Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve, auc,precision_score\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.model_selection import learning_curve\n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Импортируем даннные и сохраняем в формате DataFrame.\n",
    "Проверим, правильно ли распознались данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump_Thickness</th>\n",
       "      <th>Uniformity_of_Cell_Size</th>\n",
       "      <th>Uniformity_of_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Clump_Thickness  Uniformity_of_Cell_Size  Uniformity_of_Cell_Shape  \\\n",
       "0                5                        1                         1   \n",
       "1                5                        4                         4   \n",
       "2                3                        1                         1   \n",
       "3                6                        8                         8   \n",
       "4                4                        1                         1   \n",
       "\n",
       "   Marginal_Adhesion  Single_Epithelial_Cell_Size Bare_Nuclei  \\\n",
       "0                  1                            2           1   \n",
       "1                  5                            7          10   \n",
       "2                  1                            2           2   \n",
       "3                  1                            3           4   \n",
       "4                  3                            2           1   \n",
       "\n",
       "   Bland_Chromatin  Normal_Nucleoli  Mitoses  Class  \n",
       "0                3                1        1      2  \n",
       "1                3                2        1      2  \n",
       "2                3                1        1      2  \n",
       "3                3                7        1      2  \n",
       "4                3                1        1      2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('data/data.csv')\n",
    "df = df.drop(columns = 'id', axis =1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из описания данных было известно, что фича ***Bare_Nuclei имеет пропуски***. Проверим это"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#сказать особенности данных: что нет сведений о паиенте и пр"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     402\n",
       "10    132\n",
       "5      30\n",
       "2      30\n",
       "3      28\n",
       "8      21\n",
       "4      19\n",
       "?      16\n",
       "9       9\n",
       "7       8\n",
       "6       4\n",
       "Name: Bare_Nuclei, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bare_Nuclei'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обнаружено 16 пропусков. Bare_Nuclei является важной характеристикой опухоли. Поэтому, чтобы не исказить показатели модели, наблюдения без указанного значения не войдут в итоговую выборку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     402\n",
       "10    132\n",
       "5      30\n",
       "2      30\n",
       "3      28\n",
       "8      21\n",
       "4      19\n",
       "9       9\n",
       "7       8\n",
       "6       4\n",
       "Name: Bare_Nuclei, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Bare_Nuclei != '?']\n",
    "df['Bare_Nuclei'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(683, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на описательные характеристики данных. Действительно, данные оценены корректно (минимальные баллы - 1, максимальный балл - 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump_Thickness</th>\n",
       "      <th>Uniformity_of_Cell_Size</th>\n",
       "      <th>Uniformity_of_Cell_Shape</th>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>683.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.442167</td>\n",
       "      <td>3.150805</td>\n",
       "      <td>3.215227</td>\n",
       "      <td>2.830161</td>\n",
       "      <td>3.234261</td>\n",
       "      <td>3.445095</td>\n",
       "      <td>2.869693</td>\n",
       "      <td>1.603221</td>\n",
       "      <td>2.699854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.820761</td>\n",
       "      <td>3.065145</td>\n",
       "      <td>2.988581</td>\n",
       "      <td>2.864562</td>\n",
       "      <td>2.223085</td>\n",
       "      <td>2.449697</td>\n",
       "      <td>3.052666</td>\n",
       "      <td>1.732674</td>\n",
       "      <td>0.954592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clump_Thickness  Uniformity_of_Cell_Size  Uniformity_of_Cell_Shape  \\\n",
       "count       683.000000               683.000000                683.000000   \n",
       "mean          4.442167                 3.150805                  3.215227   \n",
       "std           2.820761                 3.065145                  2.988581   \n",
       "min           1.000000                 1.000000                  1.000000   \n",
       "25%           2.000000                 1.000000                  1.000000   \n",
       "50%           4.000000                 1.000000                  1.000000   \n",
       "75%           6.000000                 5.000000                  5.000000   \n",
       "max          10.000000                10.000000                 10.000000   \n",
       "\n",
       "       Marginal_Adhesion  Single_Epithelial_Cell_Size  Bland_Chromatin  \\\n",
       "count         683.000000                   683.000000       683.000000   \n",
       "mean            2.830161                     3.234261         3.445095   \n",
       "std             2.864562                     2.223085         2.449697   \n",
       "min             1.000000                     1.000000         1.000000   \n",
