{ "cells": [ { "cell_type": "markdown", "id": "5e959844", "metadata": {}, "source": [ "(chapter9_part1)=\n", "\n", "\n", "\n", "## Bagging Models\n", "\n", "- This is a supplement material for the [Machine Learning Simplified](https://themlsbook.com) book. It sheds light on Python implementations of the topics discussed while all detailed explanations can be found in the book. \n", "- I also assume you know Python syntax and how it works. If you don't, I highly recommend you to take a break and get introduced to the language before going forward with my code. \n", "- This material can be downloaded as a Jupyter notebook (Download button in the upper-right corner -> `.ipynb`) to reproduce the code and play around with it. \n", "\n", "\n", "This notebook is a supplement for *Chapter 9. Ensemble Models* of **Machine Learning For Everyone** book.\n", "\n", "## 1. Required Libraries, Data & Variables\n", "\n", "Let's import the data and have a look at it:" ] }, { "cell_type": "code", "execution_count": 1, "id": "c23451ec", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "data = {\n", " 'Day': list(range(1, 31)),\n", " 'Temperature': [\n", " 'Cold', 'Hot', 'Cold', 'Hot', 'Hot',\n", " 'Cold', 'Hot', 'Cold', 'Hot', 'Cold',\n", " 'Hot', 'Cold', 'Hot', 'Cold', 'Hot',\n", " 'Cold', 'Hot', 'Cold', 'Hot', 'Cold',\n", " 'Hot', 'Cold', 'Hot', 'Cold', 'Hot',\n", " 'Cold', 'Hot', 'Cold', 'Hot', 'Cold'\n", " ],\n", " 'Humidity': [\n", " 'Normal', 'Normal', 'Normal', 'High', 'High',\n", " 'Normal', 'High', 'Normal', 'High', 'Normal',\n", " 'High', 'Normal', 'High', 'Normal', 'High',\n", " 'Normal', 'High', 'Normal', 'High', 'Normal',\n", " 'High', 'Normal', 'High', 'Normal', 'High',\n", " 'Normal', 'High', 'Normal', 'High', 'Normal'\n", " ],\n", " 'Outlook': [\n", " 'Rain', 'Rain', 'Sunny', 'Sunny', 'Rain',\n", " 'Sunny', 'Rain', 'Sunny', 'Rain', 'Sunny',\n", " 'Rain', 'Sunny', 'Rain', 'Sunny', 'Rain',\n", " 'Sunny', 'Rain', 'Sunny', 'Rain', 'Sunny',\n", " 'Rain', 'Sunny', 'Rain', 'Sunny', 'Rain',\n", " 'Sunny', 'Rain', 'Sunny', 'Rain', 'Sunny'\n", " ],\n", " 'Wind': [\n", " 'Strong', 'Weak', 'Weak', 'Weak', 'Weak',\n", " 'Strong', 'Weak', 'Weak', 'Weak', 'Strong',\n", " 'Weak', 'Weak', 'Strong', 'Weak', 'Weak',\n", " 'Weak', 'Strong', 'Weak', 'Weak', 'Weak',\n", " 'Strong', 'Weak', 'Weak', 'Weak', 'Weak',\n", " 'Strong', 'Weak', 'Weak', 'Weak', 'Strong'\n", " ],\n", " 'Golf Played': [\n", " 'No', 'No', 'Yes', 'Yes', 'Yes',\n", " 'No', 'Yes', 'No', 'Yes', 'Yes',\n", " 'No', 'Yes', 'No', 'Yes', 'Yes',\n", " 'No', 'Yes', 'No', 'Yes', 'Yes',\n", " 'No', 'Yes', 'No', 'Yes', 'Yes',\n", " 'No', 'Yes', 'No', 'Yes', 'Yes'\n", " ]\n", "}\n", "\n", "# Converting the dictionary into a DataFrame\n", "df = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": 2, "id": "d7c1c4d4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DayTemperatureHumidityOutlookWindGolf Played
01ColdNormalRainStrongNo
12HotNormalRainWeakNo
23ColdNormalSunnyWeakYes
34HotHighSunnyWeakYes
45HotHighRainWeakYes
56ColdNormalSunnyStrongNo
67HotHighRainWeakYes
78ColdNormalSunnyWeakNo
