Naive_Bayes.ipynb

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      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "Naive Bayes.ipnyb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FedericoMz/DM2-FMAProject/blob/main/Naive_Bayes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cj4vf6kJO9-Z",
        "outputId": "9e19c6d7-3b4e-4928-e27f-b4023a25e660"
      },
      "source": [
        "!pip install scikit-plot"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: scikit-plot in /usr/local/lib/python3.7/dist-packages (0.3.7)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.4.1)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->scikit-plot) (1.19.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8wNW60IPGzL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc687b8c-8315-42ba-e661-56a77fa71ccc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4JaVPrJPE_D"
      },
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from scikitplot.metrics import plot_roc\n",
        "\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from scikitplot.metrics import plot_roc\n",
        "from scikitplot.metrics import plot_precision_recall\n",
        "from scikitplot.metrics import plot_cumulative_gain\n",
        "from scikitplot.metrics import plot_lift_curve\n",
        "from sklearn import metrics\n",
        "\n",
        "from collections import defaultdict\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "from sklearn.model_selection import train_test_split, cross_val_score \n",
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score, classification_report\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB, CategoricalNB, BernoulliNB, MultinomialNB\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOdH-jBguJlV"
      },
      "source": [
        "# Stuff"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvFhKxlst9i6"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/FMA Project/NeoNoOutTuttoNumeri.csv', skipinitialspace=True, na_values='?', keep_default_na=True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmsfL2qpaxiL",
        "outputId": "32fd19a2-1fb0-42ae-bb0b-c9c9e1fb6837"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4263 entries, 0 to 4262\n",
            "Data columns (total 44 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Unnamed: 0           4263 non-null   int64  \n",
            " 1   album_title          4263 non-null   object \n",
            " 2   track_duration       4263 non-null   int64  \n",
            " 3   track_listens        4263 non-null   float64\n",
            " 4   acousticness         1120 non-null   float64\n",
            " 5   danceability         1120 non-null   float64\n",
            " 6   energy               1120 non-null   float64\n",
            " 7   instrumentalness     1120 non-null   float64\n",
            " 8   liveness             1120 non-null   float64\n",
            " 9   speechiness          1120 non-null   float64\n",
            " 10  tempo                1120 non-null   float64\n",
            " 11  valence              1120 non-null   float64\n",
            " 12  artist_discovery     1120 non-null   float64\n",
            " 13  artist_familiarity   1120 non-null   float64\n",
            " 14  artist_hotttnesss    1120 non-null   float64\n",
            " 15  song_currency        1120 non-null   float64\n",
            " 16  song_hotttnesss      1120 non-null   float64\n",
            " 17  artist_favorites     4263 non-null   int64  \n",
            " 18  Classical            4263 non-null   int64  \n",
            " 19  Blues                4263 non-null   int64  \n",
            " 20  Country              4263 non-null   int64  \n",
            " 21  Easy Listening       4263 non-null   int64  \n",
            " 22  Electronic           4263 non-null   int64  \n",
            " 23  Experimental         4263 non-null   int64  \n",
            " 24  Folk                 4263 non-null   int64  \n",
            " 25  Hip-Hop              4263 non-null   int64  \n",
            " 26  Instrumental         4263 non-null   int64  \n",
            " 27  International        4263 non-null   int64  \n",
            " 28  Jazz                 4263 non-null   int64  \n",
            " 29  Old-Time / Historic  4263 non-null   int64  \n",
            " 30  Pop                  4263 non-null   int64  \n",
            " 31  Rock                 4263 non-null   int64  \n",
            " 32  Soul-RnB             4263 non-null   int64  \n",
            " 33  Spoken               4263 non-null   int64  \n",
            " 34  album_num_tracks     4263 non-null   int64  \n",
            " 35  album_favorites      4263 non-null   int64  \n",
            " 36  album_listens        4263 non-null   int64  \n",
            " 37  quality              4263 non-null   float64\n",
            " 38  popularity           4263 non-null   float64\n",
            " 39  Album                4263 non-null   int64  \n",
            " 40  Live Performance     4263 non-null   int64  \n",
            " 41  Radio Program        4263 non-null   int64  \n",
            " 42  Single Tracks        4263 non-null   int64  \n",
            " 43  EP                   4263 non-null   int64  \n",
            "dtypes: float64(16), int64(27), object(1)\n",
            "memory usage: 1.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjPdqLbraxkn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tsik_HGaxnJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwp2nJv2axpi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_iML8FsPFBb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1AzHM2wPFD2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "I6qAXnJ9O9-b",
        "outputId": "f15bbb10-fe63-4cde-f4e9-75990990ea6f"
      },
      "source": [
        "class_name = 'popularity'\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1</th>\n",
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              "      <td>183</td>\n",
              "      <td>2598.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2075</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                         album_title  ...  Single Tracks  EP\n",
              "0           0  \"...Through The Cracks\" Mix Vol. 1  ...              1   0\n",
              "1           1                       \"A.B.A.Y.A.M\"  ...              1   0\n",
              "2           2                        \"Aeroplanes\"  ...              1   0\n",
              "3           3   \"All or Nothing\" Action Orchestra  ...              0   0\n",
              "4           4          \"Cast Your Ship Upon Rain\"  ...              1   0\n",
              "\n",
              "[5 rows x 44 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYDZZ9wdO9-c",
        "outputId": "164dada2-4efc-4fcc-ef2b-95e6cce71e33"
      },
      "source": [
        "from collections import Counter\n",
        "z = df[\"popularity\"]\n",
        "Counter(z)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0.0: 4039, 1.0: 224})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwaXdVkaO9-c"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_W6wTlSSO9-c"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUcT4ot-O9-d"
      },
      "source": [
        "\n",
        "\n",
        "#eliminiamo il solito Unnamed, album title e favorites (che sballerebbe il classificatore)\n",
        "del df[\"Unnamed: 0\"]\n",
        "del df[\"album_title\"]\n",
        "del df['album_favorites']\n",
        "\n",
        "#eliminiamo le feature di echonest\n",
        "del df[\"acousticness\"]\n",
        "del df [\"danceability\"]\n",
        "del df [\"energy\"]\n",
        "del df [\"instrumentalness\"]\n",
        "del df [\"liveness\"]\n",
        "del df [\"speechiness\"]\n",
        "del df [\"tempo\"]\n",
        "del df [\"valence\"]\n",
        "del df [\"artist_familiarity\"]\n",
        "del df [\"artist_discovery\"]  \n",
        "del df [\"artist_hotttnesss\"]  \n",
        "del df [\"song_currency\"]     \n",
        "del df [\"song_hotttnesss\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xg6EVmCJO9-d"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7XhLkboO9-d"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1_WvSBkO9-e"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1j8_Sg7O9-e",
        "outputId": "bc08862a-747c-481a-9efe-9a5cca69c85d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4263 entries, 0 to 4262\n",
            "Data columns (total 28 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   track_duration       4263 non-null   int64  \n",
            " 1   track_listens        4263 non-null   float64\n",
            " 2   artist_favorites     4263 non-null   int64  \n",
            " 3   Classical            4263 non-null   int64  \n",
            " 4   Blues                4263 non-null   int64  \n",
            " 5   Country              4263 non-null   int64  \n",
            " 6   Easy Listening       4263 non-null   int64  \n",
            " 7   Electronic           4263 non-null   int64  \n",
            " 8   Experimental         4263 non-null   int64  \n",
            " 9   Folk                 4263 non-null   int64  \n",
            " 10  Hip-Hop              4263 non-null   int64  \n",
