag_y_train)\n", + "y_train = label_encoder.transform(kag_y_train)\n", + "y_test = label_encoder.transform(kag_y_test)\n", + "\n", + "# Combined Data for cross validation/etc\n", + "X = pd.concat([X_train, X_test], axis='index')\n", + "y = pd.Series([*y_train, *y_test], index=X.index)\n", + "\n", + "# Default training\n", + "xg = xgb.XGBClassifier()\n", + "xg.fit(X_train, y_train)\n", + "xg.get_params()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2aeb7a45-2d17-4492-ba2a-95a07e51c9dd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
ValidationCurve(ax=<AxesSubplot:title={'center':'Validation Curve for XGBClassifier'}, xlabel='gamma', ylabel='score'>,\n",
+       "                estimator=XGBClassifier(base_score=None, booster=None,\n",
+       "                                        callbacks=None, colsample_bylevel=None,\n",
+       "                                        colsample_bynode=None,\n",
+       "                                        colsample_bytree=None,\n",
+       "                                        early_stopping_rounds=None,\n",
+       "                                        enable_categorical=False,\n",
+       "                                        eval_metric=None, gamma=None,\n",
+       "                                        gpu_id=None, grow_poli...\n",
+       "                                        learning_rate=None, max_bin=None,\n",
+       "                                        max_cat_to_onehot=None,\n",
+       "                                        max_delta_step=None, max_depth=None,\n",
+       "                                        max_leaves=None, min_child_weight=None,\n",
+       "                                        missing=nan, monotone_constraints=None,\n",
+       "                                        n_estimators=100, n_jobs=None,\n",
+       "                                        num_parallel_tree=None, predictor=None,\n",
+       "                                        random_state=None, reg_alpha=None,\n",
+       "                                        reg_lambda=None, ...),\n",
+       "                n_jobs=-1, param_name='gamma',\n",
+       "                param_range=array([ 0. ,  0.5,  1. ,  5. , 10. , 20. , 30. ]))
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "ValidationCurve(ax=,\n", + " estimator=XGBClassifier(base_score=None, booster=None,\n", + " callbacks=None, colsample_bylevel=None,\n", + " colsample_bynode=None,\n", + " colsample_bytree=None,\n", + " early_stopping_rounds=None,\n", + " enable_categorical=False,\n", + " eval_metric=None, gamma=None,\n", + " gpu_id=None, grow_poli...\n", + " learning_rate=None, max_bin=None,\n", + " max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None,\n", + " max_leaves=None, min_child_weight=None,\n", + " missing=nan, monotone_constraints=None,\n", + " n_estimators=100, n_jobs=None,\n", + " num_parallel_tree=None, predictor=None,\n", + " random_state=None, reg_alpha=None,\n", + " reg_lambda=None, ...),\n", + " n_jobs=-1, param_name='gamma',\n", + " param_range=array([ 0. , 0.5, 1. , 5. , 10. , 20. , 30. ]))" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 7))\n", + "ms.validation_curve(xgb.XGBClassifier(), X_train, y_train, param_name='gamma', param_range=[0, .5, 1,5,10, 20, 30], n_jobs=-1, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e452eac9-66a5-468c-a877-f81759585fd0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
+       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
+       "              early_stopping_rounds=None, enable_categorical=False,\n",
+       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
+       "              importance_type=None, interaction_constraints='', learning_rate=1,\n",
+       "              max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=2,\n",
+       "              max_leaves=0, min_child_weight=1, missing=nan,\n",
+       "              monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
+       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
+       "              reg_alpha=0, reg_lambda=1, ...)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n", + " colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n", + " early_stopping_rounds=None, enable_categorical=False,\n", + " eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n", + " importance_type=None, interaction_constraints='', learning_rate=1,\n", + " max_bin=256, max_cat_to_onehot=4, max_delta_step=0, max_depth=2,\n", + " max_leaves=0, min_child_weight=1, missing=nan,\n", + " monotone_constraints='()', n_estimators=100, n_jobs=0,\n", + " num_parallel_tree=1, predictor='auto', random_state=0,\n", + " reg_alpha=0, reg_lambda=1, ...)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check impact of learning weight on scores\n", + "xg_lr1 = xgb.XGBClassifier(learning_rate=1, max_depth=2)\n", + "xg_lr1.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7d1c6e88-0e85-489c-8ea4-122dd695fa9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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(0,2,8,10,21,23,24,25,26,27,28,44,56,64,83,85,87,107,109,123,125,150,157,172,174,194,210,218,219,223,246,249,262,264,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,304,306,325,326,329,341,368,371,384,385,389,390,391,393,394) have mixed types. Specify dtype option on import or set low_memory=False.\n" + "G:\\My Drive\\SisengAI\\AlgorithmicTrading\\code_rebuilding\\MattHarrison\\EffectiveXGBoost\\EffectiveXGBoost_MyTake\\EffectiveXGBoost.py:58: DtypeWarning: Columns (0,2,8,10,21,23,24,25,26,27,28,44,56,64,83,85,87,107,109,123,125,150,157,172,174,194,210,218,219,223,246,249,262,264,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,304,306,325,326,329,341,368,371,384,385,389,390,391,393,394) have mixed types. Specify dtype option on import or set low_memory=False.\n" ] }, { @@ -107,7 +107,7 @@ " 'verbosity': None}" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "79344be9-dd6e-4ee6-bbf2-426d54ff9e4c", "metadata": {}, "outputs": [ @@ -189,7 +189,7 @@ { "data": { "text/html": [ - "
ValidationCurve(ax=<AxesSubplot:title={'center':'Validation Curve for XGBClassifier'}, xlabel='gamma', ylabel='score'>,\n",
+       "
ValidationCurve(ax=<AxesSubplot:title={'center':'Validation Curve for XGBClassifier'}, xlabel='gamma', ylabel='score'>,\n",
        "                estimator=XGBClassifier(base_score=None, booster=None,\n",
        "                                        callbacks=None, colsample_bylevel=None,\n",
        "                                        colsample_bynode=None,\n",
@@ -208,7 +208,7 @@
        "                                        random_state=None, reg_alpha=None,\n",
        "                                        reg_lambda=None, ...),\n",
        "                n_jobs=-1, param_name='gamma',\n",
-       "                param_range=array([ 0. ,  0.5,  1. ,  5. , 10. , 20. , 30. ]))
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