animal_activity_classification.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "animal-activity-classification.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKXEueq7XX_X"
      },
      "source": [
        "# Description of the dataset \n",
        "\n",
        "The dataset is captured in a farm of animals and it was manually labeled to identify the state of an animal based on its position.\n",
        "\n",
        "The following are the dataset attributes: \n",
        "\n",
        "* time_stamp = time stamp of the collected abservation\n",
        "\n",
        "* pos_x = position in axis X\n",
        "\n",
        "* pos_y = position in axis Y\n",
        "\n",
        "* pos_z = position in axis Z\n",
        "\n",
        "The target is the column **label** it consists of the possible animal states:\n",
        "\n",
        "* Walking\n",
        "* Grazing\n",
        "* Stand up\n",
        "* Sternal lying\n",
        "* Licking calf\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5ikYH6WXuHA"
      },
      "source": [
        "# Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NvTma8dXHlB"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwovH86kYP7N",
        "outputId": "0598c474-9935-462e-917a-a5fe99fcf91a"
      },
      "source": [
        "from google.colab import drive \n",
        "\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "585b7jRlYTmO"
      },
      "source": [
        "# Upload data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kxv4UeIrYSnf"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Data/Cattlechain/output_data_filtered\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DAAvBNt8YtlD",
        "outputId": "d9a3d532-9208-4771-ae1d-24aa6fcff92f"
      },
      "source": [
        "df.head()"
      ],
      "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>pos_x</th>\n",
              "      <th>pos_y</th>\n",
              "      <th>pos_z</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-6.53255</td>\n",
              "      <td>2.71072</td>\n",
              "      <td>5.54596</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-7.69155</td>\n",
              "      <td>3.19922</td>\n",
              "      <td>5.79500</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-6.97316</td>\n",
              "      <td>3.77393</td>\n",
              "      <td>4.86588</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-6.77201</td>\n",
              "      <td>2.53831</td>\n",
              "      <td>1.56130</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-7.27967</td>\n",
              "      <td>2.30842</td>\n",
              "      <td>4.32949</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pos_x    pos_y    pos_z    label\n",
              "0 -6.53255  2.71072  5.54596  Grazing\n",
              "1 -7.69155  3.19922  5.79500  Grazing\n",
              "2 -6.97316  3.77393  4.86588  Grazing\n",
              "3 -6.77201  2.53831  1.56130  Grazing\n",
              "4 -7.27967  2.30842  4.32949  Grazing"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bbei7Hysshbr",
        "outputId": "17174ea1-3c17-4c8e-a07d-406af5ca7f95"
      },
      "source": [
        "df.label.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Grazing', 'Stand up', 'Sternal lying', 'Walking', nan,\n",
