rf_var_select.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features sorted by their score:\n[(0.17, 'proline'), (0.16, 'flavanoids'), (0.16, 'color_intensity'), (0.12, 'od280/od315_of_diluted_wines'), (0.12, 'alcohol'), (0.07, 'hue'), (0.05, 'total_phenols'), (0.03, 'malic_acid'), (0.03, 'magnesium'), (0.03, 'alcalinity_of_ash'), (0.02, 'proanthocyanins'), (0.01, 'nonflavanoid_phenols'), (0.01, 'ash')]\n"
     ]
    },
    {
     "data": {
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JkqRlSKd2kiRJkiRJkiRJkiRJkiRJ0jKkUztJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x214ce785ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "\n",
    "### single feature information evaluation\n",
    "n_trees = 1000\n",
    "wine = load_wine()\n",
    "X = wine[\"data\"]\n",
    "Y = wine[\"target\"]\n",
    "features = wine[\"feature_names\"]\n",
    "rf = RandomForestClassifier(n_trees)\n",
    "rf.fit(X, Y)\n",
    "plt_x = numpy.linspace(0,len(features)-1,len(features))\n",
    "print(\"Features sorted by their score:\")\n",
    "print(sorted(zip(map(lambda x: float(\"{0:.2f}\".format(round(x, 4))), rf.feature_importances_), features),\n",
    "             reverse=True))\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 12}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "plt.figure(figsize=(25,20))\n",
    "\n",
    "plt.bar(plt_x, rf.feature_importances_, width=0.5, color=\"blue\",align='center')\n",
    "plt.gca().set_xticklabels(plt_x, rotation=60 )\n",
    "plt.xticks(plt_x, features)\n",
    "plt.ylabel(\"relative information\")\n",
    "plt.xlabel(\"features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x214ce303160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### F1-score sweep over features\n",
    "F1 = []\n",
    "plt_x = numpy.linspace(0,len(features)-1,len(features))\n",
    "n_trees = 1 # set n_trees to a limiting small number to find out features' effect on constrained models\n",
    "for i in range(len(features)):\n",
    "    rf = RandomForestClassifier(n_trees, max_features = i+1)\n",
    "    rf.fit(X[:,:i+1], Y)\n",
    "    Y_pred = rf.predict(X[:,:i+1])\n",
    "    F1.append(numpy.mean(f1_score(Y,Y_pred, average  = None)))\n",
    "plt.figure(figsize=(25,20))\n",
    "\n",
    "plt.bar(plt_x, F1, width=0.2, color=\"purple\",align='center')\n",
    "\n",
    "plt.gca().set_xticklabels(plt_x, rotation=60 )\n",
    "plt.xticks(plt_x, [ int(x+1) for x in plt_x ])\n",
    "\n",
    "plt.ylabel(\"mean F1 score for all classes\")\n",
    "plt.xlabel(\"number of features\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}