improving-generalization-through-model-validation/cross-validation-for-algorithmic-selection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Cross-validation for algorithm selection** \n"
      ],
      "metadata": {
        "id": "s8Bep2swK2dQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "In order to make sure that your algorithm is efficient and robust, it is necessary to measure its generalization performance. Here, we will illustrate how to perform  algorithmic selection using nested cross-validation. We will use the German credit dataset as an example. \n",
        "\n",
        "In this notebook, we will:\n",
        "1. Load and organize the data \n",
        "2. Pre-process the features  \n",
        "3. Perform algorithm selection thorugh nested cross-validation\n",
        "4. Show the results \n",
        "\n",
        "We will implement the SVC and the cross-validation in [scikit-learn](https://scikit-learn.org/stable/). \n"
      ],
      "metadata": {
        "id": "z9CksMshaRsU"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9TdtqyBayWw"
      },
      "source": [
        "## **Load and Organise data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LXpe5bBqayWv"
      },
      "outputs": [],
      "source": [
        "#requirements !pip freeze > requirements.txt.\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Please download the data from the following link: https://drive.google.com/file/d/1t_knbnXYYD7LZopHVc2H2DBm4QId4FEf/view?usp=sharing. If running in Colab, please upload the data to the local folder. Otherwise, place the data in the same folder as the notebook."
      ],
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        "id": "Bt7j9KwTa-b2"
      }
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        "outputId": "39497bb8-a243-49bc-a6fe-ffdc4289d233",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
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          "data": {
            "text/plain": [
              "   default account_check_status  duration_in_month  \\\n",
              "0        0               < 0 DM                  6   \n",
              "1        1    0 <= ... < 200 DM                 48   \n",
              "2        0  no checking account                 12   \n",
              "3        0               < 0 DM                 42   \n",
              "4        1               < 0 DM                 24   \n",
              "\n",
              "                                      credit_history  \\\n",
              "0  critical account/ other credits existing (not ...   \n",
              "1           existing credits paid back duly till now   \n",
              "2  critical account/ other credits existing (not ...   \n",
              "3           existing credits paid back duly till now   \n",
              "4                    delay in paying off in the past   \n",
              "\n",
              "                        purpose  credit_amount                      savings  \\\n",
              "0           domestic appliances           1169  unknown/ no savings account   \n",
              "1           domestic appliances           5951                 ... < 100 DM   \n",
              "2  (vacation - does not exist?)           2096                 ... < 100 DM   \n",
              "3              radio/television           7882                 ... < 100 DM   \n",
              "4                     car (new)           4870                 ... < 100 DM   \n",
              "\n",
              "    present_emp_since  installment_as_income_perc  \\\n",
              "0       .. >= 7 years                           4   \n",
              "1  1 <= ... < 4 years                           2   \n",
              "2  4 <= ... < 7 years                           2   \n",
              "3  4 <= ... < 7 years                           2   \n",
              "4  1 <= ... < 4 years                           3   \n",
              "\n",
              "                   personal_status_sex  ... present_res_since  \\\n",
              "0                        male : single  ...                 4   \n",
              "1  female : divorced/separated/married  ...                 2   \n",
              "2                        male : single  ...                 3   \n",
              "3                        male : single  ...                 4   \n",
              "4                        male : single  ...                 4   \n",
              "\n",
              "                                            property age  \\\n",
              "0                                        real estate  67   \n",
              "1                                        real estate  22   \n",
              "2                                        real estate  49   \n",
              "3  if not A121 : building society savings agreeme...  45   \n",
              "4                              unknown / no property  53   \n",
              "\n",
              "   other_installment_plans   housing credits_this_bank  \\\n",
              "0                     none       own                 2   \n",
              "1                     none       own                 1   \n",
