{"metadata":{"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":50160,"databundleVersionId":7921029,"sourceType":"competition"}],"dockerImageVersionId":30665,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true},"kernelspec":{"display_name":"Python 3","language":"python","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"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Example Notebook + Additional Features + Hyperparameter Optimization\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_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5"}},{"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":"2024-03-17T19:44:28.910982Z","iopub.execute_input":"2024-03-17T19:44:28.911356Z","iopub.status.idle":"2024-03-17T19:44:28.916393Z","shell.execute_reply.started":"2024-03-17T19:44:28.911330Z","shell.execute_reply":"2024-03-17T19:44:28.915462Z"},"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":"2024-03-17T19:44:34.744993Z","iopub.execute_input":"2024-03-17T19:44:34.745839Z","iopub.status.idle":"2024-03-17T19:44:34.753565Z","shell.execute_reply.started":"2024-03-17T19:44:34.745806Z","shell.execute_reply":"2024-03-17T19:44:34.752661Z"},"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_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) \ntrain_debitcard_1 = pl.read_csv(dataPath + \"csv_files/train/train_debitcard_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":"2024-03-17T19:44:35.945418Z","iopub.execute_input":"2024-03-17T19:44:35.946099Z","iopub.status.idle":"2024-03-17T19:44:49.892919Z","shell.execute_reply.started":"2024-03-17T19:44:35.946071Z","shell.execute_reply":"2024-03-17T19:44:49.892105Z"},"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_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) \ntest_debitcard_1 = pl.read_csv(dataPath + \"csv_files/test/test_debitcard_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":"2024-03-17T19:44:49.894477Z","iopub.execute_input":"2024-03-17T19:44:49.894782Z","iopub.status.idle":"2024-03-17T19:44:49.953201Z","shell.execute_reply.started":"2024-03-17T19:44:49.894757Z","shell.execute_reply":"2024-03-17T19:44:49.952414Z"},"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_pmtsoverdue_635A\").mean().alias(\"pmts_pmtsoverdue_635A_mean\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).sum().alias(\"pmts_dpdvalue_108P_sum\")\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":"2024-03-17T19:45:20.083303Z","iopub.execute_input":"2024-03-17T19:45:20.084023Z","iopub.status.idle":"2024-03-17T19:45:22.091801Z","shell.execute_reply.started":"2024-03-17T19:45:20.083992Z","shell.execute_reply":"2024-03-17T19:45:22.090721Z"},"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":"2024-03-17T19:45:22.898403Z","iopub.execute_input":"2024-03-17T19:45:22.898752Z","iopub.status.idle":"2024-03-17T19:45:22.913180Z","shell.execute_reply.started":"2024-03-17T19:45:22.898724Z","shell.execute_reply":"2024-03-17T19:45:22.912159Z"},"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":"2024-03-17T19:45:24.220812Z","iopub.execute_input":"2024-03-17T19:45:24.221400Z","iopub.status.idle":"2024-03-17T19:45:31.431222Z","shell.execute_reply.started":"2024-03-17T19:45:24.221373Z","shell.execute_reply":"2024-03-17T19:45:31.430407Z"},"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":"2024-03-17T19:45:31.439676Z","iopub.execute_input":"2024-03-17T19:45:31.440321Z","iopub.status.idle":"2024-03-17T19:45:31.452063Z","shell.execute_reply.started":"2024-03-17T19:45:31.440289Z","shell.execute_reply":"2024-03-17T19:45:31.451238Z"},"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":"2024-03-17T19:45:34.089162Z","iopub.execute_input":"2024-03-17T19:45:34.089944Z","iopub.status.idle":"2024-03-17T19:46:48.099458Z","shell.execute_reply.started":"2024-03-17T19:45:34.089915Z","shell.execute_reply":"2024-03-17T19:46:48.098705Z"},"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":"2024-03-17T19:46:48.101104Z","iopub.execute_input":"2024-03-17T19:46:48.103627Z","iopub.status.idle":"2024-03-17T19:47:09.530503Z","shell.execute_reply.started":"2024-03-17T19:46:48.103596Z","shell.execute_reply":"2024-03-17T19:47:09.529549Z"},"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":"2024-03-17T19:47:09.531775Z","iopub.execute_input":"2024-03-17T19:47:09.532083Z","iopub.status.idle":"2024-03-17T19:47:10.555875Z","shell.execute_reply.started":"2024-03-17T19:47:09.532056Z","shell.execute_reply":"2024-03-17T19:47:10.554995Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Let's make it better!","metadata":{}},{"cell_type":"code","source":"# Loaded some new dataframes and generated some features by aggregating\n\ntrain_deposit_1_feats = train_deposit_1.group_by(\"case_id\").agg(\n    pl.col(\"amount_416A\").max().alias(\"amount_416A_max\"),\n    (pl.col(\"num_group1\")).mode().max().alias(\"num_group1_mode\")\n)\ntest_deposit_1_feats = test_deposit_1.group_by(\"case_id\").agg(\n    pl.col(\"amount_416A\").max().alias(\"amount_416A_max\"),\n    (pl.col(\"num_group1\")).mode().max().alias(\"num_group1_mode\")\n)\ntrain_dc_1_feats = train_debitcard_1.group_by(\"case_id\").agg(\n    