End_to_End_heart_disease_classification.ipynb

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      "name": "End-to-End-heart-disease-classification.ipynb",
      "provenance": [],
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      "authorship_tag": "ABX9TyOwwCV9XtsJyOIGZRFUYhCd",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shauryanegi/Data-Analysis/blob/master/End_to_End_heart_disease_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gAYOmZWh8csj"
      },
      "source": [
        "# **Predicting heart disease using machine learning**\n",
        "\n",
        "The notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.\n",
        "\n",
        "\n",
        "**We are going to take the following approach:**\n",
        "\n",
        "1. Problem Definition\n",
        "2. Data\n",
        "3. Evaluation\n",
        "4. Features\n",
        "5. Modelling\n",
        "6. Experimentation\n",
        "\n",
        "#1. Problem Definition\n",
        "\n",
        "In a statement, given clinical parameters about a patient, can we predict whether a patient has heart disease or not?\n",
        "\n",
        "#2 Data\n",
        "The original data came from Cleavland data from the UCI ML Repository.\n",
        "https://archive.ics.uci.edu/ml/datasets/heart+disease\n",
        "\n",
        "There is also a version available on Kaggle.\n",
        "https://www.kaggle.com/ronitf/heart-disease-uci\n",
        "\n",
        "#3 Evaluation\n",
        "If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n",
        "\n",
        "#4. Features\n",
        "\n",
        "A data dictionary describes the data you're dealing with. Not all datasets come with them so this is where you may have to do your research or ask a subject matter expert (someone who knows about the data) for more.\n",
        "\n",
        "The following are the features we'll use to predict our target variable (heart disease or no heart disease).\n",
        "\n",
        "age - age in years\n",
        "\n",
        "sex - (1 = male; 0 = female)\n",
        "\n",
        "cp - chest pain type\n",
        "\n",
        "0: Typical angina: chest pain related decrease \n",
        "blood supply to the heart\n",
        "\n",
        "1: Atypical angina: chest pain not related to heart\n",
        "\n",
        "2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
        "\n",
        "3: Asymptomatic: chest pain not showing signs of disease\n",
        "\n",
        "trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
        "\n",
        "anything above 130-140 is typically cause for concern\n",
        "\n",
        "chol - serum cholestoral in mg/dl\n",
        "\n",
        "serum = LDL + HDL + .2 * triglycerides\n",
        "above 200 is cause for concern\n",
        "\n",
        "fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
        "'>126' mg/dL signals diabetes\n",
        "\n",
        "restecg - resting electrocardiographic results\n",
        "0: Nothing to note\n",
        "1: ST-T Wave abnormality\n",
        "can range from mild symptoms to severe problems\n",
        "signals non-normal heart beat\n",
        "2: Possible or definite left ventricular hypertrophy\n",
        "Enlarged heart's main pumping chamber\n",
        "\n",
        "thalach - maximum heart rate achieved\n",
        "\n",
        "exang - exercise induced angina (1 = yes; 0 = no)\n",
        "\n",
        "oldpeak - ST depression induced by exercise relative to rest\n",
        "looks at stress of heart during excercise\n",
        "unhealthy heart will stress more\n",
        "\n",
        "slope - the slope of the peak exercise ST segment\n",
        "0: Upsloping: better heart rate with excercise (uncommon)\n",
        "1: Flatsloping: minimal change (typical healthy heart)\n",
        "2: Downslopins: signs of unhealthy heart\n",
        "\n",
        "ca - number of major vessels (0-3) colored by flourosopy\n",
        "colored vessel means the doctor can see the blood passing through\n",
        "the more blood movement the better (no clots)\n",
        "\n",
        "thal - thalium stress result\n",
        "1,3: normal\n",
        "6: fixed defect: used to be defect but ok now\n",
        "7: reversable defect: no proper blood movement when excercising\n",
        "\n",
        "target - have disease or not (1=yes, 0=no) (= the predicted attribute)\n",
        "\n",
        "Note: No personal identifiable information (PPI) can be found in the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bN4Ro_8zDidS"
      },
      "source": [
        "## Preparing the tools\n",
        "\n",
        "We are going to use pandas,matplotlib and NumPy for data analysis and manipulation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3lnrEEbDafN"
      },
      "source": [
        "# Import all the tools we need\n",
        "\n",
        "# Regular EDA and plotting libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#We want our plots to appear inside the notebook\n",
        "%matplotlib inline \n",
        "\n",
        "#Models from Scikit-Learn\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#Model Evaluations\n",
        "from sklearn.model_selection import train_test_split,cross_val_score\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import plot_roc_curve"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLajfSy0HAup"
      },
      "source": [
        "## Load the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tH7iHd39HDBA",
        "outputId": "93c8d20a-8c6c-4060-fe5c-b92bcb3d4e66"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hliO1NQtHXCV"
      },
      "source": [
        "df = pd.read_csv(\"heart-disease.csv\")"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5m9ZtDgOHtmD",
        "outputId": "3c7c2716-ca95-46fa-af28-339cdd06684d"
      },
      "source": [
        "df.head()\n",
        "df.shape #(rows,columns)"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kdgIWf0EJvHB"
      },
      "source": [
        "##Data Exploration( Exploratory data analysis or EDA)\n",
        "The goal is know more about the data and become a SME on the dataset you are working on.\n",
        "\n",
        "1. What questions are we trying to solve?\n",
        "2. What kind of data do we have and how do we treat different types?\n",
        "3. What's missing from the data and how do you deal wirh it?\n",
        "4. What are the outliers and why should you care about them?\n",
        "5. How can you add, change or remove features to get more out of your data?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
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        "id": "ial6dpEnJv2P",
        "outputId": "aceedbcd-64f8-4ec7-d416-4dc0a0099f91"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
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              "\n",
              "[5 rows x 14 columns]"
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          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "9mvuRBfEJyWX",
        "outputId": "a64432e6-e2ef-4004-f21f-166cef3688ed"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[5 rows x 14 columns]"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6CTLhH2Jzo2",
