{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","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":8411082,"sourceType":"datasetVersion","datasetId":4550700},{"sourceId":12601673,"sourceType":"datasetVersion","datasetId":4580762}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**The goal** of this competition is to predict which clients are more likely to default on their loans. The evaluation is based on gini stability metric and will favor solutions that are stable over time. A separate chalenge is to deal with large data sizes and constantly monitor and reduce memory usage to not exceed allocated amount of RAM.  \n\n**Structure**\n\nThere are several tables classified by 'depth':\n* depth=0 - These are static features directly tied to a specific case_id.\n* depth=1 - Each case_id has an associated historical record, indexed by num_group1.\n* depth=2 - Each case_id has an associated historical record, indexed by both num_group1 and num_group2.\n\nVarious 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\n\nTransformations within a group are denoted by a capital letter at the end of the predictor name (e.g., maxdbddpdtollast6m_4187119P)\n\n**Strategy**\n\nFollowing approach for data processing will be used:\n* depth=2 files will be aggregated, grouped by case_id and num_group1. For numerical columns we will calculate the average between num_group2 values and for categoricals we will get the most frequent value. Like this they will become as depth=1 files and the same processing function as for depth=1 files will be applied to them.\n* for depth=1 files we will use an aggregation grouped by case_id. For numerical columns we will calculate the mean, std, min and max between num_group1 values. For categoricals we will get the most frequent value. After this the files will become as depth=0.\n* at depth=0 level we will merge all the files together on case_id as all the case_id are unique at this level. \n\nAfter data analysis, time delta features will be created based on timeseries columns and all the preparations for modeling will be made using a processing pipeline. Competition's stability metric will be integrated during Optuna tuning process and also in the model training as evaluation metric.","metadata":{}},{"cell_type":"code","source":"pip install scikit-learn --upgrade --no-index --find-links=file:/kaggle/input/sklearn-1-4-1/ ","metadata":{"scrolled":true,"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:10.939855Z","iopub.execute_input":"2025-11-21T13:04:10.940138Z","iopub.status.idle":"2025-11-21T13:04:15.003105Z","shell.execute_reply.started":"2025-11-21T13:04:10.940116Z","shell.execute_reply":"2025-11-21T13:04:15.002302Z"}},"outputs":[{"name":"stdout","text":"Looking in links: file:///kaggle/input/sklearn-1-4-1/\nRequirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.4.2)\nRequirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.26.4)\nRequirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.15.3)\nRequirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.5.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.6.0)\nRequirement already satisfied: mkl_fft in /usr/local/lib/python3.11/dist-packages (from numpy>=1.19.5->scikit-learn) (1.3.8)\nRequirement already satisfied: mkl_random in /usr/local/lib/python3.11/dist-packages (from numpy>=1.19.5->scikit-learn) (1.2.4)\nRequirement already satisfied: mkl_umath in /usr/local/lib/python3.11/dist-packages (from numpy>=1.19.5->scikit-learn) (0.1.1)\nRequirement already satisfied: mkl in /usr/local/lib/python3.11/dist-packages (from numpy>=1.19.5->scikit-learn) (2025.3.0)\nRequirement already satisfied: tbb4py in /usr/local/lib/python3.11/dist-packages (from numpy>=1.19.5->scikit-learn) (2022.3.0)\nRequirement already satisfied: mkl-service in /usr/local/lib/python3.11/dist-packages (from numpy>=1.19.5->scikit-learn) (2.4.1)\nRequirement already satisfied: onemkl-license==2025.3.0 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.19.5->scikit-learn) (2025.3.0)\nRequirement already satisfied: intel-openmp<2026,>=2024 in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.19.5->scikit-learn) (2024.2.0)\nRequirement already satisfied: tbb==2022.* in /usr/local/lib/python3.11/dist-packages (from mkl->numpy>=1.19.5->scikit-learn) (2022.3.0)\nRequirement already satisfied: tcmlib==1.* in /usr/local/lib/python3.11/dist-packages (from tbb==2022.*->mkl->numpy>=1.19.5->scikit-learn) (1.4.0)\nRequirement already satisfied: intel-cmplr-lib-rt in /usr/local/lib/python3.11/dist-packages (from mkl_umath->numpy>=1.19.5->scikit-learn) (2024.2.0)\nRequirement already satisfied: intel-cmplr-lib-ur==2024.2.0 in /usr/local/lib/python3.11/dist-packages (from intel-openmp<2026,>=2024->mkl->numpy>=1.19.5->scikit-learn) (2024.2.0)\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"import os, glob\nimport pandas as pd\nimport polars as pl\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport optuna\nimport joblib\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.utils import resample, shuffle\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import (\n    OneHotEncoder, PowerTransformer, \n    TargetEncoder, OrdinalEncoder\n)\nfrom sklearn.model_selection import (\n    train_test_split, StratifiedGroupKFold\n)\nfrom lightgbm import (\n    LGBMClassifier, early_stopping, log_evaluation, plot_importance,\n)\nfrom xgboost import XGBClassifier, DMatrix\nfrom xgboost.callback import EarlyStopping\nfrom catboost import CatBoostClassifier, Pool\n\n# Use gpu if available\nfrom catboost.utils import get_gpu_device_count\ndevice = 'gpu' if get_gpu_device_count() > 0 else 'cpu'\n\npd.options.display.max_colwidth = None","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","scrolled":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:15.004579Z","iopub.execute_input":"2025-11-21T13:04:15.004812Z","iopub.status.idle":"2025-11-21T13:04:18.765151Z","shell.execute_reply.started":"2025-11-21T13:04:15.004789Z","shell.execute_reply":"2025-11-21T13:04:18.764298Z"}},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"#### Pandas functions","metadata":{}},{"cell_type":"code","source":"def downcast(df):\n    \"\"\"\n    Reduce memory usage of a Pandas DataFrame by converting \n    object types to categories and downcasting numeric columns\n    \"\"\"\n    # Column types\n    object_cols, int_cols, float_cols = [], [], []\n    for col, dtype in df.dtypes.items():\n        if pd.api.types.is_object_dtype(dtype):\n            object_cols.append(col)\n        elif pd.api.types.is_integer_dtype(dtype):\n            int_cols.append(col)\n        elif pd.api.types.is_float_dtype(dtype):\n            float_cols.append(col)\n        \n    # Convert object columns to category\n    df[object_cols] = df[object_cols].astype('category')\n\n    # Downcast integer columns\n    df[int_cols] = df[int_cols].apply(pd.to_numeric, downcast='integer')\n   \n    # Downcast float columns\n    df[float_cols] = df[float_cols].apply(pd.to_numeric, downcast='float')\n        \n    return df\n\ndef cols_types(df):\n    \"\"\"\n    Create lists of feature names dtype\n    \"\"\"\n    date_cols, num_cols, cat_cols = [], [], []\n    for col, dtype in df.dtypes.items():\n        if pd.api.types.is_bool_dtype(dtype):\n            cat_cols.append(col)\n        elif pd.api.types.is_datetime64_dtype(dtype):\n            date_cols.append(col)\n        elif pd.api.types.is_numeric_dtype(dtype):\n            num_cols.append(col)\n        else:\n            cat_cols.append(col)\n            \n    return date_cols, num_cols, cat_cols","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:18.766008Z","iopub.execute_input":"2025-11-21T13:04:18.766679Z","iopub.status.idle":"2025-11-21T13:04:18.774023Z","shell.execute_reply.started":"2025-11-21T13:04:18.766645Z","shell.execute_reply":"2025-11-21T13:04:18.773237Z"}},"outputs":[],"execution_count":3},{"cell_type":"markdown","source":"#### Polars functions to read and preprocess data\nMany aggregation functions are commented out, but available for any experiments","metadata":{}},{"cell_type":"code","source":"def pl_cols_types(df):\n    \"\"\"\n    (Polars version)\n    Create lists of feature names dtype\n    \"\"\"\n    date_cols, num_cols, cat_cols = [], [], []\n    \n    num_cols = df.select(pl.col(pl.NUMERIC_DTYPES)).columns\n    date_cols = df.select(pl.col(pl.Date)).columns\n    cat_cols = [col for col in df.columns \n                if col not in num_cols and col not in date_cols]\n            \n    return date_cols, num_cols, cat_cols\n\ndef aggregate_depth1(df):\n    \"\"\"\n    (Polars version)\n    Aggregate depth=1 dataframe and return a depth=0 dataframe \n    \"\"\"\n    # Drop 'num_group1' column\n    df = df.drop('num_group1')\n\n    # Create aggregation dataframe and count repetitive case_id\n    col_to_count = df.columns[1]\n    all_agg = df.group_by('case_id').agg(\n        pl.col(col_to_count).count().alias(f'{col_to_count}_count')\n    )\n    # Columns types \n    date_cols, num_cols, cat_cols = pl_cols_types(df)\n    num_cols.remove('case_id')\n\n    # Aggregate categorical columns\n    if len(cat_cols) > 0:\n        for col in cat_cols:\n            if not df[col].is_null().all():\n                cat_agg = df.group_by('case_id').agg(\n                    pl.col(col).drop_nulls().mode().first().alias(f'{col}_mode'),\n#                     pl.n_unique(col).alias(f'{col}_n_unique'),\n#                     pl.col(col).first().alias(f'{col}_first'),\n#                     pl.col(col).drop_nulls().last().alias(f'{col}_last'),\n                )\n                # Drop aggregated column to free memory\n                df = df.drop(col)\n\n                # Merge with aggregated dataframe\n                all_agg = all_agg.join(cat_agg, on='case_id', how='left')\n\n                # Free memory\n                del cat_agg\n\n    # Aggregate date columns\n    if len(date_cols) > 0:\n        for col in date_cols:\n            date_agg = df.group_by('case_id').agg(\n                pl.mean(col).alias(f'{col}_mean'), \n#                 pl.col(col).first().alias(f'{col}_first'),\n#                 pl.col(col).drop_nulls().last().alias(f'{col}_last'),\n            )\n            # Drop aggregated column to free memory\n            df = df.drop(col)\n\n            # Merge with aggregated dataframe\n            all_agg = all_agg.join(date_agg, on='case_id', how='left')\n\n            # Free memory\n            del date_agg\n\n    # Aggregate numeric columns \n    if len(num_cols) > 0:\n        for col in num_cols:\n            num_agg = df.group_by('case_id').agg(\n                pl.mean(col).alias(f'{col}_mean'), \n#                 pl.median(col).alias(f'{col}_median'), \n#                 pl.min(col).alias(f'{col}_min'), \n#                 pl.max(col).alias(f'{col}_max'), \n#                 pl.col(col).first().alias(f'{col}_first'),\n#                 pl.col(col).drop_nulls().last().alias(f'{col}_last'),\n            )\n            # Drop aggregated column to free memory\n            df = df.drop(col)\n\n            # Merge with aggregated dataframe\n            all_agg = all_agg.join(num_agg, on='case_id', how='left')\n\n            # Free memory\n            del num_agg\n  \n    print('Depth1 aggregation finished')    \n    return all_agg\n\ndef aggregate_depth2(df):\n    \"\"\"\n    (Polars version)\n    Aggregate depth=2 dataframe to level depth=1 and then apply \n    aggregate_depth1 function to return a depth=0 dataframe\n    \"\"\"\n    df = df.drop('num_group2')\n    \n    # Columns types\n    groupby_cols = ['case_id', 'num_group1']\n    date_cols, num_cols, cat_cols = pl_cols_types(df)\n    num_cols = [col for col in num_cols if col not in groupby_cols]\n    \n    # Create aggregation dataframe\n    all_agg = df[groupby_cols]\n    all_agg = all_agg.unique(groupby_cols, maintain_order=True)\n    \n    # Aggregate categoricals \n    if len(cat_cols) > 0:\n        for col in cat_cols:\n            if not df[col].is_null().all():\n                cat_agg = df.group_by('case_id').agg(\n                    pl.col(col).drop_nulls().mode().first().alias(f'{col}_mode'),\n#                     