{"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":7602123,"sourceType":"competition"}],"dockerImageVersionId":30635,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Summary\n\nThe goal of this competition is to determine how likely a customer is going to default on an issued loan. \n\nIn this notebook you will see how to:\n* Load the data\n* Join tables with Polars  \n* Create simple aggregation features\n* Train a LightGBM model\n* Create a submission table\n\n## Load the packages and data","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"code","source":"# Load Packages used in Notebook\n\nimport 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":"2024-02-09T15:52:07.266073Z","iopub.execute_input":"2024-02-09T15:52:07.266745Z","iopub.status.idle":"2024-02-09T15:52:07.279821Z","shell.execute_reply.started":"2024-02-09T15:52:07.266697Z","shell.execute_reply":"2024-02-09T15:52:07.274170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#  Function iterates over each column in the DataFrame using a loop.  If the last letter is either \"P\" or \"A\"\n# it performs a type conversion on that column and coverts the column to a float type, and assigned value\n# back to the dataframe. \n\ndef 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\n#  Function iterates over all columns and performs a type conversoin  for columns with a dtype of 'object'\n# or 'string', and then converts these columns to a pandas categorical type, and appends an 'unknown'\n# category if needed\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":"2024-02-09T15:52:10.409173Z","iopub.execute_input":"2024-02-09T15:52:10.409652Z","iopub.status.idle":"2024-02-09T15:52:10.420539Z","shell.execute_reply.started":"2024-02-09T15:52:10.409615Z","shell.execute_reply":"2024-02-09T15:52:10.419260Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Process training data.  Uses 'polars' library to read and concatenate CSV files into dataframes.  \n\ntrain_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":"2024-02-09T15:52:13.635455Z","iopub.execute_input":"2024-02-09T15:52:13.635903Z","iopub.status.idle":"2024-02-09T15:52:39.873855Z","shell.execute_reply.started":"2024-02-09T15:52:13.635854Z","shell.execute_reply":"2024-02-09T15:52:39.870962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Process test data.  pipe method is used to set the data types of the dataframe\n\ntest_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":"2024-02-09T15:52:39.996711Z","iopub.execute_input":"2024-02-09T15:52:39.997915Z","iopub.status.idle":"2024-02-09T15:52:40.086766Z","shell.execute_reply.started":"2024-02-09T15:52:39.997879Z","shell.execute_reply":"2024-02-09T15:52:40.083935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Exploration","metadata":{"execution":{"iopub.status.busy":"2024-02-09T14:59:46.949648Z","iopub.execute_input":"2024-02-09T14:59:46.950280Z","iopub.status.idle":"2024-02-09T14:59:46.958691Z","shell.execute_reply.started":"2024-02-09T14:59:46.950208Z","shell.execute_reply":"2024-02-09T14:59:46.957273Z"}}},{"cell_type":"code","source":"# Set the maximum number of columns to display\npd.set_option('display.max_columns', None)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T15:52:40.096144Z","iopub.execute_input":"2024-02-09T15:52:40.097528Z","iopub.status.idle":"2024-02-09T15:52:40.119814Z","shell.execute_reply.started":"2024-02-09T15:52:40.097397Z","shell.execute_reply":"2024-02-09T15:52:40.114038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_basetable.tail(10)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T15:52:40.164037Z","iopub.execute_input":"2024-02-09T15:52:40.165133Z","iopub.status.idle":"2024-02-09T15:52:40.191139Z","shell.execute_reply.started":"2024-02-09T15:52:40.165036Z","shell.execute_reply":"2024-02-09T15:52:40.188417Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Count the occurrences of each distinct value in the column\nvalue_counts = train_basetable['target'].value_counts()\n\n# Display