        "outputId": "34a7c81c-a111-442a-93ab-d64da1b4a440"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ot4XSYH-J0js",
        "outputId": "a17c6981-6af6-4907-e20a-5a5f7859ef7f"
      },
      "source": [
        "#Let's find out how many type of classes are\n",
        "df[\"target\"].value_counts() #The labels are balanced"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
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          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "bsNmobqKJ9tl",
        "outputId": "323e6a88-2fa6-4e01-f261-752d1f69f95e"
      },
      "source": [
        "df[\"target\"].value_counts().plot(kind = 'bar', color = ['salmon','lightblue']);"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZhSg-piKXfH",
        "outputId": "2dc06d2f-cf89-40d1-ee48-392a847b966e"
      },
      "source": [
        "#Are there any missing values?\n",
        "df.isna().sum() #No null values"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "CASQVG2cKl28",
        "outputId": "650e977d-3203-40b8-d2b1-b0d372cef81e"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frHnOJuuK50P",
        "outputId": "7f61f9e1-89f8-4e4a-897c-d3a33e1f68f7"
      },
      "source": [
        "### Heart Disease Frequency according to Sex\n",
        "df[\"sex\"].value_counts() #More males than females as we can see from the data dictionary"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    207\n",
              "0     96\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "40dRrtVdMbZh",
        "outputId": "745c932f-5f47-44c6-fc2e-d239bf14f17c"
      },
      "source": [
        "#Compare target column with sex column\n",
        "pd.crosstab(df.target,df.sex)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>sex</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "sex      0    1\n",
              "target         \n",
              "0       24  114\n",
              "1       72   93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "CHlcYzOeMoLE",
        "outputId": "cb401742-6cf2-4eea-94b8-17397525d835"
      },
      "source": [
        "#!/usr/bin/python\n",
        "# -*- coding: utf-8 -*-\n",
        "\n",
        "            # Create a plot of crosstab\n",
        "\n",
        "pd.crosstab(df.target, df.sex).plot(kind='bar', figsize=(10, 6),\n",
        "                                    color=['salmon', 'lightblue'])\n",
        "plt.style.use('ggplot')\n",
        "plt.title('Heart Disease Frequency for Sex')\n",
        "plt.xlabel(' 0 = No Disease, 1 = Disease')\n",
        "plt.ylabel('Amount')\n",
        "plt.legend(['Female', 'Male'])\n",
        "plt.xticks(rotation=0);"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eZ-IOInNv8c",
        "outputId": "ca02087a-66f3-4bc5-e94f-9b2df9b1625a"
      },
      "source": [
        "df[\"thalach\"].value_counts()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "162    11\n",
              "160     9\n",
              "163     9\n",
              "173     8\n",
              "152     8\n",
              "       ..\n",
              "129     1\n",
              "128     1\n",
              "127     1\n",
              "124     1\n",
              "71      1\n",
              "Name: thalach, Length: 91, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnr-td8oQcRl"
      },
      "source": [
        "# Age vs Max Heart Rate for Heart Disease"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "kwuywYYLQfXO",
        "outputId": "8bc316f2-6634-4057-d67a-5cd219ad0b6a"
      },
      "source": [
        "# Create another figure\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Scatter with positive examples\n",
        "\n",
        "plt.scatter(df.age[df.target == 1], df.thalach[df.target == 1],\n",
        "            c='salmon')\n",
        "\n",
        "# Scatter with negative examples\n",
        "\n",
        "plt.scatter(df.age[df.target == 0], df.thalach[df.target == 0],\n",
        "            c='lightblue')\n",
        "\n",
        "# Add some helpful information\n",
        "\n",
        "plt.title('Heart Disease in function of Age and Max Heart Rate')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Max Heart Rate')\n",
        "plt.legend(['Disease', 'No Disease'])"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc7cbb59850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ZKCTH7mtQq5G",
        "outputId": "d704cf18-7c2f-4e0b-fdfb-4aa714f0ea4b"
      },
      "source": [
        "#Check the distribution of the age column with a histogram\n",
        "df.age.plot.hist(); #Best way to identify outliers"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbNrLedzSpHt"
      },
      "source": [
        "## **Heart Disease Frequency per Chest Pain**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zt5ppCbUS2vS"
      },
      "source": [
        "cp - chest pain type\n",
        "\n",
        "0: Typical angina: chest pain related decrease \n",
        "blood supply to the heart\n",
        "\n",
        "1: Atypical angina: chest pain not related to heart\n",
        "\n",
        "2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
        "\n",
        "3: Asymptomatic: chest pain not showing signs of disease\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "UsdOJ1juS3Nw",
        "outputId": "f8c40328-a52e-4212-ca49-1715f794f661"
      },
      "source": [
        "pd.crosstab(df.cp, df.target)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>104</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "target    0   1\n",
              "cp             \n",
              "0       104  39\n",
              "1         9  41\n",
              "2        18  69\n",
              "3         7  16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "r8ZO8A03S533",
        "outputId": "e95e4762-df27-47d3-f02d-ba7f1ca42000"
      },
      "source": [
        "# Make the crosstab more visual\n",
        "\n",
        "pd.crosstab(df.cp, df.target).plot(kind='bar', figsize=(10, 6),\n",
        "                                   color=['salmon', 'lightblue'])\n",
        "\n",
        "plt.title('Heart Disease Frequency per chest pain type')\n",
        "plt.xlabel('Chest Pain Type')\n",
        "plt.ylabel('Amount')\n",
        "plt.legend(['No Disease', 'Disease'])\n",
        "plt.xticks(rotation=0)"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3]), <a list of 4 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "MtsSX5mzTtRe",
        "outputId": "86c36672-3522-4193-9e92-d72493727ea8"
      },
      "source": [
        "#Building a Correlation Matrix\n",
        "df.corr()"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.098447</td>\n",
              "      <td>-0.068653</td>\n",
              "      <td>0.279351</td>\n",
              "      <td>0.213678</td>\n",
              "      <td>0.121308</td>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.398522</td>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.210013</td>\n",
              "      <td>-0.168814</td>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.068001</td>\n",
              "      <td>-0.225439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>-0.098447</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>-0.056769</td>\n",
              "      <td>-0.197912</td>\n",
