AssetLvl_Pred_lNN_BinaryClassifierDCP03.ipynb

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      "name": "AssetLvl-Pred-lNN BinaryClassifierDCP03.ipynb",
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      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "\n",
        "from numpy import loadtxt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler,OneHotEncoder    \n",
        "\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
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        "id": "Ydjz7mefTMRI",
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      "source": [
        "df = pd.read_csv('/content/DCP_leak_classification_train_data.csv',index_col=False)"
      ],
      "execution_count": 4,
      "outputs": []
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      "source": [
        "df.head()"
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      "execution_count": 5,
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              "      <th>cyl</th>\n",
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              "      <th>d_temp_crank</th>\n",
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              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
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              "      <th>s_pressure_mean</th>\n",
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            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1  ...            Asset  Binary\n",
              "0           0             0  ...  Ashland_Unit256     0.0\n",
              "1           1             1  ...  Ashland_Unit256     0.0\n",
              "2           2             2  ...  Ashland_Unit256     0.0\n",
              "3           3             3  ...  Ashland_Unit256     0.0\n",
              "4           4             4  ...  Ashland_Unit256     0.0\n",
              "\n",
              "[5 rows x 16 columns]"
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      "metadata": {
        "id": "A8f00GmlHbTl",
        "colab_type": "code",
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      },
      "source": [
        "# df['s_temp_mean*s_pressure_mean'] = df['s_pressure_mean']*df['s_temp_mean']\n",
        "# df['d_temp_mean*d_pressure_mean'] = df['d_pressure_mean']*df['d_temp_mean']"
      ],
      "execution_count": 4,
      "outputs": []
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      "source": [
        "# df['s_temp_crank*s_pressure_crank'] = df['s_temp_crank']*df['s_pressure_crank']\n",
        "# df['d_temp_crank*d_pressure_crank'] = df['d_pressure_crank']*df['d_temp_crank']"
      ],
      "execution_count": 5,
      "outputs": []
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        "outputId": "dfe6a544-0f36-413c-fcc5-9305bfcf6a41"
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      "source": [
        "df.head()"
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      "execution_count": 6,
      "outputs": [
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              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
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            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1  ...            Asset  Binary\n",
              "0           0             0  ...  Ashland_Unit256     0.0\n",
              "1           1             1  ...  Ashland_Unit256     0.0\n",
              "2           2             2  ...  Ashland_Unit256     0.0\n",
              "3           3             3  ...  Ashland_Unit256     0.0\n",
              "4           4             4  ...  Ashland_Unit256     0.0\n",
              "\n",
              "[5 rows x 16 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BW6XEV1sh09",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# df1 = pd.read_csv('/content/old_normalised_assets-2.csv',index_col=False)\n",
        "# df2 = pd.read_csv('/content/new_normalised_assets-2.csv',index_col=False)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MV3Kc15Tw9n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 'Clark county_6', 'RJ booster_1', 'Strawberry_3.csv'"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j78zQVikHMbk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "196480f5-b804-466d-adb0-8297d2f67912"
      },
      "source": [
        "df['Asset'].unique()   "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ashland_Unit256', 'Center station_C2', 'Crossbar_Unit1',\n",
              "       'El Dorado_Unit', 'Goliad_Unit1100', 'HobitLP_Unit102',\n",
              "       'Hobit_Unit2', 'Katy_Unit1116', 'Katy_Unit1117', 'LOHT_North',\n",
              "       'La Porte_K11A', 'Lightning_Unit2 (2)(1).csv', 'RJ_Unit1(1).csv',\n",
              "       'Lightning_Unit4', 'Reagon_Unit3', 'Saltlake_K71010B',\n",
              "       'Strawberry_Unit1', 'Reagon_Unit6', 'West Turkey_Unit2'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V46jsA_6Tsq7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2 = df.loc[(df.Asset == 'Clark county_6') | (df.Asset == 'La Porte_K11A') |\\\n",
        "             (df.Asset == 'Strawberry_Unit1') | (df.Asset == 'RJ booster_1') | (df.Asset == 'El Dorado_Unit') | (df.Asset == 'LOHT_North')]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gliOBdPxahcv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0146e398-747c-4ad9-d59b-71d97109bbb8"
      },
      "source": [
        "df2.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13656, 16)"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 25
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      "source": [
        "df2[\"Binary\"].unique()"
      ],
      "execution_count": 26,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1.])"
