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   "cell_type": "markdown",
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   "source": [
    "Задание \n",
    "\n",
    "Решить задачу: классификации\n",
    "на выборке: синтетической и https://archive.ics.uci.edu/ml/datasets/Lung+Cancer\n",
    "с использованием моделей: kNN, SVM, логистическая регрессия со структурными параметрами: число и состав признаков, критерии качества AUC, F1, число признаков.\n",
    "  \n",
    "UCI Machine Learning Repository: Lung Cancer Data Set\n",
    "archive.ics.uci.edu\n",
    "Форма отчётности:\n",
    "Выполняется в формате питон-ноутбук с кодом, выполняющим эксперимент, поясняющим текстом, графиком, и таблицей. Для тех, кто программирует на других языках, выполняется в аналогичном формате.\n",
    "\n",
    "Отчет по заданию содержит следующие разделы и графики с комментариями, достаточными для передачи сообщения тому, кто будет читать код. Графики должны иметь подписанные оси и поясняющий текст с выводом - результатом анализа.\n",
    "\n",
    "Цель вычислительного эксперимента\n",
    "Описание выборок\n",
    "Блок загрузки и предобработки выборок\n",
    "График анализа состава выборки:\n",
    "анализ выбросов, гистограмма\n",
    "анализ пропусков, статистика\n",
    "анализ мультикорреляции признаков, кор. матрица\n",
    "Список моделей\n",
    "Список функций ошибки, критериев качества\n",
    "Способ разбиения выборки на обучение-контроль (выбрать)\n",
    "Таблица модели/выборки/критерии качества на разбиении со стандартным отклонением\n",
    "Анализ выбранной модели на разбиении обучение-контроль\n",
    "График зависимости функции ошибки от значения структурного параметра со ст. откл.\n",
    "График зависимости функции ошибки от объема выборки со ст. откл.\n",
    "График скорости сходимости функции ошибки (зависимости функции ошибки от номера итерации оптимизационного алгоритма) со ст. откл.\n",
    "Задание имеет вид: решить задачу (классификации, регрессии, кластеризации) на выборках (список выборок и синтетическая выборка) с использованием моделей (список) со структурными параметрами (список), критерии качества (список)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Цель вычислительного эксперимента\n",
    "Решить задачу классификации на синтетической выборке и\n",
    "Lung Cancer Data Set с использованием моделей: kNN, SVM, логистическая регрессия. Структурные параметры: число и состав признаков. Используемый критерий качества AUC, F1, число признаков.\n",
    "\n",
    "# 2. Описание выборки\n",
    "Используются данные из Lung Cancer Data Set (archive.ics.uci.edu/ml/datasets/Lung+Cancer), описывающих 3 вида патологического рака лёгких. Первый столбец это метка предсказываемого класса (1-3), остальные 55 столбцов это целочисленные атрибуты, принимающие значения 0-3. Что они означают  −  не известно.\n",
    "Размер выборки очень мал - всего 32 примера, особенно в сравнение с количеством признаков. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Блок загрузки и предобработки выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 56)\n"
     ]
    },
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       "   1   2   3  4   5   6   7   8   9   10 ...  47  48  49  50  51  52  53  54  \\\n",
       "0   0   3   0  ?   0   2   2   2   1   1 ...   2   2   2   2   2   1   1   1   \n",
       "1   0   3   3  1   0   3   1   3   1   1 ...   2   2   2   2   2   2   2   1   \n",
       "2   0   3   3  2   0   3   3   3   1   1 ...   2   2   2   2   2   2   2   2   \n",
       "3   0   2   3  2   1   3   3   3   1   2 ...   2   2   2   2   2   2   2   2   \n",
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       "\n",
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       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline \n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12,10) \n",
    "plt.rcParams['axes.titlesize'] = 'large'\n",
    "\n",
    "# Читаем данные\n",
    "df = pd.read_csv('lung-cancer.csv', header=None, index_col=False)\n",
    "# сразу отделим метки классов\n",
    "labels = df[0]\n",
    "df.drop(0,axis=1, inplace=True)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.],\n",
       "       [1., 0., 0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# приведем классы к OneHotEncoding чтобы удобнее было потом считать AUC\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "OHE  = OneHotEncoder(sparse = False)\n",
    "labels_ohe = OHE.fit_transform(labels.values.reshape(-1,1))\n",
