chapter_2_Machine_Learning_with_Sci_Kit_Learn.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",
      "source": [
        "from scipy.stats import ttest_ind\n",
        "import pandas as pd\n",
        "\n",
        "# Dataset from Matzke et al. (2015). In this dataset, participants performed a memory task in which they recalled a list of words. \n",
        "# During the retention interval, one group of participants looked at a central fixation dot on a display. \n",
        "# Another group of participants continuously made horizontal eye movements, which is believed by some to improve memory.\n",
        "dm = pd.read_csv('matzke_et_al.csv')\n",
        "\n",
        "dm_horizontal = dm[dm.Condition=='Horizontal']\n",
        "dm_fixation = dm[dm.Condition=='Fixation']\n",
        "\n",
        "t, p = ttest_ind(dm_horizontal.CriticalRecall, dm_fixation.CriticalRecall)\n",
        "print('t = {:.3f}, p = {:.3f}'.format(t, p))"
      ],
      "metadata": {
        "id": "a25M71FdDXjh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5cafee2-52c8-4f31-bc18-d258803338c3"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t = -2.845, p = 0.007\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This reveals a significant difference (p = .0066). However, as you can see in the figure below, the effect goes in the opposite direction from the prediction, such that the fixation group performed best.\n",
        "\n",
        "# You can also use the ttest() function from pingouin. This also provides a Bayes Factor, for those who are into Bayesian statistics."
      ],
      "metadata": {
        "id": "uaXiNr5wf6og"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt # visualization\n",
        "\n",
        "sns.barplot(x='Condition', y='CriticalRecall', data=dm)\n",
        "plt.xlabel('Condition')\n",
        "plt.ylabel('Memory performance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "fhdvSQxVf4r1",
        "outputId": "21e53ad7-12c5-4bcb-8d03-6eb02dac7601"
      },
      "execution_count": 4,
      "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 scipy.stats import ttest_rel\n",
        "\n",
        "# Dataset from Moore, McCabe, & Craig of aggressive behavior from people suffering from dementia was measured during full moon and another phase of the lunar cycle. \n",
        "# Each participant was measured at both phases, i.e. this was a within-subject design.\n",
        "dm = pd.read_csv('moon-aggression.csv')\n",
        "\n",
        "t, p = ttest_rel(dm.Moon, dm.Other)\n",
        "print('t = {:.3f}, p = {:.3f}'.format(t, p))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xz2p7LIxgrui",
        "outputId": "00aff3e3-470d-4841-c79e-dd7f41c7ba61"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t = 6.452, p = 0.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.stats.anova import anova_lm\n",
        "\n",
        "# Heart-rate data from Moore, McCabe, and Craig. This dataset contains two factors that vary between subjects (Gender and Group) and one dependent variable (Heart Rate).\n",
        "dm = pd.read_csv('heartrate.csv')\n",
        "dm = dm.rename({'Heart Rate':'HeartRate'},axis=1)  # statsmodels doesn't like spaces\n",
        "df = anova_lm(ols('HeartRate ~ Gender * Group', data=dm).fit())\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dafxrxv9owl8",
        "outputId": "88e757eb-6703-4c6e-e746-98353aa4e5f6"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 df      sum_sq        mean_sq           F         PR(>F)\n",
            "Gender          1.0   45030.005   45030.005000  185.979949   3.287945e-38\n",
            "Group           1.0  168432.080  168432.080000  695.647040  1.149926e-110\n",
            "Gender:Group    1.0    1794.005    1794.005000    7.409481   6.629953e-03\n",
            "Residual      796.0  192729.830     242.122902         NaN            NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.formula.api import ols\n",
        "\n",
        "# dataset from Moore, McCabe, & Craig which contains grade-point averages (gpa) and SAT scores for mathematics (satm) and verbal knowledge (satv) for 500 high-school students. \n",
        "dm = pd.read_csv('gpa.csv')\n",
        "model = ols('gpa ~ satm + satv', data=dm).fit()\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVWmEFJ8g07p",
        "outputId": "0d244d2c-d241-4530-9f9d-ed98bd6c0bd2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                    gpa   R-squared:                       0.063\n",
            "Model:                            OLS   Adj. R-squared:                  0.055\n",
            "Method:                 Least Squares   F-statistic:                     7.476\n",
            "Date:                Tue, 15 Nov 2022   Prob (F-statistic):           0.000722\n",
            "Time:                        13:40:56   Log-Likelihood:                -254.18\n",
            "No. Observations:                 224   AIC:                             514.4\n",
            "Df Residuals:                     221   BIC:                             524.6\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      1.2887      0.376      3.427      0.001       0.548       2.030\n",