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      "cell_type": "code",
      "metadata": {
        "id": "tCoxexG3USm4",
        "colab_type": "code",
        "colab": {
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          "height": 136
        },
        "outputId": "fc42f7c2-ce79-467a-fa6a-71b222e895b1"
      },
      "source": [
        "df['Asset'].unique()    "
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ashland_Unit256', 'Center station_C2', 'Crossbar_Unit1',\n",
              "       'El Dorado_Unit', 'Goliad_Unit1100', 'HobitLP_Unit102',\n",
              "       'Hobit_Unit2', 'Katy_Unit1116', 'Katy_Unit1117', 'LOHT_North',\n",
              "       'La Porte_K11A', 'Lightning_Unit2 (2)(1).csv', 'RJ_Unit1(1).csv',\n",
              "       'Lightning_Unit4', 'Reagon_Unit3', 'Saltlake_K71010B',\n",
              "       'Strawberry_Unit1', 'Reagon_Unit6', 'West Turkey_Unit2'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMiAXyBWLMvn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ae2b043c-6aed-4656-c349-ced7d019e307"
      },
      "source": [
        "len(df['Asset'].unique() )"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlpoTL8fLSWO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5557aaf9-d190-46ee-cca3-4796b1f1efc5"
      },
      "source": [
        "len(df2['Asset'].unique() )"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XaH8vslgJIzU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 'Clark county_6') | ('La Porte_K11A') |\\\n",
        "#              ('Strawberry_Unit1') | ('RJ booster_1') | ('El Dorado_Unit'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dX25ZBbPNiRY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = df.loc[(df.Asset == 'West Turkey_Unit2') | (df.Asset == 'Crossbar_Unit1') | (df.Asset == 'Reagon_Unit6') | (df.Asset == 'Reagon_Unit3') | (df.Asset == 'Hobit_Unit2')|(df.Asset == 'Katy_Unit1116') | (df.Asset == 'Katy_Unit1117') | (df.Asset == 'HobitLP_Unit102') | (df.Asset == 'Goliad_Unit1100') | (df.Asset == 'Lightning_Unit4') | (df.Asset == 'Ashland_Unit256') | (df.Asset == 'Center station_C2') | (df.Asset == 'El Dorado_Unit J2502') | (df.Asset == 'Saltlake_K71010B') | (df.Asset == 'Lightning_Unit2 (2)(1).csv') |  (df.Asset == 'RJ_Unit1(1).csv')]"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7ca37IuL0tZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "0953b3f8-b6b0-41ea-8cac-54b0e62c59f7"
      },
      "source": [
        "df1['Asset'].unique()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ashland_Unit256', 'Center station_C2', 'Crossbar_Unit1',\n",
              "       'Goliad_Unit1100', 'HobitLP_Unit102', 'Hobit_Unit2',\n",
              "       'Katy_Unit1116', 'Katy_Unit1117', 'Lightning_Unit2 (2)(1).csv',\n",
              "       'RJ_Unit1(1).csv', 'Lightning_Unit4', 'Reagon_Unit3',\n",
              "       'Saltlake_K71010B', 'Reagon_Unit6', 'West Turkey_Unit2'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bj2HA-GDLKYE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "76c91898-948b-4100-8fa6-339b18e27fa0"
      },
      "source": [
        "len(df1['Asset'].unique() )"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySz7tAnswqa8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "8cf25108-c636-4870-abf5-3f000bb577ee"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>time</th>\n",
              "      <th>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>Asset</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>344.238677</td>\n",
              "      <td>820.900548</td>\n",
              "      <td>1357.93250</td>\n",
              "      <td>Ashland_Unit256</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>2019-12-01 01:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1358.24000</td>\n",
              "      <td>Ashland_Unit256</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
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              "      <td>1398.28125</td>\n",
              "      <td>Ashland_Unit256</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2019-12-01 03:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>104.620153</td>\n",
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              "      <td>345.283193</td>\n",
              "      <td>835.520144</td>\n",
              "      <td>1397.87125</td>\n",
              "      <td>Ashland_Unit256</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-12-01 04:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>109.567843</td>\n",
              "      <td>227.339240</td>\n",
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              "      <td>344.060655</td>\n",
              "      <td>838.433890</td>\n",
              "      <td>1397.31000</td>\n",
