{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":50160,"databundleVersionId":7602123,"sourceType":"competition"}],"dockerImageVersionId":30664,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import polars as pl\nimport numpy as np\nimport pandas as pd\nimport lightgbm as lgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score \n\ndataPath = \"/kaggle/input/home-credit-credit-risk-model-stability/\"","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:26:47.285163Z","iopub.execute_input":"2024-03-14T05:26:47.285659Z","iopub.status.idle":"2024-03-14T05:26:50.174099Z","shell.execute_reply.started":"2024-03-14T05:26:47.285619Z","shell.execute_reply":"2024-03-14T05:26:50.172687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def set_table_dtypes(df: pl.DataFrame) -> pl.DataFrame:\n    # implement here all desired dtypes for tables\n    # the following is just an example\n    for col in df.columns:\n        # last letter of column name will help you determine the type\n        if col[-1] in (\"P\", \"A\"):\n            df = df.with_columns(pl.col(col).cast(pl.Float64).alias(col))\n\n    return df\n\ndef convert_strings(df: pd.DataFrame) -> pd.DataFrame:\n    for col in df.columns:  \n        if df[col].dtype.name in ['object', 'string']:\n            df[col] = df[col].astype(\"string\").astype('category')\n            current_categories = df[col].cat.categories\n            new_categories = current_categories.to_list() + [\"Unknown\"]\n            new_dtype = pd.CategoricalDtype(categories=new_categories, ordered=True)\n            df[col] = df[col].astype(new_dtype)\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:26:50.176910Z","iopub.execute_input":"2024-03-14T05:26:50.177522Z","iopub.status.idle":"2024-03-14T05:26:50.190337Z","shell.execute_reply.started":"2024-03-14T05:26:50.177478Z","shell.execute_reply":"2024-03-14T05:26:50.188626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_basetable = pl.read_csv(dataPath + \"csv_files/train/train_base.csv\")\ntrain_static = pl.concat(\n    [\n        pl.read_csv(dataPath + \"csv_files/train/train_static_0_0.csv\").pipe(set_table_dtypes),\n        pl.read_csv(dataPath + \"csv_files/train/train_static_0_1.csv\").pipe(set_table_dtypes),\n    ],\n    how=\"vertical_relaxed\",\n)\ntrain_static_cb = pl.read_csv(dataPath + \"csv_files/train/train_static_cb_0.csv\").pipe(set_table_dtypes)\ntrain_person_1 = pl.read_csv(dataPath + \"csv_files/train/train_person_1.csv\").pipe(set_table_dtypes) \ntrain_credit_bureau_b_2 = pl.read_csv(dataPath + \"csv_files/train/train_credit_bureau_b_2.csv\").pipe(set_table_dtypes) ","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:26:50.192048Z","iopub.execute_input":"2024-03-14T05:26:50.192508Z","iopub.status.idle":"2024-03-14T05:27:10.171115Z","shell.execute_reply.started":"2024-03-14T05:26:50.192468Z","shell.execute_reply":"2024-03-14T05:27:10.169920Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### basetable contains decision dates and target","metadata":{}},{"cell_type":"code","source":"train_basetable.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.174809Z","iopub.execute_input":"2024-03-14T05:27:10.175650Z","iopub.status.idle":"2024-03-14T05:27:10.187724Z","shell.execute_reply.started":"2024-03-14T05:27:10.175602Z","shell.execute_reply":"2024-03-14T05:27:10.185449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_basetable.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.190256Z","iopub.execute_input":"2024-03-14T05:27:10.191283Z","iopub.status.idle":"2024-03-14T05:27:10.209402Z","shell.execute_reply.started":"2024-03-14T05:27:10.191230Z","shell.execute_reply":"2024-03-14T05:27:10.207850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### static table contains paid related features","metadata":{}},{"cell_type":"code","source":"train_static.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.211429Z","iopub.execute_input":"2024-03-14T05:27:10.212007Z","iopub.status.idle":"2024-03-14T05:27:10.221556Z","shell.execute_reply.started":"2024-03-14T05:27:10.211961Z","shell.execute_reply":"2024-03-14T05:27:10.219898Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_static.