              "       'Licking calf'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Wi44Xt0sul_"
      },
      "source": [
        "#deleteing rows with label equal to nan \n",
        "df= df.dropna()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAVrf1TTu8qa",
        "outputId": "740caa1f-dac8-4fdf-87d0-1604176fbee4"
      },
      "source": [
        "df.label.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Grazing', 'Stand up', 'Sternal lying', 'Walking', 'Licking calf'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thBcQw5DJtRY"
      },
      "source": [
        "# Exploratory data analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "Hf2-OntUFOBo",
        "outputId": "3a1a5d5a-f329-4bc8-9a49-1eb437e40728"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(15,8)})\n",
        "sns.countplot(x = \"label\", data = df)\n",
        "plt.title(\"Number of samples by activity in the filtered dataset\")\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdFDM2rPLVcy"
      },
      "source": [
        "df_Sternal_lying = df.query('label == \"Sternal lying\"') \n",
        "df_Grazing = df.query('label == \"Grazing\"') \n",
        "df_Stand_up = df.query('label == \"Stand up\"')  \n",
        "df_Licking_calf = df.query('label == \"Licking calf\"') \n",
        "df_Walking = df.query('label == \"Walking\"') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "GAf8inuWLk_8",
        "outputId": "717135a0-4b97-452d-bd37-e08208f45e6e"
      },
      "source": [
        "# histogram and kde plot \n",
        "fig, axs = plt.subplots(2, 3, figsize=(20, 10))\n",
        "\n",
        "df_Sternal_lying.plot.kde(ax=axs[0, 0], legend=True, title=\"Sternal lying\")\n",
        "#df_Sternal_lying.plot.hist(density=True, ax=axs[0, 0])\n",
        "df_Walking.plot.kde(ax=axs[0, 1], legend=False, title=\"Walking\")\n",
        "#df_Walking.plot.hist(density=True, ax=axs[0, 1])\n",
        "df_Grazing.plot.kde(ax=axs[0, 2], legend=False, title=\"Grazing\")\n",
        "#df_Grazing.plot.hist(density=True, ax=axs[0, 2])\n",
        "df_Licking_calf.plot.kde(ax=axs[1, 0], legend=False, title=\"Licking calf\")\n",
        "#df_Licking_calf.plot.hist(density=True, ax=axs[2, 0])\n",
        "df_Stand_up.plot.kde(ax=axs[1, 1], legend=False, title=\"Stand up\")\n",
        "#df_Stand_up.plot.hist(density=True, ax=axs[2, 2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f10b9deb690>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "nMZ1as51Tovd",
        "outputId": "27de7c3f-c559-47a5-8e68-22ff34be2237"
      },
      "source": [
        "fig, axs = plt.subplots(2, 3, figsize=(20, 10))\n",
        "\n",
        "df_Sternal_lying.boxplot(ax=axs[0, 0])\n",
        "df_Walking.boxplot(ax=axs[0, 1])\n",
        "df_Grazing.boxplot(ax=axs[0, 2])\n",
        "df_Licking_calf.boxplot(ax=axs[1, 0])\n",
        "df_Stand_up.boxplot(ax=axs[1, 1])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f859ad33e50>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGdOtq_gaNEK"
      },
      "source": [
        "# Standard Scaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4MSl2_saCQy"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "df[[\"pos_x\",\"pos_y\",\"pos_z\"]]= scaler.fit_transform(df[[\"pos_x\",\"pos_y\",\"pos_z\"]])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SP9Sc0xIwOy"