       "25%             1.000000                     2.000000         2.000000   \n",
       "50%             1.000000                     2.000000         3.000000   \n",
       "75%             4.000000                     4.000000         5.000000   \n",
       "max            10.000000                    10.000000        10.000000   \n",
       "\n",
       "       Normal_Nucleoli     Mitoses       Class  \n",
       "count       683.000000  683.000000  683.000000  \n",
       "mean          2.869693    1.603221    2.699854  \n",
       "std           3.052666    1.732674    0.954592  \n",
       "min           1.000000    1.000000    2.000000  \n",
       "25%           1.000000    1.000000    2.000000  \n",
       "50%           1.000000    1.000000    2.000000  \n",
       "75%           4.000000    1.000000    4.000000  \n",
       "max          10.000000   10.000000    4.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на распределение переменных. Для этого построим парные графики каждой переменной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1842.38x1800 with 110 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pairplot = sns.pairplot(df, hue=\"Class\", palette='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на распределение целевых классов. В целом соотношение классов допустимое и не требует дополнительных преобразований. <br>  \n",
    "Доля доброкачественных опухолей в выборке: 65.5%\n",
    "<br>Доля злокачественных опухолей в выборке: 34.5%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    444\n",
       "4    239\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заменим предложенные значения на стандартные: 0 - для доброкачественной опухоли, 1 - для злокачественной"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Class'][df['Class'] == 4] = 1 \n",
    "df['Class'][df['Class'] == 2] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    444\n",
       "1    239\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Предположим наличие линейной зависимости между целевой переменной и признаками. Тогда можем построить корреляционную матрицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2189482abe0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor=df.corr()\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(cor, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Гипотеза о наличии линейной зависимости принимается."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задача 1. Классификация пациентов ##\n",
    "### Необходимо обучить модель на выборке, чтобы определять характер опухоли пациента по внесенным в карту данным (от врача-онколога).  ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь перейдем к первичному построению моделей. На основе которых будет выбрана 1-2 модели для их доработки.\n",
    "1. Разделим данные на X и y\n",
    "2. Разобъем выборку на тренировочную и валидационную\n",
    "3. Из предположений о линейности и нормальности распределения, выполним скалирование данных\n",
    "4. Построим дерево решений, ограничив глубину, чтобы избежать переобучение. Остальные параметры оставим неизменными"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Class'],axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель 1. Дерево решений ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#не надо"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy: 92.7%\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(max_depth=3)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(f'Decision Tree Accuracy: {round(clf.score(X_test, y_test), 4)*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель 2. Логистическая регрессия ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 96.35%\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "lr.predict(X_test)\n",
    "acc = lr.score(X_test,y_test)\n",
    "print(f'Logistic Regression Accuracy: {round(acc*100, 2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель 3. Случайный лес ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 94.89%\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(random_state=42, n_estimators=20)\n",
    "rf.fit(X_train, y_train)\n",
    "pred = rf.predict(X_test)\n",
    "print(f'Random Forest Accuracy: {round(accuracy_score(y_test, pred), 4)*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель 4. Градиентный бустинг ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6215618\ttotal: 123ms\tremaining: 494ms\n",
      "1:\tlearn: 0.5626153\ttotal: 137ms\tremaining: 206ms\n",
      "2:\tlearn: 0.5113073\ttotal: 150ms\tremaining: 100ms\n",
      "3:\tlearn: 0.4677086\ttotal: 165ms\tremaining: 41.2ms\n",
      "4:\tlearn: 0.4310742\ttotal: 180ms\tremaining: 0us\n",
      "Gradient Boosting Accuracy: 94.16%\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "cb = CatBoostClassifier(learning_rate=0.1,iterations=5)\n",
    "cb.fit(X_train,y_train)\n",
    "print(f'Gradient Boosting Accuracy: {round(accuracy_score(y_test, cb.predict(X_test)), 4)*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Модель 5. Байес ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB \n",
    "bayes = GaussianNB() \n",
    "bayes.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bayes Accuracy: 95.62%\n"
     ]
    }
   ],
   "source": [
    "bayes.predict(X_test)\n",
    "print(f'Bayes Accuracy: {round(bayes.score(X_test, y_test), 4)*100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#+ вероятностная модель\n",
    "#чекнуть значимость коэфов\n",
    "#опорные вектора\n",