the counts\nprint(value_counts)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T15:52:40.194016Z","iopub.execute_input":"2024-02-09T15:52:40.195502Z","iopub.status.idle":"2024-02-09T15:52:40.347299Z","shell.execute_reply.started":"2024-02-09T15:52:40.195439Z","shell.execute_reply":"2024-02-09T15:52:40.344394Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_static_cb.tail(10)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T15:54:51.537724Z","iopub.execute_input":"2024-02-09T15:54:51.538253Z","iopub.status.idle":"2024-02-09T15:54:51.562472Z","shell.execute_reply.started":"2024-02-09T15:54:51.538215Z","shell.execute_reply":"2024-02-09T15:54:51.561117Z"},"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    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":"2024-02-09T16:01:56.933843Z","iopub.execute_input":"2024-02-09T16:01:56.934470Z","iopub.status.idle":"2024-02-09T16:01:59.962855Z","shell.execute_reply.started":"2024-02-09T16:01:56.934428Z","shell.execute_reply":"2024-02-09T16:01:59.961678Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.tail(10)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T16:02:50.088704Z","iopub.execute_input":"2024-02-09T16:02:50.089173Z","iopub.status.idle":"2024-02-09T16:02:50.116337Z","shell.execute_reply.started":"2024-02-09T16:02:50.089138Z","shell.execute_reply":"2024-02-09T16:02:50.114737Z"},"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":"2024-02-09T14:42:43.300353Z","iopub.execute_input":"2024-02-09T14:42:43.300764Z","iopub.status.idle":"2024-02-09T14:42:43.317308Z","shell.execute_reply.started":"2024-02-09T14:42:43.300728Z","shell.execute_reply":"2024-02-09T14:42:43.315248Z"},"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":"2024-02-09T14:42:43.320298Z","iopub.execute_input":"2024-02-09T14:42:43.321096Z","iopub.status.idle":"2024-02-09T14:42:53.179543Z","shell.execute_reply.started":"2024-02-09T14:42:43.321007Z","shell.execute_reply":"2024-02-09T14:42:53.178246Z"},"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":"2024-02-09T14:42:53.181583Z","iopub.execute_input":"2024-02-09T14:42:53.182102Z","iopub.status.idle":"2024-02-09T14:42:53.189472Z","shell.execute_reply.started":"2024-02-09T14:42:53.182034Z","shell.execute_reply":"2024-02-09T14:42:53.187737Z"},"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":"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\": 1000,\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":"2024-02-09T14:42:53.191696Z","iopub.execute_input":"2024-02-09T14:42:53.192120Z","iopub.status.idle":"2024-02-09T14:44:31.562379Z","shell.execute_reply.started":"2024-02-09T14:42:53.192084Z","shell.execute_reply":"2024-02-09T14:44:31.561148Z"},"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":"2024-02-09T14:44:31.564371Z","iopub.execute_input":"2024-02-09T14:44:31.564850Z","iopub.status.idle":"2024-02-09T14:44:53.881723Z","shell.execute_reply.started":"2024-02-09T14:44:31.564805Z","shell.execute_reply":"2024-02-09T14:44:53.880342Z"},"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":"2024-02-09T14:44:53.883477Z","iopub.execute_input":"2024-02-09T14:44:53.883850Z","iopub.status.idle":"2024-02-09T14:44:55.038028Z","shell.execute_reply.started":"2024-02-09T14:44:53.883819Z","shell.execute_reply":"2024-02-09T14:44:55.036882Z"},"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":"2024-02-09T14:44:55.040354Z","iopub.execute_input":"2024-02-09T14:44:55.040756Z","iopub.status.idle":"2024-02-09T14:44:55.164217Z","shell.execute_reply.started":"2024-02-09T14:44:55.040722Z","shell.execute_reply":"2024-02-09T14:44:55.162920Z"},"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":"2024-02-09T14:44:55.165656Z","iopub.execute_input":"2024-02-09T14:44:55.166022Z","iopub.status.idle":"2024-02-09T14:44:55.182420Z","shell.execute_reply.started":"2024-02-09T14:44:55.165989Z","shell.execute_reply":"2024-02-09T14:44:55.180952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}}]}