Extrapolation_Metrics_StatisticalCriteria.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TwBxbfWCg6a-"
      },
      "outputs": [],
      "source": [
        "import numpy as np #инструмент для работы с многомерными массивами с возможностью векторизации вычислений\n",
        "import pandas as pd #удобные методы реляционного взаимодействия с данными, аналогичные, например, возможностям SQL для слияния и конкатенации таблиц\n",
        "import matplotlib.pyplot as plt #графики"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.read_csv(\"/content/DataSet1_3.csv\", sep=\",\")\n",
        "df2['Date'] = pd.to_datetime(df2['Date'])\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "3MN5PNDJkts4",
        "outputId": "c90f16ac-f655-457a-f9e9-976ff53b9b9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date     Open     High      Low    Close    Volume   Name\n",
              "0    2006-01-03   211.47   218.05   209.32   217.83  13137450  GOOGL\n",
              "1    2006-01-04   222.17   224.70   220.09   222.84  15292353  GOOGL\n",
              "2    2006-01-05   223.22   226.00   220.97   225.85  10815661  GOOGL\n",
              "3    2006-01-06   228.66   235.49   226.85   233.06  17759521  GOOGL\n",
              "4    2006-01-09   233.44   236.94   230.70   233.68  12795837  GOOGL\n",
              "...         ...      ...      ...      ...      ...       ...    ...\n",
              "3014 2017-12-22  1070.00  1071.72  1067.64  1068.86    889446  GOOGL\n",
              "3015 2017-12-26  1068.64  1068.86  1058.64  1065.85    918767  GOOGL\n",
              "3016 2017-12-27  1066.60  1068.27  1058.38  1060.20   1116203  GOOGL\n",
              "3017 2017-12-28  1062.25  1064.84  1053.38  1055.95    994249  GOOGL\n",
              "3018 2017-12-29  1055.49  1058.05  1052.70  1053.40   1180340  GOOGL\n",
              "\n",
              "[3019 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85205836-e5c7-46e1-881b-f4720e383c96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006-01-03</td>\n",
              "      <td>211.47</td>\n",
              "      <td>218.05</td>\n",
              "      <td>209.32</td>\n",
              "      <td>217.83</td>\n",
              "      <td>13137450</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006-01-04</td>\n",
              "      <td>222.17</td>\n",
              "      <td>224.70</td>\n",
              "      <td>220.09</td>\n",
              "      <td>222.84</td>\n",
              "      <td>15292353</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006-01-05</td>\n",
              "      <td>223.22</td>\n",
              "      <td>226.00</td>\n",
              "      <td>220.97</td>\n",
              "      <td>225.85</td>\n",
              "      <td>10815661</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006-01-06</td>\n",
              "      <td>228.66</td>\n",
              "      <td>235.49</td>\n",
              "      <td>226.85</td>\n",
              "      <td>233.06</td>\n",
              "      <td>17759521</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-01-09</td>\n",
              "      <td>233.44</td>\n",
              "      <td>236.94</td>\n",
              "      <td>230.70</td>\n",
              "      <td>233.68</td>\n",
              "      <td>12795837</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3014</th>\n",
              "      <td>2017-12-22</td>\n",
              "      <td>1070.00</td>\n",
              "      <td>1071.72</td>\n",
              "      <td>1067.64</td>\n",
              "      <td>1068.86</td>\n",
              "      <td>889446</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3015</th>\n",
              "      <td>2017-12-26</td>\n",
              "      <td>1068.64</td>\n",
              "      <td>1068.86</td>\n",
              "      <td>1058.64</td>\n",
              "      <td>1065.85</td>\n",
              "      <td>918767</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3016</th>\n",
              "      <td>2017-12-27</td>\n",
              "      <td>1066.60</td>\n",
              "      <td>1068.27</td>\n",
              "      <td>1058.38</td>\n",
              "      <td>1060.20</td>\n",
              "      <td>1116203</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3017</th>\n",
              "      <td>2017-12-28</td>\n",