    "#посмотреть архитектура сеток для поохжих задач + посмтотреть"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы использовали метрику Accuarcy для первичного сравнения работы моделей. Логистическая регрессия и градиентный бустинг показали наилучшие результаты (97,14% и 97,14% соответственно). Поэтому, на следующем этапе мы будем проводить кросс валидацию на этих двух моделях."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Тюнинг моделей ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кросс-валидация логистической регрессии ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В контексте задачи, наша цель - с большей вероятностью определить наличие злокачественной опухоли. Поэтому мы будем оптимизировать метрику presicion. Будем использовать Stratified Folds, чтобы сохранить распределение в подвыборках для оптимизации модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf = StratifiedKFold(n_splits=10,shuffle=True,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression()\n",
    "parameters = { 'penalty' : ['l1', 'l2'], \n",
    "'C' : np.logspace(-4, 4, 20)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 40 candidates, totalling 400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0240s.) Setting batch_size=16.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done 160 tasks      | elapsed:    0.8s\n",
      "[Parallel(n_jobs=-1)]: Done 400 out of 400 | elapsed:    1.3s finished\n"
     ]
    }
   ],
   "source": [
    "gridSearch = GridSearchCV(lr,parameters, n_jobs=-1, scoring='precision', cv=skf, verbose=10)\n",
    "gridSearch = gridSearch.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_tuned = gridSearch.best_estimator_\n",
    "lr_tuned.fit(X_train, y_train)\n",
    "y_pred = lr_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision log reg tuned: 97.44%\n"
     ]
    }
   ],
   "source": [
    "lr_tuned_pres = precision_score(y_test,y_pred)\n",
    "print(f'Precision log reg tuned: {round(lr_tuned_pres, 4)*100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <td>1.610964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clump_Thickness</th>\n",
       "      <td>1.087988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <td>0.973361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>0.640652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_of_Cell_Shape</th>\n",
       "      <td>0.590282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <td>0.490593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <td>0.469863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_of_Cell_Size</th>\n",
       "      <td>0.152550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Coefficient\n",
       "Bare_Nuclei                     1.610964\n",
       "Clump_Thickness                 1.087988\n",
       "Bland_Chromatin                 0.973361\n",
       "Mitoses                         0.640652\n",
       "Uniformity_of_Cell_Shape        0.590282\n",
       "Normal_Nucleoli                 0.490593\n",
       "Marginal_Adhesion               0.469863\n",
       "Uniformity_of_Cell_Size         0.152550\n",
       "Single_Epithelial_Cell_Size     0.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = pd.DataFrame(lr_tuned.coef_[0], X.columns)\n",
    "coef.columns = {'Coefficient'}\n",
    "coef = coef.sort_values(['Coefficient'], ascending = False)\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21897126b70>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)\n",
    "df_cm = pd.DataFrame(cm, index = [i for i in \"10\"],\n",
    "                  columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (5,4))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На оптимизированной логистической модели мы получили, что 2 человека, имеющих злокачественные опухоли были определены как \"здоровые\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кросс-валидация градиентного бустинга ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.7s\n",
      "[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    1.2s\n",
      "[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    2.6s\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed:    4.7s\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    6.4s\n",
      "[Parallel(n_jobs=-1)]: Done  69 tasks      | elapsed:    8.3s\n",
      "[Parallel(n_jobs=-1)]: Done  85 out of  90 | elapsed:   10.9s remaining:    0.5s\n",
      "[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed:   11.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6856900\ttotal: 12.7ms\tremaining: 242ms\n",
      "1:\tlearn: 0.6791149\ttotal: 29.6ms\tremaining: 267ms\n",
      "2:\tlearn: 0.6732042\ttotal: 47.3ms\tremaining: 268ms\n",
      "3:\tlearn: 0.6668547\ttotal: 64.1ms\tremaining: 257ms\n",
      "4:\tlearn: 0.6605980\ttotal: 80.9ms\tremaining: 243ms\n",
      "5:\tlearn: 0.6543983\ttotal: 98.9ms\tremaining: 231ms\n",
      "6:\tlearn: 0.6481502\ttotal: 115ms\tremaining: 214ms\n",
      "7:\tlearn: 0.6425989\ttotal: 135ms\tremaining: 202ms\n",
      "8:\tlearn: 0.6371906\ttotal: 156ms\tremaining: 190ms\n",
      "9:\tlearn: 0.6317078\ttotal: 174ms\tremaining: 174ms\n",
      "10:\tlearn: 0.6262425\ttotal: 195ms\tremaining: 160ms\n",
      "11:\tlearn: 0.6200980\ttotal: 226ms\tremaining: 151ms\n",
      "12:\tlearn: 0.6145494\ttotal: 254ms\tremaining: 137ms\n",
      "13:\tlearn: 0.6089628\ttotal: 273ms\tremaining: 117ms\n",
      "14:\tlearn: 0.6040654\ttotal: 292ms\tremaining: 97.2ms\n",
      "15:\tlearn: 0.5988520\ttotal: 309ms\tremaining: 77.3ms\n",
      "16:\tlearn: 0.5939467\ttotal: 329ms\tremaining: 58ms\n",
      "17:\tlearn: 0.5890293\ttotal: 350ms\tremaining: 38.8ms\n",
      "18:\tlearn: 0.5840867\ttotal: 368ms\tremaining: 19.4ms\n",
