{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","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":7921029,"sourceType":"competition"}],"dockerImageVersionId":30635,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"This notebook is based on the baseline  https://www.kaggle.com/code/jetakow/home-credit-2024-starter-notebook with several adjustments:\n\n1) new tables and new features\n\n2)search of best hyperparameters using Optuna with pruner\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"# Example Notebook\n\nWelcome to the example notebook for the Home Credit Kaggle competition. The goal of this competition is to determine how likely a customer is going to default on an issued loan. The main difference between the [first](https://www.kaggle.com/c/home-credit-default-risk) and this competition is that now your submission will be scored with a custom metric that will take into account how well the model performs in future. A decline in performance will be penalized. The goal is to create a model that is stable and performs well in the future.\n\nIn this notebook you will see how to:\n* Load the data\n* Join tables with Polars - a DataFrame library implemented in Rust language, designed to be blazingy fast and memory efficient.  \n* Create simple aggregation features\n* Train a LightGBM model\n* Create a submission table\n\n## Load the data","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\nimport warnings\n\nwarnings.filterwarnings(\"ignore\")\n\ndataPath = \"/kaggle/input/home-credit-credit-risk-model-stability/\"","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:42:31.066340Z","iopub.execute_input":"2024-03-17T20:42:31.066918Z","iopub.status.idle":"2024-03-17T20:42:31.075460Z","shell.execute_reply.started":"2024-03-17T20:42:31.066875Z","shell.execute_reply":"2024-03-17T20:42:31.073735Z"},"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":"2024-03-17T20:42:31.096373Z","iopub.execute_input":"2024-03-17T20:42:31.096912Z","iopub.status.idle":"2024-03-17T20:42:31.108375Z","shell.execute_reply.started":"2024-03-17T20:42:31.096870Z","shell.execute_reply":"2024-03-17T20:42:31.106616Z"},"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":"2024-03-17T20:42:31.129066Z","iopub.execute_input":"2024-03-17T20:42:31.129653Z","iopub.status.idle":"2024-03-17T20:42:48.044431Z","shell.execute_reply.started":"2024-03-17T20:42:31.129606Z","shell.execute_reply":"2024-03-17T20:42:48.042738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":"2024-03-17T20:42:48.047298Z","iopub.execute_input":"2024-03-17T20:42:48.047783Z","iopub.status.idle":"2024-03-17T20:42:48.095919Z","shell.execute_reply.started":"2024-03-17T20:42:48.047743Z","shell.execute_reply":"2024-03-17T20:42:48.094397Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Feature engineering\n\nIn this part, we can see a simple example of joining tables via `case_id`. Here the loading and joining is done with polars library. Polars library is blazingly fast and has much smaller memory footprint than pandas. ","metadata":{}},{"cell_type":"code","source":"def generate_new_features(df_debitcard, df_deposit, df_other):\n    total_transactions_last_180_days = df_debitcard['last180dayturnover_1134A'].count()\n    max_amt_debit_outgoing = df_other['amtdebitoutgoing_4809440A'].max()\n    min_amt_debit_incoming = df_other['amtdebitincoming_4809443A'].min()\n    total_deposit_transactions = df_deposit['amount_416A'].count()\n    avg_amt_deposit_balance = df_deposit['amount_416A'].mean()\n    max_amt_deposit_outgoing = df_other['amtdepositoutgoing_4809442A'].max()\n    min_amt_deposit_incoming = df_other['amtdepositincoming_4809444A'].min()\n    \n    return total_transactions_last_180_days, max_amt_debit_outgoing, min_amt_debit_incoming, \\\n           total_deposit_transactions, avg_amt_deposit_balance, max_amt_deposit_outgoing, \\\n           