DataScience_Project_2.ipynb

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  "metadata": {
    "colab": {
      "name": "DataScience_Project_2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1FU3IS-P4gsSSQqRpJtTjooutO7rT-j6N",
      "authorship_tag": "ABX9TyM+3seanGm+s8kBE2b9njZP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OZebra/CaptainMarvel/blob/master/DataScience_Project_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uC5m8MP4otrs"
      },
      "source": [
        "#Projeto 2 de DataScience \n",
        "\n",
        "---\n",
        "\n",
        "##Objetivos\n",
        "\n",
        "\n",
        "1.   Escolher uma coluna para realizar predição por regressão ou classificação.\n",
        "2.   Separar os dados em treinamento, validação e teste.\n",
        "3.   Selecionar 4 algorítmos de acordo com a tarefa escolhida no passo 1\n",
        "4.   Adicionar MLFlow no treinamento dos modelos para rastreamento.\n",
        "5.   Executar uma ferramenta de seleção de hiper-parâmetros sobre o conjunto de validação.\n",
        "     1. Selecionar poucos Hiper-parâmetros por algorítmo (max: 3).\n",
        "     2. Selecionar o conjunto algorítmo + hiperparâmetros com o melhor resultado\n",
        "     3. Executar o melhor modelo para cada algorítmo e selecionar o melhor.\n",
        "\n",
        "6. Realizar o diagnóstico geral do modelo escolhido e melhorá-lo a partir do diagnóstico\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQq3OyY-pu1y"
      },
      "source": [
        "##Parte 1\n",
        "\n",
        "---\n",
        "\n",
        "O dataset escolhido, será [este](https://www.kaggle.com/adityakadiwal/water-potability/version/3).\n",
        "\n",
        "O dataset trata da predição de derrame baseado em exames e fatores pessoais da vida dos paciêntes.\n",
        "\n",
        "Vamos começar importando as bibliotecas necessárias\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bc4bNYs5y80m"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import sklearn"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0WxgJoSZXZQ",
        "outputId": "c55b1cf6-2f06-4da2-ff48-08e8fb5fdbc4"
      },
      "source": [
        "!pip install mlflow --quiet\n",
        "!pip install pyngrok --quiet\n",
        "!pip install optuna\n",
        "\n",
        "import mlflow\n",
        "import optuna"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afdsqLmjSi4Z"
      },
      "source": [
        "Configurando MLFlow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YXPuH9qPHIK",
        "outputId": "f63717d2-62cc-4b19-8040-05e7363a6b54"
      },
      "source": [
        "\n",
        "with mlflow.start_run():\n",
        "  mlflow.log_metric(\"m1\", 2.0)\n",
        "  mlflow.log_param(\"p1\", \"mlflow-colab\")\n",
        "\n",
        "get_ipython().system_raw(\"mlflow ui --port 5000 &\")\n",
        "\n",
        "from pyngrok import ngrok\n",
        "\n",
        "ngrok.kill()\n",
        "\n",
        "NGROK_AUTH_TOKEN = \"1wbnBFUrJ5h6yC62xQKhBAg2d6j_7VXzU961dGaomVXmeKr1y\"\n",
        "ngrok.set_auth_token(NGROK_AUTH_TOKEN)\n",
        "\n",
        "ngrok_tunnel = ngrok.connect(addr=\"5000\", proto=\"http\", bind_tls=True)\n",
        "print(\"MLflow Tracking UI:\", ngrok_tunnel.public_url)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MLflow Tracking UI: https://863b1642094c.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ThYdEbny9xn"
      },
      "source": [
        "# 🧭 Exploração\n",
        "\n",
        "Agora, podemos carregar o dataset e realizar uma análise exploratória dos nossos dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qo6AQfzWh_AB"
      },
      "source": [
        "df_stroke = pd.read_csv(\"/content/water_potability.csv\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Inxx3mTT2vPT",
        "outputId": "b658fd46-f213-476d-a37e-c92487c94d74"
      },
      "source": [
        "df_stroke.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ph</th>\n",
              "      <th>Hardness</th>\n",
              "      <th>Solids</th>\n",
              "      <th>Chloramines</th>\n",
              "      <th>Sulfate</th>\n",
              "      <th>Conductivity</th>\n",
              "      <th>Organic_carbon</th>\n",
              "      <th>Trihalomethanes</th>\n",
              "      <th>Turbidity</th>\n",
              "      <th>Potability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>204.890455</td>\n",
              "      <td>20791.318981</td>\n",
              "      <td>7.300212</td>\n",
              "      <td>368.516441</td>\n",
              "      <td>564.308654</td>\n",
              "      <td>10.379783</td>\n",
              "      <td>86.990970</td>\n",
              "      <td>2.963135</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.716080</td>\n",
              "      <td>129.422921</td>\n",
              "      <td>18630.057858</td>\n",
              "      <td>6.635246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>592.885359</td>\n",
              "      <td>15.180013</td>\n",
              "      <td>56.329076</td>\n",