            "satm           0.0023      0.001      3.444      0.001       0.001       0.004\n",
            "satv       -2.456e-05      0.001     -0.040      0.968      -0.001       0.001\n",
            "==============================================================================\n",
            "Omnibus:                       23.688   Durbin-Watson:                   1.715\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.838\n",
            "Skew:                          -0.809   Prob(JB):                     9.02e-07\n",
            "Kurtosis:                       3.601   Cond. No.                     5.85e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 5.85e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3T5y09gEgImd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "df = pd.DataFrame([(.2, .3,.8), (.0, .6,.9), (.6, .0,.4), (.2, .1,.9),\n",
        "                   (.1, .3,.7), (.1, .5,.6), (.7, .1,.5), (.3, .0,.8),],\n",
        "                  columns=['dogs', 'cats','birds'])\n",
        "\n",
        "corr_mat = df.corr()\n",
        "corr_mat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "OO72MNml-ZQh",
        "outputId": "719cc221-5449-488a-f0be-9db55ce121f6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           dogs      cats     birds\n",
              "dogs   1.000000 -0.740432 -0.742781\n",
              "cats  -0.740432  1.000000  0.340799\n",
              "birds -0.742781  0.340799  1.000000"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>dogs</th>\n",
              "      <th>cats</th>\n",
              "      <th>birds</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>dogs</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.740432</td>\n",
              "      <td>-0.742781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cats</th>\n",
              "      <td>-0.740432</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.340799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>birds</th>\n",
              "      <td>-0.742781</td>\n",
              "      <td>0.340799</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4de8327-ac3e-44c0-9f9e-19d521c3a429')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b4de8327-ac3e-44c0-9f9e-19d521c3a429 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b4de8327-ac3e-44c0-9f9e-19d521c3a429');\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",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "# Then, add the following syntax at the bottom of the code:\n",
        "\n",
        "sn.heatmap(corr_mat, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "X2wnUVjTAGKT",
        "outputId": "062cd9af-cd65-47c6-d366-7980f7f33230"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(123)\n",
        "df1=pd.DataFrame( np.random.randn(3,2), columns=list('ab') )\n",
        "df2=pd.DataFrame( np.random.randn(3,2), columns=list('ac') )\n",
        "\n",
        "\n",
        "corr_mat = df1.corr()\n",
        "sn.heatmap(corr_mat, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "w3eOCrGQ-qE7",
        "outputId": "7b4dcd79-1ed2-421e-b0a7-2f52ebcbe399"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# You can compare columns of df1 & df2 with corrwith. Note that only columns with the same names are compared:\n",
        "\n",
        "df1.corrwith(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uyAalfM9_Yso",
        "outputId": "2978cf7e-fa4f-4041-bbb9-97e441d749f5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    0.993085\n",
              "b         NaN\n",
              "c         NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.corrwith(df2.set_axis( df1.columns, axis='columns', inplace=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf3QP48Q_gLs",
        "outputId": "be67effb-e3d5-49aa-e13d-efe0085cfd3e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    0.993085\n",
              "b    0.969220\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_mat = pd.concat([df1,df2],axis=1).corr()\n",
        "sn.heatmap(corr_mat, annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "SG9-B6D0_kLQ",
        "outputId": "2bcc4825-aaee-469f-d761-5bfc14130ac4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "ds = pd.DataFrame(columns = ['x','y'])\n",
        "ds['x'] = [int(n>500) for n in random.sample(range(0, 1000), 100)]\n",
        "ds['y'] = [int(n<500) for n in random.sample(range(0, 1000), 100)]\n",
        "ds.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XqjFhAKCj4_2",
        "outputId": "5504dd1c-d01e-42d8-af65-950d233097f8"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   x  y\n",
              "0  0  0\n",
              "1  0  1\n",
              "2  1  0\n",
              "3  0  1\n",
              "4  1  0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-55805ce1-3d51-416b-b49a-cd9d4e828bec\">\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>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55805ce1-3d51-416b-b49a-cd9d4e828bec')\"\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-55805ce1-3d51-416b-b49a-cd9d4e828bec 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-55805ce1-3d51-416b-b49a-cd9d4e828bec');\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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base = ds[ds.x == 0]\n",