      "19:\tlearn: 0.5793845\ttotal: 385ms\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=10, random_state=42, shuffle=True),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=<catboost.core.CatBoostClassifier object at 0x0000021895B7D198>,\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'iterations': [20], 'learning_rate': [0.01, 0.1, 1], 'depth': [3, 6, 9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='precision', verbose=10)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'iterations': [20],\n",
    "    'learning_rate': [0.01,0.1,1],\n",
    "    'depth': [3,6,9] \n",
    "}\n",
    "cb_tuned = GridSearchCV(cb,params,n_jobs=-1,scoring='precision',cv=skf,verbose=10)\n",
    "cb_tuned.fit(X_train,y_train);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "cb_best = cb_tuned.best_estimator_\n",
    "cb_pred = cb_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision log reg tuned: 98.11%\n"
     ]
    }
   ],
   "source": [
    "cb_tuned_pres = precision_score(y_test,cb_pred)\n",
    "print(f'Precision log reg tuned: {round(cb_tuned_pres, 4)*100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.57680821, 15.27806863, 15.13705229,  5.78658691,  9.59678244,\n",
       "       24.40956311,  8.20099515, 10.37423221,  3.63991106])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb_best.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bare_Nuclei</th>\n",
       "      <td>24.409563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_of_Cell_Size</th>\n",
       "      <td>15.278069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniformity_of_Cell_Shape</th>\n",
       "      <td>15.137052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal_Nucleoli</th>\n",
       "      <td>10.374232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_Epithelial_Cell_Size</th>\n",
       "      <td>9.596782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bland_Chromatin</th>\n",
       "      <td>8.200995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clump_Thickness</th>\n",
       "      <td>7.576808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marginal_Adhesion</th>\n",
       "      <td>5.786587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mitoses</th>\n",
       "      <td>3.639911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Coefficient\n",
       "Bare_Nuclei                    24.409563\n",
       "Uniformity_of_Cell_Size        15.278069\n",
       "Uniformity_of_Cell_Shape       15.137052\n",
       "Normal_Nucleoli                10.374232\n",
       "Single_Epithelial_Cell_Size     9.596782\n",
       "Bland_Chromatin                 8.200995\n",
       "Clump_Thickness                 7.576808\n",
       "Marginal_Adhesion               5.786587\n",
       "Mitoses                         3.639911"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_cb = pd.DataFrame(cb_best.feature_importances_, X.columns)\n",
    "coef_cb.columns = {'Coefficient'}\n",
    "coef_cb = coef_cb.sort_values(['Coefficient'], ascending = False)\n",
    "coef_cb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#попробовать на разных поддатасетах\n",
    "#посмотреть п-статистику по фичам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2189788d588>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,cb_pred)\n",
    "df_cm = pd.DataFrame(cm, index = [i for i in \"10\"],\n",
    "                  columns = [i for i in \"10\"])\n",
    "plt.figure(figsize = (5,4))\n",
    "sns.heatmap(df_cm, annot=True,annot_kws={\"size\": 20})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На оптимизированной модели градиентного бустинга мы получили, что 2 человека, имеющих злокачественные опухоли были определены как \"здоровые\". Однако catboost  работаете немного медленнее, чем linreg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Нам удалось добиться улучшения в три наблюдения,\n",
    "#которые ранее были классифицированы ошибочно как ложно отрицательные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# рисуем кривую обучения\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'C:\\\\Users\\\\LENOVO\\\\Anaconda3\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Learning curve CatBoost'\n",
    "estimator = cb_tuned.best_estimator_\n",
    "\n",
    "plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=skf, n_jobs=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "вывод по задачке 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "fit() missing 1 required positional argument: 'X'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-9b5d1e69b5d3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mKMeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\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[0mKMeans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: fit() missing 1 required positional argument: 'X'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.63824317, 0.11685927, 0.06637342, 0.05114293, 0.04432324,\n",
       "       0.03092674, 0.0266673 ])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=0.95, random_state=17)\n",
    "pca.fit(X_test)\n",
    "X_pca = pca.transform(X_test)\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_pca[:,0], X_pca[:,1], c=y_test, s=20, cmap='plasma');\n",
    "plt.show()\n",
    "\n",
    "#DBSCAN добавить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(2,n_init =100, random_state=17)\n",
    "kmeans.fit(X_test)\n",
    "cluster_labels = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_pca[:,0], X_pca[:,1], c=kmeans.labels_,s=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%load ranking.py\n",
    "\"\"\"\n",
    "Implementation of pairwise ranking using scikit-learn LinearSVC\n",
    "\n",
    "Reference: \n",
    "\n",
    "    \"Large Margin Rank Boundaries for Ordinal Regression\", R. Herbrich,\n",