              "      <td>4.500656</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.099124</td>\n",
              "      <td>224.236259</td>\n",
              "      <td>19909.541732</td>\n",
              "      <td>9.275884</td>\n",
              "      <td>NaN</td>\n",
              "      <td>418.606213</td>\n",
              "      <td>16.868637</td>\n",
              "      <td>66.420093</td>\n",
              "      <td>3.055934</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8.316766</td>\n",
              "      <td>214.373394</td>\n",
              "      <td>22018.417441</td>\n",
              "      <td>8.059332</td>\n",
              "      <td>356.886136</td>\n",
              "      <td>363.266516</td>\n",
              "      <td>18.436524</td>\n",
              "      <td>100.341674</td>\n",
              "      <td>4.628771</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.092223</td>\n",
              "      <td>181.101509</td>\n",
              "      <td>17978.986339</td>\n",
              "      <td>6.546600</td>\n",
              "      <td>310.135738</td>\n",
              "      <td>398.410813</td>\n",
              "      <td>11.558279</td>\n",
              "      <td>31.997993</td>\n",
              "      <td>4.075075</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "         ph    Hardness        Solids  ...  Trihalomethanes  Turbidity  Potability\n",
              "0       NaN  204.890455  20791.318981  ...        86.990970   2.963135           0\n",
              "1  3.716080  129.422921  18630.057858  ...        56.329076   4.500656           0\n",
              "2  8.099124  224.236259  19909.541732  ...        66.420093   3.055934           0\n",
              "3  8.316766  214.373394  22018.417441  ...       100.341674   4.628771           0\n",
              "4  9.092223  181.101509  17978.986339  ...        31.997993   4.075075           0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KxHPzxT2xWx",
        "outputId": "d087a926-5fbf-425f-b4c9-829e43121567"
      },
      "source": [
        "df_stroke.columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ph', 'Hardness', 'Solids', 'Chloramines', 'Sulfate', 'Conductivity',\n",
              "       'Organic_carbon', 'Trihalomethanes', 'Turbidity', 'Potability'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "gsXwz7Yn2zBN",
        "outputId": "059ffd68-a8f9-46bb-eb94-81d7814b8d5b"
      },
      "source": [
        "df_stroke.describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ph</th>\n",
              "      <th>Hardness</th>\n",
              "      <th>Solids</th>\n",
              "      <th>Chloramines</th>\n",
              "      <th>Sulfate</th>\n",
              "      <th>Conductivity</th>\n",
              "      <th>Organic_carbon</th>\n",
              "      <th>Trihalomethanes</th>\n",
              "      <th>Turbidity</th>\n",
              "      <th>Potability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2785.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "      <td>2495.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "      <td>3114.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "      <td>3276.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.080795</td>\n",
              "      <td>196.369496</td>\n",
              "      <td>22014.092526</td>\n",
              "      <td>7.122277</td>\n",
              "      <td>333.775777</td>\n",
              "      <td>426.205111</td>\n",
              "      <td>14.284970</td>\n",
              "      <td>66.396293</td>\n",
              "      <td>3.966786</td>\n",
              "      <td>0.390110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.594320</td>\n",
              "      <td>32.879761</td>\n",
              "      <td>8768.570828</td>\n",
              "      <td>1.583085</td>\n",
              "      <td>41.416840</td>\n",
              "      <td>80.824064</td>\n",
              "      <td>3.308162</td>\n",
              "      <td>16.175008</td>\n",
              "      <td>0.780382</td>\n",
              "      <td>0.487849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.432000</td>\n",
              "      <td>320.942611</td>\n",
              "      <td>0.352000</td>\n",
              "      <td>129.000000</td>\n",
              "      <td>181.483754</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>0.738000</td>\n",
              "      <td>1.450000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.093092</td>\n",
              "      <td>176.850538</td>\n",
              "      <td>15666.690297</td>\n",
              "      <td>6.127421</td>\n",
              "      <td>307.699498</td>\n",
              "      <td>365.734414</td>\n",
              "      <td>12.065801</td>\n",
              "      <td>55.844536</td>\n",
              "      <td>3.439711</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.036752</td>\n",
              "      <td>196.967627</td>\n",
              "      <td>20927.833607</td>\n",
              "      <td>7.130299</td>\n",
              "      <td>333.073546</td>\n",
              "      <td>421.884968</td>\n",
