submission.ipynb

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            "text/plain": [
              "        step       customer  age gender zipcodeOri       merchant zipMerchant  \\\n",
              "0          0   'C583110837'  '3'    'M'    '28007'   'M480139044'     '28007'   \n",
              "1          0  'C1332295774'  '3'    'M'    '28007'   'M480139044'     '28007'   \n",
              "2          0  'C1160421902'  '3'    'M'    '28007'   'M857378720'     '28007'   \n",
              "3          0   'C966214713'  '3'    'M'    '28007'   'M857378720'     '28007'   \n",
              "4          0  'C1450140987'  '4'    'F'    '28007'  'M1198415165'     '28007'   \n",
              "...      ...            ...  ...    ...        ...            ...         ...   \n",
              "513588   179   'C827350474'  '4'    'F'    '28007'    'M85975013'     '28007'   \n",
              "513589   179  'C1151051721'  '3'    'M'    '28007'  'M1823072687'     '28007'   \n",
              "513590   179  'C1621267054'  '2'    'F'    '28007'    'M85975013'     '28007'   \n",
              "513591   179   'C897612698'  '4'    'M'    '28007'   'M348934600'     '28007'   \n",
              "513592   179   'C885555910'  '5'    'F'    '28007'  'M1823072687'     '28007'   \n",
              "\n",
              "                      category  amount  fraud  \n",
              "0                  'es_health'   44.26      1  \n",
              "1                  'es_health'  324.50      1  \n",
              "2           'es_hotelservices'  176.32      1  \n",
              "3           'es_hotelservices'  337.41      1  \n",
              "4       'es_wellnessandbeauty'  220.11      1  \n",
              "...                        ...     ...    ...  \n",
              "513588               'es_food'   62.62      0  \n",
              "513589     'es_transportation'   34.50      0  \n",
              "513590               'es_food'    1.30      0  \n",
              "513591     'es_transportation'   27.05      0  \n",
              "513592     'es_transportation'    5.96      0  \n",
              "\n",
              "[513593 rows x 10 columns]"
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              "      <th>merchant</th>\n",
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              "      <td>'C583110837'</td>\n",
              "      <td>'3'</td>\n",
              "      <td>'M'</td>\n",
              "      <td>'28007'</td>\n",
              "      <td>'M480139044'</td>\n",
              "      <td>'28007'</td>\n",
              "      <td>'es_health'</td>\n",
              "      <td>44.26</td>\n",
              "      <td>1</td>\n",
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              "      <td>'M480139044'</td>\n",
              "      <td>'28007'</td>\n",
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              "      <td>'28007'</td>\n",
              "      <td>'es_hotelservices'</td>\n",
              "      <td>176.32</td>\n",
              "      <td>1</td>\n",
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              "      <td>'M857378720'</td>\n",
              "      <td>'28007'</td>\n",
              "      <td>'es_hotelservices'</td>\n",
              "      <td>337.41</td>\n",
              "      <td>1</td>\n",
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              "      <td>'28007'</td>\n",
              "      <td>'es_wellnessandbeauty'</td>\n",
              "      <td>220.11</td>\n",
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              "      <td>'M85975013'</td>\n",
              "      <td>'28007'</td>\n",
              "      <td>'es_food'</td>\n",
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              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"train_hsbc_df.csv\")\n",
        "df.head(-50)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(df['amount'], kde=True)\n",
        "plt.title('Distribution of Transaction Amounts')\n",
        "plt.show()"
      ],
      "metadata": {
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          "height": 411
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.boxplot(x='category', y='amount', data=df)\n",
        "plt.title('Transaction Amounts by Merchant Category')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tnJ8WDzJnnur",
        "outputId": "4eeb4a72-4fb1-43b3-84ec-a81cd3a0b074"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "df['fraud'].value_counts().plot(kind='pie', autopct='%1.1f%%')\n",
        "plt.title('Distribution of Fraudulent vs Non-Fraudulent Transactions')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "YKUo0aBnnqos",
        "outputId": "a114d6d8-ae3b-477b-ac97-2d376be4b9b8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "df['merchant'].value_counts().plot(kind='pie', autopct='%1.1f%%')\n",
        "plt.title('Merchant ID distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "CNSj-cRWnr6f",
        "outputId": "c26dd090-732b-47f8-e237-3cbcfda8c4ae"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Ori_values = df['zipcodeOri'].unique()\n",
        "Mer_values = df['zipMerchant'].unique()\n",
        "print(f\"{Ori_values}, {Mer_values}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUl2RCDGntiI",
        "outputId": "cbb40738-f56e-432c-dbfb-6c37882f1e02"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"'28007'\"], [\"'28007'\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HkF6B8pnwWH",
        "outputId": "d5a554ee-1306-4654-f4dd-38d538f28f5d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: \n",