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.223460Z","iopub.execute_input":"2024-03-14T05:27:10.223951Z","iopub.status.idle":"2024-03-14T05:27:10.249884Z","shell.execute_reply.started":"2024-03-14T05:27:10.223919Z","shell.execute_reply":"2024-03-14T05:27:10.248676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Static_cb Contains personal information like DOB, education, marital status","metadata":{}},{"cell_type":"code","source":"train_static_cb.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.251972Z","iopub.execute_input":"2024-03-14T05:27:10.252840Z","iopub.status.idle":"2024-03-14T05:27:10.260503Z","shell.execute_reply.started":"2024-03-14T05:27:10.252792Z","shell.execute_reply":"2024-03-14T05:27:10.259572Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_static_cb.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.261886Z","iopub.execute_input":"2024-03-14T05:27:10.263722Z","iopub.status.idle":"2024-03-14T05:27:10.280882Z","shell.execute_reply.started":"2024-03-14T05:27:10.263684Z","shell.execute_reply":"2024-03-14T05:27:10.279963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_static_cb[\"maritalst_385M\"].unique()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.285214Z","iopub.execute_input":"2024-03-14T05:27:10.286372Z","iopub.status.idle":"2024-03-14T05:27:10.326178Z","shell.execute_reply.started":"2024-03-14T05:27:10.286333Z","shell.execute_reply":"2024-03-14T05:27:10.324858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_static_cb[\"maritalst_893M\"].unique()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.328007Z","iopub.execute_input":"2024-03-14T05:27:10.329038Z","iopub.status.idle":"2024-03-14T05:27:10.359299Z","shell.execute_reply.started":"2024-03-14T05:27:10.329001Z","shell.execute_reply":"2024-03-14T05:27:10.358014Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Person personal info like gender, zipcode district etc","metadata":{}},{"cell_type":"code","source":"train_person_1.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.360689Z","iopub.execute_input":"2024-03-14T05:27:10.361014Z","iopub.status.idle":"2024-03-14T05:27:10.369382Z","shell.execute_reply.started":"2024-03-14T05:27:10.360987Z","shell.execute_reply":"2024-03-14T05:27:10.368074Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_person_1.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.370860Z","iopub.execute_input":"2024-03-14T05:27:10.371339Z","iopub.status.idle":"2024-03-14T05:27:10.389260Z","shell.execute_reply.started":"2024-03-14T05:27:10.371296Z","shell.execute_reply":"2024-03-14T05:27:10.387912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### credit_bureau_b_2 ? need more information","metadata":{}},{"cell_type":"code","source":"train_credit_bureau_b_2.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.390793Z","iopub.execute_input":"2024-03-14T05:27:10.391332Z","iopub.status.idle":"2024-03-14T05:27:10.402700Z","shell.execute_reply.started":"2024-03-14T05:27:10.391294Z","shell.execute_reply":"2024-03-14T05:27:10.401232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_credit_bureau_b_2.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.404239Z","iopub.execute_input":"2024-03-14T05:27:10.404650Z","iopub.status.idle":"2024-03-14T05:27:10.418469Z","shell.execute_reply.started":"2024-03-14T05:27:10.404608Z","shell.execute_reply":"2024-03-14T05:27:10.416882Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Test data","metadata":{}},{"cell_type":"code","source":"test_basetable = pl.read_csv(dataPath + \"csv_files/test/test_base.csv\")\ntest_static = pl.concat(\n    [\n        pl.read_csv(dataPath + \"csv_files/test/test_static_0_0.csv\").pipe(set_table_dtypes),\n        pl.read_csv(dataPath + \"csv_files/test/test_static_0_1.csv\").pipe(set_table_dtypes),\n        pl.read_csv(dataPath + \"csv_files/test/test_static_0_2.csv\").pipe(set_table_dtypes),\n    ],\n    how=\"vertical_relaxed\",\n)\ntest_static_cb = pl.read_csv(dataPath + \"csv_files/test/test_static_cb_0.csv\").pipe(set_table_dtypes)\ntest_person_1 = pl.read_csv(dataPath + \"csv_files/test/test_person_1.csv\").pipe(set_table_dtypes) \ntest_credit_bureau_b_2 = pl.read_csv(dataPath + \"csv_files/test/test_credit_bureau_b_2.csv\").pipe(set_table_dtypes) ","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.420044Z","iopub.execute_input":"2024-03-14T05:27:10.420527Z","iopub.status.idle":"2024-03-14T05:27:10.491747Z","shell.execute_reply.started":"2024-03-14T05:27:10.420483Z","shell.execute_reply":"2024-03-14T05:27:10.490735Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Feature engineering","metadata":{}},{"cell_type":"code","source":"# We need to use aggregation functions in tables with depth > 1, so tables that contain num_group1 column or \n# also num_group2 column.\ntrain_person_1_feats_1 = train_person_1.group_by(\"case_id\").agg(\n    pl.col(\"mainoccupationinc_384A\").max().alias(\"mainoccupationinc_384A_max\"),\n    (pl.col(\"incometype_1044T\") == \"SELFEMPLOYED\").max().alias(\"mainoccupationinc_384A_any_selfemployed\")\n)\n\n# Here num_group1=0 has special meaning, it is the person who applied for the loan.\ntrain_person_1_feats_2 = train_person_1.select([\"case_id\", \"num_group1\", \"housetype_905L\"]).filter(\n    pl.col(\"num_group1\") == 0\n).drop(\"num_group1\").rename({\"housetype_905L\": \"person_housetype\"})\n\n# Here we have num_goup1 and num_group2, so we need to aggregate again.\ntrain_credit_bureau_b_2_feats = train_credit_bureau_b_2.group_by(\"case_id\").agg(\n    pl.col(\"pmts_pmtsoverdue_635A\").max().alias(\"pmts_pmtsoverdue_635A_max\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\")\n)\n\n# We will process in this examples only A-type and M-type columns, so we need to select them.\nselected_static_cols = []\nfor col in train_static.columns:\n    if col[-1] in (\"A\", \"M\"):\n        selected_static_cols.append(col)\nprint(selected_static_cols)\n\nselected_static_cb_cols = []\nfor col in train_static_cb.columns:\n    if col[-1] in (\"A\", \"M\"):\n        selected_static_cb_cols.append(col)\nprint(selected_static_cb_cols)\n\n# Join all tables together.\ndata = train_basetable.join(\n    train_static.select([\"case_id\"]+selected_static_cols), how=\"left\", on=\"case_id\"\n).join(\n    train_static_cb.select([\"case_id\"]+selected_static_cb_cols), how=\"left\", on=\"case_id\"\n).join(\n    train_person_1_feats_1, how=\"left\", on=\"case_id\"\n).join(\n    train_person_1_feats_2, how=\"left\", on=\"case_id\"\n).join(\n    train_credit_bureau_b_2_feats, how=\"left\", on=\"case_id\"\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:10.495479Z","iopub.execute_input":"2024-03-14T05:27:10.495882Z","iopub.status.idle":"2024-03-14T05:27:13.732630Z","shell.execute_reply.started":"2024-03-14T05:27:10.495851Z","shell.execute_reply":"2024-03-14T05:27:13.731366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_person_1_feats_1 = test_person_1.group_by(\"case_id\").agg(\n    pl.col(\"mainoccupationinc_384A\").max().alias(\"mainoccupationinc_384A_max\"),\n    (pl.col(\"incometype_1044T\") == \"SELFEMPLOYED\").max().alias(\"mainoccupationinc_384A_any_selfemployed\")\n)\n\ntest_person_1_feats_2 = test_person_1.select([\"case_id\", \"num_group1\", \"housetype_905L\"]).filter(\n    pl.col(\"num_group1\") == 0\n).drop(\"num_group1\").rename({\"housetype_905L\": \"person_housetype\"})\n\ntest_credit_bureau_b_2_feats = test_credit_bureau_b_2.group_by(\"case_id\").agg(\n    pl.col(\"pmts_pmtsoverdue_635A\").max().alias(\"pmts_pmtsoverdue_635A_max\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\")\n)\n\ndata_submission = test_basetable.join(\n    test_static.select([\"case_id\"]+selected_static_cols), how=\"left\", on=\"case_id\"\n).join(\n    test_static_cb.select([\"case_id\"]+selected_static_cb_cols), how=\"left\", on=\"case_id\"\n).join(\n    test_person_1_feats_1, how=\"left\", on=\"case_id\"\n).join(\n    