Stepik. Введение в DS. Задание 3.5 И на Марсе будут яблони цвести.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fb7ca16",
   "metadata": {},
   "source": [
    "Переберите параметры с помощью GridSearchCV и обучите Random Forest на данных, указанных в предыдущем стэпе. Передайте в GridSearchCV модель с указанием random_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "899871c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e51a16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.read_csv('E:\\\\pyrhon\\\\progectTEST\\\\training_mush.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8cd2042a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x.pop('class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9a6e3e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rf = RandomForestClassifier(random_state=0, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "929a4c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'n_estimators':range(10,51,10),\n",
    "'max_depth':range(1,13,2),\n",
    "'min_samples_leaf':range(1,8),\n",
    "'min_samples_split':range(2,10,2)   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5b8d5a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_cv_clf = GridSearchCV(clf_rf, params,n_jobs=-1, cv = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e0a4d3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(n_jobs=-1, random_state=0),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'max_depth': range(1, 13, 2),\n",
       "                         'min_samples_leaf': range(1, 8),\n",
       "                         'min_samples_split': range(2, 10, 2),\n",
       "                         'n_estimators': range(10, 51, 10)})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cv_clf.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "82dffb04",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_clf = grid_search_cv_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23f5bd31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 10}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_clf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4655c938",
   "metadata": {},
   "source": [
    "Выбранное число деревьев в лесу 10, глубина деревьев 9, минимальное число образцов в листах 1, а минимальное число образцов для сплита 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0869565",
   "metadata": {},
   "source": [
    "Задание 3.5.2 Выберите правильные утверждения о важности фичей для нашего классификатора"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3cc9f697",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_estimator = grid_search_cv_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3ce74ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = best_estimator.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fe527545",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_df = pd.DataFrame({'features': list(x), 'feature_importances': feature_importances}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "605dd8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>features</th>\n",
       "      <th>feature_importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>odor</td>\n",
       "      <td>0.188376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gill-color</td>\n",
       "      <td>0.103861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>stalk-root</td>\n",
       "      <td>0.103793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spore-print-color</td>\n",
       "      <td>0.083564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gill-spacing</td>\n",
       "      <td>0.080840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ring-type</td>\n",
       "      <td>0.070726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bruises</td>\n",
       "      <td>0.070109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gill-size</td>\n",
       "      <td>0.068461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>stalk-surface-below-ring</td>\n",
       "      <td>0.048296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>population</td>\n",
       "      <td>0.043783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>stalk-surface-above-ring</td>\n",
       "      <td>0.031802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>habitat</td>\n",
       "      <td>0.022611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cap-color</td>\n",
       "      <td>0.021773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>stalk-color-above-ring</td>\n",
       "      <td>0.017689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ring-number</td>\n",
       "      <td>0.013910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>veil-color</td>\n",
       "      <td>0.010881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>stalk-shape</td>\n",
       "      <td>0.007563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>stalk-color-below-ring</td>\n",