              "      <td>0.045032</td>\n",
              "      <td>-0.058196</td>\n",
              "      <td>-0.044020</td>\n",
              "      <td>0.141664</td>\n",
              "      <td>0.096093</td>\n",
              "      <td>-0.030711</td>\n",
              "      <td>0.118261</td>\n",
              "      <td>0.210041</td>\n",
              "      <td>-0.280937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>-0.068653</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.433798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.279351</td>\n",
              "      <td>-0.056769</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>-0.144931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.213678</td>\n",
              "      <td>-0.197912</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.085239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.121308</td>\n",
              "      <td>0.045032</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.028046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.058196</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>0.137230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.398522</td>\n",
              "      <td>-0.044020</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.421741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.141664</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>-0.436757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.210013</td>\n",
              "      <td>0.096093</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.430696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>-0.168814</td>\n",
              "      <td>-0.030711</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.345877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.118261</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>-0.391724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.068001</td>\n",
              "      <td>0.210041</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.344029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>-0.225439</td>\n",
              "      <td>-0.280937</td>\n",
              "      <td>0.433798</td>\n",
              "      <td>-0.144931</td>\n",
              "      <td>-0.085239</td>\n",
              "      <td>-0.028046</td>\n",
              "      <td>0.137230</td>\n",
              "      <td>0.421741</td>\n",
              "      <td>-0.436757</td>\n",
              "      <td>-0.430696</td>\n",
              "      <td>0.345877</td>\n",
              "      <td>-0.391724</td>\n",
              "      <td>-0.344029</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age       sex        cp  ...        ca      thal    target\n",
              "age       1.000000 -0.098447 -0.068653  ...  0.276326  0.068001 -0.225439\n",
              "sex      -0.098447  1.000000 -0.049353  ...  0.118261  0.210041 -0.280937\n",
              "cp       -0.068653 -0.049353  1.000000  ... -0.181053 -0.161736  0.433798\n",
              "trestbps  0.279351 -0.056769  0.047608  ...  0.101389  0.062210 -0.144931\n",
              "chol      0.213678 -0.197912 -0.076904  ...  0.070511  0.098803 -0.085239\n",
              "fbs       0.121308  0.045032  0.094444  ...  0.137979 -0.032019 -0.028046\n",
              "restecg  -0.116211 -0.058196  0.044421  ... -0.072042 -0.011981  0.137230\n",
              "thalach  -0.398522 -0.044020  0.295762  ... -0.213177 -0.096439  0.421741\n",
              "exang     0.096801  0.141664 -0.394280  ...  0.115739  0.206754 -0.436757\n",
              "oldpeak   0.210013  0.096093 -0.149230  ...  0.222682  0.210244 -0.430696\n",
              "slope    -0.168814 -0.030711  0.119717  ... -0.080155 -0.104764  0.345877\n",
              "ca        0.276326  0.118261 -0.181053  ...  1.000000  0.151832 -0.391724\n",
              "thal      0.068001  0.210041 -0.161736  ...  0.151832  1.000000 -0.344029\n",
              "target   -0.225439 -0.280937  0.433798  ... -0.391724 -0.344029  1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "sonEODuWUso6",
        "outputId": "3ed3a0c4-62b6-4320-d1e1-ef3010511e80"
      },
      "source": [
        "# Let us make our correlation matrix\n",
        "\n",
        "corr_matrix = df.corr()\n",
        "(fig, ax) = plt.subplots(figsize=(15, 10))\n",
        "ax = sns.heatmap(corr_matrix, cmap='YlGnBu', annot=True,\n",
        "                 linewidths=0.5, fmt='.2f')\n"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jDjZCfhYWPgw"
      },
      "source": [
        "Much better. A higher positive value means a potential positive correlation (increase) and a higher negative value means a potential negative correlation (decrease)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sh0a8YsCWVar"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLAgPmDpWW_r"
      },
      "source": [
        "## Importing D-tale library for automatic EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6mLaozkWmlM",
        "outputId": "519253dd-e60e-4850-b56c-7af5f8e84a5d"
      },
      "source": [
        "!pip install dtale\n",
        "import dtale"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: dtale in /usr/local/lib/python3.7/dist-packages (1.51.0)\n",
            "Requirement already satisfied: squarify in /usr/local/lib/python3.7/dist-packages (from dtale) (0.4.3)\n",
            "Requirement already satisfied: scipy; python_version >= \"3.7\" in /usr/local/lib/python3.7/dist-packages (from dtale) (1.4.1)\n",
            "Requirement already satisfied: numpy; python_version >= \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (1.19.5)\n",
            "Requirement already satisfied: itsdangerous; python_version >= \"3.7\" in /usr/local/lib/python3.7/dist-packages (from dtale) (1.1.0)\n",
            "Requirement already satisfied: seaborn; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.11.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from dtale) (2.23.0)\n",
            "Requirement already satisfied: dash-colorscales in /usr/local/lib/python3.7/dist-packages (from dtale) (0.0.4)\n",
            "Requirement already satisfied: Flask-Compress in /usr/local/lib/python3.7/dist-packages (from dtale) (1.10.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from dtale) (1.15.0)\n",
            "Requirement already satisfied: networkx; python_version >= \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (2.5.1)\n",
            "Requirement already satisfied: dash-daq in /usr/local/lib/python3.7/dist-packages (from dtale) (0.5.0)\n",
            "Requirement already satisfied: plotly>=5.0.0; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from dtale) (5.1.0)\n",
            "Requirement already satisfied: kaleido; python_version > \"3.6\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.2.1)\n",
            "Requirement already satisfied: dash-bootstrap-components; python_version > \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.12.2)\n",
            "Requirement already satisfied: Flask; python_version >= \"3.7\" in /usr/local/lib/python3.7/dist-packages (from dtale) (1.1.4)\n",
            "Requirement already satisfied: matplotlib; python_version > \"3.6\" in /usr/local/lib/python3.7/dist-packages (from dtale) (3.2.2)\n",