pl.col(\"last180dayaveragebalance_704A\").mean().alias(\"last180dayaveragebalance_704A_mean\"),\n    pl.col(\"last30dayturnover_651A\").mean().alias(\"last30dayturnover_651A_mean\"),\n    pl.col(\"last180dayturnover_1134A\").mean().alias(\"last180dayturnover_1134A_mean\"),\n    (pl.col(\"num_group1\")).mode().max().alias(\"num_group12_mode\")\n)\ntest_dc_1_feats = test_debitcard_1.group_by(\"case_id\").agg(\n    pl.col(\"last180dayaveragebalance_704A\").mean().alias(\"last180dayaveragebalance_704A_mean\"),\n    pl.col(\"last30dayturnover_651A\").mean().alias(\"last30dayturnover_651A_mean\"),\n    pl.col(\"last180dayturnover_1134A\").mean().alias(\"last180dayturnover_1134A_mean\"),\n    (pl.col(\"num_group1\")).mode().max().alias(\"num_group12_mode\")\n)\ntest_other_1_feats = test_other_1.group_by(\"case_id\").first()\ntrain_other_1_feats = train_other_1.group_by(\"case_id\").first()\n\ndata = data.join(\n    train_dc_1_feats, how=\"left\", on=\"case_id\"\n).join(\n    train_deposit_1_feats, how=\"left\", on=\"case_id\"\n).join(\n    train_other_1_feats, how=\"left\", on=\"case_id\"\n)\n    \n\ndata_submission = data_submission.join(\n    test_dc_1_feats, how=\"left\", on=\"case_id\"\n).join(\n    test_deposit_1_feats, how=\"left\", on=\"case_id\"\n).join(\n    test_other_1_feats, how=\"left\", on=\"case_id\"\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T19:47:32.527171Z","iopub.execute_input":"2024-03-17T19:47:32.527564Z","iopub.status.idle":"2024-03-17T19:47:32.938046Z","shell.execute_reply.started":"2024-03-17T19:47:32.527536Z","shell.execute_reply":"2024-03-17T19:47:32.937242Z"},"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":"2024-03-17T19:47:35.277792Z","iopub.execute_input":"2024-03-17T19:47:35.278463Z","iopub.status.idle":"2024-03-17T19:47:42.487654Z","shell.execute_reply.started":"2024-03-17T19:47:35.278429Z","shell.execute_reply":"2024-03-17T19:47:42.486512Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for X in [X_valid, X_train, X_test]:\n    X.loc[:, X_valid.dtypes == 'category'] = X.loc[:, X_valid.dtypes == 'category'].astype(str).fillna('').astype(pd.CategoricalDtype)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T19:47:49.237681Z","iopub.execute_input":"2024-03-17T19:47:49.237995Z","iopub.status.idle":"2024-03-17T19:47:57.118830Z","shell.execute_reply.started":"2024-03-17T19:47:49.237959Z","shell.execute_reply":"2024-03-17T19:47:57.118064Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Hyperparameter optimization","metadata":{}},{"cell_type":"code","source":"import optuna\nfrom catboost import CatBoostClassifier, Pool\n\ncf = X_valid.columns[X_valid.dtypes == 'category'].tolist()","metadata":{"execution":{"iopub.status.busy":"2024-03-17T19:47:57.120637Z","iopub.execute_input":"2024-03-17T19:47:57.120921Z","iopub.status.idle":"2024-03-17T19:47:57.174146Z","shell.execute_reply.started":"2024-03-17T19:47:57.120896Z","shell.execute_reply":"2024-03-17T19:47:57.173174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cf","metadata":{"execution":{"iopub.status.busy":"2024-03-17T19:47:57.175224Z","iopub.execute_input":"2024-03-17T19:47:57.175497Z","iopub.status.idle":"2024-03-17T19:47:57.182439Z","shell.execute_reply.started":"2024-03-17T19:47:57.175474Z","shell.execute_reply":"2024-03-17T19:47:57.181361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import optuna\nfrom catboost.utils import eval_metric\neval_set = Pool(X_valid, y_valid, cat_features=cf)\ndef objective(trial):\n    params = {\n        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.1),\n        'depth': trial.suggest_int('depth', 4, 10),\n        'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 1, 10),\n        'boosting_type': trial.suggest_categorical('boosting_type', ['Ordered', 'Plain']),\n    }\n\n    model = CatBoostClassifier(**params, task_type=\"GPU\",\n                           devices='cuda:0', use_best_model=True, random_seed=42, cat_features=cf, verbose=100, eval_metric='AUC')\n    model.fit(X_train, y_train, eval_set=eval_set, verbose=100)\n    y_pred = model.predict_proba(X_valid)\n    return eval_metric(eval_set.get_label(), y_pred[:, 1], 'AUC')\n\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=10)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T19:56:38.049770Z","iopub.execute_input":"2024-03-17T19:56:38.050158Z","iopub.status.idle":"2024-03-17T20:53:31.154394Z","shell.execute_reply.started":"2024-03-17T19:56:38.050128Z","shell.execute_reply":"2024-03-17T20:53:31.153461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = CatBoostClassifier(**study.best_params, task_type=\"GPU\",\n                           devices='cuda:0', use_best_model=True, random_seed=42, cat_features=cf)\nmodel.fit(X_train, y_train, verbose=0, eval_set=eval_set)","metadata":{"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\nX_submission.loc[:, X_submission.dtypes == 'category'] = X_submission.loc[:, X_submission.dtypes == 'category'].astype(str).fillna('').astype(pd.CategoricalDtype)\n\ny_submission_pred = model.predict_proba(X_submission)[:, 1]","metadata":{"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":{"trusted":true},"execution_count":null,"outputs":[]}]}