RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble, cross_validation, learning_curve, metrics\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Случайный лес"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bioresponce = pd.read_csv('bioresponce.csv', header=0, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Activity</th>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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      "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",
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       "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",
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       "3      0      0      0  \n",
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       "\n",
       "[5 rows x 1777 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bioresponce.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В первом столбце - целевая переменная (был ли дан биологический ответ или нет), остальное - признаки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Отрежем целевую переменную (так удобнее будет работать с данными)\n",
    "bioresponce_target = bioresponce.Activity.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bioresponse = 1: 0.54\n",
      "bioresponse = 0: 0.46\n"
     ]
    }
   ],
   "source": [
    "# Посмотрим на баланс классов\n",
    "\n",
    "bio_answer = sum(bioresponce_target) / float(len(bioresponce_target))\n",
    "no_bio_answer = 1.0 - bio_answer\n",
    "\n",
    "print('bioresponse = 1: {:.2f}\\nbioresponse = 0: {:.2f}'.format(bio_answer, no_bio_answer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Отдельно отрежем данные\n",
    "bioresponce_data = bioresponce.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Будем строить модель с 50 деревьями с глубиной не больше двух\n",
    "rf_classifier_low_depth = ensemble.RandomForestClassifier(n_estimators=50, max_depth=2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Интересно посмотреть, как меняется качество на обучающей выборке в зависимости от того,\n",
    "# на скольких объектах мы обучаемся\n",
    "\n",
    "\n",
    "# На вход подаём, на каких долях обучающей выборки мы хотим строить модель. \n",
    "# Будут возвращены размеры обучающей выборки и оценки качества на train'e/test'e\n",
    "# Сначала возбмём 0,1 от обучающей выборки, а затем будем двигаться с шагом 0,2 до 1. \n",
    "\n",
    "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": 16,
   "metadata": {},
   "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) # Размеры обучающей выборки в рамках эксперимента\n",
    "# Смотрим качество верного прогноза на обучающей и тестовой выборках\n",
    "print(train_scores.mean(axis=1)) # делаем усреднение по всем фолдам кросс-валидации; axis=1 --> усредняем по строчкам\n",
    "print(test_scores.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HvlENsIbYdziSCmob6+7Pe+y/8LG455K1ulJI9rqlc4uQrNUV7EkuBH6aahk5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bbcb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid(True)\n",
    "plt.plot(train_sizes, train_scores.mean(axis=1), 'g-', marker='o', label='train')\n",
    "plt.plot(train_sizes, test_scores.mean(axis=1), 'r-', marker='o', label='test')\n",
    "plt.ylim((0.0, 1.05))\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод по графику - дальнейший рост обучающей выборке навряд ли скажется на качестве нашей модели.\n",
    "\n",
    "Попробуем увеличить сложность модели.       \n",
    "Для этого увеличим глубину деревьев до максимума *(максимальная глубина деревьев=10)*       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_classifier = ensemble.RandomForestClassifier(n_estimators=50, max_depth=10, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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azuPARWHNDwKL8lBXpPctfOwG3gmUhG3OyXVdSfP/Cbh1BNZXumwY0c/YAHWN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\nlnmeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125f91ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid(True)\n",
    "plt.plot(train_sizes, train_scores.mean(axis=1), 'g-', marker='o', label='train')\n",
    "plt.plot(train_sizes, test_scores.mean(axis=1), 'r-', marker='o', label='test')\n",
    "plt.ylim((0.0, 1.05))\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь с ростом обучающей выборки качество на тесте продолжает расти.       \n",
    "Качество на обучающей выборке продолжает падать.        \n",
    "\n",
    "В данном случае имеет смысл увеличивать объем обучающей выборки."
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}