            " 11  Instrumental         4263 non-null   int64  \n",
            " 12  International        4263 non-null   int64  \n",
            " 13  Jazz                 4263 non-null   int64  \n",
            " 14  Old-Time / Historic  4263 non-null   int64  \n",
            " 15  Pop                  4263 non-null   int64  \n",
            " 16  Rock                 4263 non-null   int64  \n",
            " 17  Soul-RnB             4263 non-null   int64  \n",
            " 18  Spoken               4263 non-null   int64  \n",
            " 19  album_num_tracks     4263 non-null   int64  \n",
            " 20  album_listens        4263 non-null   int64  \n",
            " 21  quality              4263 non-null   float64\n",
            " 22  popularity           4263 non-null   float64\n",
            " 23  Album                4263 non-null   int64  \n",
            " 24  Live Performance     4263 non-null   int64  \n",
            " 25  Radio Program        4263 non-null   int64  \n",
            " 26  Single Tracks        4263 non-null   int64  \n",
            " 27  EP                   4263 non-null   int64  \n",
            "dtypes: float64(3), int64(25)\n",
            "memory usage: 932.7 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYdpDySmO9-g"
      },
      "source": [
        "df0 = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "_KFa4Fy9O9-g",
        "outputId": "e895711e-de9a-4761-b36d-0d30cafcc079"
      },
      "source": [
        "dfX = pd.get_dummies(df[[c for c in df.columns if c != class_name]], prefix_sep='=')\n",
        "dfX.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>track_duration</th>\n",
              "      <th>track_listens</th>\n",
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              "      <th>Blues</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>152</td>\n",
              "      <td>1787.0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>2080.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   track_duration  track_listens  ...  Single Tracks  EP\n",
              "0              66         1190.0  ...              1   0\n",
              "1             183         2598.0  ...              1   0\n",
              "2             152         1787.0  ...              1   0\n",
              "3              88         1672.0  ...              0   0\n",
              "4             205         2080.0  ...              1   0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S80vxPxMO9-g",
        "outputId": "dd979871-fbed-4af8-ca12-f71f7d32c64c"
      },
      "source": [
        "dfY = df[class_name]\n",
        "dfY.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.0\n",
              "1    0.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "Name: popularity, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "ysR6bbRYO9-g",
        "outputId": "27f07779-08bd-45eb-9fee-fdc9bc5a3441"
      },
      "source": [
        "df = pd.concat([dfX, dfY], axis=1)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th>Blues</th>\n",
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              "      <th>Easy Listening</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88</td>\n",
              "      <td>1672.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>4966</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>205</td>\n",
              "      <td>2080.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2075</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   track_duration  track_listens  ...  EP  popularity\n",
              "0              66         1190.0  ...   0         0.0\n",
              "1             183         2598.0  ...   0         0.0\n",
              "2             152         1787.0  ...   0         0.0\n",
              "3              88         1672.0  ...   0         0.0\n",
              "4             205         2080.0  ...   0         0.0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "C2IdyczrO9-g",
        "outputId": "bbf10a93-1cdf-4323-b4ab-2a6a2d2acd63"
      },
      "source": [
        "df0.copy()"
      ],
      "execution_count": null,
      "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>track_duration</th>\n",
              "      <th>track_listens</th>\n",
              "      <th>artist_favorites</th>\n",
              "      <th>Classical</th>\n",
              "      <th>Blues</th>\n",
              "      <th>Country</th>\n",
              "      <th>Easy Listening</th>\n",
              "      <th>Electronic</th>\n",
              "      <th>Experimental</th>\n",
              "      <th>Folk</th>\n",
              "      <th>Hip-Hop</th>\n",
              "      <th>Instrumental</th>\n",
              "      <th>International</th>\n",
              "      <th>Jazz</th>\n",
              "      <th>Old-Time / Historic</th>\n",
              "      <th>Pop</th>\n",
              "      <th>Rock</th>\n",
              "      <th>Soul-RnB</th>\n",
              "      <th>Spoken</th>\n",
              "      <th>album_num_tracks</th>\n",
              "      <th>album_listens</th>\n",
              "      <th>quality</th>\n",
              "      <th>popularity</th>\n",
              "      <th>Album</th>\n",
              "      <th>Live Performance</th>\n",
              "      <th>Radio Program</th>\n",
              "      <th>Single Tracks</th>\n",
              "      <th>EP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1190</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>183</td>\n",
              "      <td>2598.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2596</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>152</td>\n",
              "      <td>1787.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1785</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88</td>\n",
              "      <td>1672.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>4966</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>205</td>\n",
              "      <td>2080.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2075</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4258</th>\n",
              "      <td>1939</td>\n",
              "      <td>784.428571</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>6158</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4259</th>\n",
              "      <td>1078</td>\n",
              "      <td>1446.000000</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7230</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4260</th>\n",
              "      <td>854</td>\n",
              "      <td>613.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>4021</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4261</th>\n",
              "      <td>234</td>\n",
              "      <td>1240.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1240</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4262</th>\n",
              "      <td>877</td>\n",
              "      <td>982.500000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3801</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4263 rows × 28 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      track_duration  track_listens  ...  Single Tracks  EP\n",
              "0                 66    1190.000000  ...              1   0\n",
              "1                183    2598.000000  ...              1   0\n",
              "2                152    1787.000000  ...              1   0\n",
              "3                 88    1672.000000  ...              0   0\n",
              "4                205    2080.000000  ...              1   0\n",
              "...              ...            ...  ...            ...  ..\n",
              "4258            1939     784.428571  ...              0   0\n",
              "4259            1078    1446.000000  ...              0   1\n",
              "4260             854     613.750000  ...              0   0\n",
              "4261             234    1240.000000  ...              1   0\n",
              "4262             877     982.500000  ...              0   1\n",
              "\n",
              "[4263 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5oLrv9VO9-h"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gi7193nTO9-h"
      },
      "source": [
        "attributes = [col for col in df.columns if col != class_name]\n",
        "X = df[attributes].values\n",
        "y = df[class_name]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100, stratify=y)\n",
        "\n",
        "X_OG, y_OG = X_train, y_train "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6lnYa0mDzqo"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6abmHvFD0K9"
      },
      "source": [
        "#scaling features \n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBYjaZoOD3qH"
      },
      "source": [
        "#scaler = StandardScaler()\n",
        "#scaler.fit(X_train)\n",
        "# X_train = scaler.transform(X_train)\n",
        "# X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EcCJ3pWJdfjk"
      },
      "source": [
        "backup = df.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cm-zNhSqdsRK"
      },
      "source": [
        "df = backup"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mj9vCxhnO9-h"
      },
      "source": [
