jupyter_notebooks/III_machine_learning_supervised_hw.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Валидация моделей классификации\n",
    "\n",
    "Данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>call_diff</th>\n",
       "      <th>sms_diff</th>\n",
       "      <th>traffic_diff</th>\n",
       "      <th>customes_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.666421</td>\n",
       "      <td>0.444911</td>\n",
       "      <td>-0.273538</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.889273</td>\n",
       "      <td>-0.537896</td>\n",
       "      <td>-1.959469</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.841503</td>\n",
       "      <td>0.846665</td>\n",
       "      <td>0.727606</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.389749</td>\n",
       "      <td>0.390911</td>\n",
       "      <td>1.789246</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.228211</td>\n",
       "      <td>-1.375899</td>\n",
       "      <td>-1.434209</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   call_diff  sms_diff  traffic_diff  customes_class\n",
       "0  -0.666421  0.444911     -0.273538               0\n",
       "1  -0.889273 -0.537896     -1.959469               2\n",
       "2  -0.841503  0.846665      0.727606               0\n",
       "3   1.389749  0.390911      1.789246               1\n",
       "4  -1.228211 -1.375899     -1.434209               2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "np.set_printoptions(precision=4)\n",
    "\n",
    "df_source = pd.read_csv(\n",
    "    'data/client_segmentation.csv', \n",
    "    dtype={'call_diff': np.float32, 'call_diff': np.float32, 'traffic_diff': np.float32, 'customes_class': np.uint16}\n",
    ")\n",
    "X = df_source[['call_diff','sms_diff','traffic_diff']].values\n",
    "y = df_source.customes_class.values\n",
    "\n",
    "df_source.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдите лучший классификатор по метрике f1-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree  import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "    \n",
    "\n",
    "# --------- ВАШ КОД ТУТ --------\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Валидация моделей линейной регрессии\n",
    "\n",
    "Пользуясь методом .score подберите наилучшее значение степени полинома"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv('data/content_views_toy_set.csv', encoding='utf8')\n",
    "df.plot.scatter(x='time', y='content_views')\n",
    "plt.show()\n",
    "\n",
    "X = df['time'].values\n",
    "y = df['content_views'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "reg = LinearRegression().fit(X_train.reshape(-1, 1), y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "def generate_degrees(source_data: list, degree: int):\n",
    "    \"\"\"Функция, которая принимает на вход одномерный массив, а возвращает n-мерный\n",
    "    Для каждой степени от 1 до  degree возводим x в эту степень\n",
    "    \"\"\"\n",
    "    return np.array([\n",
    "          source_data**n for n in range(1, degree + 1)  \n",
    "    ]).T\n",
    "\n",
    "X_train_5 = generate_degrees(X_train, 5)\n",
    "X_test_5 = generate_degrees(X_test, 5)\n",
    "reg_5 = LinearRegression().fit(X_train_5, y_train)\n",
    "\n",
    "# строим график\n",
    "margin = 0.3\n",
    "support = np.arange(X.min(), X.max(), step=0.1)\n",
    "\n",
    "X_test_support = generate_degrees(support, 5)\n",
    "y_hat = reg_5.predict(X_test_support)\n",
    "\n",
    "plt.plot(support, y_hat, 'b--', alpha=0.5, label='prediction')\n",
    "plt.scatter(X_train, y_train, 40, 'g', 'o', alpha=0.8, label='data')\n",
    "plt.xlim(X.min() - margin, X.max() + margin)\n",
    "plt.ylim(y.min() - margin, y.max() + margin)\n",
    "plt.legend(loc='upper right', prop={'size': 20})\n",
    "plt.title('Fitted linear regression')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --------- ВАШ КОД ТУТ --------\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ------------------------------"
   ]
  }
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