              "2                     none       own                 1   \n",
              "3                     none  for free                 1   \n",
              "4                     none  for free                 2   \n",
              "\n",
              "                           job people_under_maintenance  \\\n",
              "0  skilled employee / official                        1   \n",
              "1  skilled employee / official                        1   \n",
              "2         unskilled - resident                        2   \n",
              "3  skilled employee / official                        2   \n",
              "4  skilled employee / official                        2   \n",
              "\n",
              "                                   telephone foreign_worker  \n",
              "0  yes, registered under the customers name             yes  \n",
              "1                                       none            yes  \n",
              "2                                       none            yes  \n",
              "3                                       none            yes  \n",
              "4                                       none            yes  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th></th>\n",
              "      <th>default</th>\n",
              "      <th>account_check_status</th>\n",
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              "      <th>credit_history</th>\n",
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              "      <th>installment_as_income_perc</th>\n",
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              "      <th>...</th>\n",
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              "      <th>property</th>\n",
              "      <th>age</th>\n",
              "      <th>other_installment_plans</th>\n",
              "      <th>housing</th>\n",
              "      <th>credits_this_bank</th>\n",
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              "      <th>telephone</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>&lt; 0 DM</td>\n",
              "      <td>6</td>\n",
              "      <td>critical account/ other credits existing (not ...</td>\n",
              "      <td>domestic appliances</td>\n",
              "      <td>1169</td>\n",
              "      <td>unknown/ no savings account</td>\n",
              "      <td>.. &gt;= 7 years</td>\n",
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              "      <td>male : single</td>\n",
              "      <td>...</td>\n",
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              "      <td>2</td>\n",
              "      <td>skilled employee / official</td>\n",
              "      <td>1</td>\n",
              "      <td>yes, registered under the customers name</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0 &lt;= ... &lt; 200 DM</td>\n",
              "      <td>48</td>\n",
              "      <td>existing credits paid back duly till now</td>\n",
              "      <td>domestic appliances</td>\n",
              "      <td>5951</td>\n",
              "      <td>... &lt; 100 DM</td>\n",
              "      <td>1 &lt;= ... &lt; 4 years</td>\n",
              "      <td>2</td>\n",
              "      <td>female : divorced/separated/married</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>real estate</td>\n",
              "      <td>22</td>\n",
              "      <td>none</td>\n",
              "      <td>own</td>\n",
              "      <td>1</td>\n",
              "      <td>skilled employee / official</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>no checking account</td>\n",
              "      <td>12</td>\n",
              "      <td>critical account/ other credits existing (not ...</td>\n",
              "      <td>(vacation - does not exist?)</td>\n",
              "      <td>2096</td>\n",
              "      <td>... &lt; 100 DM</td>\n",
              "      <td>4 &lt;= ... &lt; 7 years</td>\n",
              "      <td>2</td>\n",
              "      <td>male : single</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>real estate</td>\n",
              "      <td>49</td>\n",
              "      <td>none</td>\n",
              "      <td>own</td>\n",
              "      <td>1</td>\n",
              "      <td>unskilled - resident</td>\n",
              "      <td>2</td>\n",
              "      <td>none</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>&lt; 0 DM</td>\n",
              "      <td>42</td>\n",
              "      <td>existing credits paid back duly till now</td>\n",
              "      <td>radio/television</td>\n",
              "      <td>7882</td>\n",
              "      <td>... &lt; 100 DM</td>\n",
              "      <td>4 &lt;= ... &lt; 7 years</td>\n",
              "      <td>2</td>\n",
              "      <td>male : single</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>if not A121 : building society savings agreeme...</td>\n",
              "      <td>45</td>\n",
              "      <td>none</td>\n",
              "      <td>for free</td>\n",
              "      <td>1</td>\n",
              "      <td>skilled employee / official</td>\n",
              "      <td>2</td>\n",
