{"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"},{"sourceId":8171752,"sourceType":"datasetVersion","datasetId":4836407}],"dockerImageVersionId":30684,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;\n            background-color:#3f4d6f;font-size:200%;\n            font-family:Gill Sans;letter-spacing:0.5px\">\n<p style=\"padding: 8px;color:white;\"><b> Introduction </b></p>","metadata":{}},{"cell_type":"markdown","source":"The goal of this competition is to predict which clients are more likely to default on their loans. If data science could help better predict one’s repayment capabilities, loans might become more accessible improving the lives of people who have historically been denied due to lack of credit history.","metadata":{}},{"cell_type":"markdown","source":"![cr1.jpg](attachment:22edaef6-0f65-4abc-b58c-b8680a966892.jpg) Created this image using Image Creator from Designer \n","metadata":{},"attachments":{"22edaef6-0f65-4abc-b58c-b8680a966892.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"\nThere are many great public kernels posted in this contest that helped me a lot with ideas and code. Most of them use LightGBM and CatBoost gradient boosting solutions. This kernel focuses on Random Forests  as an alternative since they are robust to overfitting and perform well on large datasets with many features. BalancedRandomForestClassifier is a variant of the standard RF classifier from imblearn that handles imbalanced datasets with many nan features so is a promising solution for this contest, alone or combined in an ensemble with LightGBM and CatBoost. If you find something interesting please do not forget to UPVOTE! ","metadata":{}},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;\n            background-color:#3f4d6f;font-size:200%;\n            font-family:Gill Sans;letter-spacing:0.5px\">\n<p style=\"padding: 8px;color:white;\"><b> Importing Libraries </b></p>","metadata":{}},{"cell_type":"code","source":"!pip install /kaggle/input/scikit-learn-1-4-2/numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:46:13.575855Z","iopub.execute_input":"2024-05-20T09:46:13.576290Z","iopub.status.idle":"2024-05-20T09:46:19.651168Z","shell.execute_reply.started":"2024-05-20T09:46:13.576256Z","shell.execute_reply":"2024-05-20T09:46:19.649837Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install /kaggle/input/scikit-learn-1-4-2/joblib-1.4.0-py3-none-any.whl","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:46:19.653514Z","iopub.execute_input":"2024-05-20T09:46:19.653961Z","iopub.status.idle":"2024-05-20T09:46:52.985371Z","shell.execute_reply.started":"2024-05-20T09:46:19.653922Z","shell.execute_reply":"2024-05-20T09:46:52.984243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install /kaggle/input/scikit-learn-1-4-2/scikit_learn-1.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:46:52.986988Z","iopub.execute_input":"2024-05-20T09:46:52.987450Z","iopub.status.idle":"2024-05-20T09:47:29.974119Z","shell.execute_reply.started":"2024-05-20T09:46:52.987388Z","shell.execute_reply":"2024-05-20T09:47:29.972743Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install /kaggle/input/scikit-learn-1-4-2/scipy-1.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:47:29.977687Z","iopub.execute_input":"2024-05-20T09:47:29.978195Z","iopub.status.idle":"2024-05-20T09:48:10.009000Z","shell.execute_reply.started":"2024-05-20T09:47:29.978146Z","shell.execute_reply":"2024-05-20T09:48:10.007818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install /kaggle/input/scikit-learn-1-4-2/threadpoolctl-3.4.0-py3-none-any.whl","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:48:10.010859Z","iopub.execute_input":"2024-05-20T09:48:10.011220Z","iopub.status.idle":"2024-05-20T09:48:57.456112Z","shell.execute_reply.started":"2024-05-20T09:48:10.011186Z","shell.execute_reply":"2024-05-20T09:48:57.454642Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from pathlib import Path\nfrom glob import glob\nfrom datetime import datetime\nfrom sklearn.model_selection import TimeSeriesSplit, GroupKFold, StratifiedGroupKFold\nfrom sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin\nfrom sklearn.metrics import roc_auc_score\nfrom collections import Counter\nimport sys\nimport subprocess\nimport os\nimport gc\nimport numpy as np\nimport pandas as pd\nimport polars as pl\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nROOT = '/kaggle/input/home-credit-credit-risk-model-stability'","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:48:57.458833Z","iopub.execute_input":"2024-05-20T09:48:57.460134Z","iopub.status.idle":"2024-05-20T09:48:59.752737Z","shell.execute_reply.started":"2024-05-20T09:48:57.460085Z","shell.execute_reply":"2024-05-20T09:48:59.751522Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;\n            