test_person_1_feats_2, how=\"left\", on=\"case_id\"\n).join(\n    test_credit_bureau_b_2_feats, how=\"left\", on=\"case_id\"\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:13.734283Z","iopub.execute_input":"2024-03-14T05:27:13.734754Z","iopub.status.idle":"2024-03-14T05:27:13.750241Z","shell.execute_reply.started":"2024-03-14T05:27:13.734705Z","shell.execute_reply":"2024-03-14T05:27:13.748894Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"case_ids = data[\"case_id\"].unique().shuffle(seed=1)\ncase_ids_train, case_ids_test = train_test_split(case_ids, train_size=0.6, random_state=1)\ncase_ids_valid, case_ids_test = train_test_split(case_ids_test, train_size=0.5, random_state=1)\n\ncols_pred = []\nfor col in data.columns:\n    if col[-1].isupper() and col[:-1].islower():\n        cols_pred.append(col)\n\nprint(cols_pred)\n\ndef from_polars_to_pandas(case_ids: pl.DataFrame) -> pl.DataFrame:\n    return (\n        data.filter(pl.col(\"case_id\").is_in(case_ids))[[\"case_id\", \"WEEK_NUM\", \"target\"]].to_pandas(),\n        data.filter(pl.col(\"case_id\").is_in(case_ids))[cols_pred].to_pandas(),\n        data.filter(pl.col(\"case_id\").is_in(case_ids))[\"target\"].to_pandas()\n    )\n\nbase_train, X_train, y_train = from_polars_to_pandas(case_ids_train)\nbase_valid, X_valid, y_valid = from_polars_to_pandas(case_ids_valid)\nbase_test, X_test, y_test = from_polars_to_pandas(case_ids_test)\n\nfor df in [X_train, X_valid, X_test]:\n    df = convert_strings(df)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:13.752416Z","iopub.execute_input":"2024-03-14T05:27:13.752877Z","iopub.status.idle":"2024-03-14T05:27:23.042126Z","shell.execute_reply.started":"2024-03-14T05:27:13.752838Z","shell.execute_reply":"2024-03-14T05:27:23.040449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f\"Train: {X_train.shape}\")\nprint(f\"Valid: {X_valid.shape}\")\nprint(f\"Test: {X_test.shape}\")","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:23.043706Z","iopub.execute_input":"2024-03-14T05:27:23.044093Z","iopub.status.idle":"2024-03-14T05:27:23.051901Z","shell.execute_reply.started":"2024-03-14T05:27:23.044062Z","shell.execute_reply":"2024-03-14T05:27:23.049929Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Number of loan defaulters in train data : \",y_train.sum())\nprint(\"Percentage of loan defaulters in train data : \",(y_train.sum()/len(y_train)*100))\n\nprint(\"Number of loan defaulters in valid data : \",y_valid.sum())\nprint(\"Percentage of loan defaulters in valid data : \",(y_valid.sum()/len(y_valid)*100))\n\nprint(\"Number of loan defaulters in test data : \",y_test.sum())\nprint(\"Percentage of loan defaulters in test data : \",(y_test.sum()/len(y_test)*100))","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:23.053595Z","iopub.execute_input":"2024-03-14T05:27:23.053987Z","iopub.status.idle":"2024-03-14T05:27:23.081587Z","shell.execute_reply.started":"2024-03-14T05:27:23.053955Z","shell.execute_reply":"2024-03-14T05:27:23.078346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Baseline model","metadata":{}},{"cell_type":"code","source":"lgb_train = lgb.Dataset(X_train, label=y_train)\nlgb_valid = lgb.Dataset(X_valid, label=y_valid, reference=lgb_train)\n\nparams = {\n    \"boosting_type\": \"gbdt\",\n    \"objective\": \"binary\",\n    \"metric\": \"auc\",\n    \"max_depth\": 3,\n    \"num_leaves\": 31,\n    \"learning_rate\": 0.05,\n    \"feature_fraction\": 0.9,\n    \"bagging_fraction\": 0.8,\n    \"bagging_freq\": 5,\n    \"n_estimators\": 2000,\n    \"verbose\": -1,\n}\n\ngbm = lgb.train(\n    params,\n    lgb_train,\n    valid_sets=lgb_valid,\n    callbacks=[lgb.log_evaluation(50), lgb.early_stopping(10)]\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:27:23.083598Z","iopub.execute_input":"2024-03-14T05:27:23.083991Z","iopub.status.idle":"2024-03-14T05:28:55.477895Z","shell.execute_reply.started":"2024-03-14T05:27:23.083961Z","shell.execute_reply":"2024-03-14T05:28:55.476603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for base, X