pl.col(col).drop_nulls().first().alias(f'{col}_first'),\n                )\n                # Drop aggregated column to free memory\n                df = df.drop(col)\n\n                # Merge with aggregated dataframe\n                all_agg = all_agg.join(cat_agg, on='case_id', how='left')\n\n                # Free memory\n                del cat_agg\n    \n    # Aggregate date columns\n    if len(date_cols) > 0:\n        for col in date_cols:\n            date_agg = df.group_by('case_id').agg(\n                pl.mean(col).alias(f'{col}_mean'), \n#                 pl.col(col).drop_nulls().first().alias(f'{col}_first'),\n            )\n            # Drop aggregated column to free memory\n            df = df.drop(col)\n\n            # Merge with aggregated dataframe\n            all_agg = all_agg.join(date_agg, on='case_id', how='left')\n\n            # Free memory\n            del date_agg\n    \n    # Aggregate numeric columns if any\n    if len(num_cols) > 0:\n        for col in num_cols:\n            num_agg = df.group_by('case_id').agg(\n                pl.mean(col).alias(f'{col}_mean'), \n#                 pl.median(col).alias(f'{col}_median'),\n#                 pl.col(col).drop_nulls().first().alias(f'{col}_first'),\n            )\n            # Drop aggregated column to free memory\n            df = df.drop(col)\n\n            # Merge with aggregated dataframe\n            all_agg = all_agg.join(num_agg, on='case_id', how='left')\n\n            # Free memory\n            del num_agg\n \n    del df\n    print('Depth2 aggregation finished') \n    return aggregate_depth1(all_agg)\n\ndef create_df_from(path, file_name, depth=0):\n    \"\"\"\n    (Polars version)\n    Preprocess files in chunks \n    \"\"\"\n    dfs = []\n    for i, file_path in enumerate(\n        glob.glob(path + '*' + file_name + '*.parquet')\n    ):\n        df = pl.read_parquet(file_path)\n\n        for col in df.columns:\n            if (col[-1] == 'D') or (col == 'date_decision'):\n                df = df.with_columns(pl.col(col).cast(pl.Date))\n            elif col in ['case_id', 'WEEK_NUM', 'num_group1', 'num_group2']:\n                df = df.with_columns(pl.col(col).cast(pl.Int32))\n            elif 'person' in col:\n                df = df.with_columns(\n                    pl.col(col).cast(pl.String).cast(pl.Categorical))\n            elif 'month' in col and 'T' in col:\n                df = df.with_columns(\n                    pl.col(col).cast(pl.String).cast(pl.Categorical))\n        \n        if depth == 2:\n            df = aggregate_depth2(df)\n            \n        elif depth == 1:\n            df = aggregate_depth1(df) \n\n        dfs.append(df)\n        print(f'Chunk {i} added to list')\n    \n    return pl.concat(dfs, how='diagonal_relaxed')\n\ndef read_prepare_all(path, files_dict):\n    \"\"\"\n    (Polars version)\n    Read, preprocess and merge all the files together\n    Return a pandas dataframe\n    \"\"\"\n    # Read base data frame\n    df_all = create_df_from(path, 'base')\n    print(f'base created')\n\n    # Read and aggregate \n    for depth, files_list in files_dict.items():\n        for file in files_list:\n            # Create dataframe from file chunks\n            print(f'### Start read {file}')\n            df = create_df_from(path, file, depth)\n            \n            # Join with the main dataframe\n            df_all = df_all.join(df, how='left', on='case_id')\n            print(f'=== {file} merged to df_all')\n            \n            # Convert to Categorical to free memory\n            df_all = df_all.with_columns(\n                pl.col(pl.String).cast(pl.Categorical))\n            df_all = df_all.with_columns(\n                pl.col(pl.Float64).cast(pl.Float32))\n        \n    # Free memory\n    del df\n    \n    # Columns types\n    date_cols, num_cols, cat_cols = pl_cols_types(df_all)\n    \n    # Convert to pandas in chunks to not explode memory use\n    df_pd = df_all.select(pl.col(num_cols)).to_pandas()\n    df_all = df_all.drop(num_cols)\n    df_pd = df_pd.join(df_all.select(pl.col(date_cols)).to_pandas())\n    df_all = df_all.drop(date_cols)\n    df_pd = df_pd.join(df_all.select(pl.col(cat_cols)).to_pandas())\n    del df_all\n    print('df converted to pandas')\n    \n    # Create time features\n    df_pd['birth_year'] = df_pd.birth_259D_mean.dt.year\n    df_pd['decision_year'] = df_pd.date_decision.dt.year\n    df_pd['decision_quarter'] = (\n        df_pd.date_decision.dt.quarter.astype(str).astype('category'))\n    df_pd['decision_month_of_year'] = (\n        df_pd.date_decision.dt.month.astype(str).astype('category'))\n    df_pd['decision_day_of_month'] = df_pd.date_decision.dt.day\n    df_pd['decision_day_of_year'] = df_pd.date_decision.dt.dayofyear\n    df_pd['decision_week_of_year'] = df_pd.date_decision.dt.isocalendar().week\n    df_pd['decision_day_of_week'] = (\n        (df_pd.date_decision.dt.dayofweek + 1).astype(str).astype('category'))\n    \n    return downcast(df_pd)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:18.774847Z","iopub.execute_input":"2025-11-21T13:04:18.775056Z","iopub.status.idle":"2025-11-21T13:04:18.799208Z","shell.execute_reply.started":"2025-11-21T13:04:18.775039Z","shell.execute_reply":"2025-11-21T13:04:18.798503Z"}},"outputs":[],"execution_count":4},{"cell_type":"markdown","source":"#### Read and preprocess data","metadata":{}},{"cell_type":"code","source":"# Filepaths\nmain_path = '/kaggle/input/home-credit-credit-risk-model-stability/'\ntrain_path = main_path + 'parquet_files/train/'\ntest_path = main_path + 'parquet_files/test/'\n\n# Read info files\nfeat_def = pd.read_csv(main_path + 'feature_definitions.csv')\nsubmit = pd.read_csv(main_path + 'sample_submission.csv')\n\n# Lists of file names\nfiles_dict = {\n    0: ['static_0', 'static_cb_0'],\n    1: ['credit_bureau_a_1', 'credit_bureau_b_1', 'applprev_1', \n        'debitcard_1', 'deposit_1', 'other_1', 'person_1', \n        'tax_registry_a_1', 'tax_registry_b_1', 'tax_registry_c_1'],\n    2: ['credit_bureau_a_2', 'credit_bureau_b_2', 'applprev_2', 'person_2']\n}","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:18.801101Z","iopub.execute_input":"2025-11-21T13:04:18.801368Z","iopub.status.idle":"2025-11-21T13:04:18.829499Z","shell.execute_reply.started":"2025-11-21T13:04:18.801353Z","shell.execute_reply":"2025-11-21T13:04:18.82897Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"%%time\n\n# Process / restore point\nprocess = False\n\nif process:\n    # Read, preprocess and merge all the training files together\n    X = read_prepare_all(train_path, files_dict)\n    \n    # Backup processed data\n    X.to_parquet('X.parquet')\n    \nelse:\n    # Restore processed data from backup\n    X = pd.read_parquet('/kaggle/input/creditrisk-data/X.parquet') \n\nX.info()","metadata":{"scrolled":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:18.830212Z","iopub.execute_input":"2025-11-21T13:04:18.830406Z","iopub.status.idle":"2025-11-21T13:04:26.304942Z","shell.execute_reply.started":"2025-11-21T13:04:18.830389Z","shell.execute_reply":"2025-11-21T13:04:26.304249Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1526659 entries, 0 to 1526658\nColumns: 491 entries, case_id to decision_day_of_week\ndtypes: Int8(1), bool(1), category(113), datetime64[ms](58), float32(297), int16(3), int32(2), int8(4), object(12)\nmemory usage: 2.7+ GB\nCPU times: user 13.6 s, sys: 5.95 s, total: 19.5 s\nWall time: 7.47 s\n","output_type":"stream"}],"execution_count":6},{"cell_type":"markdown","source":"#### Analyze data","metadata":{}},{"cell_type":"code","source":"# Function for date features\ndef make_time_features(df, date_col):\n    plot_data = df[['target', 'WEEK_NUM']]\n    plot_data['day_of_year'] = df[date_col].dt.dayofyear.astype('int16')\n    plot_data['month_of_year'] = df[date_col].dt.month.astype('int8')\n    plot_data['day_of_month'] = df[date_col].dt.day.astype('int8')\n    plot_data['day_of_week'] = (df[date_col].dt.dayofweek + 1).astype('int8')\n    return plot_data\n\n# Create time related features for analysis\nplot_data = make_time_features(X, 'date_decision')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:26.305685Z","iopub.execute_input":"2025-11-21T13:04:26.305901Z","iopub.status.idle":"2025-11-21T13:04:26.461723Z","shell.execute_reply.started":"2025-11-21T13:04:26.305883Z","shell.execute_reply":"2025-11-21T13:04:26.460734Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# Target distribution\nplt.figure(figsize=(5, 1))\nplt.title('Target distribution')\nsns.countplot(data=plot_data, y='target')\nplt.show()\nprint(f'Targets == 1 are: {plot_data.target.mean().round(2) * 100}% from train data')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:26.462724Z","iopub.execute_input":"2025-11-21T13:04:26.462973Z","iopub.status.idle":"2025-11-21T13:04:26.694899Z","shell.execute_reply.started":"2025-11-21T13:04:26.462953Z","shell.execute_reply":"2025-11-21T13:04:26.694077Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 500x100 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"name":"stdout","text":"Targets == 1 are: 3.0% from train data\n","output_type":"stream"}],"execution_count":8},{"cell_type":"markdown","source":"* Target is unbalanced (97/3)","metadata":{"execution":{"iopub.status.busy":"2024-02-12T09:13:13.016101Z","iopub.execute_input":"2024-02-12T09:13:13.016534Z","iopub.status.idle":"2024-02-12T09:13:13.028114Z","shell.execute_reply.started":"2024-02-12T09:13:13.016502Z","shell.execute_reply":"2024-02-12T09:13:13.0268Z"}}},{"cell_type":"code","source":"# Target evolution in time\nplt.figure(figsize=(12, 3))\nplt.title('Target evolution in time')\ng = sns.lineplot(data=plot_data, x='WEEK_NUM', y='target')\ng.set(xticks=np.arange(0, 93, 4))\nplt.grid() \nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:26.695752Z","iopub.execute_input":"2025-11-21T13:04:26.696028Z","iopub.status.idle":"2025-11-21T13:04:39.02595Z","shell.execute_reply.started":"2025-11-21T13:04:26.695999Z","shell.execute_reply":"2025-11-21T13:04:39.025238Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x300 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":9},{"cell_type":"markdown","source":"* A big drop of total defaults was registered between weeks 62 - 65 ","metadata":{}},{"cell_type":"code","source":"# Target distribution by month_of_year\nplt.figure(figsize=(8, 3))\nplt.title('Target distribution by month of the year')\ng = sns.lineplot(data=plot_data, x='month_of_year', y='target')\ng.set(xticks=np.arange(1, 13, 1))\nplt.grid()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:39.026665Z","iopub.execute_input":"2025-11-21T13:04:39.026884Z","iopub.status.idle":"2025-11-21T13:04:50.008809Z","shell.execute_reply.started":"2025-11-21T13:04:39.026867Z","shell.execute_reply":"2025-11-21T13:04:50.00791Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x300 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"# Target distribution by day_of_year\nplt.figure(figsize=(12, 3))\nplt.title('Target distribution by day of the year')\ng = sns.lineplot(data=plot_data, x='day_of_year', y='target')\ng.set(xticks=np.arange(1, 366, 30))\nplt.grid()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:04:50.009661Z","iopub.execute_input":"2025-11-21T13:04:50.010292Z","iopub.status.idle":"2025-11-21T13:05:06.519599Z","shell.execute_reply.started":"2025-11-21T13:04:50.010271Z","shell.execute_reply":"2025-11-21T13:05:06.518742Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x300 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":11},{"cell_type":"markdown","source":"* Strange behaviour on day 335, the only day without any target == 1","metadata":{}},{"cell_type":"code","source":"# Target distribution by day_of_month\nplt.figure(figsize=(12, 3))\nplt.title('Target distribution by day of month')\ng = sns.lineplot(data=plot_data, x='day_of_month', y='target')\ng.set(xticks=np.arange(1, 32, 1))\nplt.grid()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:06.520455Z","iopub.execute_input":"2025-11-21T13:05:06.520753Z","iopub.status.idle":"2025-11-21T13:05:17.863778Z","shell.execute_reply.started":"2025-11-21T13:05:06.520729Z","shell.execute_reply":"2025-11-21T13:05:17.86309Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x300 