              "      <td>1062.25</td>\n",
              "      <td>1064.84</td>\n",
              "      <td>1053.38</td>\n",
              "      <td>1055.95</td>\n",
              "      <td>994249</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3018</th>\n",
              "      <td>2017-12-29</td>\n",
              "      <td>1055.49</td>\n",
              "      <td>1058.05</td>\n",
              "      <td>1052.70</td>\n",
              "      <td>1053.40</td>\n",
              "      <td>1180340</td>\n",
              "      <td>GOOGL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3019 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85205836-e5c7-46e1-881b-f4720e383c96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85205836-e5c7-46e1-881b-f4720e383c96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85205836-e5c7-46e1-881b-f4720e383c96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n1=150\n",
        "n2=300\n",
        "n3 = 350\n",
        "sdvig = 1 #количество строк для сдвига \n",
        "\n",
        "#предсказываем параметр Close следующего дня по параметрам Date Open Volume High предшествующего дня \n",
        "#делю на тестовую и тренировучную выборки\n",
        "X_train = df2[['Date','Open','Volume','High']][n1:n2] #много параметров\n",
        "X_train['Date'] = pd.to_numeric(X_train['Date'])\n",
        "y_train = df2[['Close']].shift(sdvig)[n1:n2] #делаем сдвиг всех значений колонки на 1 строку назад\n",
        "\n",
        "X_test = df2[['Date','Open','Volume','High']][n2:n3] #много параметров\n",
        "X_test['Date'] = pd.to_numeric(X_test['Date'])\n",
        "y_test = df2[['Close']].shift(sdvig)[n2:n3] #делаем сдвиг всех значений колонки на 1 строку назад"
      ],
      "metadata": {
        "id": "looT7r3olGsV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# МНК"
      ],
      "metadata": {
        "id": "MVb3wnAEYCHU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#создаю модель, учу \n",
        "from sklearn import linear_model \n",
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(X_train,y_train) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhWBpwNeYBgp",
        "outputId": "c2066f17-b3e1-42f5-efa7-77b7c125e67c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 420
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#получаю предсказанные значения\n",
        "y_pred = reg.predict(X_test)"
      ],
      "metadata": {
        "id": "GmVLgjwVgD1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#сравниваем визуально предсказанные и реальные данные\n",
        "plt.plot(X_train['Date'],y_train,'o')\n",
        "plt.plot(X_test['Date'],y_test,'o',color = 'red')\n",
        "plt.plot(X_test['Date'],y_pred, color ='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "bWo9__jqgH8W",
        "outputId": "8d3744a8-4d2d-424f-dfc2-c1da161a1542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#оцениваем\n",
        "from sklearn import metrics \n",
        "metrics.mean_squared_error(y_test,y_pred) #MSE метрика, чем меньше, тем лучше"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ww2vb_rE2Wnd",
        "outputId": "3a83c6b4-22a4-482b-8f20-68830f0092b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4446028862856135"
            ]
          },
          "metadata": {},
          "execution_count": 423
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Хи квадрат**, проверяет:\n",
        "*   h0: данные ожидаемые данные не протеворечат реальным \n",
        "*   h1: протеворечат\n",
        "<br>Если p-value больше 0,05 то H0, если меньше H1\n",
        "\n",
        "Чем меньше само число критерия хи квадрат, тем ближе ожидание к реальным"
      ],
      "metadata": {
        "id": "GAS7dOXip33i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from  scipy.stats import chi2_contingency #хи квадрат\n",
        "chi2, p, dof, ex = chi2_contingency([y_test,y_pred])\n",
        "print(chi2,p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssW2p25-xxAH",
        "outputId": "6e614955-d1c2-4204-ad6f-eb8108f2c5ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2006946788291653 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**T-критерий Стьюдента**\n",
        "<br>Это тест на нулевую гипотезу о том, что 2 независимых образца имеют одинаковые средние значения."
