RForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = make_classification(n_samples=1000,n_features = 6,n_informative=3,n_classes=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF = RandomForestClassifier(max_depth=16,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "RF = RF.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.06090509,  0.13015767,  0.18728705,  0.22498857,  0.2954665 ,\n",
       "        0.10119513])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RF.predict([ 0.96131158,  0.98905803, -2.58661193,  1.68342564,  0.40663442,\n",
    "#         0.20191016])\n",
    "RF.predict(X[4:6,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[246,   0,   2,   0],\n",
       "       [  1, 249,   0,   0],\n",
       "       [  2,   1, 251,   1],\n",
       "       [  0,   4,   3, 240]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = RF.predict(X)\n",
    "cm = confusion_matrix(Y,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3834cb9b70>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DfCVSt14C65M37v+cvCGFunUTPEbk16BBz9G79wCys6PpqVmlIyYmhvnzppO8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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f383671f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.heatmap(cm,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.98795181  0.98031496  0.98046875  0.99585062]\n",
      "[ 0.99193548  0.996       0.98431373  0.97165992]\n",
      "[ 0.98993964  0.98809524  0.98238748  0.98360656]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "\n",
    "p, r , f , _ = score(Y,y_pred)\n",
    "print(p)\n",
    "print(r)\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "params = {\n",
    "    'max_depth':range(2,25,2)\n",
    "}\n",
    "\n",
    "GR = GridSearchCV(\n",
    "    RandomForestClassifier(random_state=42),\n",
    "    param_grid=params,\n",
    "    scoring='accuracy',\n",
    "    n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "GR = GR.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'auto'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GR.best_estimator_.max_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}