        "variant = ds[ds.x == 1]\n",
        "# We use the z-score value for .95 confidency of the normal interval\n",
        "delta = variant.y.mean() - base.y.mean()\n",
        "delta_err = 1.96 * np.sqrt(\n",
        "    variant.y.var() / variant.shape[0] + \n",
        "    base.y.var() / base.shape[0])\n",
        "\n",
        "print({\"estimated_effect\": delta, \"standard_error\": delta_err})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0mLzzqGi_us",
        "outputId": "ac304224-858e-4388-fdb2-439230cee3cd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'estimated_effect': -0.02101723413198825, 'standard_error': 0.20312418042730232}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ld0hxR4ijJGs"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base = ds[ds.x == 0]\n",
        "variant = ds[ds.x == 1]\n",
        "# We use the z-score value for .95 confidency of the normal interval\n",
        "delta = variant.y.mean() - base.y.mean()\n",
        "delta_err = 1.96 * np.sqrt(\n",
        "    variant.y.var() / variant.shape[0] + \n",
        "    base.y.var() / base.shape[0])\n",
        "\n",
        "print({\"estimated_effect\": delta, \"standard_error\": delta_err})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_1jE3xVVtfT",
        "outputId": "fc36f187-e356-48cf-c558-b376b8868abe"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'estimated_effect': -0.02101723413198825, 'standard_error': 0.20312418042730232}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import chi2_contingency\n",
        "\n",
        "contingency_table = (\n",
        "    ds\n",
        "    .assign(placeholder=1)\n",
        "    .pivot_table(index=\"x\", columns=\"y\", values=\"placeholder\", aggfunc=\"sum\")\n",
        "    .values\n",
        ")\n",
        "\n",
        "_, p, _, _ = chi2_contingency(contingency_table, lambda_=\"log-likelihood\")\n",
        "\n",
        "print(\"P-value:\",p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVZi3m3yPYA8",
        "outputId": "edf939df-238a-4c51-dc32-c45fa917c856"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-value: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "observed_data_1 = pd.read_csv('observed_data_1.csv' )\n",
        "\n",
        "observed_data_1.plot.scatter(x=\"z\", y=\"y\", c=\"x\", cmap=\"rainbow\", colorbar=False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "k-YAXMkBQDY5",
        "outputId": "e52b2718-31c8-4ca7-e580-5430e74c24ff"
      },
      "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": [
        "import seaborn as sns\n",
        "\n",
        "sns.kdeplot(observed_data_1.loc[lambda df: df.x == 0].y, label=\"untreated\")\n",
        "sns.kdeplot(observed_data_1.loc[lambda df: df.x == 1].y, label=\"treated\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-Vhl_tPbQHaf",
        "outputId": "97305f7a-ab39-4c45-f493-985fac5aaaf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe063777290>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "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": [
        "!pip install causalinference"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPqflSsQQdS8",
        "outputId": "8e316f99-7a9e-4c86-9100-98ff4c3ba9a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting causalinference\n",
            "  Downloading CausalInference-0.1.3-py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 3.5 MB/s \n",
            "\u001b[?25hInstalling collected packages: causalinference\n",
            "Successfully installed causalinference-0.1.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from causalinference import CausalModel\n",
        "\n",
        "cm = CausalModel(\n",
        "    Y=observed_data_1.y.values, \n",
        "    D=observed_data_1.x.values, \n",
        "    X=observed_data_1.z.values)\n",
        "\n",
        "cm.est_via_ols(adj=1)\n",
        "\n",
        "print(cm.estimates)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfT-DjmZQcRy",
        "outputId": "30945d01-2613-4032-f5e3-4261d968a4f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Treatment Effect Estimates: OLS\n",
            "\n",
            "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
            "--------------------------------------------------------------------------------\n",
            "           ATE     -0.483      0.032    -14.993      0.000     -0.546     -0.420\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/causalinference/estimators/ols.py:21: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  olscoef = np.linalg.lstsq(Z, Y)[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x7mnMpdzMJF8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "observed_data_2 = pd.read_csv('observed_data_2.csv')\n",
        "\n",
        "observed_data_2.plot.scatter(x=\"z\", y=\"y\", c=\"x\", cmap=\"rainbow\", colorbar=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "ypF-EhXkQkvQ",