    "labels_ohe[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Анализ состава выборки:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Анализ выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
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       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
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       "      <th>mean</th>\n",
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       "      <td>2.031250</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>2.187500</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>2.406250</td>\n",
       "      <td>1.156250</td>\n",
       "      <td>1.406250</td>\n",
       "      <td>1.500000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.843750</td>\n",
       "      <td>1.843750</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>1.812500</td>\n",
       "      <td>1.718750</td>\n",
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       "      <th>std</th>\n",
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       "      <td>0.553581</td>\n",
       "      <td>1.031265</td>\n",
       "      <td>0.456803</td>\n",
       "      <td>0.737804</td>\n",
       "      <td>0.751343</td>\n",
       "      <td>0.756024</td>\n",
       "      <td>0.514899</td>\n",
       "      <td>0.559918</td>\n",
       "      <td>0.672022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.245935</td>\n",
       "      <td>0.245935</td>\n",
       "      <td>0.359211</td>\n",
       "      <td>0.359211</td>\n",
       "      <td>0.508001</td>\n",
       "      <td>0.447889</td>\n",
       "      <td>0.447889</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.456803</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>2.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>2.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1          2          3          5          6          7   \\\n",
       "count  32.000000  32.000000  32.000000  32.000000  32.000000  32.000000   \n",
       "mean    0.031250   2.375000   2.031250   0.281250   2.187500   2.125000   \n",
       "std     0.176777   0.553581   1.031265   0.456803   0.737804   0.751343   \n",
       "min     0.000000   1.000000   0.000000   0.000000   1.000000   1.000000   \n",
       "25%     0.000000   2.000000   1.750000   0.000000   2.000000   2.000000   \n",
       "50%     0.000000   2.000000   2.000000   0.000000   2.000000   2.000000   \n",
       "75%     0.000000   3.000000   3.000000   1.000000   3.000000   3.000000   \n",
       "max     1.000000   3.000000   3.000000   1.000000   3.000000   3.000000   \n",
       "\n",
       "              8          9          10         11    ...             47  \\\n",
       "count  32.000000  32.000000  32.000000  32.000000    ...      32.000000   \n",
       "mean    2.406250   1.156250   1.406250   1.500000    ...       2.062500   \n",
       "std     0.756024   0.514899   0.559918   0.672022    ...       0.245935   \n",
       "min     1.000000   1.000000   1.000000   1.000000    ...       2.000000   \n",
       "25%     2.000000   1.000000   1.000000   1.000000    ...       2.000000   \n",
       "50%     3.000000   1.000000   1.000000   1.000000    ...       2.000000   \n",
       "75%     3.000000   1.000000   2.000000   2.000000    ...       2.000000   \n",
       "max     3.000000   3.000000   3.000000   3.000000    ...       3.000000   \n",
       "\n",
       "              48         49         50         51         52         53  \\\n",
       "count  32.000000  32.000000  32.000000  32.000000  32.000000  32.000000   \n",
       "mean    2.062500   2.000000   2.000000   2.000000   1.843750   1.843750   \n",
       "std     0.245935   0.359211   0.359211   0.508001   0.447889   0.447889   \n",
       "min     2.000000   1.000000   1.000000   1.000000   1.000000   1.000000   \n",
       "25%     2.000000   2.000000   2.000000   2.000000   2.000000   2.000000   \n",
       "50%     2.000000   2.000000   2.000000   2.000000   2.000000   2.000000   \n",
       "75%     2.000000   2.000000   2.000000   2.000000   2.000000   2.000000   \n",
       "max     3.000000   3.000000   3.000000   3.000000   3.000000   3.000000   \n",
       "\n",
       "              54         55         56  \n",
       "count  32.000000  32.000000  32.000000  \n",
       "mean    1.562500   1.812500   1.718750  \n",
       "std     0.504016   0.396558   0.456803  \n",