       "      <td>0.006253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cap-surface</td>\n",
       "      <td>0.003130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cap-shape</td>\n",
       "      <td>0.002577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>veil-type</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gill-attachment</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    features  feature_importances\n",
       "4                       odor             0.188376\n",
       "8                 gill-color             0.103861\n",
       "10                stalk-root             0.103793\n",
       "19         spore-print-color             0.083564\n",
       "6               gill-spacing             0.080840\n",
       "18                 ring-type             0.070726\n",
       "3                    bruises             0.070109\n",
       "7                  gill-size             0.068461\n",
       "12  stalk-surface-below-ring             0.048296\n",
       "20                population             0.043783\n",
       "11  stalk-surface-above-ring             0.031802\n",
       "21                   habitat             0.022611\n",
       "2                  cap-color             0.021773\n",
       "13    stalk-color-above-ring             0.017689\n",
       "17               ring-number             0.013910\n",
       "16                veil-color             0.010881\n",
       "9                stalk-shape             0.007563\n",
       "14    stalk-color-below-ring             0.006253\n",
       "1                cap-surface             0.003130\n",
       "0                  cap-shape             0.002577\n",
       "15                 veil-type             0.000000\n",
       "5            gill-attachment             0.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances_df.sort_values('feature_importances', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a746c863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp = pd.DataFrame(best_estimator.feature_importances_, index=x.columns, columns=['importance'])\n",
    "imp.sort_values('importance').plot(kind='barh', figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7395bb27",
   "metadata": {},
   "source": [
    "Задние 3.5.3  Теперь у нас есть классификатор, определяющий какие грибы съедобные, а какие нет, испробуем его! Предскажите съедобность этих данных грибов и напишите в ответ число несъедобных грибов (класс равен 1).\n",
    "Заметьте, что для использования этого леса на новых грибах, нам нужно будет заполнить значения параметров гриба, часть из которых определить проще (например, цвет шляпки), а для определения части понадобится специалист-миколог.\n",
    "То есть в этом случае нам придётся самим экстрагировать признаки из объекта. Для зрительных признаков типа формы, цвета можно использовать более сложную модель (например, свёрточную нейронную сеть) и подавать на вход фотки гриба. И модель сама извлечёт признаки вместо того, чтобы нам описывать самим. Но одной фоткой тут не отделаешься - для определения запаха понадобится ещё детектор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "de48b55d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = pd.read_csv('E:\\\\pyrhon\\\\progectTEST\\\\testing_mush.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8aa56730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>stalk-shape</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
       "0          0            3          8        1     3                1   \n",
       "1          5            3          4        1     5                1   \n",
       "2          3            3          4        0     8                1   \n",
       "3          3            2          2        0     7                1   \n",
       "4          3            2          2        0     8                1   \n",
       "5          0            2          3        0     5                1   \n",
       "6          5            2          3        1     2                1   \n",
       "7          5            3          8        1     0                1   \n",
       "8          2            0          9        0     2                1   \n",
       "9          2            0          3        0     2                1   \n",
       "\n",
       "   gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
       "0             0          0           4            0  ...   \n",
       "1             0          0          10            1  ...   \n",
       "2             0          1           0            1  ...   \n",
       "3             0          1           0            1  ...   \n",
       "4             0          1           0            1  ...   \n",
       "5             1          0           7            0  ...   \n",
       "6             0          0          10            1  ...   \n",
       "7             0          0           2            0  ...   \n",