background-color:#3f4d6f;font-size:200%;\n            font-family:Gill Sans;letter-spacing:0.5px\">\n    <p style=\"padding: 8px;color:white;\"><b> Training / Test Data </b></p>","metadata":{}},{"cell_type":"markdown","source":"For this challenge we are given 32 train and 36 test files in .csv and .parquet format that follow the same naming conventions.They contain information from previous applications (monthly annuity ,days past due,cancellation reason etc),credit bureau (contract status,collateral valuation type etc), debit card details (opening date,card turnover for recent periods etc), deposit (amount, date opened etc), person details (birthday, address), static info (monthly annuity amount) , tax registry info (tax deductions, name of employer) and other (incoming/outgoing deposits,debit card transactions) . Each file of the seven categories presented before contains too many predictors,lets have a look using feature definitions file.","metadata":{}},{"cell_type":"code","source":"features = pd.read_csv('/kaggle/input/home-credit-credit-risk-model-stability/feature_definitions.csv')\nprint('\\033[1m \\033[90m',features[0:7])\nprint(' ')\nprint('\\033[1m \\033[91m There are',features.shape[0],'predictors to analyze, we ll see what to keep!')","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:48:59.754339Z","iopub.execute_input":"2024-05-20T09:48:59.754999Z","iopub.status.idle":"2024-05-20T09:48:59.784327Z","shell.execute_reply.started":"2024-05-20T09:48:59.754954Z","shell.execute_reply":"2024-05-20T09:48:59.782962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Base tables**\nBase tables (train_base.csv and test_base.csv) store the basic information about the observation and case_id, train set does not include target. This is a unique identification of every observation and you need to use it to join the other tables to base tables.</font> </br>   <span style=\"font-size:12px;\"> **case_id** - This is the unique identifier for each credit case. You'll need this ID to join relevant tables to the base table.  \n    **date_decision** - This refers to the date when a decision was made regarding the approval of the loan.  \n    **WEEK_NUM** - This is the week number used for aggregation. In the test sample, WEEK_NUM continues sequentially from the last training value of WEEK_NUM.  \n    **MONTH** - This column represents the month and is intended for aggregation purposes.  \n    **target** - This is the target value, determined after a certain period based on whether or not the client defaulted on the specific credit case (loan).  </font>  \n  </span>   ","metadata":{}},{"cell_type":"code","source":"train_path= \"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/\"\ntest_path=  \"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/test/\"\ntrain_b = pd.read_csv(train_path+ \"train_base.csv\")\ntest_b = pd.read_csv(test_path +\"test_base.csv\")\ntrain_b.head()","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:48:59.788214Z","iopub.execute_input":"2024-05-20T09:48:59.788626Z","iopub.status.idle":"2024-05-20T09:49:00.948120Z","shell.execute_reply.started":"2024-05-20T09:48:59.788584Z","shell.execute_reply":"2024-05-20T09:49:00.946915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Depth 0 features**\nFor depth=0 tables, there are static features directly tied to a specific case_id, predictors can be directly used as features.   \n**train**: train_static_0_0.csv,train_static_0_1.csv,static_cb_0 \n**test** : test_static_0_0.csv , test_static_0_1.csv,           test_static_0_2.csv,test_static_cb_0.csv   \nEach group of tables can comprise one or more individual tables. If a group contains more than one table, they are divided based on WEEK_NUM. Various predictors were transformed, therefore we have the following notation for similar groups of transformations  \n\n    P - Transform DPD (Days past due)\n    M - Masking categories\n    A - Transform amount\n    D - Transform date\n    T - Unspecified Transform\n    L - Unspecified Transform\ntransformations within a group are denoted by a capital letter at the end of the predictor name. Let's examine how they appear in Base and Depth0 levels</font> ","metadata":{}},{"cell_type":"code","source":"def transform_types(df):\n    counts = {'P': 0, 'M': 0, 'A': 0, 'D': 0, 'T': 0, 'L': 0}\n    columns= df.columns.tolist()\n    exclude=['case_id','num_group1','num_group2']\n    last_characters = [s[-1] for s in columns if s not in exclude]\n    value_counts = Counter(last_characters)\n    for value, count