       "min     1.000000   1.000000   1.000000  \n",
       "25%     1.000000   2.000000   1.000000  \n",
       "50%     2.000000   2.000000   2.000000  \n",
       "75%     2.000000   2.000000   2.000000  \n",
       "max     2.000000   2.000000   2.000000  \n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "вместо гистограммы посмотрим на вывод _.describe_ Видно что все признаки лежат в интервале 0-3 как и написано в описание данных, следовательно выбросов не наблюдается"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Анализ пропусков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column '4' has missings\n",
      "Column '38' has missings\n"
     ]
    }
   ],
   "source": [
    "for column in df:\n",
    "    if df[column].dtype != np.int64:\n",
    "        if (df[column] == '?').any() == True:\n",
    "            print(\"Column '{}' has missings\".format(column))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# выбросим все столбцы в которых есть пропуски\n",
    "df.drop(4,axis=1, inplace=True)\n",
    "df.drop(38,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Анализ мультикорреляции признаков. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ADDKZeYukW3Z47qpeX6ekfyg+qqKsRJ2I+F8RsSIiZhcf51arQQAAAANOFvrv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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b8f1b0828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sns.heatmap(corr, \n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно что есть признаки которые полностью скоррелированны (например 31-32 или 47-48). И те которые сильно коррелируют (52-52, 50-51). Выкинем полностью скоррелированные признаки. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(31,axis=1, inplace=True)\n",
    "df.drop(47,axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Список моделей:\n",
    "Будем использовать kNN, SVM и логистическая регрессию.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Критерии качества  \n",
    "Будем использовать F1.\n",
    "Auc-roc не определен для мультиклассовой классификации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Способ разбиения на обучение-контроль:\n",
    "Будем использовать кросс-валидацию с разбиением на 3 фолда. В коде используется GridSearchCV, потому что это удобная обертка, которая автоматически делит на фолды, обучает их, и потом усредняет результат. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Определим пару функций, которые потом нам понадобятся"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_subfeatures(classifier, dummy_param):\n",
    "    score = []\n",
    "    score_std = []\n",
    "    for sub_features in range(10,50,5):\n",
    "        tmp_score = []\n",
    "        tmp_score_std = []\n",
    "        # Для каждого размера фитим 20 раз c 5ю соседями и потом усредняем\n",
    "        for i in range(20):\n",
    "            cols = np.random.choice(df.columns, sub_features, replace=False)\n",
    "            df_small = df[cols]\n",
    "            labels_small = labels\n",
    "            clf = GridSearchCV(classifier, dummy_param, cv = 3, n_jobs=1, \n",
    "                       verbose=0, scoring=make_scorer(f1_score,average='micro'))\n",
    "            clf.fit(df_small, labels_small)\n",
    "            tmp_score.append(clf.cv_results_['mean_test_score'].mean())\n",
    "            tmp_score_std.append(clf.cv_results_['std_test_score'].mean())\n",
    "        score.append(np.mean(tmp_score))\n",
    "        score_std.append(np.mean(tmp_score_std))\n",
    "    return score, score_std\n",
    "\n",
    "def fit_subdata(classifier, dummy_param):   \n",
    "    score = []\n",
    "    score_std = []\n",
    "    for s in range(12,36,4):\n",
    "        # Для каждого размера фитим 20 раз\n",
    "        tmp_score = []\n",
    "        tmp_score_std = []\n",
    "        for i in range(20):\n",
    "            df_small = df.sample(s)\n",
    "            labels_small = labels[df_small.index]\n",
    "            clf = GridSearchCV(classifier, dummy_param, cv = 3, n_jobs=1, \n",
    "                       verbose=0, scoring=make_scorer(f1_score,average='micro'))\n",
    "            clf.fit(df_small, labels_small)\n",
    "            tmp_score.append(clf.cv_results_['mean_test_score'].mean())\n",
    "            tmp_score_std.append(clf.cv_results_['std_test_score'].mean())\n",
    "        score.append(np.mean(tmp_score))\n",
    "        score_std.append(np.mean(tmp_score_std))\n",
    "    return score, score_std\n",