            "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
            "\n",
            "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
            "This will raise in a future version.\n",
            "\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n"
      ],
      "metadata": {
        "id": "FVo4OWlfn3wF"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_drop = ['step', 'customer', 'zipcodeOri', 'zipMerchant']\n",
        "df = df.drop(columns=columns_to_drop)\n",
        "df['age'] = df['age'].replace(\"'U'\", np.nan).str.replace(\"'\", \"\").astype(float)\n",
        "df['age'] = df['age'].fillna(df['age'].median())"
      ],
      "metadata": {
        "id": "acStNQ9jn5pU"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "categorical_columns = ['gender', 'merchant', 'category']\n",
        "for col in categorical_columns:\n",
        "    df[col] = le.fit_transform(df[col].astype(str))\n",
        "\n",
        "scaler = StandardScaler()\n",
        "df['amount'] = scaler.fit_transform(df[['amount']])"
      ],
      "metadata": {
        "id": "w_dJvu2Pn7bm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('fraud', axis=1)\n",
        "y = df['fraud']\n",
        "smote = SMOTE(random_state=42)\n",
        "X_resampled, y_resampled = smote.fit_resample(X, y)\n"
      ],
      "metadata": {
        "id": "XmOKX8-Zn-Ac"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "oyywZhijoAhQ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model = XGBClassifier(\n",
        "    colsample_bytree=1.0, learning_rate=0.3, max_depth=10, n_estimators=600,\n",
        "    subsample=1.0, gamma=2.0, min_child_weight=1, tree_method='gpu_hist',\n",
        "    use_label_encoder=False\n",
        ")\n",
        "lgbm_model = LGBMClassifier(n_estimators=500, learning_rate=0.01, max_depth=10)\n",
        "\n",
        "stacking_clf = StackingClassifier(\n",
        "    estimators=[('xgb', xgb_model), ('lgbm', lgbm_model)],\n",
        "    final_estimator=XGBClassifier(\n",
        "        colsample_bytree=1.0, learning_rate=0.3, max_depth=10, n_estimators=600,\n",
        "        subsample=1.0, gamma=2.0, min_child_weight=1, tree_method='gpu_hist',\n",
        "        use_label_encoder=False\n",
        "    )\n",
        ")\n",
        "\n",
        "stacking_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uU1sGVPMoCnm",
        "outputId": "d30b001c-9b11-403f-b6ab-c865799574e0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 405858, number of negative: 406050\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.198770 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 578\n",
            "[LightGBM] [Info] Number of data points in the train set: 811908, number of used features: 5\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499882 -> initscore=-0.000473\n",
            "[LightGBM] [Info] Start training from score -0.000473\n",
            "[LightGBM] [Info] Number of positive: 324686, number of negative: 324840\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009679 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 579\n",
            "[LightGBM] [Info] Number of data points in the train set: 649526, number of used features: 5\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499881 -> initscore=-0.000474\n",
            "[LightGBM] [Info] Start training from score -0.000474\n",
            "[LightGBM] [Info] Number of positive: 324686, number of negative: 324840\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.025545 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 579\n",
            "[LightGBM] [Info] Number of data points in the train set: 649526, number of used features: 5\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499881 -> initscore=-0.000474\n",
            "[LightGBM] [Info] Start training from score -0.000474\n",
            "[LightGBM] [Info] Number of positive: 324686, number of negative: 324840\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009815 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 579\n",
            "[LightGBM] [Info] Number of data points in the train set: 649526, number of used features: 5\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499881 -> initscore=-0.000474\n",
            "[LightGBM] [Info] Start training from score -0.000474\n",
            "[LightGBM] [Info] Number of positive: 324687, number of negative: 324840\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009644 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 578\n",
            "[LightGBM] [Info] Number of data points in the train set: 649527, number of used features: 5\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499882 -> initscore=-0.000471\n",
            "[LightGBM] [Info] Start training from score -0.000471\n",
            "[LightGBM] [Info] Number of positive: 324687, number of negative: 324840\n",
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.009662 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 578\n",
            "[LightGBM] [Info] Number of data points in the train set: 649527, number of used features: 5\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.499882 -> initscore=-0.000471\n",
            "[LightGBM] [Info] Start training from score -0.000471\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StackingClassifier(estimators=[('xgb',\n",
              "                                XGBClassifier(base_score=None, booster=None,\n",
              "                                              callbacks=None,\n",
              "                                              colsample_bylevel=None,\n",
