Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('testX.csv')\n",
    "train_x = train.drop(['Id', 'Label'], axis = 1)\n",
    "train_y = train['Label']\n",
    "\n",
    "test = test.drop('Id', axis = 1)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(train_x, train_y, test_size=0.1, random_state=42)\n",
    "\n",
    "scalar = StandardScaler(with_std = False)\n",
    "scalar.fit(X_train)\n",
    "train_scaled = scalar.transform(X_train)\n",
    "val_scaled = scalar.transform(X_val)\n",
    "test_scaled = scalar.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "pca = PCA(random_state = 42)\n",
    "pca.fit(train_scaled)\n",
    "X_train_transformed  = pca.transform(train_scaled)\n",
    "X_val_transformed = pca.transform(val_scaled)\n",
    "X_test_transformed = pca.transform(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83\n",
      "90.2344871977032\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "variance = pca.explained_variance_ratio_.cumsum()\n",
    "for i in range(0,len(variance)):\n",
    "  if(variance[i] > 0.90):\n",
    "    print(i)\n",
    "    break;\n",
    "\n",
    "print('Variance explained by 85 components: ', variance[85]*100)\n",
    "\n",
    "plt.figure(figsize = (11,6))\n",
    "plt.plot(np.arange(0, 784), variance*100)\n",
    "plt.plot(85, variance[85]*100, 'ro', ms = 15)\n",
    "plt.xlabel('number of components', fontsize = 15)\n",
    "plt.ylabel('cumulative explained variance', fontsize = 15)\n",
    "plt.title('Scree Plot', fontsize = 15)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (11,6))\n",
    "plt.plot(np.arange(0, 25), pca.explained_variance_ratio_[:25])\n",
    "plt.xlabel('Total Components')\n",
    "plt.title('Scree plot')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest\n",
    "accuracy_list = []\n",
    "time_list = []\n",
    "def rfclass(d, n):\n",
    "    clf = RandomForestClassifier(max_depth = d, n_estimators = n, random_state = 42)\n",
    "    start = time.time()\n",
    "    clf.fit(X_train_transformed[:,:85], y_train)\n",
    "    end = time.time()\n",
    "    y_pred = clf.predict(X_val_transformed[:,:85])\n",
    "    accuracy = accuracy_score(y_val, y_pred)\n",
    "    #print(accuracy)\n",
    "    #print(end-start)\n",
    "    return (accuracy, end-start) \n",
    "\n",
    "max_depth = [3,5,7,9,11]\n",
    "n_estimators = [50,100,150,200,250]\n",
    "\n",
    "a_d = {}\n",
    "t_d = {}\n",
    "for depth in max_depth:\n",
    "    accuracy_list = []\n",
    "    time_list = []\n",
    "    for n_e in n_estimators:\n",
    "        a,t = rfclass(depth, n_e)\n",
    "        accuracy_list.append(a)\n",
    "        time_list.append(t)\n",
    "    a_d[depth] = accuracy_list\n",
    "    t_d[depth] = time_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.601833</td>\n",
       "      <td>0.691000</td>\n",
       "      <td>0.747667</td>\n",
       "      <td>0.777667</td>\n",
       "      <td>0.810833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.613833</td>\n",
       "      <td>0.689000</td>\n",
       "      <td>0.748500</td>\n",
       "      <td>0.773167</td>\n",
       "      <td>0.812333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.623833</td>\n",
       "      <td>0.691500</td>\n",
       "      <td>0.748667</td>\n",
       "      <td>0.775167</td>\n",
       "      <td>0.812833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.630833</td>\n",
       "      <td>0.695333</td>\n",
       "      <td>0.749167</td>\n",
       "      <td>0.776333</td>\n",
       "      <td>0.814833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.629500</td>\n",
       "      <td>0.696333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.777167</td>\n",
       "      <td>0.813833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         3         5         7         9         11\n",
       "0  0.601833  0.691000  0.747667  0.777667  0.810833\n",
       "1  0.613833  0.689000  0.748500  0.773167  0.812333\n",
       "2  0.623833  0.691500  0.748667  0.775167  0.812833\n",
       "3  0.630833  0.695333  0.749167  0.776333  0.814833\n",
       "4  0.629500  0.696333  0.750000  0.777167  0.813833"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(a_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>9</th>\n",