in value_counts.items():\n        if value == 'A':\n            counts['A'] = count\n        elif value == 'P':\n            counts['P'] = count\n        elif value == 'M':\n            counts['M'] = count\n        elif value == 'D':\n            counts['D'] = count\n        elif value == 'T':\n            counts['T'] = count\n        elif value == 'L':\n            counts['L'] = count\n    return counts\n\ntrain_st0_1 = pd.read_csv(train_path+ \"train_static_0_0.csv\",low_memory=False)\ntrain_st0_2 = pd.read_csv(train_path+ \"train_static_0_1.csv\",low_memory=False)\ntrain_st_12 = pd.concat([train_st0_1, train_st0_2], axis=0)\ntrain_st0_3 = pd.read_csv(train_path+ \"train_static_cb_0.csv\",low_memory=False)\ndf_trcombo = train_b.join(train_st_12.set_index('case_id'), on='case_id', how='left')\ndf_train0 = df_trcombo.join(train_st0_3.set_index('case_id'), on='case_id', how='left')\ndel train_st0_1,train_st0_2,train_st_12 ,train_st0_3,df_trcombo\ngc.collect()\ncounts_df = pd.DataFrame(list(transform_types(df_train0).items()), columns=['Type', 'Count'])\nplt.figure(figsize=(8, 4))\nsns.barplot(x='Type', y='Count', data=counts_df, palette='viridis')\nplt.title('Transformation Types in Base-Depth0')\nplt.xlabel('Type')\nplt.ylabel('Count')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:49:00.949338Z","iopub.execute_input":"2024-05-20T09:49:00.949633Z","iopub.status.idle":"2024-05-20T09:50:59.416850Z","shell.execute_reply.started":"2024-05-20T09:49:00.949607Z","shell.execute_reply":"2024-05-20T09:50:59.415504Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Class imbalance**\nExamining target variable shows that we have to deal with an extremely imbalanced dataset","metadata":{"execution":{"iopub.status.busy":"2024-05-04T12:35:22.722102Z","iopub.execute_input":"2024-05-04T12:35:22.722541Z","iopub.status.idle":"2024-05-04T12:35:22.729657Z","shell.execute_reply.started":"2024-05-04T12:35:22.722509Z","shell.execute_reply":"2024-05-04T12:35:22.728269Z"}}},{"cell_type":"code","source":"class_counts = train_b['target'].value_counts()\nlabels = ['Good Credit', 'Default']\nsizes = [class_counts[0], class_counts[1]]\ncolors = ['#26838f', '#fee825']\nexplode = (0, 0.1)\nplt.figure(figsize=(6, 4))\nplt.pie(sizes, labels=labels, explode=explode, colors=colors, autopct='%1.1f%%', shadow=True, startangle=140)\nplt.title('Default Percentage (Class 1)')\nplt.axis('equal')  \nplt.show()\ndel df_train0,train_b\ngc.collect()\n# Started EDA with Pandas but will proceed with Polars since it is a faster , memory efficient solution \n# that provides significant performance improvements, especially for large datasets.","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:50:59.420963Z","iopub.execute_input":"2024-05-20T09:50:59.421386Z","iopub.status.idle":"2024-05-20T09:51:00.061040Z","shell.execute_reply.started":"2024-05-20T09:50:59.421340Z","shell.execute_reply":"2024-05-20T09:51:00.059795Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This is a serious problem in credit scoring since classifiers overfit towards the majority class,performing poorly on minority class. There are various ways to deal with this problem like undersampling, oversampling, or by creating synthetic samples approaces that have both advantages but also disadvantages like loss of potentially valuable information, bias introduction and overfitting. The choice of BalancedRandomForestClassifier is an alternative that bybasses the need for such strategies. ","metadata":{}},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;\n            background-color:#3f4d6f;font-size:180%;\n            font-family:Gill Sans;letter-spacing:0.5px\">\n    <p style=\"padding: 8px;color:white;\"><b> Feature engineering </b></p>","metadata":{}},{"cell_type":"code","source":"class Pipeline:\n\n    def set_table_dtypes(df):\n        for col in df.columns:\n            if col in [\"case_id\", \"WEEK_NUM\", \"num_group1\", \"num_group2\"]:\n                df = df.with_columns(pl.col(col).cast(pl.Int64))\n            elif col in [\"date_decision\"]:\n                df = df.with_columns(pl.col(col).cast(pl.Date))\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        return df\n\n    def handle_dates(df):\n        for col in df.columns:\n            if col[-1] in (\"D\",):\n                df = df.with_columns(pl.col(col) - pl.col(\"date_decision\")) \n                df = df.with_columns(pl.col(col).dt.total_days())  \n        df = df.drop(\"date_decision\", \"MONTH\")\n        return df\n\n    def filter_cols(df):\n        for col in df.columns:\n            if col not in [\"target\", \"case_id\", \"WEEK_NUM\"]:\n                isnull = df[col].is_null().mean()\n                if isnull > 0.7:\n                    df = df.drop(col)\n        \n        for col in df.columns:\n            if (col not in [\"target\", \"case_id\", \"WEEK_NUM\"]) & (df[col].dtype == pl.String):\n                freq = df[col].n_unique()\n                if (freq == 1) | (freq > 200):\n                    df = df.drop(col)\n        \n        return df\n\n\nclass Aggregator:\n    def num_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"P\", \"A\")]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        expr_last = [pl.last(col).alias(f\"last_{col}\") for col in cols]\n        expr_mean = [pl.mean(col).alias(f\"mean_{col}\") for col in cols]\n        return expr_max +expr_last+expr_mean\n    \n    def date_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"D\")]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        expr_last = [pl.last(col).alias(f\"last_{col}\") for col in cols]\n        expr_mean = [pl.mean(col).alias(f\"mean_{col}\") for col in cols]\n        return  expr_max +expr_last+expr_mean\n    \n    def str_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"M\",)]\n        mode_expr = [pl.col(col).mode().first().alias(f\"max_{col}\") for col in cols]\n        return  mode_expr \n    \n    def other_expr(df):\n        cols = [col for col in df.columns if col[-1] in (\"T\", \"L\")]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols]\n        expr_last = [pl.last(col).alias(f\"last_{col}\") for col in cols]\n        return  expr_max +expr_last\n    \n    def count_expr(df):\n        cols = [col for col in df.columns if \"num_group\" in col]\n        expr_max = [pl.max(col).alias(f\"max_{col}\") for col in cols] \n        expr_last = [pl.last(col).alias(f\"last_{col}\") for col in cols]\n        return  expr_max +expr_last\n    \n    def get_exprs(df):\n        exprs = Aggregator.num_expr(df) + \\\n                Aggregator.date_expr(df) + \\\n                Aggregator.str_expr(df) + \\\n                Aggregator.other_expr(df) + \\\n                Aggregator.count_expr(df)\n\n        return exprs\n\ndef read_file(path, depth=None):\n    df = pl.read_parquet(path)\n    df = df.pipe(Pipeline.set_table_dtypes)\n    if depth in [1,2]:\n        df = df.group_by(\"case_id\").agg(Aggregator.get_exprs(df)) \n    return df\n\ndef read_files(regex_path, depth=None):\n    chunks = []\n    \n    for path in glob(str(regex_path)):\n        df = pl.read_parquet(path)\n        df = df.pipe(Pipeline.set_table_dtypes)\n        if depth in [1, 2]:\n            df = df.group_by(\"case_id\").agg(Aggregator.get_exprs(df))\n        chunks.append(df)\n    \n    df = pl.concat(chunks, how=\"vertical_relaxed\")\n    df = df.unique(subset=[\"case_id\"])\n    return df\n\ndef feature_eng(df_base, depth_0, depth_1, depth_2):\n    df_base = (\n        df_base\n        .with_columns(\n            month_decision = pl.col(\"date_decision\").dt.month(),\n            weekday_decision = pl.col(\"date_decision\").dt.weekday(),\n        )\n    )\n    for i, df in enumerate(depth_0 + depth_1 + depth_2):\n        df_base = df_base.join(df, how=\"left\", on=\"case_id\", suffix=f\"_{i}\")\n    df_base = df_base.pipe(Pipeline.handle_dates)\n    return df_base\n\ndef to_pandas(df_data, cat_cols=None):\n    df_data = df_data.to_pandas()\n    if cat_cols is None:\n        cat_cols = list(df_data.select_dtypes(\"object\").columns)\n    df_data[cat_cols] = df_data[cat_cols].astype(\"category\")\n    return df_data, cat_cols\n\ndef reduce_mem_usage(df):\n\n    start_mem = df.memory_usage().sum() / 1024**2\n    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n    \n    for col in df.columns:\n        col_type = df[col].dtype\n        if str(col_type)==\"category\":\n            continue\n        \n        if col_type != object:\n            c_min = df[col].min()\n            c_max = df[col].max()\n            if str(col_type)[:3] == 'int':\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n                    df[col] = df[col].astype(np.int8)\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n                    df[col] = df[col].astype(np.int16)\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n                    df[col] = df[col].astype(np.int32)\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n                    df[col] = df[col].astype(np.int64)  \n            else:\n                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n                    df[col] = df[col].astype(np.float16)\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n                    df[col] = df[col].astype(np.float32)\n                else:\n                    