Code-Example-021-Bagging-and-Boosting-Methods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging and Boosting\n",
    "Study this Tutorial \n",
    "https://scikit-learn.org/stable/modules/ensemble.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.stats import norm\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "from random import sample\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Some Example Data with 2 Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_feature = 2 # the number of features\n",
    "n_components = 2 # the number of clusters\n",
    "\n",
    "n = 1000 # the number of total samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.85481625,  0.26603657,  0.46247165, ...,  0.73449007,\n",
       "        -0.76130474, -0.07198028],\n",
       "       [-0.9126137 ,  0.10747966, -0.61044301, ...,  1.66951783,\n",
       "         1.07056042,  2.41455708],\n",
       "       [ 1.08141922, -0.57487904,  0.30069666, ..., -0.49893332,\n",
       "        -0.6517423 , -0.62635633],\n",
       "       ...,\n",
       "       [-1.1390147 ,  0.16402807,  2.87545616, ..., -0.98889391,\n",
       "        -0.21026866,  0.04611526],\n",
       "       [ 0.15397659,  1.14186448,  0.05271537, ..., -0.09555256,\n",
       "        -0.12484183,  0.96495438],\n",
       "       [-1.70722693, -0.11692937,  1.60531813, ...,  0.44446011,\n",
       "         0.29918428,  0.9359656 ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create random example data\n",
    "X, y = make_classification(n_samples=n, class_sep=0.5, random_state=123)\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot, data points annotated by different colors\n",
    "df = pd.DataFrame(dict(feature_1=X[:,0], feature_2=X[:,1], label=y))\n",
    "\n",
    "\n",
    "cluster_name = set(y)\n",
    "colors = dict(zip(cluster_name, cm.rainbow(np.linspace(0, 1, len(cluster_name)))))\n",
    "fig, ax = pyplot.subplots()\n",
    "\n",
    "grouped = df.groupby('label')\n",
    "\n",
    "for key, group in grouped:\n",
    "    group.plot(ax=ax, kind='scatter', x='feature_1', y='feature_2', label=key, color=colors[key].reshape(1,-1))\n",
    "    \n",
    "pyplot.title('Original 2D Data from {} Clusters'.format(n_components))\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Test/Train\n",
    "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=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple KNeighborsClassifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.645\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.94      0.72        98\n",
      "           1       0.86      0.36      0.51       102\n",
      "\n",
      "    accuracy                           0.65       200\n",
      "   macro avg       0.72      0.65      0.62       200\n",
      "weighted avg       0.73      0.65      0.61       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "predictions= model.predict(X_test)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BaggingClassifier using kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.665\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.89      0.72        98\n",
      "           1       0.81      0.45      0.58       102\n",
      "\n",
      "    accuracy                           0.67       200\n",
      "   macro avg       0.71      0.67      0.65       200\n",
      "weighted avg       0.71      0.67      0.65       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "bagging = BaggingClassifier(KNeighborsClassifier(n_neighbors=4),max_samples=0.5, max_features=0.5)\n",
    "\n",
    "\n",
    "bagging.fit(X_train, y_train)\n",
    "predictions= bagging.predict(X_test)\n",
    "y_pred = bagging.predict(X_test)\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forests "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.92\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.92      0.92        98\n",
      "           1       0.92      0.92      0.92       102\n",
      "\n",
      "    accuracy                           0.92       200\n",
      "   macro avg       0.92      0.92      0.92       200\n",
      "weighted avg       0.92      0.92      0.92       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=10)\n",
    "\n",
    "# fit and check the results\n",
    "model.fit(X_train, y_train)\n",
    "predictions= model.predict(X_test)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Accuracy Score:\", accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.80 (+/- 0.03) [Logistic Regression]\n",
      "Accuracy: 0.92 (+/- 0.02) [Random Forest]\n",
      "Accuracy: 0.89 (+/- 0.02) [naive Bayes]\n",
      "Accuracy: 0.89 (+/- 0.02) [Ensemble]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "clf1 = LogisticRegression(random_state=1)\n",
    "clf2 = RandomForestClassifier(n_estimators=10, random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "\n",
    "eclf = VotingClassifier(\n",
    "    estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],\n",
    "    voting='hard')\n",
    "\n",
    "for clf, label in zip([clf1, clf2, clf3, eclf], ['Logistic Regression', 'Random Forest', 'naive Bayes', 'Ensemble']):\n",
    "    scores = cross_val_score(clf, X, y, scoring='accuracy', cv=5)\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boosting - AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.931"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "clf = AdaBoostClassifier(n_estimators=100, algorithm=\"SAMME\")\n",
    "scores = cross_val_score(clf, X, y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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