              "      <td>14.218338</td>\n",
              "      <td>66.622485</td>\n",
              "      <td>3.955028</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.062066</td>\n",
              "      <td>216.667456</td>\n",
              "      <td>27332.762127</td>\n",
              "      <td>8.114887</td>\n",
              "      <td>359.950170</td>\n",
              "      <td>481.792304</td>\n",
              "      <td>16.557652</td>\n",
              "      <td>77.337473</td>\n",
              "      <td>4.500320</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>323.124000</td>\n",
              "      <td>61227.196008</td>\n",
              "      <td>13.127000</td>\n",
              "      <td>481.030642</td>\n",
              "      <td>753.342620</td>\n",
              "      <td>28.300000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>6.739000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                ph     Hardness  ...    Turbidity   Potability\n",
              "count  2785.000000  3276.000000  ...  3276.000000  3276.000000\n",
              "mean      7.080795   196.369496  ...     3.966786     0.390110\n",
              "std       1.594320    32.879761  ...     0.780382     0.487849\n",
              "min       0.000000    47.432000  ...     1.450000     0.000000\n",
              "25%       6.093092   176.850538  ...     3.439711     0.000000\n",
              "50%       7.036752   196.967627  ...     3.955028     0.000000\n",
              "75%       8.062066   216.667456  ...     4.500320     1.000000\n",
              "max      14.000000   323.124000  ...     6.739000     1.000000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o52nRBWE3I1c"
      },
      "source": [
        "Como podemos perceber, algumas propriedades como 'hypertension', e 'stroke' provavelmente variam bináriamente indicando presença ou não da doença/condição"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "SEEwpaxu3Hmo",
        "outputId": "d6a5793a-d6c8-4b94-cf5f-ea16f8ead6c2"
      },
      "source": [
        "df_stroke.info()\n",
        "df_stroke.isnull().sum()\n",
        "sns.countplot(x='Potability', data=df_stroke);"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3276 entries, 0 to 3275\n",
            "Data columns (total 10 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   ph               2785 non-null   float64\n",
            " 1   Hardness         3276 non-null   float64\n",
            " 2   Solids           3276 non-null   float64\n",
            " 3   Chloramines      3276 non-null   float64\n",
            " 4   Sulfate          2495 non-null   float64\n",
            " 5   Conductivity     3276 non-null   float64\n",
            " 6   Organic_carbon   3276 non-null   float64\n",
            " 7   Trihalomethanes  3114 non-null   float64\n",
            " 8   Turbidity        3276 non-null   float64\n",
            " 9   Potability       3276 non-null   int64  \n",
            "dtypes: float64(9), int64(1)\n",
            "memory usage: 256.1 KB\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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKO5-sr8WWlu"
      },
      "source": [
        "Observando a descrição do dataset no kaggle, podemos perceber que existem três colunas que possuem algum valor inválido, sendo elas: \"Trihalomethanes\", \"Sulfate\" e \"pH\". Vamos filtrar esses valores inválidos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpki-f1fW3zO"
      },
      "source": [
        "df_stroke = df_stroke.drop(df_stroke[df_stroke.Trihalomethanes.isna()].index)\n",
        "df_stroke = df_stroke.drop(df_stroke[df_stroke.ph.isna()].index)\n",
        "df_stroke = df_stroke.drop(df_stroke[df_stroke.Sulfate.isna()].index)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwEEPHATXQ9k"
      },
      "source": [
        "E em seguida, podemos verificar novamente o dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDCz2qG3Xnc6",
        "outputId": "092c5352-55b0-4d06-8dcf-2c3484cf9fb6"
      },
      "source": [
        "df_stroke.count()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ph                 2011\n",
              "Hardness           2011\n",
              "Solids             2011\n",
              "Chloramines        2011\n",
              "Sulfate            2011\n",
              "Conductivity       2011\n",
              "Organic_carbon     2011\n",
              "Trihalomethanes    2011\n",
              "Turbidity          2011\n",
              "Potability         2011\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NsaKkjIXx38"
      },
      "source": [
        "A partir daqui, dividiremos o dataframe em treino, teste e validação e começaremos a iterar em cima dos algorítmos com o auxílio do MLFlow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_CfaiGj4Se8"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x = df_stroke.drop(columns = ['Potability']).copy()\n",
        "y = df_stroke['Potability']\n",
        "\n",
        "\n",
        "x_train, x_rem, y_train, y_rem = train_test_split(x,y, train_size=0.8)\n",
