{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","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":30786,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import polars as pl\nimport numpy as np\nimport pandas as pd\nimport lightgbm as lgb\nimport random\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":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:20:56.722477Z","iopub.execute_input":"2024-10-19T09:20:56.723904Z","iopub.status.idle":"2024-10-19T09:20:56.736061Z","shell.execute_reply.started":"2024-10-19T09:20:56.723841Z","shell.execute_reply":"2024-10-19T09:20:56.734566Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:20:57.750731Z","iopub.execute_input":"2024-10-19T09:20:57.752226Z","iopub.status.idle":"2024-10-19T09:20:57.763559Z","shell.execute_reply.started":"2024-10-19T09:20:57.752161Z","shell.execute_reply":"2024-10-19T09:20:57.761453Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:20:59.544159Z","iopub.execute_input":"2024-10-19T09:20:59.544700Z","iopub.status.idle":"2024-10-19T09:21:16.040883Z","shell.execute_reply.started":"2024-10-19T09:20:59.544651Z","shell.execute_reply":"2024-10-19T09:21:16.038961Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:21:16.043932Z","iopub.execute_input":"2024-10-19T09:21:16.044560Z","iopub.status.idle":"2024-10-19T09:21:16.098823Z","shell.execute_reply.started":"2024-10-19T09:21:16.044486Z","shell.execute_reply":"2024-10-19T09:21:16.097357Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Feature Engineering","metadata":{}},{"cell_type":"code","source":"# train_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.\n# train_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.\n# train_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.\n# selected_static_cols = []\n# for col in train_static.columns:\n#     if col[-1] in (\"A\", \"M\"):\n#         selected_static_cols.append(col)\n# print(selected_static_cols)\n\n# selected_static_cb_cols = []\n# for col in train_static_cb.columns:\n#     if col[-1] in (\"A\", \"M\"):\n#         selected_static_cb_cols.append(col)\n# print(selected_static_cb_cols)\n\n# # Join all tables together.\n# data = 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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T08:52:54.960141Z","iopub.execute_input":"2024-10-19T08:52:54.960775Z","iopub.status.idle":"2024-10-19T08:52:58.763774Z","shell.execute_reply.started":"2024-10-19T08:52:54.960722Z","shell.execute_reply":"2024-10-19T08:52:58.762527Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Step 1: Aggregate features for train_person_1\ntrain_person_1_feats = train_person_1.group_by(\"case_id\").agg(\n    pl.max(\"mainoccupationinc_384A\").alias(\"mainoccupationinc_384A_max\"),\n    (pl.col(\"incometype_1044T\") == \"SELFEMPLOYED\").max().alias(\"mainoccupationinc_384A_any_selfemployed\"),\n    pl.when(pl.col(\"num_group1\") == 0).then(pl.col(\"housetype_905L\")).alias(\"person_housetype\")\n).filter(pl.col(\"person_housetype\").is_not_null())\n\n# Step 2: Aggregate features for train_credit_bureau_b_2\ntrain_credit_bureau_feats = train_credit_bureau_b_2.group_by(\"case_id\").agg(\n    pl.max(\"pmts_pmtsoverdue_635A\").alias(\"pmts_pmtsoverdue_635A_max\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\")\n)\n\n# Step 3: Select relevant static columns from train_static and train_static_cb\nselected_static_cols = [col for col in train_static.columns if col.endswith((\"A\", \"M\"))]\nselected_static_cb_cols = [col for col in train_static_cb.columns if col.endswith((\"A\", \"M\"))]\n\n# Step 4: Join all tables together\ndata = (\n    train_basetable\n    .join(train_static.select([\"case_id\"] + selected_static_cols), how=\"left\", on=\"case_id\")\n    .join(train_static_cb.select([\"case_id\"] + selected_static_cb_cols), how=\"left\", on=\"case_id\")\n    .join(train_person_1_feats, how=\"left\", on=\"case_id\")\n    .join(train_credit_bureau_feats, how=\"left\", on=\"case_id\")\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:29:25.701473Z","iopub.execute_input":"2024-10-19T09:29:25.703050Z","iopub.status.idle":"2024-10-19T09:29:33.230021Z","shell.execute_reply.started":"2024-10-19T09:29:25.702970Z","shell.execute_reply":"2024-10-19T09:29:33.228426Z"}},"outputs":[],"execution_count":null},{"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\n# test_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\n# test_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\n# data_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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T08:53:18.952773Z","iopub.execute_input":"2024-10-19T08:53:18.954236Z","iopub.status.idle":"2024-10-19T08:53:18.979120Z","shell.execute_reply.started":"2024-10-19T08:53:18.954152Z","shell.execute_reply":"2024-10-19T08:53:18.977519Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Step 1: Aggregate features for test_person_1\ntest_person_1_feats = test_person_1.group_by(\"case_id\").agg(\n    pl.max(\"mainoccupationinc_384A\").alias(\"mainoccupationinc_384A_max\"),\n    (pl.col(\"incometype_1044T\") == \"SELFEMPLOYED\").max().alias(\"mainoccupationinc_384A_any_selfemployed\"),\n    pl.when(pl.col(\"num_group1\") == 0).then(pl.col(\"housetype_905L\")).alias(\"person_housetype\")\n).filter(pl.col(\"person_housetype\").is_not_null())\n\n# Step 2: Aggregate features for test_credit_bureau_b_2\ntest_credit_bureau_feats = test_credit_bureau_b_2.group_by(\"case_id\").agg(\n    pl.max(\"pmts_pmtsoverdue_635A\").alias(\"pmts_pmtsoverdue_635A_max\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\")\n)\n\n# Step 3: Join all tables together\ndata_submission = (\n    test_basetable\n    .join(test_static.select([\"case_id\"] + selected_static_cols), how=\"left\", on=\"case_id\")\n    .join(test_static_cb.select([\"case_id\"] + selected_static_cb_cols), how=\"left\", on=\"case_id\")\n    .join(test_person_1_feats, how=\"left\", on=\"case_id\")\n    .join(test_credit_bureau_feats, how=\"left\", on=\"case_id\")\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:29:36.343142Z","iopub.execute_input":"2024-10-19T09:29:36.343683Z","iopub.status.idle":"2024-10-19T09:29:36.362396Z","shell.execute_reply.started":"2024-10-19T09:29:36.343631Z","shell.execute_reply":"2024-10-19T09:29:36.360499Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# case_ids = data[\"case_id\"].unique().shuffle(seed=1)\n# case_ids_train, case_ids_test = train_test_split(case_ids, train_size=0.6, random_state=1)\n# case_ids_valid, case_ids_test = train_test_split(case_ids_test, train_size=0.5, random_state=1)\n\n# cols_pred = []\n# for col in data.columns:\n#     if col[-1].isupper() and col[:-1].islower():\n#         cols_pred.append(col)\n\n# print(cols_pred)\n\n# def 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# base_train, X_train, y_train = from_polars_to_pandas(case_ids_train)\n# base_valid, X_valid, y_valid = from_polars_to_pandas(case_ids_valid)\n# base_test, X_test, y_test = from_polars_to_pandas(case_ids_test)\n\n# for df in [X_train, X_valid, X_test]:\n#     df = convert_strings(df)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T08:53:32.909423Z","iopub.execute_input":"2024-10-19T08:53:32.910498Z","iopub.status.idle":"2024-10-19T08:53:41.746703Z","shell.execute_reply.started":"2024-10-19T08:53:32.910446Z","shell.execute_reply":"2024-10-19T08:53:41.745131Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Step 1: Shuffle and split case_ids\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# Step 2: Identify prediction columns\ncols_pred = [col for col in data.columns if