              "      <td>none</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>&lt; 0 DM</td>\n",
              "      <td>24</td>\n",
              "      <td>delay in paying off in the past</td>\n",
              "      <td>car (new)</td>\n",
              "      <td>4870</td>\n",
              "      <td>... &lt; 100 DM</td>\n",
              "      <td>1 &lt;= ... &lt; 4 years</td>\n",
              "      <td>3</td>\n",
              "      <td>male : single</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>unknown / no property</td>\n",
              "      <td>53</td>\n",
              "      <td>none</td>\n",
              "      <td>for free</td>\n",
              "      <td>2</td>\n",
              "      <td>skilled employee / official</td>\n",
              "      <td>2</td>\n",
              "      <td>none</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/askoshiyama/mli-cohort/master/german_credit.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxm8Mam2ayWx"
      },
      "source": [
        "## **Pre-process variables**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We start by observing which variables are in the dataset, then transform the categorical variables in binary values through one hot encoding. "
      ],
      "metadata": {
        "id": "IEsy4tA3bTCl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2X8KRY69ayWy",
        "outputId": "2507e040-732b-451d-b172-03aa3056802c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "default                        int64\n",
              "account_check_status          object\n",
              "duration_in_month              int64\n",
              "credit_history                object\n",
              "purpose                       object\n",
              "credit_amount                  int64\n",
              "savings                       object\n",
              "present_emp_since             object\n",
              "installment_as_income_perc     int64\n",
              "personal_status_sex           object\n",
              "other_debtors                 object\n",
              "present_res_since              int64\n",
              "property                      object\n",
              "age                            int64\n",
              "other_installment_plans       object\n",
              "housing                       object\n",
              "credits_this_bank              int64\n",
              "job                           object\n",
              "people_under_maintenance       int64\n",
              "telephone                     object\n",
              "foreign_worker                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# variable types\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wzt1LV8WayWy"
      },
      "outputs": [],
      "source": [
        "# categorical variables\n",
        "cat_variables = ['account_check_status', 'credit_history', 'purpose', 'savings', 'present_emp_since', 'personal_status_sex',\n",
        "                'property', 'other_installment_plans', 'housing', 'job', 'telephone', \"other_debtors\", 'foreign_worker']\n",
        "\n",
        "# target variable\n",
        "output_variable = [\"default\"]\n",
        "\n",
        "# other integer variables\n",
        "int_variables = ['credits_this_bank', 'present_res_since', 'duration_in_month', 'people_under_maintenance', \n",
        "                 'installment_as_income_perc', 'age', 'credit_amount']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GDYqRu4CayWz"
      },
      "outputs": [],
      "source": [
        "# pre-allocation\n",
        "df_cat = pd.DataFrame(index=df.index)\n",
        "\n",
        "# one-hot encoding of categorical variables\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# I will do a loop for pedagogical reasons, but it is not entirely necessary\n",
        "for cat in cat_variables:\n",
        "    # one-hot encoding fitting\n",
        "    one_hot_func = OneHotEncoder().fit(df[[cat]])\n",
        "    \n",
        "    # mapping\n",
        "    cat_mapped = one_hot_func.transform(df[[cat]]).toarray()\n",
        "    \n",
        "    # storing\n",
        "    for (k, cat_label) in enumerate(one_hot_func.categories_[0]):\n",
        "        df_cat[cat + \"_\" + cat_label] = cat_mapped[:, k]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then bin the variable age into three categories: young, adult, senior."
      ],
      "metadata": {
        "id": "1b1CqE418JAC"
      }
    },
    {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   young  adult  senior  age\n",
              "0    0.0    0.0     1.0   67\n",
              "1    1.0    0.0     0.0   22\n",
              "2    0.0    0.0     1.0   49\n",
              "3    0.0    0.0     1.0   45\n",
              "4    0.0    0.0     1.0   53\n",
              "5    0.0    1.0     0.0   35\n",
              "6    0.0    0.0     1.0   53\n",
              "7    0.0    1.0     0.0   35\n",
              "8    0.0    0.0     1.0   61\n",
              "9    0.0    1.0     0.0   28"
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              "      <th>young</th>\n",
              "      <th>adult</th>\n",