    "    T. Graepel, K. Obermayer 1999\n",
    "\n",
    "    \"Learning to rank from medical imaging data.\" Pedregosa, Fabian, et al., \n",
    "    Machine Learning in Medical Imaging 2012.\n",
    "\n",
    "\n",
    "Authors: Fabian Pedregosa <fabian@fseoane.net>\n",
    "         Alexandre Gramfort <alexandre.gramfort@inria.fr>\n",
    "\n",
    "See also https://github.com/fabianp/pysofia for a more efficient implementation\n",
    "of RankSVM using stochastic gradient descent methdos.\n",
    "\"\"\"\n",
    "\n",
    "import itertools\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import svm, linear_model\n",
    "\n",
    "\n",
    "def transform_pairwise(X, y):\n",
    "    \"\"\"Transforms data into pairs with balanced labels for ranking\n",
    "\n",
    "    Transforms a n-class ranking problem into a two-class classification\n",
    "    problem. Subclasses implementing particular strategies for choosing\n",
    "    pairs should override this method.\n",
    "\n",
    "    In this method, all pairs are choosen, except for those that have the\n",
    "    same target value. The output is an array of balanced classes, i.e.\n",
    "    there are the same number of -1 as +1\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X : array, shape (n_samples, n_features)\n",
    "        The data\n",
    "    y : array, shape (n_samples,) or (n_samples, 2)\n",
    "        Target labels. If it's a 2D array, the second column represents\n",
    "        the grouping of samples, i.e., samples with different groups will\n",
    "        not be considered.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    X_trans : array, shape (k, n_feaures)\n",
    "        Data as pairs\n",
    "    y_trans : array, shape (k,)\n",
    "        Output class labels, where classes have values {-1, +1}\n",
    "    \"\"\"\n",
    "    X_new = []\n",
    "    y_new = []\n",
    "    y = np.asarray(y)\n",
    "    if y.ndim == 1:\n",
    "        y = np.c_[y, np.ones(y.shape[0])]\n",
    "    comb = itertools.combinations(range(X.shape[0]), 2)\n",
    "    for k, (i, j) in enumerate(comb):\n",
    "        if y[i, 0] == y[j, 0] or y[i, 1] != y[j, 1]:\n",
    "            # skip if same target or different group\n",
    "            continue\n",
    "        X_new.append(X[i] - X[j])\n",
    "        y_new.append(np.sign(y[i, 0] - y[j, 0]))\n",
    "        # output balanced classes\n",
    "        if y_new[-1] != (-1) ** k:\n",
    "            y_new[-1] = - y_new[-1]\n",
    "            X_new[-1] = - X_new[-1]\n",
    "    return np.asarray(X_new), np.asarray(y_new).ravel()\n",
    "\n",
    "\n",
    "class RankSVM(svm.LinearSVC):\n",
    "    \"\"\"Performs pairwise ranking with an underlying LinearSVC model\n",
    "\n",
    "    Input should be a n-class ranking problem, this object will convert it\n",
    "    into a two-class classification problem, a setting known as\n",
    "    `pairwise ranking`.\n",
    "\n",
    "    See object :ref:`svm.LinearSVC` for a full description of parameters.\n",
    "    \"\"\"\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit a pairwise ranking model.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array, shape (n_samples, n_features)\n",
    "        y : array, shape (n_samples,) or (n_samples, 2)\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        self\n",
    "        \"\"\"\n",
    "        X_trans, y_trans = transform_pairwise(X, y)\n",
    "        super(RankSVM, self).fit(X_trans, y_trans)\n",
    "        return self\n",
    "\n",
    "    def decision_function(self, X):\n",
    "        return np.dot(X, self.coef_.ravel())\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        Predict an ordering on X. For a list of n samples, this method\n",
    "        returns a list from 0 to n-1 with the relative order of the rows of X.\n",
    "        The item is given such that items ranked on top have are\n",
    "        predicted a higher ordering (i.e. 0 means is the last item\n",
    "        and n_samples would be the item ranked on top).\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array, shape (n_samples, n_features)\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        ord : array, shape (n_samples,)\n",
    "            Returns a list of integers representing the relative order of\n",
    "            the rows in X.\n",
    "        \"\"\"\n",
    "        if hasattr(self, 'coef_'):\n",
    "            return np.argsort(np.dot(X, self.coef_.ravel()))\n",
    "        else:\n",
    "            raise ValueError(\"Must call fit() prior to predict()\")\n",
    "\n",
    "    def score(self, X, y):\n",
    "        \"\"\"\n",
    "        Because we transformed into a pairwise problem, chance level is at 0.5\n",
    "        \"\"\"\n",
    "        X_trans, y_trans = transform_pairwise(X, y)\n",
    "        return np.mean(super(RankSVM, self).predict(X_trans) == y_trans)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задача 2. Ранжирование пациентов.##\n",
    "### Из всех пациентов, определенных как \"больные\" необходимо определить приоритет каждого пациента, чтобы лечить наиболее опасные опухоли в первую очередь ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "       [0.00559751, 0.        ],\n",
       "       [0.00559751, 0.        ],\n",
       "       [0.00551712, 0.        ],\n",
       "       [0.00549011, 0.        ],\n",
       "       [0.00543788, 0.        ],\n",
       "       [0.00543788, 0.        ],\n",
       "       [0.00405714, 0.        ],\n",
       "       [0.00372635, 0.        ],\n",
       "       [0.00372635, 0.        ],\n",
       "       [0.00372635, 0.        ],\n",