            "Requirement already satisfied: future>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from dtale) (0.16.0)\n",
            "Requirement already satisfied: flask-ngrok; python_version > \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.0.25)\n",
            "Requirement already satisfied: et-xmlfile; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from dtale) (1.1.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from dtale) (1.1.5)\n",
            "Requirement already satisfied: openpyxl; python_version >= \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (2.5.9)\n",
            "Requirement already satisfied: strsimpy in /usr/local/lib/python3.7/dist-packages (from dtale) (0.2.0)\n",
            "Requirement already satisfied: scikit-learn; python_version > \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.22.2.post1)\n",
            "Requirement already satisfied: missingno<=0.4.2 in /usr/local/lib/python3.7/dist-packages (from dtale) (0.4.2)\n",
            "Requirement already satisfied: statsmodels; python_version > \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.10.2)\n",
            "Requirement already satisfied: ppscore; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from dtale) (1.2.0)\n",
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.7/dist-packages (from dtale) (1.1.0)\n",
            "Requirement already satisfied: xarray; python_version >= \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (0.18.2)\n",
            "Requirement already satisfied: lz4; python_version > \"3.0\" in /usr/local/lib/python3.7/dist-packages (from dtale) (3.1.3)\n",
            "Requirement already satisfied: dash>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from dtale) (1.20.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->dtale) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->dtale) (3.0.4)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9Nzb56IWoEv"
      },
      "source": [
        "d = dtale.show(df)"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kaqCetAqXi3w"
      },
      "source": [
        "# **Preparing and modelling our data for Machine Learning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "UDFtOy9OXmTM",
        "outputId": "bca56042-563c-44ce-fa2c-74c13050af2a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "610R8C0abXLC"
      },
      "source": [
        "X = df.drop(['target'], axis=1)\n",
        "y = df['target']\n",
        "\n",
        "# Split data into train and test sets\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "(X_train, X_test, y_train, y_test) = train_test_split(X, y,\n",
        "        test_size=0.2)\n",
        "\n",
        "\t\t\t"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F583g0vlcBDP",
        "outputId": "9ac37f56-9ff4-4870-c16f-dd0ac6b0f782"
      },
      "source": [
        "#Checking if all of our data has same amount of rows and columns\n",
        "print(len(X_train)), print(len(y_train)), print(len(X_test)), print(len(y_test))"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "242\n",
            "242\n",
            "61\n",
            "61\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None, None, None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MN9oRBj1c2iU"
      },
      "source": [
        "### Now we have got our data split into training and test sets, it is time to train the model.\n",
        "\n",
        "We are going to try 3 different machine learning models.\n",
        "\n",
        "1. Logistic Regression\n",
        "2. K-Nearest Neighbours Classifier\n",
        "3. Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTXjG789c9CW"
      },
      "source": [
        "# Put models in a dictionary\n",
        "\n",
        "models = {'Logistic Regression': LogisticRegression(),\n",
        "          'KNN': KNeighborsClassifier(),\n",
        "          'Random Forest': RandomForestClassifier()}\n",
        "\n",
        "\n",
        "# Create a function to fit and score the models\n",
        "\n",
        "def fit_and_score(\n",
        "    models,\n",
        "    X_train,\n",
        "    X_test,\n",
        "    y_train,\n",
        "    y_test,\n",
        "    ):\n",
        "    \"\"\"\n",
        "    Fits and evaluates given machine learning models.\n",
        "    models: a dict of different Scikit-Learn machine learning models\n",
        "    X_train : Training data (no labels)\n",
        "    X_test: testing data (no labels)\n",
        "    y_train : training labels\n",
        "    y_test : test labels\n",
        "    \"\"\"\n",
        "\n",
        "    # Set random seed\n",
        "\n",
        "    np.random.seed(42)\n",
        "\n",
        "    # Make a dictionary to keep model scores\n",
        "\n",
        "    model_scores = {}\n",
        "\n",
        "    # Loop through models\n",
        "\n",
        "    for (name, model) in models.items():\n",
        "\n",
        "      # Fit the model to the data\n",
        "\n",
        "        model.fit(X_train, y_train)\n",
        "\n",
        "      # Evaulaute the model and append the score to the model score\n",
        "\n",
        "        model_scores[name] = model.score(X_test, y_test)\n",
        "    return model_scores"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EUPUQOUtfFP5"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8pYkm710esW7",
        "outputId": "98ba0f96-43e3-4702-dd87-8f624367b72d"
      },
      "source": [
        "model_scores = fit_and_score(models=models, X_train=X_train,\n",
        "                             X_test=X_test, y_train=y_train,\n",
        "                             y_test=y_test)\n",
        "model_scores"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'KNN': 0.6885245901639344,\n",
              " 'Logistic Regression': 0.8852459016393442,\n",
              " 'Random Forest': 0.8360655737704918}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7g8QSGn3hETr"
      },
      "source": [
        "## Model Comparison"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "1H0SntDkhJAZ",
        "outputId": "6b7c7b80-688f-44a2-8c56-9bfc60f09e89"
      },
      "source": [
        "model_compare = pd.DataFrame(model_scores, index = ['accuracy'])\n",
        "model_compare.plot.bar(linewidth = 20, figsize = (10,6));\n",
        "plt.title(\"Machine Learning Algorithms\")\n",
        "plt.ylabel(\"Scores\")\n",
        "plt.xticks(rotation = 0)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0]), <a list of 1 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUZlDCndkt5Y"
      },
      "source": [
        "Now we got a baseline model and we know that our first model might not be the best perfoming model. We have to work on hyperparameter tuning.\n",
        "\n",
        "Let's look at the following:\n",
        "*Hyperparamter tuning\n",
        "\n",
        "*Feature importance\n",
        "\n",
        "*Confusion matrix\n",
        "\n",
        "*Cross-validation\n",
        "\n",
        "*Precision\n",
        "\n",
        "*Recall\n",
        "\n",
        "*F1-Score\n",
        "\n",
        "*Classification report\n",
        "\n",
        "*ROC Curve\n",
        "\n",