       "8             0          0           2            0  ...   \n",
       "9             0          0           3            0  ...   \n",
       "\n",
       "   stalk-surface-below-ring  stalk-color-above-ring  stalk-color-below-ring  \\\n",
       "0                         2                       7                       7   \n",
       "1                         2                       6                       6   \n",
       "2                         2                       7                       6   \n",
       "3                         1                       6                       7   \n",
       "4                         1                       6                       6   \n",
       "5                         2                       7                       7   \n",
       "6                         0                       7                       7   \n",
       "7                         2                       7                       7   \n",
       "8                         1                       6                       0   \n",
       "9                         1                       0                       0   \n",
       "\n",
       "   veil-type  veil-color  ring-number  ring-type  spore-print-color  \\\n",
       "0          0           2            1          4                  2   \n",
       "1          0           2            1          4                  3   \n",
       "2          0           2            1          0                  7   \n",
       "3          0           2            1          0                  7   \n",
       "4          0           2            1          0                  7   \n",
       "5          0           2            2          4                  7   \n",
       "6          0           2            1          4                  1   \n",
       "7          0           2            1          4                  2   \n",
       "8          0           2            1          2                  1   \n",
       "9          0           2            1          2                  1   \n",
       "\n",
       "   population  habitat  \n",
       "0           3        3  \n",
       "1           5        0  \n",
       "2           4        4  \n",
       "3           4        0  \n",
       "4           4        4  \n",
       "5           3        1  \n",
       "6           4        5  \n",
       "7           2        3  \n",
       "8           4        0  \n",
       "9           5        0  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "22dd2f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = grid_search_cv_clf.predict(x_test) #предсказываем обученый классификатор на тестовых данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5590c10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result # получился массив 0 и 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bf8c9697",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1055\n",
       "1     976\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(result).value_counts() #создаем серию из получившихся значений и  сортируем ее"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd175960",
   "metadata": {},
   "source": [
    "Задание 3.5.4 Создайте confusion matrix по предсказаниям, полученным вами в прошлом уроке и правильным ответам, (воспользуйтесь паролем из предыдущего задания, чтобы открыть их). Выберите из предложенных вариантов правильный"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "95039485",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "465f2b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_y_mush = pd.read_csv('E:\\\\pyrhon\\\\progectTEST\\\\testing_y_mush.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4a004977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "578570b6",
   "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>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2031 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class\n",
       "0         0\n",
       "1         0\n",
       "2         1\n",
       "3         1\n",
       "4         1\n",
       "...     ...\n",
       "2026      1\n",
       "2027      0\n",
       "2028      1\n",
       "2029      0\n",
       "2030      1\n",
       "\n",
       "[2031 rows x 1 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_y_mush"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "918d6d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1055,    0],\n",
