{"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":7921029,"sourceType":"competition"}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"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 \nfrom sklearn.ensemble import RandomForestRegressor\n\ndataPath = \"/kaggle/input/home-credit-credit-risk-model-stability/\"","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:23:01.468709Z","iopub.execute_input":"2024-03-21T17:23:01.469134Z","iopub.status.idle":"2024-03-21T17:23:01.545900Z","shell.execute_reply.started":"2024-03-21T17:23:01.469104Z","shell.execute_reply":"2024-03-21T17:23:01.544586Z"},"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":"2024-03-21T17:23:03.608021Z","iopub.execute_input":"2024-03-21T17:23:03.608513Z","iopub.status.idle":"2024-03-21T17:23:03.618090Z","shell.execute_reply.started":"2024-03-21T17:23:03.608475Z","shell.execute_reply":"2024-03-21T17:23:03.616785Z"},"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 = train_static[['case_id', 'amtinstpaidbefduel24m_4187115A', \n                             'annuity_780A', \n                             'annuitynextmonth_57A', \n                             'avginstallast24m_3658937A', \n                             'avglnamtstart24m_4525187A', \n                             'avgoutstandbalancel6m_4187114A', \n                             'avgpmtlast12m_4525200A', \n                             'credamount_770A', \n                             'currdebt_22A', \n                             'currdebtcredtyperange_828A']]\nprint(train_static.columns)\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) \n\ntrain_static_cb = train_static_cb[['case_id', 'birthdate_574D', 'contractssum_5085716L', 'days120_123L', 'days180_256L', 'days30_165L', 'days360_512L', 'days90_310L', 'numberofqueries_373L', 'pmtcount_4527229L', 'pmtcount_4955617L']]\n\n\n\ntrain_person_1 = train_person_1[['num_group1', 'mainoccupationinc_384A','incometype_1044T', 'case_id','birthdate_87D', 'childnum_185L', 'education_927M', 'empl_employedfrom_271D', 'empl_employedtotal_800L', 'empl_industry_691L', 'familystate_447L', 'gender_992L', 'housetype_905L', 'housingtype_772L']]\n\n","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:23:06.354857Z","iopub.execute_input":"2024-03-21T17:23:06.355339Z","iopub.status.idle":"2024-03-21T17:23:22.732194Z","shell.execute_reply.started":"2024-03-21T17:23:06.355297Z","shell.execute_reply":"2024-03-21T17:23:22.730914Z"},"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) \n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:23:22.733929Z","iopub.execute_input":"2024-03-21T17:23:22.734305Z","iopub.status.idle":"2024-03-21T17:23:22.776506Z","shell.execute_reply.started":"2024-03-21T17:23:22.734275Z","shell.execute_reply":"2024-03-21T17:23:22.775479Z"},"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":"# 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    \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)\n        \n\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":"2024-03-21T17:23:25.673755Z","iopub.execute_input":"2024-03-21T17:23:25.674542Z","iopub.status.idle":"2024-03-21T17:23:27.477033Z","shell.execute_reply.started":"2024-03-21T17:23:25.674491Z","shell.execute_reply":"2024-03-21T17:23:27.475703Z"},"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":"2024-03-21T17:23:35.138689Z","iopub.execute_input":"2024-03-21T17:23:35.139091Z","iopub.status.idle":"2024-03-21T17:23:35.151783Z","shell.execute_reply.started":"2024-03-21T17:23:35.139063Z","shell.execute_reply":"2024-03-21T17:23:35.150654Z"},"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    \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\n\n        \n\n\nfor df in [X_train, X_valid, X_test]:\n    df = convert_strings(df)","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:23:37.967754Z","iopub.execute_input":"2024-03-21T17:23:37.971829Z","iopub.status.idle":"2024-03-21T17:23:41.329591Z","shell.execute_reply.started":"2024-03-21T17:23:37.971773Z","shell.execute_reply":"2024-03-21T17:23:41.326480Z"},"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":"2024-03-21T17:23:43.758618Z","iopub.execute_input":"2024-03-21T17:23:43.761302Z","iopub.status.idle":"2024-03-21T17:23:43.775054Z","shell.execute_reply.started":"2024-03-21T17:23:43.761145Z","shell.execute_reply":"2024-03-21T17:23:43.772337Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training Random Forest\n\nMinimal example of Random Forest training is shown below.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='median')\n\n\n\n\n\nparams = {\n    'n_estimators': 300,\n    'max_depth': 20,\n    'min_samples_split': 2,\n    'min_samples_leaf': 2,\n    'max_features': 'sqrt', \n    'n_jobs': -1\n}\n\n\nX_train_encoded = pd.get_dummies(X_train)\nX_train_imputed = imputer.fit_transform(X_train_encoded)\n\n\nrf = RandomForestRegressor(**params)\nrf.fit(X_train_imputed, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:23:47.589278Z","iopub.execute_input":"2024-03-21T17:23:47.589730Z","iopub.status.idle":"2024-03-21T17:38:44.120316Z","shell.execute_reply.started":"2024-03-21T17:23:47.589694Z","shell.execute_reply":"2024-03-21T17:38:44.118970Z"},"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":"import pandas as pd\nfrom sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='median')\n\nfor base, X in [(base_train, X_train), (base_valid, X_valid), (base_test, X_test)]:\n    y_pred = rf.predict(imputer.fit_transform(pd.get_dummies(X)))\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\"])}')  \n\n\n\n","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:38:54.889239Z","iopub.execute_input":"2024-03-21T17:38:54.890284Z","iopub.status.idle":"2024-03-21T17:39:59.427340Z","shell.execute_reply.started":"2024-03-21T17:38:54.890245Z","shell.execute_reply":"2024-03-21T17:39:59.426044Z"},"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":"2024-03-21T17:40:09.156768Z","iopub.execute_input":"2024-03-21T17:40:09.157223Z","iopub.status.idle":"2024-03-21T17:40:10.287216Z","shell.execute_reply.started":"2024-03-21T17:40:09.157184Z","shell.execute_reply":"2024-03-21T17:40:10.285713Z"},"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":"import pandas as pd\nfrom sklearn.impute import SimpleImputer\nimputer = SimpleImputer(strategy='median')\n\nX_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 = rf.predict(imputer.fit_transform(pd.get_dummies(X_submission)))","metadata":{"execution":{"iopub.status.busy":"2024-03-21T17:40:20.158512Z","iopub.execute_input":"2024-03-21T17:40:20.159045Z","iopub.status.idle":"2024-03-21T17:40:20.192925Z","shell.execute_reply.started":"2024-03-21T17:40:20.159009Z","shell.execute_reply":"2024-03-21T17:40:20.191691Z"},"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":"2024-03-21T17:40:23.180291Z","iopub.execute_input":"2024-03-21T17:40:23.180756Z","iopub.status.idle":"2024-03-21T17:40:23.189921Z","shell.execute_reply.started":"2024-03-21T17:40:23.180720Z","shell.execute_reply":"2024-03-21T17:40:23.188445Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Best of luck, and most importantly, enjoy the process of learning and discovery! \n\n<img src=\"https://i.imgur.com/obVWIBh.png\" alt=\"Image\" width=\"700\"/>","metadata":{}}]}