      },
      "source": [
        "The data suffers from skewness, one possibility to handle is is by applying the log transform. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8jJjeerV4On"
      },
      "source": [
        "# Log transform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "keBt5xR1V3Wz"
      },
      "source": [
        "dff= df.copy()"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqvTbOjEU91v"
      },
      "source": [
        "# Log transform \n",
        "dff['pos_x'] = dff['pos_x'].apply(np.log)\n",
        "dff['pos_y'] = dff['pos_y'].apply(np.log)\n",
        "dff['pos_z'] = dff['pos_z'].apply(np.log)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0XhuLbIW6Pt"
      },
      "source": [
        "#Negative Values Handling\n",
        "#Log transform\n",
        "dff['log_pos_x'] = (dff['pos_x']-dff['pos_x'].min()+1).transform(np.log)\n",
        "dff['log_pos_y'] = (dff['pos_y']-dff['pos_y'].min()+1).transform(np.log)\n",
        "dff['log_pos_z'] = (dff['pos_z']-dff['pos_z'].min()+1).transform(np.log)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-PCTN4QYA2L"
      },
      "source": [
        "dff = dff.drop(columns=[\"pos_x\",\"pos_y\",\"pos_z\"])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRT8ImFiVpMh"
      },
      "source": [
        "dff_Sternal_lying = dff.query('label == \"Sternal lying\"') \n",
        "dff_Grazing = dff.query('label == \"Grazing\"') \n",
        "dff_Stand_up = dff.query('label == \"Stand up\"')  \n",
        "dff_Licking_calf = dff.query('label == \"Licking calf\"') \n",
        "dff_Walking = dff.query('label == \"Walking\"')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKe2bQGEVqOt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "outputId": "fd9d378e-f348-40a6-95cb-ff8e252eff39"
      },
      "source": [
        "#kde plot after log transform \n",
        "fig, axs = plt.subplots(2, 3, figsize=(20, 10))\n",
        "\n",
        "dff_Sternal_lying.plot.kde(ax=axs[0, 0], legend=True, title=\"Sternal lying\")\n",
        "dff_Walking.plot.kde(ax=axs[0, 1], legend=False, title=\"Walking\")\n",
        "dff_Grazing.plot.kde(ax=axs[0, 2], legend=False, title=\"Grazing\")\n",
        "dff_Licking_calf.plot.kde(ax=axs[1, 0], legend=False, title=\"Licking calf\")\n",
        "dff_Stand_up.plot.kde(ax=axs[1, 1], legend=False, title=\"Stand up\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f859a46bd50>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xjriIhfT9f0"
      },
      "source": [
        "The training has been performed with data which has be transformed with a log function but the accuracy of the model did not change than without using it. \n",
        "\n",
        "So in this Notebook, the following steps are performed on the features without applying the log transform on"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1lz5Uu6gULi"
      },
      "source": [
        "# Label Encoding "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWrXQjwtgcxf"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "Y=df.label\n",
        "label_encoder = LabelEncoder()\n",
        "label_encoder = label_encoder.fit(Y)\n",
        "label_encoded_y = label_encoder.transform(Y)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlG-ak-shJSe"
      },