       "       [   0,  976]], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(testing_y_mush, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e5207be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAAD4CAYAAAAw/yevAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAW2ElEQVR4nO3de3hV1ZnH8e+bhAAqyEUTIgSLGrGgbbWW2trxUmrBSxuoOtJRixZN2wdb7dQq1OfRsW1a7MXRqtSJV1QUqaMD3rBIvVcFWh1uSo2XhkhIqIKgRYTknT+yYQ6Qy8nhJGdl+fv47Cfn7NtaW+LvLN+z18bcHRERCUterjsgIiK7UjiLiARI4SwiEiCFs4hIgBTOIiIBKujsBnoffoFuB5FdrFt0fa67IAHqVYDt7jk6kjmbXrp+t9vrLBo5i4gEqNNHziIiXcriGHMqnEUkLnn5ue5BViicRSQuFmwZuUMUziISF5U1REQCpJGziEiANHIWEQlQJCPnOD5iRES2yctPf2mHmd1qZg1mtixl3QAzm29mryU/+6dsm2pm1Wa20szGpKz/rJktTbb9zqz9TxCFs4jExfLSX9p3OzB2p3VTgAXuXgYsSN5jZiOACcDI5JjpZrbtE+D3QAVQliw7n3MXCmcRiYtZ+ks73P1p4N2dVpcDM5LXM4BxKetnuftmd38TqAZGmVkJ0Nfdn/fmv93kjpRjWqVwFpG4dGDkbGYVZrY4ZalIo4Vid68DSH4WJesHA6tS9qtN1g1OXu+8vk36QlBE4tKBuzXcvQqoylbLLTXRxvo2KZxFJC75nT59u97MSty9LilZNCTra4HSlP2GAKuT9UNaWN8mlTVEJC5ZrDm3Yi4wMXk9EZiTsn6CmfU0s2E0f/G3MCl9bDSzo5K7NL6VckyrNHIWkbhkcRKKmd0DHAfsY2a1wBXANGC2mU0CaoDTAdx9uZnNBlYAW4HJ7t6YnOp7NN/50Rt4NFnapHAWkbhkcRKKu3+zlU2jW9m/EqhsYf1i4NCOtK1wFpG4aPq2iEiAIpm+rXAWkbjoYfsiIgFSWUNEJEAqa4iIBEgjZxGRACmcRUQCpC8ERUQCpJqziEiAVNYQEQmQRs4iIuFJ46/n6xYUziISFYWziEiALE/hLCISHI2cRUQCpHAWEQmQwllEJERxZLPCWUTiopGziEiA8vI0Q1BEJDgaOYuIhCiObFY4i0hcNHIWEQmQwllEJECavi0iEiCNnEVEAqRwFhEJkMJZRCRACmcRkRDFkc0KZxGJi6Zvi4gEKJayRhwfMSIi21gHlvZOZfZDM1tuZsvM7B4z62VmA8xsvpm9lvzsn7L/VDOrNrOVZjZmdy5D4dyGG684k78v+CWL//CTFrcf/IlinpzxI9a/+J9cdPborLRZ2KOAO6edy7I5V/D0HRcztGQAAENL+vPczEt4YdYU/nLfZZx32pey0p7k1nPPPM3XTx7DKWNP4JabqnLdnSiYWdpLO+cZDPwAONLdDwXygQnAFGCBu5cBC5L3mNmIZPtIYCww3czyM70OhXMb7nzwBcon39Dq9nXvfcCPrvoD19zxpw6fe2jJAB676cJd1p8z7gus27iJQ8uv5LqZT1B5YTkAdWs3cPw5V3PUhGkcc/avufjcEyjZd+8OtyvhaGxs5BeVP2X6jTfzwNyHmffIQ7xeXZ3rbnV72QrnRAHQ28wKgD2A1UA5MCPZPgMYl7wuB2a5+2Z3fxOoBkZleh3thrOZHWJml5rZ78zs2uT1JzNtsDt57q+v8+57/2x1+9p17/OXFTVs2dq4y7YJJ32OZ+68mBdmTeG6yyaQl+aU0lOO+xQzH3wRgPsff4njRg0HYMvWRj7ashWAnoU9yIukrvZxtmzpEkpL92dIaSk9CgsZe9LJPPnEglx3q9vrSDibWYWZLU5ZKradx93fBn4D1AB1wHvu/keg2N3rkn3qgKLkkMHAqpSu1CbrMtJmOJvZpcAsmqszC4FFyet7zGxKpo3GbviwYk776hEcf27zSLexqYkJJ30urWP3K9qb2jXrAGhsbGLD+5sY2G9PAIYU92PhvVN57dGf8dvbH6du7Xuddg3S+Rrq6xlUMmj7+6LiYurr63PYozhYnqW9uHuVux+ZsmyvLSW15HJgGLAfsKeZndVW0y2s80yvo727NSYBI919yw49MLsaWA5Ma+mg5NOnAqBgyHEU7DMy0/51S8ePGs4RI4by7F2XANC7Zw/Wvvs+APf+9nz2HzyQwh75lA4awAuzmj/jbrj7Se6c+0KL/6vlyR9vbf16Rp3xS0r23ZvZV5/PA4+/RMO7G7vmoiTrvIX/bmO50yCXsvjv8CvAm+6+Njnv/cAXgXozK3H3OjMrARqS/WuB0pTjh9BcBslIe+HcRPMnxt93Wl+SbGtR8ulTBdD78Asy/uTorsyMux58kcuvm7vLtjN+dBPQXHO+6adnM+b8a3fY/nb9eoYM6s/bDevJz8+j7169efe9D3bYp27te6x4fQ1HH3EgDzz+cqddh3Su4uJBrKlbs/19Q309RUVFbRwh6chiONcAR5nZHsAmYDSwGPgAmEjz4HQiMCfZfy5wdzJ43Q8oo7nikJH2as4XAQvM7FEzq0qWeTR/Q7nrt1kCwBMLVzL+K59h3/57AdC/7x4MLenfzlHNHn5qKWd+7fMAfOMrh/PUor8BMLioH7169gCgX5/efOEzB/C3txpaPY+Eb+Shh1FT8xa1tavY8tFHzHvkYY49/su57la3Z5b+0hZ3fxG4D/grsJTmvKyiOZRPMLPXgBOS97j7cmA2sAKYB0x2912/kEpTmyNnd59nZgfT/I3jYJprKrXAot1ptLuY8ctz+JfPlrFPv72onvczfnbjI/QoaL4z5ub7nqV4YB+em3kJffbsRZM7F5x5HIefWsmrb6zhyhse4sHfX0CeGVu2NvLDabOpqVvXbpu3/8+fufXn32LZnCtYt+EDzp5yGwDDhw1i2r+Px3EM45o7FrC8OuP/Y5IAFBQUMPWyy/lexXk0NTUybvypHHRQWa671e1lszTk7lcAV+y0ejPNo+iW9q8EKrPRtrl3btXh41jWkPatW3R9rrsgAepVsPtPxhh+6WNpZ87Kq8YEW+TX9G0RiUos36kqnEUkKunOKQidwllEoqKRs4hIgGK5V1zhLCJRiSSbFc4iEhc9bF9EJEAaOYuIBEg1ZxGRAEWSzQpnEYmLRs4iIgGKJJsVziISF80QFBEJkMoaIiIBiiSbFc4iEheNnEVEAhRJNiucRSQu+kJQRCRAKmuIiARI4SwiEqBIslnhLCJx0chZRCRAkWSzwllE4qK7NUREApQXydBZ4SwiUYkkmxXOIhIXfSEoIhKgSErOCmcRiYu+EBQRCZChcBYRCU4kA2eFs4jEJZYvBPNy3QERkWwyS39p/1zWz8zuM7NXzewVM/uCmQ0ws/lm9lrys3/K/lPNrNrMVprZmN25DoWziEQlzyztJQ3XAvPc/RDg08ArwBRggbuXAQuS95jZCGACMBIYC0w3s/yMryPTA0VEQpSXZ2kvbTGzvsAxwC0A7v6Ru68HyoEZyW4zgHHJ63Jglrtvdvc3gWpgVMbXkemBIiIh6khZw8wqzGxxylKRcqoDgLXAbWb2kpndbGZ7AsXuXgeQ/CxK9h8MrEo5vjZZlxF9ISgiUenIszXcvQqoamVzAXAE8H13f9HMriUpYbSipYY97c7sRCNnEYmKdWBpRy1Q6+4vJu/vozms682sBCD52ZCyf2nK8UOA1Zleh8JZRKJiZmkvbXH3NcAqMxuerBoNrADmAhOTdROBOcnrucAEM+tpZsOAMmBhptehsoaIRCXLk1C+D8w0s0LgDeBcmge1s81sElADnA7g7svNbDbNAb4VmOzujZk2rHAWkahk89ka7v4ycGQLm0a3sn8lUJmNthXOIhKVWGYIKpxFJCp6toaISIA0chYRCVAc0axwFpHI5EdS11A4i0hUVNYQEQlQJNmscBaRuHTk2RohUziLSFQiyebOD+d1i67v7CakG+p/4lW57oIEaNP8S3f7HKo5i4gEKF/hLCISnkjupFM4i0hcFM4iIgFSzVlEJEAaOYuIBCiSgbPCWUTiUhBJOiucRSQqkWSzwllE4qLp2yIiAYokmxXOIhIX3a0hIhIgPWxfRCRAkWSzwllE4mKR/C2CCmcRiYpGziIiAVI4i4gESA8+EhEJUH5ernuQHQpnEYmKZgiKiARINWcRkQBFMnBWOItIXPIiuc85ktK5iEgzs/SX9M5n+Wb2kpk9lLwfYGbzzey15Gf/lH2nmlm1ma00szG7cx0KZxGJSkGepb2k6ULglZT3U4AF7l4GLEjeY2YjgAnASGAsMN3M8jO9DoWziEQlmyNnMxsCnAzcnLK6HJiRvJ4BjEtZP8vdN7v7m0A1MCrT61A4i0hU8szSXsyswswWpywVO53uGuASoCllXbG71wEkP4uS9YOBVSn71SbrMqIvBEUkKh25W8Pdq4Cqls9jpwAN7v4XMzsunaZbaiL93uxI4SwiUcliOeBo4OtmdhLQC+hrZncB9WZW4u51ZlYCNCT71wKlKccPAVZn2rjKGiISlY6UNdri7lPdfYi7f4LmL/r+5O5nAXOBicluE4E5yeu5wAQz62lmw4AyYGGm16GRs4hEpQumb08DZpvZJKAGOB3A3Zeb2WxgBbAVmOzujZk2onAWkah0RjS7+5PAk8nrd4DRrexXCVRmo02Fs4hERdO3RUQCpOc5i4gEKJa7HBTOIhIVPc9ZRCRAKmuIiARIZQ0RkQBp5CwiEqA4olnhLCKRydfIWUQkPJFks8JZROJikRQ2FM4iEhWNnEVEAhTL376tcBaRqGjkLCISIE3fFhEJUF4c2axwFpG46G4NEZEARVLVUDh3leeeeZqrplXS1NjE+FNPZ9L5FbnukmRo8vjPcu6Jn8bMuO2R/+X6BxbvsL3vHoXcOuVrlBb1pSA/j2vuW8idjy3drTYLe+RzyyUnc3jZIN7dsImzKudQU7+BTx1YxO9+8FX67NGTxqYmfnX389z31Ku71VZ3F8vIOZYHOAWtsbGRX1T+lOk33swDcx9m3iMP8Xp1da67JRkY8Yl9OPfET/Mv37+DUd+5lROPOpADB/ffYZ/vlB/BqzX/4PPfvY0xF9/NtIrj6VGQ3n9qQ4v78thvvrnL+nPGfop173/IoedUcd39i6k87zgA/vnhFib96mE+e/4tlP/kD/zqe6PZe8+eu32d3Vmepb+ETOHcBZYtXUJp6f4MKS2lR2EhY086mSefWJDrbkkGDhk6kIWvrmbT5q00NjnPLFlF+dFlO+zjDnv1LgRgz96FrNv4IVsbmwCYMHoEz1x3Ni/ceA7XXTiGvDQT4pQvljHzj8sAuP/pVznu8P0BqH57Ha+/vQ6AunfeZ+36f7JPvz2ycq3dVZ5Z2kvIFM5doKG+nkElg7a/Lyoupr6+Poc9kkwtf+sffOmwUgb06UXvngWMHXUAQ/btu8M+N875K4cMHcgbsyazuOrbXDz9cdxh+NCBnHbsJzn+opkc9d3baWxqYsKXR6TV7n4D96J27UYAGpucDR9sZmDf3jvsc+TwEgp75PPG6nXZudhuyjqwhCzjmrOZnevut7WyrQKoALh++n997Ourju+yLpZnzn7crKx5h9/e+yIPXXUGH2zawpI3GraPirc54chhLHm9gbE/nsUB+/Xj4Wln8Nx3b+P4w/fniIOLefaGbwHQu7CAtev/CcC9V4xn/5K9KSzIp7SoLy/ceA4ANzzwF+58bGmLvy/u//97NWjAntxy6cmc/+tH8F1/3T5WQh8Rp2t3vhC8EmgxnN29CqgC+HBrC8n0MVNcPIg1dWu2v2+or6eoqCiHPZLdMWPeEmbMWwLAld8+hreTEe02Z485jN/OegGAN1av56017zG8dCAG3PXHZVx+69O7nPOMKx8AmmvON/34ZMZcfM8O29/+x0aG7NuHt/+xkfw8o++ePXl344cA9NmjkPt/fhpX3v4MC19Zne3L7XbiiOZ2yhpmtqSVZSlQ3EV97PZGHnoYNTVvUVu7ii0ffcS8Rx7m2OO/nOtuSYb2TWq6pfv2ofzog5n9xIodtq9q2LC9JlzUbw8OLh3Am3XreeKlvzP+mOHbj+/fpxdDi3YsibTm4edf48yvHgrAN445hKdergGgR0Ee9/7HeO6ev5z7n16Zlevr9iKpa7Q3ci4GxgA7F7EM+HOn9ChCBQUFTL3scr5XcR5NTY2MG38qBx1U1v6BEqR7Lh/HgL692bK1iYuun8/69zdz3imfAeDmh15m2sw/U/Xjk1hU9W0MuOzmJ3lnwybe2bCJK297hgen/St5ZmzZ2sQPr59PTcOGdtu8/dEl3DrlFJbdXsG6jZs4u3IuAKcee0hzDbxvb84a0xzeFb9+hCWvN3TW5QcvlrKGeRsFKjO7BbjN3Z9tYdvd7v5v7TWgsoa0pP+JV+W6CxKgTfMv3e1kXfTGe2lnzucO2DvYJG9z5Ozuk9rY1m4wi4h0uWDjtmM0Q1BEohLLDEGFs4hEJZKSs8JZROISSTYrnEUkLrFM8NL0bRGJiln6S9vnsVIze8LMXjGz5WZ2YbJ+gJnNN7PXkp/9U46ZambVZrbSzMbsznUonEUkKlmcg7IV+JG7fxI4CphsZiOAKcACdy8DFiTvSbZNAEYCY4HpZpaf6XUonEUkLllKZ3evc/e/Jq83Aq8Ag4FyYEay2wxgXPK6HJjl7pvd/U2gGhiV6WUonEUkKtaBf9I+p9kngMOBF4Fid6+D5gAHtj0oZzCwKuWw2mRdRhTOIhKVjtSczazCzBanLLs8QtPM9gL+G7jI3duaa99S2mc8Q1p3a4hIVDpys0bqEzRbPpf1oDmYZ7r7/cnqejMrcfc6MysBtj3IpBYoTTl8CJDxYwI1chaRqGSrrGHN9+TdArzi7lenbJoLTExeTwTmpKyfYGY9zWwYUAYszPQ6NHIWkahk8Tbno4GzgaVm9nKy7ifANGC2mU0CaoDTAdx9uZnNBlbQfKfHZHdvzLRxhbOIRCVb2Zw8jbO1041u5ZhKoDIb7SucRSQucUwQVDiLSFxiedi+wllEohJHNCucRSQ2kaSzwllEoqKH7YuIBCiSkrPCWUTiEkk2K5xFJC6xPGxf4SwiUYkkmxXOIhKXSLJZ4SwikYkknRXOIhIV3UonIhIg1ZxFRAKUp3AWEQlRHOmscBaRqKisISISoEiyWeEsInHRyFlEJECavi0iEqA4olnhLCKRiWTgrHAWkbhohqCISIjiyGaFs4jEJZJsVjiLSFzyIik6K5xFJCqRZDN5ue6AiIjsSiNnEYlKLCNnhbOIREW30omIBEgjZxGRACmcRUQCpLKGiEiAYhk561Y6EYmKdWBp91xmY81spZlVm9mUTupyixTOIhKXLKWzmeUDNwAnAiOAb5rZiM7q9s5U1hCRqGRx+vYooNrd3wAws1lAObAiWw20pdPDuVdBJNX5LDCzCnevynU/QrBp/qW57kIw9HuRXR3JHDOrACpSVlWl/FkMBlalbKsFPr/7PUyPyhpdq6L9XeRjSL8XOeLuVe5+ZMqS+iHZUsh7V/VN4Swi0rJaoDTl/RBgdVc1rnAWEWnZIqDMzIaZWSEwAZjbVY3rC8GupbqitES/FwFy961mdgHwGJAP3Oruy7uqfXPvshKKiIikSWUNEZEAKZxFRAKkcO4iuZwGKmEys1vNrMHMluW6LxIehXMXyPU0UAnW7cDYXHdCwqRw7hrbp4G6+0fAtmmg8jHm7k8D7+a6HxImhXPXaGka6OAc9UVEugGFc9fI6TRQEel+FM5dI6fTQEWk+1E4d42cTgMVke5H4dwF3H0rsG0a6CvA7K6cBiphMrN7gOeB4WZWa2aTct0nCYemb4uIBEgjZxGRACmcRUQCpHAWEQmQwllEJEAKZxGRACmcRUQCpHAWEQnQ/wFtfa+fMqhzkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(testing_y_mush, result), annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "487d8e22",
   "metadata": {},
   "source": [
    "Задание 3.5 Часть 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff61e1c",
   "metadata": {},
   "source": [
    "Перейдём к следующему датасэту.\n",
    "\n",
    "На Землю нападают войска жукеров, и в их флоте присутствуют транспортники, истребители и крейсеры. Для борьбы с каждым типом кораблей используется свой вид оружия. Как аналитику из Штаба Обороны, вам поручено разработать модель, предсказывающую какие корабли участвуют в атаке, чтобы успешно отбить нападения на различные области планеты\n",
    "\n",
    "Данных удалось собрать немного, и предсказывать придётся гораздо больший по объёму массив.\n",
    "\n",
    "Обучите модель и предскажите классы кораблей для новых поступающих данных. Укажите в ответе через пробел число крейсеров, транспортников и истребителей.\n",
    "\n",
    "От вашего ответа зависит судьба человечества!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2f8d0fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#считываем тренировочный датасет\n",
    "data_train = pd.read_csv('E:\\\\pyrhon\\\\progectTEST\\\\invasion.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "79a906b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#считываем тестовый датасет\n",
    "data_test = pd.read_csv('E:\\\\pyrhon\\\\progectTEST\\\\operative_information.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3042193d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Берем из тренировочного датасета Х\n",
    "X_train = data_train.drop(['class'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9eb48615",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Берем из тренировочного датасета у\n",
    "y_train = data_train['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5f0198b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#заводим классификатор\n",
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7a0129f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#параметры для классификатора( диапазоны котрые он переберет)\n",
    "parameters = {'n_estimators': range(10, 51, 10), 'max_depth': range(1, 13, 2),\n",