              "      <th>senior</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-955ef4ec-8366-4320-9d05-86caeb8a1207')\"\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-955ef4ec-8366-4320-9d05-86caeb8a1207 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-955ef4ec-8366-4320-9d05-86caeb8a1207');\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": 6
        }
      ],
      "source": [
        "# bracketing integer variable - age\n",
        "from sklearn.preprocessing import KBinsDiscretizer\n",
        "kbin_func = KBinsDiscretizer(n_bins=3, encode='onehot', strategy='quantile').fit(df[[\"age\"]])\n",
        "df_age = pd.DataFrame(kbin_func.transform(df[[\"age\"]]).toarray(), columns=[\"young\", \"adult\", \"senior\"])\n",
        "\n",
        "# checking\n",
        "pd.concat([df_age, df[[\"age\"]]], axis=1).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also add the log-transformed variables to the datset. "
      ],
      "metadata": {
        "id": "lVFYdJf080gs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lrklWI5NayW0",
        "outputId": "640831e5-9f51-4e33-de54-24fdba3513bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# other types of transformations possible - log transformation \n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "log_mapping = FunctionTransformer(func=np.log, inverse_func=np.exp)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n",
        "ax1.hist(df[\"credit_amount\"])\n",
        "ax1.set_title(\"Pre-transformation\")\n",
        "\n",
        "ax2.hist(log_mapping.transform(df[\"credit_amount\"]))\n",
        "ax2.set_title(\"After log-transformation\")\n",
        "df_log = pd.DataFrame(log_mapping.transform(df[\"credit_amount\"]).values, columns=[\"log(credit)\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iygd2FWwayW0"
      },
      "source": [
        "Finally, we can finalize and print our dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n6VR-3GFayW1",
        "outputId": "578cba6d-9db2-4f8d-feae-8d9d6f24117d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   credits_this_bank  present_res_since  duration_in_month  \\\n",
              "0                  2                  4                  6   \n",
              "1                  1                  2                 48   \n",
              "2                  1                  3                 12   \n",
              "3                  1                  4                 42   \n",
              "4                  2                  4                 24   \n",
              "\n",
              "   people_under_maintenance  installment_as_income_perc  \\\n",
              "0                         1                           4   \n",
              "1                         1                           2   \n",
              "2                         2                           2   \n",
              "3                         2                           2   \n",
              "4                         2                           3   \n",
              "\n",
              "   account_check_status_0 <= ... < 200 DM  account_check_status_< 0 DM  \\\n",
              "0                                     0.0                          1.0   \n",
              "1                                     1.0                          0.0   \n",
              "2                                     0.0                          0.0   \n",
              "3                                     0.0                          1.0   \n",
              "4                                     0.0                          1.0   \n",
              "\n",
              "   account_check_status_>= 200 DM / salary assignments for at least 1 year  \\\n",
              "0                                                0.0                         \n",
              "1                                                0.0                         \n",
              "2                                                0.0                         \n",
              "3                                                0.0                         \n",
              "4                                                0.0                         \n",
              "\n",
              "   account_check_status_no checking account  \\\n",
              "0                                       0.0   \n",
              "1                                       0.0   \n",
              "2                                       1.0   \n",
              "3                                       0.0   \n",
              "4                                       0.0   \n",
              "\n",
              "   credit_history_all credits at this bank paid back duly  ...  \\\n",
              "0                                                0.0       ...   \n",
              "1                                                0.0       ...   \n",
              "2                                                0.0       ...   \n",
              "3                                                0.0       ...   \n",
              "4                                                0.0       ...   \n",
              "\n",
              "   other_debtors_co-applicant  other_debtors_guarantor  other_debtors_none  \\\n",
