{"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":"# 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--------------------\nСтудент: Чирин Сергей\n\nЧто было добавлено:\n\n* добавлены данные из следующих файлов: deposit_1, debitcard_1 с агрегацией данных по каждому case_id. Из уже используемых файлов были добалены признаки, которые кончаются на P (P - Transform DPD (Days past due)), а также был добавлен возраст. \n\nТаким образом количество признаков увеличилось с 48 до 87.\n\n* подобраны гиперпараметры (код закомменчен из-за долгого времени работы)\n\nБазовый score: 0.361\nТекущий score: 0.418 \n\nДальнейшее развитие: добавление остальных файлов глубины 1 и добавление файлов с глубиной 2, а также их обработка, выкинуть не влияющие на резульатат признаки. Такие должны быть, т.к. признаков станет значительно больше. ","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"## Load the data","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":"2024-03-17T13:04:58.984077Z","iopub.execute_input":"2024-03-17T13:04:58.984561Z","iopub.status.idle":"2024-03-17T13:04:58.991731Z","shell.execute_reply.started":"2024-03-17T13:04:58.984506Z","shell.execute_reply":"2024-03-17T13:04:58.990490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# добавляем для M и D\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 in (\"WEEK_NUM\", \"case_id\", \"num_group_1\"):\n            df = df.with_columns(pl.col(col).cast(pl.Int64))\n        elif col[-1] in (\"P\", \"A\"):\n            df = df.with_columns(pl.col(col).cast(pl.Float64))\n        elif col[-1] in (\"M\"):\n            df = df.with_columns(pl.col(col).cast(pl.String))\n        elif col[-1] in (\"D\"):\n            df = df.with_columns(pl.col(col).cast(pl.Date))\n        elif col == \"date_decision\" or 'dateofbirth' in col:\n            df = df.with_columns(pl.col(col).cast(pl.Date))\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\n\ndef calculate_age(df):\n    for col in df.columns:\n        if 'birthdate_574D' in col:\n            df = df.with_columns(\n                (((pl.col(\"date_decision\") - pl.col(col)).dt.total_days()) //365).alias(\"agE\").cast(pl.Int64)\n            )\n            df = df.drop([col, \"date_decision\"])\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:04:58.994231Z","iopub.execute_input":"2024-03-17T13:04:58.994705Z","iopub.status.idle":"2024-03-17T13:04:59.010959Z","shell.execute_reply.started":"2024-03-17T13:04:58.994669Z","shell.execute_reply":"2024-03-17T13:04:59.009478Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_basetable = pl.read_csv(dataPath + \"csv_files/train/train_base.csv\").pipe(set_table_dtypes)\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) \n\n##\ntrain_deposit = pl.read_csv(dataPath + \"csv_files/train/train_deposit_1.csv\").pipe(set_table_dtypes)\ntrain_debitcard = pl.read_csv(dataPath + \"csv_files/train/train_debitcard_1.csv\").pipe(set_table_dtypes)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:04:59.012810Z","iopub.execute_input":"2024-03-17T13:04:59.013219Z","iopub.status.idle":"2024-03-17T13:05:27.097962Z","shell.execute_reply.started":"2024-03-17T13:04:59.013185Z","shell.execute_reply":"2024-03-17T13:05:27.096505Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_basetable = pl.read_csv(dataPath + \"csv_files/test/test_base.csv\").pipe(set_table_dtypes)\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##\ntest_deposit = pl.read_csv(dataPath + \"csv_files/test/test_deposit_1.csv\").pipe(set_table_dtypes)\ntest_debitcard = pl.read_csv(dataPath + \"csv_files/test/test_debitcard_1.csv\").pipe(set_table_dtypes)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:05:27.099928Z","iopub.execute_input":"2024-03-17T13:05:27.100490Z","iopub.status.idle":"2024-03-17T13:05:27.158575Z","shell.execute_reply.started":"2024-03-17T13:05:27.100450Z","shell.execute_reply":"2024-03-17T13:05:27.157397Z"},"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":"for i in train_static_cb.columns:\n    if i[-1] == 'D':\n        print(i)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:05:27.161544Z","iopub.execute_input":"2024-03-17T13:05:27.161908Z","iopub.status.idle":"2024-03-17T13:05:27.167976Z","shell.execute_reply.started":"2024-03-17T13:05:27.161878Z","shell.execute_reply":"2024-03-17T13:05:27.167073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Все кроме даты рождения можно выкинуть, т.к. эта информация о дате ответа","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().cast(pl.Categorical).