              "      <td>Ashland_Unit256</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1  ...            Asset  Binary\n",
              "0           0             0  ...  Ashland_Unit256     0.0\n",
              "1           1             1  ...  Ashland_Unit256     0.0\n",
              "2           2             2  ...  Ashland_Unit256     0.0\n",
              "3           3             3  ...  Ashland_Unit256     0.0\n",
              "4           4             4  ...  Ashland_Unit256     0.0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdsRIaE8w7kC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "731d236a-6034-4e25-adc3-ca963a0f243e"
      },
      "source": [
        "df1.columns"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'time', 'cyl', 's_temp_crank',\n",
              "       'd_temp_crank', 's_pressure_crank', 'd_pressure_crank', 'rpm_crank',\n",
              "       's_temp_mean', 'd_temp_mean', 's_pressure_mean', 'd_pressure_mean',\n",
              "       'rpm_mean', 'Asset', 'Binary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBrWvkAjwx6S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "32eb834f-a6f4-472b-ae13-7aaa28d8f774"
      },
      "source": [
        "df1['Binary'] = df1['Binary'].fillna(0)\n",
        "df1 = df1.fillna(0)\n",
        "#df1['multi_class'] = df1['multi_class'].fillna(0)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3TrxRtjxE3f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "67d462ad-0f08-46a4-add0-df96de53358d"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Unnamed: 0</th>\n",
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              "      <th>time</th>\n",
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              "      <th>d_pressure_crank</th>\n",
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              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "      <th>Asset</th>\n",
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              "      <td>Ashland_Unit256</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2019-12-01 03:30:00</td>\n",
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              "      <td>Ashland_Unit256</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-12-01 04:30:00</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1  ...            Asset  Binary\n",
              "0           0             0  ...  Ashland_Unit256     0.0\n",
              "1           1             1  ...  Ashland_Unit256     0.0\n",
              "2           2             2  ...  Ashland_Unit256     0.0\n",
              "3           3             3  ...  Ashland_Unit256     0.0\n",
              "4           4             4  ...  Ashland_Unit256     0.0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJlXj7AOtvSl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9f5b09f0-6157-4b99-e17b-42df1491c3c7"
      },
      "source": [
        "df1.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(124842, 16)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuozQPwtzzkn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "9bf70e0d-317e-4dbc-cb91-825c5ad0c181"
      },
      "source": [
        "df1.columns"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'time', 'cyl', 's_temp_crank',\n",
              "       'd_temp_crank', 's_pressure_crank', 'd_pressure_crank', 'rpm_crank',\n",
              "       's_temp_mean', 'd_temp_mean', 's_pressure_mean', 'd_pressure_mean',\n",
              "       'rpm_mean', 'Asset', 'Binary'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_FhJ9ekCX79",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_columns = [ 'cyl', 's_temp_crank', 'd_temp_crank', 's_pressure_crank',\\\n",
        "       'd_pressure_crank', 'rpm_crank', 's_temp_mean', 'd_temp_mean',\\\n",
        "       's_pressure_mean', 'd_pressure_mean', 'rpm_mean']"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noBsBx2QzZf0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e7502e22-0311-4cc0-8eb8-48979aee2beb"
      },
      "source": [
        "len(X_columns)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzRQrtp3xKTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df1[X_columns]"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1a92LSVIsCgl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c3e4afd7-d7e4-40f9-af87-62c60d3eb7b6"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(124842, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X76N5os4_x5r",
        "colab_type": "text"
      },
      "source": [
        "# build  with mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41ZpjzWqDeIW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "0bd8fd56-4f7d-4bfe-8575-d55c7f3266e8"
      },
      "source": [
        "X"
      ],
      "execution_count": 80,
      "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>cyl</th>\n",
