{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem 16: По описанию условий посева предсказать прорастут семена растений или нет. Провести бинарную классификацию семян с помощью метода Парзеновского окна. Построить график зависимости ошибки на контроле от ширины окна. Подобрать оптимальную ширину окна."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Сп"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#импортируем нужные модули\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import cross_validation\n",
    "from sklearn import neighbors\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>Age</th>\n",
       "      <th>Diseases</th>\n",
       "      <th>Traumas</th>\n",
       "      <th>Surguries</th>\n",
       "      <th>Fevers</th>\n",
       "      <th>Alcohol_Consumption</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>Sitting_time</th>\n",
       "      <th>Output</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.88</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.31</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.38</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.50</td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season   Age  Diseases  Traumas  Surguries  Fevers  Alcohol_Consumption  \\\n",
       "0   -0.33  0.69         0        1          1       0                  0.8   \n",
       "1   -0.33  0.94         1        0          1       0                  0.8   \n",
       "2   -0.33  0.50         1        0          0       0                  1.0   \n",
       "3   -0.33  0.75         0        1          1       0                  1.0   \n",
       "4   -0.33  0.67         1        1          0       0                  0.8   \n",
       "\n",
       "   Smoking  Sitting_time Output  \n",
       "0        0          0.88      N  \n",
       "1        1          0.31      O  \n",
       "2       -1          0.50      N  \n",
       "3       -1          0.38      N  \n",
       "4       -1          0.50      O  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Обрабатываем данные, создаем переменную с тренировочными данными: входы и цели\n",
    "upper_row = ['Season','Age','Diseases','Traumas','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time','Output']\n",
    "dataframe  = pd.read_csv('fertility_Diagnosis.txt',names = upper_row ,sep = ',');\n",
    "Targets = dataframe['Output']\n",
    "Inputs = dataframe[['Season','Age','Diseases','Traumas','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time']]\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#проводим перемешивание данных для тренировки классификатора и проверки работы\n",
    "Inputs_train, Inputs_test, Targets_train, Targets_test = cross_validation.train_test_split(dataframe.loc[:, dataframe.columns != 'Output'], dataframe['Output'], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#экспериментально определяем, что для большинства выборок радиус классификатора менее 2.0 не находит \n",
    "#для некоторых элементов соседей, соответственно, обрывая работу алгоритма. Экспериментально пробуя изменять \n",
    "#шаг радиуса, понимаем, что точность алгоритма меняется при шаге ~0.1 наглядно\n",
    "accuracy_arr = []\n",
    "a = np.linspace(2.0,3.5,10)\n",
    "for i in a:\n",
    "    neighbor = neighbors.RadiusNeighborsClassifier(radius = int(i),weights = 'distance',metric= 'manhattan')\n",
    "    neighbor.fit(Inputs_train,Targets_train)\n",
    "    predictions = neighbor.predict(Inputs_test)\n",
    "    accuracy = metrics.accuracy_score(Targets_test, predictions)\n",
    "    accuracy_arr.append(accuracy)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.90000000000000002,\n",
       " 0.90000000000000002,\n",
       " 0.90000000000000002,\n",
       " 0.90000000000000002]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHVCAYAAADLiU4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234d67ace80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(2,3.5,10)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(x, accuracy_arr, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Оптимальный радиус окна Парзена - 2.9 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.80000000000000004,\n",
       " 0.90000000000000002,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#для примера покажем аналогичную работу классификатора ближайших соседей для этих же данных\n",
    "accuracy_arr_2 = []\n",
    "\n",
    "for k in range(1,6):\n",
    "    neighbor = neighbors.KNeighborsClassifier(n_neighbors = k)\n",
    "    neighbor.fit(Inputs_train,Targets_train)\n",
    "    predictions = neighbor.predict(Inputs_test)\n",
    "    accuracy = metrics.accuracy_score(Targets_test, predictions)\n",
    "    accuracy_arr_2.append(accuracy)\n",
    "accuracy_arr_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHVCAYAAADLiU4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZJqwNIlSZJUA/8/fAHlrCcZHgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234d681cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(1,6,5)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(x, accuracy_arr_2, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}