CH2/templates/10_sklearn.random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sklearn.ensemble.RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "документация:  http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import ensemble, cross_validation, learning_curve, metrics \n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Данные"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Задача на kaggle: https://www.kaggle.com/c/bioresponse\n",
    "\n",
    "Данные: https://www.kaggle.com/c/bioresponse/data\n",
    "\n",
    "По данным характеристикам молекулы требуется определить, будет ли дан биологический ответ (biological response).\n",
    "\n",
    "Признаки нормализаваны.\n",
    "\n",
    "Для демонстрации используется обучающая выборка из исходных данных train.csv, файл с данными прилагается."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bioresponce = pd.read_csv('bioresponse.csv', header=0, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activity</th>\n",
       "      <th>D1</th>\n",
       "      <th>D2</th>\n",
       "      <th>D3</th>\n",
       "      <th>D4</th>\n",
       "      <th>D5</th>\n",
       "      <th>D6</th>\n",
       "      <th>D7</th>\n",
       "      <th>D8</th>\n",
       "      <th>D9</th>\n",
       "      <th>...</th>\n",
       "      <th>D1767</th>\n",
       "      <th>D1768</th>\n",
       "      <th>D1769</th>\n",
       "      <th>D1770</th>\n",
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       "      <th>D1774</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.678031</td>\n",
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       "      <td>0.366667</td>\n",
       "      <td>0.606291</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111209</td>\n",
       "      <td>0.803455</td>\n",
       "      <td>0.106105</td>\n",
       "      <td>0.411754</td>\n",
       "      <td>0.836582</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.033300</td>\n",
       "      <td>0.480124</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.209791</td>\n",
       "      <td>0.610350</td>\n",
       "      <td>0.356453</td>\n",
       "      <td>0.517720</td>\n",
       "      <td>0.679051</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538825</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.196344</td>\n",
       "      <td>0.724230</td>\n",
       "      <td>0.235606</td>\n",
       "      <td>0.288764</td>\n",
       "      <td>0.805110</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.517794</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494734</td>\n",
       "      <td>0.781422</td>\n",
       "      <td>0.154361</td>\n",
       "      <td>0.303809</td>\n",
       "      <td>0.812646</td>\n",
       "      <td>...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1777 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Activity        D1        D2    D3   D4        D5        D6        D7  \\\n",
       "0         1  0.000000  0.497009  0.10  0.0  0.132956  0.678031  0.273166   \n",
       "1         1  0.366667  0.606291  0.05  0.0  0.111209  0.803455  0.106105   \n",
       "2         1  0.033300  0.480124  0.00  0.0  0.209791  0.610350  0.356453   \n",
       "3         1  0.000000  0.538825  0.00  0.5  0.196344  0.724230  0.235606   \n",
       "4         0  0.100000  0.517794  0.00  0.0  0.494734  0.781422  0.154361   \n",
       "\n",
       "         D8        D9  ...    D1767  D1768  D1769  D1770  D1771  D1772  D1773  \\\n",
       "0  0.585445  0.743663  ...        0      0      0      0      0      0      0   \n",
       "1  0.411754  0.836582  ...        1      1      1      1      0      1      0   \n",
       "2  0.517720  0.679051  ...        0      0      0      0      0      0      0   \n",
       "3  0.288764  0.805110  ...        0      0      0      0      0      0      0   \n",
       "4  0.303809  0.812646  ...        0      0      0      0      0      0      0   \n",
       "\n",
       "   D1774  D1775  D1776  \n",
       "0      0      0      0  \n",
       "1      0      1      0  \n",
       "2      0      0      0  \n",
       "3      0      0      0  \n",
       "4      0      0      0  \n",
       "\n",
       "[5 rows x 1777 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bioresponce.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3751, 1777)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bioresponce.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Activity', u'D1', u'D2', u'D3', u'D4', u'D5', u'D6', u'D7', u'D8',\n",
       "       u'D9',\n",
       "       ...\n",
       "       u'D1767', u'D1768', u'D1769', u'D1770', u'D1771', u'D1772', u'D1773',\n",
       "       u'D1774', u'D1775', u'D1776'],\n",
       "      dtype='object', length=1777)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bioresponce.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bioresponce_target = bioresponce.Activity.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bioresponse = 1: 0.54\n",
      "bioresponse = 0: 0.46\n"
     ]
    }
   ],
   "source": [
    "# проверить сбалансированность данных по классам\n",
    "print 'bioresponse = 1: {:.2f}\\nbioresponse = 0: {:.2f}'.format(sum(bioresponce_target)/float(len(bioresponce_target)), \n",
    "                1.0 - sum(bioresponce_target)/float(len(bioresponce_target)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bioresponce_data = bioresponce.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Кривые обучения для деревьев небольшой глубиной "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 50 деревьев с глубиной не более 2\n",
    "rf_classifier_low_depth = ensemble.RandomForestClassifier(n_estimators = 50, max_depth = 2, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve.learning_curve(rf_classifier_low_depth, bioresponce_data, bioresponce_target, \n",
    "                                                                       train_sizes=np.arange(0.1,1., 0.2), \n",
    "                                                                       cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 250  750 1250 1750 2250]\n",
      "[ 0.74933333  0.71333333  0.68453333  0.69104762  0.69022222]\n",
      "[ 0.62356685  0.64195598  0.65369955  0.66248974  0.66728527]\n"
     ]
    }
   ],
   "source": [
    "print train_sizes\n",
    "print train_scores.mean(axis = 1)\n",
    "print test_scores.mean(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff463017dd0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vJM0e02s3s1lAhbu/Hl/umYSfOW2MkAXE3h/JrmViZ9Ht7u3x+0eBLcAcIvje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\nMwcUXeUXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff475292cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.grid(True)\n",
    "pylab.plot(train_sizes, train_scores.mean(axis = 1), 'g-', marker='o', label='train')\n",
    "pylab.plot(train_sizes, test_scores.mean(axis = 1), 'r-', marker='o', label='test')\n",
    "pylab.ylim((0.0, 1.05))\n",
    "pylab.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Кривые обучения для деревьев большей глубины"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_classifier = ensemble.RandomForestClassifier(n_estimators = 50, max_depth = 10, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve.learning_curve(rf_classifier, bioresponce_data, bioresponce_target, \n",
    "                                                                       train_sizes=np.arange(0.1,1, 0.2), \n",
    "                                                                       cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff46118cfd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fMbOlwGpi5aAH3H2zmV0Xe9p/4u6/NbPLzezvxG5Y88XcdltERDKR1ytyRUQk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff49ca4bb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.grid(True)\n",
    "pylab.plot(train_sizes, train_scores.mean(axis = 1), 'g-', marker='o', label='train')\n",
    "pylab.plot(train_sizes, test_scores.mean(axis = 1), 'r-', marker='o', label='test')\n",
    "pylab.ylim((0.0, 1.05))\n",
    "pylab.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}