       "       [0.00372635, 0.        ],\n",
       "       [0.00372635, 0.        ],\n",
       "       [0.00367273, 0.        ],\n",
       "       [0.00367273, 0.        ],\n",
       "       [0.00367273, 0.        ],\n",
       "       [0.00365472, 0.        ],\n",
       "       [0.00344205, 0.        ],\n",
       "       [0.00344205, 0.        ],\n",
       "       [0.00247913, 0.        ],\n",
       "       [0.00247913, 0.        ],\n",
       "       [0.00247913, 0.        ],\n",
       "       [0.00247913, 0.        ],\n",
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       "       [0.00247913, 0.        ],\n",
       "       [0.00247913, 0.        ]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal_prob = lr_tuned.predict_proba(X_test)[:,1]\n",
    "mal_prob = pd.DataFrame(mal_prob)\n",
    "mal_prob.columns = ['Вероятность']\n",
    "mal_prob['Приоритет'] = 0\n",
    "mal_prob = mal_prob.sort_values(['Вероятность'], ascending = False)\n",
    "\n",
    "#mal_prob[mal_prob['Вероятность'] >= 0.75]['Приоритет'] = 2 \n",
    "mal_prob.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98035639],\n",
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       "       [0.9995107 ],\n",
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       "       [0.6323594 ],\n",
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       "       [0.09012128],\n",
       "       [0.89720907],\n",
       "       [0.00559751],\n",
       "       [0.02922334],\n",
       "       [0.00559751],\n",
       "       [0.99185041],\n",
       "       [0.97384651],\n",
       "       [0.00367273],\n",
       "       [0.00828003],\n",
       "       [0.99535207],\n",
       "       [0.99111524],\n",
       "       [0.99943556],\n",
       "       [0.93685231],\n",
       "       [0.06154496],\n",
       "       [0.99997888],\n",
       "       [0.01803678],\n",
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       "       [0.9985515 ],\n",
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       "       [0.0122323 ],\n",
       "       [0.23455897],\n",
       "       [0.00816143],\n",
       "       [0.06790772],\n",
       "       [0.9075328 ],\n",
       "       [0.00543788],\n",
       "       [0.98630952],\n",
       "       [0.24842187],\n",
       "       [0.00559751],\n",
       "       [0.00367273],\n",
       "       [0.99981893],\n",
       "       [0.01803678],\n",
       "       [0.99718958],\n",
       "       [0.99999508],\n",
       "       [0.93176723],\n",
       "       [0.02384626],\n",
       "       [0.00247913],\n",
       "       [0.01188571],\n",
       "       [0.14783355],\n",
       "       [0.01418405],\n",
       "       [0.05078755],\n",
       "       [0.01778093],\n",
       "       [0.83902111],\n",
       "       [0.99999714],\n",
       "       [0.00247913],\n",
       "       [0.00247913],\n",
       "       [0.00344205],\n",
       "       [0.01691922],\n",
       "       [0.901527  ],\n",
       "       [0.03118798],\n",
       "       [0.69574811],\n",
       "       [0.00405714],\n",
       "       [0.01205778],\n",
       "       [0.0122323 ],\n",
       "       [0.99916653],\n",
       "       [0.99923205],\n",
       "       [0.99742856],\n",
       "       [0.00372635],\n",
       "       [0.00816143]])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mal_prob = lr_tuned.predict_proba(X_test)[:,1]\n",
    "mal_prob = pd.DataFrame(mal_prob)\n",
    "mal_prob.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#вероятностная модель для мультиклассовой "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Float64Index([ 0.9964731999489324,  0.9929369528175512,  0.9890290954793411,\\n                0.985293296243271,  0.9839409124626367,  0.9836805514694685,\\n               0.9808554094041847,  0.9795439026645792,  0.9785219598773379,\\n               0.9724047441839064,\\n              ...\\n              0.07888519303914948, 0.07139122905403567, 0.07139122905403567,\\n              0.07139122905403567, 0.07139122905403567, 0.07139122905403567,\\n              0.07139122905403567, 0.07139122905403567, 0.07139122905403567,\\n              0.06668911877452169],\\n             dtype='float64', length=140)] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-275-c29f942825a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmal_prob\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmal_prob\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Вероятность'\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[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2932\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2933\u001b[0m             indexer = self.loc._convert_to_indexer(key, axis=1,\n\u001b[1;32m-> 2934\u001b[1;33m                                                    raise_missing=True)\n\u001b[0m\u001b[0;32m   2935\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2936\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[0;32m   1352\u001b[0m                 kwargs = {'raise_missing': True if is_setter else\n\u001b[0;32m   1353\u001b[0m                           raise_missing}\n\u001b[1;32m-> 1354\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\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   1355\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1356\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1159\u001b[0m         self._validate_read_indexer(keyarr, indexer,\n\u001b[0;32m   1160\u001b[0m                                     \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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[1;32m-> 1161\u001b[1;33m                                     raise_missing=raise_missing)\n\u001b[0m\u001b[0;32m   