df[col] = df[col].astype(np.float64)\n        else:\n            continue\n    end_mem = df.memory_usage().sum() / 1024**2\n    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n    \n    return df","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:51:00.062978Z","iopub.execute_input":"2024-05-20T09:51:00.063328Z","iopub.status.idle":"2024-05-20T09:51:00.098456Z","shell.execute_reply.started":"2024-05-20T09:51:00.063297Z","shell.execute_reply":"2024-05-20T09:51:00.097040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ROOT            = Path(\"/kaggle/input/home-credit-credit-risk-model-stability\")\n\nTRAIN_DIR       = ROOT / \"parquet_files\" / \"train\"\nTEST_DIR        = ROOT / \"parquet_files\" / \"test\"","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:51:00.099580Z","iopub.execute_input":"2024-05-20T09:51:00.100599Z","iopub.status.idle":"2024-05-20T09:51:00.107750Z","shell.execute_reply.started":"2024-05-20T09:51:00.100554Z","shell.execute_reply":"2024-05-20T09:51:00.106508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\ndata_store = {\n    \"df_base\": read_file(TRAIN_DIR / \"train_base.parquet\"),\n    \"depth_0\": [\n        read_file(TRAIN_DIR / \"train_static_cb_0.parquet\"),\n        read_files(TRAIN_DIR / \"train_static_0_*.parquet\"),\n    ],\n    \"depth_1\": [\n        read_files(TRAIN_DIR / \"train_applprev_1_*.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_tax_registry_a_1.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_tax_registry_b_1.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_tax_registry_c_1.parquet\", 1),\n        read_files(TRAIN_DIR / \"train_credit_bureau_a_1_*.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_credit_bureau_b_1.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_other_1.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_person_1.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_deposit_1.parquet\", 1),\n        read_file(TRAIN_DIR / \"train_debitcard_1.parquet\", 1),\n    ],\n    \"depth_2\": [\n        read_file(TRAIN_DIR / \"train_credit_bureau_b_2.parquet\", 2),\n        read_files(TRAIN_DIR / \"train_credit_bureau_a_2_*.parquet\", 2),\n        read_file(TRAIN_DIR / \"train_applprev_2.parquet\", 2),\n        read_file(TRAIN_DIR / \"train_person_2.parquet\", 2)\n    ]\n}","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:51:00.109208Z","iopub.execute_input":"2024-05-20T09:51:00.109592Z","iopub.status.idle":"2024-05-20T09:53:25.640751Z","shell.execute_reply.started":"2024-05-20T09:51:00.109561Z","shell.execute_reply":"2024-05-20T09:53:25.639359Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\ndf_train = feature_eng(**data_store)\nprint(\"train data shape:\\t\", df_train.shape)\ndel data_store\ngc.collect()\ndf_train = df_train.pipe(Pipeline.filter_cols)\ndf_train, cat_cols = to_pandas(df_train)\ndf_train = reduce_mem_usage(df_train)\nprint(\"train data shape:\\t\", df_train.shape)\n\nnums=df_train.select_dtypes(exclude='category').columns\nfrom itertools import combinations, permutations\nnans_df = df_train[nums].isna()\nnans_groups={}\nfor col in nums:\n    cur_group = nans_df[col].sum()\n    try:\n        nans_groups[cur_group].append(col)\n    except:\n        nans_groups[cur_group]=[col]\ndel nans_df; x=gc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.642236Z","iopub.execute_input":"2024-05-20T09:53:25.642589Z","iopub.status.idle":"2024-05-20T09:53:25.666489Z","shell.execute_reply.started":"2024-05-20T09:53:25.642559Z","shell.execute_reply":"2024-05-20T09:53:25.665493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;\n            background-color:#3f4d6f;font-size:180%;\n            font-family:Gill Sans;letter-spacing:0.5px\">\n    <p style=\"padding: 8px;color:white;\"><b> Correlation-based Feature Selection </b></p>","metadata":{}},{"cell_type":"code","source":"def reduce_group(grps):\n    use = []\n    for g in grps:\n        mx = 0; vx = g[0]\n        for gg in g:\n            n = df_train[gg].nunique()\n            if n>mx:\n                mx = n\n                vx = gg\n        use.append(vx)\n    return use\n\ndef group_columns_by_correlation(matrix, threshold=0.8):\n    correlation_matrix = matrix.corr()\n    groups = []\n    remaining_cols = list(matrix.columns)\n    while remaining_cols:\n        col = remaining_cols.pop(0)\n        group = [col]\n        correlated_cols = [col]\n        for c in remaining_cols:\n            if correlation_matrix.loc[col, c] >= threshold:\n                group.append(c)\n                correlated_cols.append(c)\n        groups.append(group)\n        remaining_cols = [c for c in remaining_cols if c not in correlated_cols]\n    \n    return