        "# Gaussian"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "617H0-SCO9-h"
      },
      "source": [
        "dfNB = pd.DataFrame()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16jNob2ndbri"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "9TjkAU8tdbzd",
        "outputId": "3bfee2b4-3a63-4c26-f899-e00804655897"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "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>track_duration</th>\n",
              "      <th>track_listens</th>\n",
              "      <th>artist_favorites</th>\n",
              "      <th>Classical</th>\n",
              "      <th>Blues</th>\n",
              "      <th>Country</th>\n",
              "      <th>Easy Listening</th>\n",
              "      <th>Electronic</th>\n",
              "      <th>Experimental</th>\n",
              "      <th>Folk</th>\n",
              "      <th>Hip-Hop</th>\n",
              "      <th>Instrumental</th>\n",
              "      <th>International</th>\n",
              "      <th>Jazz</th>\n",
              "      <th>Old-Time / Historic</th>\n",
              "      <th>Pop</th>\n",
              "      <th>Rock</th>\n",
              "      <th>Soul-RnB</th>\n",
              "      <th>Spoken</th>\n",
              "      <th>album_num_tracks</th>\n",
              "      <th>album_listens</th>\n",
              "      <th>quality</th>\n",
              "      <th>Album</th>\n",
              "      <th>Live Performance</th>\n",
              "      <th>Radio Program</th>\n",
              "      <th>Single Tracks</th>\n",
              "      <th>EP</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66</td>\n",
              "      <td>1190.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1190</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>183</td>\n",
              "      <td>2598.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2596</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>152</td>\n",
              "      <td>1787.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1785</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>88</td>\n",
              "      <td>1672.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>4966</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>205</td>\n",
              "      <td>2080.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2075</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4258</th>\n",
              "      <td>1939</td>\n",
              "      <td>784.428571</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4259</th>\n",
              "      <td>1078</td>\n",
              "      <td>1446.000000</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>7230</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4260</th>\n",
              "      <td>854</td>\n",
              "      <td>613.750000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>4021</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4261</th>\n",
              "      <td>234</td>\n",
              "      <td>1240.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1240</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4262</th>\n",
              "      <td>877</td>\n",
              "      <td>982.500000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3801</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4263 rows × 28 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      track_duration  track_listens  ...  EP  popularity\n",
              "0                 66    1190.000000  ...   0         0.0\n",
              "1                183    2598.000000  ...   0         0.0\n",
              "2                152    1787.000000  ...   0         0.0\n",
              "3                 88    1672.000000  ...   0         0.0\n",
              "4                205    2080.000000  ...   0         0.0\n",
              "...              ...            ...  ...  ..         ...\n",
              "4258            1939     784.428571  ...   0         0.0\n",
              "4259            1078    1446.000000  ...   1         0.0\n",
              "4260             854     613.750000  ...   0         0.0\n",
              "4261             234    1240.000000  ...   0         0.0\n",
              "4262             877     982.500000  ...   1         0.0\n",
              "\n",
              "[4263 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeeZM3KrdaQk"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5LauylgO9-h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "9b66518b-5415-4824-d40f-2eb884d4fc83"
      },
      "source": [
        "clf = GaussianNB()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "y_pred0 = y_pred\n",
        "y_score=clf.predict_proba(X_test)\n",
        "\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9452697419859265\n",
            "F1-score [0.97085762 0.55128205]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.96      0.97      1212\n",
            "         1.0       0.48      0.64      0.55        67\n",
            "\n",
            "    accuracy                           0.95      1279\n",
            "   macro avg       0.73      0.80      0.76      1279\n",
            "weighted avg       0.95      0.95      0.95      1279\n",
            "\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": "code",
      "metadata": {
        "id": "LzoWlhQAO9-h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "8e0bc79c-9cbd-4f96-f0e3-2279e54472e7"
      },
      "source": [
        "clf = GaussianNB(var_smoothing=0.01)\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "y_pred0 = y_pred\n",
        "y_score=clf.predict_proba(X_test)\n",
        "\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9710711493354183\n",
            "F1-score [0.98486708 0.67256637]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.99      0.98      1212\n",
            "         1.0       0.83      0.57      0.67        67\n",
            "\n",
            "    accuracy                           0.97      1279\n",
            "   macro avg       0.90      0.78      0.83      1279\n",
            "weighted avg       0.97      0.97      0.97      1279\n",
            "\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": "code",
      "metadata": {
        "id": "xZlNKyLKO9-h"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "phoBgoJzO9-h",
        "outputId": "5b548117-f53c-4856-d7d0-92422d144abd"
      },
      "source": [
        "X_train_num, X_test_num, y_train, y_test = train_test_split(\n",
        "    df[[c for c in df.columns if c in ['album_listens']]], df[class_name], \n",
        "       test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "\n",
        "clf = GaussianNB()\n",
        "clf.fit(X_train_num, y_train)\n",
        "y_pred = clf.predict(X_test_num)\n",
        "y_pred0 = y_pred\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-21ec62471613>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m X_train_num, X_test_num, y_train, y_test = train_test_split(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'album_listens'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m        test_size=0.3, random_state=100, stratify=df[class_name])\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "EDGjlZVM1drX",
        "outputId": "14659cbe-5229-46bc-a84d-1aecd0976043"
      },
      "source": [
        "X_train_num, X_test_num, y_train, y_test = train_test_split(\n",
        "    df[[c for c in df.columns if c in ['track_listens', 'artist_favorites', 'track_duration']]], df[class_name], \n",
        "       test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "\n",
        "clf = GaussianNB(var_smoothing=0.01)\n",
        "clf.fit(X_train_num, y_train)\n",
        "y_pred = clf.predict(X_test_num)\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9609069585613761\n",
            "F1-score [0.97949139 0.58333333]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      0.99      0.98      1212\n",
            "         1.0       0.66      0.52      0.58        67\n",
            "\n",
            "    accuracy                           0.96      1279\n",
            "   macro avg       0.82      0.75      0.78      1279\n",