1162\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\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[0;32m   1163\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1244\u001b[0m                 raise KeyError(\n\u001b[0;32m   1245\u001b[0m                     u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[1;32m-> 1246\u001b[1;33m                         key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[0;32m   1247\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1248\u001b[0m             \u001b[1;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Float64Index([ 0.9964731999489324,  0.9929369528175512,  0.9890290954793411,\\n                0.985293296243271,  0.9839409124626367,  0.9836805514694685,\\n               0.9808554094041847,  0.9795439026645792,  0.9785219598773379,\\n               0.9724047441839064,\\n              ...\\n              0.07888519303914948, 0.07139122905403567, 0.07139122905403567,\\n              0.07139122905403567, 0.07139122905403567, 0.07139122905403567,\\n              0.07139122905403567, 0.07139122905403567, 0.07139122905403567,\\n              0.06668911877452169],\\n             dtype='float64', length=140)] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "mal_prob[mal_prob['Вероятность']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in randge len(mal_prob):\n",
    "    if()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "mal_prob.columns = {'prob'}\n",
    "mal_prob2 = mal_prob[mal_prob.prob > 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2189ebce4a8>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GhEdtyxnaewa4cF6u21HOa2R/21pbJtkxIRV8VX1OVf8EWA0cA/4gIptF5OMikjjWY0TkRqBJVbeN99wisl5EakSkprm5eZLxjTGh2Ha8DYCL5kdvwQcoz8+wZZIdFHIXjYjkAXcDnwS2A/9J4AXgD+d5yFrgJhE5BvwMeLeI/OTcg1T1AVWtVtXqgoKCyaU3xoRk2/E2ctISzy5hEK0WFqTb/rYOCrUP/xfARiAN+ICq3qSqj6nq54CMsR6jqv+gqqWqWgbcDrygqneGKbcxZhLeOOHnonm5UbVC5lhG9retb7P9bZ0Qagv/QVVdpqpfU9UGABFJBlDVasfSGWOmzd/dz+GmM6yO8u4cCFx8BYExBxN+oRb8fxvjti2hnkRVX1TVG0M93hgTPttPBBZMi/b+exi1iJpNzXREwnh3ishsoARIFZELgZH3g1kEuneMMVFu2/E2vB6hKspWyBxLfkYSmSm2v61Txi34wHsJDNSWAt8cdXsn8I8OZTLGhNG2420sK84iLWmiP3f3Bfa3zbAuHYeM+xugqhuADSJyi6o+GaFMxpgwGRwaZkedn9vWzHU7SsjK89N5tbbV7Rgz0kRdOneq6k+AMhH5m3PvV9VvjvEwY0yU2H+qk56BoZgYsB1Rnp/OL7fX090/GBPvSmLJRIO2I5N2M4DMMT6MMVEsVi64Gm1kpo7144ffRF06Pwj++5XIxDHGhFPN8TZmZ6UwJzvF7SghGz1T54I50T/QHEtCvfDqGyKSJSKJIvK8iLSIiF1EZUwUU1W2Hm2luiz6L7gabaTgWws//EKdh/8eVe0AbgR8wGLgC46lMsZM2/HWbho7+rikPM/tKJOSmuRlTnaKLaLmgFAL/sgCaeuAR1X1tEN5jDFhMjLTJdYKPth2h04JteA/LSL7gWrgeREpAHqdi2WMma7Xjp4mPyOZhQXRvWDaWMoL0jna3IWqLaIWTqEuj/xF4FKgWlUHgC7gg04GM8ZMnaryam0r7yqfFVP99yMW5KfT2TdI85k+t6PMKJOZ5FpJYD7+6Mf8T5jzGGPC4MTpbhrae7lkwSy3o0zJ2amZzV0UZsbODKNoF1LBF5FHgIXADmAoeLNiBd+YqPRabWCYLRb774Gz6/bXtnTxrhj9HqJRqC38amCZWoeaMTHh1dpW8tKTWFQ45nYVUW9OTipJCR6bqRNmoQ7a7gFmOxnEGBMeqsprR0/HbP89gNcjLMhLt7n4YRZqCz8f2CsiW4GzoyiqepMjqYwxU1Z3uod6fw/rryx3O8q0LMhP52Bjp9sxZpRQC/6XnQxhjAmfjYebAVi7KN/lJNNTXpDOc/saGRgaJtEb8vbbZhyhTst8CTgGJAY/fx14w8Fcxpgp2niwhTnZKTE5/3608oIMBoeVutPdbkeZMUJdS+dTwBPAD4I3lQC/ciqUMWZqBoeG2XSkhSsXF8Rs//0I2+4w/EJ9n/RZYC3QAaCqh4BCp0IZY6Zmp89PZ+8gV1QUuB1l2kbeodjAbfiEWvD7VLV/5IvgxVfjTtEUkRQR2SoiO0XkTRGxJZaNcdjLB1sQgbWLYn/uek5aErlpibbdYRiFWvBfEpF/JLCZ+fXA48DTEzymD3i3qq4EVgE3iMglU49qjJnIxkPNVJXmkJOW5HaUsCgvyOCIdemETagF/4tAM7Ab+DTwG+Cfx3uABoy8NCcGP+zCLWMc0t4zwI46P1dVxPbsnNHK89OtDz+MQp2lM0xgkPbPVPVWVf1hKFfdiohXRHYATcAfVPW1MY5ZLyI1IlLT3Nw82fzGmKAtR1oYVrhicez3349YVJhBy5k+/N39Ex9sJjRuwZeAL4tIC7AfOCAizSJyXyhPrqpDqroKKAUuFpHlYxzzgKpWq2p1QcHM+UU1JtJeOthMRnICq+bmuB0lbCqKAktDHG6yfvxwmKiF/1cEZuesUdU8VZ0FvAtYKyJ/HepJVNUPvAjcMNWgxpjzGx5Wnt/XxJWL82fURUoVhZkAHLKCHxYT/WZ8DLhDVY+O3KCqtcCdwfvOS0QKRCQn+HkqcB2BdwnGmDDbXd9OU2cf11UWuR0lrEpyUklN9NoSC2Ey0dIKiaracu6NqtosIoljPWCUYmCDiHgJvLD8XFWfmWJOY8w4nt/XiEfgmiUz6/IYj0dYVJhhXTphMlHBH2+kZNxRFFXdBVw46UTGmEl7bl8T1fNnkZs+M6ZjjlZRmMHmI61ux5gRJurSWSkiHWN8dAIrIhHQGDO+en8Pexs6uLZyZrXuR1QUZXKqo5eO3gG3o8S8cVv4quqNVBBjzNS8sK8RgGtnWP/9iIrgJi6HGs9w0fxcl9PEtpkznG9MnPr93kbK8tJifnXM83lraqYN3E6XFXxjYlhbVz+bj7Ryw/LimF8d83xKc9NISfRwqNEGbqfLCr4xMez3e08xNKy8f0Wx21Ec4/UICwsybC5+GFjBNyaGPbv7FPNmpbG8JMvtKI6qKMzgkM3FnzYr+MbEKH93P5sPt7BuxcztzhlRUZTJyfZeOm2mzrRYwTcmRv3+zUYGZ3h3zoiRmTq2VPL0WME3JkY9u7uBubNSZ3x3DgRa+IB160yTFXxjYlBTZy+vHG7hxqo5M747B2DerDSSEjw2cDtNVvCNiUFP7TjJ0LByy+oSt6NExMhMnQOnrIU/HVbwjYlBv3ijnpWl2SwKLh8cDypnZ7L/VIfbMWKaFXxjYsy+hg72NnTwodWlbkeJqMriLBo7+jjdZbtfTZUVfGNizC+315PgET6wco7bUSJqaXHg3cy+BmvlT5UVfGNiyODQML/cXs81SwuZNQOXQh5PZXFgNpIV/Kmzgm9MDNl4qIXmzr64GawdLT8jmYLMZPY12MDtVFnBNyaG/PS14+RnJPPupTNzKeSJLJ2daS38abCCb0yMqPf38ML+Jm5bU0pSQnz+6S4rzuJw0xkGhobdjhKT4vO3xpgY9NjWEyhw+5p5bkdxTWVxFv1Dw9TaEgtT4ljBF5G5IvJHEdknIm+KyF86dS5jZrqBoWF+9nod1ywpZO6sNLfjuMZm6kyPky38QeBvVbUSuAT4rIgsc/B8xsxYz+1tpKmzjz95V/y27gEWFmSQ5PVYwZ8ixwq+qjao6hvBzzuBfUD8TS0wJgz+Z8txSnJSuXrJzNyoPFSJXg+LCjPYawV/SiLShy8iZcCFwGuROJ8xM8me+na21LbysUvn4/XM/IXSJrJsThZ7T3agqm5HiTmOF3wRyQCeBP5KVd/xsiwi60WkRkRqmpubnY5jTMz54cZaMpITuCPOu3NGVJVm09rVz8n2XrejxBxHC76IJBIo9j9V1V+MdYyqPqCq1apaXVBQ4GQcY2LOSX8Pz+xq4LY1c8lKSXQ7TlRYUZINwK46v8tJYo+Ts3QEeAjYp6rfdOo8xsxkP9p0FICPry1zN0gUqSzOIsEj7KpvdztKzHGyhb8W+FPg3SKyI/ixzsHzGTOjdPQO8LOtdaxbUUxpbvxOxTxXSqKXJbMz2e2zgj9ZCU49saq+AtgIkzFTtGHTMTr7Bvn0leVuR4k6VaXZPLurAVWNix2/wsWutDUmCnX0DvDgK0e5rrKQ5cE+a/OWFSU5dPQOcuJ0t9tRYooVfGOi0IZNx2jvGeAvr13sdpSoVFUaHLi1bp1JsYJvTJTpHNW6X1FqrfuxLC7KJCnBwy6fzdSZDCv4xkSZH1vrfkJJCR4qi7OshT9JVvCNiSItZ/r4wcu1XL+syFr3E6gqyWZPfTtDw3bFbais4BsTRf7r+UP0DAzxxfctdTtK1Fs9P4eu/iH2n7J1dUJlBd+YKFHbfIb/fe0Ed1w8l4UFGW7HiXrV82cBsO14m8tJYocVfGOixDd+d4DkBI/13YeoNDeVwsxkao5ZwQ+VFXxjosCmwy387s1TfPqqhRRkJrsdJyaICNVludbCnwQr+Ma4rG9wiC/9ag/z89JYb1fVTspF82dR7++hob3H7SgxwQq+MS774cu11LZ08ZWbLiAl0et2nJhSPT8XsH78UFnBN8ZFdae7uf+Fw6xbMTvud7OaimVzskhN9Fo/fois4BvjkuFh5e