              "      <th>s_temp_crank</th>\n",
              "      <th>d_temp_crank</th>\n",
              "      <th>s_pressure_crank</th>\n",
              "      <th>d_pressure_crank</th>\n",
              "      <th>rpm_crank</th>\n",
              "      <th>s_temp_mean</th>\n",
              "      <th>d_temp_mean</th>\n",
              "      <th>s_pressure_mean</th>\n",
              "      <th>d_pressure_mean</th>\n",
              "      <th>rpm_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>103.113613</td>\n",
              "      <td>216.591245</td>\n",
              "      <td>354.642761</td>\n",
              "      <td>836.740658</td>\n",
              "      <td>1357.93250</td>\n",
              "      <td>103.113613</td>\n",
              "      <td>218.012416</td>\n",
              "      <td>344.238677</td>\n",
              "      <td>820.900548</td>\n",
              "      <td>1357.932500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>99.965045</td>\n",
              "      <td>214.136948</td>\n",
              "      <td>352.424753</td>\n",
              "      <td>837.714329</td>\n",
              "      <td>1358.24000</td>\n",
              "      <td>99.965045</td>\n",
              "      <td>215.236945</td>\n",
              "      <td>342.992523</td>\n",
              "      <td>822.153967</td>\n",
              "      <td>1358.240000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>98.503206</td>\n",
              "      <td>213.432090</td>\n",
              "      <td>352.148637</td>\n",
              "      <td>842.562702</td>\n",
              "      <td>1398.28125</td>\n",
              "      <td>98.503206</td>\n",
              "      <td>214.106646</td>\n",
              "      <td>344.690998</td>\n",
              "      <td>829.080473</td>\n",
              "      <td>1398.281250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>104.620153</td>\n",
              "      <td>220.973225</td>\n",
              "      <td>352.706318</td>\n",
              "      <td>848.715357</td>\n",
              "      <td>1397.87125</td>\n",
              "      <td>104.620153</td>\n",
              "      <td>221.697270</td>\n",
              "      <td>345.283193</td>\n",
              "      <td>835.520144</td>\n",
              "      <td>1397.871250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>109.567843</td>\n",
              "      <td>227.339240</td>\n",
              "      <td>351.392953</td>\n",
              "      <td>851.224013</td>\n",
              "      <td>1397.31000</td>\n",
              "      <td>109.567843</td>\n",
              "      <td>228.114859</td>\n",
              "      <td>344.060655</td>\n",
              "      <td>838.433890</td>\n",
              "      <td>1397.310000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138493</th>\n",
              "      <td>1.0</td>\n",
              "      <td>96.835182</td>\n",
              "      <td>99.422598</td>\n",
              "      <td>13.660536</td>\n",
              "      <td>14.204035</td>\n",
              "      <td>1152.12875</td>\n",
              "      <td>96.835182</td>\n",
              "      <td>171.098857</td>\n",
              "      <td>13.698061</td>\n",
              "      <td>39.353878</td>\n",
              "      <td>1152.128750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138494</th>\n",
              "      <td>1.0</td>\n",
              "      <td>95.006198</td>\n",
              "      <td>97.633450</td>\n",
              "      <td>13.558118</td>\n",
              "      <td>14.108873</td>\n",
              "      <td>1152.18375</td>\n",
              "      <td>95.006198</td>\n",
              "      <td>169.042154</td>\n",
              "      <td>13.567354</td>\n",
              "      <td>39.004776</td>\n",
              "      <td>1152.295000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138495</th>\n",
              "      <td>1.0</td>\n",
              "      <td>94.047816</td>\n",
              "      <td>96.736952</td>\n",
              "      <td>13.624233</td>\n",
              "      <td>14.190248</td>\n",
              "      <td>1152.29625</td>\n",
              "      <td>94.047816</td>\n",
              "      <td>168.547145</td>\n",
              "      <td>13.454798</td>\n",
              "      <td>38.888198</td>\n",
              "      <td>1152.406875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138496</th>\n",
              "      <td>1.0</td>\n",
              "      <td>93.322582</td>\n",
              "      <td>96.065229</td>\n",
              "      <td>13.553225</td>\n",
              "      <td>14.129440</td>\n",
              "      <td>1152.01875</td>\n",
              "      <td>93.322582</td>\n",
              "      <td>168.032948</td>\n",