      "source": [
        "y = label_encoded_y"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSWhLX4Dq29-",
        "outputId": "44518e67-b26f-40b7-c193-01a41cdd98ca"
      },
      "source": [
        "list(label_encoder.inverse_transform([0,1,2,3,4]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Grazing', 'Licking calf', 'Stand up', 'Sternal lying', 'Walking']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "RWvNCrMLb1Fc",
        "outputId": "9609e929-8b58-41e0-a77b-a89e3a57478a"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 13,
      "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>pos_x</th>\n",
              "      <th>pos_y</th>\n",
              "      <th>pos_z</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.441070</td>\n",
              "      <td>0.682508</td>\n",
              "      <td>-1.426030</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.751247</td>\n",
              "      <td>0.919277</td>\n",
              "      <td>-1.291752</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.558988</td>\n",
              "      <td>1.197831</td>\n",
              "      <td>-1.792719</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.505155</td>\n",
              "      <td>0.598943</td>\n",
              "      <td>-3.574496</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.641018</td>\n",
              "      <td>0.487519</td>\n",
              "      <td>-2.081932</td>\n",
              "      <td>Grazing</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      pos_x     pos_y     pos_z    label\n",
              "0 -1.441070  0.682508 -1.426030  Grazing\n",
              "1 -1.751247  0.919277 -1.291752  Grazing\n",
              "2 -1.558988  1.197831 -1.792719  Grazing\n",
              "3 -1.505155  0.598943 -3.574496  Grazing\n",
              "4 -1.641018  0.487519 -2.081932  Grazing"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XwFR9sjSgrCQ"
      },
      "source": [
        "# Stratified train-test-split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Dh_TEvxgs2F"
      },
      "source": [
        "Stratified splits are desirable in some cases, like when you’re classifying an imbalanced dataset, a dataset with a significant difference in the number of samples that belong to distinct classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGBM4DyMpQgb"
      },
      "source": [
        "X=df.iloc[:,0:3]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "ldeGxT8HpaqK",
        "outputId": "5dad7120-e583-4af8-ae7b-6566c5404459"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 32,
      "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>pos_x</th>\n",
              "      <th>pos_y</th>\n",
              "      <th>pos_z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.441070</td>\n",
              "      <td>0.682508</td>\n",
              "      <td>-1.426030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.751247</td>\n",
              "      <td>0.919277</td>\n",
              "      <td>-1.291752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.558988</td>\n",
              "      <td>1.197831</td>\n",
              "      <td>-1.792719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.505155</td>\n",
              "      <td>0.598943</td>\n",