        "*Area under the curve (AUC)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHLsW90Llt4w"
      },
      "source": [
        "**Hyperparameter tuning (by hand manually)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kxik2hYslxLV"
      },
      "source": [
        "#Let us tune KNN\n",
        "\n",
        "train_scores = []\n",
        "test_scores = []\n",
        "\n",
        "#Create a list of values for N-Neighbors\n",
        "neighbors = range(1,21)\n",
        "\n",
        "#Setup KNN instance\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "#Loop through different n_neighbors\n",
        "for i in neighbors:\n",
        "  knn.set_params(n_neighbors = i)\n",
        "\n",
        "  #Fit the algorithm\n",
        "  knn.fit(X_train, y_train)\n",
        "\n",
        "  #Update training scores list\n",
        "  train_scores.append(knn.score(X_train,y_train))\n",
        "\n",
        "  #Update the test scores list\n",
        "  test_scores.append(knn.score(X_test,y_test))\n"
      ],
      "execution_count": 168,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "DwOR-eMrmoE7",
        "outputId": "62a89b87-2457-43f8-d3f4-daafddb8d46e"
      },
      "source": [
        "plt.plot(neighbors, train_scores, label = \"Train scores\")\n",
        "plt.plot(neighbors, test_scores, label = \"Test scores\")\n",
        "plt.xticks(np.arange(1,21,1))\n",
        "plt.xlabel(\"Number of neighbors\")\n",
        "plt.ylabel(\"Model Score\")\n",
        "plt.legend()\n",
        "\n",
        "print(f\"Maximum KNN score on test data: {max(test_scores) * 100:.2f}%\")"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maximum KNN score on test data: 75.41%\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCWrXZbmoleV"
      },
      "source": [
        "# **Hyperparamater tuning with RandomizedSearchCV**\n",
        "\n",
        "We are going to tune:\n",
        "* LogisticRegression()\n",
        "* RandomForestClassifier()\n",
        "\n",
        "  ...using RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B67KHKDSorHN"
      },
      "source": [
        "#Create a hyperparameter grid for Logistic Regression\n",
        "\n",
        "log_reg_grid = {\"C\": np.logspace(-4,4,20),\n",
        "                \"penalty\": [\"l2\", \"elasticnet\"],\n",
        "                \"solver\": [\"liblinear\"]}\n",
        "\n",
        "\n",
        "#Create a hyperparameter grid for Random Forest Classifier\n",
        "rf_grid = {\"rf_grid_estimators\": np.arange(10,1000,50),\n",
        "           \"max_depth\" : [None,3,5,10],\n",
        "           \"min_samples_split\": np.arange(2,20,2),\n",
        "           \"max_features\" : [\"auto\", \"sqrt\"]\n",
        "           }\n"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GxTw6rsCuEM"
      },
      "source": [
        "Now we've got hyperparameter grids setup for each of our models, let's tune them using RandomizedSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKTKOiUHAh45",
        "outputId": "eb0bdedf-589c-47d0-ace7-1031b3208c82"
      },
      "source": [
        "#Tune Logistic Regression\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "#Setup random hyperparameter search for Logistic Regression\n",
        "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
        "                                param_distributions = log_reg_grid,\n",
        "                                cv = 14,\n",
        "                                n_iter = 20,\n",
        "                                verbose = True)\n",
        "\n",
        "#Fit random hyperparameter search model for Logistic Regression\n",
        "rs_log_reg.fit(X_train,y_train)"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 14 folds for each of 20 candidates, totalling 280 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "[Parallel(n_jobs=1)]: Done 280 out of 280 | elapsed:    0.8s finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=14, error_score=nan,\n",
              "                   estimator=LogisticRegression(C=1.0, class_weight=None,\n",
              "                                                dual=False, fit_intercept=True,\n",
              "                                                intercept_scaling=1,\n",
              "                                                l1_ratio=None, max_iter=100,\n",
              "                                                multi_class='auto', n_jobs=None,\n",
              "                                                penalty='l2', random_state=None,\n",
              "                                                solver='lbfgs', tol=0.0001,\n",
              "                                                verbose=0, warm_start=False),\n",
              "                   iid='deprecated', n_iter=20, n_jobs=None,\n",
              "                   param_distributions={'C'...\n",
              "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
              "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
              "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
              "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
              "                                        'penalty': ['l2', 'elasticnet'],\n",
              "                                        'solver': ['liblinear']},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cB0saXvgDX_w",
        "outputId": "2a26e570-d839-4e40-868f-df5ae65f2c42"
      },
      "source": [
        "#Tune Random Forest Classification\n",
        "np.random.seed(42)\n",
        "\n",
        "#Setup random hyperparameter search for Logistic Regression\n",
        "rs_rf_reg = RandomizedSearchCV(RandomForestClassifier(),\n",
        "                                param_distributions = rf_grid,\n",
        "                                cv = 14,\n",
        "                                n_iter = 20,\n",
        "                                verbose = True)\n",
        "\n",
        "#Fit random hyperparameter search model for Logistic Regression\n",
        "rs_rf_reg.fit(X_train,y_train)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 14 folds for each of 20 candidates, totalling 280 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done 280 out of 280 | elapsed:  2.8min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=14, error_score=nan,\n",
              "                   estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                    ccp_alpha=0.0,\n",
              "                                                    class_weight=None,\n",
              "                                                    criterion='gini',\n",
              "                                                    max_depth=None,\n",
              "                                                    max_features='auto',\n",
              "                                                    max_leaf_nodes=None,\n",
              "                                                    max_samples=None,\n",
              "                                                    min_impurity_decrease=0.0,\n",
              "                                                    min_impurity_split=None,\n",