    "              'min_samples_leaf': range(1,8), 'min_samples_split': range(2,10,2)}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ef14e3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': range(1, 13, 2),\n",
       "                         'min_samples_leaf': range(1, 8),\n",
       "                         'min_samples_split': range(2, 10, 2),\n",
       "                         'n_estimators': range(10, 51, 10)})"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Передаем в GridSearchCV для поиска наилучших параметров\n",
    "GridSearchCV_clf = GridSearchCV(rf, parameters, cv=3, n_jobs=-1)\n",
    "#обучаем классификатор на тренировочных данных\n",
    "GridSearchCV_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "10def548",
   "metadata": {},
   "outputs": [],
   "source": [
    "#получаем наилучшие параметры\n",
    "best_clf = GridSearchCV_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "eaaa8044",
   "metadata": {},
   "outputs": [],
   "source": [
    "#предсказываем на нашем лучшем классификаторе по тестовым данным\n",
    "predictions = best_clf.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "46e062e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fighter', 'transport', 'transport', ..., 'transport', 'fighter',\n",
       "       'transport'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#результат - массив из предсказанных величин\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "271def4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fighter      675\n",
       "transport    595\n",
       "cruiser      230\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# получаем количество уникальных предсказанных значений\n",
    "pd.Series(predictions).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bc6af9",
   "metadata": {},
   "source": [
    "Теперь найдем самую важную переменную для классификации кораблей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "66542972",
   "metadata": {},
   "outputs": [],
   "source": [
    "#из лучших параметров берем значение feature_importances\n",
    "feature_importances = best_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "18d5103b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# строим датасет переменная - значение feature_importances\n",
    "feature_importances_df = pd.DataFrame({'features': list(X_train), 'feature_importances': feature_importances})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0798886a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>features</th>\n",
       "      <th>feature_importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>volume</td>\n",
       "      <td>0.407308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>brightness</td>\n",
       "      <td>0.273746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speed</td>\n",
       "      <td>0.151540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>g_reflection</td>\n",
       "      <td>0.150344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>i_reflection</td>\n",
       "      <td>0.017062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>time_of_observance</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             features  feature_importances\n",
       "5              volume             0.407308\n",
       "3          brightness             0.273746\n",
       "2               speed             0.151540\n",
       "0        g_reflection             0.150344\n",
       "1        i_reflection             0.017062\n",
       "4  time_of_observance             0.000000"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# сортируем,чтобы определить важнейшую переменную\n",
    "feature_importances_df.sort_values('feature_importances', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c92d6bb8",
   "metadata": {},
   "source": [
    "Благодаря вашим стараниям войска захватчиков были разгромлены, но война ещё не окончена! Вас повысили и перевели на новое направление (новые должности - новые задачи) - теперь нужно выявлять опасные регионы космоса, где могут находиться жукеры.\n",
    "\n",
    "Проанализируйте имеющиеся данные об опасности разных регионов космоса и укажите наиболее вероятные причины угрозы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3943e1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_space = pd.read_csv('E:\\\\pyrhon\\\\progectTEST\\\\space_can_be_a_dangerous_place.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "72491957",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>phi</th>\n",
       "      <th>peradventure_index</th>\n",
       "      <th>dustiness</th>\n",
       "      <th>black_hole_is_near</th>\n",
       "      <th>buggers_were_noticed</th>\n",
       "      <th>nearby_system_has_planemo</th>\n",
       "      <th>dangerous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003336</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.005788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phi</th>\n",
       "      <td>0.001023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.002875</td>\n",