              "      <td>-3.574496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.641018</td>\n",
              "      <td>0.487519</td>\n",
              "      <td>-2.081932</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      pos_x     pos_y     pos_z\n",
              "0 -1.441070  0.682508 -1.426030\n",
              "1 -1.751247  0.919277 -1.291752\n",
              "2 -1.558988  1.197831 -1.792719\n",
              "3 -1.505155  0.598943 -3.574496\n",
              "4 -1.641018  0.487519 -2.081932"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cGXXlVUtgqLZ"
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)\n",
        "\n",
        "for train_ix, test_ix in kfold.split(X, y):\n",
        "\tx_train, x_test = X.iloc[train_ix], X.iloc[test_ix]\n",
        "\ty_train, y_test = y[train_ix], y[test_ix]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jChfQjLlSIQ",
        "outputId": "55e21817-8914-4253-bc76-8be9f82c2dba"
      },
      "source": [
        "#Result of Stratified train-test-split: the number of samples in each class \n",
        "a=y_test\n",
        "unique, counts = np.unique(a, return_counts=True)\n",
        "dict(zip(unique, counts))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 3045, 1: 426, 2: 4777, 3: 4887, 4: 1159}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "kVhHZub5rwp5",
        "outputId": "7c0e4b37-17ad-42ee-c461-4bf3fc1d7665"
      },
      "source": [
        "sns.countplot(a)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6b425ce050>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4kAAAHUCAYAAACXoW1VAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAacElEQVR4nO3df2yV9f338VcPrPUXUFt+FTBRcZpGYow2M0vclkEMZkHiH1sgvd2yIbppXIyLP4gwICozBWJmJsoSjeZO/GrmncxINeK2Zskwzug9iUGMGoNGRwVpYYLjx749vf/43vfnO25ldgc4p8jj8VfP9Tmn593kylWeXNfp1TQ8PDwcAAAASFJp9AAAAACMHiIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAACjGNnqARtq9+9NUq+4AAgAAnFwqlaaceebpn7t2UkditTosEgEAAP6Jy00BAAAoRCIAAACFSAQAAKAQiQAAABQiEQAAgKJuf9109uzZaW5uTktLS5Lk1ltvzTe+8Y1s3rw5y5cvz8GDBzN9+vSsWbMm7e3tSVLzGgAAALVpGh4erss9IGbPnp3169fn/PPPL9uq1Wrmzp2be++9N11dXXnwwQfzwQcf5N5776157d8xMLDPLTAAAICTTqXSlPb2Mz5/rc6zHGbLli1paWlJV1dXkmThwoV5/vnnj2oNAACA2tXtctPkvy4xHR4ezqWXXpqf/exn6e/vz7Rp08p6W1tbqtVq9uzZU/Naa2trPX8kAACAL5W6ReLjjz+ejo6OHDp0KKtWrcpdd92VK664ol5v/7mOdHoVAADgZFW3SOzo6EiSNDc3p7u7OzfccEN+8IMfZPv27eU5g4ODqVQqaW1tTUdHR01r/w6fSQQAAE5GDf9M4t///vfs3bs3STI8PJznnnsunZ2dmTVrVg4cOJBXX301SfLkk0/myiuvTJKa1wAAAKhdXf666QcffJCf/vSnGRoaSrVazcyZM7Ns2bJMnjw5f/nLX7JixYrDbmUxceLEJKl5baScSQQAAE5G/+pMYt1ugTEaiUQAAOBk1PDLTQEAADgxiEQAAAAKkQgAAEAhEgEAACjqdp9EAIDRrHVcc75ySkujx2CU+ceBg9mz91Cjx4C6EokAAEm+ckpLnvvBjxo9BqPMd/7no4lI5CTjclMAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAwn0SAaib8RNa0tLc3OgxGGUOHjqUT/52sNFjAPB/iUQA6qaluTk/fPTmRo/BKPPYj+5PIhIBRguXmwIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAARd0j8YEHHsgFF1yQt99+O0myefPmzJ8/P3Pnzs2iRYsyMDBQnlvrGgAAALWpayS+8cYb2bx5c6ZPn54kqVarue2227J8+fJs3LgxXV1dWbt27VGtAQAAULu6ReKhQ4dy1113ZeXKlWXbli1b0tLSkq6uriTJwoUL8/zzzx/VGgAAALUbW683uv/++zN//vzMmDGjbOvv78+0adPK47a2tlSr1ezZs6fmtdbW1hHP1N5+xlH+VADAsTBp0rhGjwBHZP/kZFOXSHzttdeyZcuW3HrrrfV4uxEbGNiXanW40WMAnDT8Q4sj+fjjvY0ewf7JEY2G/ROOtUql6YgnzeoSia+88krefffdzJkzJ0ny0Ucf5dprr833v//9bN++vTxvcHAwlUolra2t6ejoqGkNAACA2tXlM4nXX399Nm3alL6+vvT19WXq1Kl55JFHsnjx4hw4cCCvvvpqkuTJJ5/MlVdemSSZNWtWTWsAAADUrm6fSfw8lUolq1evzooVK3Lw4MFMnz49a9asOao1AAAAateQSOzr6ytfX3LJJdmwYcPnPq/WNQAAAGpT1/skAgAAMLqJRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQDG20QOcqMaNPyWntHyl0WMwyhw4+I/s/eRAo8cAAICa1S0Sb7zxxnz44YepVCo57bTT8vOf/zydnZ3Ztm1blixZkj179qS1tTU9PT05++yzk6TmtXo4peUr6b798bq9HyeG/1j9P7I3IhEAgBNX3S437enpyTPPPJOnn346ixYtyp133pkkWbFiRbq7u7Nx48Z0d3dn+fLl5TW1rgEAAFCbukXiuHHjytf79u1LU1NTBgYGsnXr1sybNy9JMm/evGzdujWDg4M1rwEAAFC7un4mcenSpXnxxRczPDychx9+OP39/ZkyZUrGjBmTJBkzZkwmT56c/v7+DA8P17TW1tY24nna28849j8kJ71Jk8Z98ZMAOIxjJ6OZ/ZOTTV0jcdWqVUmSp59+OqtXr87NN99cz7f/jIGBfalWh2t6rYMFR/Lxx3sbPQKMWo6dHMloOHbaPzmS0bB/wrFWqTQd8aRZQ26BcfXVV+fll1/O1KlTs2PHjgwNDSVJhoaGsnPnznR0dKSjo6OmNQAAAGpXl0j89NNP09/fXx739fVlwoQJaW9vT2dnZ3p7e5Mkvb296ezsTFtbW81rAAAA1K4ul5vu378/N998c/bv359KpZIJEyZk/fr1aWpqysqVK7NkyZI8+OCDGT9+fHp6esrral0DAACgNnWJxIkTJ+Y3v/nN567NnDkzTz311DFdAwAAoDYN+UwiAAAAo5NIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAMWII/GRRx753O2PPvroMRsGAACAxhpxJK5bt+5ztz/00EPHbBgAAAAaa+wXPeGll15KklSr1fz5z3/O8PBwWfvwww9z+umnH7/pAAAAqKsvjMSlS5cmSQ4ePJg777yzbG9qasqkSZOybNmy4zcdAAAAdfWFkdjX15ckuf3227N69erjPhAAAACN84WR+P/8cyBWq9XD1ioVfyQVAADgy2DEkfjGG2/krrvuyltvvZWDBw8mSYaHh9PU1JQ333zzuA0IAABA/Yw4EpcsWZJvf/vb+cUvfpFTTjnleM4EAABAg4w4Ev/617/mlltuSVNT0/GcBwAAgAYa8YcJr7jiimzatOl4zgIAAECDjfhM4sGDB3PTTTfl0ksvzcSJEw9b81dPAQAAvhxGHInnnXdezjvvvOM5CwAAAA024ki86aabjuccAAAAjAIjjsSXXnrpiGtf//rXj8kwAAAANNaII3Hp0qWHPd69e3f+8Y9/ZMqUKfnDH/5wzAcDAACg/kYciX19fYc9HhoaykMPPZTTTz/9mA8FAABAY4z4Fhj/vzFjxuQnP/lJHn744WM5DwAAAA1UcyQmyYsvvpimpqZjNQsAAAANNuLLTb/1rW8dFoT79+/PoUOHsmLFiuMyGAAAAPU34khcs2bNYY9PPfXUnHPOOTnjjDOO+VAAAAA0xogj8Wtf+1qSpFqtZteuXZk4cWIqlaO6WhUAAIBRZsSVt2/fvtx+++256KKL8s1vfjMXXXRR7rjjjuzdu/d4zgcAAEAdjTgS77nnnuzfvz8bNmzI66+/ng0bNmT//v255557jud8AAAA1NGILzf905/+lN///vc59dRTkyTnnHNO7r333lxxxRXHbTgAAADqa8RnEltaWjI4OHjYtt27d6e5ufmYDwUAAEBjjPhM4ne/+90sWrQoP/zhDzNt2rRs3749jz32WL73ve8dz/kAAACooxFH4g033JApU6Zkw4YN2blzZyZPnpzFixeLRAAAgC+REV9uumrVqpxzzjl57LHH8txzz+Wxxx7LzJkzs2rVquM5HwAAAHU04kjs7e3NrFmzDts2a9as9Pb2HvOhAAAAaIwRR2JTU1Oq1eph24aGhj6zDQAAgBPXiCOxq6sr999/f4nCarWaX/3qV+nq6jpuwwEAAFBfI/7DNUuXLs2Pf/zjXH755Zk2bVr6+/szadKkrF+//njOBwAAQB2NOBKnTp2a3/72t3n99dfT39+fjo6OXHTRRalURnwyEgAAgFFuxJGYJJVKJRdffHEuvvji4zUPAAAADeQ0IAAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAEVdInH37t257rrrMnfu3Fx11VW56aabMjg4mCTZvHlz5s+fn7lz52bRokUZGBgor6t1DQAAgNrUJRKbmpqyePHibNy4MRs2bMhZZ52VtWvXplqt5rbbbsvy5cuzcePGdHV1Ze3atUlS8xoAAAC1q0sktra25rLLLiuPL7744mzfvj1btmxJS0tLurq6kiQLFy7M888/nyQ1rwEAAFC7sfV+w2q1mieeeCKzZ89Of39/pk2bVtba2tpSrVazZ8+emtdaW1tHPEt7+xnH5oeCfzJp0rhGjwBwwnHsZDSzf3KyqXsk3n333TnttNNyzTXX5He/+1293/4wAwP7Uq0O1/RaBwuO5OOP9zZ6BBi1HDs5ktFw7LR/ciSjYf+EY61SaTriSbO6RmJPT0/ef//9rF+/PpVKJR0dHdm+fXtZHxwcTKVSSWtra81rAAAA1K5ut8C47777smXLlqxbty7Nzc1JklmzZuXAgQN59dVXkyRPPvlkrrzyyqNaAwAAoHZ1OZP4zjvv5Ne//nXOPvvsLFy4MEkyY8aMrFu3LqtXr86KFSty8ODBTJ8+PWvWrEmSVCqVmtYAAACoXV0i8atf/Wreeuutz1275JJLsmHDhmO6BgAAQG3qdrkpAAAAo59IBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAMXYRg8AAAAc2YTxp6a5xT/bOdyhg/+Zv32y/7h8b3sbAACMYs0tY/OLpf+r0WMwyty56rvH7Xu73BQAAIBCJAIAAFCIRAAAAAqRCAAAQCESAQAAKEQiAAAAhUgEAACgEIkAAAAUdYnEnp6ezJ49OxdccEHefvvtsn3btm1ZsGBB5s6dmwULFuS999476jUAAABqV5dInDNnTh5//PFMnz79sO0rVqxId3d3Nm7cmO7u7ixfvvyo1wAAAKhdXSKxq6srHR0dh20bGBjI1q1bM2/evCTJvHnzsnXr1gwODta8BgAAwNEZ26g37u/vz5QpUzJmzJgkyZgxYzJ58uT09/dneHi4prW2trZG/TgAAABfCg2LxNGgvf2MRo/Al9CkSeMaPQLACcexk9HM/slodbz2zYZFYkdHR3bs2JGhoaGMGTMmQ0ND2blzZzo6OjI8PFzT2r9rYGBfqtXhmuZ3sOBIPv54b6NHgFHLsZMjGQ3HTvsnR9Lo/dO+yZEczb5ZqTQd8aRZw26B0d7ens7OzvT29iZJent709nZmba2tprXAAAAODp1OZN4zz335IUXXsiuXbvyox/9KK2trXn22WezcuXKLFmyJA8++GDGjx+fnp6e8ppa1wAAAKhdXSJx2bJlWbZs2We2z5w5M0899dTnvqbWNQAAAGrXsMtNAQAAGH1EIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBjb6AGAY+/MCc0Z29zS6DEYZf7z0MHs/tuhRo8BAIxyIhG+hMY2t+R/r17c6DEYZS69/eEkIhEA+NdcbgoAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQiEQAAAAKkQgAAEAhEgEAAChEIgAAAIVIBAAAoBCJAAAAFCIRAACAQiQCAABQnNCRuG3btixYsCBz587NggUL8t577zV6JAAAgBPaCR2JK1asSHd3dzZu3Jju7u4sX7680SMBAACc0MY2eoBaDQwMZOvWrXn00UeTJPPmzcvdd9+dwcHBtLW1jeh7VCpNRzXDxDNPP6rX8+V0tPvVsdI8vr3RIzAKjYb9c+IZIztGc3IZDftmkpw60bGTzxoN++eE1tMaPQKj0NHsm//qtU3Dw8PDNX/nBtqyZUvuuOOOPPvss2Xbd77znaxZsyYXXnhhAycDAAA4cZ3Ql5sCAABwbJ2wkdjR0ZEdO3ZkaGgoSTI0NJSdO3emo6OjwZMBAACcuE7YSGxvb09nZ2d6e3uTJL29vens7Bzx5xEBAAD4rBP2M4lJ8u6772bJkiX55JNPMn78+PT09OTcc89t9FgAAAAnrBM6EgEAADi2TtjLTQEAADj2RCIAAACFSAQAAKAQiQAAABQiEQAAgEIkclS2bduWBQsWZO7cuVmwYEHee++9Ro8ESZKenp7Mnj07F1xwQd5+++1GjwPF7t27c91112Xu3Lm56qqrctNNN2VwcLDRY0GS5MYbb8z8+fNz9dVXp7u7O2+++WajR4LDPPDAA36314FI5KisWLEi3d3d2bhxY7q7u7N8+fJGjwRJkjlz5uTxxx/P9OnTGz0KHKapqSmLFy/Oxo0bs2HDhpx11llZu3Zto8eCJP/1H2zPPPNMnn766SxatCh33nlno0eC4o033sjmzZv9bq8DkUjNBgYGsnXr1sybNy9JMm/evGzdutX/iDMqdHV1paOjo9FjwGe0trbmsssuK48vvvjibN++vYETwX8bN25c+Xrfvn1pampq4DTw3w4dOpS77rorK1eubPQoJ4WxjR6AE1d/f3+mTJmSMWPGJEnGjBmTyZMnp7+/P21tbQ2eDmD0q1areeKJJzJ79uxGjwLF0qVL8+KLL2Z4eDgPP/xwo8eBJMn999+f+fPnZ8aMGY0e5aTgTCIANMjdd9+d0047Lddcc02jR4Fi1apV+eMf/5hbbrklq1evbvQ4kNdeey1btmxJd3d3o0c5aYhEatbR0ZEdO3ZkaGgoSTI0NJSdO3e6xA9gBHp6evL+++/nl7/8ZSoVv44Zfa6++uq8/PLL2b17d6NH4ST3yiuv5N13382cOXMye/bsfPTRR7n22muzadOmRo/2peW3EjVrb29PZ2dnent7kyS9vb3p7Ox0qSnAF7jvvvuyZcuWrFu3Ls3NzY0eB5Ikn376afr7+8vjvr6+TJgwIa2trQ2cCpLrr78+mzZtSl9fX/r6+jJ16tQ88sgjufzyyxs92pdW0/Dw8HCjh+DE9e6772bJkiX55JNPMn78+PT09OTcc89t9FiQe+65Jy+88EJ27dqVM888M62trXn22WcbPRbknXfeybx583L22WfnlFNOSZLMmDEj69ata/BknOx27dqVG2+8Mfv370+lUsmECRNyxx135MILL2z0aHCY2bNnZ/369Tn//PMbPcqXlkgEAACgcLkpAAAAhUgEAACgEIkAAAAUIhEAAIBCJAIAAFCIRAAAAAqRCAAAQPF/AAArOB4hgSdrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yf8GzJ-mdiu"
      },
      "source": [
        "# XGboost "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpKo4qJMmGOI"
      },
      "source": [
        "import xgboost as xgb\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "model = XGBClassifier(max_depth=5, objective=\"multi:softmax\", n_estimators=100, \n",
        "                        num_classes=5,verbosity=1) \n",
        "\n",
        "\n",
        "xgbm = model.fit(x_train.values, y_train)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7LQkljvXwpOT"
      },
      "source": [
        "# Model metrics "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jir-hNi0qber"
      },
      "source": [
        "pred = xgbm.predict(x_test.values)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7s1YWMuhwXfG",
        "outputId": "8c743586-9cf2-4856-fc2c-e0e5eedfc09f"
      },
      "source": [
        "pred"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXYh0zePwhA4"
      },
      "source": [
        "# Reverse the label encoding to get the predicted labels \n",
        "predictions = label_encoder.inverse_transform(pred)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubs3FY4Iwm2m"