              "                                              colsample_bynode=None,\n",
              "                                              colsample_bytree=1.0, device=None,\n",
              "                                              early_stopping_rounds=None,\n",
              "                                              enable_categorical=False,\n",
              "                                              eval_metric=None,\n",
              "                                              feature_types=None, gamma=2.0,\n",
              "                                              grow_policy=None,\n",
              "                                              importance_type=None,\n",
              "                                              interaction_constraints=None,\n",
              "                                              learning_ra...\n",
              "                                                 feature_types=None, gamma=2.0,\n",
              "                                                 grow_policy=None,\n",
              "                                                 importance_type=None,\n",
              "                                                 interaction_constraints=None,\n",
              "                                                 learning_rate=0.3,\n",
              "                                                 max_bin=None,\n",
              "                                                 max_cat_threshold=None,\n",
              "                                                 max_cat_to_onehot=None,\n",
              "                                                 max_delta_step=None,\n",
              "                                                 max_depth=10, max_leaves=None,\n",
              "                                                 min_child_weight=1,\n",
              "                                                 missing=nan,\n",
              "                                                 monotone_constraints=None,\n",
              "                                                 multi_strategy=None,\n",
              "                                                 n_estimators=600, n_jobs=None,\n",
              "                                                 num_parallel_tree=None,\n",
              "                                                 random_state=None, ...))"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StackingClassifier(estimators=[(&#x27;xgb&#x27;,\n",
              "                                XGBClassifier(base_score=None, booster=None,\n",
              "                                              callbacks=None,\n",
              "                                              colsample_bylevel=None,\n",
              "                                              colsample_bynode=None,\n",
              "                                              colsample_bytree=1.0, device=None,\n",
              "                                              early_stopping_rounds=None,\n",
              "                                              enable_categorical=False,\n",
              "                                              eval_metric=None,\n",
              "                                              feature_types=None, gamma=2.0,\n",
              "                                              grow_policy=None,\n",
              "                                              importance_type=None,\n",
              "                                              interaction_constraints=None,\n",
              "                                              learning_ra...\n",
              "                                                 feature_types=None, gamma=2.0,\n",
              "                                                 grow_policy=None,\n",
              "                                                 importance_type=None,\n",
              "                                                 interaction_constraints=None,\n",
              "                                                 learning_rate=0.3,\n",
              "                                                 max_bin=None,\n",
              "                                                 max_cat_threshold=None,\n",
              "                                                 max_cat_to_onehot=None,\n",
              "                                                 max_delta_step=None,\n",
              "                                                 max_depth=10, max_leaves=None,\n",
              "                                                 min_child_weight=1,\n",
              "                                                 missing=nan,\n",
              "                                                 monotone_constraints=None,\n",
              "                                                 multi_strategy=None,\n",
              "                                                 n_estimators=600, n_jobs=None,\n",
              "                                                 num_parallel_tree=None,\n",
              "                                                 random_state=None, ...))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StackingClassifier</label><div class=\"sk-toggleable__content\"><pre>StackingClassifier(estimators=[(&#x27;xgb&#x27;,\n",
              "                                XGBClassifier(base_score=None, booster=None,\n",
              "                                              callbacks=None,\n",