              "      <td>13.353085</td>\n",
              "      <td>38.787718</td>\n",
              "      <td>1152.185000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138497</th>\n",
              "      <td>1.0</td>\n",
              "      <td>92.461638</td>\n",
              "      <td>94.666968</td>\n",
              "      <td>13.540495</td>\n",
              "      <td>14.003428</td>\n",
              "      <td>1152.01750</td>\n",
              "      <td>92.461638</td>\n",
              "      <td>166.238691</td>\n",
              "      <td>13.409664</td>\n",
              "      <td>38.583256</td>\n",
              "      <td>1152.238750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>124842 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        cyl  s_temp_crank  ...  d_pressure_mean     rpm_mean\n",
              "0       1.0    103.113613  ...       820.900548  1357.932500\n",
              "1       1.0     99.965045  ...       822.153967  1358.240000\n",
              "2       1.0     98.503206  ...       829.080473  1398.281250\n",
              "3       1.0    104.620153  ...       835.520144  1397.871250\n",
              "4       1.0    109.567843  ...       838.433890  1397.310000\n",
              "...     ...           ...  ...              ...          ...\n",
              "138493  1.0     96.835182  ...        39.353878  1152.128750\n",
              "138494  1.0     95.006198  ...        39.004776  1152.295000\n",
              "138495  1.0     94.047816  ...        38.888198  1152.406875\n",
              "138496  1.0     93.322582  ...        38.787718  1152.185000\n",
              "138497  1.0     92.461638  ...        38.583256  1152.238750\n",
              "\n",
              "[124842 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osDrTPHBDgws",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_true = df1.iloc[:,15]"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhjDVPj6kHHe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08d9e6be-9860-4211-ace8-6a64928f1068"
      },
      "source": [
        "y_true.unique()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUjimy6IJ_wS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#seed = 42\n",
        "test_size = 0.15\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_true, test_size=test_size,shuffle= True)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5KNo3We0J-4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# X_val, X_test, y_val, y_test = train_test_split(X_Test, y_Test, test_size=0.75)"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeA4Ad6-TG2m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import Counter"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6qs--2goqXC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8bcb78f5-2e26-4dc2-fa64-fb35338c808c"
      },
      "source": [
        "print(X_train.shape,y_train.shape)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(106115, 11) (106115,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kz_JxO2XFUGw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "22cf4550-5edb-4c6f-9fb3-98cff698095f"
      },
      "source": [
        "len(X_test)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18727"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8Pk0uESFcGx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#len(X_val)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zxib685OjhRK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "220ed9cb-0774-461b-fcaf-6755070970dd"
      },
      "source": [
        "Counter(y_train)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0.0: 73179, 1.0: 32936})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x46e8C02_USW",
        "colab_type": "text"
      },
      "source": [
        "64->16->4->1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QK8nARFrb0jE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class binaryClassification(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(binaryClassification, self).__init__()\n",
        "        # Number of input features is 11.\n",
        "        self.layer_1 = nn.Linear(11, 64) \n",
        "        self.layer_2 = nn.Linear(64, 64)\n",
        "        self.layer_3 = nn.Linear(64, 64)\n",
        "        self.layer_4 = nn.Linear(64, 10)\n",
        "        self.layer_5 = nn.Linear(10, 4)\n",
        "        self.layer_out = nn.Linear(4, 1) \n",
        "        \n",
        "        self.relu = nn.ReLU()\n",
        "        self.dropout = nn.Dropout(p=0.1)\n",
        "        self.batchnorm1 = nn.BatchNorm1d(64)\n",
        "        self.batchnorm2 = nn.BatchNorm1d(64)\n",
        "        self.batchnorm3 = nn.BatchNorm1d(64)\n",
        "        self.batchnorm4 = nn.BatchNorm1d(10)\n",
        "\n",
        "        \n",