       "      <td>-0.004726</td>\n",
       "      <td>-0.000803</td>\n",
       "      <td>0.003035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peradventure_index</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003336</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.005788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dustiness</th>\n",
       "      <td>-0.003336</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>-0.003336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004691</td>\n",
       "      <td>-0.001290</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>-0.000499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_hole_is_near</th>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.002875</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>-0.004691</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001874</td>\n",
       "      <td>0.002661</td>\n",
       "      <td>0.052968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buggers_were_noticed</th>\n",
       "      <td>0.001797</td>\n",
       "      <td>-0.004726</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>-0.001290</td>\n",
       "      <td>-0.001874</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003031</td>\n",
       "      <td>0.344469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nearby_system_has_planemo</th>\n",
       "      <td>0.000326</td>\n",
       "      <td>-0.000803</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>0.002661</td>\n",
       "      <td>-0.003031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dangerous</th>\n",
       "      <td>0.005788</td>\n",
       "      <td>0.003035</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>-0.000499</td>\n",
       "      <td>0.052968</td>\n",
       "      <td>0.344469</td>\n",
       "      <td>0.299508</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  r       phi  peradventure_index  dustiness  \\\n",
       "r                          1.000000  0.001023            1.000000  -0.003336   \n",
       "phi                        0.001023  1.000000            0.001023   0.001921   \n",
       "peradventure_index         1.000000  0.001023            1.000000  -0.003336   \n",
       "dustiness                 -0.003336  0.001921           -0.003336   1.000000   \n",
       "black_hole_is_near         0.002337  0.002875            0.002337  -0.004691   \n",
       "buggers_were_noticed       0.001797 -0.004726            0.001797  -0.001290   \n",
       "nearby_system_has_planemo  0.000326 -0.000803            0.000326   0.003370   \n",
       "dangerous                  0.005788  0.003035            0.005788  -0.000499   \n",
       "\n",
       "                           black_hole_is_near  buggers_were_noticed  \\\n",
       "r                                    0.002337              0.001797   \n",
       "phi                                  0.002875             -0.004726   \n",
       "peradventure_index                   0.002337              0.001797   \n",
       "dustiness                           -0.004691             -0.001290   \n",
       "black_hole_is_near                   1.000000             -0.001874   \n",
       "buggers_were_noticed                -0.001874              1.000000   \n",
       "nearby_system_has_planemo            0.002661             -0.003031   \n",
       "dangerous                            0.052968              0.344469   \n",
       "\n",
       "                           nearby_system_has_planemo  dangerous  \n",
       "r                                           0.000326   0.005788  \n",
       "phi                                        -0.000803   0.003035  \n",
       "peradventure_index                          0.000326   0.005788  \n",
       "dustiness                                   0.003370  -0.000499  \n",
       "black_hole_is_near                          0.002661   0.052968  \n",
       "buggers_were_noticed                       -0.003031   0.344469  \n",
       "nearby_system_has_planemo                   1.000000   0.299508  \n",
       "dangerous                                   0.299508   1.000000  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_space.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "af2dc326",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>dangerous</th>\n",
       "      <td>0.005788</td>\n",
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       "      <td>0.299508</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>buggers_were_noticed</th>\n",
       "      <td>0.001797</td>\n",
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       "      <td>0.344469</td>\n",
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       "    <tr>\n",
       "      <th>nearby_system_has_planemo</th>\n",
       "      <td>0.000326</td>\n",