    "\n",
    "def plot_results(clf, algorithm, parameter):\n",
    "    plt.figure(figsize=(12,8))   \n",
    "    x = clf.param_grid[parameter]\n",
    "    y = clf.cv_results_['mean_test_score']\n",
    "    plt.plot(x, y)\n",
    "    plt.fill_between(x, y-clf.cv_results_['std_test_score'], \\\n",
    "                     y+clf.cv_results_['std_test_score'], \\\n",
    "                     color = 'cyan')\n",
    "    plt.title(algorithm+' F1 (micro averaged) score with a confidence interval', fontsize=15)\n",
    "    plt.xlabel(parameter, fontsize=15)\n",
    "    plt.ylabel('F1 score', fontsize=15)\n",
    "    plt.legend(loc='best', prop={'size': 18})\n",
    "    plt.grid()    \n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "def plot_results2(model:str, score, score_std, subfeatures=True):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    x = range(10,50,5) if subfeatures else range(12,36,4)\n",
    "    y = np.array(score)\n",
    "    plt.plot(x, y)\n",
    "    plt.fill_between(x, y-score_std, \\\n",
    "                     y+score_std, \\\n",
    "                     color = 'cyan')\n",
    "    plt.title('{} F1 (micro averaged) score with a confidence interval'.format(model), fontsize=15)\n",
    "    if subfeatures:\n",
    "        plt.xlabel('Количество признаков', fontsize=15)\n",
    "    else:\n",
    "        plt.xlabel('Объем выборки', fontsize=15)\n",
    "    plt.ylabel('F1 score', fontsize=15)\n",
    "    plt.legend(loc='best', prop={'size': 18})\n",
    "    plt.grid()    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import f1_score, make_scorer\n",
    "neighbor_clf = KNeighborsClassifier()\n",
    "max_n_neighbors = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  50 out of  50 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "parameters = {'n_neighbors': np.arange(1, max_n_neighbors, 2)}\n",
    "clf = GridSearchCV(neighbor_clf, parameters, cv = 5, n_jobs=4, \n",
    "                   verbose=1, scoring=make_scorer(f1_score,average='micro'))\n",
    "clf.fit(df, labels)\n",
    "knn_score_no_scaling = clf.cv_results_['mean_test_score']\n",
    "knn_score_std_no_scaling = clf.cv_results_['std_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MSU4u9jyyEvglMBv4M1CBRmfMJk3APODusAMRkRYpyZa883fUO4K0XS70PLIE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\nC+aLZIQBz8yloKrODzqjMhEJUe3IYko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qvvMke6+ZRftZ44OuTCTlDBmy3fAXmJEU111b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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b8f1aaeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(clf, 'kNN classifier', 'n_neighbors')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно что качество слегка растет при увеличении соседей, а потом падает. Так и ожидалось. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN. Исследуем зависимость от количества признаков. Будем брать случайные подмножества признаков и усреднять качество на них"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p/h731oxtHiT0a57Zz3M3lG/s7o1+vref9enX4bx+zpXtQvKDle31cTCv17SlwBHRe+Wr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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b740395c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummy_param = {'n_neighbors':[5]}\n",
    "score, score_std = fit_subfeatures(neighbor_clf, dummy_param)\n",
    "plot_results2(\"kNN\", score, score_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "видно что качество слегка растет при использовании бОльшего количества признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Исследуем зависимость от размера выборки (хотя у нас и так уже меньше некуда, всего ~30 примеров, но давайте). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nL8B+HZ+T3oQfZSQ1dXoXRwbUujIkIjKxKfiWQe3AjzpxR3A/zvAPTSaSD+HU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\nklQSk29JkiSpJP8foKOz0DAJ3mcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b8f26a3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummy_param = {'n_neighbors':[5]}\n",
    "score, score_std  = fit_subdata(neighbor_clf, dummy_param)\n",