+f3EWCR/jSjbbd81Qkej2smpvD68dOux0lJljBN8Ylj7x6nM1HWvnnG5dRnJ3qdpyYdenCPPY2dNDW1e92lKhnBd8YF9Q2n+Frv93H1UsKuH3NXLfjxLS1i/JQhS21rW5HiXpW8I2JsIGhYf7m5ztJTvDy9VuqbD33aaoqzSE9ycumwy1uR4l6jm2AYowZ29d+s58ddX7++6OrKcpKcTtOzEv0enhXeR6bj1gLfyLWwjcmgp7d1cDDm45y92VlvL+q2O04M8ZlC/M42tJFvd/m44/HCr4xEXK4qZO/e2Inq+fl8I/rKt2OM6NcXpEPYN06E7CCb0wENHf2cfePXic1KYHvfHQ1SQn2pxdOS4oyyc9IZuMhK/jjcey3TkQeFpEmEdnj1DmMiQXd/YN8YsPrtJ7p5+G7q5mTY1Mww01EuGZJAS8eaGJgaNjtOFHLyWbGj4EbHHx+Y6Je78AQn35kG3vq27n/jgupKs1xO9KMdW1lEZ29g3YR1jgcK/iq+jJgP3kTt/oGA8X+lcMtfP2WKq5bVuR2pBntiop8krwent/X5HaUqOV6R6KIrBeRGhGpaW5udjuOMWHR2TvAJzfU8NLBZr528wo+XG0XVzktPTmByxbl8dy+RlRt28OxuF7wVfUBVa1W1eqCggK34xgzbU0dvdz2g1fZfKSVb9xaxe0Xz3M7Uty4trKI463dHGk+43aUqOR6wTdmJtnl83PzdzdzrLWLh+6q5iPWso+o6yoDK47+35uNLieJTlbwjQkDVeWRV49z6/e2APDY+kttuWMXFGenctH8XJ7eedLtKFHJyWmZjwJbgCUi4hORTzh1LmPc1NTRy6cf2caXfrWHtYvyeOZzl7OiNNvtWHHrg6vmsP9UJwdOdbodJeo4OUvnDlUtVtVEVS1V1YecOpcxblBVHq+p47pvvsRLB5v5p3WVPHTXGnLTk9yOFtfWrSjG6xGe2lnvdpSoY4unGTMFb5xo41+f2cv2E37WlOXy9VuqKC/IcDuWAfIzkrlsYR6/3nGSv71+CR6PrUY6wvrwjZmEYy1d/MWj2/nQdzfja+vh67es4LH1l1qxjzK3XlSKr62HTUdsqYXRrIVvTAgON3XynRcO89TOkyR6Pfz5NYu49+qFZCTbn1A0umH5bHLTEnl06wmuqLDp3iPst9WYcbx5sp3vvXiEZ3c3kJLg5ZNXlPPJKxZQmGnr2Eez5AQvt15Uyo82HaOps9f+v4Ks4BtzjqFh5Q97G/nRpqO8dvQ0GckJfOaqhXzi8gXkZSS7Hc+E6I6L5/HDjUf52dY6/uLaCrfjRAUr+MYEdfQO8PPX69iw5Rh1p3soyUnln99fyYer55Kdmuh2PDNJ5QUZXLOkgA2bj/GpK8pJTfK6Hcl1VvDNtKgq3f1DdPYOMqyKCHhEyEhOIC3JG/X7taoqO+r8/GxrHU/vOkl3/xAXl83in9ZVcl1lEQlem9cQyz5z9SI+8oMtPL6tjo9dWuZ2HNdZwTcTUlV8bT3srm9nX0MHJ053c+J0N3Wne/B39zM4PPZCVUleDzlpiRRmJTM/L52yvDTK8tJZOjuLxbMzSE5wr8Xl7+7n1ztO8ujWE+w/1UlqopcPrCzmTy8ps4umZpA1ZbmsnpfDAy/XcvuaeXG/8YwVfPMOqsqhpjNsPNTCpsMtbD/RRlv3AAAegTk5qczPS+O6ykJmpSeRlZpIZkoCXhGUQB/4mb5B2rr78XcNcKqjlzfr2/ndnlMMBV8cEjzC4qJMlpdksbwkm+Ul2SwrziIl0bkXgfaeAX7/5ime3d3AK4daGBxWVpRk8+83L+emlXPITLFum5lGRPiLayu4+0ev89PXjvPxtQvcjuQqK/gGgP7BYbbUtvLb3Q28sL+Jps4+ABbkp3P9siKqSnOoKs1myezMKbfMB4aGqTvdzf5Tneyub2dPfTvP7Wvi5zU+ALzBF4EVJVmsKMlmRWkOS2dnTvlFYGBomH0NHWw81MLGQ81sO97GwJBSkpPKJ65YwE0r53DBHGvNz3RXLS5g7aI8/uv5Q3xodWlcj8dINK0bXV0Ah+KJAAAKUElEQVRdrTU1NW7HiBuDQ8NsPNzC0ztP8tzeRjp6B0lP8nL10kKuqijgskV5lOamOZpBVTnZ3stuXzu76/3sru9gt89/9h3FyDuBRYUZlOSmUpqbSlFmCmlJXtKSE0jwCH2DQ/T0D9Pa1YevrQdfWw97GzrY19BB/2Bgu7vK4iyurMjnhuWzWTU3J+rHFkx47alv58b7X+GetQu47wPL3I4TViKyTVWrQznWWvhx6GhLF4/X1PHkGz4aO/rISkng+mWzed/y2Vxeke9ot8q5RISSnFRKclK5YflsIPAiUO/vCb4IBD6217Xxm90N5x0vGC03LZHFRZl87JL5rCjN5tKFeTYPO84tL8nmo++ax483H+UDK4u5cF6u25FcYS38ONHVN8izuxt4osbH1mOn8Qhcs6SQD1eX8u6lRTExmDU0rDR19tLc2Ud3/xDd/YMMDCmpiV5SEr3kpiUyJyeVdLv61Yyhs3eA93zrZTJTEnjqzy+PaMPGSdbCN0CgpbzteBs/r6njmV0NdPcPUV6Qzhfft5QPXVhCYVZstXq9HqE4O5Xi7FS3o5gYlJmSyNc+tIK7f/Q6X/rVHr5xa1Xcde1ZwZ+BGjt6efINH0/U+Kht6SI9yctNK+fw4epSVs/LjbtfcmNGXL2kkM+9exH3v3CYFaXZcTc33wr+DNE3OMRze5t4fFsdLx9sZljh4gWz+LNrFrFuxWzSkuy/2hiAv7puMfsaOviXp94kJy2Jm1bOcTtSxFgViGHDw8r2Oj9P7ajn1ztP4u8eoDg7hc9es4hbLyplfl662xGNiTpej/Cdj67mroe38teP7aCnf5Db1sTHRvNW8GOMqrLT186zu07y7K4GTrb3kpTg4T3LivhI9VzWLsrHaxs+GDOulEQvD929hj/76Rv8/ZO7Odh4hr+7YYmrV39HghX8GNDZO8Cmw628dLCZlw40cbK9l0SvcGVFAZ9/7xKuW1ZEll0lasykZCQn8NBd1fzrM3t56JWjbDrcwr/fvIKL5s/cKZuOTssUkRuA/wS8wIOq+h/jHW/TMgP83f1sP+Fn2/E2th47zRvH2xgcVjKSE1i7KI9rK4t477LZZKdZkTcmHF7Y38gXn9xNU2cf71lWxPory7lofmxMcJjMtEzHCr6IeIGDwPWAD3gduENV957vMfFW8HsHhvC19XCosZMDjZ0cbOxk/6lOapu7gEBf47LiLC6vyOeqxQVcND+XRFu90RhHdPUN8vArR3lgYy2dvYNUFGbw3gtmc83SQpaXZEVtd0+0FPxLgS+r6nuDX/8DgKp+7XyPicWCPzys9A8N0zswRN/gMH0Dw/QODtHdP0R7zwD+7n783QP4uwdo6+6nsaOXk/4e6v29tJzpO/s8IjB/VhqLizJZOTeHi+bnUlWabbNrjImw7v5Bnt55kl+8UU/N8TaGhpUkr4elxZlUFGYyd1YqpblpFGYmk5OWSHZq4CMtKYFEr0T8XUG0XHhVAtSN+toHvMuJE914/0Z6+odQAAUlMLgZ+BcUDfwbfG079z7Ofj5yjI76etSxwc9HztE/NHx2rZZQZCYnUJiVzJycVJbNyWJOdioluaksLspkYUGGbdBgTBRIS0rgtjXzuG3NPPzd/Ww+0spOn5/dvnY2HW6hsbOX8drJSV4PiV4hMcFDktdDguetF4GR/SJEQAgsLSJAXkYSj997mePfm5MFf6yXuXf8mERkPbAeYN68qU2NqijMDBTec36I5/5QA/eP/mG/9TXBrznP/SPPw6jbkxI8JCd4SEn0vuPf1CQP2alJ5KQlkhNsAdhmGsbElpy0JNatKGbdiuKzt/UNDnEy+A69vXuA9p7AR8/AEP2Dw/QPDTMw8u/QMAND+lbjMthYHNa3NyojtTS3kwXfB8wd9XUpcPLcg1T1AeABCHTpTOVE37pt1VQeZowxk5ac4GVBfjoL8mPvOhcnm5yvAxUiskBEkoDbgaccPJ8xxphxONbCV9VBEflz4P8ITMt8WFXfdOp8xhhjxufoFBBV/Q3wGyfPYYwxJjQ2imiMMXHCCr4xxsQJK/jGGBMnrOAbY0ycsIJvjDFxIqo2MReRZuC42zlGyQda3A4xgVjICLGRMxYyQmzktIzhM1HO+apaEMoTRVXBjzYiUhPqokRuiYWMEBs5YyEjxEZOyxg+4cxpXTrGGBMnrOAbY0ycsII/vgfcDhCCWMgIsZEzFjJCbOS0jOETtpzWh2+MMXHCWvjGGBMnrOAT2GxdRA6IyGER+eIY998tIs0isiP48cloyxg85iMisldE3hSR/410xmCGiX6W3xr1czwoIv4ozDhPRP4oIttFZJeIrIvCjPNF5PlgvhdFpNSFjA+LSJOI7DnP/SIi/xX8HnaJyOpIZwzmmCjnUhHZIiJ9IvL5SOcLZpgo458Ef4a7RGSziKyc0olUNa4/CCzdfAQoB5KAncCyc465G/hOlGesALYDucGvC6Mx5znHf47AstlRlZFAn+lngp8vA45FYcbHgbuCn78beMSF/+8rgdXAnvPcvw74LYEN4y4BXot0xhBzFgJrgH8HPh+lGS8b9bf9vqn+LK2FDxcDh1W1VlX7gZ8BH3Q507lCyfgp4L9VtQ1AVZsinBEm/7O8A3g0IsneEkpGBbKCn2czxk5tDgsl4zLg+eDnfxzjfsep6svA6XEO+SDwPxrwKpAjIsXjHO+IiXKqapOqvg4MRC7VOzJMlHHzyN828CqBHQQnzQr+2Jutl4xx3C3Bt1NPiMjcMe53UigZFwOLRWSTiLwqIjdELN1bQv1ZIiLzgQXACxHINVooGb8M3CkiPgL7OXwuMtHOCiXjTuCW4Oc3A5kikheBbJMR8u+DmZRPEHjnNGlW8EPbbP1poExVq4DngA2Op3q7UDImEOjWuZpAy/lBEclxONe5Qtq4Puh24AlVHXIwz1hCyXgH8GNVLSXQLfGIiETybyWUjJ8HrhKR7cBVQD0w6HSwSZrM74MJgYhcQ6Dg//1UHm8FP4TN1lW1VVX7gl/+ELgoQtlGhLIhvA/4taoOqOpR4ACBF4BICmnj+qDbiXx3DoSW8RPAzwFUdQuQQmA9k0gJ5XfypKp+SFUvBP4peFt75CKGZDK/D2YCIlIFPAh8UFVbp/IcVvBD2Gz9nH7Hm4B9EcwHoW0I/yvgGgARySfQxVMb0ZQhblwvIkuAXGBLhPNBaBlPANcCiEglgYLfHE0ZRSR/1LuOfwAejmC+UD0FfCw4W+cSoF1VG9wOFYtEZB7wC+BPVfXglJ/IjRHpaPsg8Lb9IIGZEf8UvO2rwE3Bz78GvEmg3/SPwNIozCjAN4G9wG7g9mj8WQa//jLwH1H8/70M2BT8/94BvCcKM94KHAoe8yCQ7ELGR4EGAoOdPgLvjO4F7h31O/nfwe9hN1Dt0v/3RDlnB2/vAPzBz7OiLOODQFvw93EHUDOV89iVtsYYEyesS8cYY+KEFXxjjIkTVvCNMSZOWME3xpg4YQXfGGPihBV8Y4yJE1bwjTEmTljBN8aYOPH/AQ3eYgYm/FSUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mal_prob2.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}