        "    def forward(self, inputs):\n",
        "        x = self.relu(self.layer_1(inputs))\n",
        "        x = self.batchnorm1(x)\n",
        "        x = self.relu(self.layer_2(x))\n",
        "        x = self.batchnorm2(x)\n",
        "        x = self.relu(self.layer_3(x))\n",
        "        x = self.batchnorm3(x)\n",
        "        x = self.relu(self.layer_4(x))\n",
        "        x = self.batchnorm4(x)\n",
        "        x = self.relu(self.layer_5(x))\n",
        "        x = self.dropout(x)\n",
        "        x = self.layer_out(x)\n",
        "        \n",
        "        return x"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXiXsGllj-Pp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "#X_val = scaler.fit_transform(X_val)\n",
        "X_test = scaler.fit_transform(X_test)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7K8tRCb1MOY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "59deb53b-aea1-4eeb-edef-1f2be1f2419a"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.19492408,  0.33206065, -0.69527269, ...,  1.02432582,\n",
              "         0.47888903,  0.27490141],\n",
              "       [-0.25140916,  1.69077262,  1.396364  , ..., -0.71298618,\n",
              "        -0.0531538 ,  0.6839079 ],\n",
              "       [-1.19492408, -1.49768951, -0.11198699, ..., -0.26135794,\n",
              "         0.44810608,  0.51473448],\n",
              "       ...,\n",
              "       [-0.25140916,  1.39758973,  1.40970769, ..., -0.68273908,\n",
              "         0.11907178,  0.68318109],\n",
              "       [ 1.63562067,  0.37462322,  0.01408412, ..., -1.02581729,\n",
              "        -1.14428728, -1.41711287],\n",
              "       [-1.19492408, -0.54158127, -0.48774594, ...,  0.30671641,\n",
              "         0.36637245,  0.12747495]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uam-SPjJlGsT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS = 10\n",
        "BATCH_SIZE = 75\n",
        "LEARNING_RATE = 0.0792"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdQAqwpOlTYo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## train data\n",
        "class trainData(Dataset):\n",
        "    \n",
        "    def __init__(self, X_data, y_data):\n",
        "        self.X_data = X_data\n",
        "        self.y_data = y_data\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        return self.X_data[index], self.y_data[index]\n",
        "        \n",
        "    def __len__ (self):\n",
        "        return len(self.X_data)\n",
        "\n",
        "y_train = y_train.values\n",
        "train_data = trainData(torch.FloatTensor(X_train),torch.FloatTensor(y_train))\n",
        "## test data    \n",
        "class testData(Dataset):\n",
        "    \n",
        "    def __init__(self, X_data):\n",
        "        self.X_data = X_data\n",
        "        \n",
        "    def __getitem__(self, index):\n",
        "        return self.X_data[index]\n",
        "        \n",
        "    def __len__ (self):\n",
        "        return len(self.X_data)\n",
        "# ## val data    \n",
        "# class valData(Dataset):\n",
        "    \n",
        "#     def __init__(self, X_data):\n",
        "#         self.X_data = X_data\n",
        "        \n",
        "#     def __getitem__(self, index):\n",
        "#         return self.X_data[index]\n",
        "        \n",
        "#     def __len__ (self):\n",
        "#         return len(self.X_data)\n",
        "\n",
        "test_data = testData(torch.FloatTensor(X_test))\n",
        "# val_data = valData(torch.FloatTensor(X_val))"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ADj3luUlV2O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\n",
        "# val_loader = DataLoader(dataset=val_data, batch_size=1)\n",
        "test_loader = DataLoader(dataset=test_data, batch_size=1)"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ipz_O-d2ndxr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b171e94c-3820-411b-8efe-aee053a497d5"
      },
      "source": [
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(device)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmrpoOEmnaqx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "b514f95c-abcc-44df-d2e6-e624cbf3d77e"
      },
      "source": [
        "model = binaryClassification()\n",
        "model.to(device)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "binaryClassification(\n",
              "  (layer_1): Linear(in_features=11, out_features=64, bias=True)\n",
              "  (layer_2): Linear(in_features=64, out_features=64, bias=True)\n",
              "  (layer_3): Linear(in_features=64, out_features=64, bias=True)\n",
              "  (layer_4): Linear(in_features=64, out_features=10, bias=True)\n",