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA/oAAAE8CAYAAABwyMohAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAD9mElEQVR4nOzdd3xb1fn48c/V3vKeSezsPSBA2DMkzJBSdsv68uVLC7RAgLbQllHKj1JGgQJltIy27NGUDWGPQAJk7ziJ95Rl7a17f3/INnHiJJYtWbJz3n3xSi1f3XMka9xzznOeR1IURUEQBEEQBEEQBEEQhGFBlekOCIIgCIIgCIIgCIKQOmKgLwiCIAiCIAiCIAjDiBjoC4IgCIIgCIIgCMIwIgb6giAIgiAIgiAIgjCMiIG+IAiCIAiCIAiCIAwjYqAvCIIgCIIgCIIgCMOIGOgLgiAIgiAIgiAIwjAiBvqCIAiCIAiCIAiCMIyIgb4gCIIgCIIgCIIgDCNioC8IgiAIfXTsscdy7LHHdv9cXV2NJEk888wzaW/7mWeeQZIkqquru2+rrKzktNNOS3vbAJ9++imSJPHpp58OSns7G8zHuatMPu6+2rp1K/PmzcNutyNJEosXL850l1Iik393QRCEoU4M9AVBEIQ+kySpT/9l26Bo6dKl3Hbbbbhcrkx3BYBHH310UCYH+iOb+yb07uKLL2bt2rXceeed/Otf/+Kggw7KdJf6bMOGDdx22209JrAEQRCEgdNkugOCIAjC0PGvf/2rx8///Oc/WbJkyW63T548eTC7tU9Lly7l9ttv55JLLiEnJydl562oqCAYDKLVapO636OPPkpBQQGXXHJJn+9z4YUXct5556HX65PsZXL21Lejjz6aYDCITqdLa/tCcoLBIF9//TW//e1vufrqqzPdnaRt2LCB22+/nWOPPZbKyspMd0cQBGHYEAN9QRAEoc9++tOf9vj5m2++YcmSJbvd3h+KohAKhTAajQM+12CRJAmDwZDWNvx+P2azGbVajVqtTmtbe6NSqdL+WIXktbW1AaR0AksQBEEY+kToviAIgpBSTz/9NMcffzxFRUXo9XqmTJnC3/72t92O69p/+/7773PQQQdhNBp5/PHHAaipqWHBggWYzWaKioq47rrreP/993vdFrBs2TJOOukk7HY7JpOJY445hq+++qr797fddhs33ngjAKNHj+7eXrCvUOEnnniCsWPHYjQaOeSQQ/jiiy92O6a3PfrNzc1ceumljBgxAr1eT2lpKWeccUZ3e5WVlaxfv57PPvusuy9d+/679uF/9tlnXHnllRQVFTFixIgev+ut3x988AGzZs3CYDAwZcoUXn/99R6/v+2225Akabf77XrOvfVtT3vVX3nlFWbPno3RaKSgoICf/vSnNDQ09DjmkksuwWKx0NDQwMKFC7FYLBQWFnLDDTcQj8f38BfY3d4e5/bt25Ekib/85S+73W/p0qVIksQLL7yw1/PX19ezcOHCHq+7cDi823FffPEFZ599NqNGjUKv1zNy5Eiuu+46gsFg9zFPP/00kiSxcuXK3e7///7f/0OtVu/2PO1q5cqVnHzyydhsNiwWCyeccALffPNN9+9vu+02KioqALjxxhuRJGmvq+Jdf8OXX36Z22+/nfLycqxWK2eddRZut5twOMy1115LUVERFouFSy+9dLfHH4vFuOOOOxg7dix6vZ7Kykpuvvnm3Y7ren9/+eWXHHLIIRgMBsaMGcM///nP7mOeeeYZzj77bACOO+64PW792ds5BEEQhN6JFX1BEAQhpf72t78xdepUFixYgEaj4c033+TKK69ElmWuuuqqHsdu3ryZ888/nyuuuILLL7+ciRMn4vf7Of7442lqauKaa66hpKSE559/nk8++WS3tj7++GNOPvlkZs+eza233opKpeqeaPjiiy845JBDOPPMM9myZQsvvPACf/nLXygoKACgsLBwj4/hH//4B1dccQWHH3441157Ldu3b2fBggXk5eUxcuTIvT7+H//4x6xfv55f/OIXVFZW0traypIlS6itraWyspIHHniAX/ziF1gsFn77298CUFxc3OMcV155JYWFhdxyyy34/f69trd161bOPfdcfvazn3HxxRfz9NNPc/bZZ/Pee+9x4okn7vW+u+pL33b2zDPPcOmll3LwwQdz11130dLSwoMPPshXX33FypUre6wyx+Nx5s+fz5w5c7j33nv58MMPue+++xg7diw///nP99m3fT3OMWPGcMQRR/Dcc89x3XXX9bjvc889h9Vq5Ywzztjj+YPBICeccAK1tbX88pe/pKysjH/96198/PHHux37yiuvEAgE+PnPf05+fj7Lly/nr3/9K/X19bzyyisAnHXWWVx11VU899xzHHDAAbv159hjj6W8vHyP/Vm/fj1HHXUUNpuNX/3qV2i1Wh5//HGOPfZYPvvsM+bMmcOZZ55JTk4O1113Heeffz6nnHIKFotln8/lXXfdhdFo5De/+Q1VVVX89a9/RavVolKp6Ojo4LbbbuObb77hmWeeYfTo0dxyyy3d9/3f//1fnn32Wc466yyuv/56li1bxl133cXGjRv5z3/+06OdqqoqzjrrLC677DIuvvhinnrqKS655BJmz57N1KlTOfroo/nlL3/JQw89xM0339y95WfnrT/7OocgCIKwB4ogCIIg9NNVV12l7PpVEggEdjtu/vz5ypgxY3rcVlFRoQDKe++91+P2++67TwGUxYsXd98WDAaVSZMmKYDyySefKIqiKLIsK+PHj1fmz5+vyLLco/3Ro0crJ554Yvdt99xzjwIoO3bs2OdjikQiSlFRkTJr1iwlHA533/7EE08ogHLMMcd037Zjxw4FUJ5++mlFURSlo6NDAZR77rlnr21MnTq1x3m6PP300wqgHHnkkUosFuv1dzs/hq7n8LXXXuu+ze12K6WlpcoBBxzQfdutt966299pT+fcU98++eSTHs9/1/M0bdo0JRgMdh/31ltvKYByyy23dN928cUXK4Dyhz/8occ5DzjgAGX27Nm7tbWrvj7Oxx9/XAGUjRs3dt8WiUSUgoIC5eKLL95rGw888IACKC+//HL3bX6/Xxk3blyPx60ovb/G77rrLkWSJKWmpqb7tvPPP18pKytT4vF4920rVqzo8ZrZk4ULFyo6nU7Ztm1b922NjY2K1WpVjj766O7bul6D+3rNKcoPf8Np06YpkUikRz8lSVJOPvnkHscfdthhSkVFRffPq1atUgDlf//3f3scd8MNNyiA8vHHH3ff1vU3+/zzz7tva21tVfR6vXL99dd33/bKK6/s9vwmew5BEARhdyJ0XxAEQUipnffYu91uHA4HxxxzDNu3b8ftdvc4dvTo0cyfP7/Hbe+99x7l5eUsWLCg+zaDwcDll1/e47hVq1axdetWLrjgAtrb23E4HDgcDvx+PyeccAKff/45siwn3f/vvvuO1tZWfvazn/VIPHfJJZdgt9v3+dh1Oh2ffvopHR0dSbfd5fLLL+/zfvyysjJ+9KMfdf9ss9m46KKLWLlyJc3Nzf3uw750PU9XXnllj737p556KpMmTeLtt9/e7T4/+9nPevx81FFHsX379j6115fHec4552AwGHjuuee6j3v//fdxOBz7zCPxzjvvUFpayllnndV9m8lk4v/+7/92O3bn17jf78fhcHD44YejKEqPUP2LLrqIxsbGHtEozz33HEajkR//+Md77Es8HueDDz5g4cKFjBkzpvv20tJSLrjgAr788ks8Hs9eH8/eXHTRRT0SSM6ZMwdFUfif//mfHsfNmTOHuro6YrEYkHiOABYtWtTjuOuvvx5gt7/5lClTOOqoo7p/LiwsZOLEiX3+m6fqHIIgCPsjMdAXBEEQUuqrr75i7ty5mM1mcnJyKCws5OabbwbodaC/q5qaGsaOHbvbnvJx48b1+Hnr1q1AorRYYWFhj//+/ve/Ew6Hd2uvL2pqagAYP358j9u1Wm2PQVdv9Ho9d999N++++y7FxcUcffTR/PnPf056wN3b87In48aN2+25mjBhAkBaS5Z1PU8TJ07c7XeTJk3q/n0Xg8Gw23aJ3NzcPk+I9OVx5uTkcPrpp/P88893H/Pcc89RXl7O8ccfv8/H01sbvT2+2tpaLrnkEvLy8rrzDRxzzDFAz9f4iSeeSGlpaffEgyzLvPDCC5xxxhlYrdY99qWtrY1AINBr25MnT0aWZerq6vb6ePZm1KhRPX7umsDadVuK3W5HluXux1RTU4NKpdrtvVhSUkJOTs5uf/Nd24Hk/uapOocgCML+SOzRFwRBEFJm27ZtnHDCCUyaNIn777+fkSNHotPpeOedd/jLX/6y2wr7QDLsd53rnnvuYdasWb0e05f9yql27bXXcvrpp7N48WLef/99fv/733PXXXfx8ccf77ZXe09SXXmgt0R8QFKJ8AZqsCoGXHTRRbzyyissXbqU6dOn88Ybb3DllVeiUqVmbSMej3PiiSfidDr59a9/zaRJkzCbzTQ0NHDJJZf0eI2r1WouuOACnnzySR599FG++uorGhsbU1KlYiD29LfY0+2KovT4eU+vp/6eL93nEARB2B+Jgb4gCIKQMm+++SbhcJg33nijx0pcb4n09qSiooINGzagKEqPAUVVVVWP48aOHQskQrjnzp2713P2dWDS1T4kIgZ2XgWORqPs2LGDmTNn7vMcY8eO5frrr+f6669n69atzJo1i/vuu49///vfSfdnX6qqqnZ7rrZs2QLQnYE9NzcXAJfL1SNB3q4rsMn0ret52rx5826r5Zs3b+7+far05XECnHTSSRQWFvLcc88xZ84cAoEAF1544T7PX1FRwbp163ZrY/PmzT2OW7t2LVu2bOHZZ5/loosu6r59yZIlvZ73oosu4r777uPNN9/k3XffpbCwcLftKrsqLCzEZDLt1jbApk2bUKlU+0wKmQ4VFRXIsszWrVt7JMxraWnB5XL162+eyveCIAiC8AMRui8IgiCkTNfq286rbW63m6effrrP55g/fz4NDQ288cYb3beFQiGefPLJHsfNnj2bsWPHcu+99+Lz+XY7T1d9cQCz2QwkBrr7ctBBB1FYWMhjjz1GJBLpvv2ZZ57Z5/0DgQChUKjHbWPHjsVqtfYoP2Y2m/vUl75obGzske3c4/Hwz3/+k1mzZlFSUtLdB4DPP/+8+zi/38+zzz672/n62reDDjqIoqIiHnvssR6P7d1332Xjxo2ceuqp/X1IverL4wTQaDScf/75vPzyyzzzzDNMnz6dGTNm7PP8p5xyCo2Njbz66qvdtwUCAZ544okex/X2GlcUhQcffLDX886YMYMZM2bw97//nddee43zzjsPjWbv6yxqtZp58+bx3//+t8f2i5aWFp5//nmOPPJIbDbbPh9Tqp1yyilAojrDzu6//36Afv3Nk3lvCoIgCH0nVvQFQRCElJk3bx46nY7TTz+dK664Ap/Px5NPPklRURFNTU19OscVV1zBww8/zPnnn88111zTvce5K+Fb1wqgSqXi73//OyeffDJTp07l0ksvpby8nIaGBj755BNsNhtvvvkmkJgUAPjtb3/Leeedh1ar5fTTT+8eZOxMq9Xyxz/+kSuuuILjjz+ec889lx07dvD000/vc4/+li1bOOGEEzjnnHOYMmUKGo2G//znP7S0tHDeeed1Hzd79mz+9re/8cc//pFx48ZRVFS0zz3kezJhwgQuu+wyvv32W4qLi3nqqadoaWnpMbkyb948Ro0axWWXXcaNN96IWq3mqaeeorCwkNra2h7n62vftFotd999N5deeinHHHMM559/fnd5vcrKyt1K3A1UXx5nl4suuoiHHnqITz75hLvvvrtP57/88st5+OGHueiii/j+++8pLS3lX//6FyaTqcdxkyZNYuzYsdxwww00NDRgs9l47bXX9rpn/KKLLuKGG24A6HPY/h//+EeWLFnCkUceyZVXXolGo+Hxxx8nHA7z5z//uU/nSLWZM2dy8cUX88QTT+ByuTjmmGNYvnw5zz77LAsXLuS4445L+pyzZs1CrVZz991343a70ev1HH/88RQVFaXhEQiCIOxHMpPsXxAEQRgOeiuv98YbbygzZsxQDAaDUllZqdx9993KU0891WtpuFNPPbXX827fvl059dRTFaPRqBQWFirXX3+98tprrymA8s033/Q4duXKlcqZZ56p5OfnK3q9XqmoqFDOOecc5aOPPupx3B133KGUl5crKpWqT6X2Hn30UWX06NGKXq9XDjroIOXzzz9XjjnmmL2W13M4HMpVV12lTJo0STGbzYrdblfmzJnTo2SboihKc3OzcuqppypWq7VHyb6ucnfffvvtbv3ZU3m9U089VXn//feVGTNmKHq9Xpk0aZLyyiuv7Hb/77//XpkzZ46i0+mUUaNGKffff3+v59xT33Ytr9flpZdeUg444ABFr9creXl5yk9+8hOlvr6+xzEXX3yxYjabd+vTnsr+7SqZx9ll6tSpikql2q0ve1NTU6MsWLBAMZlMSkFBgXLNNdco77333m6Pe8OGDcrcuXMVi8WiFBQUKJdffrmyevXqPZbNa2pqUtRqtTJhwoQ+90VREqX45s+fr1gsFsVkMinHHXecsnTp0h7H9Ke83q7P255ed11/n7a2tu7botGocvvttyujR49WtFqtMnLkSOWmm25SQqFQj/vu6f2963tIURTlySefVMaMGaOo1eoez3Uy5xAEQRB6khRFZDMRBEEQst8DDzzAddddR319PeXl5ZnujpDlDjjgAPLy8vjoo48y3RUcDgelpaXccsst/P73v890dwRBEIT9gNijLwiCIGSdYDDY4+dQKMTjjz/O+PHjxSBf2KfvvvuOVatW9UiWl0nPPPMM8Xi8T0kBBUEQBCEVxB59QRAEIeuceeaZjBo1ilmzZuF2u/n3v//Npk2buuuRC0Jv1q1bx/fff899991HaWkp5557bkb78/HHH7NhwwbuvPNOFi5c2KM6gCAIgiCkkxjoC4IgCFln/vz5/P3vf+e5554jHo8zZcoUXnzxxYwP3ITs9uqrr/KHP/yBiRMn8sILL3QncMyUP/zhDyxdupQjjjiCv/71rxntiyAIgrB/EXv0BUEQBEEQBEEQBGEYEXv0BUEQBEEQBEEQBGEYEQN9QRAEQRAEQRAEQRhGxB79fpJlmcbGRqxWK5IkZbo7giAIgiAIgiAIwjCnKAper5eysjJUqj2v24uBfj81NjYycuTITHdDEARBEARBEARB2M/U1dUxYsSIPf5eDPT7yWq1Aokn2GazZbg3exaNRvnggw+YN28eWq1WtDcE2xTtDe32MtGmaE+0l+1tivaGdnuZaFO0J9rL9jZFe0O7vUy12R8ej4eRI0d2j0f3RAz0+6krXN9ms2X9QN9kMmGz2QbtTTmc28tEm6K9od1eJtoU7Yn2sr1N0d7Qbi8TbYr2RHvZ3qZob2i3l6k2B2Jf28dFMj5BEARBEARBEARBGEbEQF8QBEEQBEEQBEEQhhEx0BcEQRAEQRAEQRCEYUQM9AVBEARBEARBEARhGBEDfUEQBEEQBEEQBEEYRsRAXxAEQRAEQRAEQRCGETHQFwRBEARBEARBEIRhRJPpDgiCIAiCIAhDRygaZ2uTJ9PdEARBEPZCDPQFQRAEQRCEPnEHo2xt8dLsCgAQjsbRarUZ7pUgCIKwKxG6LwiCIAiCIOyVoii0eEKsqXfhCkQpsxsBcAaiGe6ZIAiC0Bsx0BcEQRAEQRD2KC4r7HD4WVvvRlGg2GZArZIAaHIHictKhnsoCIIg7EoM9AVBEARBEIRehaJxNjZ52NrqxWrQkGvS9fi9OxClIxDJUO8EQRCEPREDfUEQBEEQBGE37kCUtQ1u6p0BCi0GTLrdUztJQLM7NPidEwRBEPYq4wP9Rx55hMrKSgwGA3PmzGH58uV7Pf6VV15h0qRJGAwGpk+fzjvvvNPj97fddhuTJk3CbDaTm5vL3LlzWbZs2W7nefvtt5kzZw5Go5Hc3FwWLlyYyoclCIIgCIIwJCmKQrM7xOp6F+5AlNIcI1p175eMVoMWhzeMJyT26guCIGSTjA70X3rpJRYtWsStt97KihUrmDlzJvPnz6e1tbXX45cuXcr555/PZZddxsqVK1m4cCELFy5k3bp13cdMmDCBhx9+mLVr1/Lll19SWVnJvHnzaGtr6z7mtdde48ILL+TSSy9l9erVfPXVV1xwwQVpf7yCIAiCIAjZLBaX2d7mZ12DG0jsx1dJ0h6PN+rUhGIyDm94sLooCIIg9EFGy+vdf//9XH755Vx66aUAPPbYY7z99ts89dRT/OY3v9nt+AcffJCTTjqJG2+8EYA77riDJUuW8PDDD/PYY48B7DZgv//++/nHP/7BmjVrOOGEE4jFYlxzzTXcc889XHbZZd3HTZkyZa99DYfDhMM/fIl5PIn6sdFolGg0e2exu/o2WH0c7u1lok3R3tBuLxNtivZEe9nepmgvO9sLReJsc/hocofIMWox6VTI8Vivx3bdLsdjmDXQ4PRRbNGg06gH1Ic9GarPqWgvM+1lok3R3tBuL1Nt9kdf+ycpipKRVKmRSASTycSrr77aI2z+4osvxuVy8d///ne3+4waNYpFixZx7bXXdt926623snjxYlavXt1rGw899BB//OMfqaqqoqCggOXLlzNnzhyeeuopHnroIZqbm5k1axb33HMP06ZN22N/b7vtNm6//fbdbn/++ecxmUzJPXhBEARBEARBEARBSFIgEOCCCy7A7XZjs9n2eFzGVvQdDgfxeJzi4uIetxcXF7Np06Ze79Pc3Nzr8c3NzT1ue+uttzjvvPMIBAKUlpayZMkSCgoKANi+fTuQGLjff//9VFZWct9993HssceyZcsW8vLyem37pptuYtGiRd0/ezweRo4cybx58/b6BGdaNBplyZIlnHjiiWi1WtHeEGxTtDe028tEm6I90V62tynay572FEWh1RumqtVLLK6Qb9HvNVS/ixyPUbv2G0ZNPxSVWoPTF8Zs0DBzRA4q1b7vn6yh9JyK9jLfXibaFO0N7fYy1WZ/dEWW70tGQ/fT5bjjjmPVqlU4HA6efPJJzjnnHJYtW0ZRURGyLAPw29/+lh//+McAPP3004wYMYJXXnmFK664otdz6vV69Hr9brdrtdqsfiF0Gex+Dvf2MtGmaG9ot5eJNkV7or1sb1O0l9n2YnGZmnY/OxwBDBot+Vbdvu+0C5Vag0qtwW5R0e6L4Isq5FuSP09fZftzKtrLrvYy0aZob2i3l6k2k9HXvmUsGV9BQQFqtZqWlpYet7e0tFBSUtLrfUpKSvp0vNlsZty4cRx66KH84x//QKPR8I9//AOA0tJSoOeefL1ez5gxY6itrR3w4xIEQRAEQch2wUicDU0eqlp92A1ackwDG5xr1SqQFFo8otSeIAhCNsjYQF+n0zF79mw++uij7ttkWeajjz7isMMO6/U+hx12WI/jAZYsWbLH43c+b1civdmzZ6PX69m8eXP376PRKNXV1VRUVPT34QiCIAiCIAwJHf4Iq+tdNLqCFFkNGHWpSaBnM2hp9YbxilJ7SQtEEskN2/2RDPdEEIThIqOh+4sWLeLiiy/moIMO4pBDDuGBBx7A7/d3Z+G/6KKLKC8v56677gLgmmuu4ZhjjuG+++7j1FNP5cUXX+S7777jiSeeAMDv93PnnXeyYMECSktLcTgcPPLIIzQ0NHD22WcDYLPZ+NnPfsatt97KyJEjqaio4J577gHoPkYQBEEQBGG4URSFJneIrS2J/fildmOf9uP3lUmnoSMQod0XxmrI3rDXbOT0JQb4te1+8q3GRISEIAjCAGR0oH/uuefS1tbGLbfc0p39/r333utOuFdbW4tK9cMH3eGHH87zzz/P7373O26++WbGjx/P4sWLu7Plq9VqNm3axLPPPovD4SA/P5+DDz6YL774gqlTp3af55577kGj0XDhhRcSDAaZM2cOH3/8Mbm5uYP7BAiCIAiCIAyCaFym2uGnut2PUashz5yegbhFr6XRFaI0x4g+TaX2hptoXKapc8uD0x+h0RWkIt+c4V4JgjDUZTwZ39VXX83VV1/d6+8+/fTT3W47++yz97jybjAYeP311/fZplar5d577+Xee+9Nqq+CIAiCIAhDTSASY2uLjyZ3kDyTPmWh+r2xGjQ0uUM4/RFK7ca0tTOcdAQi3dsdLHoN1e1+cs06bCIqQhCEARBxQYIgCIIgCMOU0x9hTb2bJneQ4hTux98TlSSh16hodAWRZSWtbQ0HiqLQ7A6hIrGFwmrQEoomoi/E8ycIwkCIgb4gCIIgCMIwoygKDa4ga+pd+MMxyuxGNIO079tu1NLhj+IKiqR8++INx2j3hbEZf1i9L7ToaXaHaPGKCgaCIPSfGOgLgiAIgiAMI9G4zNYWH+sb3GhUKoqsBqQUJt3bF61ahYJCsyc4aG0OVe3eMOGYgkH7Q6SFVq3CoFFT7fATjMQz2DtBEIYyMdAXBEEQBEEYJvzhGBsaPWx3+Mg16bAbM7PP22bQ0uYJ4wvHMtL+UBCJyTS5Q1j0u6fMyjFpcQej1DkDKIoI4RcEIXlioC8IgiAIgjAMOP0R1ja4afYk9uPvvEo82Ew6DcFoHIcIP98jpz+CNxzDath9oC9JEnkmPXWuAE5/JAO9EwRhqBMDfUEQBEEQhGFgQ4ObQDhOqW3w9uPvTVepvUhMznRXsk4iCV8QrUqFag/bKow6NchQ3e4nGhfPoSAIycn8t4AgCIIgCILQb95wIumdWiVRaNUP6n78vbEaNHjDMbEi3Qt3MEp7IILNuPdK1/kWPW3eMI0uke9AEITkiIG+IGQxWVYIR0UiHkEQBKF3sqxQ70wMAu0mXYZ705NKktCqVDS5Ram9XbV5w0RjMnrN3rdXqFUSNoOW6nY/npCoYiAIQt/tfRpREIS0i8sKkZhMJCYTjscJR2VC0Tj+cJxgJE44EgYgGImh1WYmqZIgCIKQndp8YZrd2bvam2PS0u6L4ApGyTNn10REpoSicVo8IWyGvn2nWw1aGt1Bqh1+ppXZUamyI2JDEITsJgb6gjAIugbz4Vi889/EYN4bjhKKyETjMtG4QkxWkACVSkKrkhIldrQa3EB1e4BpRoP4ghcEQRAACMfi7HD40apUZOtQX6tWISsKrZ6QGOh3cvoj+MIxyuzGPt+n0KKn2R2iyGqgxG5IY+8EQRguxEBfEFIkFpeJxOUfVudjMoFIDH8kRiiS+F0srhBXFFASg3mdWoVWLWHSadCqVah7GcTL8URpokZXkHxbiPKcvl8YCIIgCMNXvTOIKxCl2KLDk+nO7IXdqKXFG2JknglzL6Xk9ieyrNDoCmLQqJPKpaBVq9Br1FQ7/OSYtBmtqCAIwtCwf3/aCkI/xDoz37Z6w8hECURieEMxwrHEynxiMJ84Ri2p0HYO5s17Gcz3hUmnYVurD6tB0+dwP0EQBGF4cgei1HUEyDFqyfZAL5NOQ0cggsMX3u8H+q5gFFegf9sYck2JEP7a9gATSqxp6J0gCMPJ/v1pKwhJCkXjbGr2ArCuwQUqNZruwbxqwIP5vbEbtbT4omxv9TG13I42C0onCYIgCIMvLivdJdfyzfruyK9sZtZpaHKFKMsx7tffX62eELKi9Os5kCSJPJOeOleAfIuOfIs+DT0UBGG4EAN9QegjXzjG5mYPrZ4QACU2Iyr14L6FCi16mj0h7EYtowstg9q2IAiCkB1avSFaPCEKhtBAz2rQ0uwJ4vRHKLbtn3vMA5EYLd6+J+HrjVGnxhuKUt3ux2bU7teTJoIg7J34dBCEPnAFIqxrcOPwRSi2Zu4CRaNWYTfqqG730+4LZ6wfgiAIQmaEonGqHX4MGvWQGuSpVRI6tZomVxBF2T9L7bX7IgQjMibdwPbX51v0tHnDNLqyNQWjIAjZYOh8QwhChrR5w6xrcOMPxyi1GdISlp8Mi16DLMN2h59QNJ7RvgiCIAiDq84ZwB2MkmMaerla7EYtDn8EV2D/qwcfi8s0uYOYtMkl4euNWiVhM2ipaQ/gDe1/z6UgCH0jBvqCsBeNriDrG93E4gpFVsOAv5xTpcCqp90XprY9sN+ujAiCIOxvOvwR6joC5Jp0WfN9lAydRkVcVmjp3AK3P+kIRHEHo9iMqZmgsRq0BCNxatoDyLK4