min_amt_deposit_incoming","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:42:48.097816Z","iopub.execute_input":"2024-03-17T20:42:48.098222Z","iopub.status.idle":"2024-03-17T20:42:48.107272Z","shell.execute_reply.started":"2024-03-17T20:42:48.098189Z","shell.execute_reply":"2024-03-17T20:42:48.105697Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":"2024-03-17T20:42:48.110978Z","iopub.execute_input":"2024-03-17T20:42:48.112138Z","iopub.status.idle":"2024-03-17T20:42:50.580943Z","shell.execute_reply.started":"2024-03-17T20:42:48.112078Z","shell.execute_reply":"2024-03-17T20:42:50.579153Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Add new features to the train dataset\ntrain_debitcard_1 = pl.read_csv(dataPath + \"csv_files/train/train_debitcard_1.csv\").pipe(set_table_dtypes)\ntrain_deposit_1 = pl.read_csv(dataPath + \"csv_files/train/train_deposit_1.csv\").pipe(set_table_dtypes)\ntrain_other_1 = pl.read_csv(dataPath + \"csv_files/train/train_other_1.csv\").pipe(set_table_dtypes)\n\n\nnew_features_train = generate_new_features(train_debitcard_1, train_deposit_1, train_other_1)\n\n\ndata = data.lazy().with_columns(\n    total_transactions_last_180_days=new_features_train[0],\n    max_amt_debit_outgoing=new_features_train[1],\n    min_amt_debit_incoming=new_features_train[2],\n    total_deposit_transactions=new_features_train[3],\n    avg_amt_deposit_balance=new_features_train[4],\n    max_amt_deposit_outgoing=new_features_train[5],\n    min_amt_deposit_incoming=new_features_train[6]\n).collect()","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:42:50.583014Z","iopub.execute_input":"2024-03-17T20:42:50.583633Z","iopub.status.idle":"2024-03-17T20:42:50.732443Z","shell.execute_reply.started":"2024-03-17T20:42:50.583572Z","shell.execute_reply":"2024-03-17T20:42:50.730305Z"},"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":"2024-03-17T20:42:50.736717Z","iopub.execute_input":"2024-03-17T20:42:50.737365Z","iopub.status.idle":"2024-03-17T20:42:50.761844Z","shell.execute_reply.started":"2024-03-17T20:42:50.737306Z","shell.execute_reply":"2024-03-17T20:42:50.759720Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create new features for test data\ntest_debitcard_1 = pl.read_csv(dataPath + \"csv_files/test/test_debitcard_1.csv\").pipe(set_table_dtypes)\ntest_deposit_1 = pl.read_csv(dataPath + \"csv_files/test/test_deposit_1.csv\").pipe(set_table_dtypes)\ntest_other_1 = pl.read_csv(dataPath + \"csv_files/test/test_other_1.csv\").pipe(set_table_dtypes)\n\n\nnew_features_test = generate_new_features(test_debitcard_1, test_deposit_1, test_other_1)\n\ndata_submission = data_submission.lazy().with_columns(\n    total_transactions_last_180_days=new_features_test[0],\n    max_amt_debit_outgoing=new_features_test[1],\n    min_amt_debit_incoming=new_features_test[2],\n    total_deposit_transactions=new_features_test[3],\n    avg_amt_deposit_balance=new_features_test[4],\n    max_amt_deposit_outgoing=new_features_test[5],\n    min_amt_deposit_incoming=new_features_test[6]\n).collect()\n","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:42:50.764138Z","iopub.execute_input":"2024-03-17T20:42:50.764751Z","iopub.status.idle":"2024-03-17T20:42:50.792753Z","shell.execute_reply.started":"2024-03-17T20:42:50.764699Z","shell.execute_reply":"2024-03-17T20:42:50.791090Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split data into train, validation, and test sets\ncase_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\n\ncols_pred = []\nfor col in data.columns:\n    if col[-1].isupper() and col[:-1].islower():\n        cols_pred.append(col)\n\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\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\n\nfor df in [X_train, X_valid, X_test]:\n    df = convert_strings(df)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:42:50.794970Z","iopub.execute_input":"2024-03-17T20:42:50.796247Z","iopub.status.idle":"2024-03-17T20:43:01.103250Z","shell.execute_reply.started":"2024-03-17T20:42:50.796187Z","shell.execute_reply":"2024-03-17T20:43:01.102045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training LightGBM\n\nMinimal example of LightGBM training is shown below.","metadata":{}},{"cell_type":"code","source":"import optuna\nimport logging\nlgb_train = lgb.Dataset(X_train, label=y_train)\nlgb_valid = lgb.Dataset(X_valid, label=y_valid, reference=lgb_train)\n\ndef search(trial):\n    params = {\n        \"boosting_type\": \"gbdt\",\n        \"objective\": \"binary\",\n        \"metric\": \"auc\",\n        \"max_depth\": trial.suggest_int(\"max_depth\", 3, 10),\n        \"num_leaves\": trial.suggest_int(\"num_leaves\", 10, 100),\n        \"learning_rate\": trial.suggest_loguniform(\"learning_rate\", 0.01, 0.1),\n        \"feature_fraction\": trial.suggest_uniform(\"feature_fraction\", 0.5, 1.0),\n        \"bagging_fraction\": trial.suggest_uniform(\"bagging_fraction\", 0.5, 1.0),\n        \"bagging_freq\": trial.suggest_int(\"bagging_freq\", 1, 10),\n        \"n_estimators\": 1000,\n        \"verbose\": -1,\n    }\n\n    \n    gbm = lgb.train(\n    params,\n    lgb_train,\n    valid_sets=lgb_valid,\n    verbose_eval=False,\n    callbacks=[lgb.log_evaluation(50), lgb.early_stopping(5)])\n    \n    auc = gbm.best_score[\"valid_0\"][\"auc\"]\n    return auc\n\n\noptuna.logging.set_verbosity(optuna.logging.WARNING)\nstudy = optuna.create_study(direction=\"maximize\", pruner=optuna.pruners.MedianPruner())\nstudy.optimize(search, n_trials=10)\n\nbest_params = study.best_params\nprint(\"Best params:\", best_params)\n        \n","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:43:01.104685Z","iopub.execute_input":"2024-03-17T20:43:01.105942Z","iopub.status.idle":"2024-03-17T20:56:28.656348Z","shell.execute_reply.started":"2024-03-17T20:43:01.105892Z","shell.execute_reply":"2024-03-17T20:56:28.654754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gbm = lgb.train(\n    best_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":"2024-03-17T20:56:28.661000Z","iopub.execute_input":"2024-03-17T20:56:28.661547Z","iopub.status.idle":"2024-03-17T20:56:49.812738Z","shell.execute_reply.started":"2024-03-17T20:56:28.661482Z","shell.execute_reply":"2024-03-17T20:56:49.811661Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Evaluation with AUC and then comparison with the stability metric is shown below.","metadata":{}},{"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":"2024-03-17T20:56:49.814261Z","iopub.execute_input":"2024-03-17T20:56:49.815491Z","iopub.status.idle":"2024-03-17T20:56:58.041872Z","shell.execute_reply.started":"2024-03-17T20:56:49.815439Z","shell.execute_reply":"2024-03-17T20:56:58.040393Z"},"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":"2024-03-17T20:56:58.043801Z","iopub.execute_input":"2024-03-17T20:56:58.044184Z","iopub.status.idle":"2024-03-17T20:56:59.367593Z","shell.execute_reply.started":"2024-03-17T20:56:58.044153Z","shell.execute_reply":"2024-03-17T20:56:59.365824Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Submission\n\nScoring the submission dataset is below, we need to take care of new categories. Then we save the score as a last step. ","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":"2024-03-17T20:56:59.369877Z","iopub.execute_input":"2024-03-17T20:56:59.370332Z","iopub.status.idle":"2024-03-17T20:56:59.523627Z","shell.execute_reply.started":"2024-03-17T20:56:59.370295Z","shell.execute_reply":"2024-03-17T20:56:59.521885Z"},"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":"2024-03-17T20:56:59.526244Z","iopub.execute_input":"2024-03-17T20:56:59.526821Z","iopub.status.idle":"2024-03-17T20:56:59.551262Z","shell.execute_reply.started":"2024-03-17T20:56:59.526780Z","shell.execute_reply":"2024-03-17T20:56:59.548469Z"},"trusted":true},"execution_count":null,"outputs":[]}]}