      ],
      "metadata": {
        "id": "mOEKAjSMp8EU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind #t критерий Стьюдента\n",
        "stat, p = ttest_ind(y_test,y_pred)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y7tQvpWP6BOl",
        "outputId": "7b773ece-3a83-496b-f391-a6aac0924788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.45093362]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Z-тест Фишера**\n",
        "<br>H0: средние равны\n",
        "<br> H1: неравны"
      ],
      "metadata": {
        "id": "hZ4dgh77p_yC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats import weightstats #z-тест Фишера\n",
        "stat, p = weightstats.ztest(y_test,y_pred)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PGcaA-DBajN9",
        "outputId": "fe6a0f0c-08b7-429b-a091-f5ce4526d0da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.44911775]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LASSO"
      ],
      "metadata": {
        "id": "C34eobArZ70A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "clf = linear_model.Lasso(alpha=0.5)\n",
        "clf.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBgEMU42Z7Pf",
        "outputId": "45657812-eac3-443e-bd11-c36bf29989ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_coordinate_descent.py:648: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.970e+02, tolerance: 6.590e+00\n",
            "  coef_, l1_reg, l2_reg, X, y, max_iter, tol, rng, random, positive\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso(alpha=0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 429
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = pd.DataFrame()\n",
        "y_pred['y_pred'] = pd.DataFrame(clf.predict(X_test))"
      ],
      "metadata": {
        "id": "wmUqxQJoh3sk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train['Date'],y_train,'o')\n",
        "plt.plot(X_test['Date'],y_test,'o',color = 'red')\n",
        "plt.plot(X_test['Date'],y_pred, color ='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "ieykv8H_h6Qd",
        "outputId": "012590c3-a2e4-4705-f92f-e6522d7deef8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics \n",
        "metrics.mean_squared_error(y_test,y_pred) #MSE метрика, чем меньше, тем лучше"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYMp7agA2VvO",
        "outputId": "cd38e4bc-0cea-4eef-8732-715b9af7683d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5363587003294286"
            ]
          },
          "metadata": {},
          "execution_count": 432
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from  scipy.stats import chi2_contingency #Статистика хи квадрат\n",
        "chi2, p, dof, ex = chi2_contingency([y_test,y_pred])\n",
        "print(chi2,p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzx2OYqrxx8j",
        "outputId": "df8d3bd2-a08d-4ee4-fd03-550cd37cf783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2006642431022123 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind #t критерий Стьюдента\n",
        "stat, p = ttest_ind(y_test,y_pred)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2gk_0Oga6CR_",
        "outputId": "c038ff07-c415-492f-c229-e991dd3dd48b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.41441288]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats import weightstats #z-тест Фишера\n",
        "stat, p = weightstats.ztest(y_test,y_pred)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovh_9wltaoe6",
        "outputId": "3a37b753-ca77-44d3-e478-6518ea128000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.41242393]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ridge"
      ],
      "metadata": {
        "id": "6FOc2dq5aelT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "rdg = Ridge(alpha=1.0)\n",
        "rdg.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xm8vTWbahDy",
        "outputId": "a3bdf913-8e08-4d36-e6b9-db647a37e303"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_ridge.py:157: LinAlgWarning: Ill-conditioned matrix (rcond=6.38012e-32): result may not be accurate.\n",
            "  return linalg.solve(A, Xy, sym_pos=True, overwrite_a=True).T\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge()"
            ]
          },
          "metadata": {},
          "execution_count": 438
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rdg.predict(X_test)"
      ],
      "metadata": {
        "id": "Azti5PGBiJXp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(X_train['Date'],y_train,'o')\n",
        "plt.plot(X_test['Date'],y_test,'o',color = 'red')\n",
        "plt.plot(X_test['Date'],y_pred, color ='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "6MuCYuPLiFvy",
        "outputId": "204cb413-fa3f-4105-96a5-498355bbdce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics \n",
        "metrics.mean_squared_error(y_test,y_pred) #MSE метрика, чем меньше, тем лучше"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3vpHwkX2OJk",
        "outputId": "4ec9a9c1-b194-475a-b30a-df5af189813a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4445648210415887"
            ]
          },
          "metadata": {},
          "execution_count": 441
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from  scipy.stats import chi2_contingency #хи квадрат\n",
        "chi2, p, dof, ex = chi2_contingency([y_test,y_pred])\n",
        "print(chi2, p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SWKT3unxyrd",
        "outputId": "c0f4fdf9-65bf-4fb4-c3b5-b18e7d6fe137"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.20059656341125232 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind #t критерий Стьюдента\n",
        "stat, p = ttest_ind(y_test,y_pred)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HG9ID2eT6DGn",
        "outputId": "1230857e-5169-4bae-b11b-52c433f9c386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.45050961]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats import weightstats #z-тест Фишера\n",
        "stat, p = weightstats.ztest(y_test,y_pred)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyzNZu7qap7q",
        "outputId": "fa91c171-d408-4702-cfa3-8b0f77078ccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.44869177]\n"
          ]
        }
      ]
    }
  ]
}