DhAEYXdioC8IvZBlhZp2PxsaPWhUqqxLeKOSJPLNemqcAVq9IoRfyKy4uMgUhLSLxWVq2v3IciKL/VBlNyRK7QUi2Z9AMJVaPCEkpJRGBRZa9TS6guI6QBCEXomBviDsIi4rbHf42NzsxazXYE/R7HuqGbRqdGoV21p9+90Fk5AdFEXB6Y+wodENQLs/kuEeCcLw1eIN0+wJk9+PsmyppCgKb69tYtGra6nyJH9/k05NMBLHsR8NTr2hKA5fOOWlcbVqFXqNmmqxlU8QhF6Igb4g7CQSk9nc4qGq1UeOUYcly+v95pq0eEJRtrf5xaqqMKj84Ribm72srOug3ZcY4G9t8eIR+0UFIeUCkRg7HH7MOjWaDCbg84dj3P3+Zh77bBvb2vz8p1qd9PYxSZIw6TQ0ukNE43Kaeppd2n1hQtE4xgEm4etNrkmLKxihzhlI+bkFQRjaxEBfEDqFonE2NnuobQ9SaDGk5Qs51SRJoshqoKEjQJNbZN8V0i8Sk6lt97OitoPq9gA2vZaizlJZwUhi8B+MiJUlQUgVRVGobQ/gC8UyGmFW1erjupdX8VWVA7VKQqOSqPdLrG/yJn0um0GLOxChYz+IAorGZZrdYcxp2m4hSRJ5Jj21HQGc+8HzKQhC34mBviAAvnCM9Y1umjqCFFv16DRD562hVasw67Vsa/PhDorVVCE9ZFmh1RNidb2LjU0eVEiU5xgxaH+YECu0Gmj3hdnS4iUS2z9W6gQh3dr9EepdQfLNmUnA1xWqf+Orq2lyhyiy6rn7zBmcMKkQgDdWNyV9TrVKQqNW0eQODfuEsh3+CO5gBGuKw/Z3ZtSpUWTY4fDtN1ESgiDsW3bHJQvCIHAHomxs9uAORCmxGzNePq8/7EYtzZ4g29t8TCu3D6naykL2cwei1Dj9tHhCaFUqim29v09UkkSx1UCTO4ROIzGh2DYk30+CkC2inQn4JOgxqTZYApEYf/24ii+rHADMGZ3HNSeMx2rQYtTA+xtaWV7dQaMrSFmOMalzJ0rthTtLBWY270C6KIpCkzuERqVK+2dhgUVPizdIkyvIqHxzWtsSBGFoEKMBYb/W5g2zttGNLxSj1G4Y0oOSQouBFk+I2naxT09IjVA0TlWrl5W1HTS7Q+Sb9eRb9Ht9n2jUKgosOmqdQWrb/cN+tU4Q0qnJFaTNGybfPPiVX7a1+bj2pVV82Rmqf9kRo/ntKZO7V6ZH5BqZkiOjAG+ubkz6/HqNmlhModUzfJPyeUIx2v3hlJXU2xu1SsKi01LdHsArcqUIgoAY6Av7sSZ3kPWNbmIxmWKbYUjWJN6ZWiWRa9JR4/Tj8A3fCych/WJxmQZXkO9rOqhq9WHUqSm1G/scKaLXqMkxatnW5qfRvf/VyxaEVPCFY9Q4A1j12kGdhFYUhXfX/RCqX2jV86czp7PwgPLdviePLU1M5H24qQVfOPnqLzajlmZPaNjm9XB4w0Ri8qBFY9iMWoKRODXtAWSRoFcQ9ntioC/sdxRFoabdz/pGD2pJIt8y+Csl6WLSaVAU2N7mE6V2hKQpioLDF2Z1vYu19W5kWaHMbuxXzW6TToNBq2Zrs1dMPAlCkhIJ+PwEIrFBWQ3uEojEuOeDzTz66TaicYVDKvN48NxZTCqx9Xr8BLtCRZ6JUFTmg/XNSbdn1qkJROLD8jMiHIvT5Alh1Q9uAsUCi55GV5DW/ah8oSAIvRMDfWG/EpcVtrX52NzsxazVDMt9gQUWPU5/hGqHCJsW+s4birKxycOqWheuQJQSm4Ec08CSf3VlCN/c7BWJIgUhCW2+MA2u0KCG7G/vDNX/YusPofq/O3XyXpPISRIsmFECwJtrmpIu8ypJEiatmgZXkNgwSyLn9EfwhaJYDIObDkunUaHXqKlu94sJf0HYz4mBvrDfiMRktrR4qGr1kWPUDfqX72BRSRL5Zj11HQFahvHeRyE1wrE41Q4fK2o7qHUGsBu1FFlTl68i36InGImzpUWU3ROEvojEZKodfjQqCb0m/SHfXaH6N3SG6hdY9PzpR72H6vfm6PEF5Bi1OHxhlm5zJN2+zajFE4wOq9JwsqzQ7A6h16hRZWBbYK5JiysQoc4pcvYMFllWaHQFu/+/IGQDMdAX9guhaJxNzR5q2oMUWgwYdYOfvXgwGbRq9Go129p8+Puxb1IY/uKdF6Ira11savahU6kpzzGlZS9poVUvyu4JQh81uhL10HMHIeIsEIlx7wdbukP1D6rITYTql/Yeqt8bnUbFydMSq/r/XZV8Uj61SkItScOq1J47GKXdH+mOahpskpTI2VPXEciaCZSuiA3PMEwUGIwkrjE3NnkAcAaG32MUhiYx0BeGPX84xvpGN40dQYqtenSa/eNln2PS4gtH2dbmSzqcUhjeOvwR1jW4WVPvIhyVKbUb0hrhsnPZvW1tXvF6FIQ98ISi1LQHsBt0aU/At8Ph47qXVvH51jZUElx6eCW/P21Kv3ICnDy9FI1KYnOLl02dg51k2E1a2v1hPMHhMTHd5g0Tl5WMlro16TTIMlQ7/EQzuC0iFI3T4Aqyut4FwJo6F9UOX0b7lCqKotDqDbG63kWtM9i91abZHRSr+kJWGJ6xy4LQyR2IsrHZgzsQpcTee+3v4UqSJAoticFVrknLyDxRV3d/F4jEqHcGqXcFkOVEPofBuhDVqFUUWvTUtAfRqdWMKTQP+UoXgpBKsqxQ7fATisnkpXFvvqIovL++hSe+SKziF1h0/Gr+JCYnsYq/q1yTjmMnFvLhxlYWr27kN0meS69RE42HafWGsJsyswqeKsFInBZvCNtechsMlgKLnhZvkCaXjlH5g3sN4AvHaPOEaHCH8Iei6Du/anRqNZuavXQEoowuMA/ZXEmRmEyt0091ewCNJFFmN6DIie1pbd4wzkCEgmGU7FkYmsRAXxi2HL4wm5q9hCJxSu1Dv3xef2jVKiw6Ddvb/NgMuiF/ASX0TzQu0+wOUtMewBeOk2fSZWT7ik6jItekZbvDj0GnpjzHOOh9EIRs1eYL0+wOUZjGwUEgEuORT7bx+dY2AA6qyOW6uRNSktn/jJnlfLixla+3OWj1hCiyGZK6v02vo9kTYmReerYQDZZ2fxh/OEaZPfOfb2qVhEWnpbo9QK5Zt9fEiqmgKAquQJRmT5AWT5hQJI7VoKXUbkSR47gBi0GD2ajG4QvjDkSpLDBRnmvKaPRDstyBKFVtXtq8EXJN2u7KNF1r+BLQ0BEkz6RDtR8tMAnZZ+i8qwQhCU3uIOsa3ERjMsW2/XOQ38Vm1BKNK1S1if3R+xtZToQVrqpzsb7RAyRWHTKZo8Kk02DsLLvXJso/CQKQCG/e4fCjU6vSNuDZ4fCz6OXVKQnV701lgZmZI+zISiIDf7LM+qFfai8uKzS5Qxi1mqy57rAZtQQjcWraA2kLJ4/FZVo9IVbXufi+toM6ZxCjVk15rgmbUbvbc6FWSRTbDOi1ajY3e1lT76IjS3IJ7E1cVqhzBlhV30GHP1GdprfyszkmHW3eMB2B7H9MwvCWFQP9Rx55hMrKSgwGA3PmzGH58uV7Pf6VV15h0qRJGAwGpk+fzjvvvNPj97fddhuTJk3CbDaTm5vL3LlzWbZsWa/nCofDzJo1C0mSWLVqVaoekpAhXbWH1zd6UEuSCJvqVGDR0+YNU+v0Z7orwiDa3OJldZ0bXyhGic2IvZcLrkzoLrvXIsruCQJAfUcAVyBCrjn1YcyJUP1mbnhlNQ2uIAUWHXedOYMzDxyR8ozwZ8wqB+CDDc0EIsntt5ckCaNGTeMQLrXnCkRwBaLY+pHzZF2jh3Ud6fl8LrDoaXIHaUvxJEooGqe+I8B3NR2sqnPR7o+Qa9RRajf2OgDelUWvodhmpMMfZXWdix1t2bt3PxCJsbHJw/pGNxpJRbFtz9VpunJBNbiCwybBpDA0ZXyg/9JLL7Fo0SJuvfVWVqxYwcyZM5k/fz6tra29Hr906VLOP/98LrvsMlauXMnChQtZuHAh69at6z5mwoQJPPzww6xdu5Yvv/ySyspK5s2bR1tb227n+9WvfkVZWVnaHp8weOKywrY2H5uavZi1miG77ysd1KpEBt4aZ0Csou4HHL7EKkKjOxE6WGDRZ11+inyLnlAkzuZmT9IDAkEYTlyBCPXOIDlGXcoH3sFInPuXbOHhT6qIxGVmV+TywLkHMGUA+/H3ZnZFLuU5RgKROB9u7P06bm9sRi2uQBTnEF0JbfGGAAVNklEZTn+EW9/cyJOb1Czb4Ux5v3QaFVqVih0OP6HowMucekNRtrX6+K7ayboGN5GoTJHVQJHVkHTC4x6r+y3Zt7qvKAqtnhBr6tzUdwQoshr6FAWTa9LR6glnTdUDYf+U8YH+/fffz+WXX86ll17KlClTeOyxxzCZTDz11FO9Hv/ggw9y0kknceONNzJ58mTuuOMODjzwQB5++OHuYy644ALmzp3LmDFjmDp1Kvfffz8ej4c1a9b0ONe7777LBx98wL333pvWxyikXzQus6XFQ1WrnxyjLq0ZxIcqk06DColtbb6UfNEL2UlRFFo8iVq+pTZjVleZKLTqcfojbG3xiW0lwn4pLivUtAeIyQpmfWq/t6odfq57eRWfbkmE6l98WCW3nDYlrSXfVJLEgpmJxZM3VjckXWFDrZJQSRLNQ7DUnj8co80T7lcSvnfWNhHrfK7++sn2tGxfyDXrOieVAv26vywrOP0RNjS6+b6mg62tPlSSRJndSK554FUiLHoNJZ2r+6vqOtjWmvnvhUhMpqrVx5p6N6FonDK7sc9ba8SqvpANMjoaikQifP/999x0003dt6lUKubOncvXX3/d632+/vprFi1a1OO2+fPns3jx4j228cQTT2C325k5c2b37S0tLVx++eUsXrwYk8m0z76Gw2HC4R8+eD2eRPmYaDRKNJq9oaddfRusPmaqvU2NHbT4YuSbdejVCnI8fSuEXedOZxvpai/HoKLZHaSqBcYXWntNErO/vGYG8307mG36wlGcnQP9ofAaLTJraezwoZLijC+09ulicbi/Zob7a1S094Nmd4imDh8FFv2A3q87vwcVReHDTW088cUOInGFfLOOG04cl1jFl+Okaui0p/f9sePz+Nc31bR4wnyzrY3DxuQldV6rTqLN7cdp1/UYNGfr37BLqytAIBwhx2BM6m8Zjsm8uy6R08CsUfCGY9z3wSb+cPqUlEdi2fUqahwebAYVFm3i3Pt6fNG4TEcgSrM7iNMXQUHBZtCRa038bRQ5Tl+GsX35npCAQrMGfzjGlmYX7d4AlQVmcvsZoTmQ14w7GGGHw4/Dl9iOYNSp9/lYd32MNr1Ei8tPiVXb78ewN9n+nhhq7WWqzf7oa/8kJYPTTI2NjZSXl7N06VIOO+yw7tt/9atf8dlnn/W6r16n0/Hss89y/vnnd9/26KOPcvvtt9PS0tJ921tvvcV5551HIBCgtLSUxYsXc/DBBwOJFa9TTjmFI444gt/97ndUV1czevRoVq5cyaxZs3rt62233cbtt9++2+3PP/98nyYKBEEQBEEQ0ikch5e3q/jOkVhNnJwj89NxMpZBLrjyZq2KDxtUjLUq/HKaiCDbm69bJF7criZPr/CzyXHuW6MmLEucMjLO/BFiJVgQhN0FAgEuuOAC3G43Ntuet2IN2/jm4447jlWrVuFwOHjyySc555xzWLZsGUVFRfz1r3/F6/X2iCTYl5tuuqlHJIHH42HkyJHMmzdvr09wpkWjUZYsWcKJJ56IVpv+b/rBbs8dCPHVpx9TOPFgzMbBSbwnx2PUrv2GUdMPRaVO/1soHe25AhEklcSMEXbMuyTMGe6vmcFubzDbjMVlVta5iIajuLZ9P6Reo4FIDG8oysQSG2X7KLs33F8zw/k1Ktr7QVWLj2qnn9IUVIaR4zG+WfYN/66x0OAKoZLgJ4eM5MwDylK+73/nNvf0vr9gXIRPnlvJNi/EymcxrtCS1Lm9oSiyonDgqFz0naX2svFv2KXNF2ZtvYtCy54TtPVGURTue2kNEGTBASMplqr52dFjePDTHbxXr+bo2VOZXGod4CPpKS4rtPpCjM83su7bL3d7fN5wlDZPmBZPCH8kjkmrxmrQDji6oL/fE4FIDFcwSr5ZR0W+mbwkElYm+5oJRGLUtAdodAcxazVJV6To7TGGY3HcwSizRuakPHdUNr8nhmJ7mWqzP7oiy/clowP9goIC1Gp1j5V4SITVl5SU9HqfkpKSPh1vNpsZN24c48aN49BDD2X8+PH84x//4KabbuLjjz/m66+/Rq/vOTA86KCD+MlPfsKzzz67W7t6vX634wG0Wm1WvxC6DHY/B6s9Tyix18xs1A/KgGZnKrVmUNtMZXt5Vg2NriC1HWGmlvV+YTJcXzOZam8w2nSFwvgiCgVmAy6G1mvUYtQgo2Z7exCjQUeRdd81uIf7a2Y4vkZFewlOf4QmX4R8iwm1ZuDlLr/Z4eT+tWqicog8s45fzZ/I1DL7gM/bF7297wvtGo4aV8CnW9p4c00L18/LSeqcNpOaRncQd0Sh3NTz+cuWv2EXRVFw+P2o1cn3a0VtB7UdiVJ086aU0LaxmuMnF7O6ycenm9u476MqHjr3gJTmHVKpwWKABk8iSZxWq0Wt1tARiNDsDtHmDROKxbEZtJSbjSmv1JLs94TFqMGo19PuC7O+2ceoPBMj80zok3jf9OVv2OYNs63NjzsYpchmHlCZy50fo1GtwR2WafHFKLCZ0lL5JtveE0O9vUy1mYy+9i2jWZp0Oh2zZ8/mo48+6r5NlmU++uijHqH8OzvssMN6HA+wZMmSPR6/83m79tg/9NBDrF69mlWrVrFq1aru8nwvvfQSd95550AekjCIonGZZnco091Iiw5/hM+3tPHwx1u55uU1vFuX+i+GQqueRleI+o7+JeYRsk+rJ4RKkrIuw35f2YxaJCS2NHtxB7J7f5wg9FcsLlPT7kdRwKgb+CC/wx/hoY+3EZUlDhhp56HzDhi0Qf7edJXa+6LKQXuSyeUkScKo1dDQEUg6od9g84ZjOH2RfiU5/O+qRgDmTi7qkYzx58eMpdRuoM0b5q+fbE15Mje7UUugMylviyfMqvoOVta6aHKHMOk0lOeYsBqyoxwrJJI0FtkMmHUaqlr9rKl3pyxhYTgWZ2tLIuFeOConlXCvr3KMOlq8IVFOVhh0GQ/dX7RoERdffDEHHXQQhxxyCA888AB+v59LL70UgIsuuojy8nLuuusuAK655hqOOeYY7rvvPk499VRefPFFvvvuO5544gkA/H4/d955JwsWLKC0tBSHw8EjjzxCQ0MDZ599NgCjRo3q0QeLJRFSNnbsWEaMGDFYD10YoA5/BE9oeHxouoNR1jW4WdPgZm29i7qOYI/fV6Pm4JoODhlTmLI2tWoVNoOGHQ4/dqNWlCMc4vzhGA5fGOsQrziRb9HT4gmxucXDtHJ7n2oxC8JQ0uwJ0eIJU2xNzXazv3+5HX8kzkizwu9PmZQ1q1DjiixMLbOxvtHD22ubuOiwyqTubzNocPgT5ckKU/RcpUO7N0woJpNvSW7Sps4ZYEVtBxJw+syeZZ5NOg03zJvIr15bw9Jt7XywoYX5U3uPdO2vfJOeRmBdowu9VkeeWZfyAW6qmXQa9Bo17f4wq+tdVOSaGJmf3Or+zlyBCFWtPtp8YfJN+pRMvPXGoFXTEYjQ6ApiN2bPBIow/GX8Curcc8+lra2NW265hebmZmbNmsV7771HcXExALW1tahUP3zwHH744Tz//PP87ne/4+abb2b8+PEsXryYadOmAaBWq9m0aRPPPvssDoeD/Px8Dj74YL744gumTp2akccopJ6iKDS5Q2gkiaFYFd4XirG2MTGoX9vgprq956q6BIwuMDO93E6HP8znVe389ZPtPFyak9LSSFaDlhZPiG1tPqaX52R1KTZh75z+CMFonDzzwLJ3Z4NCq55mT5AtzV4ml9n6fREnCNkmEIlR7fBj0WuSrrXem+9rOvh8qwOVBOeOiWddNM8ZM8tY3+jhvXXNnHPQSAzavr+XE8+PRLMnSIElOyeiIzGZJncISz9KI76xOrGaP2dMHqX23TP1Tyi2ctGhFTy9tJonvtjO5FIbo/JSl/y56/u+yGLImsmhvlCrJIqsBgKRGFVtPjqCUUYXmCmw9H0yKC4rNHQE2O7wE5cVSm3GtL93ck06mj0hynKMYmFFGDQZH+gDXH311Vx99dW9/u7TTz/d7bazzz67e3V+VwaDgddffz2p9isrK0WNyyHGE4rR7g9jNWrxZ7ozfRCIxFjf6GFNvZu1DS62t/l3K9EyKs/EjHI700fYmVZm704CEwxH2NLgoDkY5ZFPqrjp5EkpnQ0usOhp9oSodfoZm2TCJCE7xGWFJncQkzYrPtIHTCVJFFuNNHuC6DQqJpbYsm4AIwjJUhSFmnY/vnCcMvu+c1DsSyga52+fVQFw6vQSRprrB3zOVDtkdD4lNgPNnhCfbG7l5GmlSd0/x6jF4Q3jDccwZuF8n9MfwRuOUWJL7u/pCUb5eHMrAGfMLN/jcQsPKGdVnYuVdS7ueX8T9509K+UT8kP1s9Wk02DQqmn3RVhV56Kic+/+viaT/OEY29p8NLpC2AwarObBmeQwaNU4O1f1xUBfGCzD46pQ2O84vGEiMZl8Y3Z+WIaicTY0ehKh+A0uqlp97LrNsDzHyIwRdqaXJ/7b0we/XqPiwvFx/rJOy9fb2/loUytzJxenrK9qlUSuSUt1ewCbUUuuIQuvpoS9cgUiuIMxCpLIRpzt1CqJQouBWmcAnUbF2EKLCHcUhrR2f4SGjhD5Zl1KXssvf1dHiydMgUXHBQePpHVD9g301SqJ02eW8uQXO/jvqkbmTy1JqgqAQZsI027zhBmVm13h+4qi0OwOolWpkq5s8P76ZiIxmTGFZqaW7blyk0qSuG7uBH754kqq2wM8/dUOrjhm7EC7PmyoJIlCq55AJDF47whEGF1gocCy+3tMURRavWGqWn34wlGKrIZB36qQZ9LR4glRnmPCbho6URTC0CUG+sKQE47FafaEsOqz50MyHIuzqdnL2vrEPvstLd7dEgiV2Aw9Bvb5SYSZjTDD+QeP4F/L6nji8+1MK7cnvYKwNyadhmAkzvY2P1OLzSk7rzA42rxhQElJKHA20WlU5Jn07HD4MWjVjMhNXdiqIAymaFym2uFHkkgqfH1Patr9vL6yAYArjh6LKU17i1Nh7uRinltWS4MryIqaDg6qzEvq/la9liZPiGJLdl2yuoNR2gPJJ+GLxmXeWtsEJFbz9zXpk2vWce3cCdz25nreWtvErFE5zBmd3+9+D0c7r+6vrncxKtfEqHwTXe+KSCzOjvYQtc4AWrWKUlvqqwn0RfeqvjuA3ZT5hJnC8Jddn5qC0AdOfwRvKEap3QByPCN9iMmwvtHDuiYfa+pdbGr2EttlYF9o1TO93N4djt+XcmF786NZZXxX62Zjk4cHPtzCnQunpzTkLs+so9Edoto5FDZDCF2CkTit3nBWTXylklGnJiZr2NriRadRDfh9JAiZ0OQK4vCFKbEZB3wuWVF45JMq4rLCoWPyOHRMflbn5TDpNMybUsziVY38d3Vj8gN9g4ZGdxBnllXicHjDxOJy0jlEvqpy4PRHyDPpOGp8QZ/uM7sil4Wzylm8qoEHP9zKX8+3JLVYsD/oWt0PRuJsd/hwBiLdUSDrGz04g3HyzfqUTLQNRK5RR4s7TFlONKU5lwShN2KgLwwpsqzQ7A6h1yRC5eRBbn+Hw8/TX21nXYOaqLyhx+/yzLruQf2M8hyKbfqUzhirVRKLOkP41jd6WLyqgR8fmLoqEZIkUWjR0+gSA/2hxBmIEIjEyLEPfACRrawGLVG/wpZmL3q1GhHxKAwl3lCU6vYAVr02JZOzH6xvYWOzF6NWzf8dNTTCuE+bUcYbqxtZVedih8PP6IK+R45JkoRBo6bJHdz3wYMkFO1fZKGiKN0l9U6ZUZpU6PhFh1WwrsFNVZuP+5Zs4Y4zpg3Z/fXpZNSpKdUacfojrK93A+AORCmxm7Pi+TLqEhn4mzoz8AtCOg2vOE9h2POEorT7I9gMg//h+MXWNm58dTUr69xEZQm7UcuR4wq48tixPPaT2TxzycFcP28i86aUUGI3pCUsrMRu4PKjRgPw729q2OHwpfT8Oo0KY+fqRCCSvStEQoIsKzS5ghg06mG/fz3PrCMUldnY7BGvTWHIUBSFOmeAQCTWnWB1IDr8EZ75egcAPz10VFaXndtZsc3AYWMS4eZvrG5I+v52oxZ3IJLqbvWb0x/BF44lnW1/Q5OHqjYfOrWKk5Isl6dVq7hx/kQMWhVrG9y8+n1dUvffn6gkiQKLvvs9V2QzZMUgv0uOSUuzOzRsSkQL2UsM9IUhpc0bJh5XBrUMXFxWeGbpDv78/mbCMZlZI+38emaMZy8+kF+fNImTp5VSnjt4+73mTi7m0DF5xGSFez/YQiSW2rgGe2dSwMaOUErPK6SeOxjFFYimZAAxFBRZ9biDEba1pnaCSxDSxeGL0OAKJVX6a2/+/uUO/OE44wotnDq9bN93yCJnzEpkl/90cxsdSQ7aNWoVEonvWFcgktFKSbKs0NjPCdau1fzjJhb2azW3LMfIzzuT8T2/vJYNTZ6kz7E/yXSY/p6YdBrC8ThNLnGdJaSXGOgLQ0ZXqNxgruZ7Q1Fuf3M9r61IrED8+MBybjllEmUmMraCKkkSVx83nhyjllpngH99U52WdprcAVxZtIIi7M7hCxNXlEHPHJwpUmfZvRZvGAB3MJLyiS5BSKVapx+tSkp6H3dvVtR08PnWNlQSXHXcuKxaoeyLSSVWJhZbickK73Ymo0tGXmdVkdX1LjY1ezK2Gurq5wRrsyfEsh3tACyYteeSevty3MQijp1QiKzAvR9sxhcWEU5DUY5RR5M7KFb1hbTaP64OhWGh3R/BH45h1g/ODG21w8+il1ezss6FXqPiV/Mncsnho7Pi4spu1PKL48cDsHhVI6vrXSlvIyYr1DoDyLvWBRSyQigap8UTwjZMk/DtSaLsXmJ1dGWNi+XVTlbXuahzBmj3hQlF4xld7ROEnXUEouSmoOxlKBrn0c+qgMR+93FFlgGfc7BJksQZsxJRCO+sa056kq6rqohVr6WmPcDKmg6qWr0EI4OblLfVE0LuxwTrm6sbkRU4YGQOo/L6X0FEkiR+fuxYSu0G2rxhHv6kSnzmDUEmnSaxgCVW9YU0EgN9YUiIDyBUrj++rHJww6urafaEKLbpueesGRw1vjDt7SbjkNF5zO/c4/fAh1tTPqufa9LT4gnj8IdTel4hNToCEfyRwZv42pO4rPBdTQfNgcFrs+sCO9+iRy1JOP0RNjR6+L6mg2U72vm+poPtbT5aPSF84ZiYrBIGncOXiIayGbRJ11jvzcvf1dHiCVNg0fGTOaMGfL5MOXxsAQUWPe5glM+2tPbrHAatmvIcE3qNmqpWH9/XdlDb7h+U6J5AJEarN5x0ZGEgEmPJhhbghy0MA2HSabhh3kTUKomvqhx80HluYWjJNSVW9b1iVV9IEzHQF4YEVyAyKHuR47LCs0urufu9TZ378XO4/+xZjC7IztWTy44YTandgMMX5vHPt6X03DqNCrUkUecMEIuL8OhsoiiJ6hM6deaS8CmKwvIdTq55cSV3vLOZ+9aq2dY2uBUb1CoJi15DgUVPWY6RYpsBg0aNPxxna6uPlXUuvtvhZHm1k83NHprcQdzBqHg9C2nlCUWpavUCJJ2srTc17X5eX5nYPnbF0WMx6TJfMCkuK7R6kl+JVKskTp9RCsAbqxsHtBJt1msosxtBSSS5W1HbQZM7mNb3d7svQiASx6RLboJ1yYYWgtE4I3ONHDgqJyV9mVBs5aJDKwB44ovt1DkHcbZVSAmTTkMwGqfZLVb1hfQQA31hSGjxhoD07kX2hWL84a31vLqiHoAzDyjnttOnZnWiM6NOzaK5E1BJiQRHX2xtS+n588w62ryR7j3RQnbwBGM4A5mpPgGwvtHNr19fyx1vb6Cm8+IyIkvc8c6mfl38p4pKkjDpNOSZdZTZjZTaDFgMGmJxhVpnkDX1br6tdrJsh5N1DS7qOwJ0+COEooMb+isMX6FonK0t3pRVhpAVhUc+3UZcVpgzOo9DOzPXZ1IoGqfJE8TeWecy2YH1vKklGLQqqtsDrO4sf9ZfkpSogFNqNxKOyqyuc7G63kWbN5zySJ5YXKbJHcSkTW6CNS4rvLkmkYRvwczylE7OLjygnFkjc4jEZP78/iaRs2QIyjHqaHQHRa4FIS3EQF/Iev5wjLZ+hMolo6bdz6JXVrGi1oVOo+LGeRO59Ijs2I+/L5NKbZw9eyQAj366jXZf6gblapWESaemrj0gBkNZxOELE4vLg1p9AhJ5K/7w1np+8/paNjZ50KlV/PjAETz50wMoNSl0BKLc9taGrLlgkaREEjS7UUuJzUCZ3UieSYcEtHjCrGtw8111B99WO1lZ20G1w0ebN0wgEhN7XoWkxeIyVa2J11ChxZCScy7Z0MLGJg8GrYorjh6bknMOhDsYxRWMMLbAwuQSGwDeUHLvd4tew9xJxQD8d1XypfZ6o5Ik8sw6iq0GXIEoq+pcrGt0pzShbEcgijuYfGTh8h3ttHjCWPUajp2Y2i2AKkli0dwJ2I1aqtsDPL10R0rPL6SfWa8hGJFpdgcz3RVhGBIDfSHrOf0RgpF42sIVv+rcj9/kDlFk1XPvWTM4ekJ27cffl/MOHsm4Qgu+cIwHPtqKnMJBit2opSMYocklvoSyQTiWqD5h0Q3ean6zJ8T9SzbzyxdX8m11ByoJTppawhMXzuaSwyspsur52aQ4eWYtdc4Ad72zkWiWhsdr1SqsBi1FVgPlOSYKrXp0ahXuQJTNzT5W1nawfIeTb6udVLV6afGE8IXF/klh7xRFoaY9QIMrQJE1NTW7OwKR7oHbT+dUUGhNTYm+/ojLCi2dSeimltkZX2zB0Bm+HowlnwDz9JllSMB3NR3UdaQu5FyjVlFkNZBn0tHiCbGitoNNTZ6U7IFu8YSQkJL+2/53dWI1/6RpJWkp95Zr1nHt3ERy3rfWNLG8M7O/MHTYjVoaXEH8WTJJLgwfmd/oJQh7EYvLNLiCaRnkx2WF55bV8Mr3iVD9mSPs/Gr+pKwO1d8TjVrFonkTuPbFVayqc/HO2iZOm5GaGssqSSLHoKOuI0ihzZCSPadC/7kCUXzhGCW21KwY7r2tCC9/V8e765qJdYbBHjmugJ/OqaA819jj2Bw93HLKJG5avIE1DW4e+ngri+ZOyFgOgb5KRK1ouj9jFEUhHJMJRuPscPiRFQUNiUmL76qdaLSJz4euRyVJnf91/q/rth7/dh8n7XS/H46Vuo+RkCSIxxIXe65AhEL70Ps82h81uUNsd/jJMerQqlXIKZjo+seXO/CH44wtNKfs87w/wrE4Dl+YAoue8UXW7pD9LmadGl84hjWJqLuyHCOHjM5j2Q4nb65u5Mpjx6W0zzqNihKbkWAkTo3TT4s3xIgcE+W5xn4Ntr2hKA5f8pGFVa0+1jd60KgkTp1emnS7fXVQRR4LZ5WxeFUjD3y0lb+eZyHfkrmJISE5Fr2Gho4Ize4QY4dgRQ0he4krdiGrOQMRPMEoRdbUDmp8oRj3LtnM9zUdAPzogHIuPqxySITq78nIXBOXHlHJ459v5+mvqpk5MoeRuf0v4bMzi0FDgytAvTPApFJbSs4pJK8rCZ9GJaUkk/eeBCIxFq9sYPGqRoKdWzZmjczhokMrGF9s3eP9RheY+c3Jk7j9zfV8urmNYquBn3YmixoqJEnCoFX3GAyEwhF8QDQmE1Pi9Fi7VHr80+vK5g+/6/p5lzvtfAygxBMD/Y1NHlRqjbhgz3JOf4StLV6MWnXKJqVX1Hbw2ZY2VBJcfdz4jH03uYNR/JEYlflmRhea0Wt2HyQX2wzscIaSGugDnDGzjGU7nHy0qZWfzqlIyyS7UafGqDPhC8fY2ual2RNiVJ6JYpshqa1PXaU7883JvRe7tiYcOb4g7e/jiw6rZG2Dm21tfu5bsoU7zpg2pK9p9jd2o44GV5ASuwGzWFARUkS8koSs1uoJo5KSD5Xbm5p2P3e+s5EmdwidRsUvjhvHsROLUnb+TDpleinLdzhZWefi/g+2cM9ZM7prDw9UnklPoztIsc2QkrrQQvK84RhOf/ryVUTjMu+ua+Klb+vwdO67HVdk4ZLDKpk5MqdP5zhwVC5XHzeOhz6u4qXv6iiy6Zk3pSQt/R0sXQMCu0mHSp3+r005HqMaiMYUNjZ5mFJmJ0+857KSPxxjS4uXmKyQZ07N+zIci/O3TxNVVE6bUca4DKzwyYpCmzeMVi0xtcxGmd2Iag/fwwVWPY2eRFLLZFbLp5XbGVNgZrvDz3vrmznnoJGp6v5uLHoNZp0aTyjG+kY3Da4glflmCq36fV5fROMyze4w5iQncdp9Yb6ocgBwxsyBl9TbF61axY3zJnHtyytZ2+Dm1RX1nJvG51RIra4FFbGqL6SS2KMvZC1vKIrDG8aewln+Xffj3/PjGcNmkA+JMPtrThiPRa+hqs3Hi9/VpezcRp0aWYZaZ0DUJc8Qpy9MOKakfJ9nXFb4eFMLP/v39zz5xQ48oRjlOUZ+c9Ik7j97Zp8H+V1OnFLCuQcnLjAf+aSKFZ2RM0JyCqx6wjGZDU0eOvypSyompEYkJrO1xYs7EKUwhau1L31bR7MnRL5Zx0/mjErZefsqEktkl7ebtMwcmcOIXNMeB/kAVr2GAqseVzC5ffCSJHHGrMSWhLfXNKU9r0dXhv4Sm5FQJM7qug7W1Ltw+MJ7zTHgCkRxByNJRyy8vbaJuKwwtcw2aJM15blGfn5MImnj88tq2NjkGZR2hUQum799tp3V7f1fmLIbEhn4U1W1QxDEQF/IWl2hcqkY1MRlhX99U8Of3ttEKCozY4Sd+8+ZxZjC4Tdrmm/Rc+WxiS/6V76rY1MKv+jzzDpavSEcKczsL/RN16pSKnMkKIrC8h3tXPPiSv7y4VZavWHyzDquPm4cj1xwIEeMK+j3HvufHDKK4yYWIivwp/c2sb3Nl7J+70+KrAbCkTgbmjwpzSAuDExcVtjWGQpebDOkLBdFTbuf11cmwr2vOGZs2pLQ7oknGMXhCzEq38T0cjs5pn1HkkiSRInNCChJl9o7anwhuSYtzkCELztXv9NNrZLIt+gpshpw+iKsqnWxvnHP768WTwiNSpVUZGEoGue9dc1AYovCYDpuYhHHTkh89t77weasqIISisb5eHMbr25XUetMXfLFbKAoCu+vb+aXL6zkvQ2t/LtK1e/ParNejT8co8WduTK1wvAiBvpCVorEZBpdISwpCFH2hWP88e0NvNy5ur1wVhl/WDAtpZEC2eao8YXdX/T3f7iFYCQ1pfG0ahUalYoaZyBrs6oPVx2BCJ5QNGUD/fWNbn79+lrueHsjNc4AZr2aSw6v5PGfzmb+1JIBb5eRJIlfHD+eGeV2gtE4t7+1gTavmCDqjyKbgWAkzsYmD+6AqACQDeqcfmqdQQotqcmwD4lw+Uc/3UZcVpgzOo/DxuSn5Lx9bbvVEyIqy0wpszOp2JbUJHueWUeOSde95aevtGpVd5K6/65qGNSylhq1iiKbgVyTjiZ3kJW1HWxu9u42MHb6w0nnD/hkcyvecIxim55DRg/e3xESn70/P3YsJTYDrd4wD39SlZFyoYqisLXFyyOfVHHx08t58ONtfNGi4rpX1vLC8tphcQ3h9Ef4w1sbePiTKoLROBqVRESWeHVlY7/OJ0kSNkMiA3+qrtuE/ZsY6AtZKVWDmlpngOtfXsV3NR3o1CquP3EClx05Zr9IUHPFMWMpsOhpcof4x1epq62ba9Lh9Idp8YgZ58GUqnwV1Q4/f3hrPb95fS0bmzzoNCrOOnAEf7/wYH584IiUbgvQqlXcdMpkRuWZcPoj3P7melE+qJ+KrHr84c7BfpIh0kJqNbtDVLX6sRu0SSV025clG1rY0OTBoFVxxdFjU3befYnEZJpcQaxGDTNG5DAyb++h+r1RqyTKcoyEorGkB5UnTStFp1axrc3P+sbBDzXvytBv0mnY4fCxoqaDHW0+wp2JSCPx5LZLyYrCG50l9U6fUZaR6w2TTsON8yeiVkl8VeVgycaWQWvbE4zyxupGfvniSha9spr31jcTiMQpsuoZZ5OJyQrPL6/l2pdWsbnZO2j9SrXPt7Rx1fMr+K6mA61a4n+OqOTmkyYA8N76ln5HPlr0GnzhGC0eUdJYGDiRjE/IOl2ZxbXq5ELldvX1Ngd/+XArwWicQquem0+enJGkRpli0Wu4bu54frt4He+vb+aQyjwOGZ034POqVRImrYaa9gAFFn1a6gILPfnCsX6VdtpZsyfE88tq+HRzGwqgkmDelBLOO3hkWrNBW/Qabj19Cje+soYaZ4A/vbeJW06bgjZFSSL3F5IkUWTV0+oNdybos6UtKaOwZ65AhC0tXnQaVUozY3cEIjy9NDEh+9M5FRRaB6fSgi8UwxOOMjLfxNhCy4A+z/PNOsydg5Rk9rPbjVqOm1TE++ub+e/qBqaV2/vdh4HoKrPpC8XY3OKlQZu4/rAkuX1iZa2L+o4gRq2aE6cUp6OrfTKh2MqFh1bwzNJqnvh8O5NLbIzMS00lnl3JisLqOhdLNrbw9bb27nKsWrXEYWMKmDelmKmlZmpWfUWdZQJ//6qGWmeAG19dzekzy7jw0Iohcy3hCUb522fbureajC00c93cCVTkm4nHooy1Kmzzwsvf1fWrbKQkSVj1Wuo7ghTbjBh1Q+N5EbKTuNISso4nGMPh738SPllR+Pc3Nfy/dzcRjMaZUW7nL+fM2q8G+V1mjMhhYWeyo79+vDVlK4F2oxZvOEpDh5hxHgxOX5hQLN6vL3xXIMLjn2/j5//+nk86B/lHjivg0Qtmc9Vx4waldFuR1cAtp0/BoFWxqs6VsVDSoa5rsO8NRdnQ6MEbEiv7gykQSWTYj8Zlcvuwdz0ZT325A384zphCM6fNSP+ebqUzq34oFmNyiZVJJcmF6vfGoFVTajfi6cfrsmsf+7LtTprcmf1esRg0lNmN3UlnLYbkBvpdJfXmTSke9BwLu/rRAeXMGplDOCZzzwebicRSGy7f6g3xwvJaLv/nd9zyxnq+2OogJiuMKTBzxdFjePbSQ7hx/kRmjsxBJUlIEhw9voBHLziQ4yYWogBvrG7kqudXsKI2+5O2flvt5OoXVvBllQOVBOcfPJJ7z5pJRb4ZSHxGnzIqEQnywYYWmvsZ+Wg1JCbMWr0iclIYGDHQF7JOmy9ELC73Wq93X3zhGHe8tYGXOvfjL5hZxh/OGN778fflwkMrGZVnwhWM8vAnW1MywJIkCbtBR31HoF8XdULfxeIyje4QJm1yF4yhGDy/vI7L//Udb61pIiYrzBqZw1/OmcWvT5pEea4xTT3u3dhCC78+aRIqCT7e1MoLy2sHtf3hQpIkiq0GPMEoG5s8WZFoa38QjctsbfHh9EcoSPHk2MraDj7d0oZKgquPHZf2UO9o12eKXs2METmMyjenrM1Cmx69RkUomtz+4pF5Jg4clYsCvLm6f/ubU0mSJOydkzmqJBIt1rT7WVnnQiXBaYOchK83KkniurkTsBu17HD4eWbpwLfxReMyX1Y5uOW/6/jfZ7/j+eW1tHrDmHVqTpleyl/OmcWD5x3AaTPK9hjZYTNqWXTiRG47fSqFnZFKt76xnr98uCUrJzADkRgPfbyVP7y1gY5AlJG5Ru49ayYXzKnYrYTxOBvMGmEnLiu82M/vOUmSsOi11HUEk34vCcLOxEBfyCqhaJxmTxirPvmB+a778a+bO4HLj9o/9uPvjU6TyE2gUUl8s93Jhynaq2fRawhFZeqdAbE6m0auYBRvKNrnMG1FUXhrbTN/WKnmpe8bCEVlxhdZ+OMZ07jjjGkZjWw5qCKPnx+TCGV84du6lL0W9zeSJFFsM9ARSAz2Rd6D9JJlhR1tfprcQYqthqQGfvsSjsX522fbADh1einji60pO3dvEquEYUbkGpk5IiflET1WvYZCq6Ff0WNdpfY+3Ng6ZF/TXZMUc0bnU2IzZLg3CXlmHdeeMB6AN9c0sXxHe7/OU9Pu58kvtnPx08u5+71NrKxzoQAzyu1cf+IEnv2fQ/j5MWOT+o6ZXZHLw+cfwGkzSpFITAJf+VxixTxbrivW1rv4xQsrWbKhBYlEQue/nDtrr+/VnxySKC/7yeZW6jv6V2XAZkhsIxH5kISBEHv0hazi9Efwh6OU2pJbbfxmh5MHPtpGMBqnwKLnt6fsX/vx92VMoYWfzKng2a+refKLHUwvz6HEPvCLkDyzjkZ3orzUYISA749avSEk+p6E75PNrTz5ZTUgUWY3cNFhlRw+Nj9l5b8G6qRpJbR6Q7zyfT0Pf1JFvlnHAaNyM92tIUclSZTYDDS7Q9179jMdJjxc1XcEqG73k2/W77Z6N1Avf1dPkztEvlnHTw+tSOm5d6YoCu3+CAoKk0osjMxL3Sr+zromoZrcQWJxOann64CROYzKM1HrDPDBhmZ+dMCIlPcvndzBKJ9sbgN+mLTIFgdV5nHGzDL+u7qRBz7ayl/Ps5Br3HfUZCAS4/MtDpZsbGZLyw8lUvPMOk6YVMSJU4optQ8sOsyk03DF0WM5ZnwhD328lbqOIHe/t4k5o/P4+TFjM3ZtEY7F+efXNd2JFYuseq6dO4HpfcghMaHYwpzReSzb4eSF5bXcOH9S0u0nVvU1nXv1DUMmh4GQXZL+xqqtre11lk1RFGprRSim0H+yrNDkDqJXq/s8KJEVhXdqVdz13haC0TjTy+08cO7+uR9/X350QDlTy2wEo3Hu/3ALcXngs+UGrRoJqHMGUnI+oadAJEabN4w1iT2ib65pAuCYUpmHz5vJEeMKsmaQ3+XCQys4ZkIhcVnhrnc3scPhz3SXhiSVJFFiN9DuTyToE+WYUq/VG6KqzYdVr035hXZNu5/XV9QDcMXRY9I2UZMI1U8kh5sxIofKAktaI926Su0lu6ovSRILOsPd31zTNOS+U95b10QkLjOuyMKUUlvS9w9EElEMLn//arDvy8WHVzKm0Iw3FOP+JXu+BlAUhfWNbh74cAsXPbWcRz6tYkuLD7VK4rAx+dxy2hSeuvhgLjqscsCD/J1NKrXx4HkHcN7BI9GoJJbtcHLl8yt4b10z8iCv7m9p8XLtS6u6B/nzpxTz1/MP6NMgv8tP5owC4POtjn5/x9kMGryhGK1iVV/op6QH+qNHj6atrW23251OJ6NHj05Jp4T9kzsYpcMfTape7Qvf1vN+Q+JlvGBmGX9YMHW/3o+/N2pVYq+eUatmY5OH11fWp+S8uSYdrd6wqJGeBk5/hGBExtjHAcbWFi9VrT40Kol55XLWbluRJIlrThjPtM6Jp9vfXN/vUkT7O5UkUWw10uYVg/1U84SibGnxoUJKOiHbvsiKwqOfbiMmK8wZncehY9JTaz0QidHmC1GWY2TGSHvK8wv0pqvUXjgWTzr8+tiJhdgMGtq8Yb7e3r8Q80yIxmXeXpuYZD1jZlm/Jle79qaH4/G07MvWqlX8at4kDFoVaxrcvL5LrfcOf4TXVtTz8+dW8JvX1/LRplbCMZkRuUYuPbySpy85mJtPmczBlXlp+27RqlX8ZE4FD5w7iwnFFgKROI98WsVv/7OWRlf6kzRG4zL/XlbDja+upr4jSJ5Jx62nTeHq48cnPRE3usDCkeMKAHhuWU2/+rPzqr7Yqy/0R9IDfUVRev0A8/l8GAzZsR9JGJpavSHiitLnsluhaJy31zYD8H9HVXL5UWNSHlY53BTbDPzf0WMAeH5ZLdvafPu4x75p1Sp0ahW1Tn/KM/ruz+KyQpM7hFHb9wiXd9cl3g9HjM3HkuXzXVq1it+eMoWRuUba/RFuf3N994qWkBy1SqLEZqTFG2JTs0dcEKZAKBpnS7OXYCSWltDhDze2sKHJg0Gr4v+OHpPyqBtFUWj3hfFFYowvsjKldHC3duSbdZgN2qSTReo1ak6eXgrA4pUN6ehaWnyx1UFHIEqeWccRnYO7ZETjMhKJ10B5rol2fzgtq9jluUZ+dvRYAJ7/to5tHlhe3cEf397AJc8s55ml1TS4ghi0Kk6cXMyffzyDRy84kDMPHJHyShN7U5Fv5s8/nsn/HjkavUbFukYPv3hhJa9+X5+2SI+adj83vLqal76tQ1YS1QEevuAADqrsf1niCw4ZhUqCZTucbGnx9uscVoMGTygqFlOEfunzp/6iRYuAxOzS73//e0ymH2pxxuNxli1bxqxZs1LeQWH/EIzEafEkVyf8081t+CNxCvQKJ0/NXK3aoeaESUUs3+Hk6+3t3LdkCw+cMwudZmATJDkmHS2eIM3uIKM6y8wIA+MORnF3Xjj2hS8U47OtiWirk6cWQ0tzOruXEhaDhltPn8oNr66muj3An97dxC2nTRETdv2gVkmU2ow0e0JIeJhUOvByafurWFymqtWHwxumNCf11SlcgQhPf1UNwE/mVFBkTe0iSSwu09q55WdcsSXl5+8Lg1ZNqc3A1lbfHjOv78mp00p57ft6Nrd42dTsYVJJ8mHwg0lRFP67OjEpcdr00j4vVuzMHYySY9YSACryTPijCu2+CIXW1E8yHT+piJV1Lj7b0sZD6zWwfnP37yaVWDlxSjFHjivIeM4PtUrijFnlzBmTzyOfVLGqzsWzX1fzRVUbvzx+PGMLU7NFMy4r/HdVA//6poaYrGDVa/j5sWM5anzhgM89Ms/EsROK+HhzK88tq+H2BdOSPodKkjDrNNR3BCiy6ftVkUrYf/X502jlypWsXLkSRVFYu3Zt988rV65k06ZNzJw5k2eeeSaNXRWGs3Z/mEAkjrmPdcIVReHttYmwsyNL5JRmQR7uJEniquPGkWPSUucM8OzX1QM+p1olYdZrqe0IitDhFGnzhpCTiHD5eHMLkZhMZb6JSSVDJ0dFsc3AradNRa9RsbLOxaOfbsuabMtDTWJlP5Ggb3Ozl3BMvBeTpSgK1e3+zovq1GbY7/KPr3bgC8cYU2jm9BmpTdoWiCRqb5fYDcwYmZORQX6XRKk9KekIk1yzjqMnJAZZ/12V+VJ7+7Ku0cP2Nj86jYr5U0uSvr+sKIRjMqX2xAKaXqtmTKEFJCUtUU6SJHHlsWMpsSUmEWwGDQtnlfHIBQdyz1kzmTelJOOD/J2V2Az8YcFUrj1hPBa9hu1tfha9vIpnllYP+DOuyR3k5v+s5eml1cRkhYMqcnn4ggNTMsjvct4hI1GrJFbUuljf6O7XOWxGLZ6gWNUXktfnd/Inn3wCwKWXXsqDDz6IzZbdM6zC0BGXFRpdwaRClNc1eqhuD6DXqJhTJMJ9k2U3avnl8eP5w1sbeGN1I4dU5jFzZM6AzmkzaGh0B2noCDAuzSWihrtQNN65Itf3knpdYfsnTyvNuuR7+zKuyMKv5k/iznc2sGRjC0U2PecdPCrT3RqS1Kofsp5LEkwssYoVoCQ0ukPscPjJM+n7tTK7L6vqXHy6uQ2VBFcfOy7le519oRjjiu2Myjelpf/JsBm0FFoTE0/JRpcsnFXGx5taWbrNQas3lNEJi315o3M1//iJRUnlGOriDcWwGTTkmn64b4FFT0Wema2tXvQadcpfJyadhrt/NJVl337L8Ucegl43eGH5/SFJEidMLubAilwe/3w7X1U5eG1FPV9vc3D18eOTSpIHie/M99Y389RXOwhFE3lw/veo0Zw4uTjl35+ldiNzJxfz/vpm/vVNDXf9aHrSbagkCaNOQ70zSKFVrOoLfZf0t8DTTz+NzWajqqqK999/n2AwkRxDrMAI/dURiOAORrElkezo7TWJWf5jJxRgyp6J5yHl4Mo8Tp6WWH144KMt+EIDmzCRJIlck456V7BfNZSFHzj9EQLhWJ8jXNY2uKnvSEyWHTsxdSsRg+mQ0Xn87JjE3tHnltXy8aaWDPdo6NKoVRRbDTS6gmxu9orcGX3U7guztcWLUavB2Mf3XjLCsTiPfloFwKnTS/dahztZXSu/k8tsjCk0Z3yQ36XEbkBBIRZP7jU4usDCjHI7sgJvdVYSyUZN7iDLtjsBWNDPknq+cJSyHMNuW+hG5Zsothlo86Un43qOScdEe9+jxrJBrknHb06axG9Pmdxd3vfm/6zlkU+q8PcxH0S7L8xtb67n0U+3EYrKTC+389fzD2DelJK0TZKfe1CiksD6Rg+r6/u3qm83anEFI2JVX0hK0u9up9PJCSecwIQJEzjllFNoakp8AF922WVcf/31Ke+gMPy1ehJ1wvu6L9fh+yEb7ynTkg+TE37wP0eMptRuwOGL8Njn2wZ8PpNOQzgmU+cMiMm/fuouM6npe4TLO53Zno+dWJhVIZfJOnlaKT8+MFE7+6GPq1hd58psh4YwjVpFUedgf0uLGOzviy8cY0uLF1kmbZVbXvmuniZ3iDyzjp8eWpGy88qKgqtzcrXYZsiqiJ5cU/9K7cEPteg/WN+ctVvC3lzdiALMrshlZK5pn8fvKhCJYdSqKeglYkGrVjGm0IJOo8IjJs97OHRMPo9ecGD3Von31jdz5fMrWLZjz5UaFEXh082tXPXCClbUutCpVVx+1Gj+uHAaxbb0RowUWvWc0plk8t/f1PTr+kglSZi0iQz84vNc6KukB/rXXnstWq2W2traHgn5zj33XN57771+deKRRx6hsrISg8HAnDlzWL58+V6Pf+WVV5g0aRIGg4Hp06fzzjvv9Pj9bbfdxqRJkzCbzeTm5jJ37lyWLVvW/fvq6mouu+wyRo8ejdFoZOzYsdx6661EIumpXSrsmS+cfJ3wd9c1IyswrcxGZX7yX6xDRSQm4wpEaPIkombSkWnWoFVz/YkTUUnw2ZY2Pt+ye+nMZOWbdTS7Q7SnqRbwcOcJRekI9L3MpNMf4ZsdiRWlk6eVprNrg+Kiwyo4enwBcVnh/727kZr2/tUfFhIDhSKrgfqOIFtbvESTXFXdX4RjiQz7nlCMAkt6QphrnQFeW5EoaXrF0WNSOiHX4Y9kbVlZtUqiPMdIqB+l9g6qzKPMbsAfifPhxuyL8PGHY3y4sRVIlPftD3cwSpFNj0Xf++vBbtQypsCCNxwV799dmPUarj5uHP/vR9MptRtw+iP88e2N3P3eJjoCPa8/3MEof3pvE/ct2YI/HGd8kYUHzpvFgpnlg5bj6awDR6DXqNjc4uXb6o5+ncNu0uIORmgT5WiFPkp6oP/BBx9w9913M2LEiB63jx8/npqa5OtEvvTSSyxatIhbb72VFStWMHPmTObPn09ra2uvxy9dupTzzz+fyy67jJUrV7Jw4UIWLlzIunXruo+ZMGECDz/8MGvXruXLL7+ksrKSefPm0daWGMRs2rQJWZZ5/PHHWb9+PX/5y1947LHHuPnmm5PuvzAw7b4wwWi8zxc90bjM++sTe5FPS3ESo0yLywq+cCKRUqM7iDsUQatRMSY/kVjNmaYP9oklVs45aCQAj35WRbtvYAN0vUaNBNQ5A2krgzOcOXxh4vG+h1N+sKGZuKwwucTK6IKhX/FAJUlcc8IEppbZCETi3PbmBtrFRU2/JQb7emqdAba2eJMOoR7u4rLCtlYfrd4Qxdb0rIbLisIjn1QRkxUOqczjsDH5KTt3KBonJitUZvF7P8+sw2LQ4k1ye5hKklgwqxyAN9c0Zt33yZINLQSjcUblmTigHzluovFEIuFi294rO5TlGBmRa6LNFxKRcr3oCr3/8YEjUEnwZZWDq55bwcebWhIJxJ0Sv3hpDUu3taNWSfx0zijuOWtmvyIwBiLXrOu+bn1uWU2/yieqJAmjRkOdMyAmfoQ+SXqg7/f7e6zkd3E6nej1yZcBuf/++7n88su59NJLmTJlCo899hgmk4mnnnqq1+MffPBBTjrpJG688UYmT57MHXfcwYEHHsjDDz/cfcwFF1zA3LlzGTNmDFOnTuX+++/H4/GwZs0aAE466SSefvpp5s2bx5gxY1iwYAE33HADr7/+etL9F/ovGpdpcocwJ7Gy8WWVA3cwSoFFx6EpvFjKlFA0Toc/QqMrSJsvRFyWKbbpmV5u56DKPA6pzGN0YeICTqWiz3vQknXuQSMZV2TBH47z4CfbGOj1VJ5ZR6snRKs3PXsLh6twLE6zp+8RLnFZ6Z746qo9PRzoNCp+e8pkynOMOHxh/vDWhrRkn95faNUqim2GxGC/VQz2uyiKQk27n1pnkEKLIeUJz7p8uLGFDU0eDFoVVxwzJmWTCYqi0O6PUJ5roKCPZTgzoavUnrcf318nTCrCotfQ5A7xXU3/VkHTIS4rvNmZK2jBzLJ+/U1dgSj5Fh05+4jGUKskRheYsei1dARECH9v9Bo1lxxeyX1nz2JMoRlvOMZfPtzK