              "                                              colsample_bylevel=None,\n",
              "                                              colsample_bynode=None,\n",
              "                                              colsample_bytree=1.0, device=None,\n",
              "                                              early_stopping_rounds=None,\n",
              "                                              enable_categorical=False,\n",
              "                                              eval_metric=None,\n",
              "                                              feature_types=None, gamma=2.0,\n",
              "                                              grow_policy=None,\n",
              "                                              importance_type=None,\n",
              "                                              interaction_constraints=None,\n",
              "                                              learning_ra...\n",
              "                                                 feature_types=None, gamma=2.0,\n",
              "                                                 grow_policy=None,\n",
              "                                                 importance_type=None,\n",
              "                                                 interaction_constraints=None,\n",
              "                                                 learning_rate=0.3,\n",
              "                                                 max_bin=None,\n",
              "                                                 max_cat_threshold=None,\n",
              "                                                 max_cat_to_onehot=None,\n",
              "                                                 max_delta_step=None,\n",
              "                                                 max_depth=10, max_leaves=None,\n",
              "                                                 min_child_weight=1,\n",
              "                                                 missing=nan,\n",
              "                                                 monotone_constraints=None,\n",
              "                                                 multi_strategy=None,\n",
              "                                                 n_estimators=600, n_jobs=None,\n",
              "                                                 num_parallel_tree=None,\n",
              "                                                 random_state=None, ...))</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=2.0, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=600, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>lgbm</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(learning_rate=0.01, max_depth=10, n_estimators=500)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>final_estimator</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=2.0, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=600, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_proba_stacking = stacking_clf.predict_proba(X_test)[:, 1]\n",
        "thresholds = np.linspace(0, 1, 100)\n",
        "best_threshold = 0\n",
        "best_f1 = 0\n",
        "for threshold in thresholds:\n",
        "    y_pred_stacking = (y_pred_proba_stacking >= threshold).astype(int)\n",
        "    f1 = f1_score(y_test, y_pred_stacking)\n",
        "    if f1 > best_f1:\n",
        "        best_f1 = f1\n",
        "        best_threshold = threshold"
      ],
      "metadata": {
        "id": "feU8nBuVoobj"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_stacking = (y_pred_proba_stacking >= best_threshold).astype(int)\n",
        "precision_stacking = precision_score(y_test, y_pred_stacking)\n",
        "recall_stacking = recall_score(y_test, y_pred_stacking)\n",
        "f1_stacking = f1_score(y_test, y_pred_stacking)\n",
        "auc_roc_stacking = roc_auc_score(y_test, y_pred_proba_stacking)"
      ],
      "metadata": {
        "id": "dVJc2dbworYI"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "VcR41dlnotFc"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred_stacking).ravel()"
      ],
      "metadata": {
        "id": "ks7WEkXUpL2N"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Stacking Classifier:\")\n",
        "print(f\"Precision: {precision_stacking:.4f}\")\n",
        "print(f\"Recall: {recall_stacking:.4f}\")\n",
        "print(f\"F1 Score: {f1_stacking:.4f}\")\n",
        "print(f\"AUC-ROC: {auc_roc_stacking:.4f}\")\n",
        "print(f\"True Negatives (TN): {tn}\")\n",
        "print(f\"False Positives (FP): {fp}\")\n",
        "print(f\"False Negatives (FN): {fn}\")\n",
        "print(f\"True Positives (TP): {tp}\")\n",
        "print(f\"True Positive Rate (TPR) / Recall: {recall_stacking:.4f}\")\n",
        "print(f\"False Positive Rate (FPR): {fp / (fp + tn):.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taLc3ozepSBj",
        "outputId": "b8ed163e-ceab-4cb0-8e95-b7703ed1b50b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stacking Classifier:\n",
            "Precision: 0.9796\n",
            "Recall: 0.9915\n",
            "F1 Score: 0.9855\n",
            "AUC-ROC: 0.9987\n",
            "True Negatives (TN): 99293\n",
            "False Positives (FP): 2100\n",
            "False Negatives (FN): 862\n",
            "True Positives (TP): 100723\n",
            "True Positive Rate (TPR) / Recall: 0.9915\n",
            "False Positive Rate (FPR): 0.0207\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BL8cSq_ZpU6I"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}