              "                                                    min_samples_leaf=1,\n",
              "                                                    min_samples_split=2,\n",
              "                                                    min_weight_fraction_leaf=0.0,\n",
              "                                                    n_estimators=100,\n",
              "                                                    n_job...\n",
              "                   iid='deprecated', n_iter=20, n_jobs=None,\n",
              "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
              "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
              "       660, 710, 760, 810, 860, 910, 960])},\n",
              "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZ7ag55MD_xV",
        "outputId": "50ca4fba-15a9-4d32-92cf-03918d1f74f8"
      },
      "source": [
        "print(rs_log_reg.best_params_)\n",
        "print(rs_rf_reg.best_params_)"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'solver': 'liblinear', 'penalty': 'l2', 'C': 0.23357214690901212}\n",
            "{'n_estimators': 410, 'min_samples_split': 10, 'max_features': 'sqrt', 'max_depth': 3}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFP41mYHEMc5",
        "outputId": "48af8642-20ef-434b-c4b2-e1b0af909bde"
      },
      "source": [
        "print(rs_log_reg.score(X_test,y_test))\n",
        "print(rs_rf_reg.score(X_test,y_test))"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8852459016393442\n",
            "0.8688524590163934\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPXgw6T3EX4t",
        "outputId": "17024805-07f3-47dd-f415-8ebfd116d424"
      },
      "source": [
        "model_scores"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'KNN': 0.6885245901639344,\n",
              " 'Logistic Regression': 0.8852459016393442,\n",
              " 'Random Forest': 0.8360655737704918}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55KYOhevFuWk"
      },
      "source": [
        "1. Hyperparameter tuning using hand\n",
        "2. Hyperparameter tuning using GridSearch and RandomizedSearch CV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUAm14diFg7F"
      },
      "source": [
        "## Hyperparameter tuning with GridSearchCV\n",
        "#Since our LogisticRegression model provides the best scores so far, we would try to improve them again using GridSearchCV"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SA5oPCoGYO3",
        "outputId": "04b0b866-01ff-4984-8feb-5e07497d327c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Different hyperparameters for our LogisticRegression model\n",
        "\n",
        "log_reg_grid = {\"C\": np.logspace(-4,4,30),\n",
        "                \"penalty\": [\"l2\", \"elasticnet\"],\n",
        "                \"solver\": [\"liblinear\",\"lbfgs\"]}\n",
        "\n",
        "#Setup random hyperparameter search for Logistic Regression\n",
        "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
        "                                param_grid = log_reg_grid,\n",
        "                                cv = 14,\n",
        "                                verbose = False)\n",
        "\n",
        "#Fit grid hyperparameter search model\n",
        "gs_log_reg.fit(X_train, y_train);\n"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning:\n",
            "\n",
            "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",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
            "\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning:\n",
            "\n",
            "Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
            "\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrE7Er1-HORW",
        "outputId": "bce2d595-704e-4d22-d8dd-e975e9d12c06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Check the best parameters\n",
        "gs_log_reg.best_params_"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.1082636733874054, 'penalty': 'l2', 'solver': 'liblinear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOeU7ix1HZBS",
        "outputId": "aafb2a68-819f-448b-9a2b-bdfb0f48094e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(gs_log_reg.score(X_test,y_test))"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8852459016393442\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3lLc_qzHdx9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBLvCPc_IJ9U"
      },
      "source": [
        "## Evaluating our tuned machine learning classifier, beyond accuracy\n",
        "\n",
        "* ROC curve and AUC score\n",
        "*Confusion matrix\n",
        "*Classification report\n",
        "*Precision\n",
        "*Recall\n",
        "*F1-score\n",
        "\n",
        "...and it would be great if cross-validation was used where possible."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MNKSL-JIawm"
      },
      "source": [
        "#Make predictions with tuned model\n",
        "y_preds = gs_log_reg.predict(X_test)"
      ],
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohkDmPI5Io-P",
        "outputId": "90eb0b60-9f50-4de6-a2c9-4d5280d72a87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "y_preds"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7K-QMniIrfL",
        "outputId": "2a974f11-31f7-4d78-dffc-d67ea01674bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "#Import ROC curve function from the sklearn.metrics module.\n",
        "# Plot ROC curve and calculate and calculate AUC metric\n",
        "\n",
        "plot_roc_curve(gs_log_reg, X_test, y_test)"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fc7cbab4dd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiebJtnFJTRT",
        "outputId": "6b6916d5-1e72-400e-bc1f-57c3fb5abb43",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Confusion Matrix\n",
        "print(confusion_matrix(y_test,y_preds))"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[25  4]\n",
            " [ 3 29]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHKD8bknJm7j",
        "outputId": "fc163545-494f-42b5-b245-df07a2257a03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "source": [
        "# Import Seaborn\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set(font_scale=1.5)  # Increase font size\n",
        "\n",
        "\n",
        "def plot_conf_mat(y_test, y_preds):\n",
        "    \"\"\"\n",
        "    Plots a confusion matrix using Seaborn's heatmap().\n",
        "    \"\"\"\n",
        "\n",
        "    (fig, ax) = plt.subplots(figsize=(3, 3))\n",
        "    ax = sns.heatmap(confusion_matrix(y_test, y_preds), annot=True,\n",
        "                     cbar=True)  # Annotate the boxes\n",
        "    plt.xlabel('Predicted label')  # predictions go on the x-axis\n",
        "    plt.ylabel('True label')  # true labels go on the y-axis\n",
        "\n",
        "\n",
        "plot_conf_mat(y_test, y_preds)"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 216x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrLhOMDIMBfU"
      },
      "source": [
        "Now we have got a ROC curve, an AUC metric and a confusion matrix, let us get a classificatom report as well as cross-validated precision,recall and f1-score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mvPvpizMQ8H",