            "weighted avg       0.96      0.96      0.96      1279\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p0waAiRMn0r59ex588MHb2l5hcv5EoRWKknqlkJf9+y/x1Vd/m5bDwyOtJoqcJ+W0NMsnS1tPvpbLWj8Bu7vnTBQZVhKF+bq8Tuq2JgBbjx2MXURLqsLswZSdwWDgo48+YsKECbi5uREREUHFihXx8PAoFkkCdKK4qxS0mqmka9myutnyzp2RVsve7rfv/E7+WVxdFQaD9RNRkyZVSElJx93dFXd3F6zl9YAAX4YNux93d1dKlXKladMqVrc5aVJbYmOTcHd3wd3dlYYNLQ+sWLt2BSIiRlKqlCvu7q54ehav04ajqoYKw+HDhxk2bBjbtxt7gHXq1ImkpCQqVqzo4MjM2W0ID3uxaQgPzSJLddpQ8hqkT568ym+/nSE8PJLw8EgmTWpL//4hucpdv55MhQpvAcaTdlhYVbZuHWxxaIUvvjhIZOQN04n6sccCLdar37yZxs6dkZknVRfKlClFcLDluuWUlPTMfbvi4mL5d6PlL2vYC3vq5tuNnxoX3v9IYmIi06dPZ+7cuaSnp1O5cmXeffdd+vXrZ/X/9E4VyyE8tOLL2b4cWJKRYbDYQwjgo4/+4v/+7w/T8s6d5y0mCh8fT5Yt60lQkB9NmlTJ85vy00/b1h2xdGl3OnSobVNZDw/971eY7FU1ZA99+vQx3TQ3YsQI3njjDXx8crd5FRfO+5eadaMd3JqLwNbRKTWnIyK8996fHDgQzYED0Rw7FktMzHiLQznkrFIKD7c+er21NgnNcZy5KslWEydOJDo6mkWLFtGiRfEfPcJ5E0XWjXZgPlub5rQMBiEpKY2yZXPfpauU4t13/+TMmeumdUeOxHD//VVzlW3RwjxRHDwYTUpKuv4G7yRuJ0kUxs1t9pKens7777/PmTNnmDdvHgDt27dnz549Drkn4nY473+OvtGu2BMxjuV/7VoyNWqUt1gPHxERS+PGi0lPN5CRIQwZ0pQlSx6xuL1GjSqbJYoDB6ItJgp//zI8+2xjateuQKtW1WnWrJpOEk7ImaqSrNm1axdDhw5l/37jvBJDhgyhYcOGAE6TJMCZE8WmJ4w/Z+2CX87oG+2KWHq6gZs30yyOognQtevnbNp0iowM4z/72bMvUqNG7sZfV1dFSsqtnkH790db3WdoaGXWrDlmWs5ryGdrw0trhe9uqCoqqOvXrzNp0iQWL16MiFCzZk3mz59vShLOxnkTRaPMniRf9HBsHCVMQkIqZcq4W+x5sWnTKUaPXs/ly4lcuXKTvn0b8PXXfS1ux8VFmZIEGK8cLCWKnD2MDh6MJj3dYLHn0SOP3IePjyehoZUJDa2c5x3CWtGxR5IozlVJ+Vm5ciUvvvgi0dHRuLm5MXbsWKZOnUqZMs779+q8iUIrNL16fcXRo7FcuBDPjRspREePs3oSPno01vT88uVEq9vMOSDbsWOxdO5cN1e5nAmhdGk3oqJuWLzruVmzajRrVi3PY9EcpyRUFRWGX375hejoaFq3bs2iRYsICcnd487Z6EShcezYFSIibiWAqKgbFhNFznUxMdYHcqtY0ThmUpky7lSoUJrExDSL5apVK0dS0iTc3Fxwc3OxWx9yrfDoqiZzKSkpREVFUaeOcTyqWbNm0bZtWwYNGuRU7RB50YlCo2pVb44cudVz7MKFeJo0yX1Hb85EkdddxzNnPsycOV0sDjORnYuLstjFVSu+LCUJZ64quhObN29m+PDhuLi4cODAAUqVKoWfnx+DBw92dGiFSicKjapVb03X5e7uYnXCl0qVvDhwYBj+/mXw9S2da1iL7MqUsT4RjVYy3M1VTdHR0YwbN47PP/8cgMDAQCIjI01XFSWN8yaKA5fNlxs5bgje4u7mzTR+//2s1dcnT27Lyy+3pGpVb3x9vawOJ+Hq6kJoaGV7halpxZ7BYOCDDz7glVde4fr163h6ejJlyhTGjx9PqVIl98uR8yaKh782X44Z5Zg4ijkR4T//+YEVKw4B7YHfc5W57z7fog6rWNJ171p+Hn/8cdauNY4I0aVLFxYsWEDdurk7aZQ0JaOlRbPqrbe2ZyYJMCaKviQmpjowouJLJwnb3a1tEr169eKee+7hq6++Yv369XdFkgBnvqLQ8hUVdYPXXtuSY20ls3kNtNzu5rp3zdzatWuJjIxkxIgRAAwcOJBevXrh7e2dzztLFudNFKGWx87XbqlWrRybNg2kV6+vMruy3gS+pHz5+Y4OrUjpKiWtoM6dO8fo0aNZs2YNHh4edO3alTp16qCUuuuSBDhzotAjxdqkTZsa7N79PL17f83evTMB68NelFQFSRJ3a5WKZpSWlsZ7773HtGnTSExMxNvbmxkzZlCzZk1Hh+ZQzpsoNJvVrOnDrl3P4+o61NGhOJSuUtLyEh4eztChQzl48CAAffv25Z133qFaNT0agE4Ud4mSOoOarlbSCsvUqVM5ePAgtWvXZv78+XTrpq8us+heTyVIXqOpllS2JgldpaTlZBwG/9Z0BfPnz2fSpEkcPnxYJ4kc9BVFCbF582m6dPmcl15qyZtvdrQ6TWhJpauVtII4duwYI0aMQCnFxo0bUUoREBDAG2+84ejQiiWdKEqAkyev0rfvN6SnG3j77R0cORLDihW9KVfO8lwRurpGu1slJyfz5ptvMnPmTFJTU/H19eXMmTPUrm3bPOd3K+dNFC9vNl+e28ExcTiYiNCv37dcvXrTtO6nn/5hx47zdO1az+J7SlqS0NVKmi02btzIiBEjOHHiBADPPvsss2bNwtdXj0yQH7smCqVUV2Ae4Ap8KCIzc7xeA/gU8Mks84qI2HYW++yI+fJdmiiUUnh4bAJqAFn9u3/lX/96Ld/36uoa7W4gIjz33HN8/PHHADRo0IDFixfTtm1bB0fmPOyWKJRSrsACoBMQCexWSq0Vkexn+CnA1yKySCnVAFgH1LJXTCWJWfXRdCDWG/77JFS9BpO3Qcns5KRpBaaUolatWpQuXZr//ve/vPzyyyV6AD97sOcVRXPghIicAlBKrQQeBbInCgHKZT4vD1ywYzwlQvfu3Vm3bh1syvGCXzy88zGIsilJ6OoarSTbv38/Fy9e5F//+hcAEydOZMCAAbot4jbZM1FUA85nW44EWuQo8xrwi1LqBaAM8LClDSmlhgBDAGrUqGFcOad9YcZa7IkIx45dMSaJ7DI/sW7duvHTTz8VfWCaVozEx8czbdo05s2bh6+vLxEREVSsWBEPDw+dJO6AoxuznwI+EZE5SqlWwGdKqWARMRu1TkSWAksBwsLCjBXrA4OLOlaHOn78CkFBC6DiWGh0Bn45AZ0PIKLbGTRNRFi9ejWjR48mMjISFxcX+vfvj7u7nj2xMNgzUUQB92Zbrp65LrvngK4AIrJTKeUJ+AE5ZiXSfvvtjPHJVW/4LQRueNHtKT20gKadPXuWUaNG8eOPPwIQFhbGkiVLaNq0qYMjKznseVfWbqC+Uqq2UqoU8CSwNkeZc0BHAKVUEOAJxKDlYkoUmf6v9/P81FhXNWl3NxGhd+/e/Pjjj5QrV4758+cTHh6uk0Qhs1uiEJF0YBTwM3AUY++mv5VS05VSPTOLjQWeV0odAL4EnhFdl2KRh4creN6acOihh3R9q3b3MhiMtdNKKWbPnk2/fv2IiIhg5MiRuLpan8tduz3K2c7LYWFhsmfPHkeH4RDqZ1c4XhVG1yI1dQvu7vofQru7XLlyhVdeeQWADz74wMHROBel1F4RCbud9zq6Mfv2VMox8c7dMl+2mwEaRAKROklodxURYfny5YwbN47Y2FhKlSrFtGnTqF69uqNDuys4Z6KwIz0OkqYVL0ePHmX48OH8/vvvALRv355FixbpJFGE7q4hRm1Q7JPEn44OQNOKhogwdepUGjVqxO+//46fnx+ffvopmzdvJjAw0NHh3VX0FYUVjhwHacOGE6xZE8HixS8CZ4AMs9f1WPna3UApRVRUFGlpaTz//PPMnDmTihUrOjqsu9Jd3ZidVzWTIxPFgAGr+Pzzg5lLKXz6aT8GDmzksHg0rahcuHCB2NhYQkNDAYiNjeXYsWO0bt3awZE5vztpzL6rq56sJQlHjoOUlpbBjz8ez7bGg1q1fBwWj6YVhYyMDObPn09QUBBPPvkkqanGruB+fn46SRQDuuqJ4jXc9rZt57h+PTnbmiQeeOBeq+U1zdnt27ePoUOHklVT8OCDD3Ljxg38/PwcHJmW5a6+oiiOHnjgXn76qT/PP98USACO4eamf01ayXPjxg3GjBlDs2bN2LNnD9WrV+f7779n7dq1OkkUMza3USilvEQkyc7x5Ksw2yjUr8bxuIvTFUV2SrkAHojczLespjkTEaFJkyYcOHAAV1dXxowZw2uvvYa3t3f+b9Zui13bKJRSDyiljgARmcuNlFILb2dnWkEJkJxvKU1zNkopXnrpJZo3b86ePXuYM2eOThLFWL5XFEqpP4E+wFoRaZK57rCIOGSc77CwMNkz+hPzlTYOOW6tl1PxvaLIvOJxsp5pmpZTamoqc+fOxdXVlfHjxwPGv2uDwaDHZioidh/CQ0TOZ520MmVYK1skxm4xX7YxUVhKEnqmN02zr23btjFs2DCOHDmCh4cHAwcOpHLlyiildJJwErYkivNKqQcAUUq5A2MwjgbrtIrrFYSmlSSxsbFMmDCBjz/+GID69euzcOFCKleu7ODItIKypTvNMGAkxqlNo4DGwAh7BnU3WrXqKPPn7+L8+ThHh6Jpd0RE+PjjjwkMDOTjjz82DeB38OBBHn7Y4mzHWjFnyxVFgIg8nX2FUqo1sN0+IdlgQIMCv6X7/u52CKTwzJv3J7//fpYXXlhP06ZVWLBAV4lpzuvzzz/nypUrdOjQgYULFxIQEODokLQ7YEuieB/IOV2UpXVFZ26HAr8lq32iOLZJXLmSxLZt50zL+/ZdxN+/jAMj0rSCSUpKIi4ujipVqqCUYuHChezevZunn36aHO2bmhOymiiUUq2AB4BKSqmXs71UDnDaFqjiOH3ojz8ex2C41W4SEuJPnToVHBiRptlu/fr1jBw5kjp16rBx40aUUgQEBOiriBIkrzaKUkBZjMnEO9vjBsbusk6h+/7uphvriqv27Wsxa9bDPPDAvSgFjz6q/8G04i8qKoq+ffvSrVs3Tp8+TUxMDFeuXHF0WJod2HIfRU0ROVtE8eSroHdmZ08S3Xy7FcsriuyioxMAqFy5rL6PQiuWMjIyWLBgAVOmTCE+Pp4yZcowffp0Ro8ejZubHj6uuLL3fRRJSqm3gYaAZ9ZKESl4Q4EDFZcusVFRN0hNzaB2bctVS5Urly3iiDTNdgaDgXbt2rF9u7Evy2OPPca8efOoUaOGgyPT7MmW7rFfYBy+ozbwP4wz6ey2Y0yFpjj1dEpPNzBvXjhBQQt46aWfHR2Opt0WFxcXOnfuzL333suaNWtYtWqVThJ3AVuqnvaKyP1KqYMiEpq5breINCuSCHMICwuTPeXHm6/8tZ/FslnVTo6ucjp/Po5HH13JX39dMq3bufM5WrbMe85fXfWkOZqI8PXXX+Pm5kbv3r0BSElJIS0tjbJl9dWvM7F31VNa5s+LSqnuwAXAsfMRHowpUHFHt0tUrlyWlBTzUU9effVXNm8eqLsOasXWyZMnGTFiBL/88guVKlWiQ4cOVKhQAQ8PDzw8PBwdnlaEbKl6mqGUKg+MBcYBHwIv2jWqO5DVy6k49XQqVcqVRYtuVYOVK+dB795BDoxI06xLSUlhxowZBAcH88svv1ChQgXeeOMNypcv7+jQNAfJ94pCRH7MfBoHPASmO7OLpZwD/xWXG+wefLAmzzzTmOTkdObO7UyVKnpIZa342bJlC8OHDyciIgKAAQMGMHv2bPz9/R0cmeZIed1w5wo8gXGMpw0iclgp1QOYBJQGmhRNiBZseiLfIsWll1N2H3zwiJ6tTiu2MjIyGDFiBBEREQQEBLBo0SIeeughR4elFQN5XVEsA+4FdgHvKaUuAGHAKyKyuiiCs6qRc3670UlCK24MBgPJycl4eXnh6urKokWL2Lp1KxMmTNDtEJpJXokiDAgVEYNSyhO4BNQVEX3rpaaVAIcOHWLYsGEEBgaybNkyANq1a0e7du0cHJlW3OT1FTdVRAwAIq7I4x4AACAASURBVJIMnNJJwnZffnmIY8dizcZw0rTiIDExkYkTJ9K0aVN27NjB+vXruXbtmqPD0oqxvK4oApVSBzOfK6Bu5rICJOueiuLA2hSnjhIZeYP+/b8HoEIFT9q1q8X33z+hu8JqDvfDDz8watQozp07h1KKESNG8MYbb+Dj4+Po0LRiLK9E4TT9N4tbT6fw8EjT82vXkrl8OVEnCc2h0tPT6devH99/b/wC07hxY5YsWULz5s0dHJnmDKwmiuI0EGAuBy5bXF1cejr9+Wek2XLLltUcFImmGbm5uVG+fHnKli3L66+/zqhRo/QAfprN7PqXopTqCszDOH/FhyIy00KZJ4DXAAEOiEj/fDf88NfmyyvvPNbC1KBBJTp3rsuff0YSF5eS71AdmmYPf/75JwAtWrQA4O2332b69OlUr67/HrWCsVuiyLwPYwHQCYgEdiul1orIkWxl6gOvAq1F5JpSyjn7veYweHATBg9ugsEgHDsWS9Wq+uY6rehcv36dV199lSVLlhAYGMj+/fspVaoUvr6+jg5Nc1I2dexXSpVWShV0Np3mwAkROSUiqRi/9z+ao8zzwAIRuQYgIpbrlJyUi4siKKgS5ct75lu2e/fuKKXMHppWECLCihUrCAwMZPHixbi6utKzZ08yMjLyf7Om5SHfKwql1CPAbIwz3tVWSjUGpotIz3zeWg04n205EmiRo8x9mfvYjrF66jUR2ZBv1KGV8i3ibNats9xrq1u34jEEiVa8/fPPP4wYMYJNmzYB0Lp1axYvXkxwcLCDI9NKAluqnl7DeHWwBUBE9iulahfi/usD7YHqwFalVIiIXM9eSCk1BBgCGMe+35NjWPFfnyykcBxPDymuFVRaWhodOnQgMjKSihUrMmvWLAYPHoyLix4JQCsctvwlpYlIXI51tpzNojAOAZKleua67CKBtSKSJiKngeMYE4f5zkSWikiYiIRVqlTyriY07XZkfalwd3fnjTfe4JlnniEiIoLnnntOJwmtUNny1/S3Uqo/4KqUqq+Ueh/YYcP7dgP1lVK1lVKlgCeBtTnKrMZ4NYFSyg9jVdQpW4MvbtLTDWRkGBwdhlbCRUdHM2DAAGbMmGFaN3DgQD7++GP0FynNHmxJFC9gnC87BViBcbjxfOejEJF0YBTwM3AU+FpE/lZKTVdKZbVv/AxcUUodAX4DxjvzMCG//HISH5+36NhxOZMn/8rWrcX3VhTN+RgMBlNPps8//5y5c+cSHx/v6LC0u4AtbRSBIjIZmFzQjYvIOmBdjnX/zfZcgJczH04vPDyShIRUNm8+zebNp7l2LZkHH6yZq1z37t2tNl5rmiUHDhxg2LBhhIeHA9C1a1cWLFiAt7fueq3Zny2JYo5S6h7gW+ArETls55jytDd+b7GavS677EN3AFZvtNM9nDRbpaWl8eqrr/Luu++SkZFBlSpVmDdvHn369NFdqLUiY8sMdw9lJoongCVKqXIYE8aMfN5qN0uWmvdyGjpkpcPHdxIRTp0yH4EzvzuydQ8nLT9ubm789ddfGAwGXnjhBV5//XU9JalW5FRBTlZKqRBgAtBPRErZLaq8YghQIlffN18ZM8oRoeSSlSzCwyP5669LvP12J4vf+rLW6UShWXLu3DkyMjKoXdvYC/2ff/4hLi6OsLAwB0emOTOl1F4Rua0/onwThVIqCOgH9AauAF8B3znqLurinChspROFZklaWhrz5s1j2rRptGrVio0bN+rqJa3Q3EmisKWN4iOMyaGLiFy4nZ2UdCKi/6G1O7Jz506GDRvGwYPGKWAqVqxIUlISZcqUcXBkmmZbG0WrogikQOa0d3QE/PrrKVavjmDz5jNMnfogTz5peagE3cNJy8u1a9d45ZVXWLp0KQC1a9dmwYIF/Otf/3JwZJp2i9VEoZT6WkSeUEodwvxObMfPcDfQcePXiAgjRvzE4sV7Tes2bz5tNVHoHk6aNSkpKTRu3Jhz587h7u7O+PHjmTx5Ml5eXo4OTdPM5HVFMSbzZ4+iCMRZxMQksX79CbN1v/12Jt/36fYILScPDw+ee+45fv31VxYtWkSDBg0cHZKmWWRLY/ZbIjIxv3VFRQUokWOOPenGxCTSu/fXbNt2DgBXV0VU1MtUrlw2V1ndcK1lSU5O5s033yQgIID+/Y3zc6Wnp+Pq6qrbuDS7s3evp30i0jTHuoOOqnoqDokCIDU1gxdeWEdYWFWeeKKh1TkndKLQADZu3MiIESM4ceIE/v7+nDlzhtKlSzs6LO0uYpdeT0qp4cAIoI5S6mC2l7yB7bezs5KkVClXlix5xNFhaMXcpUuXePnll/nyyy8BaNiwIYsXL9ZJQnMqebVRrADWA28Cr2RbHy8iV+0aVR7uP3UvVJp/a4WT3UOh3R0yMjJYsmQJkyZNIi4ujtKlSzNt2jReeuklSpVyyL2qmnbb8ho9VkTkDDASiM/2QClV0f6hOV5cXDJz5uwgJSXd0aFoTiYjI4P333+fuLg4unXrxt9//83EiRN1ktCcUn5XFD2AvRi7x2ZvbROgjh3jcri//rpI377fcPLkNVatiuC7756w2FitaVni4+PJyMjAx8eHUqVK8cEHHxAdHU2vXr10Y7Xm1KxeUYhIj8yftUWkTubPrEeJThJnz16nVatlnDxpHORv+/bzNGv2AQcOXHJwZFpxJCJ8//33BAUFMXbsWNP6Nm3a0Lt3b50kNKeX78RFSqnWSqkymc//rZSaq5SqYf/QLNtb57yxXSLrYQc1a/rw/PNmHb3w9fXC29vDLvvTnNeZM2fo2bMnvXv3JioqisOHD5OcnOzosDStUNkyw90iIEkp1QgYC5wEPrNrVMXA7NmdCQurCsCwYfezc+dz1KlTwcFRacVFWloab731Fg0aNODHH3+kXLlyzJ8/nx07duDpabmrtKY5K1sGBUwXEVFKPQrMF5FlSqnn7B2Yo3l4uPH11334888oq8NzaHenpKQkWrZsyaFDhwB48sknmTt3LlWqVHFwZJpmH7Ykinil1KvAAKCtUsoFcLdvWMVD7doVqF1bX0Vo5ry8vAgLCyMpKYmFCxfSuXNnR4ekaXZly53Z9wD9gd0isi2zfaK9iCwvigBzxVNM7sy2lb4z2/mJCMuXL6du3bq0adMGgLi4OEqVKqVvnNOchl2H8MjcQWWgWebiLkdNWgQ6UWhF6+jRowwfPpzff/+doKAg9u/fr++F0JySXScuUko9AbwNbMF4L8X7SqnxIvLt7ezwTvndKAPLD99aUUhDjl+/nszWrWfx9i5FuXIe+Pp6UauWT6FsW3M+N2/e5I033mDWrFmkpaVRqVIlXn31Vdzd74paV00zY0sbxWSgWdZVhFKqErAJcEiiqBlbEcZuubWikBLFsWOxPProStNyWFhVdu9+vlC2rTmXDRs2MHLkSE6dOgXA888/z8yZM6lY8a4YkEDTcrElUbjkqGq6gm3dap3KjRspZsvlyul7Ju5GCQkJDBgwgNjYWIKDg1m8eDGtW7d2dFia5lC2JIoNSqmfgS8zl/sBJW5uTz8/LxYs6MbIkcZD04ni7pGRkYHBYMDd3Z2yZcsyb948IiMjeemll3RVk6Zh25zZ45VSvYA2mauWisgq+4ZlXYx3Agwo/JnAmjSpQr16FXn55Z9JScmgSZN7Cn0fWvGzd+9ehg4dyqOPPsrUqVMBTJMKaZpmZLXXk1KqPjAbqAscAsaJSFQRxmaRvXs99er1FSKwbFlPKla8866PutdT8XTjxg2mTp3K/PnzMRgM1K9fn7///ltfQWglll26xyqltgHLga3AI8ADItLrtqMsJPZOFGlpGbi7uxba9nSiKF5EhG+//ZYxY8Zw8eJFXF1dGTNmDP/73/8oW1aPDqyVXPbqHustIh9kPj+mlNp3Ozsojq5evcnly4nUr18RV1fzdvnCTBJa8RIfH0+/fv1Yv349AC1atGDx4sU0btzYwZFpWvGWV6LwVEo14dY8FKWzL4uIUyaOuLhkHnhgGceOXcHNzYWqVb3p2fM+3n+/m6ND0+ysbNmypKSkUL58eWbOnMmQIUNwcSlxHfg0rdDllSguAnOzLV/KtixAB3sFZU/9+3/PsWNXAEhPN3DuXBxubvpkUVJt3bqVKlWqUL9+fZRSfPTRR3h6elK5cmVHh6ZpTsOmITyKkzttozh4MJp27T7h+nXjnAHBwf5s2TIIX1+vwgrRjG6jcIzY2FgmTJjAxx9/TMeOHdm4caOeQEi7q91JG4XTfZUOiqwMHb+69Sig0NDKrFvXHy8vd1q2rM7vvz9jtyShFT2DwcBHH31EQEAAH3/8MaVKlaJt27ZkZGQ4OjRNc1q23HB325RSXYF5gCvwoYjMtFKuN8YhQZqJyJ68tumVWgoOxtxRXK1a3cvmzQNp2NCfsmX1AG8lxd9//83w4cPZtm0bAB07dmThwoXcd999Do5M05yb3RKFUsoVWAB0AiKB3UqptSJyJEc5b2AM8GdhxyAiVqsbWrSoXti70xwoLi6Oli1bkpCQgL+/P3PnzqV///66uknTCoEtc2arzLmy/5u5XEMp1dyGbTcHTojIKRFJBVYCj1oo9zrwFlCoEw0nJqbSrt0nnD17vTA3qxUzWW0/5cuXZ+LEiQwbNoyIiAiefvppnSQ0rZDY0kaxEGgFPJW5HI/xSiE/1YDz2ZYjM9eZKKWaAveKyE95bUgpNUQptUcptedotUuw6YlbDwtEhDFjNrBt2zkGDlxNRobBhnA1ZxIVFUWfPn34/PPPTesmT57MokWLqFBBz0qoaYXJlkTRQkRGkvmNX0SuAXdcsZ85pepcYGx+ZUVkqYiEiUhYkkcaNPK/9chdllde2cSyZX8BsHXrWebM2YnBoHsdlQTp6enMmzePwMBAvvvuO6ZNm2ZqqNZXEJpmH7YkirTM9gYB03wUtnxFjwLuzbZcPXNdFm8gGNiilDoDtATWKqVuq/tWlps30/nttzNm6z7//CBpabrXi7PbvXs3LVq04MUXXyQhIYHHHnuM33//HVdXfTe9ptmTLYniPWAV4K+UegP4A/g/G963G6ivlKqtlCoFPAmszXpRROJExE9EaolILSAc6Jlfr6f8eHm5s2HDvwkNNd5QVb68B5999jgeHnbt4KXZUWJiIqNGjaJFixbs27ePGjVqsGbNGlatWsW9996b/wY0Tbsj+SYKEfkCmAC8ifFu7cdE5Bsb3pcOjAJ+Bo4CX4vI30qp6UqpnncWdt4qVizNxo0DaNOmBps3D6JRIz1kuDNzc3Nj06ZNuLi4MH78eI4cOULPnnb9E9I0LZt878xWStWwtF5EztklonwU5M7svLrHFhV9Z/btOXnyJD4+Pvj6+gLGaidPT09CQkIcHJmmOSd735n9E/Bj5s9fgVPA+tvZWWHwSnGHA5dvPfLg6CShFVxKSgozZswgODiYiRMnmtY3a9ZMJwlNcxBbZrgz++/M7NI6wm4R5SMo6h54+OtbK2JGOSoUrZBt2bKF4cOHExERARh7OGVkZOjGak1zsAKP9ZQ5vHgLO8Ry2wwG4euv/6ZDh09JSkpzdDhaAV2+fJlBgwbx0EMPERERQUBAAJs3b+aTTz7RSULTioF8ryiUUi9nW3QBmgIX7BZRAe3bd5FBg1Zz+LCxGmrRot2MHfuAg6PSbBUbG0tQUBBXr17Fw8ODyZMnM2HCBDw8PBwdmqZpmWxpzJ6WbTEdOAN8JyKFOuSGrRqUuUeOtJxnDMYgVD8aSXR0oul1Pz8vTp8eU2wG+9ON2fl79tlniYyMZOHChdSrV8/R4WhaiWSXObMzN+wKvCUi4243uMKWvdfTsWOxBAaajybi4qJYvbofjzwS4IjwctGJwlxiYiLTp0+ne/fuPPjggwAkJyfj4eGhOx9omh3ZJVEopdxEJF0ptVNEWt1RhIUoZ/fYmzfTuHw5kfPnb/DGG9uYN68r993n68AIzelEccsPP/zAqFGjOHfuHA0bNuTgwYN6KlJNKyL2ShT7RKSpUmoRxsH8vgFMdTwi8v3t7PBOWbuPojjcM2GJThRw/vx5xowZw6pVqwBo0qQJS5YsoVmzZg6OTNPuHva+j8ITuIJxjuwewCOZP4uV4pAkunfvjlLK7HE3S09PZ+7cuQQFBbFq1SrKli3Lu+++y65du3SS0DQnktcVRSTG0V0VxgEBs5/1RETm2j88C3Flu6IwGAQXl+JzMraWGLp168ZPP+U5knqxlpaWRmRkJMnJBeu/YDAYiIqKwmAw4OXlRYUKFXBz02NuaZo9eXp6Ur16ddzd3c3W38kVRV7/ta5AWcwTRBbH1aOkujJjxlZWrjzMCy80Z+jQOxps1i5KWjVTZGQk3t7e1KpVK9+rpPT0dFxcXExtD1WrVkUphY+PT1GEqml3NRHhypUrREZGUrt27ULbbr5tFIW2p0KiVFWBoQC0r16e386/6JA4unfvzrp16yy+VtISxdGjRwkMDMwzSYgIV69e5fz58/j7+1O1atUijFDTtCwiQkREBEFBQWbr7XVFUXzqdKz4PTKOCxfiqVrVu8j3bS1JdOvWrYgjKRp5JYnk5GTOnj1LfHw8AAkJCcW2c4GmlXT2+L/LK1F0LPS9FQIvFEmZz12A8PBIevUKyustdlXSrh4KwmAwcOnSJS5evIiI4ObmRvXq1fH19dVJQtNKEKu9nkTkalEGYquKLor27m4sLlOGixUrODRJ3M3S0tL4+++/uXDhAiKCr68vDRs2xM/Pr9CThKurK40bNyY4OJhHHnmE69evm177+++/6dChAwEBAdSvX5/XX3/dLHmvX7+esLAwGjRoQJMmTRg7Nt+Zd4uNp556itDQUN555x2bypctW9YucYgIo0ePpl69eoSGhrJv3z6L5fbu3UtISAj16tVj9OjRVr9EvfvuuyxfvtwusRaG06dP06JFC+rVq0e/fv1ITU3NVSYtLY1BgwYREhJCUFAQb775pum1WrVqERISQuPGjQkLu1XTM27cODZv3lwkx1DoRMSpHn73lBH59NCth4NgbNB32P6L0pEjR3KtMxgMEhERIYcOHZIbN27Ydf9lypQxPR84cKDMmDFDRESSkpKkTp068vPPP4uISGJionTt2lXmz58vIiKHDh2SOnXqyNGjR0VEJD09XRYuXFiosaWlpRXq9rJcvHhR6tatW6D3ZP+cCtNPP/0kXbt2FYPBIDt37pTmzZtbLNesWTPZuXOnGAwG6dq1q6xbty5XmbS0NAkJCSnQ52avz9iavn37ypdffikiIkOHDrX4N/PFF19Iv379RMT4d1ezZk05ffq0iIjUrFlTYmJicr3nzJkz0qlTJ/sFno2l/1lgj9zmedfhJ/4CB3xf8Tg5322JwmAwyOXLl03HXdiPvGQ/AS5atEiGDx8uIiIffvihDBgwwKzsiRMnpHr16iIiMmDAAFm2bFm+xxcfHy/PPPOMBAcHS0hIiHz77be59vvNN9/IoEGDRERk0KBBMnToUGnevLm89NJLUrNmTbl27ZqpbL169eTSpUty+fJl6dWrl4SFhUlYWJj88ccfufZ98+ZN074bN24smzdvFhGRkJAQ8fT0lEaNGsnWrVvN3nPp0iV57LHHJDQ0VEJDQ2X79u1m8cbHx0uHDh2kSZMmEhwcLKtXrxYRkYSEBOnWrZuEhoZKw4YNZeXKlSIiMnHiRAkKCpKQkBAZO3ZsrhiHDBkiK1asMC3fd999cuHCBbMyFy5ckICAANPyihUrZMiQIbm29fPPP5s+RxGRpUuXSlhYmISGhkqvXr0kMTHR4md84sQJ6dKlizRt2lTatGljSv5r166V5s2bS+PGjaVjx45y6dKlXPssCIPBIL6+vqbktGPHDuncuXOucitWrJAePXpIWlqaxMbGSv369eXKlSsiYj1RiIg0bdpULl68eEcx2qKwE4Xu1K7lKzU1lYiICBITE/MvbEcZGRn8+uuvPPfcc4Cx2un+++83K1O3bl0SEhK4ceMGhw8ftqmq6fXXX6d8+fIcOnQIgGvXruX7nsjISHbs2IGrqysZGRmsWrWKwYMH8+eff1KzZk0qV65M//79eemll2jTpg3nzp2jS5cuHD161Gw7CxYsQCnFoUOHiIiIoHPnzhw/fpy1a9fSo0cP9u/fn2vfo0ePpl27dqxatYqMjAwSEhLMXvf09GTVqlWUK1eO2NhYWrZsSc+ePdmwYQNVq1Y13dMTFxfHlStXWLVqFRERESilzKr1skRFRZnNTV69enWioqKoUqWKWZnq1avnKpPT9u3bzX5nvXr14vnnnwdgypQpLFu2jBdeeCHXZ9yxY0cWL15M/fr1+fPPPxkxYgSbN2+mTZs2hIeHo5Tiww8/ZNasWcyZM8dsn8eOHaNfv365YgHjHCjZu25fuXIFHx8f0/0+1o6jT58+rFmzhipVqpCUlMQ777xDxYoVAWNjcufOnVFKMXToUIYMGWJ6X9OmTdm+fTu9e/e2GE9xpROFZlVCQgKvvfYaDz/8MH5+fri7u3PlyhUqVKhQpI3VN2/epHHjxkRFRREUFESnTp0KdfubNm1i5cqVpuUKFSrk+56+ffua5sro168f06dPZ/DgwaxcudJ0Utq0aRNHjhwxvefGjRskJCSYtSX88ccfphNjYGAgNWvW5Pjx45QrV87qvjdv3myq43d1daV8+fJmr4sIkyZNYuvWrbi4uBAVFUV0dDQhISGMHTuWiRMn0qNHD9q2bUt6ejqenp4899xz9OjRgx497DvowsWLF826bR4+fJgpU6Zw/fp1EhIS6NKli+m1rM84ISGBHTt20LdvX9NrKSkpgDGZ9OvXj4sXL5Kammrx3oGAgACLCfdO7Nq1C1dXVy5cuMC1a9do27YtDz/8MHXq1OGPP/6gWrVqXL58mU6dOhEYGGgaANPf358LF4rNLA020yOyaRatXr2aoKAg07czf39/goODqVixYpH3aCpdujT79+/n7NmziAgLFhhHDG7QoAF79+41K3vq1CnKli1LuXLlaNiwYa7XCyL7cea8K71MmTKm561ateLEiRPExMSwevVqevXqBRh7hYWHh7N//372799PVFSU3Rqcs/viiy+IiYlh79697N+/n8qVK5OcnMx9993Hvn37CAkJYcqUKUyfPh03Nzd27dpFnz59+PHHH+natWuu7VWrVo3z58+bliMjI6lWrVquMpGRkXmWAePvMvtn+cwzzzB//nwOHTrEtGnTzF7L+owNBgM+Pj6mz3H//v2mK7MXXniBUaNGcejQIZYsWWJx9IBjx47RuHFji4+cV1C+vr5cv36d9PT0PI9jxYoVdO3aFXd3d/z9/WndujV79uwxfRZg/J95/PHH2bVrl+l9ycnJlC5dOtf2ijudKLRcoqKiePLJJ4mMjOT+++/nnnvuoUaNGg6fbc7Ly4v33nuPOXPmkJ6eztNPP80ff/zBpk2bAOOVx+jRo5kwYQIA48eP5//+7/84fvw4YDzhLF68ONd2O3XqZEo+cKvqqXLlyhw9ehSDwWAa0NASpRSPP/44L7/8MkFBQfj6Gkcv7ty5M++//76pnKVvtW3btuWLL74A4Pjx45w7d46AgLyHyO/YsSOLFi0CjNVxcXFxZq/HxcXh7++Pu7s7v/32G2fPngXgwoULeHl58e9//5vx48ezb98+EhISiIuLo1u3brzzzjscOHAg1/569uzJ8uXLERHCw8MpX768WbUTQJUqVShXrhzh4eGICMuXL+fRRx/Nta2goCBOnDhhWo6Pj6dKlSqkpaWZPoecypUrR+3atfnmm28A4xVTVpxxcXGmE/Onn35q8f1ZVxSWHjlHDFBK8dBDD/Htt9+atmnpOGrUqGHqwZSYmEh4eDiBgYEkJiaa7idKTEzkl19+ITg42PS+48ePmy07jdtt3HDUQzdm20dqaqoYDAbT8uzZs+W9996T9PR0iw1jRSlnb54ePXrI8uXLRUTk4MGD0q5dO7nvvvukbt268tprr5kdxw8//CBNmzaVwMBACQoKkvHjx+fafnx8vAwcOFAaNmwooaGh8t1334mIsQG7Tp060qJFCxk5cqRZY/Y333xjto3du3cLIJ988olpXUxMjDzxxBMSEhIiQUFBMnTo0Fz7ttaYffr0aWnYsKHFz+PSpUvSs2dPCQ4OlkaNGsmOHTvMPqeYmBhp2bKlBAcHyzPPPCOBgYFy+vRp2bBhg4SEhEijRo0kLCxMdu/eLRcuXJBmzZpJSEiIBAcHm8WfxWAwyIgRI6ROnToSHBwsu3fvNr3WqFEjs8+gYcOGUqdOHRk5cqTZ7yHLmTNnpG3btqblhQsXSq1ataRZs2YyatQoq5/xqVOnpEuXLhIaGipBQUHyv//9T0REVq9eLbVr15amTZvKuHHjpF27dhY/s4I4efKkNGvWTOrWrSt9+vSR5ORkERFZs2aNTJ06VUSMfzN9+vSRBg0aSFBQkMyaNcv03qxOBg0aNDD10BMx/o8FBgYWSS+uwm7MzneGu+ImzL2G7PGZcGtFzCiHxFGShg/fsWMHw4YNY/z48QwYMCDX60ePHs01HICm3a7HH3+cWbNmUb9+fUeHUqRWrVrFvn37eP311+2+L0v/s/YeZvyuV1KHD7969SpDhw6ldevWHDp0iIULF5aIxKcVbzNnzuTixYuODqPIpaenO9UNn9npRGGDkjauk4jw2WefERgYyNKlS3F3d2fy5Mls3ry5xCRBrfgKCAgw9QK6m/Tt29dpR1HW3WMLoCR8246Ojuapp57it99+A6Bdu3YsWrRIVy1pmmaV0yWKvXXOwzHHtEuUBD4+Ply8eBE/Pz9mz57NwIED9VWEpml5crpEoRXcxo0badq0Kb6+vnh4ePDNN99QpUoVUzdOTdO0vOg2ihLs4sWLPPXUU3Tu3JmJEyea1gcHB+skoWmazXSiKIEyMjJYuHAhgYGBrFy5ktKlSxMQnGiBOAAAIABJREFUEOC0bSx6mHHHDjMeERFBq1at8PDwYPbs2VbL2TI8Nxjv+p8+fbpdYi0MV69epVOnTtSvX59OnTpZHftrwoQJNGzYkKCgILNh1du3b09AQIDp7u/Lly8DMH/+fD766KMiO45Cdbs3YDjq4Ygb7nCim+v27t0rzZo1M8XcvXt30/DHt6s43XCnhxm3zl7DjEdHR8uuXbtk0qRJ8vbbb1stZ8vw3CIirVq1sjq6qiVFPcz4+PHj5c033xQRkTfffFMmTJiQq8z27dvlgQcekPT0dElPT5eWLVvKb7/9JiIi7dq1M7spMUtiYqI0btzYrrFnKewb7pzuisLvRhlYftj4sANnvmfizJkzNG/enN27d1OtWjW+++47fvjhB2rVqlVo+1C/Krs8bNWqVSvTaJ4rVqygdevWdO7cGTAO8TF//nxmzpwJwKxZs5g8eTKBgYGA8cpk+PDhubaZkJDA4MGDCQkJITQ0lO+++w4w/4b+7bff8swzzwDG8YmGDRtGixYtmDBhArVq1TK7yqlfvz7R0dHExMTQu3dvmjVrRrNmzdi+fXuufScnJ5v23aRJE1NvtM6dOxMVFUXjxo3Ztm2b2Xuio6N5/PHHadSoEY0aNWLHjh25jqdjx440bdqUkJAQ1qxZAxiHlOjevTuNGjUiODiYr776CoBXXnmFBg0aEBoayrhx43LF6O/vT7NmzXB3d7f4OwHjF87NmzfTp08fAAYNGsTq1atzlTt+/DgeHh74+fkB8MMPP9CiRQuaNGnCww8/THR0NACvvfYaAwYMoHXr1gwYMMDqZ7lr1y5atWpFkyZNeOCBBzh27JjVGG21Zs0aBg0alOdxKKVITk4mNTWVlJQU0tLSqFy5cp7b9fLyolatWmZjPzkLuzZmK6W6AvMAV+BDEZmZ4/WXgf8A6UAM8KyInM1rmzVjK8LYLcaFgYU/Zooz3zNRq1YtBg8ejLe3N//73//w9i76ucTtSQ8zblTUw4zbwtbhubdv307Tpk1Ny3kNE37kyBH++OMPSpcubfWzDAwMZNu2bbi5ubFp0yYmTZpkSvRZ4uPjadu2rcW4V6xYQYMGDczWRUdHm8ayuueee0zJK7tWrVrx0EMPUaVKFUSEUaNGmXUxHzx4MK6urvTu3ZspU6aYvnCGhYWxbds2mjdvnu9nWpzYLVEopVyBBUAnIBLYrZRaKyJHshX7CwgTkSSl1HBgFmB54PgiJk5Qn3/mzBleeOEFxo0bR7t27QBYunSpXa+CpGPRfy56mHFzzj7MeKVKlUzLeQ0T3rNnT9NIq9Y+y7i4OAYNGsQ///yDUoq0tLRc+/T29r7tYcat1SqcOHGCo0ePmkbM7dSpE9u2bTMN8litWjXi4+Pp3bs3n332GQMHDgSMV2cRERG3FYsj2bPqqTlwQkROiUgq/H97Zx5WVbn98c8bmjhramaGSoIio4Cg6M15tkuD86w5ZGamlXMl2WAOcX+m9piWUlpp2dVLNpiWQ9fUwKnrUNYNrqmYIKgoiiDr98c+Z8twDhwVOIDv53n24977vHu/6yw3Z+13+i7WAjlkGEVkm4ikWQ73AA+gKZCMjAzmzZuHt7c3mzZtYvr06eZnpamrzFG0zPjNUdgy447gqDx3bpnx/GTCs/vYni9feuklOnTowOHDh/niiy9syoynpqbalRnPHnys1K1b15QYSUhI4N57781TZsOGDbRq1YoqVapQpUoVevTowe7du4EbMuNVq1Zl0KBBWma8AOoDf2Y7Pmk5Z49RwNe2PlBKjVVKxSqlYhOrXoKh3sZ2B/Lvf/+bwMBApk+fzpUrVxgwYAD//Oc/nW1WsaBlxg2KW2bcERyV584tM+6ITDjY92X266Oiomxea21R2NpydzuB0ZKx2pKfzPiOHTvIzMwkIyODHTt20KxZMzIzM0lKSgKMF7pNmzZpmfH8NqAPxriE9XgosMRO2SEYLYoKBd63iGc9UUJnOCUnJ8uoUaNM+xo3bmzO9ilqStKsJxEtM17cMuMJCQlSv359qVq1qlSvXl3q168vFy5cEBGRHj16yKlTp0TEvjx3di5fvize3t7m/5E9mfDZs2fnmGFlz5c//vijeHp6SvPmzWXWrFnSsGFDmz67GZKSkqRjx47i4eEhnTp1MnNhx8TEyKhRo0TEmEE3duxY87maPHmyiBh5yYOCgsTPz0+8vb1l4sSJkpmZad47MDBQkpKSbtvGgijsWU9FGSjCgM3ZjmcAM2yU6wwcA+516L53aKBISkqS2rVrS/ny5eWll16StLS0Yqvb2YFCU7aYOHGibNmyxdlmFDv79++XIUOGFEtdhR0oinLWUwzgqZRyB04BA4BB2QsopQKBd4HuInK2CG0plfzyyy+4u7tToUIFatWqxUcffUSDBg3M6Z4aTWlk5syZ7N2719lmFDtJSUnFkouiKCiyMQoRyQQmAJsxWgyfisgRpdQcpVS4pdgCoArwmVLqoFIquqjsKU2kpaUxa9Ys/P39mT9/vnm+a9euOkhoSj1169YlPDy84IJljC5duhTqmqbipEjXUYjIV8BXuc69nG2/c1HWXxr55ptvGD