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_over31A\")\n)\n\n##\ntrain_deposit_feats_2 = train_deposit.group_by(\"case_id\").agg(\n    pl.col(\"amount_416A\").max().alias(\"amount_416a_maX\"),\n    pl.col(\"amount_416A\").sum().alias(\"amount_416a_suM\")\n)\n\ntrain_debitcard_feats = train_debitcard.group_by(\"case_id\").agg(\n    pl.col(\"last180dayaveragebalance_704A\").max().alias(\"last180dayaveragebalance_704a_maX\"),\n    pl.col(\"last180dayaveragebalance_704A\").mean().alias(\"last180dayaveragebalance_704a_meaN\"),\n    pl.col(\"last180dayaveragebalance_704A\").min().alias(\"last180dayaveragebalance_704a_miN\"),\n    pl.col(\"last180dayturnover_1134A\").max().alias(\"last180dayturnover_1134a_maX\"),\n    pl.col(\"last180dayturnover_1134A\").mean().alias(\"last180dayturnover_1134a_meaN\"),\n    pl.col(\"last180dayturnover_1134A\").min().alias(\"last180dayturnover_1134a_miN\"),\n    pl.col(\"last30dayturnover_651A\").max().alias(\"last30dayturnover_651a_maX\"),\n    pl.col(\"last30dayturnover_651A\").mean().alias(\"last30dayturnover_651a_meaN\"),\n    pl.col(\"last30dayturnover_651A\").min().alias(\"last30dayturnover_651a_miN\")\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\", 'P'):\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\", 'P') or 'birthdate_574D' in col:\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).join(\n    train_deposit_feats_2, how=\"left\", on=\"case_id\"\n).join(\n    train_debitcard_feats, how=\"left\", on=\"case_id\"\n)\n\ndata = data.pipe(calculate_age)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:05:27.169524Z","iopub.execute_input":"2024-03-17T13:05:27.170181Z","iopub.status.idle":"2024-03-17T13:05:29.168148Z","shell.execute_reply.started":"2024-03-17T13:05:27.170142Z","shell.execute_reply":"2024-03-17T13:05:29.167229Z"},"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().cast(pl.Categorical).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_over31A\")\n)\n\n##\ntest_deposit_feats_2 = test_deposit.group_by(\"case_id\").agg(\n    pl.col(\"amount_416A\").max().alias(\"amount_416a_maX\"),\n    pl.col(\"amount_416A\").sum().alias(\"amount_416a_suM\")\n)\n\ntest_debitcard_feats = test_debitcard.group_by(\"case_id\").agg(\n    pl.col(\"last180dayaveragebalance_704A\").max().alias(\"last180dayaveragebalance_704a_maX\"),\n    pl.col(\"last180dayaveragebalance_704A\").mean().alias(\"last180dayaveragebalance_704a_meaN\"),\n    pl.col(\"last180dayaveragebalance_704A\").min().alias(\"last180dayaveragebalance_704a_miN\"),\n    pl.col(\"last180dayturnover_1134A\").max().alias(\"last180dayturnover_1134a_maX\"),\n    pl.col(\"last180dayturnover_1134A\").mean().alias(\"last180dayturnover_1134a_meaN\"),\n    pl.col(\"last180dayturnover_1134A\").min().alias(\"last180dayturnover_1134a_miN\"),\n    pl.col(\"last30dayturnover_651A\").max().alias(\"last30dayturnover_651a_maX\"),\n    pl.col(\"last30dayturnover_651A\").mean().alias(\"last30dayturnover_651a_meaN\"),\n    pl.col(\"last30dayturnover_651A\").min().alias(\"last30dayturnover_651a_miN\")\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).join(\n    test_deposit_feats_2, how='left', on='case_id'\n).join(\n    test_debitcard_feats, how=\"left\", on=\"case_id\"\n)\n\ndata_submission = data_submission.pipe(calculate_age)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:05:29.169565Z","iopub.execute_input":"2024-03-17T13:05:29.170125Z","iopub.status.idle":"2024-03-17T13:05:29.193001Z","shell.execute_reply.started":"2024-03-17T13:05:29.170090Z","shell.execute_reply":"2024-03-17T13:05:29.191664Z"},"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# странная проверка, но окей\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":"2024-03-17T13:05:29.194715Z","iopub.execute_input":"2024-03-17T13:05:29.195086Z","iopub.status.idle":"2024-03-17T13:05:33.384802Z","shell.execute_reply.started":"2024-03-17T13:05:29.195054Z","shell.execute_reply":"2024-03-17T13:05:33.383683Z"},"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":"2024-03-17T13:05:33.386338Z","iopub.execute_input":"2024-03-17T13:05:33.386708Z","iopub.status.idle":"2024-03-17T13:05:33.392917Z","shell.execute_reply.started":"2024-03-17T13:05:33.386678Z","shell.execute_reply":"2024-03-17T13:05:33.391794Z"},"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#   Заменяем на найденные лучшие гиперпараметры: \n    \"boosting_type\": \"gbdt\",\n    \"objective\": \"binary\",\n    \"metric\": \"auc\",\n    \"max_depth\": 3,\n    \"num_leaves\": 20,\n    \"learning_rate\": 