groups\n\nuses=[]\nfor k,v in nans_groups.items():\n    if len(v)>1:\n            Vs = nans_groups[k]\n            grps= group_columns_by_correlation(df_train[Vs], threshold=0.8)\n            use=reduce_group(grps)\n            uses=uses+use\n   \n    else:\n        uses=uses+v\n#print(uses)\n#print(len(uses))\nuses=uses+list(df_train.select_dtypes(include='category').columns)\n#print(len(uses))\ndf_train=df_train[uses]","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.667752Z","iopub.execute_input":"2024-05-20T09:53:25.668087Z","iopub.status.idle":"2024-05-20T09:53:25.850465Z","shell.execute_reply.started":"2024-05-20T09:53:25.668060Z","shell.execute_reply":"2024-05-20T09:53:25.848852Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")\ndevice='gpu'\nn_est=6000\nDRY_RUN = True if sample.shape[0] == 10 else False   \nif DRY_RUN:\n    device='cpu'\n    df_train = df_train.iloc[:50000]\n    n_est=600\nprint(device)","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.851409Z","iopub.status.idle":"2024-05-20T09:53:25.851836Z","shell.execute_reply.started":"2024-05-20T09:53:25.851649Z","shell.execute_reply":"2024-05-20T09:53:25.851666Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_store = {\n    \"df_base\": read_file(TEST_DIR / \"test_base.parquet\"),\n    \"depth_0\": [\n        read_file(TEST_DIR / \"test_static_cb_0.parquet\"),\n        read_files(TEST_DIR / \"test_static_0_*.parquet\"),\n    ],\n    \"depth_1\": [\n        read_files(TEST_DIR / \"test_applprev_1_*.parquet\", 1),\n        read_file(TEST_DIR / \"test_tax_registry_a_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_tax_registry_b_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_tax_registry_c_1.parquet\", 1),\n        read_files(TEST_DIR / \"test_credit_bureau_a_1_*.parquet\", 1),\n        read_file(TEST_DIR / \"test_credit_bureau_b_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_other_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_person_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_deposit_1.parquet\", 1),\n        read_file(TEST_DIR / \"test_debitcard_1.parquet\", 1),\n    ],\n    \"depth_2\": [\n        read_file(TEST_DIR / \"test_credit_bureau_b_2.parquet\", 2),\n        read_files(TEST_DIR / \"test_credit_bureau_a_2_*.parquet\", 2),\n        read_file(TEST_DIR / \"test_applprev_2.parquet\", 2),\n        read_file(TEST_DIR / \"test_person_2.parquet\", 2)\n    ]\n}","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.853680Z","iopub.status.idle":"2024-05-20T09:53:25.854108Z","shell.execute_reply.started":"2024-05-20T09:53:25.853895Z","shell.execute_reply":"2024-05-20T09:53:25.853910Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test = feature_eng(**data_store)\nprint(\"test data shape:\\t\", df_test.shape)\ndel data_store\ngc.collect()\ndf_test = df_test.select([col for col in df_train.columns if col != \"target\"])\nprint(\"train data shape:\\t\", df_train.shape)\nprint(\"test data shape:\\t\", df_test.shape)\n\ndf_test, cat_cols = to_pandas(df_test, cat_cols)\ndf_test = reduce_mem_usage(df_test)\n\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.855332Z","iopub.status.idle":"2024-05-20T09:53:25.855758Z","shell.execute_reply.started":"2024-05-20T09:53:25.855563Z","shell.execute_reply":"2024-05-20T09:53:25.855581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y = df_train[\"target\"]\nweeks = df_train[\"WEEK_NUM\"]\ndf_train= df_train.drop(columns=[\"target\", \"case_id\", \"WEEK_NUM\"])\ncv = StratifiedGroupKFold(n_splits=5, shuffle=False)\ndf_train[cat_cols] = df_train[cat_cols].astype(str)\ndf_test[cat_cols] = df_test[cat_cols].astype(str)","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.857138Z","iopub.status.idle":"2024-05-20T09:53:25.857573Z","shell.execute_reply.started":"2024-05-20T09:53:25.857352Z","shell.execute_reply":"2024-05-20T09:53:25.857369Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\n%%time\nparams = {\n    \"boosting_type\": \"gbdt\",\n    \"objective\": \"binary\",\n    \"metric\": \"auc\",\n    \"max_depth\": 7,  \n    \"learning_rate\": 0.05,\n    \"n_estimators\": 1000,  \n    \"colsample_bytree\": 0.8,\n    \"colsample_bynode\": 0.8,\n    \"verbose\": -1,\n    \"random_state\": 42,\n    \"reg_alpha\": 0.1,\n    \"reg_lambda\": 10,\n    \"extra_trees\":True,\n    'num_leaves':64,\n    \"device\": device, \n    \"verbose\": -1,\n}\nfrom catboost import CatBoostClassifier, Pool\nimport lightgbm as lgb\n\ncv = StratifiedGroupKFold(n_splits=5, shuffle=False)\n\nfitted_models_cat = []\nfitted_models_lgb = []\n\ncv_scores_cat = []\ncv_scores_lgb = []\n\n#train_scores = []\n#valid_scores = []\n\nfor idx_train, idx_valid in