Credit_Card_Fraud_LightGBM_Classification_and_SMOTE.ipynb

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      "name": "LightGBM Classification and SMOTE for Credit Card Fraud.ipynb",
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      "cell_type": "code",
      "source": [
        "#@title Installing and upgrading packages\n",
        "!pip install lightgbm --upgrade\n",
        "!pip install optuna"
      ],
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        "cellView": "form",
        "id": "SP8rMWQ5rGcP"
      },
      "execution_count": null,
      "outputs": []
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      "execution_count": 39,
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        "#@title Preparing the data\n",
        "# Importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "pd.set_option(\"display.precision\", 4)\n",
        "# Metrics and tools\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score\n",
        "# Optuna packages\n",
        "import optuna.integration.lightgbm as lgb\n",
        "from lightgbm import early_stopping\n",
        "from lightgbm import log_evaluation\n",
        "\n",
        "# Loading the data\n",
        "# [https://www.kaggle.com/mlg-ulb/creditcardfraud]\n",
        "df = pd.read_csv('creditcard.csv') \n",
        "df.drop('Time', axis = 1, inplace = True)\n",
        "\n",
        "X = df.iloc[:, :-1]\n",
        "y = df.iloc[:, -1]\n",
        "\n",
        "# Splitting the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                                    test_size = 0.25,\n",
        "                                                    random_state = 42)\n",
        "\n",
        "# Scaling the 'Amount' column\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train['Amount'] = sc.fit_transform(X_train['Amount'].to_numpy().reshape(-1, 1))\n",
        "X_test['Amount'] = sc.transform(X_test['Amount'].to_numpy().reshape(-1, 1))"
      ]
    },
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        "X_train.head()"
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Applying SMOTE to balance the data \n",
        "from collections import Counter\n",
        "\n",
        "print('Frequency of each class before SMOTE')\n",
        "print('Training set: {}'.format(Counter(y_train)))\n",
        "print('Testing set: {}'.format(Counter(y_test)))\n",
        "\n",
        "from imblearn.over_sampling import SMOTE \n",
        "\n",
        "# add artificial samples to training data only\n",
        "sm = SMOTE(random_state=42)\n",
        "X_train_sm, y_train_sm = sm.fit_resample(X_train, y_train)\n",
        "\n",
        "print('\\nFrequency of each class after SMOTE')\n",
        "print('Training set: {}'.format(Counter(y_train_sm)))\n",
        "print('Testing set: {}'.format(Counter(y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "buegw3Pz08SX",
        "outputId": "a1f1187f-ca16-4b8e-c8b0-b4e889de7361"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequency of each class before SMOTE\n",
            "Training set: Counter({0: 213226, 1: 379})\n",
            "Testing set: Counter({0: 71089, 1: 113})\n",
            "\n",
            "Frequency of each class after SMOTE\n",
            "Training set: Counter({0: 213226, 1: 213226})\n",
            "Testing set: Counter({0: 71089, 1: 113})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Optuna Lightgbm tuner\n",
        "def tuner(X_train, X_test, y_train, y_test):\n",
        "  # Setting up the data for the model\n",
        "  dtrain = lgb.Dataset(X_train, label = y_train)\n",
        "  dtest = lgb.Dataset(X_test, label = y_test)\n",
        "\n",
        "  # Parameters for classification\n",
        "  params = {\n",
        "      'objective' : 'binary',\n",
        "      'metric' : 'binary_logloss',\n",
        "      'verbose' : -1,\n",
        "      'boosting_type' : 'gbdt'\n",
        "  }\n",
        "\n",
        "  # Training the model\n",
        "  model = lgb.train(\n",
        "      params,\n",
        "      dtrain,\n",
        "      valid_sets = [dtrain, dtest],\n",
        "      callbacks = [early_stopping(100), log_evaluation(100)]\n",
        "  )\n",
        "\n",
        "  # Results\n",
        "  y_pred = np.rint(model.predict(X_test, \n",
        "                                     num_iteration = model.best_iteration))\n",
        "  score = f1_score(y_test, y_pred)\n",
        "  return score"
      ],
      "metadata": {
        "cellView": "form",
        "id": "ztwqm9gDrUym"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score_no_smote = tuner(X_train.values, X_test.values, y_train.values, y_test.values)"
      ],
      "metadata": {
        "id": "N8wMVCApq9Dp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score_with_smote = tuner(X_train_sm.values, X_test.values, y_train_sm.values, y_test.values)"
      ],
      "metadata": {
        "id": "Zvq1HlSctPBr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Results\n",
        "results = pd.DataFrame(np.array([f1_score_no_smote, f1_score_with_smote]),\n",
        "                       columns = ['LightGBM'],\n",
        "                       index = ['f1 score no SMOTE', 'f1 score with SMOTE'])\n",
        "results.style"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "cellView": "form",
        "id": "SuDvzVEbJtgQ",
        "outputId": "3a732d66-f803-477a-98ba-1c86ebbdb2bc"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<table id=\"T_ed6ea_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >LightGBM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_ed6ea_level0_row0\" class=\"row_heading level0 row0\" >f1 score no SMOTE</th>\n",
              "      <td id=\"T_ed6ea_row0_col0\" class=\"data row0 col0\" >0.8558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ed6ea_level0_row1\" class=\"row_heading level0 row1\" >f1 score with SMOTE</th>\n",
              "      <td id=\"T_ed6ea_row1_col0\" class=\"data row1 col0\" >0.8398</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    }
  ]
}