        "outputId": "168e2e3c-6f52-4ebd-9957-7dc9e29498f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe062fa6750>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "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": [
        "cm = CausalModel(\n",
        "    Y=observed_data_2.y.values, \n",
        "    D=observed_data_2.x.values, \n",
        "    X=observed_data_2.z.values)\n",
        "\n",
        "cm.est_via_ols(adj=1)\n",
        "\n",
        "print(cm.estimates)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlEMxZ87QsCd",
        "outputId": "de76c868-1ab0-47b8-ed79-d3b51f7d8205"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Treatment Effect Estimates: OLS\n",
            "\n",
            "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
            "--------------------------------------------------------------------------------\n",
            "           ATE      0.472      0.090      5.236      0.000      0.295      0.648\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/causalinference/estimators/ols.py:21: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
            "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
            "  olscoef = np.linalg.lstsq(Z, Y)[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "x_train = pd.DataFrame([[ 1., -1.,  2.],\n",
        "                    [ 2.,  0.,  0.],\n",
        "                    [ 0.,  1., -1.]],columns=['x','y','z'])\n",
        "scaler = preprocessing.StandardScaler().fit(x_train)\n",
        "scaler.mean_"
      ],
      "metadata": {
        "id": "9vV-DffxRaGg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e796fefd-e758-414a-f913-95412c4599aa"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.        , 0.        , 0.33333333])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYwR8BazOx3J",
        "outputId": "45fefe35-9039-4b41-ed39-9390aa8b03af"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81649658, 0.81649658, 1.24721913])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_scaled = scaler.transform(x_train)\n",
        "x_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bOUJShFOskG",
        "outputId": "136e89ab-75ab-4d4b-9cdf-4569baab956d"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.        , -1.22474487,  1.33630621],\n",
              "       [ 1.22474487,  0.        , -0.26726124],\n",
              "       [-1.22474487,  1.22474487, -1.06904497]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "x_train_minmax = min_max_scaler.fit_transform(x_train)"
      ],
      "metadata": {
        "id": "u6yDklp2qi3B"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "data = load_iris()\n",
        "x, y = data['data'],data['target']\n",
        "quantile_transformer = preprocessing.QuantileTransformer(n_quantiles=5)\n",
        "x_train_qt = quantile_transformer.fit_transform(x)\n",
        "x_train_qt[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7qUlMQCMPaHK",
        "outputId": "71343827-e42f-4140-cd4b-9637f20efeca"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.25      , 0.79545455, 0.16666667, 0.125     ],\n",
              "       [0.1875    , 0.5       , 0.16666667, 0.125     ],\n",
              "       [0.125     , 0.66666667, 0.125     , 0.125     ],\n",
              "       [0.09375   , 0.58333333, 0.20833333, 0.125     ],\n",
              "       [0.21875   , 0.81818182, 0.16666667, 0.125     ]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quantile_transformer.quantiles_ "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2ZAoNaPrLef",
        "outputId": "b8d0e5ec-9d1a-4b22-f91c-287cb266b92d"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.3 , 2.  , 1.  , 0.1 ],\n",
              "       [5.1 , 2.8 , 1.6 , 0.3 ],\n",
              "       [5.8 , 3.  , 4.35, 1.3 ],\n",
              "       [6.4 , 3.3 , 5.1 , 1.8 ],\n",
              "       [7.9 , 4.4 , 6.9 , 2.5 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nX26AdE3SDo2"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "kmeans = KMeans(n_clusters=len(set(y)), random_state=0).fit(x)\n",
        "kmeans.labels_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWCWrLh5R3Lt",
        "outputId": "3a403f66-ce42-42c2-fff0-e501576ec44c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
              "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
              "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.predict(x[0].reshape(1,-1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw3MXuE1SQCD",
        "outputId": "5cdda34c-fa73-4e7b-8a82-b4c2a340d79e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans.cluster_centers_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iaf90aIyomaM",
        "outputId": "0b57953f-6589-4af2-cc1e-e01d32cd8f7f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.9016129 , 2.7483871 , 4.39354839, 1.43387097],\n",
              "       [5.006     , 3.428     , 1.462     , 0.246     ],\n",
              "       [6.85      , 3.07368421, 5.74210526, 2.07105263]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=2)\n",
        "pca.fit(x)"
      ],
      "metadata": {