    "plot_results2(\"kNN\", score, score_std, subfeatures=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно что качество слегка растет при увеличении размера выборки. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "график скорости сходимости ошибка для KNN отсутствует, потому что KNN сходится за одну итерацию"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM. Исследуем зависимость от количества признаков. Будем брать случайные подмножества признаков и усреднять качество на них"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UuBcftSIXH7e5p85DsZa2u/BZ20bh48h+EO+wI8NHB/AZfNIXBSCDTws+usa5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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bcfd39160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "SVM_clf = LinearSVC()\n",
    "dummy_param = {'penalty':['l2']}\n",
    "score, score_std = fit_subfeatures(SVM_clf, dummy_param)\n",
    "plot_results2(\"SVM\", score, score_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно что качество с количеством признаков особо не растет. Это связано с тем, что признаков в задаче больше чем обучающих примеров, поэтому их добавление не улучшает качества. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM. Исследуем зависимость от размера выборки. Будем брать случайные подмножества примеров и усреднять качество на них"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wFLgXuA8oBCYAk4LbuNBtbPC3HLBsVFZEhg0F3yIyqFqA9cBc4EfAR7NaGxnp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\niITk/wM4x56vZ4aTiwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b73fac400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummy_param = {'penalty':['l2']}\n",
    "score, score_std = fit_subdata(SVM_clf, dummy_param)\n",
    "plot_results2(\"SVM\", score, score_std, subfeatures=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "качество незначительно растет при увеличении выборки (я не фиксирую сид, поэтому возможно при вашем запуске качество даже не будет расти)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "график скорости сходимости ошибка для SVС отсутствует, т.к. он сходится за одну итерацию"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogReg. Исследуем зависимость от количества признаков. Будем брать случайные подмножества признаков и усреднять качество на них"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6w4Gnk9wW2V0LcDHw3yjwlvTXCCwApgH/iTqMYqFSgQNLwbfsM1U0kYHWie+Z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\n/w9Y6R3RuMH0agAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b8f279208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, LogisticRegression\n",
    "linear_clf = LogisticRegression()\n",
    "dummy_param = {'penalty':['l2']}\n",
    "score, score_std = fit_subfeatures(linear_clf, dummy_param)\n",
    "plot_results2(\"LogReg\", score, score_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "видно что качество слегка растет с увеличением количества признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogReg. Исследуем зависимость от размера выборки. Будем брать случайные подмножества данных и усреднять качество на них"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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13d0UfemQvbMd5rMzLMpYbyXwDxk7hmVuE+k+3HuQ7asthJDE/+lnbl+DzcQxD9ic8XwH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\nfgrUoFbw3sSAQ4BfAa8D70ahWyQXKXyLiEikDL8gzxLgFHwfZukRAyY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b8f24f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linear_clf = LogisticRegression()\n",
    "dummy_param = {'penalty':['l2']}\n",
    "score, score_std = fit_subdata(linear_clf, dummy_param)\n",
    "plot_results2(\"LogReg\", score, score_std, subfeatures=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут тоже качество незначительно растет при увеличении размера выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Основная проблема данной работы - плохой датасет. Из-за него не очень видна зависимость качества от объема выборки и количества признаков. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Посмотрим качество моделей при полной выборке и всех признаках. \n",
    "Цифры могут немного отличаться от тех, что вы увидите выше, т.к. у меня не фиксируется сид. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Модель/Качество|  F1-score   |\n",
    "▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔|▔▔▔▔▔▔▔▔▔▔▔▔▔|\n",
    "    kNN        |  0.57±0.09  |\n",
    "▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔|▔▔▔▔▔▔▔▔▔▔▔▔▔|\n",
    "    SVM        |  0.49±0.08  |     \n",
    "▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔|▔▔▔▔▔▔▔▔▔▔▔▔▔|\n",
    "Linear Regress.|  0.53±0.09  |          \n",
    "▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔"
   ]
  }
 ],
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