1S+u5u+b1biDUUblmbj3rJmce/CotL3X9+XMA8oxatVsd/j5etuetxnsjc2oxRUQGfiFvkk6duyoo47in//8J3fccQeQSMAlyzJ//vOfOe6445I6VyQS4fvvv+emm27qvk2lUjF37ly+/vrrXu/z9ddfs2jRoh63zZ8/n8WLF++xjSeeeAK73c7MmTP32Be3201eXt4efx8OhwmHf3hTeTweAKLRKNFo9n7wdvVtsPqYTHtt3jBuf+ICS4737QLgzc7stvOnFCMp8e779fX+qTCQNuNyolxOMBInrijo1SpMBg3ldgMWgxaLXo1up2yq8Xis+7kst+vZ7gyiU6X+glQFXHf8WK57dS2r6918oZGoHMBzqgL0KtjR6sGmk3o8pl1l82t0sNts84TxBkKU2Pr2nli+w4nDF8Fm0HBYZc5ur83Bel+koz2zVuKWUyfyq9fXs93h5+53N/K7UyahVknD4vENdntqIN+oobrNixyPM6bA3CMvynB/H/bWXosnxLZmD3aDBo0kI6dwAqTrb+f0BXn6qx0AXHDwSApMmpT9XV3+CBatxAibnlgscc5s/FwDyDWp0Esy/mA4qUSHOhXMm1LE6ysb+e/qRv6vMjveh0u3tdPqDWMzaDh6XF7SfYrLCpFYhEKziXg8Rjy+9+dTp4LKPAPrG9wE1ErSVQx6Mxw+13Y1Jt/APT+ayn/XNPHit/XUu0JIKCycWcpP5oxCq1altf19PUaLTuKMmSW8+F0D/15WwyEV9qSv5yTAoFKobvNg0SQiPvenz+3h2GZ/9LV/kpJkHNC6des44YQTOPDAA/n4449ZsGAB69evx+l08tVXXzF27Ng+n6uxsZHy8nKWLl3KYYcd1n37r371Kz777LMe++q76HQ6nn32Wc4///zu2x599FFuv/12Wlp+2MP11ltvcd555xEIBCgtLWXx4sUcfPDBvfajqqqK2bNnc++993L55Zf3esxtt93G7bffvtvtzz//fK8RDkLq1Xjh/nUa1JLC7bPjWLNzS+KQ9kWzxKs71GhVCr+dFSc3+SAdYRD9bYOKTW4VJ5TJLKgYnqu0NT7463o1UVnisCKZc8fIZFGuMUHYq39vVfGtQ0W5SeH6GXHU4rWbNFcYbl+pRlYkbpwRY0QW7FJ4YJ2aHV6JeeUyp44anp+9Q11rEL5uUTEjX2Z0FlX8DcbgDyvUBOISPx0X5+BCsR1DSF4gEOCCCy7A7XbvteR90iv606ZNY8uWLTz88MNYrVZ8Ph9nnnkmV111FaWl2RM6etxxx7Fq1SocDgdPPvkk55xzDsuWLaOoqKjHcQ0NDZx00kmcffbZexzkA9x00009Igk8Hg8jR45k3rx5e32CMy0ajbJkyRJOPPFEtNr0j4z72p43HGVVTQdmvbbPM9P/+agKcHDU+EKmHzwOSMya1q79hlHTD0WlHpxs4/tqMxaXCUTiBKOJVXuDRoXFoCHfrMdq0GDWa5IqZ7PzcxqMw9p6F2pJ1a96vftSoShsWLyeDc0+PvYUcf2JEwZ0Pk8wioLCzJE5e8zDkK2v0cFu0xOKsqq2A6tB26cauU3uEJu+XoUEnHPcgZTslDV4sN8X6WyvEjCUO/nT+1v4ulXF2MoKfjyreNg8vky0F4rGcQYiVOaZGV1oRq2Shv37cOf2YorEukYPgVCMwjRl25bjMZZ8uYxvHSok4LqTpzO2yJKy8zd5gpTZjUwstqLKwN8Pkv8btvsjrKlzkW/W9bnKTpcjPVv5vKqdf21Vc+UJE5k5cs8RmKmyp/fh1lYfO75eh0Ylcf4JB5HXj20TjZ4gE4usjMz7YaGoL89nOBpnXaMbXyhGYS+Z+pMx3D7XdlUJHJSlj/HHqgb+tayOD1tN/Oj4mUm/HyCRt0mvUWjb9N1+8bmdrZ9rmdIVWb4v/XrV2+12fvvb3/bnrj0UFBSgVqt7rMQDtLS0UFLSe9m0kpKSPh1vNpsZN24c48aN49BDD2X8+PH84x//6LFNoLGxkeOOO47DDz+cJ554Yq991ev1veYg0Gq1Wf1C6DLY/dxXey5XmIiiptDQtyVjVyDCl1WJ/Uynzyzf7QNUpdYM2kB/1zYVRSEYjROMxAnF4mhUKkw6DRU5JnJMOqwGDUZt30ul7YlWq8Vk0jK2WGF9oxczqrTUv/3fI0dz/atr+LzKyekzA0wq7f9EVo5FQ4MrSIs3xoSSvSccyrbX6GC36XaFiSoqjH3MdfLBxkRy0QNG5VKW2/sAYrDfF+lq77BxRfxfIMZjn2/n38vrKLTqqUxje3syXNozdZ63xhVGo9UwtvCH189wfx8qkpod7UG8EZnSHHPaStFFYjIvb098Pp86o5SJpTkpO7c7GMVi0DOm2I5+l2zt2fa5trNCm4Y8axhvKEa+JbnX9bmHVPB9rYvmYJxb3trCwZW5XHr46B4D5XTZ9X34xprENejR4wspsPWvpJ5Jr6M4x4xWu/vzsLfnU6vVMr5EYnW9i2AskXl+oIbL51o2tbmv9hbMGsGba5pp9oT5ZKuzu0xgMnKtaprdPmD4f25n8+dapvS1b0mPEtasWdPrf2vXrmXr1q099rHvi06nY/bs2Xz00Ufdt8myzEcffdQjlH9nhx12WI/jAZYsWbLH43c+7859a2ho4Nhjj2X27Nk8/fTTqFSpHzAJvQvH4jS5Q3ssJ9ObDza0EJMVJhRbmFCcHTFY7mCUZk+QRneQUCxOjknH1DI7B1XmcsjoPCaW2Ci2GTDpNCm9mCzLMVGWY0hbIpaxhWYO6Qwl+/uXO/qVGXZnuSYtDa4AroAot7cnkZhMszuERd+3D+5ITGZJZ7mpU6Ynf4GQauFYosZ1OjNCnzqjjIWdGbj/+sk2NrkkkYF6AAxaNflmHdvafGx3+JCzLKN5ulS3+2lyhyi2GtNab/6VFQ04QhJ5Zi0XHlqRsvNG4zK+cJTKAnNS36HZQK2SKOsstZfs98qoPBN/u2AWRxXLqCT4trqDq19YwaOfVu1WSi2dHL4wX21zALBgVv9L6hVbDf0epOdb9FTmm3EHI1lXiUDoG4NWzVmzE9XLXvy2lkgs+e0fapWEvnPsEun8DhaEXSX9KTNr1qzuL8eui6ydvyy1Wi3nnnsujz/+OAbDvsOKFi1axMUXX8xBBx3EIYccwgMPPIDf7+fSSy8F4KKLLqK8vJy77roLgGuuuYZjjjmG++67j1NPPZUXX3yR7777rntF3u/3c+edd7JgwQJKS0txOBw88sgjNDQ0cPbZZwM/DPIrKiq49957u8vuAXuMJBBSp8MfxRuKUWrvW9hZXFZ4d10TAKdOz2xJvbis0NJZ116jlijLM2EzarHqtUklGBoItUpiTKEFTzCKKxAhx5T6bMunjZJZ7dKwucXLZ1vaOG5i0b7vtAcmnQZPKEqtM4DNoEWVoWy32cwViOANRfdZZqnLV9sceEMxCix6DqpIfwjrnsiKQoc/QqQzKUyHP0KBPX0z4JceUUmbN8RX29r520Y1T2/9lrJcI+U5RspyEv92/f+hNgjKBINWTZ5Jz7ZWH/Iwv1Dsul6pcwYosJnSmnV7+Q4nr3yfSBx7+RGVfS4f2xcOX5hSu5FSe98+K7JNV6k9XyiW9PYzu1HLWWNkzjvuAP61vI5vtjt5d10zn25u48cHlnPGrPKUJKnbm7fXNBGXFaaV2XpEwvRVJCajUkkUD3DLyMg8E55glDZfiJI+fm8I2eXkaaX8Z2UDDl+E99c3c/rM5K9v7SYdHmBLi4/J5X3fCivsP5L+9vnPf/7Dr3/9a2688UYOOeQQAJYvX859993HrbfeSiwW4ze/+Q2/+93vuPfee/d5vnPPPZe2tjZuueUWmpubmTVrFu+99x7FxcUA1NbW9lhtP/zww3n++ef53e9+x80338z48eNZvHgx06ZNA0CtVrNp0yaeffZZHA4H+fn5HHzwwXzxxRdMnToVSEQAVFVVUVVVxYgRI3r0R6wQpZeiKDS5g+jUKlR9XE35Zns7Dl8Eu1HLUeML0tzDPQtF47T7wxSY9TQAB47KxaDPTEkji17DmEIL6xrcmGIyOk1qI1JsOjj7wHL+tayOZ5dWc9iY/AF9geSZ9LR4wpTYwxQNcF/hcKMoCs2eEBqVqs+Dj3fXJia+TppanLEyQYFIDKc/Qq5Zx4QiE99uh5iSqCqRyoHNzlSSxKITJyLLm1i2o51QTGZ7m5/tbf7djs0xarsH/4l/DZTlJAZIqX6/DGVGnRoFHTvaEyGgnlCUPE1qo5AyKS4rOP0RGpxegD7nwOivHQ4/936wGQU4rEjmsDGpm4jzhqLotSoqC8wZe98PlEGrpsxuYEuLr995ZkbkGvntKVNY1+Dmqa92sLXVx7+X1fLuumZ+emgFx00sSsvzE4rGea+znOkZndFFyXIHoxSYdeSYBjYhqlWrGF1owROK4Q5GsachZ4+QXjqNinMPHsmjn27j5e/rOHFKcdLXWV2v82ZPCJXay8QSqxjsCz0kfTV255138uCDDzJ//vzu26ZPn86IESP4/e9/z/LlyzGbzVx//fV9GugDXH311Vx99dW9/u7TTz/d7bazzz67e3V+VwaDgddff32v7V1yySVccsklfeqbkFruYBRnIEKOse8D5Lc7BzXzp5akZU96X3iCUfyRGJX5Zkbm6GlYS8YvtEpsBlyBCLXOIGV2Q8ovzBfMKOX9Da20esO8vqKeC+b0P/xUp1GhliTqnAHyTMknYhrOfOEYTl+kzxe9Oxw+NjZ7Uask5k0Z/AikuKzQ7guDCsYVWRiZZ0JNIuywIs+cKAGpVqXtb6zTqPjNSROoWvElhtGzafZGaHAFaXCFaHQFaXAFcfojuIJRXMEoG5p6JqyRgEKrnhG5xl0mAowUWPRpeV9HYjK+cAxvKNr5bwxfKJb4/73c7g1FkWJqfp7v4sDK9E9umnQalLiOELC6toPCnAglNiN5Zl3GP+f6KxyL0+6L0OgKJkK744mIhXRGenT4I9zx9gaC0Tgzym2cPcKZss/luKzgCUWZXGob8oO6Qque2vYAwUh8QJFw08rt3Hv2TL7Y6uCfX1fT6g3z4EdbeWN1I/9zxGhmjcxJXaeBjze14gsnohEPrkx+AicuK0TicUpzbCl5XdiNWsYUmlnf4MakU2fs+kjov7mTi3ltRT0tnjDvrG3izANH7PtOvSiy6GlyJ6JNxWBf2FnS33hr166lomL3C/6KigrWrl0LJML7m5qaBt47Ydhp84aJxZQ+r6hVO/ysbXCjkuDkaYM/qFEUhTZvGLVaYkqZjTK7kfgg1ZzdF5VKYnSBBU8wsbKab0ltLTydRsX/HDGaP723iddWNnDilBIKrf1vI8+so9kTosUbpjxHhBp2cfojhGLxPv/93lmbWFE6dEw+uf3I9jwQvnAMdzBCgUXP6AJzd5+j0cRAf0SekUBMocUbpizNocUaVWJlb1TB7jk7ApEYjTsN/Lv+bXAFCUTitHrDtHrDrKh19bifVi1Rav9hC0B5jpGyXCOlVi2KAoFInEA0MTj3hXYapO80aO8aqHcN3L3hWL/2X4LEXe9t4c4f6QclL0nXgMus19LiDtPsDpFj0lGWYyTfrBsyF46BSIw2b5iGjiDecBS9Wk2BWY8Kmeo0thuOxbnznY20dX6+/Xr+BBwbv0nZ+R2+MMW2RFTKUGc1aCm06mlyhwa85U0lSRwzoZDDxuTz1ppGXv6ujh0OP7//7zpmV+Ry6eGVVOQPvB6frCi8sboRgNNmlPVrAswbipJj0vUrS/+elNmNuAJR6jsClNnTm3dCSD2tWsX5B4/igY+28uqKek6aVtKviDiNWkWx1SAG+wPkC8fY0dq3bPZDRdKvpkmTJvGnP/2JJ554Ap0u8WEVjUb505/+xKRJk4DEHviu0HtB6BKKxmnxhLAZ+r4a0bWaf9iYfApSPJDdl2hcptWbuNidUGzt/nKOZ9FWVqNOzZhCM2vq3YSi8ZR/sB8+Np+pZTbWN3p49utqbpg3sd/nUqskTDo1de2BITVwSKdYXKbJHcLcxy/2QCTGp1taAThlECe+YnEZhz+MVq1iYrGV8lxTr5N1WrWKsUUWfOEYHZ1h/Zlg0mkYV2Rh3C7lzBRFwRWM9jIBEKLJFSQaV6h1Bqh1BnY7p4Qa5Ztv+90nlZTIkG3Va7AatFgMif9vMWiw6DVYDRosei1WgwaTVuKZj9eyyQ23v7mee86aOWgDPINWjcmgIRaX8YRirKt3YTZoKbUZKLTpk/r8HiyKouAJxmjxhGjyBAlG41h0Wkpsxu4tYnI8fbXOFUXhoY+2srnFi0Wv4ZbTpmDRa3Ck6Pz+cAyNSqIy3zxsVm2L7QYa3SGicTklj0mnUXHmgSOYO7mYl76r4521TXxf08HK2g7mTi7mJ3MqBjTAXlHTQYMriEmnZu7k/uWs8UdiTMm3pfRvqFJJjCk04w1F6QhEUzqJIAyOYycW8cr39TS4gryxupHzDh7Vr/No1CpKbEaaXGKwn6xoXKbJFaS6PYDTF2Q4TZclPdB/5JFHWLBgASNGjGDGjBlAYpU/Ho/z1ltvAbB9+3auvPLK1PZUGPLa/RH84Vifkwj5QjE+2ZwY1Jw6Y3CT8AUiMVzBCGU5RsYXWQct0V5/FFr1jMozsd3ho9Ru7HPug76QJIn/PXIMi15exWdb2jhteumAyu3ZjVqa3IlB1eh+JDIabjoCUTzBaJ/zFnyyuY1QVGZErpHp5fY09y7BHUysTpfY9IwusGDfx95Sq0HLuCIraxvSM/k0EJIkkWvSkdtZIWNncTkRvdOwyyRAoytImzeM0vnVr1VLWPWdA/VeBuk//NxzQG/Uqfv83pTjMS6dKPPEDivb2vzc8sY67vnxzEGdONGoVeSZdSiKFl84RlWrj1qnn0KrgRK7gVxT5sP6u/bfN7mDOHxhojEFu1FLnmlwJ4Vf/LaOz7c6UKskbj55EmU5RuQURX7F5cTk1PgiS8YmztIh8T7U4glGUxqNZjNqufyoMZw6vZR/fl3NV9va+WBDC59vbePMA0awcFZ5v77P/9u5mj9vSv9WXP3hGEadmoIBRMXtiUmXyNmzNk0T/kJ6qVUSP5kzij+/v5n/rGzg1OmlWPs5oapWSZTYE4N9RVGYVGoTr4e9UBSFdn+Eaocfhy+cSKytVRHKdMdSKOlPq8MPP5wdO3bw3HPPsWXLFiCxZ/6CCy7Aak2EF1544YWp7aUw5MmyQpMriF7T93ryH25qIRyTqcgzMa2s/4PLZHX4I0TiMuMKrVTkm7J+P7kkSYzKN+EKRmj3RQYUXt+bcUUW5k4uZsnGFp78cjv3nDWz35MJKknCbtBS1xGk0GbY7zOjt3pCqCSpTwMmRVG6k/CdPK007SGakZiMwxfGpFczrdxGic3Q5/dCsU2PJ2him8NHqc2Y8QFhXyQukBKD2NkVuT1+FwxH2LRiKZMOPBzjICXgNKjhllMm8pvFG2hyh7jtrfXc9aPpaUt0uCeSJGE1aLEatISicZrdIZrcQXJMOspzEvv4B/tCMhKTafcnwvM7AhEkJOxGLQbL4F/QfraljeeX1wJw5bFjmT4iJ6Xnb/eHybfoBqVe/GDqKrW3tsGFrCgpnaAGKMsx8puTJ7OxycM/vtzB5hYvzy+v5d11TfxkTgVzJ/c9kWl1e4BVdS5UEpw+o7Rf/XGHoozON6ft/VuUxgl/If2OGFdA5Xd1VLcH+M/KBi46rLLf5+oa7Dd3hvGLwX7vApEYNe1+GjpCSBKUdF6r+ELDaZgPSY1gotEoY8eOpb6+np/97Gfcf//93H///VxxxRXdg3xB6I0rGMUViPY54ZisKLzTOag5bUbZoOw7i8uJigAqFUwfYWdMoTnrB/ldDFo1YwotKFIi63mqXXhoBUatmi0tPj7b0rbvO+yFxaAhEIlR30t49P7EH44lZpANfbvw29DkocYZQK9Rcfyk/pc73Bels2Sewx+mPNfIAaNyGZGb3ISXJElUFpgpsupx+MJp6+tg0WtU2HWJfwdTjknH7QumkmPUsr3Nz/97ZyPRNIag74tBq6bYZqDArMcfirGm3sV3NR1sb/PhDUXT3n7Xhdl31U7W1LvwhWIUmPUU2wwZuZDd1OzhwY8SCx4/OqA85ckxg5E4CjC6wDwsK0XkW3RY9IlSe+kyudTGPWfN4NcnTaLEZqAjEOXhT6q45sWVfF/T0adKS2+u+WELYVE/yuJFYjJqlUSRLX2RJpIkUVFgIt+ix+Ed+p+5+xuVJPGTzoTHb65pxBWIDOh8Pwz2Q2xq8hCKZtGe0wyLxWXqOwKsqOmgpj2I3ailyGoYEgsS/ZHUN4dWqyU0zGY6hMHR6gkhK0qf96atqOno3Lus5tiJhWnuXSKRUrMnSL5Fx4yRORTbUp/FPt0KLHoq88x0BCLE5dSWicw16zj7oEQ22GeXVg/4SyPPpKfRHaTDP7Avs6HM6Y8QjMb7vMLz7rpEEr6jJxSmLRIiFI3T6AqiUUvMGGFnSqmt323pNCrGFVrRaiTcwfQPAoerUruRW0+fikGrYnW9mwc+3Iqc4TKwGrWKfIueUrsRSUnUcP6+uoP1DW4cvnBKP38URcEdiLKl2cu31U42NnuQlcTqS75Fn7HJ2FZPiDvf3kg0rjBndB4XD2AFrjeyouAMhKnIM5E/jEL2d6bXJErtecPpTXArSRJHjivg0Z8cyGVHjsai11DjDHDbm+u55Y317HD49nhfbxQ+25rIttDfknquYIRCiz7t1RL0GjVjCy2oVImkYsLQMmd0HuOKLISiMq+tqB/w+boH+56wGOx3cvojrG1ws67BA0iU2TMzSTyYkv6GvOqqq7j77ruJxcSHiNA3gUiMFm9ySfje6lzNnzs5+bqiyfKGorT7wlTkm5hWbs/KZFN9NTLPRGGaVlHPmFlOsU1Puz8y4C8ho06NLEOtM4Cc4kmJoaAresSk7dsg2h2M8lVV4mLzlGn9Cx3dG1lRcPjCuINRKgvMHDAqNxH+OcAZbrspsV/fH4kRjomLjP4aV2ThppMno1ZJfL61jX98uaNPK5HpppIkbEYt5TlGTDoNTe4QK2tdrKztoNEVHNDfvCtnwtoGN9/XOtnh8KNXqym3m7AbtRkNTQ5EYvzhrQ24glFGF5i5/sSJKV8N6vBHyDElQvaH2qRzMgqsegwaFcFI+j8ftGoVC2eV8+SFB7FwVjkalcSqOhfXvLiKBz/akigbuouvmiWicYUJxRYmlSQfuRqXFWJxmdI0lMDtTZ5ZR2W+GXcwQiyD0T97k8mopGwmSRIXdq7qv722qdfXY7LUKokSm4FmT5iN+/FgPxiJs6XZy6q6Dpz+CMXWxMTbcP5s7ZL0QP/bb7/l9ddfZ9SoUcyfP58zzzyzx3+CsKt2X4RQRMbcx5XBRleQ72s6kIBTpqd+UNNFURT+f3v3HSdVef0P/HPvnd5nd9r2BRaWDiqioBEjCAhijRg1scY0SSy/YK8xxm40lhjzVTTGFhPFjiIIakQU6b2zy/Yyvd659/n9MbvrLiyyszttd8/79eKlDLNz7szOLec+z3NOoz+CWFzG6EITKp0mqBX9+86eSsFjmN0AhcClfDqkSsHjiqlDAABvra1Bo79vs3vy9Co0+iNoGYSj+p5QDN5wvMfT9pdubUBcZhjeTSX5vgrF4qjzhqFXCxhfYkalK7XFJwtMGpTmadEciGZ9JLo/O7bUiuumDwcAvLuhFm+vq8nyFnWlVSWm9efrVQhE4thU48Ga/W7sS3Jafywuo84bxroqN9ZXu9HoSxRIKrRoe3wOSSdJZnj44x040BqCVafEHXNHp7xYa0SUIMoMQ236AT/aZNQo4TCp4cvA0o92Bo0CV508BH/72XE4ZbgNDMCn2xrxy399h399faBj+ZsoyfiyIXGZfPaEol4lBb6ICLNOldFCiiV5OrhMGjTl0LKpiCihzpdYM95+U3kg8rW9r97eiD2m1IIxhSaIEsMba6pTsk3tyX5DW7KfiZtquUKSGWo9ifPJ3uYA9CoFHMae1xsaCJJ+pxaLBeeffz5mzZqFwsJCmM3mLn8I6SwuyajxhKFN4mKlvaXecWXWtLWUEiUZtd4w9GoFxhWbUZKn7/PoZa6w6FQYYtPDF039Hf32dnsxScZLXx3o02spBR4Knke1e/Ct1W/yRwGwHp1sZMawZEtin0jlaL4kMzT4IgjG4qhwGDChxAKHMfWjTjzPYYjNQGtHU+DUSgeuPKkcALDoq/0dXUlyibJtWr/LpAVjwI56P7474MbWWi9aAtEjzuDpvP5+Q7U36+vvj2TR//ZhzQE3VAKP2+eOTnnx0/Yq0MVWTcpfO1c5TRowZH6k12XSYOGskXjkJxMwusCEWFzGG2uq8auXv8NHm+uwYmcz/CKHfL0KU4flJ/36jDGEYhKKLNqMtkVUCDyGOgzQKIWsJ9QRUUKdNwx/VERR2/XcqAITJCaj3hdO+TLDbIlLbe+HJb7DvV06wXEcftY2qr90awPqfalZLv39yH4E2+sHR7LvCcWwucaLTQc9iEsMhWZtxovZ5oKk3/GiRYvSsR1kgEq2fVg4JmHZtgYAiSJ86RCKxeEOJVrnVTgMA3LHL7Jo4QmJaPBFetzOsCc4jsPVPxqK699Yj893NeHM8QUY1Yd2e1adCvXeI6+PHIjCMQmN/sQoZU+srXKjwReFXi3g5OG2lGyDPyLCFxFhNyZa5qW797JGKaDCbsCGgx74I2KvWwcR4NxjitEajGHx+lo8sWwXzFolji21Hv0HM4znEpXwzVolQrF4W6vCCKz6xOi8SZW4oeQLi2hpiaDeF0EoFodBrUSBWZOTVcM/2lzX0Wbt+tNHYIQz9UWIPSERRo0CZfn6QTGtFEjcnE5Hq72eqnQZ8cB54/D13hYs+mo/6rwRPLNiT8e/zx3n7NUIYDAmQacSkG/IfI0Fk0aJYQ4DNh/0QKsUMl7MMSJKcIdiUAgciq1aFFq00CmAvUh0RLDotdjTFEC9LwyrTtWvr8MC0Ti84RhcJg1KrGp8uRPwR+Mw6Viv9uGxRWZMLLFgfbUHb3xbhWunj0jJdgo8hwKTFvW+MBhL3HDJ5dbRvRURJRx0h3CwNQxRlmE3ajJ6oy3XDN53TjKiwRcB18P2YQCwYmcjgjEJBWYNjim1pHx73MEY/JE4hjuMGF1g6tcnlx+iEHgMsevTckd/mN2AGaOdAID/+3Jvn6ZjCzwHXdtyiXR0C8hFraEYQrE4dD08wbZ3n5g+su/1KkQpMS1alGSMKjBhfLEl7Ul+O6tehaE2PfzROK3R7KMrThqCU4bbIckM93+0Dbsa/NnepB+kUyngMiVa8fnCcWyo9mJdlQcAsL7ajX3NQagEHkWW7K+/P5L11R48uzKR/P3shFKcXJGam26diZKMSFzCUHv62rDlovZWe1FJytryHo7jMGWYDU9ffCx++aOhHcuqVDzDzFHOXr2mLyzCZdJk7XdZYNKgyKpDYyCSsZoeETFR2NjXNoJ/bKkVowpMsOhUXZJes06JccVmVDgMCMQSHWhyoe5IMiQ5sfwzKkoY6TJ2qfFkUCvg68PyyfZR/eXbG1HjDqdke4H2kX0tGv2RATeNX5YZ6r0RrK/2YHdjABqlAJcps7NpclGvjj7/+c9/8O9//xtVVVWIxbqur127dm1KNoz0f/6IiOZAFOYejt4xxvB+WxubueMKUnqxJ8kMTYEItEoBY11mOE3qAT9aYtIoMdSux5YaL3QqIaUHu5+fUIYvdzVjZ0MAK3Y09andm0mrhBvAtjofRhUp0l6ZOJtkmaHOE4ZGIfTo+9foi2DNfjcA4IyxfWvd5Q2LCERFFJi1KLfps/I5F1l18EXiqHaHUGTWDvh9MF14jsN1M4bDFxGxvtqDe97fiofOH5+2pU6pohR42AxqyIzBG0xMSTWoldBpcnuK+kF3CA8s2QaZAaeOsGP+pJK0xGnyJ1paOns4A24gaW+154/Es3oOUAo85k0oxI9HOrB0Sx00nr09rqXSWTQuQaHg4OxFO75U4XkOQ+16BCJxtAZjaZ0tEY0nRvB5nkOBWYsiixYW3Q8XO1MKPCocRpi1KuxpCqDWE4bNqO4XtZISM0NF2AwqDLMbOmowiHIicS62aLGjKQSjRtGra9lKlxGTy/Pwzf5WvPpNFRbOqkzZtrcn+/W+MBgYRheY+/3Ivi8iYn9zEPXeCFQCnygmTNcXAHoxov/Xv/4VV1xxBZxOJ9atW4fJkycjPz8fe/fuxRlnnJGObST9VEsgiogo9fgAsrnGi6rWEDRKHtN7eQe9O+2t8/L0KowrtsCVoeq3uaDQrEWRVYemFN/R79Jub1Xf2u21/y784Tg2tbXnGqh8ERGekAhTDy9kl2ypBwMwvtiMYquuVzFjcRm13hAAhnHFFowtMmftQlrgucRFkU41KIswppJS4HHLGSMx1K6HNyzirne3wN3H3suZ0j6tH0BOrb/vji8s4o/vb0UwKmGUy4jfnTY8LecPb1iETi2g3DZw6sUko73VXiCDRfl+iEGtwNkTClBp7t150xsSYdOrYdJmd2aGTqXAULsecZmlZfQ2GpfQ4IvAE07cRD62xIoxhSZY9aoe7yd2oxoTSywotenQGoz1uYd8Osks0Q0kEIujwqHH+GJLt4UWHSY1LFpVn2ZUXnJCKQDgi11N2N8c7PXrdKc92W/yR7G1zttvZ1TG4jL2Nwewrq0lt82gRr5BTUl+J0kn+s888wyee+45PPnkk1CpVLjxxhuxdOlS/P73v4fX603HNpJ+SJRk1HujMPRwHTIAvNc2mv/jSkfK+oQf2jpvII8WdydRCE0Pg1oJTyi1F1Dt7fZaU9BuDwAcJg3icRmba7yo9YT73TS+nmgJxCAx1qPZFaIkY+nWRL2K3hbhcwdjaA1FUWLV4ZhSK4os2pS3AUuWViWgwmEAA0OQej33iU6lwN1njoHTpEa9L4J73tvSby/YcpEoybj/o22o80bgMKpx65xRaVnrLEoygrF427F68EzZP5TNqIZGJfT76cSSzCAxlrGWekdjN6pRmqdDayiasuJ3sbjckeA7TGocW5p8gt+ZRilgpNOEsUVmgANqveGcaw/YXljQoBEwodiCCofxiMcDlUJASb4OYTHe6898qN2AkyoSXSFe/aaqD1vevc7J/rY6X786d7R3zVpf7cb2ej9UgoBCM03T707Sn0hVVRWmTp0KANBqtfD7E2sDf/7zn+O1115L7daRfssdEuENx3p80dLoj2D1vhYAiWn7fcXa7rrG4om1yAOhdV5v6dUKDLMbEJWklPYyT3W7PQDIN6ih4HlsrfXhQEvwiBW6+6tGfwSmHt78+npvCzxhEXk6FU4YkpdUnFg8cYGkUvAYX2zBSJcpJ1qTtbMZ1BiSr4c3EqP1+n1k1avwx7PGwqxVYk9TEPd/tJ0+0xRgjOFvK/Zgc60PWqWAO88cDYsuPfUsmgNRuEyalBZO7Y+MGiUcxsy22ksHXzgxaytT9U+OhuM4lNl0yDeo+9ybvT3Bd4diHQn+uCIz8nqZ4HfGt9VqmFhiSbSD80d6Xb0+lRhjaGlrCTjEZsD4YgtsPVgG4TCqYdWp+jRD4ZLJpeA5YNXeFuxuTH3h4vZkvznQf5L9QDSObXU+bKj2IhiV4DJpYejF8prBIulE3+VyobW1FQBQWlqKr7/+GgCwb9++ATkCR3qn0ReBgud7PHq4ZHM9ZAaMLzKjLF/fp9hxSUadLwydWsC4YjNK8wfnVMjOnCY1iq1aNPtTW/Amle322pm1ShjUCuyo92N3YyDn7ur3RUiUoFcnV4Rv5pjkKz63hBIXc+OLzXCaNDn5/U/0ek4UBaJzR98UWrS488zR0Ch5rK/24Illu7JW1GygeHtdDZZuawDPATfNHtnn89KRBCJxqBU8ym36rM+2yQVOkwaMZb7VXqowxhASEy31cqlXt1ohYJjdAIHnepU8i1LXBP+YUkvKEvxDGTVKjCk0YaTLiIgYR6M/krXjWTQuodYbgUYpYHyxGSOchh4vN1IKPErydIjEpV6P6pfk6TBthB0A8K/VqbnGOpTAc3AaE8n+1trcTfZFSUZVSxBrD7hR7Q7BqlPCZlDTcfMokj4KnXbaaXj33XcBAFdccQWuv/56nH766bjwwgtx7rnnpnwDSf/UGoz2eB1yLC7j4y31AIC54/s2mh+KJU4KLpMW44vNWWnVk4s4LtHL3KJXoTWFa6Pb2+1xAD7f1YRtdb6UvK5erUCeXo29zQFsr/endCZCNrQnsiqe79FFUVVrCJtrfeA5YNaY5IrwxeIyhLYY6hxe/6wQeFQ4DDBqlHCneFnJYDTCacTNs0dB4Dms3NmERf/bn+1N6rdW7W3Bi1/tBwD84uShOK4sPe0LJZnBG4llrThmLrLqVLDqE632+qNgNHEztycjvpmWp1eh3KaDLxLr8Q309gS/NRiD3fh9gp9vSG9BY4XAo7xt9NykVaLOG+5TLaDecIdiaA3GUJqnxYQSCxym5Jdi2A1q2AzqPtVPuWhyKQSew3cH3NiaomusQ7Un+y3BRLKfa8vqWoIxbKj2YGudDwLHodCsG7SzdJOVdKJ/22234ZZbbgEAXHPNNXjhhRcwatQo/PGPf8TNN9+c8g0k/Uv7XcuYxHp81/PL3U3wReKwGdQ4YUh+r2O7Q4nWecMcBowpHLit83pLoxTSUpSnc7u9f3zRt3Z7nWmUAhxGDQ66Q9iWw3eZe8Lf1manp/3jP9qcGM2fPCQv6QtGTziWkxeZ3dGrFRjuMECU5H79+80Vx5VZ8fvThgMAFq+vwdvr+l47Y7DZ0xTAo5/sAAMwZ1wBzuzjzecf0hKMwmHU5Hy3hEzieQ5F1uy22usLXzTRTz1Xq5gXW9tmUh1lCv+hCf7EDCX4h8o3qDGuyIzyfD08YRHuYCztM8BESUatJwye5zCu2IyRrt73m1cIPErzdBAludezVArMWsxoK1D9ytfpGdUHDp/Gn81kPy7J8IZE1HkTS0I3HfTAGxbhMml7PIhIEpJO9CsqKuDxeDr+/tOf/hR//etfcfHFF2PkyJGp3DbSj0TjEmo9YWw86AGApFrqtRfhmzPW1espOI2+CDgOGFtkxjC7IaemzOUSu0GNsvzUFuUBEu32tEoBuxoT7fZSRSnwcJm0aPBHsLnG26cKttniCcVQ1RoCgB4V8oqIEpZvbwQAnJFkET5JZohLMhxZbOmULLtRjXKbDu5QLKXfycHqtJEOXD61HADwwv/2Y8WOxuxuUD/SEoji3ve3IhqXcUyJBb/80dC0JTWhWBw8Bwyx6amA1CHy9N+32utPIqIEpcDDkcPtERUCjyF2PXQqodvzqSjJaPRF0HJIgm8zqLO2BEyjFFDpMmJckRmCwKHWG07b0g5vWESjP4ICiwYTSyyJNm19fN/5BjXsRjXcfZhNeeGkEih4DhtrvNhQ7enT9vwQnku0R2wJxDKa7Lcn9jWeMLbUeLF6Xyu+PdCKrbWJIu8mjRIOo4am6fdC0meXI91JCwQC0Ghy9+BG0iMQjWN/cwBr9ruxqcaDYNuJuaeFv3Y2BLC7MQClwGFmklOUOzPrlBg/yFrn9QbHcSjNT01Rns6selVHb+m+tts7VPtdZl84js39qP1eRJSwu9GPdVVuNPt7vs0rdzYhFJNQYE5caCTDFxFh1qlg1fWfO94cx6EsXw+nSZOSgo4EOO+YIpw1oRAA8MSyXVhX5c7yFuW+iCjhTx9uQ0swhhKrFjfOHpm2i0pJZnCHRJTm6bttzTXYdbTai/avG7vesAibIfst9Y7GpFFiqN2AsPh9Etc5wc83qDCxJPsJfmccx8HVdk4stGjR6I/Cn8KijXFJRp03DJkxjC0yY0yhOWUdMASeQ7FVBxnfF8pNlt2oxhljE9fIL399IK2zGvi2zzqdyX5ckuENi6j1hLG1zotv2hL7zQe9qPdGwAHI16k6CpTmehvWXNbjb/ENN9wAILGz3XnnndDpvu/pLEkSVq9ejYkTJ6Z8A0nuYYzBExLR4IugwRdBWJRgUCvhMmkBWUIyK4je31QLAPjRcHuv1ii2T0Ef5jDQGsceai/Ks6HajWA0nrJq7GdNKMSSLXVo8EXxn7UH8bMTylLyukDixOM0adAciGJzjReVLmPOVqiWZYZGfxT7m4PwhGOw6lTQaBXY34OfZYx1TNufPcaVVC9YxhhCMalfjhAqBR7DHAYEonG4Q4nPjPQex3G46uQhcIdi+GJXM+7/aDv+fO44VDgM2d60nCQzhsc/3YndjQEYNQrccebotLa5a21LpkrydEd/8iBlM6pxoDWEUCzeL5bhSTKDzFi/GWwoMGnQatFiL4AmfwSMF2A3qFFk1SFfr8qJ5L47erUCYwrNsGiV2NscRIMv0ueCbIFIHL5oDE6jBkPs6bmWzNer4DSp0eiLwtnLGXcXHFeCj7c2YEeDH2sOuHF8eXLdeJLRnuzXeyPYWufD6IK+de6RZIZANI5gNA5POAZPUERIlCBJDAqeg0YpIE+nOuzaRe6nRTlzSY9/a+vWrQOQuJjctGkTVKrvL8RUKhUmTJiAP/zhD6nfQpIz4pKM1mAMdd4ImgKJqd8WrRJ5+u/XAyezS7pDMXy5qxkAcGYvW+r5IompUKYeLhUgCXl6Fcrz9djZ4IdGKaRk5Kq93d4DS7bj7bU1mDnamfIpjDaDGp5QDFtqfIjFZZRYdTl1QeINizjQEkS9NwK1QkhM++M4yFLP7ojvagxgT1MQSoHrWJPXU8GYBJ2qvQhU/zs5mjRKVDgM2FzjRUSU6A5+H/Ech+tnjIA3LGLjQS/ueW8LHvrJ+Jy9QZZNr6yuwv/2tEDBc7htzqi0fkbhmAQZDENs+h4t5Rms2lvt1XjC/SLR94ZFWHRK5PWTm5Q8z6E8T4+9SMzIK7EZYdPnxuj90Qg8h9J8PUxaJfY0BVDviyBPp0p6Hb0kMzQHolDwHCqdRhRbdWlb9sm3jeo3+qOIxqVeFZKz6lWYN74A/11bg3+tPpC2IqHtDk32RxWYoO7hxyPJDMFYW2IfEhOJfTyOuMQgcBy0R0jsSer1+Oj52WefAUhU2n/iiSdgMpnStlEkt0RECc2BKA66w/CFRQg8B6tW1eeLlE+21CMuM1Q6jRjuNCb987G4DA65f1LKVcV5OnjCIpoDvb/DfKipw/IxttCEzbU+vPTVASycVZmS1+3MolMhEI1jR70fsbiMITZ91msyROMSatxhVLWGEIvLsBnUvTqBfdDWUu/kClvSBWd8YRFD7XpoVQJEsf8l+gDgMmngC8exrzkAl0lL6/H6SCnwuG3OKNz81ibsaw7irne34KHzx6etH3x/tHx7I/69phoAsODHFRhTaE5bLJkxtIaiGGozIJ+m7B+V06RBrScCUZJzOiFgjCEsxjHMYc76uSgZmrbEeEyhGRp1//s+WnQqjCuy4EBLEAdaErM/etruLxSLozWYaBU4zG7IyDHRqlPCZdKg1hPu9c3E844pxoeb6rG3KYhVe1owZYgltRt5iI5k3xfBtjofhtu73+7Oib03JMLdTWJv1VJinw1Jf+KLFi2iJH+Q8EVE7GkMYM3+Vmyu8SImynAYNXAYNX1O8uOSjI82J1rq9baqsScUg83YP6qL5yKlwGOozQClwKVsrRvHcfhFGtrtHcqgVsCqU2FPUwA7GrLXfk+WGRp9Eayr8mBnQ6BjFL83JzN/ROyY4TInySJ8EVGCUsHldBGonuA4DuU2HWwGdb+pxZDrdCoF7p43Bg6jGnXeCO55b2tKu270Z1vrfHhy+S4AwAXHFWN6krNokuUOxmDRqVCar+sX07uzrb+02gtGJRjUin7T7eRQ/fmGqkqRaNM6vsQMjVJArTf8g+vgZcbQ6I8gEItjuNOAcUWWjN345DgOxVYtFALX62OwSavE2RMT9Vde+aYqIwVseY6Dy6RBazCGHfV+AInE3hcRUecNY0e9D9/sbcWa/a3YWO1BtTsMiTFYtSoUmrVwmjQwaZWU5GcJfeqkC1lmaAlEsaXGi7X73djV6AfPcSg0a2HVq1J2Qvh6XytagjFYtEqcVGFL+ufjkgyJMTjN/TuxyTazTokhNj18kXjKqtimq93eodrb71W3Zqf9nj8iYmudDxsOehBpK57Xl3W9y7Y1IibJGGrTo9KV3AwXb1hEvj73i0D1hFohoMKRuAHVH7ss5KI8vQp/PGssTBoFdjcFcP9H29JWtbq/qPdGcN8HWxGXGaYMzcfPTkxdTZHuROMSRJlhqE1Py1J6iOc5FFq0iMTlnG6154vG4DJr6PeaJRyXuMk9vsSMYqsOzYFIt+eOiJjoDmXUKDCh2IIKhzHjy2csOhUKzBq0hnp/I/vsiUUwqBWobg3hi90tKdy6I2tP9t2hxHLZtVXujsS+qjWR2Fu0KhRadHCZNDBTYp8z6LdAACSmwdd7I1hX7ca6Kg/qvBHoVAoUWXQwapQpH314f2OiCN+ssa5eHQy8YRFWvarfrIfLZUVWHQotGjQlURn+aH5+Yud2e+lr75WN9nuxuIwDLUGsrXKjxh1Gnk6NfIM6qcJ5h5I7FeE7Y2xBUvtbexGoAkv/KALVExadCsMcBgSjYq+rFJOuiqxa3HnmGKgVPNZVe/DX5btyOnlKp2A0jj9+sBW+SBwVdgNuOH1En/bfo2GMoTkQQ5FVAzvNQktKnl4Fk0aRs632IqIElcDD3s9nUw0EOpUCowtMGFNkhswY6n3hjhHv1kAU3rCIYXYDxhdbsjr7osiqg0Yp9HpwwqBW4LxjigAAr685iEzds+U5Ds6273lcYjBrKLHvD+i3MsiFYvFE0nLAjfXVHvjCiTVOTpMm6cImPbWvOYgttT7wHHBGL1rqyYwhEpdRbKU1vKkg8ByG2PTQqwV4Qr3v89qZVde53d6BtE4VzlT7PcYYmvxRbDjowbY6H5Q8j0KLNiUjAhsPelHrjUCrFDBthD2pn+1vRaB6qtCsRUmeDk3+yKBNSFOt0mXEzWeMBM8BK3Y04aWv9md7kzJOkhke+ng7qltDyNOrcPvcUWkfifWERRg1CpTn6wfMzbhMUSsEFFpyt9WeNywi36CGSdP/Z1MNBO1F7yaUJJL5hraWrWqlgPHFZgx3GrI+88KkUaLIooU71Pvv9LwJhbBolajzRvBNU+aOKe03RM1aJRUT7SfotzQIMcbgDYnYWe/Hmv1ubKvzQZRkuEyaXhcRS8YHbaP5U4bZkN+Lu6q+sAiLVol8PY2MpIqxo6+ulLIR1LMmFMJpUqM1GMN/1x1MyWseSXv7vVhcxpYaL+q84ZS+fjAax7Z6HzZUe+APx+EyaWFMYaeHD9uK8J020pHUDbb2IlCFFm2/KgLVEzzPYajdgDyDitbrp9Cksjz8/rThAIC31tVg8fqaLG9RZj3/v/1YW+WBWsHjjrmje3UOSoYoyYiIEoba9f2ienwushk1fRoBTZe4JIOBocCspRs4OcasVWJskRkV9kRL0XFFZjhMuTPrrcCihVbFI9DLmSoapYCfHFcMAPiwmkedN5LKzSMDyMC6MiQ/SJITRUg21XixpqoV+5qDUAk8Cs1aWHSpW3//QwKROD7b2QQAmNeLInyMMQRjEoqsqRlJJd9zmTQotmrRFExNUqVS8LjypCEAgLfX1qDRl/4Tka1tCv2WGh8OtAQh97FQjSjJqG5NTNOvagnBrFXCbuxbz95DtQSiWL0vsc7ujLHJzXAJROP9ugjU0WiUAobZDeA59PqCiBxu+ignLp2SWJP+/Jf78HnbMXmg+6KewwebGwAA/+/0EahwGNIeszkQRaFF2zHllSTPoFbAYVSnrGhsqvgicVh0KuRRB4WcpBR4lNv0AL7vMJArDGoFii06eCMxsF7OWDtjbAGKLRr4RA63LN6CqtZQireSDASUKQ0Sdd4I1la5sb7Kg0ZfFGaNEoUWLfRqRUbvcC7dVo9YXEZ5vg6jC5Lv3pBIbARa55gGPM+h3KaHOYVTEKcMTbTbi0kyXlq1P2Wv+0MsOhX0agV21PuxpymAeC8WsDGWKEq58aAHW+t84MChyKJLy5S/T7Y2QGbAmEITyvL1Sf2sLyKiwKzN+lTEdMo3qDHUboA3Ig76AnKp9JNjizs6nvzl053YcNCb5S1Kr7VVHry1L3HJc9mUckwZlnwR2N7QqQSU5ev6RX/yXOY0aXKqnS5jDJG22VS0hJD0RqFFC4O69/UnVAoefzp7NAq0DO6QiFve2oi9TYEUbyXp7yjRH+DaL4y31HoRikqwG9RwmjRQKzKfGEgyw4eb2lvqFfbqBoMvEh/wiU026VQKlNsSo1wRse/r6ru222tOW7u9Q3Vuv7czyfZ7oVgcO+r9WFftgScowmnUwpxkT/uekmSGj7ck9okzetFST6MQYDcN/JtexVYdiixaNPojvR79yFUyY4hLMmLxxBTvUCzeMXshnTc2OI7DL04eipMqbIjLDPcv2Yn1LVyvbozlKsYYNhz04P6PtuHeD7dDBofTKu04/9iitMdu/92V5ulTusxnsLLqVLDoc+dzDETj0KsVyKfRfNJLWpWAkjwd/NF4r89rVp0KvxsjYZg90T3p1sWbsL0+M9dZpH+gBWMDXPvBw6JVwqDN7glpbZUb9b4I9OrkC44BiQRMo+ThGASJTTbZDYnvSUsoBq1a1ecZH8PsBpw+2olPtjbguS/24tELJqS1wnW79vZ7Va0hxOIyRriMP7hGNi7JqPdFsL8lhEAkjny9Ku03lL7Z14KWYAxmrRJTh+Un9bPesAiXWQPTIEgiBJ7DMIcewWgcrcEYrNrs3+hjjEFmiURdZgyy3On/WaJVqcwYpE5/B5c4HnPgwABwSNSX4HhAQNt/OQ5A4nlNgSgcJkXalikJPIcbZoyALyxiU40Xi3YKWHxwHaaPdOL00U4UWrRpiZtugUgcy7Y34KPN9ajxfF+vY5xVxm+nDUn7LDZJZmhqqyvhohawKcHzHArMOtQCaPRFkG/SZbXKty8iYrjDSIMOpE+cJg0OusNtRXV7d42uVwL3zhuFez/cgW31ftz5zhbcMXcUxhVbUruxpF+iRJ9kTHtLvdNHuXp1cvSERZTl6Wh0JM3aL4INakWfTj6d/ezEMnyxqxm729rtnTbS2efX7In29nv1vjBEmWGE0whdN0c9dzCGAy1B1Pui0KsEFJozU7Tnw82J0fzTRzmTumhNFIEaXEmETqXAMIcBG2s8aSnKJcms65+2kXaJMcTjibXBdb4weF4AY4n9pLsknec4KAQOShUPpcBDwfNQChyUAg+B58DzXOJ5bc/v/JjAJ35eluL4eDtQkqfFQU8EDqMmbUmNSsHj9rmj8MY3B/DJ5lq4QyL+s/Yg/rP2IMYVmTFztBNThuVnZRZYsnY3BvDhpjqs3NXUUVRUqxTw45EOzBplB6rXpj05ZIyhwR+B3aBGDUDTulPIYUyci2xGNZqDUSh4HtYM1RfqLCJKUCv4QTGbiqSXRimgNE+HrbVemLTKXg+C6NUK/PHssfjTB1ux4aAXd7+3FbfOGYXjyqwp3mLS3+REov/000/j4YcfRn19PSZMmIAnn3wSkydPPuLz33zzTdxxxx3Yv38/hg8fjgcffBBz5szp+Pe7774br7/+Oqqrq6FSqXDcccfhvvvuwwknnNDxnNbWVvzud7/De++9B57ncf755+OJJ56AwZD+4jyDUY07jLVVHnAA5o5LvghfNC5BIXCDKrHJtvJ8PbbWB6BXK/p8cdzebu+lVfvx0qoDmDLUlrb2jYcSeA4F5sS07801XlTYvh+ljIgSqltDqHaHIMuA06jOWPX6Wk8Y66sT+8TsJIvwecMirDolrAOspd7R2I1qlOfpsbPe06PnSzJDXJYPT+LbEvn2UXWG9qQ7MXKo4HkoBA4GtQoqBQ8lp8bWvcCYAjNUqsTFmNA5YW9L0L9/rG+Jh8gSPz/UZgC4CA66Q3AaNWn7bupUClw2pQwnqavRZBmNpdubsPaAG5tqvNhU44X+cwE/HuHAzDFODLHl1jkyIkr4clczPtxch12N369PLc/XYc64AkwbYYdOpYAsxbG/Ov3b0xyIwaxRYphDi8HVzyD92m++ji4wwRuVUdUaQr0vAp1SgFnX+yQpWYNpNhVJP6dJgxpPGN6QCGsfloJolALuPHMM7v9oG9YccONPH2zFjbMqM1aPhOSmrCf6b7zxBm644QY8++yzOOGEE/D4449j1qxZ2LFjBxwOx2HP/+qrr3DRRRfh/vvvx5lnnolXX30V55xzDtauXYuxY8cCAEaMGIGnnnoKQ4cORTgcxl/+8hfMnDkTu3fvht2emDJ+ySWXoK6uDkuXLoUoirjiiivwy1/+Eq+++mpG3/9g8eHmRPuwSeXWXiXr7pAIp0mdtrXS5HAOoxrusIRGfwQuU9+n8J49sRAfb6lHvS+C/649iJ+dWJaCrewZnuPgNGrQHIhhW22i6FiDL4JqT7QtaVZlvPXVR237xHFlVjhNPd8nZMYQlSSMsBgH5WhhWb4OvlAEVUi02mRc26h721R5gLWV7GobXec5CEIiAdeoBKgEHholD7VCSIy8t420K3kegpB4voLnuiTVoihiKxIzKJTKzB2DlAKPEU4jZMZQ6wnDZUpv4S8FD0wZmoeThjvQ5I/i020N+HRbAxr9Uby/qQ7vb6pDhcOAmaOdHQl0ttS4w/hocx2WbW9EIBpv234OJ1fYcMa4AoxyGTPeSssbFsHzwHCnAQY1lUBKF57n4DBpkKdXocEfRXVLCHXeCIxqBUxpvkZob6lHgw4kVVQKHqV5Omw86IVJZn06xqsUPG6dMwqPLt2J/+1uxgNLtuP6GSNwauXh+RQZHLKe6D/22GO4+uqrccUVVwAAnn32WXzwwQd44YUXcPPNNx/2/CeeeAKzZ8/GwoULAQD33nsvli5diqeeegrPPvssAODiiy8+LMbzzz+PjRs3Yvr06di2bRuWLFmCb7/9FpMmTQIAPPnkk5gzZw4eeeQRFBYWpvMtDzrhmIRPtyVaGp05LvnPtr2oUaGFetVmksBzKM/Xwx2KdbRx6wulwOOKk8px/0fb8fa6Gswc7YQjiQS3rziOg92ohtufKMy3ucYLnVqNwiz0QI7GJXy6rREAMCfJGS7+SBwGtRL5hsE1mt9OIfAYYtejCokbOGq1ALWCh0YpQK0QIAgclHxidF0h8G1Je2KUvj/eGFEpeFS6jGAs0T3FZdJk5H3YjWpcNLkUFx5fgg3VHny8tQGr97Zgd2MAuxsDeP7LfTi5woaZY1wZS6olmeGbfS34cHM91ld7Oh53GNU4Y2wBTh/tzNrN4FAsjrAoYXShCfkGNUQxt1rBDUQKgUeRRYt8vQoNvgiqWkOocYdg1afvxm37crbBNpuKpJfdqEaeXglPKIb8PrbLVQo8Fs6shFrBY/n2Rjy2dCeicRmzxiQ3c5AMDFlN9GOxGL777jvccsstHY/xPI8ZM2Zg1apV3f7MqlWrcMMNN3R5bNasWVi8ePERYzz33HMwm82YMGFCx2tYLJaOJB8AZsyYAZ7nsXr1apx77rmHvU40GkU0+n1/cZ8vUdVSFMWcPqHH44mRDlmKQ5bS34e6PUbnWMu31SMUk1Bo1mB8kSHp7fAEorBoFTAqucM+6/a/Z/J3kOmY2YynUypRbFJhV1MAar7vCcYJZWaMLTRhc60PL361D384fXi335l0Mqp5uAHkaQWoVTyYLCHdddwPfY9f7GhCIBqHw6jCxCJjUu/dH45ghMMAnskQxe4rpA/076iqrajdxGIDVKqjXXAzgDHIkgy5l40ksv158gCG5WsgiiLqvQE4jZqUT1P+of1wQpERE4qM8IbLsGJnMz7Z1oiD7jCWbW/Esu2NKLZocPooB04dYYdF17NEO5n9viUYw9KtjfhkWyNagjEAiSUXk8osmD3GiWNKLB3HpiO9XjqPM6Ikwx2MYki+Hnad0OW6YKDug9mIeaR4AoBCkwpWDY9aTwR1njDcwTCs2r4VVD30O8MYQzgWw1Cbtu2aqtcv3a1c+TwHSrxsxOxLvEKTGptrPIjFuB4v0zrScY0D8LtTh0AtcPhoSwOe+mw3wlERZ01IfulsT+KlS6bjAQBr27FzObcDer59HMtir6La2loUFRXhq6++wpQpUzoev/HGG7Fy5UqsXr36sJ9RqVR46aWXcNFFF3U89swzz+Cee+5BQ0NDx2Pvv/8+fvrTnyIUCqGgoACLFy/G8ccfDwD485//jJdeegk7duzo8toOhwP33HMPfvOb3xwW9+6778Y999xz2OOvvvoqdDpd8m9+kGAMeGCDgPowh/PKJUwrGFitsUjyDgaBRzYKYOBw7Zg4hpqyvUWZ99gmAQcCHM4slXB6Ee0TpH9hDNgfAFY18FjXwiEmJ5JsgWMYZ2U40cFQaWHoy31BxoCdPg7/q+ewqZWD3LYgw6BgONHJMNUhI59mTxNCyBExBrx7gMfyusSNg