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       "      <td>0.000326</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.299508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_hole_is_near</th>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.002875</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>-0.004691</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.002661</td>\n",
       "      <td>0.052968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003336</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.005788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peradventure_index</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003336</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.005788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phi</th>\n",
       "      <td>0.001023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001023</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>0.002875</td>\n",
       "      <td>-0.004726</td>\n",
       "      <td>-0.000803</td>\n",
       "      <td>0.003035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dustiness</th>\n",
       "      <td>-0.003336</td>\n",
       "      <td>0.001921</td>\n",
       "      <td>-0.003336</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004691</td>\n",
       "      <td>-0.001290</td>\n",
       "      <td>0.003370</td>\n",
       "      <td>-0.000499</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                  r       phi  peradventure_index  dustiness  \\\n",
       "dangerous                  0.005788  0.003035            0.005788  -0.000499   \n",
       "buggers_were_noticed       0.001797 -0.004726            0.001797  -0.001290   \n",
       "nearby_system_has_planemo  0.000326 -0.000803            0.000326   0.003370   \n",
       "black_hole_is_near         0.002337  0.002875            0.002337  -0.004691   \n",
       "r                          1.000000  0.001023            1.000000  -0.003336   \n",
       "peradventure_index         1.000000  0.001023            1.000000  -0.003336   \n",
       "phi                        0.001023  1.000000            0.001023   0.001921   \n",
       "dustiness                 -0.003336  0.001921           -0.003336   1.000000   \n",
       "\n",
       "                           black_hole_is_near  buggers_were_noticed  \\\n",
       "dangerous                            0.052968              0.344469   \n",
       "buggers_were_noticed                -0.001874              1.000000   \n",
       "nearby_system_has_planemo            0.002661             -0.003031   \n",
       "black_hole_is_near                   1.000000             -0.001874   \n",
       "r                                    0.002337              0.001797   \n",
       "peradventure_index                   0.002337              0.001797   \n",
       "phi                                  0.002875             -0.004726   \n",
       "dustiness                           -0.004691             -0.001290   \n",
       "\n",
       "                           nearby_system_has_planemo  dangerous  \n",
       "dangerous                                   0.299508   1.000000  \n",
       "buggers_were_noticed                       -0.003031   0.344469  \n",
       "nearby_system_has_planemo                   1.000000   0.299508  \n",
       "black_hole_is_near                          0.002661   0.052968  \n",
       "r                                           0.000326   0.005788  \n",
       "peradventure_index                          0.000326   0.005788  \n",
       "phi                                        -0.000803   0.003035  \n",
       "dustiness                                   0.003370  -0.000499  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_space.corr().sort_values(by='dangerous', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c313d21",
   "metadata": {},
   "source": [
    "Расставьте способы взять среднее значение по скорости выполнения сверху вниз - наверху самый быстрый.\n",
    "\n",
    " \n",
    "\n",
    "Пример датасэта для тестирования\n",
    "\n",
    "При использовании jupyter notebook можно оттестировать скорость выполнения кода при помощи \"магии\" timeit, которая проведёт несколько прогонов кода и выдаст среднее значение\n",
    "\n",
    "%%timeit\n",
    "your_command\n",
    "Если вы пользуетесь чем-то другим, воспользуйтесь библиотекой time\n",
    "\n",
    "from time import time\n",
    "before = time()\n",
    "your_command\n",
    "after = time()\n",
    "print(after - before)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a90715e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "603 µs ± 36.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "import numpy as np\n",
    "data.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "809791aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.apply(np.mean)   2.39 ms\n",
    "mean(axis=0)  643 \n",
    "describe().loc['mean'] 43 ms\n",
    "apply('mean')   713 µs"
   ]
  }
 ],
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