      },
      "source": [
        "# Reverse the label encoding to get the actual labels \n",
        "y_test_cat = label_encoder.inverse_transform(y_test)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tWuG-ek074d",
        "outputId": "ee1cf8ef-1b2f-4eb0-e536-c746b18da669"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "print(classification_report(y_test_cat, predictions))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "      Grazing       0.86      0.91      0.88      3045\n",
            " Licking calf       0.57      0.48      0.52       426\n",
            "     Stand up       0.78      0.80      0.79      4777\n",
            "Sternal lying       0.82      0.83      0.82      4887\n",
            "      Walking       0.81      0.59      0.68      1159\n",
            "\n",
            "     accuracy                           0.81     14294\n",
            "    macro avg       0.77      0.72      0.74     14294\n",
            " weighted avg       0.80      0.81      0.80     14294\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7UJtHhr4c-WX"
      },
      "source": [
        "# Saving the model "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "13gYuVXDvkVz"
      },
      "source": [
        "Pickle is the standard way of serializing objects in Python.\n",
        "\n",
        "You can use the Python pickle API to serialize your machine learning algorithms and save the serialized format to a file, for example:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZoMmYdRvfCQ"
      },
      "source": [
        "import pickle\n",
        "\n",
        "# save model to file\n",
        "pickle.dump(xgbm, open(\"/content/drive/MyDrive/Cattlechain/animal_activity_model.pkl\", \"wb\"))"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkCpmHiF1naF"
      },
      "source": [
        "Loading the saved pickle file to deserialize the xgboost trained model and use it to make new predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFQyIjhc11ua"
      },
      "source": [
        "import pickle\n",
        "\n",
        "# load model from file\n",
        "loaded_model = pickle.load(open(\"/content/drive/MyDrive/Cattlechain/animal_activity_model.pkl\", \"rb\"))"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Gt3dsqAdlwF"
      },
      "source": [
        "Loading model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYoLv1Ygd-sG"
      },
      "source": [
        "predict = loaded_model.predict(x_test)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sX3KDc4BeKIm",
        "outputId": "0835384c-89ff-489e-e3fe-14b2aaa595df"
      },
      "source": [
        "predict"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTZWjngjlRj7"
      },
      "source": [
        "input= [[-1.751247,3.751247,0.598943 ]]"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2TPNO4bqsqQ"
      },
      "source": [
        "pred_name = loaded_model.predict([[-1.751247,3.751247,0.598943 ]])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjHgjNxkrqgH",
        "outputId": "045a3998-7d71-49cd-e00d-af62a2e96a07"
      },
      "source": [
        "label_encoder.inverse_transform(pred_name)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Walking'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}