        "outputId": "4b3a12c8-e5cd-4e62-e3c5-88f81c243798",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(classification_report(y_test,y_preds))"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.86      0.88        29\n",
            "           1       0.88      0.91      0.89        32\n",
            "\n",
            "    accuracy                           0.89        61\n",
            "   macro avg       0.89      0.88      0.88        61\n",
            "weighted avg       0.89      0.89      0.89        61\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GJa6IWcOPs7"
      },
      "source": [
        "## Calculate evaluation metrics using cross validation. Using cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ch0G5o8LMhsf",
        "outputId": "6b23ab1e-e8b0-44be-b2c5-6f38932818dd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Check the best hyperparameters\n",
        "gs_log_reg.best_params_"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.1082636733874054, 'penalty': 'l2', 'solver': 'liblinear'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqTKFffBOe14"
      },
      "source": [
        "#Create a new classifier with best hyperparameters\n",
        "clf = LogisticRegression(C =0.1082636733874054, penalty = 'l2',\n",
        "                         solver = 'liblinear')"
      ],
      "execution_count": 213,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIOReNCFOskJ",
        "outputId": "bbf177e8-80d0-4e0b-bee8-8eee5d58399f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Cross validated accuracy\n",
        "cv_acc = cross_val_score(clf,X,y, cv = 14, scoring = \"accuracy\")\n",
        "cv_acc = np.mean(cv_acc)\n",
        "cv_acc"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.840599876314162"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_ZcR_LsO5K0",
        "outputId": "74092d3b-0bfb-4518-ae62-d02d80638857"
      },
      "source": [
        "#Cross validated precision\n",
        "cv_precision = cross_val_score(clf,X,y, cv = 14, scoring = \"precision\")\n",
        "cv_precision = np.mean(cv_precision)\n",
        "cv_precision"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8160125588697016"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxlRbpH4PMIs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyLsHOwTPUXT",
        "outputId": "1aa2c3a9-b9e1-4701-a546-a5824fa42ca1"
      },
      "source": [
        "#Cross validated recall\n",
        "cv_recall = cross_val_score(clf,X,y, cv = 14, scoring = \"recall\")\n",
        "cv_recall = np.mean(cv_recall)\n",
        "cv_recall"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9193722943722943"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tmy5U2INPeGg",
        "outputId": "33d5334c-a007-4fde-fedb-a457d0f94f23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Cross validated f1\n",
        "cv_f1 = cross_val_score(clf,X,y, cv = 14, scoring = \"f1\")\n",
        "cv_f1 = np.mean(cv_f1)\n",
        "cv_f1"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.862605017480794"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_N9lIEA6Pjxt",
        "outputId": "9530ae38-d8ca-4998-d2d4-28422736849b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        }
      },
      "source": [
        "# Visualizing cross-validated metrics\n",
        "cv_metrics = pd.DataFrame({\"Accuracy\": cv_acc,\n",
        "                            \"Precision\": cv_precision,\n",
        "                            \"Recall\": cv_recall,\n",
        "                            \"F1\": cv_f1},\n",
        "                          index=[0])\n",
        "\n",
        "cv_metrics.plot.bar(title = 'Cross-validated classification metrics', legend = True, figsize = (10,8))\n",
        "plt.xlabel(\"Logistic Regression Metrics\")\n",
        "plt.xticks(rotation = 0);"
      ],
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRCHn8SBPwMK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULMsyMecTIga"
      },
      "source": [
        "## Feature Importance\n",
        "\n",
        "Feature importance is a way of asking which features contributed most to the outcomes of the model and how did they contribute.\n",
        "\n",
        "Finding Feature Importance is different for each Machine Learning model.\n",
        "\n",
        "Let's find the feature importance for our Logistic Regresison."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcbV1jGPTRYM",
        "outputId": "29eb5156-37f8-40e8-9d2d-44410b91901b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0SOA0BQTSBM",
        "outputId": "0650d959-67fd-4f29-c451-129ead5d07d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Fit an instance of Logistic Regression\n",
        "\n",
        "gs_log_reg.best_params_\n",
        "clf = LogisticRegression(C=0.1082636733874054, penalty='l2',\n",
        "                         solver='liblinear')\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.1082636733874054, class_weight=None, dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HGwZgORT8Y3",
        "outputId": "30a8415c-0cb9-4b1c-9913-b73cccd30d4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Check coef_ (Feature Importance)\n",
        "clf.coef_"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 6.05000871e-05, -6.48812516e-01,  5.86851379e-01,\n",
              "        -1.15577165e-02, -1.50181906e-03,  2.45434729e-02,\n",
              "         2.20601838e-01,  2.39710262e-02, -4.71371751e-01,\n",
              "        -5.24222207e-01,  3.53142990e-01, -5.57092301e-01,\n",
              "        -5.64594843e-01]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3GvyjCTUMlU",
        "outputId": "e52e4702-798c-417f-cb85-8267e0083c9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Match the coef of features to the columns\n",
        "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
        "feature_dict"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'age': 6.050008712704012e-05,\n",
              " 'ca': -0.5570923005318527,\n",
              " 'chol': -0.0015018190618504858,\n",
              " 'cp': 0.5868513791226628,\n",
              " 'exang': -0.47137175068316706,\n",
              " 'fbs': 0.024543472852260224,\n",
              " 'oldpeak': -0.5242222072262522,\n",
              " 'restecg': 0.22060183797812405,\n",
              " 'sex': -0.6488125156676056,\n",
              " 'slope': 0.35314299039813973,\n",
              " 'thal': -0.5645948425160071,\n",
              " 'thalach': 0.023971026179066112,\n",
              " 'trestbps': -0.01155771651409749}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_eUypBaUaXk"
      },
      "source": [
        "#Visualize feature importance\n",
        "feature_df = pd.DataFrame(feature_dict, index = [0])"
      ],
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uR2MWS9xUfQP",
        "outputId": "18c1adb0-30e5-407c-daa0-fd70fe56f710",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "feature_df.T.plot.bar(title = 'Feature Importance', legend = False, figsize = (10,6))"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7c8ee0d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tf76C1NdVsaT"
      },
      "source": [