9+PHFxcQDmwJhGo9E4C60eW0I4ffo0kyZNMhPI+/n5sWzZMlq3bu1kyzQazZ1OqQsUzU7WhU6G9ADflYi1ebfN8ePHadGiBampqVSqVImIiAgmTZqUr2SCRqPRFBelLlBUunY3/JzobDMKFU9PT0JCQqhcuTKLFy+mYcOGzjZJo9FoTEqdKGBZ4OLFi0yaNMlcCKaUIjo6mujoaB0kbpHo6GhTDPBOJioqijp16tC8eXO8vLzySJQvX74cLy8vvLy8CA0N5d///rf5WUZGBtOnT8fT05OgoCDCwsL4+muba2CdyqRJk9i5c6ezzbDLvn378PPzw8PDI4f8eHZSUlJ47LHH8Pf3JzQ0lMOHb4ic/uMf/8DHxwdfX18GDhxorjYfMGAAv/32W7F9jxzc6rxaZ23B5dxEai82tiKAIlxHkZWVJZ9++qnUq1dPAOnWrVuR1FPY5JmTbfV/Qf8PH/wnZ7nJ3xWtoQ6SlZUl169fd1r9RSmbvWrVKnn66adFxFh7U6tWLTlx4oSI3Fh8aJX43rdvn7i5uUlCQoKIiEybNk2GDRtmLpQ7c+aMrFu3rlDty7747FZISkqSli1b3tQ1xS1THhISIrt375asrCzp3r27fPXVV3nKvPDCCxIRESEiIseOHZOOHTuKiMjJkyelUaNG5jqpvn37yqpVq0REZPv27TJ69GiHbLjjZcaP1T8DW/sZ221SnJLif/zxB7169aJfv34kJCTQqlUr5s2bV2T1lRXi4+Px8vJixIgRNGnShMGDB7N161batGmDp6enqaMTFRXFhAlGLnVbMtzx8fE0bdqUYcOG4evry59//smUKVPw9fXFz8/PlNzOjT0Z61atWnHkyBGzXPv27YmNjeXy5cs88cQThIaGEhgYaEp8R0VFER4eTseOHenUqZNdKXAw1GybNm3K3/72NwYOHGgmC/rvf/9L9+7dCQ4O5qGHHipQXK5WrVp4eHiYukXz5s1jwYIFpsR3UFAQw4cPZ+nSpaSlpbFixQoWL15MhQoVAGMaa79+ef/OYmJiaN26NQEBAYSGhpKamprD/wAPP/ww27dvBwy59ueff56AgADmzp1L3759zXLbt283hQi//fZbwsLCCAoKom/fvnlUcQE+//zzHHpUc+bMISQkBF9fX8aOHWu+vbdv355JkybRokULFi1axL59+2jXrh3BwcF069bN9MmKFSsICQkhICCA3r17k5aWlqfOmyEhIYGLFy/SqlUrlFIMGzbMpkz50aNH6dixI2CIQcbHx5sqtZmZmVy5coXMzEzS0tK4//77AUPuZevWraaeVrFyqxHGWVthrszG0nrIvfXs2bPQ6khPT5fXX39dXF1dBZAaNWrIsmXLnPpGe7M4s0URFxcnLi4u8vPPP8v169clKChIRo4cKVlZWbJx40Z55JFHRCTnm3S/fv3kH//4h4gYb7Dnz5+XuLg4UUrJ7t27RURk/fr10rlzZ8nMzJQzZ86Im5ubnD59Ok/9Fy5cMN9It2zZIo8//riIiERGRsrLL78sIiKnT5+WJk2aiIjIjBkzZPXq1SIikpKSIp6ennLp0iVZtWqV1K9f35SDyMjIMGUwEhMTpXHjxpKVlSU//fSTBAQEyJUrV+TixYvi4eFhSll07NhRjh8/LiIie/bskQ4dOuSxN7sf/ve//5n3EhGpWbOmnD9/Pkf5jRs3ymOPPSaHDh1yKKlOenq6uLu7y08//ZTDP9nrFRHp1auXmcgHMFsmGRkZ4ubmJpcuXRIRkXHjxsnq1aslMTFRHnroIfP8m2++Ka+88kqe+ocNGybR0dHmsdWfIiJDhgwxP2vXrp089dRTIiJy7do1CQsLk7Nnz4qIyNq1a2XkyJEiIjnkNGbNmiVvv/12njq///57CQgIyLOFhYXlKRsTEyOdOnUyj3fu3Cm9evXKU27GjBkyadIkERHZu3evuLi4SGxsrIiI/N///Z9UrlxZateuLYMGDcpxXefOnc1y+VGaVmaXGsRGH2Jh8eeffzJnzhzS09MZPHgwb731VoEJTjQ5cXd3x8/PDwAfHx86deqEUgo/Pz/i4+PzlLclw52SkkLDhg1p1aoVYIgrDhw4EBcXF+rWrUu7du2IiYnJsxDMnox1v3796Nq1K6+88gqffvqpmbDn22+/JTo62mwFXL16lRMnTgDGgqt77rkHsC8FvmvXLh555BFcXV1xdXXl73//O2AkI/rxxx9zvI2np6fb9Ne6devYuXMnv/zyC0uWLMHV1fXmnW6HX3/9lXr16hESEgKQrxy6FWteBoBy5crRvXt3vvjiC/r06cOXX37J/Pnz2bFjB0ePHqVNmzYAXLt2jbCwsDz3yi1Tvm3bNubPn09aWhrJycn4+PiYPrPKvf/6668cPnzYlKe/fv26mW/i8OHDvPjii5w/f55Lly7RrVu3PHV26NDhlmXK7TF9+nSeffZZmjdvbiatcnFxISUlhX/961/ExcVRo0YN+vbty5o1axgyZAhgyJSfPn06Tx6WokYHiiIgJSWFGjVqoJSicePGLFq0CA8PDzp16uRs0wqHxAkFlwEjsVQhJJeydoUA3HXXXebxXXfddVPN8Oyy1fZYunQpK1asAIwkVlYZ6w0bNhAfH0/79u0BQ0q6Vq1a/Pzzz6xbt85UpRURPv/88zwKsHv37s1Rf3Yp8PLly9OoUSObEtlWsrKyqFGjhkM/WP3792fJkiXExsbStWtXwsPDue+++0xZdmuXBxgDrz4+Pnh4eHDixAkuXrzo0I9/bsqVK0dWVpZ5nP27uLq6mrk7wBiUXbJkCffccw8tWrSgatWqiAhdunThk08+ybee7DLlV69eZfz48cTGxuLm5kZERIRNmXIRwcfHx5QBz86IESPYuHEjAQEBREVFmd1l2dm2bRuTJ0/Oc75SpUp5sgvWr1/fzFEB9uXWq1WrxqpVq0z73N3defDBB9m8eTPu7u5mMHz88cf58ccfzUDhLJnyUjdGUZLJyspi5cqVeHh4sGbNGvP8k08+WXaCRCmgIBluMPp7161bx/Xr10lMTGTnzp2Ehoby9NNPmxLU999/f74y1v3792f+/PlcuHABf39/ALp168bixYvNVuqBAwds2mhPCrxNmzZmXoVLly6xadMmwPhhcXd3NxdkikiBkuAtWrRg6NChLFq0CICpU6cybdo0zp07BxhS3VFRUYwfP55KlSoxatQonn32Wa5duwZAYmKiWZ+Vpk2bkpCQQExMDGDkesjMzKRRo0YcPHiQrKws/vzzz3zTfbZr1479+/ezYsUKBgwYABhjPrt27TIlyC9fvmzOCsxOdplya1CoXbs2ly5dMiXOc9O0aVMSExPNQJGRkWGOL6WmplKvXj0yMjJMuffcWFsUubfcQQKgXr16VKtWjT179iAifPjhhzZlys+fP2/6+b333qNt27ZUq1aNBg0asGfPHtLS0hARvvvuuxyZ85wlU64DRSFx5MgR2rdvz6hRo0hOTi6R0wrvFBYtWsS2bdvw8/MjODjYZnIa69TEgIAAOnbsyPz587nvvvvylJs6dSozZswgMDAwT+ulT58+rF27NseA70svvURGRgb+/v74+Pjw0ksv2bRx8ODBxMbG4ufnx4cffmhqeIWEhBAeHo6/vz89evTAz8/PzGD30Ucf8f777xMQEICPj0+OAXB7TJs2jVWrVpGamkp4eDhPPPEErVu3xsvLizFjxrBmzRqzG+a1116jTp06eHt74+vry8MPP5yndXH33Xezbt06nnnmGQICAujSpQtXr16lTZs2uLu74+3tzcSJE3OkO82Ni4sLDz/8MF9//bU5kF2nTh2ioqIYOHAg/v7+hIWF2Rys79Wrl/nWX6NGDcaMGYOvry/dunUzu8Nyc/fdd7N+/XqmTZtGQEAAzZs3N3/kX331VVq2bEmbNm0KTUftnXfeYfTo0Xh4eNC4cWN69OgBwLJly8yW57Fjx/D19aVp06Z8/fXXZjBv2bIlffr0MSc5ZGVlMXbsWMCYpFGxYkWbz2mRc6uDG87aKjUsL3LwL2O7TSiEqbCXL1+W6dOnS7ly5QSQe++9Vz766KMcORFKO1pmvHhJTU0VEePZCg4Oln379jnZopJFmzZtJCUlxdlmFDuRkZHy3nvvOVT2jh/MbnbqPuj8qXHgaF85xpvIV199VXDBm+D48eN069aN+Ph4lFKMGzeON954w6GcyxqNPcaOHcvRo0e5evUqw4cPz/ft/E7krbfe4sSJE9SoUcPZphQrNWrUYOjQoU6pu9QFCke4maDQs2fPW66nYcOGuLq6EhAQwLJly8wZNRrN7fDxxx8724QSTcuWLZ1tglMYOXKk0+ouk2MU9oJEz5498zSpvvzyS4fvm5mZyZIlS8zBwAoVKvDNN98QGxurg4RGoymzlLoWRdrd18C/TsEFKdz1ET/99BPjxo3jwIEDHDx4kPfeew9AazNpNJoyT6kLFMce+KtY5cUvXLjArFmzeOeddxARGjRoYHO6m0aj0ZRVymTXU2EgIqxduxYvLy+WLl2Ki4sLU6dO5ejRo+bKT41Go7kT0IHCDocOHWLgwIGcOXOG1q1bs3//fubNm+fQ6l6NpiQQHx9PxYoVad68Od7e3gwbNsyUIAFDxiQ0NNSUHV++fHmO6z/88ENTNDEwMNCUJSlJbNy4kTlz5jjbDLskJyfTpUsXPD096dKlCykpKTbLTZ06FR8fH5o1a5ZDmvzatWuMHTuWJk2a4OXlxeeffw7AkiVLWLlyZbF9D6evi7jZzRFRQG5xfURuCeTJkyfLihUrSpWAX1GQe042ROTY7PHuu7E5yo0ZE223rDO5Xenr26EoJc/j4uLEx8dHRIzv2KFDB1mzZo2IiCQkJIibm5u5RiMxMVGCgoJk06ZNIiLy1VdfSWBgoJw6dUpERK5evSrLly8vVPsKQ/47LCzMlE0vrjpvhilTpsjcuXNFRGTu3LkyderUPGV27dolrVu3lszMTMnMzJRWrVqZgoovv/yyzJo1S0RErl+/bn7Xy5cv5yvieMfLjBcV27Ztw9fXN0dClMjISEaPHs1dd2k3OQtHZcbtyYFfv36dF154AV9fX/z9/Vm8eDEAjRo1Ytq0aQQFBfHZZ5/xySef4Ofnh6+vL9OmTbNpiz1p8OnTp7N06VKzXEREhPn2vWDBAkJCQvD392f27Nnmd8otef7UU0/RokULfHx8zHJgzODz8vIiODiYiRMnmiuZ7cmZ28PFxYXQ0FBOnToFGJpWI0aMMNdo1K5dm/nz55vJn+bOncvChQtNiesKFSowZsyYPPe1J+meXWZi4cKFREREADnlv19//XUaNmxoakRdvnwZNzc3MjIyHJJUP378OBUqVDBl07/44gtatmxJYGAgnTt3NmW7IyIiGDp0KG3atGHo0KEkJibSu3dvQkJCCAkJYdeuXYD9Z+h2+Ne//sXw4cMBGD58uE3JcaUUV69e5dq1a6Snp5ORkWEKh65cuZIZM2YAhraZ9btWqlSJRo0a5SuVUqjcaoRx1tagZs0cctU9e/a0KxfuCH/99ZcMGzbMvMYqW625gTNbFI7KjNuTA3/nnXekd+/e5mdWWeqGDRvKvHnzRETk1KlT4ubmJmfPnpWMjAzp0KGDbNiwIY8t9qTB9+/fL23btjXLNWvWTE6cOCGbN2+WMWPGmK2GXr16yY4dO/JInme3KzMzU9q1ayeHDh2SK1euyAMPPCB//PGHiIgMGDDAlKy2J2ee23fWFsWVK1ekffv2cujQIREReeyxx2Tjxo05yp8/f15q1qwpIrYlyW1hT9LdWq+IyIIFC2T27NkiklP+W0QkPDxcvv/+exEx5L9HjRolIo5Jqq9cuVKee+458zg5OdlURFixYoX52ezZsyUoKMhMBjRw4ED54YcfRMSQYvfy8hIR+89Qdi5evGhTcjwgIECOHDmSp3z16tXN/aysrBzH2Xn++eelevXqUq1aNZk5c6aIGP+vDzzwgEyePFkCAwOlT58+cubMGfOa1157TRYuXGjzfnf8yuw6qVVg9VGINBQw81szkR9ZWVm8//77TJs2jZSUFCpUqMCLL77IlClTCt1mze3hiMy4PTnwrVu3Mm7cOMqVMx51q8w33JChjomJoX379qZi5+DBg9m5cyePPvpoDjtEbEuDBwYGcvbsWU6fPk1iYiI1a9bEzc2NRYsW8e233xIYGAgYLZLffvuNBg0a5JA8B/j0009Zvnw5mZmZJCQkcPToUbKysnjwwQdxd3cHYODAgeY4gj058+wCcmAkO2revDlxcXH06tXLFC8sLOxJuueH1e/W/XXr1tGhQwfWrl3L+PHjHZZUzy05fvLkSfr3709CQgLXrl0z/QYQHh5uqq5u3bo1h/7XxYsXuXTpkt1nKDtVq1a9Zclxe8nRfv/9d44dO2aqznbp0oUffviBZs2acfLkSVq3bk1kZCSRkZG88MILrF69GjAkxwtKXlVYlLpAYQ+5iTUTcXFxDBkyxBQG69q1K0uXLsXDw6OozCtTiMwuuBAwdmwwY8fevm6+IzLj9uTA86OgiQl79+7lySefBIxMasnJyXalwfv27cv69es5c+aM+UMoIsyYMcO8h5X4+PgcdcfFxbFw4UJiYmKoWbMmI0aMyFdy3HpvW3LmuWncuDEHDx4kKSmJNm3aEB0dTXh4uCk5nn2qt1VyHIyAnFuS3FHykxyHnH4PDw9n5syZJCcnm/VdvnzZIUn1ihUr5lAGfuaZZ3juuecIDw9n+/btZndX7jqzsrLYs2dPnjwdEyZMKPAZSk1N5aGHHrJpz8cff4y3t3eOc3Xr1iUhIYF69eqRkJDAvffem+e6DRs20KpVK6pUqQJAjx492L17N3/729+oVKkSjz/+OGA8Y++//755XXFKjt+Rne/VqlXj+PHj3Hfffaxdu5ZvvvlGB4lSjj058C5duvDuu++aASU5OTnPtaGhoezYsYOkpCSuX7/OJ598Qrt27WjZsqUpKR0eHm5XGhyMN+O1a9eyfv168024W7durFy50kzpeerUKc6ePZun/osXL1K5cmWqV6/OX3/9ZSoPN23alD/++MNsNWVP1+qonLmV2rVr8+abbzJ37lwAnn76aaKioswf43PnzjFt2jSmTp0KwIwZM5gyZQpnzpwBjNk31kWm2bEl6V63bl3Onj3LuXPnSE9PN6XSbVGlShVCQkJ49tlnefjhh3FxcXFYUj275DjkfAY++OADu3V27drVHKsCTB/kJylvxdqisLXlDhJgBEKrLR988IHNNVgNGjRgx44dZGZmkpGRwY4dO2jWrBlKKf7+97+barnfffddjjqKU3K81AWK/9VOZmzqJzed43rz5s1m87VWrVpER0fzyy+/0L9//yLNla0pHuzJgY8ePZoGDRqYkuK2dJTq1avHm2++SYcOHQgICCA4ONjmH7Q9aXAw3sBTU1OpX7++KdvdtWtXBg0aRFhYGH5+fvTp04fU1NQ89w0ICCAwMBAvLy8GDRpkZnmrWLEi77zzjjmoW7VqVVNy3FE58+w8+uijpKWl8cMPP1CvXj3WrFnDmDFj8PLyonXr1jzxxBPmGqGePXsyYcIEOnfujI+PD0FBQVy8eDHPPW1JupcvX56XX36Z0NBQunTpUqB8d//+/VmzZk2OLilHJNXbtm3LgQMHzGAZERFB3759CQ4ONgd9bfH2228TGxuLv78/3t7epvR3fpLyt8r06dPZsmULnp6ebN26lenTpwMQGxvL6NGjAUOuvnHjxvj5+ZmTAqz/D/PmzSMiIgJ/f39Wr17NW2+9Zd57165dZta+IudWBzectdHk5nJcnzhxQh599FEB5NVXX7VbTmMfLTPuPKyS41lZWfLUU09JZGSkky0qWUycOFG2bNnibDOKnf3798uQIUPsfq6nx5I3uNkS9svMzCQyMpJmzZqxceNGqlSpkmMgU6MpDaxYsYLmzZvj4+PDhQsX8ox33OnMnDmTtLQ0Z5tR7CQlJfHqq68WW31KbmIQuCSgmiqRX/O3ec+ePYwbN87s1+zduzeLFi2ymbtWUzDHjh3LM5tGo9GUXGz9zSql9olIi1u5X5mZ9WRl7969tG7dGhGhUaNGLFmyhF69ejnbrFKPiOixHI2mFFAUL/9lLlCEhobSrVs3AgMDefHFF6lUqZKzTSr1uLq6cu7cOWrVqqWDhUZTghERzp07l2fq7+1S6ruefvvtNyZPnkxkZCRNmjQBjHnSWnaj8MjIyODkyZMFzu3XaDTOx9XVlQceeIDy5cvnOH9HdT0F/+EGdZaQfnKMOS88PT0dV1dX1q9fD6CDRCFTvnz5HKtcNRrNnUWR/qIqpborpX5VSv2ulJpUFmIGAAAIxklEQVRu4/MKSql1ls/3KqUaOXLf7679ir+/PxEREaSnpzNy5EhzLrRGo9FoCpci63pSSrkAx4EuwEkgBhgoIkezlRkP+IvIOKXUAOAxEck3fV2tuypLshjT4Zo1a8ayZcto27ZtkXwHjUajKSvcTtdTUbYoQoHfReQPEbkGrAVyL3d9BLCutV8PdFIFjJamSBqulOeNN97g4MGDOkhoNBpNEVOULYo+QHcRGW05Hgq0FJEJ2coctpQ5aTn+r6VMUq57jQXGWg59gcNFYnTpozaQVGCpOwPtixtoX9xA++IGTUWk6q1cWCoGs0VkObAcQCkVe6vNp7KG9sUNtC9uoH1xA+2LGyilYm/12qLsejoFuGU7fsByzmYZpVQ5oDpwrght0mg0Gs1NUpSBIgbwVEq5K6XuBgYA0bnKRAPDLft9gO+ltC3s0Gg0mjJOkXU9iUimUmoCsBlwAVaKyBGl1BwMFcNo4H1gtVLqdyAZI5gUxPKisrkUon1xA+2LG2hf3ED74ga37ItStzJbo9FoNMWLXsKs0Wg0mnzRgUKj0Wg0+VJiA0VRyX+URhzwxXNKqaNKqZ+VUt8ppRo6w87ioCBfZCvXWyklSqkyOzXSEV8opfpZno0jSqm8eWDLCA78jTRQSm1TSh2w/J30dIadRY1SaqVS6qxljZqtz5VS6m2Ln35WSgU5dONbTY1XlBvG4Pd/gQeBu4FDgHeuMuOBZZb9AcA6Z9vtRF90ACpZ9p+6k31hKVcV2AnsAVo4224nPheewAGgpuX4Xmfb7URfLAeesux7A/HOtruIfNEWCAIO2/m8J/A1oIBWwF5H7ltSWxRFIv9RSinQFyKyTUSs+SD3YKxZKYs48lwAvArMA8qyLrojvhgDLBWRFAAROVvMNhYXjvhCgGqW/erA6WK0r9gQkZ0YM0jt8QjwoRjsAWoopeoVdN+SGijqA39mOz5pOWezjIhkAheAWsViXfHiiC+yMwrjjaEsUqAvLE1pNxHJm0i9bOHIc9EEaKKU2qWU2qOU6l5s1hUvjvgiAhiilDoJfAU8UzymlThu9vcEKCUSHhrHUEoNAVoA7ZxtizNQSt0FRAIjnGxKSaEcRvdTe4xW5k6llJ+InHeqVc5hIBAlIm8ppcIw1m/5ikiWsw0rDZTUFoWW/7iBI75AKdUZmAWEi0h6MdlW3BTki6oYopHblVLxGH2w0WV0QNuR5+IkEC0iGSIShyH771lM9hUnjvhiFPApgIjsBlwxBAPvNBz6PclNSQ0UWv7jBgX6QikVCLyLESTKaj80FOALEbkgIrVFpJGINMIYrwkXkVsWQyvBOPI3shGjNYFSqjZGV9QfxWlkMeGIL04AnQCUUs0wAkVisVpZMogGhllmP7UCLohIQkEXlciuJyk6+Y9Sh4O+WABUAT6zjOefEJFwpxldRDjoizsCB32xGeiqlDoKXAemiEiZa3U76IvngRVKqckYA9sjyuKLpVLqE4yXg9qW8ZjZQHkAEVmGMT7TE/gdSANGOnTfMugrjUaj0RQiJbXrSaPRaDQlBB0oNBqNRpMvOlBoNBqNJl90oNBoNBpNvuhAodFoNJp80YFCUyJRSl1XSh3MtjXKp+ylQqgvSikVZ6lrv2X17s3e4z2llLdlf2auz368XRst97H65bBS6gulVI0Cyjcvq0qpmuJDT4/VlEiUUpdEpEphl83nHlHAJhFZr5TqCiwUEf/buN9t21TQfZVSHwDHReT1fMqPwFDQnVDYtmjuHHSLQlMqUEpVseTa2K+U+o9SKo9qrFKqnlJqZ7Y37ocs57sqpXZbrv1MKVXQD/hOwMNy7XOWex1WSk2ynKuslPpSKXXIcr6/5fx2pVQLpdSbQEWLHR9ZPrtk+XetUqpXNpujlFJ9lFIuSqkFSqkYS56AJx1wy24sgm5KqVDLdzyglPpRKdXUskp5DtDfYkt/i+0rlVI/WcraUt/VaHLibP10venN1oaxkvigZduAoSJQzfJZbYyVpdYW8SXLv88Dsyz7LhjaT7UxfvgrW85PA162UV8U0Mey3xfYCwQD/wEqY6x8PwIEAr2BFdmurW75dzuW/BdWm7KVsdr4GPCBZf9uDCXPisBY4EXL+QpALOBuw85L2b7fZ0B3y3E1oJxlvzPwuWV/BLAk2/VvAEMs+zUw9J8qO/v/W28leyuREh4aDXBFRJpbD5RS5YE3lFJtgSyMN+m6wJls18QAKy1lN4rIQaVUO4xENbss8iZ3Y7yJ22KBUupFDA2gURjaQBtE5LLFhn8CDwHfAG8ppeZhdFf9cBPf62tgkVKqAtAd2CkiVyzdXf5KqT6WctUxBPzicl1fUSl10PL9jwFbspX/QCnliSFRUd5O/V2BcKXUC5ZjV6CB5V4ajU10oNCUFgYDdYBgEclQhjqsa/YCIrLTEkh6AVFKqUggBdgiIgMdqGOKiKy3HiilOtkqJCLHlZH3oifwmlLqOxGZ48iXEJGrSqntQDegP0aSHTAyjj0jIpsLuMUVEWmulKqEoW30NPA2RrKmbSLymGXgf7ud6xXQW0R+dcRejQb0GIWm9FAdOGsJEh2APHnBlZEr/C8RWQG8h5EScg/QRillHXOorJRq4mCdPwCPKqUqKaUqY3Qb/aCUuh9IE5E1GIKMtvIOZ1haNrZYhyHGZm2dgPGj/5T1GqVUE0udNhEjo+FE4Hl1Q2bfKhc9IlvRVIwuOCubgWeUpXmlDOVhjSZfdKDQlBY+Aloopf4DDAN+sVGmPXBIKXUA4219kYgkYvxwfqKU+hmj28nLkQpFZD/G2MVPGGMW74nIAcAP+MnSBTQbeM3G5cuBn62D2bn4FiO51FYxUneCEdiOAvuVUocxZOPzbfFbbPkZIynPfGCu5btnv24b4G0dzMZoeZS32HbEcqzR5IueHqvRaDSafNEtCo1Go9Hkiw4UGo1Go8kXHSg0Go1Gky86UGg0Go0mX3Sg0Gg0Gk2+6ECh0Wg0mnzRgUKj0Wg0+fL/J2OKxLLDEJEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEIirMzEO9-i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "KfRxMAM3O9-i",
        "outputId": "99cc3bb1-1e3d-4e96-fce5-e19ebe9a47a3"
      },
      "source": [
        "X_train_num, X_test_num, y_train, y_test = train_test_split(\n",
        "    df[[c for c in df.columns if c in ['album_listens']]], df[class_name], \n",
        "       test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "\n",
        "clf = GaussianNB(var_smoothing=0.005)\n",
        "clf.fit(X_train_num, y_train)\n",
        "y_pred = clf.predict(X_test_num)\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9616888193901486\n",
            "F1-score [0.9798768  0.60162602]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.98      0.98      1212\n",
            "         1.0       0.66      0.55      0.60        67\n",
            "\n",
            "    accuracy                           0.96      1279\n",
            "   macro avg       0.82      0.77      0.79      1279\n",
            "weighted avg       0.96      0.96      0.96      1279\n",
            "\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": "code",
      "metadata": {
        "id": "vWLpbczQ1u8Y"
      },
      "source": [
        "X_train_num, X_test_num, y_train, y_test = train_test_split(\n",
        "    df[[c for c in df.columns if c in ['album_listens']]], df[class_name], \n",
        "       test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "\n",
        "clf = GaussianNB(var_smoothing=0.001)\n",
        "clf.fit(X_train_num, y_train)\n",
        "y_pred = clf.predict(X_test_num)\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKXoEDURhC8O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "f0f1fd99-7cf9-4661-dd99-b32010fa6129"
      },
      "source": [
        "X_OG, y_OG = X_train, y_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-77e99ca367c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_OG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_OG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JbgpLPYVO9-i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pya639D2O9-i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asmiQ0xPO9-i"
      },
      "source": [
        "cat = df0[[\"Classical\", \"Country\", \"Easy Listening\", \"Electronic\", \"Experimental\", \"Folk\", \"Blues\", \"Hip-Hop\", \"Instrumental\", \"International\",  \"Jazz\",  \"Old-Time / Historic\",  \"Pop\",  \"Rock\", \"Soul-RnB\", \"Spoken\"]].apply(LabelEncoder().fit_transform)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6hN-WpMO9-i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "97Vk5xkPO9-j"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtc5xVCLiSTX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHBXK3LeiSV-"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE, SMOTENC, RandomOverSampler\n",
        "from sklearn.utils import resample\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.under_sampling import CondensedNearestNeighbour\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN7nQHEK9XE5"
      },
      "source": [
        "# Categorical"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3ss1w13lOtr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "47a052be-1af6-48f1-8075-742d92b3ba7b"
      },
      "source": [
        "df0_cat = df0[[\"Classical\", \"Country\", \"Easy Listening\", \"Electronic\", \"Experimental\", \"Folk\", \"Blues\", \"Hip-Hop\", \"Instrumental\", \"International\",  \"Jazz\",  \"Old-Time / Historic\",  \"Pop\",  \"Rock\", \"Soul-RnB\", \"Spoken\", \"Album\", \"EP\", \"Live Performance\", \"Radio Program\",'Single Tracks']].apply(LabelEncoder().fit_transform)\n",
        "\n",
        "X_train_cat, X_test_cat, y_train, y_test = train_test_split(\n",
        "    df0_cat, df[class_name], test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "clf = BernoulliNB()\n",
        "clf.fit(X_train_cat, y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test_cat)\n",
        "y_pred1 =y_pred\n",
        "y_score=clf.predict_proba(X_test_cat)\n",
        "\n",
        "y_pred = clf.predict(X_test_cat)\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9483971853010165\n",
            "F1-score [0.97349398 0.02941176]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      1.00      0.97      1212\n",
            "         1.0       1.00      0.01      0.03        67\n",
            "\n",
            "    accuracy                           0.95      1279\n",
            "   macro avg       0.97      0.51      0.50      1279\n",
            "weighted avg       0.95      0.95      0.92      1279\n",
            "\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": "code",
      "metadata": {
        "id": "tOkGYWEniSYg"
      },
      "source": [
        "adf = df\n",
        "adf[\"Genre\"] = \" \"\n",
        "for i in range(len(adf)):\n",
        "    if ((adf[\"Classical\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 1\n",
        "    if ((adf[\"Easy Listening\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 2\n",
        "    if ((adf[\"Electronic\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 3\n",
        "    if ((adf[\"Experimental\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 4\n",
        "    if ((adf[\"Folk\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 5\n",
        "    if ((adf[\"Hip-Hop\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 6\n",
        "    if ((adf[\"Instrumental\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 7\n",
        "    if ((adf[\"International\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 8\n",
        "    if ((adf[\"Jazz\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 9\n",
        "    if ((adf[\"Old-Time / Historic\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 10\n",
        "    if ((adf[\"Pop\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 11\n",
        "    if ((adf[\"Rock\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 12\n",
        "    if ((adf[\"Soul-RnB\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 13\n",
        "    if ((adf[\"Spoken\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 14\n",
        "    if ((adf[\"Blues\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 15\n",
        "    if ((adf[\"Country\"].values[i] == 1.0)):\n",
        "        adf[\"Genre\"].values[i] = 16\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ac6kdv28lYr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "f0b5576a-9a8e-4c61-f8f8-d356d1645069"
      },
      "source": [
        "df0_cat = df0[[\"Genre\"]].apply(LabelEncoder().fit_transform)\n",
        "\n",
        "X_train_cat, X_test_cat, y_train, y_test = train_test_split(\n",
        "    df0_cat, df[class_name], test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "clf = BernoulliNB()\n",
        "clf.fit(X_train_cat, y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test_cat)\n",
        "y_pred1 =y_pred\n",
        "y_score=clf.predict_proba(X_test_cat)\n",
        "\n",
        "y_pred = clf.predict(X_test_cat)\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.947615324472244\n",
            "F1-score [0.97310317 0.        ]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.95      1.00      0.97      1212\n",