0.055,\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)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:40:55.629829Z","iopub.execute_input":"2024-03-17T13:40:55.630356Z","iopub.status.idle":"2024-03-17T13:42:10.297234Z","shell.execute_reply.started":"2024-03-17T13:40:55.630318Z","shell.execute_reply":"2024-03-17T13:42:10.295770Z"},"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":"2024-03-17T13:42:17.497472Z","iopub.execute_input":"2024-03-17T13:42:17.497897Z","iopub.status.idle":"2024-03-17T13:42:38.909482Z","shell.execute_reply.started":"2024-03-17T13:42:17.497864Z","shell.execute_reply":"2024-03-17T13:42:38.908202Z"},"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":"2024-03-17T13:42:54.125031Z","iopub.execute_input":"2024-03-17T13:42:54.125531Z","iopub.status.idle":"2024-03-17T13:42:55.273709Z","shell.execute_reply.started":"2024-03-17T13:42:54.125479Z","shell.execute_reply":"2024-03-17T13:42:55.272527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Перебор гиперпараметров \n\nЗакомментил, чтобы ноутбук не считался час при загрузке. Снизу выведены лучшие гиперпараметры модели ","metadata":{}},{"cell_type":"code","source":"# from sklearn.model_selection import RandomizedSearchCV\n# from sklearn.metrics import roc_auc_score\n\n# initial_params = {\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\n# param_dist = {\n#     'learning_rate': np.linspace(0.01, 0.1, 5),\n#     'n_estimators': [100, 500, 1000],\n#     'max_depth': [3, 4, 5, 6],\n#     'num_leaves': [20, 31, 40, 50, 60]\n# }\n\n# lgb_estimator = lgb.LGBMClassifier(**initial_params)\n\n# random_search = RandomizedSearchCV(\n#     estimator=lgb_estimator,\n#     param_distributions=param_dist,\n#     n_iter=10,\n#     scoring='roc_auc',\n#     cv=3, \n#     verbose=1,\n#     random_state=42,\n#     n_jobs=-1\n# )\n\n# random_search.fit(X_train, y_train)\n\n# print(\"Лучшие параметры:\", random_search.best_params_)\n# print(\"Лучшее значение AUC:\", random_search.best_score_)\n\n# best_model = random_search.best_estimator_\n# print(\"Лучшая модель:\", best_model)\n\n# y_valid_pred = best_model.predict_proba(X_valid)[:, 1]\n# auc_score = roc_auc_score(y_valid, y_valid_pred)\n# print(\"AUC на валидационном наборе:\", auc_score)","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:06:58.899814Z","iopub.execute_input":"2024-03-17T13:06:58.900307Z","iopub.status.idle":"2024-03-17T13:06:58.907754Z","shell.execute_reply.started":"2024-03-17T13:06:58.900243Z","shell.execute_reply":"2024-03-17T13:06:58.906372Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Лучшие параметры: {'num_leaves': 20, 'n_estimators': 1000, 'max_depth': 6, 'learning_rate': 0.05500000000000001}\n\nЛучшее значение AUC: 0.7599200382244584\n\nЛучшая модель: LGBMClassifier(bagging_fraction=0.8, bagging_freq=5, feature_fraction=0.9,\n               learning_rate=0.05500000000000001, max_depth=6, metric='auc',\n               n_estimators=1000, num_leaves=20, objective='binary',\n               verbose=-1)\n\nAUC на валидационном наборе: 0.7861698714813334","metadata":{}},{"cell_type":"code","source":"# lgb_train = lgb.Dataset(X_train, label=y_train)\n# lgb_valid = lgb.Dataset(X_valid, label=y_valid, reference=lgb_train)\n\n# params = {\n#     \"boosting_type\": \"gbdt\",\n#     \"objective\": \"binary\",\n#     \"metric\": \"auc\",\n#     \"max_depth\": 6,\n#     \"num_leaves\": 20,\n#     \"learning_rate\": 0.055,\n#     \"feature_fraction\": 0.9,\n#     \"bagging_fraction\": 0.8,\n#     \"bagging_freq\": 5,\n#     \"n_estimators\": 1000,\n#     \"verbose\": -1,\n# }\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# )","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:06:58.913606Z","iopub.execute_input":"2024-03-17T13:06:58.914221Z","iopub.status.idle":"2024-03-17T13:07:55.940795Z","shell.execute_reply.started":"2024-03-17T13:06:58.914159Z","shell.execute_reply":"2024-03-17T13:07:55.939548Z"},"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\nprint(categorical_cols)\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    if len(new_categories) > 0:\n        X_submission.loc[X_submission[col].isin(new_categories), col] = 'Unknown'\n        new_dtype = pd.CategoricalDtype(categories=train_categories, ordered=False)\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":"2024-03-17T13:43:04.293121Z","iopub.execute_input":"2024-03-17T13:43:04.293617Z","iopub.status.idle":"2024-03-17T13:43:04.409042Z","shell.execute_reply.started":"2024-03-17T13:43:04.293575Z","shell.execute_reply":"2024-03-17T13:43:04.407899Z"},"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\")\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-03-17T13:43:07.768976Z","iopub.execute_input":"2024-03-17T13:43:07.769404Z","iopub.status.idle":"2024-03-17T13:43:07.783776Z","shell.execute_reply.started":"2024-03-17T13:43:07.769368Z","shell.execute_reply":"2024-03-17T13:43:07.782655Z"},"trusted":true},"execution_count":null,"outputs":[]}]}