        "x_val, x_test, y_val, y_test = train_test_split(x_rem,y_rem, test_size=0.5)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGhG8jw5cLyz"
      },
      "source": [
        "Também estaremos utilizando o optuna para realizar a otimização de hiper-parâmetros"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0CzJs_mXcTn_",
        "outputId": "eb2a33af-5ad3-4096-e867-54d81dac3df2"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def KNNOptimization(trial):\n",
        "    # hyperparameter setting\n",
        "    neighbors = trial.suggest_int('n_neighbours', 20, 200)\n",
        "    \n",
        "    # model training and evaluation\n",
        "    with mlflow.start_run():\n",
        "      knn = KNeighborsClassifier(n_neighbors=neighbors)\n",
        "      knn.fit(x_train, y_train)\n",
        "      y_pred = knn.predict(x_test)\n",
        "\n",
        "      cr = classification_report(y_test, y_pred, output_dict=True)\n",
        "\n",
        "      precision = cr['1']['precision']\n",
        "      f1score = cr['1']['precision']\n",
        "      error = sklearn.metrics.mean_squared_error(y_test, y_pred)\n",
        "\n",
        "      mlflow.log_param(\"n_neighbors\", neighbors);\n",
        "      mlflow.log_metric(\"precision\", precision);\n",
        "      mlflow.log_metric(\"f1-score\", f1score);\n",
        "      mlflow.log_metric(\"error\", error);\n",
        "      return error\n",
        "\n",
        "study = optuna.create_study(direction='minimize')\n",
        "study.optimize(KNNOptimization, n_trials=20)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[32m[I 2021-08-12 04:38:50,808]\u001b[0m A new study created in memory with name: no-name-30ffb734-6f8a-402b-8b17-56563d015562\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:50,847]\u001b[0m Trial 0 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 92}. Best is trial 0 with value: 0.3613861386138614.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:50,883]\u001b[0m Trial 1 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 90}. Best is trial 0 with value: 0.3613861386138614.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:50,917]\u001b[0m Trial 2 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 123}. Best is trial 0 with value: 0.3613861386138614.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:50,949]\u001b[0m Trial 3 finished with value: 0.35148514851485146 and parameters: {'n_neighbours': 68}. Best is trial 3 with value: 0.35148514851485146.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:50,983]\u001b[0m Trial 4 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 99}. Best is trial 3 with value: 0.35148514851485146.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,015]\u001b[0m Trial 5 finished with value: 0.33663366336633666 and parameters: {'n_neighbours': 53}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,046]\u001b[0m Trial 6 finished with value: 0.36633663366336633 and parameters: {'n_neighbours': 20}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:51,086]\u001b[0m Trial 7 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 99}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:51,127]\u001b[0m Trial 8 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 198}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:51,162]\u001b[0m Trial 9 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 99}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,195]\u001b[0m Trial 10 finished with value: 0.3811881188118812 and parameters: {'n_neighbours': 25}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,228]\u001b[0m Trial 11 finished with value: 0.3415841584158416 and parameters: {'n_neighbours': 54}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,262]\u001b[0m Trial 12 finished with value: 0.3415841584158416 and parameters: {'n_neighbours': 54}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:51,304]\u001b[0m Trial 13 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 151}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,339]\u001b[0m Trial 14 finished with value: 0.35148514851485146 and parameters: {'n_neighbours': 52}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,373]\u001b[0m Trial 15 finished with value: 0.3712871287128713 and parameters: {'n_neighbours': 46}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:51,411]\u001b[0m Trial 16 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 133}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,454]\u001b[0m Trial 17 finished with value: 0.36633663366336633 and parameters: {'n_neighbours': 74}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "\u001b[32m[I 2021-08-12 04:38:51,496]\u001b[0m Trial 18 finished with value: 0.3613861386138614 and parameters: {'n_neighbours': 170}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n",
            "\u001b[32m[I 2021-08-12 04:38:51,535]\u001b[0m Trial 19 finished with value: 0.3564356435643564 and parameters: {'n_neighbours': 36}. Best is trial 5 with value: 0.33663366336633666.\u001b[0m\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiVnaCB6cLND"
      },
      "source": [
        ""
      ]
    }
  ]
}