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" ], "text/plain": [ " Day Temperature Humidity Outlook Wind Golf Played\n", "0 1 Cold Normal Rain Strong No\n", "1 2 Hot Normal Rain Weak No\n", "2 3 Cold Normal Sunny Weak Yes\n", "3 4 Hot High Sunny Weak Yes\n", "4 5 Hot High Rain Weak Yes\n", "5 6 Cold Normal Sunny Strong No\n", "6 7 Hot High Rain Weak Yes\n", "7 8 Cold Normal Sunny Weak No\n", "8 9 Hot High Rain Weak Yes\n", "9 10 Cold Normal Sunny Strong Yes" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Displaying the DataFrame\n", "df.head(10)" ] }, { "cell_type": "markdown", "id": "3714f7f1", "metadata": {}, "source": [ "## 2. Preparation of the Dataset\n", "\n", "One-hot encoding the categorical variables" ] }, { "cell_type": "code", "execution_count": 3, "id": "2ece4f7f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/andrewwolf/.pyenv/versions/3.10.7/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n", " warnings.warn(\n" ] } ], "source": [ "from sklearn.preprocessing import OneHotEncoder\n", "\n", "encoder = OneHotEncoder(sparse=False)\n", "encoded_features = encoder.fit_transform(df[['Temperature', 'Humidity', 'Outlook', 'Wind']])\n", "encoded_df = pd.DataFrame(encoded_features, columns=encoder.get_feature_names_out(['Temperature', 'Humidity', 'Outlook', 'Wind']))" ] }, { "cell_type": "markdown", "id": "21f25d5f", "metadata": {}, "source": [ "Visualizing the first 10 records of the encoded dataframe:" ] }, { "cell_type": "code", "execution_count": 4, "id": "478e00f0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DayTemperatureHumidityOutlookWindGolf PlayedTemperature_ColdTemperature_HotHumidity_HighHumidity_NormalOutlook_RainOutlook_SunnyWind_StrongWind_Weak
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45HotHighRainWeakYes0.01.01.00.01.00.00.01.0
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" ], "text/plain": [ " Day Temperature Humidity Outlook Wind Golf Played Temperature_Cold \\\n", "0 1 Cold Normal Rain Strong No 1.0 \n", "1 2 Hot Normal Rain Weak No 0.0 \n", "2 3 Cold Normal Sunny Weak Yes 1.0 \n", "3 4 Hot High Sunny Weak Yes 0.0 \n", "4 5 Hot High Rain Weak Yes 0.0 \n", "\n", " Temperature_Hot Humidity_High Humidity_Normal Outlook_Rain \\\n", "0 0.0 0.0 1.0 1.0 \n", "1 1.0 0.0 1.0 1.0 \n", "2 0.0 0.0 1.0 0.0 \n", "3 1.0 1.0 0.0 0.0 \n", "4 1.0 1.0 0.0 1.0 \n", "\n", " Outlook_Sunny Wind_Strong Wind_Weak \n", "0 0.0 1.0 0.0 \n", "1 0.0 0.0 1.0 \n", "2 1.0 0.0 1.0 \n", "3 1.0 0.0 1.0 \n", "4 0.0 0.0 1.0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.join(encoded_df)\n", "\n", "df.head(5)" ] }, { "cell_type": "markdown", "id": "c34c1ba8", "metadata": {}, "source": [ "Preparing the features by removing categorical variables." ] }, { "cell_type": "code", "execution_count": 6, "id": "3263541d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Temperature_ColdTemperature_HotHumidity_HighHumidity_NormalOutlook_RainOutlook_SunnyWind_StrongWind_Weak
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" ], "text/plain": [ " Temperature_Cold Temperature_Hot Humidity_High Humidity_Normal \\\n", "0 1.0 0.0 0.0 1.0 \n", "1 0.0 1.0 0.0 1.0 \n", "2 1.0 0.0 0.0 1.0 \n", "3 0.0 1.0 1.0 0.0 \n", "4 0.0 1.0 1.0 0.0 \n", "\n", " Outlook_Rain Outlook_Sunny Wind_Strong Wind_Weak \n", "0 1.0 0.0 1.0 0.0 \n", "1 1.0 0.0 0.0 1.0 \n", "2 0.0 1.0 0.0 1.0 \n", "3 0.0 1.0 0.0 1.0 \n", "4 1.0 0.0 0.0 1.0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = df.drop(['Day', 'Temperature', 'Humidity', 'Outlook', 'Wind', 'Golf Played'], axis=1)\n", "X.head(5)" ] }, { "cell_type": "markdown", "id": "52bd6b59", "metadata": {}, "source": [ "Defining y:" ] }, { "cell_type": "code", "execution_count": 7, "id": "ac05086c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 No\n", "1 No\n", "2 Yes\n", "3 Yes\n", "4 Yes\n", "5 No\n", "6 Yes\n", "7 No\n", "8 Yes\n", "9 Yes\n", "10 No\n", "11 Yes\n", "12 No\n", "13 Yes\n", "14 Yes\n", "15 No\n", "16 Yes\n", "17 No\n", "18 Yes\n", "19 Yes\n", "20 No\n", "21 Yes\n", "22 No\n", "23 Yes\n", "24 Yes\n", "25 No\n", "26 Yes\n", "27 No\n", "28 Yes\n", "29 Yes\n", "Name: Golf Played, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = df['Golf Played']\n", "\n", "y" ] }, { "cell_type": "markdown", "id": "bdd1a969", "metadata": {}, "source": [ "Splitting the dataset into training and testing sets" ] }, { "cell_type": "code", "execution_count": 8, "id": "c0e9d84a", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "markdown", "id": "a2197f47", "metadata": {}, "source": [ "## 3. Bagging Ensemble\n", "\n", "### 3.1. Building a Boosting Ensemble\n", "\n", "Creating the Gradient Boosting classifier" ] }, { "cell_type": "code", "execution_count": 9, "id": "f2e0d664", "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier" ] }, { "cell_type": "code", "execution_count": 10, "id": "bfe562c1", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'DecisionTreeClassifier' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn [10], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Creating the Bagging classifier\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Using a DecisionTreeClassifier as the base classifier\u001b[39;00m\n\u001b[1;32m 3\u001b[0m model \u001b[38;5;241m=\u001b[39m BaggingClassifier(\n\u001b[0;32m----> 4\u001b[0m base_estimator\u001b[38;5;241m=\u001b[39m\u001b[43mDecisionTreeClassifier\u001b[49m(), \n\u001b[1;32m 5\u001b[0m n_estimators\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, \u001b[38;5;66;03m# Number of trees\u001b[39;00m\n\u001b[1;32m 6\u001b[0m max_samples\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, \u001b[38;5;66;03m# Fraction of samples to draw from X to train each base estimator\u001b[39;00m\n\u001b[1;32m 7\u001b[0m max_features\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, \u001b[38;5;66;03m# Fraction of features to draw from X to train each base estimator\u001b[39;00m\n\u001b[1;32m 8\u001b[0m random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 10\u001b[0m model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n", "\u001b[0;31mNameError\u001b[0m: name 'DecisionTreeClassifier' is not defined" ] } ], "source": [ "# Creating the Bagging classifier\n", "# Using a DecisionTreeClassifier as the base classifier\n", "model = BaggingClassifier(\n", " base_estimator=DecisionTreeClassifier(), \n", " n_estimators=10, # Number of trees\n", " max_samples=0.8, # Fraction of samples to draw from X to train each base estimator\n", " max_features=0.8, # Fraction of features to draw from X to train each base estimator\n", " random_state=42\n", " )\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "id": "b68d36fb", "metadata": {}, "source": [ "### 3.2. Visualizing boosted ensemble" ] }, { "cell_type": "code", "execution_count": null, "id": "ba349ad7", "metadata": {}, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeClassifier, plot_tree" ] }, { "cell_type": "code", "execution_count": null, "id": "c9649614", "metadata": {}, "outputs": [], "source": [ "# Building 5 decision trees\n", "feature_names = encoder.get_feature_names_out(['Temperature', 'Humidity', 'Outlook', 'Wind'])\n", "trees = [DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=42 + i) for i in range(5)]\n", "for tree in trees:\n", " tree.fit(X_train, y_train)\n", "\n", "# Plotting all 5 trees\n", "fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 4), dpi=300)\n", "for i, tree in enumerate(trees):\n", " plot_tree(tree, feature_names=feature_names, class_names=['No', 'Yes'], filled=True, ax=axes[i])\n", " axes[i].set_title(f'Tree {i+1}')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d8b36e47", "metadata": {}, "source": [ "### 3.3. Predicting the Results\n", "\n", "Predicting the test set results" ] }, { "cell_type": "code", "execution_count": null, "id": "fdb4d46d", "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(X_test)" ] }, { "cell_type": "code", "execution_count": null, "id": "30b5c848", "metadata": {}, "outputs": [], "source": [ "y_pred" ] }, { "cell_type": "markdown", "id": "944a257e", "metadata": {}, "source": [ "### 3.4. Evaluating the model" ] }, { "cell_type": "code", "execution_count": null, "id": "16346dd0", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score, classification_report" ] }, { "cell_type": "code", "execution_count": null, "id": "11bf9725", "metadata": {}, "outputs": [], "source": [ "accuracy = accuracy_score(y_test, y_pred)\n", "report = classification_report(y_test, y_pred)\n", "\n", "print(\"Accuracy:\", accuracy)\n", "print(\"Classification Report:\\n\", report)" ] }, { "cell_type": "markdown", "id": "c5740b49", "metadata": {}, "source": [ "## 4. Random Forest Classifier\n", "\n", "### 4.1. Building a Boosting Ensemble\n", "\n", "Creating the Random Forest Classifier" ] }, { "cell_type": "code", "execution_count": null, "id": "14fc483f", "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": null, "id": "16dd1014", "metadata": {}, "outputs": [], "source": [ "random_forest = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=3, random_state=42)\n", "\n", "random_forest.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "id": "0cce2575", "metadata": {}, "source": [ "### 4.2. Predicting the Results" ] }, { "cell_type": "code", "execution_count": null, "id": "f95b2cb8", "metadata": {}, "outputs": [], "source": [ "# Making predictions on the test set\n", "y_pred = random_forest.predict(X_test)\n", "\n", "y_pred" ] }, { "cell_type": "markdown", "id": "a09cb020", "metadata": {}, "source": [ "### 4.3. Evaluating the model" ] }, { "cell_type": "code", "execution_count": null, "id": "435bccda", "metadata": {}, "outputs": [], "source": [ "# Evaluating the model\n", "accuracy = accuracy_score(y_test, y_pred)\n", "report = classification_report(y_test, y_pred)\n", "\n", "print(\"Accuracy:\", accuracy)\n", "print(\"Classification Report:\\n\", report)" ] } ], "metadata": { "jupytext": { "formats": "md:myst", "text_representation": { "extension": ".md", "format_name": "myst" } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.7" }, "source_map": [ 11, 31, 83, 86, 93, 99, 104, 106, 111, 115, 120, 123, 128, 132, 137, 141, 150, 155, 166, 171, 176, 191, 198, 203, 205, 210, 215, 221, 230, 235, 239, 244, 249, 254 ] }, "nbformat": 4, "nbformat_minor": 5 }