       "      <th>11</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.547820</td>\n",
       "      <td>11.244939</td>\n",
       "      <td>14.074400</td>\n",
       "      <td>17.218939</td>\n",
       "      <td>20.418440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.276513</td>\n",
       "      <td>23.567022</td>\n",
       "      <td>29.449270</td>\n",
       "      <td>34.402002</td>\n",
       "      <td>46.117820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.636871</td>\n",
       "      <td>34.818941</td>\n",
       "      <td>42.455525</td>\n",
       "      <td>51.724692</td>\n",
       "      <td>66.284755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28.731162</td>\n",
       "      <td>43.071878</td>\n",
       "      <td>58.586375</td>\n",
       "      <td>69.785476</td>\n",
       "      <td>89.346154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.029027</td>\n",
       "      <td>56.805439</td>\n",
       "      <td>70.495510</td>\n",
       "      <td>87.800274</td>\n",
       "      <td>110.330015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          3          5          7          9           11\n",
       "0   7.547820  11.244939  14.074400  17.218939   20.418440\n",
       "1  16.276513  23.567022  29.449270  34.402002   46.117820\n",
       "2  20.636871  34.818941  42.455525  51.724692   66.284755\n",
       "3  28.731162  43.071878  58.586375  69.785476   89.346154\n",
       "4  34.029027  56.805439  70.495510  87.800274  110.330015"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(t_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time:  12.093667984008789\n",
      "Training Accuracy:  0.9557962962962963\n",
      "Testing Time:  0.059839487075805664\n",
      "Validation Accuracy:  0.863\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth = 15, n_estimators = 50, random_state = 42)\n",
    "start = time.time()\n",
    "clf.fit(X_train_transformed[:,:23], y_train)\n",
    "end = time.time()\n",
    "y_t_pred = clf.predict(X_train_transformed[:,:23]) \n",
    "training_accuracy = accuracy_score(y_train, y_t_pred)\n",
    "print('Training Time: ', end-start)\n",
    "print('Training Accuracy: ', training_accuracy)\n",
    "\n",
    "start = time.time()\n",
    "y_pred = clf.predict(X_val_transformed[:,:23])\n",
    "end = time.time()\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print('Testing Time: ', end-start)\n",
    "print('Validation Accuracy: ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time:  12.730020999908447\n",
      "Training Accuracy:  0.9999444444444444\n",
      "Testing Time:  0.07480001449584961\n",
      "Validation Accuracy:  0.8735\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth = 30, n_estimators = 50, random_state = 42)\n",
    "start = time.time()\n",
    "clf.fit(X_train_transformed[:,:23], y_train)\n",
    "end = time.time()\n",
    "y_t_pred = clf.predict(X_train_transformed[:,:23]) \n",
    "training_accuracy = accuracy_score(y_train, y_t_pred)\n",
    "print('Training Time: ', end-start)\n",
    "print('Training Accuracy: ', training_accuracy)\n",
    "\n",
    "start = time.time()\n",
    "y_pred = clf.predict(X_val_transformed[:,:23])\n",
    "end = time.time()\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print('Testing Time: ', end-start)\n",
    "print('Validation Accuracy: ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time:  52.23238825798035\n",
      "Training Accuracy:  1.0\n",
      "Testing Time:  0.3251307010650635\n",
      "Validation Accuracy:  0.8781666666666667\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth = 30, n_estimators = 200, random_state = 42)\n",
    "start = time.time()\n",
    "clf.fit(X_train_transformed[:,:23], y_train)\n",
    "end = time.time()\n",
    "y_t_pred = clf.predict(X_train_transformed[:,:23]) \n",
    "training_accuracy = accuracy_score(y_train, y_t_pred)\n",
    "print('Training Time: ', end-start)\n",
    "print('Training Accuracy: ', training_accuracy)\n",
    "\n",
    "start = time.time()\n",
    "y_pred = clf.predict(X_val_transformed[:,:23])\n",
    "end = time.time()\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print('Testing Time: ', end-start)\n",
    "print('Validation Accuracy: ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time:  50.937859535217285\n",
