Mazurov2018problem16/Problem16 (1).ipynb

{
 "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": 208,
   "metadata": {},
   "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": 209,
   "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": 209,
     "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",
    "\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataframe_withoutseason = dataframe[['Age','Diseases','Traumas','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time','Output']]\n",
    "dataframe_withoutage = dataframe[['Season','Diseases','Traumas','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time','Output']]\n",
    "dataframe_withoutdisease = dataframe[['Age','Season','Traumas','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time','Output']]\n",
    "dataframe_withouttraumas = dataframe[['Age','Season','Diseases','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time','Output']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([32, 70, 4, 90, 63, 17, 77, 64, 23, 11, 88, 3, 73, 56, 95, 59, 12,\n",
       "            8, 46, 68],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Inputs_test.index[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "labels ['Season' 'Age' 'Diseases' 'Traumas' 'Surguries' 'Fevers'\n 'Alcohol_Consumption' 'Smoking' 'Sitting_time'] not contained in axis",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-139-a4d7670d3c66>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mInputs_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mInputs_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m18\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mTargets_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTargets_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m18\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mInputs_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mInputs_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mTargets_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTargets_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[0;32m   2048\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2049\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2050\u001b[1;33m                 \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2051\u001b[0m             \u001b[0mdropped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2052\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Dell\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   3573\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'ignore'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3574\u001b[0m                 raise ValueError('labels %s not contained in axis' %\n\u001b[1;32m-> 3575\u001b[1;33m                                  labels[mask])\n\u001b[0m\u001b[0;32m   3576\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3577\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: labels ['Season' 'Age' 'Diseases' 'Traumas' 'Surguries' 'Fevers'\n 'Alcohol_Consumption' 'Smoking' 'Sitting_time'] not contained in axis"
     ]
    }
   ],
   "source": [
    "Inputs_train.drop(Inputs_train.loc[[1, 2, 3, 5, 16, 18]])\n",
    "Targets_train.drop(Targets_train.loc[[1, 2, 3, 5, 16, 18]])\n",
    "Inputs_test.drop(Inputs_test.loc[[4,19]])\n",
    "Targets_test.drop(Targets_test.loc[[4,19]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80000000000000004"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighbor = neighbors.RadiusNeighborsClassifier(radius = 3.0,weights = 'distance',metric='euclidean' ,algorithm = 'auto')\n",
    "neighbor.fit(Inputs_train,Targets_train)\n",
    "predictions = neighbor.predict(Inputs_test)\n",
    "accuracy = metrics.accuracy_score(Targets_test, predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "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='euclidean' ,algorithm = 'auto')\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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004,\n",
       " 0.80000000000000004]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAHjCAYAAAB8R1jMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ebd7b51898>"
      ]
     },
     "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.ylabel('accuracy ')\n",
    "plt.xlabel('radius of window')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Оптимальный радиус окна Парзена - 2.9 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.80000000000000004,\n",
       " 0.94999999999999996,\n",
       " 0.94999999999999996,\n",
       " 0.94999999999999996,\n",
       " 0.94999999999999996]"
      ]
     },
     "execution_count": 211,
     "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": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAHVCAYAAADLiU4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ebd924d9b0>"
      ]
     },
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'distance'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = neighbor.weights\n",
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80000000000000004"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задача 3 - построение графика зависимости средней ошибки(точности) в зависимости от сложности данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "сюда будем записывать данные об ошибках и средних отклонений для последующего вывода на график"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Errors_test = []\n",
    "Errors_train = []\n",
    "Mean_dev_tests = []\n",
    "Mean_dev_trains = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Сперва сделаем модель для вычисления средней ошибки на тренировочных и тестовых данных. k - количество проходов алгоритма(кросс-валидация, расчет) по данным. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8695   0.8905  0.00505069630861   0.00598046348189\n"
     ]
    }
   ],
   "source": [
    "Error_train = 0\n",
    "Error_test = 0\n",
    "Mean_deviation_test = 0\n",
    "Mean_deviation_train = 0\n",
    "k = 40\n",
    "for i in range(k):\n",
    "    Inputs_train, Inputs_test, Targets_train, Targets_test = cross_validation.train_test_split(dataframe.loc[:, ['Alcohol_Consumption']], dataframe['Output'], test_size=0.5)\n",
    "    #['Season','Age','Diseases','Traumas','Surguries','Fevers','Alcohol_Consumption','Smoking','Sitting_time'] - так должна выглядеть базовая выборка по столбцам \n",
    "    accuracy_arr = []\n",
    "    accuracy_arr_2 = []\n",
    "    a = np.linspace(2.0,3.5,10)\n",
    "    for i in a:\n",
    "        neighbor = neighbors.RadiusNeighborsClassifier(radius = int(i),weights = 'distance',metric='euclidean' ,algorithm = 'auto')\n",
    "        neighbor.fit(Inputs_train,Targets_train)\n",
    "        predictions = neighbor.predict(Inputs_test)\n",
    "        predictions2 = neighbor.predict(Inputs_train)\n",
    "        accuracy = metrics.accuracy_score(Targets_test, predictions)\n",
    "        accuracy2 = metrics.accuracy_score(Targets_train, predictions)\n",
    "        accuracy_arr.append(accuracy)\n",
    "        accuracy_arr_2.append(accuracy2)\n",
    "    Error_test += max(accuracy_arr)\n",
    "    Error_train += max(accuracy_arr_2)\n",
    "    Mean_deviation_test += (0.875 - max(accuracy_arr))**2\n",
    "    Mean_deviation_train += (0.87 - max(accuracy_arr_2))**2\n",
    "    \n",
    "Error_test = Error_test/k\n",
    "Error_train = Error_train/k\n",
    "Mean_deviation_test = (Mean_deviation_test**(1/2))/(k-1)\n",
    "Mean_deviation_train = (Mean_deviation_train**(1/2))/(k-1)\n",
    "\n",
    "print(Error_test,' ',Error_train,'',Mean_deviation_test,' ',Mean_deviation_train)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "сюда записывать данные после каждого уменьшения(ручного) сложности данных. \n",
    "Делается это удалением названия столбца в предыдущей ячейке в 7-ой строке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Errors_test.append(Error_test)     \n",
    "Errors_train.append(Error_train)\n",
    "Mean_dev_tests.append(Mean_deviation_test)\n",
    "Mean_dev_trains.append(Mean_deviation_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.87549999999999994,\n",
       " 0.87850000000000006,\n",
       " 0.88000000000000023,\n",
       " 0.88099999999999989,\n",
       " 0.8600000000000001,\n",
       " 0.86300000000000021,\n",
       " 0.88899999999999968,\n",
       " 0.88249999999999995,\n",
       " 0.86949999999999983]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Errors_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAHjCAYAAABMy0ntAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ebd95ca9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,8,9)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(x, Errors_test, color='r',label = 'test')\n",
    "plt.plot(x, Errors_train, color='y',label = 'train')\n",
    "plt.gca().invert_xaxis()\n",
    "plt.ylabel(' Mean accuracy')\n",
    "plt.xlabel('SLozhnost of data')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAHkCAYAAABRznlVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERGRMKQiTkRERCQM/R/UexNeRP13OwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ebd97a26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,8,9)\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(x, Mean_dev_tests, color='g',label = 'test')\n",
    "plt.plot(x, Mean_dev_trains, color='b',label = 'train')\n",
    "plt.gca().invert_xaxis()\n",
    "plt.ylabel('Mean deviation ')\n",
    "plt.xlabel('SLozhnost of data')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "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
}