              "  (layer_5): Linear(in_features=10, out_features=4, bias=True)\n",
              "  (layer_out): Linear(in_features=4, out_features=1, bias=True)\n",
              "  (relu): ReLU()\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (batchnorm1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (batchnorm2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (batchnorm3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (batchnorm4): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKX1xn8OnRQl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "criterion = nn.BCEWithLogitsLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CX-alqvfpa1D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def binary_acc(y_pred, y_test):\n",
        "    y_pred_tag = torch.round(torch.sigmoid(y_pred))\n",
        "\n",
        "    correct_results_sum = (y_pred_tag == y_test).sum().float()\n",
        "    acc = correct_results_sum/y_test.shape[0]\n",
        "    acc = torch.round(acc * 100)\n",
        "    \n",
        "    return acc"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVcAHAH7pmWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.train()\n",
        "for e in range(1, EPOCHS+1):\n",
        "    epoch_loss = 0\n",
        "    epoch_acc = 0\n",
        "    for X_batch, y_batch in train_loader:\n",
        "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        #X_batch = X_batch\n",
        "  \n",
        "        y_pred = model(X_batch)\n",
        "        \n",
        "        loss = criterion(y_pred, y_batch.unsqueeze(1))\n",
        "        acc = binary_acc(y_pred, y_batch.unsqueeze(1))\n",
        "        \n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        epoch_loss += loss.item()\n",
        "        epoch_acc += acc.item()"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkg9Nwg7MH-E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_list = []\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for X_batch in test_loader:\n",
        "        X_batch = X_batch.to(device)\n",
        "        y_test_pred = model(X_batch)\n",
        "        y_test_pred = torch.sigmoid(y_test_pred)\n",
        "        y_pred_tag = torch.round(y_test_pred)\n",
        "        y_pred_list.append(y_pred_tag.cpu().numpy())\n",
        "\n",
        "y_pred_list = [a.squeeze().tolist() for a in y_pred_list]"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aad4gs6eB_O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5e7dc5c3-5b81-45fd-85d1-a7571eb5ff96"
      },
      "source": [
        "y_pred_list"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOx8PsecMNoW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "bdb07407-9282-4281-c9de-b7eb93e1950e"
      },
      "source": [
        "confusion_matrix(y_test, y_pred_list)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11476,  1387],\n",
              "       [ 1149,  4715]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUpQ6vwKSj6V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "ba481d51-3ee2-433b-aed5-5c162e70ea30"
      },
      "source": [
        "df2['Binary'] = df2['Binary'].fillna(0)\n",
        "df2 = df1.fillna(0)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqsLKC9z6NLu",
        "colab_type": "text"
      },
      "source": [
        "# Old Assets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NXduzphPVfU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "outputId": "2db7d4f1-50c0-428d-9c53-95afaf1c3c7d"
      },
      "source": [
        "print(classification_report(y_test, y_pred_list))"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-146-cbbb342dd146>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_list\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[1;32m   1969\u001b[0m     \"\"\"\n\u001b[1;32m   1970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m     \u001b[0mlabels_given\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindicator\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \"\"\"\n\u001b[0;32m---> 80\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     81\u001b[0m     \u001b[0mtype_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0mtype_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype_of_target\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 212\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [18727, 13656]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqpTDFyBSuw9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "filename = 'binarynn_dcp_model.pkl'"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dpg2BIcQb2IM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.save(model.state_dict(),filename)\n"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcfxrAPHc44k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "36811b77-e969-4a1a-b80c-6e9455cc7c72"
      },
      "source": [