        "id": "JcPi76BzRbyL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "418555ce-9000-417f-be4d-727e312bea05"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=2)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pca.explained_variance_ratio_)\n",
        "print(pca.singular_values_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9ALlItQr0Kc",
        "outputId": "16646e84-177b-40e1-9913-41a2453525c3"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.92461872 0.05306648]\n",
            "[25.09996044  6.01314738]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.datasets import load_diabetes\n",
        "data_reg = load_diabetes()\n",
        "x,y = data_reg['data'],data_reg['target']\n",
        "reg = LinearRegression().fit(x, y)\n",
        "reg.score(x, y) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5aVMaI-r5fl",
        "outputId": "d7f589b2-04b5-40f2-9e83-408d55af3982"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5177494254132934"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxVh8O-9sHkM",
        "outputId": "a04de6e8-9ad0-40c2-dc4d-852cd36c2a74"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ -10.01219782, -239.81908937,  519.83978679,  324.39042769,\n",
              "       -792.18416163,  476.74583782,  101.04457032,  177.06417623,\n",
              "        751.27932109,   67.62538639])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_ "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMDTwBpisJdw",
        "outputId": "7dfe8c94-b483-4855-ece8-219354082892"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "152.1334841628965"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Predicted:',reg.predict(x[10].reshape(1,-1)),'Actual:',y[10])  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4IwGLQRwsP1j",
        "outputId": "389fc993-fcc3-4e9a-dddc-dd25c19da21f"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted: [97.07853583] Actual: 101.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.datasets import load_digits\n",
        "\n",
        "data_class = load_digits()\n",
        "x,y = data_class['data'],data_class['target']\n",
        "clf = make_pipeline(StandardScaler(), LogisticRegression(penalty='l2',C=.1))\n",
        "clf.fit(x, y)\n",
        "clf.predict(x[:2, :]) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2OBvw6ssp5p",
        "outputId": "10e3e3e0-c9cc-461d-d4ad-65febab10d81"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlWbthmusp23",
        "outputId": "bd5449a9-b91b-4e7a-ffff-cb24af8185a2"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.988313856427379"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = load_iris(return_X_y=True)\n",
        "x.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxJZmYt8s8qc",
        "outputId": "a856400a-2b2e-4d7d-f6b0-b6371d5259f0"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((150, 4), (150,))"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=0)"
      ],
      "metadata": {
        "id": "D9vI7xhAs-HH"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsT3BdP3s-Ee",
        "outputId": "bedaaad6-9a46-4a8e-df05-32dda40a8aee"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((90, 4), (90,))"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape, y_test.shape  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDzgRWofs-B1",
        "outputId": "ef3f0572-5383-40d8-d8b3-d8acf05d85c7"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60, 4), (60,))"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = svm.SVC(kernel='linear', C=.7).fit(x_train, y_train)\n",
        "clf.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-32mDpwMs-a5",
        "outputId": "0128f6c4-2fbc-4f4d-ba38-2494ffd31519"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(clf, x, y, cv=5)"
      ],
      "metadata": {
        "id": "0t7Vh6i5tVEN"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean:',scores.mean(),'Standard Deviation:', scores.std())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_jIHMnStU_-",
        "outputId": "107fc93a-f024-4a04-b2a1-955e9d207cba"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 0.9866666666666667 Standard Deviation: 0.01632993161855452\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "scores = cross_val_score(clf, x, y, cv=5, scoring='f1_macro')\n",
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jMxK7j-9tdq6",
        "outputId": "8c8a8349-7316-4269-b621-d3b2c4aa7942"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.96658312, 1.        , 1.        , 0.96658312, 1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import ShuffleSplit\n",
        "n_samples = x.shape[0]\n",
        "cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=0)\n",
        "cross_val_score(clf, x, y, cv=cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I4PztbFXtnzv",
        "outputId": "066a1f70-8cfe-4eff-dae1-b18992c64725"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.97777778, 0.97777778, 1.        , 0.95555556, 1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3a4cGj8jtnxq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cG4dWhF6tdoz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mqouQ_c_tdmi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}