      "Training Accuracy:  1.0\n",
      "Testing Time:  0.32111310958862305\n",
      "Validation Accuracy:  0.876\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(max_depth = 50, n_estimators = 200, random_state = 42)\n",
    "start = time.time()\n",
    "clf.fit(X_train_transformed[:,:23], y_train)\n",
    "end = time.time()\n",
    "y_t_pred = clf.predict(X_train_transformed[:,:23]) \n",
    "training_accuracy = accuracy_score(y_train, y_t_pred)\n",
    "print('Training Time: ', end-start)\n",
    "print('Training Accuracy: ', training_accuracy)\n",
    "\n",
    "start = time.time()\n",
    "y_pred = clf.predict(X_val_transformed[:,:23])\n",
    "end = time.time()\n",
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print('Testing Time: ', end-start)\n",
    "print('Validation Accuracy: ', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.878167\n",
      "Precision: 0.878167\n",
      "Recall: 0.878167\n",
      "F1 score: 0.878167\n",
      "Cohens kappa: 0.847692\n",
      "ROC AUC: 0.984929\n",
      "[[[4740   70]\n",
      "  [  74 1116]]\n",
      "\n",
      " [[4598  172]\n",
      "  [ 164 1066]]\n",
      "\n",
      " [[4611  207]\n",
      "  [ 225  957]]\n",
      "\n",
      " [[4649  176]\n",
      "  [ 169 1006]]\n",
      "\n",
      " [[4671  106]\n",
      "  [  99 1124]]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "import numpy as np\n",
    "\n",
    "yhat_classes = y_pred\n",
    "yhat_probs = clf.predict_proba(X_val_transformed[:,:23])\n",
    "\n",
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = accuracy_score(y_val, yhat_classes)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(y_val, yhat_classes, average = 'micro')\n",
    "print('Precision: %f' % precision)\n",
    "\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(y_val, yhat_classes, average = 'micro')\n",
    "print('Recall: %f' % recall)\n",
    "\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(y_val, yhat_classes, average = 'micro')\n",
    "print('F1 score: %f' % f1)\n",
    " \n",
    "# kappa\n",
    "kappa = cohen_kappa_score(y_val, yhat_classes)\n",
    "print('Cohens kappa: %f' % kappa)\n",
    "\n",
    "# ROC AUC\n",
    "auc = roc_auc_score(y_val, yhat_probs, multi_class = 'ovr')\n",
    "print('ROC AUC: %f' % auc)\n",
    "\n",
    "# Confusion Matrix\n",
    "matrix = multilabel_confusion_matrix(y_val, yhat_classes, labels = [0,1,2,3,4])\n",
    "print(matrix)\n",
    "\n",
    "#Reference:\n",
    "#https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "n_classes = 5\n",
    "\n",
    "y_val = label_binarize(y_val, classes = [0,1,2,3,4])\n",
    "y_pred = label_binarize(y_pred, classes = [0,1,2,3,4])\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_val[:, i], y_pred[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# Compute micro-average ROC curve and ROC area\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_val.ravel(), y_pred.ravel())\n",
    "roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "import itertools\n",
    "# First aggregate all false positive rates\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "# Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "# Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "fpr[\"macro\"] = all_fpr\n",
    "tpr[\"macro\"] = mean_tpr\n",
    "roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n",
    "\n",
    "# Plot all ROC curves\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"micro\"]),\n",
    "         color='deeppink', linestyle=':', linewidth=4)\n",
    "\n",
    "plt.plot(fpr[\"macro\"], tpr[\"macro\"],\n",
    "         label='macro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"macro\"]),\n",
    "         color='navy', linestyle=':', linewidth=4)\n",
    "\n",
    "colors = itertools.cycle(['aqua', 'darkorange', 'cornflowerblue'])\n",
    "for i, color in zip(range(n_classes), colors):\n",
    "    plt.plot(fpr[i], tpr[i], color=color, linewidth=4,\n",
    "             label='ROC curve of class {0} (area = {1:0.2f})'\n",
    "             ''.format(i, roc_auc[i]))\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Some extension of Receiver operating characteristic to multi-class')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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