        "model.load_state_dict(torch.load(filename))"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_U7E2CDgdA7_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "796a6d20-6dd1-402a-b52f-60b2de6e31db"
      },
      "source": [
        "model.eval()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "binaryClassification(\n",
              "  (layer_1): Linear(in_features=11, out_features=64, bias=True)\n",
              "  (layer_2): Linear(in_features=64, out_features=64, bias=True)\n",
              "  (layer_3): Linear(in_features=64, out_features=64, bias=True)\n",
              "  (layer_4): Linear(in_features=64, out_features=10, bias=True)\n",
              "  (layer_5): Linear(in_features=10, out_features=4, bias=True)\n",
              "  (layer_out): Linear(in_features=4, out_features=1, bias=True)\n",
              "  (relu): ReLU()\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (batchnorm1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (batchnorm2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (batchnorm3): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (batchnorm4): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVk1VqtroSpE",
        "colab_type": "text"
      },
      "source": [
        "# Test Pred New Assets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tfrWLwNHoMJH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df2[X_columns]"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUAKUZ2qphUh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_true = df2.iloc[:,15]"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLY9dv05QlWN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dd0d8305-f8b4-4b3b-b94a-642c1e27bc33"
      },
      "source": [
        "y_true.unique()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8O01xdkpqpg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = scaler.fit_transform(X)"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ib_UUpr8qEVN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data = testData(torch.FloatTensor(X_test))"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEAoUZssqQcj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_loader = DataLoader(dataset=test_data, batch_size=1)"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oruB7-cC6Xyb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c0757290-479d-42b7-ae21-12588ada20a5"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(124842, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdNyu3TcaKsU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_list = []\n",
        "with torch.no_grad():\n",
        "    for X_batch in test_loader:\n",
        "        X_batch = X_batch.to(device)\n",
        "        y_test_pred = model(X_batch)\n",
        "        y_test_pred = torch.sigmoid(y_test_pred)\n",
        "        y_pred_tag = torch.round(y_test_pred)\n",
        "        y_pred_list.append(y_pred_tag.cpu().numpy())\n",
        "\n"
      ],
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJoFz7QCKgdq",
        "colab_type": "text"
      },
      "source": [
        "# New Assets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJYAUph2iNYL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions=[]\n",
        "predictions.append([y_pred_list[i][0][0] for i in range(len(y_pred_list))])"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEIhDwizQzOM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# predictions"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AGVAiLGQctA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9ca7f78-fde9-4d5d-abbb-39c7c658c492"
      },
      "source": [
        "len(y_true)"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "124842"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWe_b7tZRUmR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7626c326-d1e4-4aff-9803-49797ff9af17"
      },
      "source": [
        "len(predictions[0])"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "124842"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-g5NcqySR2q-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6e4a3cba-f641-4bc3-9f8d-9a5a98327986"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtiN-zLylj5K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "98f5c6f2-782e-47d5-acfa-92ce80b31331"
      },
      "source": [
        "print(classification_report(y_true, predictions[0]))"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.91      0.89      0.90     86042\n",
            "         1.0       0.76      0.81      0.78     38800\n",
            "\n",
            "    accuracy                           0.86    124842\n",
            "   macro avg       0.84      0.85      0.84    124842\n",
            "weighted avg       0.86      0.86      0.86    124842\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}