        "You'll notice some are negative and some are positive.\n",
        "\n",
        "The larger the value (bigger bar), the more the feature contributes to the models decision.\n",
        "\n",
        "If the value is negative, it means there's a negative correlation. And vice versa for positive values.\n",
        "\n",
        "For example, the sex attribute has a negative value of -0.904, which means as the value for sex increases, the target value decreases.\n",
        "\n",
        "We can see this by comparing the sex column to the target column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYBVxwZ2UzNA",
        "outputId": "e6ec629b-e232-4863-e4de-257c2d3e78f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "pd.crosstab(df[\"sex\"], df[\"target\"])"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>114</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "target    0   1\n",
              "sex            \n",
              "0        24  72\n",
              "1       114  93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHLXvODxVuX4"
      },
      "source": [
        "You can see, when sex is 0 (female), there are almost 3 times as many (72 vs. 24) people with heart disease (target = 1) than without.\n",
        "\n",
        "And then as sex increases to 1 (male), the ratio goes down to almost 1 to 1 (114 vs. 93) of people who have heart disease and who don't.\n",
        "\n",
        "What does this mean?\n",
        "\n",
        "It means the model has found a pattern which reflects the data. Looking at these figures and this specific dataset, it seems if the patient is female, they're more likely to have heart disease.\n",
        "\n",
        "How about a positive correlation?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FF0fRzOwVwIS",
        "outputId": "60b66de8-5ef7-4aea-c4a8-f54e044345a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        }
      },
      "source": [
        "# Contrast slope (positive coefficient) with target\n",
        "pd.crosstab(df[\"slope\"], df[\"target\"])"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>91</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "target   0    1\n",
              "slope          \n",
              "0       12    9\n",
              "1       91   49\n",
              "2       35  107"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZO40aOQdVyFY"
      },
      "source": [
        "Looking back the data dictionary, we see slope is the \"slope of the peak exercise ST segment\" where:\n",
        "\n",
        "0: Upsloping: better heart rate with excercise (uncommon)\n",
        "1: Flatsloping: minimal change (typical healthy heart)\n",
        "2: Downslopins: signs of unhealthy heart\n",
        "According to the model, there's a positive correlation of 0.470, not as strong as sex and target but still more than 0.\n",
        "\n",
        "This positive correlation means our model is picking up the pattern that as slope increases, so does the target value.\n",
        "\n",
        "Is this true?\n",
        "\n",
        "When you look at the contrast (pd.crosstab(df[\"slope\"], df[\"target\"]) it is. As slope goes up, so does target.\n",
        "\n",
        "What can you do with this information?\n",
        "\n",
        "This is something you might want to talk to a subject matter expert about. They may be interested in seeing where machine learning model is finding the most patterns (highest correlation) as well as where it's not (lowest correlation).\n",
        "\n",
        "Doing this has a few benefits:\n",
        "\n",
        "Finding out more - If some of the correlations and feature importances are confusing, a subject matter expert may be able to shed some light on the situation and help you figure out more.\n",
        "\n",
        "Redirecting efforts - If some features offer far more value than others, this may change how you collect data for different problems. See point \n",
        "\n",
        "3.Less but better - Similar to above, if some features are offering far more value than others, you could reduce the number of features your model tries to find patterns in as well as improve the ones which offer the most. This could potentially lead to saving on computation, by having a model find patterns across less features, whilst still achieving the same performance levels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9DWmPtNWcJC"
      },
      "source": [
        "#**Experimentation**\n",
        "Well we've completed all the metrics your boss requested. You should be able to put together a great report containing a confusion matrix, a handful of cross-valdated metrics such as precision, recall and F1 as well as which features contribute most to the model making a decision.\n",
        "\n",
        "But after all this you might be wondering where step 6 in the framework is, experimentation.\n",
        "\n",
        "Well the secret here is, as you might've guessed, the whole thing is experimentation.\n",
        "\n",
        "From trying different models, to tuning different models to figuring out which hyperparameters were best.\n",
        "\n",
        "What we've worked through so far has been a series of experiments.\n",
        "\n",
        "And the truth is, we could keep going. But of course, things can't go on forever.\n",
        "\n",
        "So by this stage, after trying a few different things, we'd ask ourselves did we meet the evaluation metric?\n",
        "\n",
        "Remember we defined one in step 3.\n",
        "\n",
        "If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursure this project.\n",
        "\n",
        "In this case, we didn't. The highest accuracy our model achieved was below 90%.\n",
        "\n",
        "What next?\n",
        "You might be wondering, what happens when the evaluation metric doesn't get hit?\n",
        "\n",
        "Is everything we've done wasted?\n",
        "\n",
        "No.\n",
        "\n",
        "It means we know what doesn't work. In this case, we know the current model we're using (a tuned version of LogisticRegression) along with our specific data set doesn't hit the target we set ourselves.\n",
        "\n",
        "This is where step 6 comes into its own.\n",
        "\n",
        "A good next step would be to discuss with your team or research on your own different options of going forward.\n",
        "\n",
        "Could you collect more data?\n",
        "\n",
        "Could you try a better model? If you're working with structured data, you might want to look into CatBoost or XGBoost.\n",
        "\n",
        "Could you improve the current models (beyond what we've done so far)?\n",
        "\n",
        "If your model is good enough, how would you export it and share it with others? (Hint: check out Scikit-Learn's documentation on model persistance)\n",
        "The key here is to remember, your biggest restriction will be time. Hence, why it's paramount to minimise your times between experiments.\n",
        "\n",
        "The more you try, the more you figure out what doesn't work, the more you'll start to get a hang of what does."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iN63lKsjV5Q5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}