in [(base_train, X_train), (base_valid, X_valid), (base_test, X_test)]:\n    y_pred = gbm.predict(X, num_iteration=gbm.best_iteration)\n    base[\"score\"] = y_pred\n\nprint(f'The AUC score on the train set is: {roc_auc_score(base_train[\"target\"], base_train[\"score\"])}') \nprint(f'The AUC score on the valid set is: {roc_auc_score(base_valid[\"target\"], base_valid[\"score\"])}') \nprint(f'The AUC score on the test set is: {roc_auc_score(base_test[\"target\"], base_test[\"score\"])}')  ","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:28:55.479540Z","iopub.execute_input":"2024-03-14T05:28:55.480837Z","iopub.status.idle":"2024-03-14T05:29:15.244086Z","shell.execute_reply.started":"2024-03-14T05:28:55.480787Z","shell.execute_reply":"2024-03-14T05:29:15.242032Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def gini_stability(base, w_fallingrate=88.0, w_resstd=-0.5):\n    gini_in_time = base.loc[:, [\"WEEK_NUM\", \"target\", \"score\"]]\\\n        .sort_values(\"WEEK_NUM\")\\\n        .groupby(\"WEEK_NUM\")[[\"target\", \"score\"]]\\\n        .apply(lambda x: 2*roc_auc_score(x[\"target\"], x[\"score\"])-1).tolist()\n    \n    x = np.arange(len(gini_in_time))\n    y = gini_in_time\n    a, b = np.polyfit(x, y, 1)\n    y_hat = a*x + b\n    residuals = y - y_hat\n    res_std = np.std(residuals)\n    avg_gini = np.mean(gini_in_time)\n    return avg_gini + w_fallingrate * min(0, a) + w_resstd * res_std\n\nstability_score_train = gini_stability(base_train)\nstability_score_valid = gini_stability(base_valid)\nstability_score_test = gini_stability(base_test)\n\nprint(f'The stability score on the train set is: {stability_score_train}') \nprint(f'The stability score on the valid set is: {stability_score_valid}') \nprint(f'The stability score on the test set is: {stability_score_test}')  ","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:29:15.246033Z","iopub.execute_input":"2024-03-14T05:29:15.246474Z","iopub.status.idle":"2024-03-14T05:29:16.468606Z","shell.execute_reply.started":"2024-03-14T05:29:15.246442Z","shell.execute_reply":"2024-03-14T05:29:16.467030Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"code","source":"X_submission = data_submission[cols_pred].to_pandas()\nX_submission = convert_strings(X_submission)\ncategorical_cols = X_train.select_dtypes(include=['category']).columns\n\nfor col in categorical_cols:\n    train_categories = set(X_train[col].cat.categories)\n    submission_categories = set(X_submission[col].cat.categories)\n    new_categories = submission_categories - train_categories\n    X_submission.loc[X_submission[col].isin(new_categories), col] = \"Unknown\"\n    new_dtype = pd.CategoricalDtype(categories=train_categories, ordered=True)\n    X_train[col] = X_train[col].astype(new_dtype)\n    X_submission[col] = X_submission[col].astype(new_dtype)\n\ny_submission_pred = gbm.predict(X_submission, num_iteration=gbm.best_iteration)","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:29:16.470815Z","iopub.execute_input":"2024-03-14T05:29:16.471337Z","iopub.status.idle":"2024-03-14T05:29:16.597157Z","shell.execute_reply.started":"2024-03-14T05:29:16.471296Z","shell.execute_reply":"2024-03-14T05:29:16.595517Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.DataFrame({\n    \"case_id\": data_submission[\"case_id\"].to_numpy(),\n    \"score\": y_submission_pred\n}).set_index('case_id')\nsubmission.to_csv(\"./submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:29:16.598983Z","iopub.execute_input":"2024-03-14T05:29:16.599550Z","iopub.status.idle":"2024-03-14T05:29:16.614823Z","shell.execute_reply.started":"2024-03-14T05:29:16.599495Z","shell.execute_reply":"2024-03-14T05:29:16.612940Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-14T05:29:16.616526Z","iopub.execute_input":"2024-03-14T05:29:16.616941Z","iopub.status.idle":"2024-03-14T05:29:16.634302Z","shell.execute_reply.started":"2024-03-14T05:29:16.616910Z","shell.execute_reply":"2024-03-14T05:29:16.632807Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}