cv.split(df_train, y, groups=weeks):#\n    X_train, y_train = df_train.iloc[idx_train], y.iloc[idx_train]# \n    X_valid, y_valid = df_train.iloc[idx_valid], y.iloc[idx_valid]\n    train_pool = Pool(X_train, y_train,cat_features=cat_cols)\n    val_pool = Pool(X_valid, y_valid,cat_features=cat_cols)\n    clf = CatBoostClassifier(eval_metric='AUC',task_type='GPU',\n    learning_rate=0.03,iterations=n_est)\n    random_seed=3107\n    clf.fit(train_pool, eval_set=val_pool,verbose=300)\n    fitted_models_cat.append(clf)\n    y_pred_valid = clf.predict_proba(X_valid)[:,1]\n    auc_score = roc_auc_score(y_valid, y_pred_valid)\n    cv_scores_cat.append(auc_score)\n    \n    \n    X_train[cat_cols] = X_train[cat_cols].astype(\"category\")\n    X_valid[cat_cols] = X_valid[cat_cols].astype(\"category\")\n    \n    model = lgb.LGBMClassifier(**params)\n    model.fit(X_train, y_train,\n              eval_set = [(X_valid, y_valid)],\n              callbacks = [lgb.log_evaluation(200), lgb.early_stopping(100)] )\n    \n    #train_scores.append(model.evals_result_['training']['auc'])\n    #valid_scores.append(model.evals_result_['valid_1']['auc'])    \n    fitted_models_lgb.append(model)\n    y_pred_valid = model.predict_proba(X_valid)[:,1]\n    auc_score = roc_auc_score(y_valid, y_pred_valid)\n    cv_scores_lgb.append(auc_score)\n    \n    \nprint(\"CV AUC scores: \", cv_scores_cat)\nprint(\"Maximum CV AUC score: \", max(cv_scores_cat))\n\n\nprint(\"CV AUC scores: \", cv_scores_lgb)\nprint(\"Maximum CV AUC score: \", max(cv_scores_lgb))\n'''","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.858360Z","iopub.status.idle":"2024-05-20T09:53:25.858754Z","shell.execute_reply.started":"2024-05-20T09:53:25.858572Z","shell.execute_reply":"2024-05-20T09:53:25.858588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div style=\"color:white;display:fill;\n            background-color:#3f4d6f;font-size:150%;\n            font-family:Gill Sans;letter-spacing:0.5px\">\n    <p style=\"padding: 8px;color:white;\"><b> Balanced RF Classifier </b></p>","metadata":{"execution":{"iopub.status.busy":"2024-05-17T20:59:36.514603Z","iopub.execute_input":"2024-05-17T20:59:36.515167Z","iopub.status.idle":"2024-05-17T20:59:36.52411Z","shell.execute_reply.started":"2024-05-17T20:59:36.515122Z","shell.execute_reply":"2024-05-17T20:59:36.522348Z"}}},{"cell_type":"markdown","source":"### **Encoding categorical variables**\n\n  \n  Well RFs can handle nan values but we also have to hanldle categorical values in our dataset properly. The most popular options like One Hot / Label Encoding will lead to the Curse of Dimensionality due to high cardinality in many features. Target encoding and WOE encoding are two alternatives that replace different classes with numerical values without increasing existing large set of predictors.   \n**Target Encoding:**  replaces each category with the mean of the target variable for that category, useful in scenarios where the dataset has high cardinality categorical features  \n**WOE Encoding:** is a technique used primarily in credit scoring and other financial applications. It transforms categorical features by replacing each category with the weight of evidence (WOE) value, which measures the strength of evidence in favor of a particular category being associated with a target class\n\nAfter testing target encoding is selected, and applied within cross-validation folds to avoid target leakage.\n\n### **Balanced RF Classifier**\n\nA balanced random forest differs from a classical random forest by the fact that it will draw a bootstrap sample from the minority class and sample with replacement the same number of samples from the majority class. It retains the inherent advantages of Random Forests, such as robustness to overfitting, ability to handle high-dimensional data, and providing feature importance scores.","metadata":{}},{"cell_type":"code","source":"#from imblearn.ensemble import BalancedRandomForestClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import TargetEncoder\nencoder = TargetEncoder()\n#import category_encoders as ce\n#encoder = ce.WOEEncoder(cols=cat_cols)   # WOE encoding\n\nfitted_modelsRF = []\nauc_scoresRF = []\nfeature_importances = []\n\nfor idx_train, idx_valid in cv.split(df_train, y, groups=weeks):\n    X_train, y_train = df_train.iloc[idx_train], y.iloc[idx_train]\n    X_valid, y_valid = df_train.iloc[idx_valid], y.iloc[idx_valid]\n    X_train[cat_cols] = encoder.fit_transform(X_train[cat_cols], y_train)\n    X_valid[cat_cols] = encoder.transform(X_valid[cat_cols])\n    model = RandomForestClassifier(n_estimators = 500,n_jobs = -1,random_state =50,\n                    min_samples_split =10,max_depth = 20, max_features = \"sqrt\", criterion =\"entropy\",min_samples_leaf = 10)\n    #model = BalancedRandomForestClassifier(n_estimators = 600,n_jobs = -1,random_state =50,\n    #                 replacement=True)\n    model.fit(X_train, y_train)\n    fitted_modelsRF.append(model)\n    y_pred_proba = model.predict_proba(X_valid)[:, 1]\n    \n\n    auc_scoreRF = roc_auc_score(y_valid, y_pred_proba)\n    auc_scoresRF.append(auc_scoreRF)\n    print(\"Fold RF AUC Score:\", auc_scoreRF)\n    feature_importances.append(model.feature_importances_)\n    \naverage_aucRF = sum(auc_scoresRF) / len(auc_scoresRF)\nprint(\"Average AUC Score:\", average_aucRF)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.860942Z","iopub.status.idle":"2024-05-20T09:53:25.861816Z","shell.execute_reply.started":"2024-05-20T09:53:25.861532Z","shell.execute_reply":"2024-05-20T09:53:25.861556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"avg_feat_importances = np.mean(feature_importances, axis=0)\nfeat_importance_df = pd.DataFrame({\n    'feature': df_train.columns,\n    'importance': avg_feat_importances\n}).sort_values(by='importance', ascending=False)\nplt.figure(figsize=(10, 8))\nsns.barplot(x='importance', y='feature', data=feat_importance_df)\nplt.title('Feature Importances')\nplt.show()\ndel df_train,X_train, y_train,X_valid, y_valid\ngc.collect","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class VotingModel(BaseEstimator, ClassifierMixin):\n    def __init__(self, estimators):\n        super().__init__()\n        self.estimators = estimators\n        \n    def fit(self, X, y=None):\n        return self\n    \n    def predict(self, X):\n        y_preds = [estimator.predict(X) for estimator in self.estimators]\n        return np.mean(y_preds, axis=0)\n    \n    def predict_proba(self, X):\n        \n        y_preds = [estimator.predict_proba(X) for estimator in self.estimators[:5]]\n        \n        X[cat_cols] = X[cat_cols].astype(\"category\")\n        y_preds += [estimator.predict_proba(X) for estimator in self.estimators[5:]]\n       \n        return np.mean(y_preds, axis=0)\n\nmodel= VotingModel(fitted_modelsRF)\n\ndel fitted_modelsRF\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.863206Z","iopub.status.idle":"2024-05-20T09:53:25.863881Z","shell.execute_reply.started":"2024-05-20T09:53:25.863485Z","shell.execute_reply":"2024-05-20T09:53:25.863509Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test = df_test.drop(columns=[\"WEEK_NUM\"])\ndf_test = df_test.set_index(\"case_id\")\ndf_test[cat_cols]=encoder.transform(df_test[cat_cols])\n\ny_pred = pd.Series(model.predict_proba(df_test)[:, 1], index=df_test.index)\n\ndf_subm = pd.read_csv(ROOT / \"sample_submission.csv\")\ndf_subm = df_subm.set_index(\"case_id\")\n\ndf_subm[\"score\"] = y_pred \ndf_subm.to_csv(\"submission.csv\")\ndf_subm","metadata":{"execution":{"iopub.status.busy":"2024-05-20T09:53:25.865734Z","iopub.status.idle":"2024-05-20T09:53:25.866313Z","shell.execute_reply.started":"2024-05-20T09:53:25.866021Z","shell.execute_reply":"2024-05-20T09:53:25.866044Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### **Furure improvements**\n\nThere is only one week left and this is a small list with things that I tried super fast for now\n\n* Changed aggregation functions using mod for string aggregations as well as median,sum etc for other but with small differences in LB.  \n* Examined WOE / TARGET encoders for BalancedRandomForestClassifier\n* Tried different % of nan to drop best results around 70-80%\n* Tuned only very basic RF hyperparameters like min_samples_leaf , max_features, n_estimators by hand, an excaustive method could be used to improve predictions.\n* Should take care of possible small differences in features - values in train/test set\n* Used RF as a feature selector \n* Tried to ensemble with LightGBM and CatBoost to combine the advantages of these learners but for now submission times out....\n","metadata":{}},{"cell_type":"markdown","source":"### **REFERENCES**\n\nThere are many public inspiring notebooks and discussions published for this contest covering various aspects, that helped me a lot in to build this kernel (thank you all for sharing) to mention a few:\n\n    https://www.kaggle.com/code/jetakow/home-credit-2024-starter-notebook\n    https://www.kaggle.com/code/greysky/home-credit-baseline\n    https://www.kaggle.com/code/shadesh/home-credit-credit-risk-model-stability-v2\n    https://www.kaggle.com/code/daviddirethucus/home-credit-risk-lightgbm\n\nThank you very much for your time reading this kernel. And don't forget , if you found something that you liked or gave you an idea, do UPVOTE!\n","metadata":{}}]}