              "0                         0.0                      0.0                 1.0   \n",
              "1                         0.0                      0.0                 1.0   \n",
              "2                         0.0                      0.0                 1.0   \n",
              "3                         0.0                      1.0                 0.0   \n",
              "4                         0.0                      0.0                 1.0   \n",
              "\n",
              "   foreign_worker_no  foreign_worker_yes  young  adult  senior  log(credit)  \\\n",
              "0                0.0                 1.0    0.0    0.0     1.0     7.063904   \n",
              "1                0.0                 1.0    1.0    0.0     0.0     8.691315   \n",
              "2                0.0                 1.0    0.0    0.0     1.0     7.647786   \n",
              "3                0.0                 1.0    0.0    0.0     1.0     8.972337   \n",
              "4                0.0                 1.0    0.0    0.0     1.0     8.490849   \n",
              "\n",
              "   default  \n",
              "0        0  \n",
              "1        1  \n",
              "2        0  \n",
              "3        0  \n",
              "4        1  \n",
              "\n",
              "[5 rows x 64 columns]"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>credits_this_bank</th>\n",
              "      <th>present_res_since</th>\n",
              "      <th>duration_in_month</th>\n",
              "      <th>people_under_maintenance</th>\n",
              "      <th>installment_as_income_perc</th>\n",
              "      <th>account_check_status_0 &lt;= ... &lt; 200 DM</th>\n",
              "      <th>account_check_status_&lt; 0 DM</th>\n",
              "      <th>account_check_status_&gt;= 200 DM / salary assignments for at least 1 year</th>\n",
              "      <th>account_check_status_no checking account</th>\n",
              "      <th>credit_history_all credits at this bank paid back duly</th>\n",
              "      <th>...</th>\n",
              "      <th>other_debtors_co-applicant</th>\n",
              "      <th>other_debtors_guarantor</th>\n",
              "      <th>other_debtors_none</th>\n",
              "      <th>foreign_worker_no</th>\n",
              "      <th>foreign_worker_yes</th>\n",
              "      <th>young</th>\n",
              "      <th>adult</th>\n",
              "      <th>senior</th>\n",
              "      <th>log(credit)</th>\n",
              "      <th>default</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.063904</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.691315</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.647786</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.972337</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.490849</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 64 columns</p>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-3bbc1cab-bfac-4e7e-a3d3-5b8be2800200');\n",
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              "            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": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df_model = pd.concat([df[int_variables[:-2]], df_cat, df_age, df_log, df[output_variable]], axis=1)\n",
        "df_model.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CzEVgZV8ayW5"
      },
      "source": [
        "## **Algorithm selection**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we define the list of our models and their hyperparameters: Gradient Boosting Classifier, Decision Tree, MultiLayer Perceptron, k-Nearest Neighbour, Logistic Regression, Lasso, Elastic Net, SVM, and two baseline classifiers which predict the label at random, uniformly or respective to the class distribution in the training set. "
      ],
      "metadata": {
        "id": "uW6S-obF-2ip"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jLQYcoluayW5"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "\n",
        "# models\n",
        "model_dict = {\"GBT\": GradientBoostingClassifier(random_state=10),\n",
        "              \"Tree\": DecisionTreeClassifier(random_state=10),\n",
        "              \"MLP\": Pipeline([(\"std\", StandardScaler()), \n",
        "                               (\"mlp\", MLPClassifier(random_state=10))]),\n",
        "              \"KNN\": Pipeline([(\"std\", StandardScaler()),\n",
        "                               (\"knn\", KNeighborsClassifier())]),\n",
        "              \"Logistic\": LogisticRegression(penalty=\"none\", solver=\"lbfgs\"),\n",
        "              \"LassoLogistic\": Pipeline([(\"std\", StandardScaler()), \n",
        "                                         (\"lasso\", LogisticRegression(penalty=\"l1\", solver='liblinear'))]),\n",
        "              \"ElasticNet\": Pipeline([(\"std\", StandardScaler()), \n",
        "                                      (\"en\", LogisticRegression(penalty=\"elasticnet\", solver=\"saga\"))]),\n",
        "              \"UnifRandom\": DummyClassifier(strategy=\"uniform\"), \n",
        "              \"StratRandom\": DummyClassifier(strategy=\"stratified\"),\n",
