{"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":"### За основу взят ноутбук: https://www.kaggle.com/code/jetakow/home-credit-2024-starter-notebook\n### Мною были добавлены новые признаки (см. код), а также использована Optuna для улучшения качества, полученного с помощью простого lightgbm","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":"2024-03-17T20:00:32.077421Z","iopub.execute_input":"2024-03-17T20:00:32.077898Z","iopub.status.idle":"2024-03-17T20:00:35.285237Z","shell.execute_reply.started":"2024-03-17T20:00:32.077862Z","shell.execute_reply":"2024-03-17T20:00:35.283875Z"},"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":"2024-03-17T20:00:43.121084Z","iopub.execute_input":"2024-03-17T20:00:43.121626Z","iopub.status.idle":"2024-03-17T20:00:43.133136Z","shell.execute_reply.started":"2024-03-17T20:00:43.121586Z","shell.execute_reply":"2024-03-17T20:00:43.131591Z"},"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_1 = pl.read_csv(dataPath + \"csv_files/train/train_credit_bureau_b_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":"2024-03-17T20:00:46.014152Z","iopub.execute_input":"2024-03-17T20:00:46.014714Z","iopub.status.idle":"2024-03-17T20:01:06.755637Z","shell.execute_reply.started":"2024-03-17T20:00:46.014670Z","shell.execute_reply":"2024-03-17T20:01:06.754531Z"},"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_1 = pl.read_csv(dataPath + \"csv_files/test/test_credit_bureau_b_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":"2024-03-17T20:01:06.757619Z","iopub.execute_input":"2024-03-17T20:01:06.758241Z","iopub.status.idle":"2024-03-17T20:01:06.835494Z","shell.execute_reply.started":"2024-03-17T20:01:06.758167Z","shell.execute_reply":"2024-03-17T20:01:06.834528Z"},"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# Add new features\ntrain_person_1_feats_2 = train_person_1.select([\"case_id\",\n                                                \"num_group1\",\n                                                \"housetype_905L\",\n                                                \"birth_259D\", \n                                                \"education_927M\",\n                                                \"empl_industry_691L\",\n                                                \"sex_738L\"\n                                               ]).filter(pl.col(\"num_group1\") == 0).drop(\"num_group1\").rename({\"housetype_905L\": \"person_housetype\",\n                                                                                                               \"birth_259D\": \"birth_date\",\n                                                                                                               \"education_927M\": \"education\",\n                                                                                                               \"empl_industry_691L\": \"empl_industry\",\n                                                                                                               \"sex_738L\": \"sex\"})\n#Add new features\ntrain_credit_bureau_b_1_feats = train_credit_bureau_b_1.group_by(\"case_id\").agg(\n    pl.col(\"credlmt_1052A\").max().alias(\"credlmt_1052A_max\"),\n    pl.col(\"debtpastduevalue_732A\").max().alias(\"debtpastduevalue_732A_max\"),\n    pl.col(\"numberofinstls_810L\").mean().alias(\"numberofinstls_810L_mean\")\n)\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\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_1_feats, 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":"2024-03-17T20:01:06.837380Z","iopub.execute_input":"2024-03-17T20:01:06.838290Z","iopub.status.idle":"2024-03-17T20:01:09.261633Z","shell.execute_reply.started":"2024-03-17T20:01:06.838246Z","shell.execute_reply":"2024-03-17T20:01:09.260373Z"},"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\",\n                                                \"num_group1\",\n                                                \"housetype_905L\",\n                                                \"birth_259D\", \n                                                \"education_927M\",\n                                                \"empl_industry_691L\",\n                                                \"sex_738L\"\n                                               ]).filter(pl.col(\"num_group1\") == 0).drop(\"num_group1\").rename({\"housetype_905L\": \"person_housetype\",\n                                                                                                               \"birth_259D\": \"birth_date\",\n                                                                                                               \"education_927M\": \"education\",\n                                                                                                               \"empl_industry_691L\": \"empl_industry\",\n                                                                                                               \"sex_738L\": \"sex\"})\n\ntest_credit_bureau_b_1_feats = test_credit_bureau_b_1.group_by(\"case_id\").agg(\n    pl.col(\"credlmt_1052A\").max().alias(\"credlmt_1052A_max\"),\n    pl.col(\"debtpastduevalue_732A\").max().alias(\"debtpastduevalue_732A_max\"),\n    pl.col(\"numberofinstls_810L\").mean().alias(\"numberofinstls_810L_mean\")\n)\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\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_1_feats, 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":"2024-03-17T20:01:16.596405Z","iopub.execute_input":"2024-03-17T20:01:16.597289Z","iopub.status.idle":"2024-03-17T20:01:16.618784Z","shell.execute_reply.started":"2024-03-17T20:01:16.597244Z","shell.execute_reply":"2024-03-17T20:01:16.617326Z"},"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\n#cols_pred = data.drop(columns=['case_id','target']).columns\n\ncols_pred = []\nfor col in data.columns:\n    if col[-1].isupper() and col[:-1].islower():\n        cols_pred.append(col)\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":"2024-03-17T20:01:19.563281Z","iopub.execute_input":"2024-03-17T20:01:19.563787Z","iopub.status.idle":"2024-03-17T20:01:30.720586Z","shell.execute_reply.started":"2024-03-17T20:01:19.563744Z","shell.execute_reply":"2024-03-17T20:01:30.719050Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training Optuna","metadata":{}},{"cell_type":"code","source":"#We use optuna to find best params of simply LGBM\nimport optuna\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\nlgb_train = lgb.Dataset(X_train, label=y_train)\nlgb_valid = lgb.Dataset(X_valid, label=y_valid, reference=lgb_train)\n\ndef objective(trial):\n    params = {\n        \"boosting_type\": \"gbdt\",\n        \"objective\": \"binary\",\n        \"metric\": \"auc\",\n        \"max_depth\": trial.suggest_int(\"max_depth\", 2, 8),\n        \"num_leaves\": trial.suggest_int(\"num_leaves\", 4, 64),\n        \"learning_rate\": trial.suggest_float(\"learning_rate\", 1e-3, 1e-1, log=True),\n        \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 1000),\n        \"verbose\": -1\n    }\n    gbm = lgb.train(\n        params,\n        lgb_train,\n        valid_sets=lgb_valid,\n        callbacks=[lgb.log_evaluation(50), lgb.early_stopping(10)]\n    )\n    y_valid_pred = gbm.predict(X_valid, num_iteration=gbm.best_iteration)\n    score = roc_auc_score(base_valid[\"target\"], y_valid_pred)\n    return score\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=10)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T20:01:35.922001Z","iopub.execute_input":"2024-03-17T20:01:35.922472Z","iopub.status.idle":"2024-03-17T20:10:40.021785Z","shell.execute_reply.started":"2024-03-17T20:01:35.922412Z","shell.execute_reply":"2024-03-17T20:10:40.020516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trial = study.best_trial\nbest_params = trial.params\n\nparams = {\n    \"boosting_type\": \"gbdt\",\n    \"objective\": \"binary\",\n    \"metric\": \"auc\",\n    \"verbose\": -1,\n    **best_params\n}\n\nlgb_train = lgb.Dataset(X_train, label=y_train)\nlgb_valid = lgb.Dataset(X_valid, label=y_valid, reference=lgb_train)\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":"2024-03-17T20:10:40.024129Z","iopub.execute_input":"2024-03-17T20:10:40.024563Z","iopub.status.idle":"2024-03-17T20:13:06.110670Z","shell.execute_reply.started":"2024-03-17T20:10:40.024529Z","shell.execute_reply":"2024-03-17T20:13:06.109765Z"},"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":"2024-03-17T20:13:12.810701Z","iopub.execute_input":"2024-03-17T20:13:12.811171Z","iopub.status.idle":"2024-03-17T20:14:21.995349Z","shell.execute_reply.started":"2024-03-17T20:13:12.811136Z","shell.execute_reply":"2024-03-17T20:14:21.994076Z"},"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":"2024-03-17T20:14:27.770845Z","iopub.execute_input":"2024-03-17T20:14:27.771349Z","iopub.status.idle":"2024-03-17T20:14:28.893296Z","shell.execute_reply.started":"2024-03-17T20:14:27.771308Z","shell.execute_reply":"2024-03-17T20:14:28.891957Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":"2024-03-17T20:15:08.154353Z","iopub.execute_input":"2024-03-17T20:15:08.154978Z","iopub.status.idle":"2024-03-17T20:15:08.316666Z","shell.execute_reply.started":"2024-03-17T20:15:08.154926Z","shell.execute_reply":"2024-03-17T20:15:08.315256Z"},"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":"2024-03-17T20:15:16.089895Z","iopub.execute_input":"2024-03-17T20:15:16.090337Z","iopub.status.idle":"2024-03-17T20:15:16.103994Z","shell.execute_reply.started":"2024-03-17T20:15:16.090304Z","shell.execute_reply":"2024-03-17T20:15:16.102964Z"},"trusted":true},"execution_count":null,"outputs":[]}]}