            "         1.0       0.00      0.00      0.00        67\n",
            "\n",
            "    accuracy                           0.95      1279\n",
            "   macro avg       0.47      0.50      0.49      1279\n",
            "weighted avg       0.90      0.95      0.92      1279\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: 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",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "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": {
        "id": "9K2qoX3d8lba"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZlugFTc8ld8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enkZHNyAgFAN"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jq4b3bkIgFDU"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDEkbUWvgFF1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4jRcc-kgFIW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUFGXBbMgFKx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5w8V245gFNa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wvq4mCrxgFP5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ok9w-POg8lge"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/FMA Project/Binnato.csv', skipinitialspace=True, na_values='?', keep_default_na=True)\n",
        "class_name = 'popularity'\n",
        "del df['Unnamed: 0']\n",
        "del df['album_favorites']\n",
        "attributes = [col for col in df.columns if col != class_name]\n",
        "X = df[attributes].values\n",
        "y = df[class_name]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100, stratify=y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25tJshFX8li7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8114421-3810-491b-94b5-151825ef0099"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4263 entries, 0 to 4262\n",
            "Data columns (total 26 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   track_listens        4263 non-null   float64\n",
            " 1   artist_favorites     4263 non-null   int64  \n",
            " 2   Classical            4263 non-null   int64  \n",
            " 3   Blues                4263 non-null   int64  \n",
            " 4   Country              4263 non-null   int64  \n",
            " 5   Easy Listening       4263 non-null   int64  \n",
            " 6   Electronic           4263 non-null   int64  \n",
            " 7   Experimental         4263 non-null   int64  \n",
            " 8   Folk                 4263 non-null   int64  \n",
            " 9   Hip-Hop              4263 non-null   int64  \n",
            " 10  Instrumental         4263 non-null   int64  \n",
            " 11  International        4263 non-null   int64  \n",
            " 12  Jazz                 4263 non-null   int64  \n",
            " 13  Old-Time / Historic  4263 non-null   int64  \n",
            " 14  Pop                  4263 non-null   int64  \n",
            " 15  Rock                 4263 non-null   int64  \n",
            " 16  Soul-RnB             4263 non-null   int64  \n",
            " 17  Spoken               4263 non-null   int64  \n",
            " 18  album_listens        4263 non-null   int64  \n",
            " 19  quality              4263 non-null   float64\n",
            " 20  popularity           4263 non-null   float64\n",
            " 21  Album                4263 non-null   int64  \n",
            " 22  Live Performance     4263 non-null   int64  \n",
            " 23  Radio Program        4263 non-null   int64  \n",
            " 24  Single Tracks        4263 non-null   int64  \n",
            " 25  EP                   4263 non-null   int64  \n",
            "dtypes: float64(3), int64(23)\n",
            "memory usage: 866.0 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knRhFn140RPv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6TCTLKjiSa5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "8746e0fd-d75a-45c5-9ea8-05a698490e05"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "clf = CategoricalNB()\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "y_pred1 =y_pred\n",
        "y_score=clf.predict_proba(X_test)\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "y_pred2 =y_pred\n",
        "\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy 0.9741985926505082\n",
            "F1-score [0.98656899 0.67326733]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.97      1.00      0.99      1212\n",
            "         1.0       1.00      0.51      0.67        67\n",
            "\n",
            "    accuracy                           0.97      1279\n",
            "   macro avg       0.99      0.75      0.83      1279\n",
            "weighted avg       0.97      0.97      0.97      1279\n",
            "\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": "code",
      "metadata": {
        "id": "GgiLK_x3iSdb"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHyj3kvLiSf3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOBbujLUiSiG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzvL2GsXO9-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlOt2IpWO9-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mx5tM7T2O9-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agv65ftjO9-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiG3ai1bO9-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAKSotocO9-k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "e03a3682-61e9-4d23-e3f4-4783310ecd8a"
      },
      "source": [
        "fpr0, tpr0, _ = roc_curve(y_test, y_pred0)\n",
        "roc_auc0 = auc(fpr0, tpr0)\n",
        "\n",
        "fpr1, tpr1, _ = roc_curve(y_test, y_pred1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "\n",
        "\n",
        "plt.plot(fpr0, tpr0, color='darkorange', lw=3, label='$AUC$ = %.3f   Gaussian' % (roc_auc0))\n",
        "plt.plot(fpr1, tpr1, color='green', lw=3, label='$AUC$ = %.3f  Categorical' % (roc_auc1))\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate', fontsize=16)\n",
        "plt.ylabel('True Positive Rate', fontsize=16)\n",
        "plt.title('ROC curve', fontsize=16)\n",
        "plt.legend(loc=\"lower right\", fontsize=14, frameon=False)\n",
        "plt.tick_params(axis='both', which='major', labelsize=16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": {
        "id": "8lKnF6qbTBGQ"
      },
      "source": [
        "#Bern si usa per le binarie, quindi uguale a prima senza quality\n",
        "df0_cat = df0[[\"Classical\", \"Country\", \"Easy Listening\", \"Electronic\", \"Experimental\", \"Folk\", \"Blues\", \"Hip-Hop\", \"Instrumental\", \"International\",  \"Jazz\",  \"Old-Time / Historic\",  \"Pop\",  \"Rock\", \"Soul-RnB\", \"Spoken\", \"Album\", \"EP\", \"Live Performance\", \"Radio Program\",'Single Tracks']].apply(LabelEncoder().fit_transform)\n",
        "\n",
        "X_train_cat, X_test_cat, y_train, y_test = train_test_split(\n",
        "    df0_cat, df[class_name], test_size=0.3, random_state=100, stratify=df[class_name])\n",
        "\n",
        "clf = BernoulliNB()\n",
        "clf.fit(X_train_cat, y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test_cat)\n",
        "y_pred2 =y_pred\n",
        "y_score=clf.predict_proba(X_test_cat)\n",
        "\n",
        "y_pred = clf.predict(X_test_cat)\n",
        "print('Accuracy %s' % accuracy_score(y_test, y_pred))\n",
        "print('F1-score %s' % f1_score(y_test, y_pred, average=None))\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "plot_roc(y_test, y_score)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKPXtlwHTBI_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1W03l7ATBLY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwFIIf-fO9-k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VtEAJPskO9-l"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMlrqwO1SwB8"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TI8_TFxHO9-l"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9K_7av4dO9-l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "ced95802-3083-4e2e-b458-025c6fec1cb4"
      },
      "source": [
        "\n",
        "fpr0, tpr0, _ = roc_curve(y_test, y_pred0)\n",
        "roc_auc0 = auc(fpr0, tpr0)\n",
        "\n",
        "fpr1, tpr1, _ = roc_curve(y_test, y_pred1)\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "\n",
        "fpr2, tpr2, _ = roc_curve(y_test, y_pred2)\n",
        "roc_auc2 = auc(fpr2, tpr2)\n",
        "\n",
        "\n",
        "plt.plot(fpr2, tpr2, color='lightblue', lw=3, label='$AUC$ = %.3f  Bernoulli' % (roc_auc2))\n",
        "plt.plot(fpr0, tpr0, color='darkorange', lw=3, label='$AUC$ = %.3f   Gaussian' % (roc_auc0))\n",
        "plt.plot(fpr1, tpr1, color='green', lw=3, label='$AUC$ = %.3f  Categorical' % (roc_auc1))\n",
        "\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate', fontsize=16)\n",
        "plt.ylabel('True Positive Rate', fontsize=16)\n",
        "plt.title('ROC curve', fontsize=16)\n",
        "plt.legend(loc=\"lower right\", fontsize=14, frameon=False)\n",
        "plt.tick_params(axis='both', which='major', labelsize=16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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": {
        "id": "NAjbDG_FO9-l"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYgIe4DuwkMR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}