        "              \"SVM\": Pipeline([(\"std\", StandardScaler()), \n",
        "                               (\"svc\", SVC(kernel=\"rbf\", probability=True))]),\n",
        "             }\n",
        "\n",
        "# models hyperparams\n",
        "hyper_params = {\"GBT\": {\"learning_rate\": [10.0 ** -1, 10.0 ** 0, 2.0],\n",
        "                        \"max_depth\": [1, 2, 3],\n",
        "                        \"max_features\": [0.1, 0.25, 0.5],\n",
        "                        \"n_estimators\": [200]},\n",
        "                \"Tree\": {\"max_depth\": [1, 3, 5, 7, 9],\n",
        "                         \"class_weight\": [None, \"balanced\"]},\n",
        "                \"MLP\": {\"mlp__hidden_layer_sizes\": [(5,), (10,), (50,)], \n",
        "                        \"mlp__activation\": ['relu', 'tanh']\n",
        "                       },\n",
        "                \"KNN\": {\"knn__n_neighbors\": [1, 3, 5, 7]\n",
        "                       },\n",
        "                \"Logistic\": {\"class_weight\": [None, \"balanced\"]},\n",
        "                \"LassoLogistic\": {\"lasso__C\": [10.0 ** -4, 10.0 ** -3, 10.0 ** -2, 10.0 ** -1, 10.0 ** 0],\n",
        "                                  \"lasso__class_weight\": [None, \"balanced\"]},\n",
        "                \"ElasticNet\": {\"en__C\": [10.0 ** -4, 10.0 ** -3, 10.0 ** -2, 10.0 ** -1, 10.0 ** -0],\n",
        "                               \"en__l1_ratio\": [0.1, 0.3, 0.5, 0.7, 0.9],\n",
        "                               \"en__class_weight\": [None, \"balanced\"]},\n",
        "                \"UnifRandom\": {},\n",
        "                \"StratRandom\": {},\n",
        "                \"SVM\": {\"svc__C\": [10.0 ** -2, 10.0 ** -1, 10.0 ** 0, 10.0 ** 1, 10.00 ** 2], \n",
        "                        \"svc__gamma\": (np.array([10.0 ** -2, 10.0 ** -1, 10.0 ** 0, 10.0 ** 1, 10.0 ** 2]) * 1.0/df_model.shape[1]).tolist(),\n",
        "                        \"svc__class_weight\": [None, \"balanced\"]}\n",
        "                }"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, we split our data into 3 inner folds and 2 outer folds, which we will use to perform our nested cross-validation. We also select a few metrics to score the performance of the algorithm.\n"
      ],
      "metadata": {
        "id": "vUS7npyloBo8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# k-fold params\n",
        "inner_folds = StratifiedKFold(n_splits=3, random_state=10, shuffle=True) # inner k-fold\n",
        "outer_folds = StratifiedKFold(n_splits=2, random_state=10, shuffle=True) # outer k-fold\n",
        "\n",
        "# performance metrics\n",
        "perf_metrics = {\"Accuracy\": metrics.accuracy_score, \n",
        "                \"Precision\": metrics.precision_score, \n",
        "                \"Recall\": metrics.recall_score,\n",
        "                \"AUC\": metrics.roc_auc_score, \n",
        "                \"F1-Score\": metrics.f1_score, \n",
        "                \"Brier\": metrics.brier_score_loss,\n",
        "               }\n",
        "scorer_metrics = {}\n",
        "for pf in perf_metrics:\n",
        "    scorer_metrics[pf] = metrics.make_scorer(perf_metrics[pf])"
      ],
      "metadata": {
        "id": "4ksroWfRoWNq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ne5DWrxaayW5"
      },
      "source": [
        "We then proceed to perform our nested cross-validation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TpMM99czayW6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcdf67fe-06c3-4b84-9510-af073f3f6ac0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'GBT')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'Tree')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'MLP')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'KNN')\n",
            "(0, 'Logistic')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'LassoLogistic')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'ElasticNet')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 'UnifRandom')\n",
            "(0, 'StratRandom')\n",
            "(0, 'SVM')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'GBT')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'Tree')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'MLP')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'KNN')\n",
            "(1, 'Logistic')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'LassoLogistic')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'ElasticNet')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 'UnifRandom')\n",
            "(1, 'StratRandom')\n",
            "(1, 'SVM')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "# pre-allocation\n",
        "X, y = df_model.drop(labels=output_variable, axis=1).values, df_model[output_variable].values\n",
        "k, z, innercv_results, inner_best_model = 0, 0, {}, {}\n",
        "df_outer_results = pd.DataFrame(index=[0], columns=[\"Model\"] + list(perf_metrics.keys()))\n",
        "\n",
        "# outer loop\n",
        "for (out_train, out_test) in outer_folds.split(X, y):\n",
        "    # separation: train, test\n",