col[-1].isupper() and col[:-1].islower()]\nprint(cols_pred)\n\n# Step 3: Function to convert Polars DataFrame to Pandas\ndef from_polars_to_pandas(case_ids: pl.Series) -> tuple:\n    filtered_data = data.filter(pl.col(\"case_id\").is_in(case_ids))\n    return (\n        filtered_data[[\"case_id\", \"WEEK_NUM\", \"target\"]].to_pandas(),\n        filtered_data[cols_pred].to_pandas(),\n        filtered_data[\"target\"].to_pandas()\n    )\n\n# Step 4: Create train, validation, and test sets\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# Step 5: Convert string columns in DataFrames\nfor df in [X_train, X_valid, X_test]:\n    df = convert_strings(df)  # Ensure this function modifies df in place or return modified df\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:29:40.636246Z","iopub.execute_input":"2024-10-19T09:29:40.637842Z","iopub.status.idle":"2024-10-19T09:29:48.124429Z","shell.execute_reply.started":"2024-10-19T09:29:40.637782Z","shell.execute_reply":"2024-10-19T09:29:48.122743Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(f\"Train: {X_train.shape}\")\nprint(f\"Valid: {X_valid.shape}\")\nprint(f\"Test: {X_test.shape}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T09:29:48.127419Z","iopub.execute_input":"2024-10-19T09:29:48.128054Z","iopub.status.idle":"2024-10-19T09:29:48.136816Z","shell.execute_reply.started":"2024-10-19T09:29:48.127984Z","shell.execute_reply":"2024-10-19T09:29:48.134949Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Training LightGBM","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    \"metric\": \"auc\",\n    \"max_depth\": 100,\n    \"num_leaves\": 100,\n    \"learning_rate\": 0.01,\n    \"feature_fraction\": 0.8,\n    \"bagging_fraction\": 0.7,\n    \"bagging_freq\": 5,\n    \"min_data_in_leaf\": 30,\n    \"lambda_l1\": 0.1,\n    \"lambda_l2\": 0.1,\n    \"n_estimators\": 1000,\n    \"verbose\": -1,\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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T10:43:22.133850Z","iopub.execute_input":"2024-10-19T10:43:22.134469Z","iopub.status.idle":"2024-10-19T10:47:10.404068Z","shell.execute_reply.started":"2024-10-19T10:43:22.134385Z","shell.execute_reply":"2024-10-19T10:47:10.402362Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T10:47:28.492505Z","iopub.execute_input":"2024-10-19T10:47:28.493218Z","iopub.status.idle":"2024-10-19T10:49:04.299906Z","shell.execute_reply.started":"2024-10-19T10:47:28.493159Z","shell.execute_reply":"2024-10-19T10:49:04.298582Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T10:49:10.404254Z","iopub.execute_input":"2024-10-19T10:49:10.406006Z","iopub.status.idle":"2024-10-19T10:49:11.609442Z","shell.execute_reply.started":"2024-10-19T10:49:10.405942Z","shell.execute_reply":"2024-10-19T10:49:11.608195Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T10:49:15.107582Z","iopub.execute_input":"2024-10-19T10:49:15.108125Z","iopub.status.idle":"2024-10-19T10:49:15.215565Z","shell.execute_reply.started":"2024-10-19T10:49:15.108073Z","shell.execute_reply":"2024-10-19T10:49:15.214294Z"}},"outputs":[],"execution_count":null},{"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":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T10:49:17.699057Z","iopub.execute_input":"2024-10-19T10:49:17.699673Z","iopub.status.idle":"2024-10-19T10:49:17.710836Z","shell.execute_reply.started":"2024-10-19T10:49:17.699617Z","shell.execute_reply":"2024-10-19T10:49:17.709392Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-10-19T10:49:19.779845Z","iopub.execute_input":"2024-10-19T10:49:19.781341Z","iopub.status.idle":"2024-10-19T10:49:19.794578Z","shell.execute_reply.started":"2024-10-19T10:49:19.781280Z","shell.execute_reply":"2024-10-19T10:49:19.793124Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}