        "    Xtrain, Xtest = X[out_train], X[out_test]\n",
        "    ytrain, ytest = y[out_train], y[out_test]\n",
        "        \n",
        "    # inner loop -- all models -- hyperparameter fine-tuning\n",
        "    innercv_results[\"fold_\" + str(k)] = {}\n",
        "    inner_best_model[\"fold_\" + str(k)] = {}\n",
        "    \n",
        "    for class_model in model_dict.keys():\n",
        "        print((k, class_model))\n",
        "        innercv_results[\"fold_\" + str(k)][class_model] = GridSearchCV(estimator=model_dict[class_model], \n",
        "                                                                       param_grid=hyper_params[class_model], \n",
        "                                                                       scoring=scorer_metrics, \n",
        "                                                                       cv=inner_folds, \n",
        "                                                                       refit=\"F1-Score\")\n",
        "        innercv_results[\"fold_\" + str(k)][class_model].fit(Xtrain, ytrain.ravel())\n",
        "        inner_best_model[\"fold_\" + str(k)][class_model] = innercv_results[\"fold_\" + str(k)][class_model].best_estimator_\n",
        "    \n",
        "    # prediction -- all models -- compute performance metrics at the same level playing field\n",
        "    for class_model in model_dict.keys():\n",
        "        # prediction\n",
        "        ypred_class = inner_best_model[\"fold_\" + str(k)][class_model].predict(Xtest)\n",
        "        ypred_prob = inner_best_model[\"fold_\" + str(k)][class_model].predict_proba(Xtest)[:, 1]\n",
        "        df_outer_results.loc[z, \"Model\"] = class_model\n",
        "    \n",
        "        # compute performance metrics\n",
        "        for pf in perf_metrics.keys():\n",
        "            if pf in [\"AUC\", \"Brier\"]:\n",
        "                df_outer_results.loc[z, pf] = perf_metrics[pf](ytest, ypred_prob)\n",
        "            else:\n",
        "                df_outer_results.loc[z, pf] = perf_metrics[pf](ytest, ypred_class)\n",
        "        z += 1\n",
        "    \n",
        "    # iteration\n",
        "    k += 1\n",
        "\n",
        "# final organisation\n",
        "df_outer_results[list(perf_metrics.keys())] = df_outer_results[list(perf_metrics.keys())].astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D3YQw03xayW6"
      },
      "outputs": [],
      "source": [
        "df_outer_results[list(perf_metrics.keys())] = df_outer_results[list(perf_metrics.keys())].astype(float)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6PKp2ptayW6"
      },
      "source": [
        "## **Display the results**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can print the results over all possible algorithms, and compare. Here, we can see that the MLP performs best in terms of accuracy."
      ],
      "metadata": {
        "id": "ceQkLQTIpKSh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IXQ1hfqRayW6",
        "outputId": "e0492e29-9c0a-4756-ad5c-59daf820a303",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    AUC  Accuracy     Brier  F1-Score  Precision    Recall\n",
              "Model                                                                     \n",
              "MLP            0.757124     0.734  0.185065  0.538732   0.559398  0.520000\n",
              "ElasticNet     0.769352     0.707  0.195486  0.587593   0.509800  0.696667\n",
              "SVM            0.777219     0.704  0.171398  0.594141   0.504553  0.723333\n",
              "GBT            0.725962     0.699  0.230611  0.476844   0.499470  0.456667\n",
              "Logistic       0.762457     0.696  0.204069  0.566488   0.494990  0.666667\n",
              "LassoLogistic  0.762790     0.694  0.202065  0.566270   0.492568  0.670000\n",
              "KNN            0.625952     0.687  0.313000  0.476427   0.482576  0.473333\n",
              "Tree           0.700524     0.619  0.222694  0.563929   0.430285  0.820000\n",
              "StratRandom    0.509524     0.577  0.404000  0.303268   0.299958  0.306667\n",
              "UnifRandom     0.500000     0.480  0.250000  0.370331   0.290739  0.510000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ead7ebf-002a-4f31-9b6d-0d1979ac5312\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AUC</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Brier</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MLP</th>\n",
              "      <td>0.757124</td>\n",
              "      <td>0.734</td>\n",
              "      <td>0.185065</td>\n",
              "      <td>0.538732</td>\n",
              "      <td>0.559398</td>\n",
              "      <td>0.520000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>0.769352</td>\n",
              "      <td>0.707</td>\n",
              "      <td>0.195486</td>\n",
              "      <td>0.587593</td>\n",
              "      <td>0.509800</td>\n",
              "      <td>0.696667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM</th>\n",
              "      <td>0.777219</td>\n",
              "      <td>0.704</td>\n",
              "      <td>0.171398</td>\n",
              "      <td>0.594141</td>\n",
              "      <td>0.504553</td>\n",
              "      <td>0.723333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GBT</th>\n",
              "      <td>0.725962</td>\n",
              "      <td>0.699</td>\n",
              "      <td>0.230611</td>\n",
              "      <td>0.476844</td>\n",
              "      <td>0.499470</td>\n",
              "      <td>0.456667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logistic</th>\n",
              "      <td>0.762457</td>\n",
              "      <td>0.696</td>\n",
              "      <td>0.204069</td>\n",
              "      <td>0.566488</td>\n",
              "      <td>0.494990</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLogistic</th>\n",
              "      <td>0.762790</td>\n",
              "      <td>0.694</td>\n",
              "      <td>0.202065</td>\n",
              "      <td>0.566270</td>\n",
              "      <td>0.492568</td>\n",
              "      <td>0.670000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>0.625952</td>\n",
              "      <td>0.687</td>\n",
              "      <td>0.313000</td>\n",
              "      <td>0.476427</td>\n",
              "      <td>0.482576</td>\n",
              "      <td>0.473333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tree</th>\n",
              "      <td>0.700524</td>\n",
              "      <td>0.619</td>\n",
              "      <td>0.222694</td>\n",
              "      <td>0.563929</td>\n",
              "      <td>0.430285</td>\n",
              "      <td>0.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>StratRandom</th>\n",
              "      <td>0.509524</td>\n",
              "      <td>0.577</td>\n",
              "      <td>0.404000</td>\n",
              "      <td>0.303268</td>\n",
              "      <td>0.299958</td>\n",
              "      <td>0.306667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UnifRandom</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.480</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.370331</td>\n",
              "      <td>0.290739</td>\n",
              "      <td>0.510000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ead7ebf-002a-4f31-9b6d-0d1979ac5312')\"\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-0ead7ebf-002a-4f31-9b6d-0d1979ac5312 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-0ead7ebf-002a-4f31-9b6d-0d1979ac5312');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df_outer_results.pivot_table(index=[\"Model\"], aggfunc=\"mean\").sort_values('Accuracy', ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interestingly, the MLP does not perform equally well in terms of the F1-score, where SVM and ElasticNet seem to outperform, displaying a higher mean score with less variance. "
      ],
      "metadata": {
        "id": "6EiLD6lWpwWX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7kXQZBkvayW6",
        "outputId": "d3d42d7f-5fdb-4473-d344-20ab4f8341c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa451258dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1,1,figsize=(13,5))\n",
        "sns.pointplot(x=\"Model\", y=\"F1-Score\", data=df_outer_results, ax=ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now proceed to analyse the models across all the metrics chosen. "
      ],
      "metadata": {
        "id": "A-M3QkAVqtqK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "avTW_rgsayW6",
        "outputId": "9a5c3f88-3f05-4c9f-96d5-90ffbabbc1ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa44d14f490>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_melt = df_outer_results.melt(id_vars=[\"Model\"], value_vars=list(perf_metrics.keys()))\n",
        "fig, ax = plt.subplots(1,1,figsize=(13,5))\n",
        "sns.pointplot(x=\"Model\", y=\"value\", hue=\"variable\", data=df_melt, ax=ax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8sFhiy6VayW7",
        "outputId": "92877be8-8e24-43cd-d6b2-ff75b4c8e053",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Model\n",
              "SVM              9.166667\n",
              "ElasticNet       8.666667\n",
              "MLP              7.500000\n",
              "LassoLogistic    6.500000\n",
              "Logistic         6.500000\n",
              "Tree             5.166667\n",
              "GBT              4.833333\n",
              "KNN              3.166667\n",
              "UnifRandom       2.000000\n",
              "StratRandom      1.500000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "a = df_outer_results.pivot_table(index=[\"Model\"], aggfunc=\"mean\")\n",
        "a[[\"Brier\"]] = 1 - a[[\"Brier\"]]\n",
        "a.rank().mean(axis=1).sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We take into account the ranking of each algorithm under each performance metric, and aggregate the results. We can see that SVM is the algorithm which performs best consistently across all measures. "
      ],
      "metadata": {
        "id": "DF4fcFPHq9IX"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "Cross-validation for algorithmic selection.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "g9TdtqyBayWw",
        "mxm8Mam2ayWx",
        "CzEVgZV8ayW5"
      ]
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}