7klEmYW0zXHQBAKhXDxxRfD6/XCZDryhXTWp+6ny49//GOsX78ezc3N+Mc//oH58+dj9erV3a7774lbbrmly0wCn8+HkpISzJw58wc/4GwLRaJYsXwZbJWTYNCm/6pIluKo2vQ1SsedCF5QYONBL+rD26BR8rhg+qSkp9N5gjEolTwmlli6LQgniiKWLl2K008/PWNrZzMdMxfiNQdi2HzQA5NW2ed2QuUA1ot7sXRbIz5oNOPBqSNxcPPqju9Muh36Hc2EzjH3t0ZxYNUmKHgOF/x4co9HQAGgwRdGgVWLkc4fPubkwneG4qUnXjAWx/Y6H3xhEQ5j6rpDJLtfDAHwYySmrH+5uwWfbGvErsYg1rdyWN8K2AwqzBhpx/SRDjiMh09FPVK8QDSO5dubsGRbA2o8kY7HR7uMOGOsE1OG5vWqOGg69vv2Cvt5ehVGuUxQdzo25tJ3ZqDETCYeYwzukIhaTxiN/giUPA9LkhX6D/3O1HrDGGYzYIhd39e30q1c/jz7Y7xsxOxrvKZAFJsPepCnV/foONeT49rvJzI419TgtTUH8UG1AK2tED87oaRX545MXz9l43qtzhNAdP/6jH5Pe6N9ZvnRZDXRt9lsEAShy0g8ADQ0NMDl6n4ticvl6tHz9Xo9KioqUFFRgRNPPBHDhw/H888/j1tuuQUulwuNjY1dnh+Px9Ha2nrEuGq1Gmr14RcrSqUyp78IinhiCgovKDK2k3SO9+GWxOd82khn0jcaGGMIy8AwmxE6zQ+vWcrG7yHTMbMZz2VRwBuRsL85iEK1qs/Jxc+nlOPL3S3Y3RTE53s8GIrsfUcziRcUWLI1Ufp76rB85Bl7XuRQlGTwghJFVmOPvweD6Ts6WOJZlEqMLlZga40PzaF4UoUceyLZ/cKgVWD2uCLMHleEfc1BLN1aj892NKE5EMPra2rwxpoaHFNqwczRLkwecniS3h7vSK3xTq20Y87YApTbUpNcpXK/bw5EYdRqMLLQDMMRKrDnwndmoMXsaTynSgW7SYemQBRVrSE0BqLQqRSwaJVJncN4QYGoxEGrUsGVp0/7e83Vz7O/xstGzN7Gc5kVaPSLaA7E4DT1vA7E0Y5rF59YDo1KgUVf7cd/1tUiKjH84kdDe70EbCBfr3F84oZtrud3Pd22rJaFbW99t2zZso7HZFnGsmXLukzl72zKlCldng8AS5cuPeLzO79u+xr7KVOmwOPx4Lvvvuv49+XLl0OW5S4t+EjfNPoiWL2vBUDvWur5I3EYNErY+liYhPQdx3Eoy9fDpFXCE+r7uiWrToULjy8BAPzz6ypEU7zWMVcFo3Gs2NkEADhjbHL7hDcsIt+ggoU6Twx6Jo0SowpM0KoENPojR/+BDBli0+OXpwzDS1dMxh9mVmJ8sRkMwNoqDx5Ysh1XvPgtnv9yH6pbQwCAmAR8ur0RN/x7Pa7/93os3daAWFxGeb4Ovz11GF684nj89tSKlCX5qeQLi+AAjHAZYaQ2azmL5zk4TRpMLLFgXLEFSoFDrTcCfyS585gvIsJuVFNLPZJWPM+hOC+xHLj9hmeqnHdsMX4zbRgA4L2NdXjqs92QZJrGP9Blfer+DTfcgMsuuwyTJk3C5MmT8fjjjyMYDHZU4b/00ktRVFSE+++/HwBw7bXXYtq0aXj00Ucxd+5cvP7661izZg2ee+45AEAwGMR9992Hs846CwUFBWhubsbTTz+NmpoaXHDBBQCAUaNGYfbs2bj66qvx7LPPQhRFLFiwAD/96U+p4n4KfbS5HjIDJhSbUZqXfB0Df1REpdPY56niJDW0KgFD7HpsOuiFKMm9mj7b2VkTCrFkc6Ld3qc1PCqPS9GG5rAVO5sRjcsozdNhTGHPl/xIMkNMklFg1va5RzsZGMy6RLK/pdaLlkC0z5WaU0ml4DFthB3TRthR5w1j6dYGLNvWiNZQDIvX12Dx+hoMd+hR0yIgJO0FkGiNd1KFDXOy1BovGeGYhJAYx6gCE92I7ieUnSr013vDqGoNo8YThlWnPOqSQlGSwQA4qaUeyYB8vQoOkxqNvmjKZ2zNGVcAtYLHX5fvwtKtDYiKMq6fMbzHxf9I/5P1RP/CCy9EU1MT7rzzTtTX12PixIlYsmQJnE4nAKCqqgo8//0XcOrUqXj11Vdx++2349Zbb8Xw4cOxePFijB07FgAgCAK2b9+Ol156Cc3NzcjPz8fxxx+PL774AmPGjOl4nVdeeQULFizA9OnTwfM8zj//fPz1r3/N7JsfwGJxGR9vrQcAzB2f/M2TYDQOrUrIaPs1cnROowZN5ijqvBEUmns+7bw7SoHHlSeV488fbcfyWg4nHHBj8lB7irY09zAGfLQlsezojLGupBIZf0SEWaNEnp5aOpHv5elVHcm+OxiDNQe/HwVmLS6dUo5LTijDdwda8cnWBny7vxW7GoMAODiMaswe68Lpo5yw9IOWZaIkozUURYXDgCJL346BJPM0SgHlNgPsRg1q3GHUesLwhkXk6VVQK7ofVPCHRVj1GmqpRzKC4ziUWBNLTiKilPLBrumjnFArBTzyyQ58vqsJ0biEm2aP7PPgDclNWU/0AWDBggVYsGBBt/+2YsWKwx674IILOkbnD6XRaPDWW28dNWZeXh5effXVpLaT9NwXu5vhj8RhN6oxuTwv6Z/3hkUMsemh72PvdpJaPM+h3KaHOxSDPyL2ecrqiUPzcXyZFd8ecONPH+7AFSdFcc7EopwezeutPX6g2h2GWsHjx5XJFQUNxhKjhyoFnYhJVzaDGqNcJmyt9cETiuVssizwHCYPycfkIfloCUSxem8z5MbdmDVtYk6vg+xMkhkafREU5+lQnq8fkMepwUKvVmCEywinSYNqdwj13giARMJ/aMITlWVUWrR9bi9LSE9ZdEoUmDSodof7PKjSnZMrbFAJPB5Ysg2r97Xi3ve34tY5o2gG7QBEV40k5RgDPtiUGLmcM7Yg6ZNjRJSgUnApn7JEUsOkUaI8Xw9fROzz+i6O43DTrOGY4khMjXzhf/vx1+W7IEqpXZuWC76sTxxuTx1hT+oGVvvsFns3lcsJAQCHSYPKAiNisgxfOLd7/wJAvkGN2WOcGG1l/SZ5Yoyh0R+BzahGhcNAU10HCLNOiTGFJhxTaoHNoEJzIIqWQLTLuU2vUiDfkJs30MjAxHEciiw6qAQO4Vh6ihhNHpKHu84cA7WCx7pqD+5+bwtCscz1qyeZQWcqknL7A8Ce5iCUAofTRzuT/nlvWITdqIFJS6P5uarQooXdqEZLINrn11IKPC4cKuMXJ5WB54BPtzXi9sWb4e0HCUtPeUIxbGxNJDRzkixM6Y2IcJk0SbemJINLgVmLSqcRYVFCIEIXa6nWGox1jALTqNfAwnEc8g1qjCu2YEKJBTq1gAZ/GN5QDABQYNIccVo/Ieli1ilRYNbCHe77ddaRTCix4I9nj4VOJWBLrQ93vLM56UKVJLdRok9S7ou2kctpI+wwJ1khvH0kt8Ccuv7QJPWUAo/yfD3AIyV3mzkOmDe+AHeeOQY6lYCtdT7c8O/12N8cTMHWZt/SbU2QGIdKpwFD7YYe/1wsLkPgaXYL6ZkiixYjnAYEYiKCUUr2U8UXFsHAMMJppKrrA1j7sfaYUivGFJo7Cp/mG2k0n2RHkVULjUJI6/F8dIEJ950zDka1AjsbArj17U3wtN3kGsgYY4hLMiJtN8fdoRia/FE0+aMIDKB7HTRERFLKHYphfUvi5Dh3XPJF+NyhGGwGFRW96QfyDWqUWHTY2xxAoVKbkhszx5VZ8chPJuDeD7aizhvBjf/diP83cwROGJKfgi3ODklm+HhrYinL7DHJzXDxhGOwG9RJ3zAjgxPHcSjJ00FmDDsbAuA40EyQPoqIUkeNDFo+MzgoBR7FVh0sah7LtgMGNR1/SXYYNUoUWrTY0xRMa82qCocB9583Dre/sxn7W0K4+a1NuO+csVnt5tIajGFbrQebDnIwxKohMQ5xWUZcYhDlRJIe7/RfUWId/37E50ms7bmJx45keiGP+Rl8r+lEVwAkpT7c3NAxclnh6PnIJZBIiCSZodBCLcT6i9J8HVqDMbhDYsoqwpfk6fDITybgwY+3Y+NBL+77YBsunVKO84/tf0X6GGP4ZGs9mgIx6BQMJw/r+Q2L9v2BZreQZHAch9I8PeIyw56mIHiOo6nmvSRKMlqCUQyzG1BspQr7g42a9huSAwotWtR5IwhE4jBo0pe2leXr8eB543Hb4s2o8YRx01sb8adzxsGVgRmFcUnGvuYgttf7sb3eh+31fjT625csCEB1Tdq3AQCUAgeB48BzA6dOFCX6JCUkmeHlrw/gv2sTO+OZ41xJv4Y3LMKioxZi/UmiVZEeGw96EIvLKasKb9Iqcc+8MXjui734aHM9Xlq1H1WtQSz48fB+U3m+zhvG3z/fi+8OuAEAUx0sqW33hUWYtLQ/kOTxPIehNgMYA/Y0BWAzqGmNcZJklqiwX2jVYoiNKuwTQrJDr1ag2KrFzkY/9GohrceiQosWD7aN7Nd5I7j5vxvxp3PGotiqS2kcdyiG7fV+7GhL6nc1BhCLd02uOQCleTo4hABsjgIoFQKUAgcFz0PR6b9KnoNC4KHo9N8uzxP4758jcFB2/Hzn5/PgucSN8lq3H5G936X0/WYTJfqkz1oCUTz8yQ5sqfUBAE5xyTi5Irmp1jJjiMTjqHBaqJpxP+MwqlFo0aLGHUZhCvtKKwQevz21AmV5Ojz3xV58tqMJtZ4IbpszKif7hbeLxiX897uD+M/agxAlBgXP4dyJhThReaDHr8EYQ0iUMMSup/2B9ArPcxhqN0CSGfa3BOEwaqhPchLaK+yPcBppHySEZFWBWYtabwS+SDztS/kcJg3uP3cc7nhnM6rdYdzy1ib88eyxGGLT9+r1JJlhX3OwI6nfXu9HvS9y2PP0agGVThNGFRgx0mXCCKcBGgHYv/5LlE8cAl6glLU36FMjfbKuyo1Hl+6ENyxCqxTwux8PRZF/G/gk7zj6I3EY1UrYqIVNv8PzHMrapvC3j0Kn0tzxhYm7zB9vx44GP254cwPumDsqqaJ2mbJmfyv+/vnejpPYxBILfn3KMBSYlNi/vueJfjAqQa8WYMvi+jjS/wk8hwqHATJjqGoNw2FUU7LfAy2BKLQqqrBPCMkNWpWAEqsW2+r8MGoUSV9jJyvfoMb9543Hne9sxt7mIG59exPuOWsMKmxHH8zxhsXE9Pu6xDT8XY0BRLsZrS/J02Gky9j2x4Qiq/aw9yVLVFS2ryjRJ70iyQyvf1uFN76tBgMwxKbHzbNHwmVUYv/65F6LMYZAVMSoAhNNL+2njBolyvN12Fbvg16tSHlv7GNKrXj0JxNx7wdbUeMJ48b/bsQNp4/A1GG2lMbprUZfBP/4ci++3tsKAMjXq/CLHw3FScPywXFc0icrXzSGoTYDtCraH0jfKAQeFQ4jJBmo8YTgNGpohPoH+CMiZDCMcBqowj4hJGc4TRrUuMPwhUVYMlCw2qxV4r5zx+Hud7dgR4Mfty/ejDvmVKLzuL4kMxxo6bq2vs7bzWi9SsAIZ1tSX2DCCKcRhjQWFyTfo0+ZJM0diuHRT3Zgw0EvAGDWGBeu/tEQqBVCr+6+JUYvFVTRuJ8rtGjREoyhJRiFw5j64i1FVm1Hkb711R7c/9F2/OzEMsw/rjhr62dFScbidTV4fU11Ryu8syYU4qfHl/S62nlElKAUeDiopR5JEZWCxwhXYmS/zhOGy6xN+c24gSAiSvBH4xhdYEzLMYwQQnpLoxRQmq/D5hovTFpl2kf1AcCgVuCPZ4/Bnz7Yhk01Xtz9wXacVcLhi9VV2NkQxM5GPyLi4YXrSqxajHSZUNk2Yl+Sp8vI9pLDUaJPkrKpxouHP94Od0iERsnjmlMrcGqlo0+v6Y3EUOEwUBuofk4h8CjL18MdiiEUi6fl92nQKHD3vDH4vy/34v2NdfjX1wdQ1RLC76dXZHw2yPpqD55duQc1njAAYGyhCb+eNgxl+b1bx9bOGxbhMKlhSmN1XTL4qBUCKl1GyIyh3heGy0TJfmftFfaH2gwosqS28BQhhKSCw6iBRReGJ4Wdjo5Gp1Lgrnmjcf9H2/HdATf+s08A9tV2/LtWmTi3VLqMGOUyodJpTGt3AJIc+k2QHpEZw3++O4hXVh+AzBKVMG+ePRIleX27IArHJGgUAo1eDhB5ehVK83TY3RiARimk5Q6uwHP41SnDUJqnw98/34vPdzWhzhvGbXNGZaTna0sgiv/7ch++3N0MALDolLjqpCGYNsLe55kFcUkGA0OBWUtVvknKaZQCRrpMkGSGBl8ELrOGRlnQVmHfH0WhRYshdj21dyWE5CSVgkdpng4bD3ohyQyZOlKpFQJumzMKTy3fha1VjRhd6sCoAnPHaD3dNM5dlOiTo/KGRTy2dCfWViXahJ1W6cBvTh2WkiJFnnAMxVYtrYUcQIqtOjQHYnAHY2lNvM8YW4AiixYPfLQduxoD+H9vbsDtc0ejwpGeIn1xScZ7G2vx2jfVCIsSeA6YO64Al5xQBn2K1pr5InFYdCpqqUfSRqsSMKrAhC213kSyb9IM+ptKTf4o8g0qDHcYqVghISSn2Y1q5BtU8IRisGozN5NRKfC49rRh2L++DuUTh1EV/H6CzmjkB22r8+G6N9ZhbZUbKoHH70+rwHUzhqckyY/FZfA8hwJz6lqykezTKAUMtekhygzRuJTWWOOLLXjkggkosSbqA9z01saOkfZU2lzjxXVvrMcL/9uPsCih0mnEY/Mn4penDEtZks8YQ0SMo9BCU6pJeunVCowqMMGkVaLRHwVjLNublDUtgSg0Sh4jnEYqfkkIyXlKITGqH5NkxKXD18cT0hndjiHdYozhnfW1eHHVfkgyQ5FFi5tmj+x1H83ueEIx2I1qWHQ0mj/Q2I1qFFk0qHaHUGhO73rXQosWD/9kAh7+ZAe+O+DGg0u2o+r4Evx0cmmfpyW7QzEs+t8+fLajCQBg1Chw+dRyzBjlTPmUZ38kDr1GiXwazScZYNQoEyP7NV40+aOw6Qff5UAgEocEhtEuU9p7UxNCSKrYDGrYDGq0+kPZ3hSS4wbfmZ0cVSASx+PLdmL1vkSrsFOG23DNjytSWlwtLsmQGEOBhaaNDkQcx6EsX4/WYAzesJj2i2i9WoE75o7Gi1/tw+L1tXjt22pUucO4bnrvZp9IMsNHmxPF/oIxCRwS3SV+fmIZTGl6L/5oHMMdBurbTTLGrG1L9ut8aPZHs705GZWosC9ipIsq7BNC+heB51Ccp0WzL5jtTSE5jhJ90sWuBj8eWLIdjf4oFDyHq380FGeMdaU8GfeGExVD8/XUUm+g0qsVKLfpsbnGB71KSHvvboHncNXJQ1GSp8PfVuzB/3Y3o94bxu1zR8OWRK2A7fU+/G3lHuxtSpxAK+wG/ObUYRjhNKZr09uKUvKwm2h/IJll1aswymXE5urEjV1JZuAH+L2meKcK+8VWqrBPCOl/bHo17EYNqrK9ISSnUaJPACSm6n+4qQ7/9+U+xGUGp0mNm2ePSkthM0lmiMRlVFppLfJA5zJp0OyPojkQgzNDnRVmjnah0KzF/R9tw56mIP7fvzfgtrmjjpqoe8Mi/rlqPz7Z2gAA0KsFXHpiOWaNcaX9e+qLiCgwa6goJcmKfIMalQUmrN4JNPojUKtkWLTKtN+cywaZMTQEIigwU4V9Qkj/xfMcCi1aVAHwR0SYB+HyK3J09K0gCMXieHL57o4iZicOzcO100fAkKIiY4fyRURYdEoazR8EFAKPcpsenpCIYDSessJ1RzO2yIxH50/Eve9vRVVrCDe/tRG/P204Tq10HPZcmTEs3dqAl77aD380DgCYPtKBy6eWw6JL/3p5UZLBALjMNH2YZE97bYixRRbU+2NoCkQh8BysOtWAqkTf7I8iz6DBCCdV2CeE9G/WthpXkbgEORSDNQPXLKR/oUR/kNvXHMADH21HrTcCgedwxdRynDWhMG3r5hljCMUkDLHpoVLQRdZgYNGpUJKnw67GALQqIWN9u10mDR7+yXg88skOfLvfjUeX7kS1O4yLJhV2PGd3YwB/W7kbOxsCAIDyfB1+PW0YxhSaM7KNAOALi8jTK+kETXKC06RGgVWPlkAUNZ4wmgMxcACsOtWAOGarlTwqnSaqsE8I6ffar9VHuszY0xJKFFY1qKj2FelAif4gxRjDJ1sb8NznexGTZNgMatw0uxIjXaa0xg1E4zCoBdiNNJo/mBTnadESjKI1GEtqvXxf6VQK3DZnNF7+ej/+u7YG/15TjaqWAM6wAs9+vg9LtjSAAdAqBVxyQinOHF+Y0eUkMmtbxmLR0hRikjMEnoPDpElUdQ7FUOMOo6mtWJ9Zq+xXBSMlmSEQjSMQjgAAKhxGmKnTCyFkAHGa1NCqldhe70ejPwq7UZ2xQRWS2yjRH4QiooRnVuzuaBk2qcyK62eMSFs18c58YRHDncZ+daFI+k6tEFBu02NjtRcRUcro71/gOVw+dQhKrDo89dlufL3PjdX7BDAk1uKfMtyOK08qR34Gb0C080fiMGkUyKOWeiQH8TwHm0GNfL0K7pCIWk8Yjf4IWkMxWLWqnB0VlxlDKCohEBPBGGBQKzDEpsf2faCbzISQASnfoMbYIh476n2o94XhNFIdLEKJ/qBT1RrCA0u2o7o1BJ4Dfn5iOc47tigjd/5CsTi0KgFOWos8KNkNahRZNdjfEkKRWZvxqWXTRzlRaNHivg+3wRsWUWzR4NenVmBCsSWj29FZoK29l1qRmwkTIUBiemieXgWrTonisBa13jAavVG4w1FYtKqUtl7trfZlYYFoHHFZhkGtQGmeDvkGdaK9pyxhe7Y3khBC0sisVWJskRk76/2o94XhMGqoFskgl/2zM8mY5dsb8cyK3YjGZeTpVFg4qxJjizK3FtkTFlGWp0tbkT+S2ziOQ1m+Hi1BEd6wmJFCd4caVWDCE/PH4Yuvv8UZ08ZDrcreSHooFodWKcBGPbxJP8FxHCw6FSw6FYosIhq8EdR5w3CHRJg1Shg0mT+2h2MS/FERcVmGVqlAgVkDu1ENs07Z5QaaKEsZ3zZCCMk0nUqBUYUmKAQeB90h5OvVNIt2EKOMaxCIScBzX+zHZzsTVfUnlljw/04fkdFEKyJKUAgcVRYf5HSqxBTaTQc90KsVWbnTbNWpMD6PZf0utzcsoiRPSze+SL9k1iph1ipRaNWi3htGrSeCGk8IRrUSRo0irTN2onEJgUgckbgMjZKHzaCGw6SGJYeXExBCSKaoFQIqXYnOIvuaA7DqcmPmFck8+q0PcPtbQvjLZgG1oWZwAC6aXIr5k0oyvm7HGxbhMLVNoSSDmsukQbNZi0Z/BC6TNtubkxWiJIPnuEH7/snAYVArUOEwotCiRYM3ghpPGLXeCAxqBYwaRcqWhYmSjEAkjpAYh0rBw6xVosKkgUWnoptlhBByCKXAY7jDAJXAYXdTEHGJZaQWF8ktdHYcwHY2+DH/uW8QjHEwaRS4cdZITCixZHw72vuEF1oyvy6b5B6B51Cer4c7FEMgGoduEB6FPCER+QYV3fgiA4ZOpcAQuwEFFi0afREcdIdR7w1Dq1LApFH26uayJDP4IyKCsTgUQiK5H2LXw6xTwqhO76wBQgjp73ieQ7lND6WCx84GP9xBBisV/x1UBuEl9uBRYTdgQokZDY0tuGHuaJTa0ts670i8bX3C86hPOGlj1ilRlqfDjgY/1PrBlexKMoMoyyiklnpkANIoBZTm6+E0a9DkjyYSfl8EGgUPi0511IRfkhmC0TgCsTh4joNRo0Cl1QirXgWTRkn7DCGEJIHjOBRbdVAJfFv7vQjsBjXdKB0kKNEfwHiew2M/GYevVi7LWvsuSWaIxSUUW010gUa6KLLq0ByMoTUQzvamZJQ/IsKsVVJLPTKgqRUCiq06OIwaNAeiOOgOocEXhkohwKJVonOFDLmtYn4wGofMGPRqBYba9MjTJ2a9KKhqNCGE9InDpIFC4Nsq8kfgNGky0nGLZBcl+gOcUaOAkMX92BcWYdapKKkhh1EpeAyx6bHOP3gSfcYYgrE4Ruebsl4MkJBMUCl4FFq0cBjVaAnGUN0aQlMgCp4lquA3+SKIcwL0agHFVm1HOzyVgvYPQghJpTy9CqOLTNhR70edNwyXSZvxml0ksyjRJ2nDGENYjGOow0xJDelWvl6FQqsWu5H4vgx0oZgEnUoBm1Gd7U0hJKMUAg+nSQObQY2WYBQHm/0IAnCYNXBa9DBrldQCihBC0sykUWJsoRk76n2o94XhMGroGn0Ao98sSRtfJA6DRgmbgZIa0j2O41Bq1QEAGvwRSPLATvZ9EREuk4ba3JBBS+A5OIwajCk0AwBGFZjgNGkoySeEkAzRqgSMKjShJE+HRn8EEVHK9iaRNKFEn6RNICqiyEIXcOSHadr6XufpVaj3hRGLy1neovRRCBwcJrrxRQjVbCGEkOxRKwRUOo0YajOgNRhFMBrP9iaRNKBEn6RFMBqHViXAbtRke1NIPzG6wITSPB2ag1EEBugJJ1+vppZ6hBBCCMk6hcCjwmFApcuIQDQOb1jM9iaRFMt6ov/000+jvLwcGo0GJ5xwAr755psffP6bb76JkSNHQqPRYNy4cfjwww87/k0URdx0000YN24c9Ho9CgsLcemll6K2trbLa+zcuRNnn302bDYbTCYTTj75ZHz22WdpeX+DlS8qosCkhV5NU5RJz6gUAipdJlQ6DQiJcbQEotnepJRpX5LgNGuopQ0hhBBCcgLPcyjL12NUoQlxSR5Q114ky4n+G2+8gRtuuAF33XUX1q5diwkTJmDWrFlobGzs9vlfffUVLrroIlx11VVYt24dzjnnHJxzzjnYvHkzACAUCmHt2rW44447sHbtWrz11lvYsWMHzjrrrC6vc+aZZyIej2P58uX47rvvMGHCBJx55pmor69P+3seLFQ8B6eZRvNJcgSeQ7nNgHFFZigUPOq84QGxbt8bigEArDSaTwghhJAcwnEciixajC4yQRA4NPgig6JA8mCQ1eHWxx57DFdffTWuuOIKAMCzzz6LDz74AC+88AJuvvnmw57/xBNPYPbs2Vi4cCEA4N5778XSpUvx1FNP4dlnn4XZbMbSpUu7/MxTTz2FyZMno6qqCqWlpWhubsauXbvw/PPPY/z48QCABx54AM888ww2b94Ml8vV7bZGo1FEo9/f5fL5fAASswhEMXenusTjiSnQshSHLKV/OnR7jDydAlqBpf2zaX/9TP4OMh1zMMazagSMdumwpzGAOk8ANr06Ze222r+jmdgfRElGczAKNZc4YTJZysjnmgu/Q4rXf+JlIybF69/xshGT4lG8XI/Z3+NZNQJGOXXY2RBArScAp1EDvtMsxExeP2UjHpC4TgMy+z3tjZ5uH8eydMsmFotBp9PhP//5D84555yOxy+77DJ4PB688847h/1MaWkpbrjhBlx33XUdj911111YvHgxNmzY0G2cTz/9FDNnzoTH44HJZAJjDKNGjcKPfvQjPP7441Cr1Xj88cfx8MMPY/v27bBard2+zt1334177rnnsMdfffVV6HS65N48IYQQQgghhBCSpFAohIsvvhherxcmk+mIz8vaiH5zczMkSYLT6ezyuNPpxPbt27v9mfr6+m6ff6Qp95FIBDfddBMuuuiijg+B4zh8+umnOOecc2A0GsHzPBwOB5YsWXLEJB8AbrnlFtxwww0df/f5fCgpKcHMmTN/8APOtlAkihXLl8FWOQkGbfqm0suMockfgUYA3Lu+w+mnnw6lMv3TlEVRxNKlSzMWLxsxB3s8SWaocYewryUIARzy+tiuUZbiqNr0NUrHnQheSP0hMCJKaA3FYFArUJ6vh92ohizFc+ozpXgUL9sxKV7/jpeNmBSP4uV6zIEULypK2NUYQJ0vAnvbrMpUXT/JjCEWlxGLyxAlGTFZBhggcByUCg4qhQCjRgGdwGHzt19g5HEnoTkoIRATwYODXq2ATiWkpeZRnSeA6P71Gf2e9kb7zPKjGbCV0kRRxPz588EYw9/+9reOxxljuOaaa+BwOPDFF19Aq9Xi//7v/zBv3jx8++23KCgo6Pb11Go11OrDEwylUpnTXwRFPDEFhRcUaUlqACAuyWgIRGEz6jDUpsEXuzL/uWTj9zDQ32OuxFMCGOZSwajXYGdDAA0BEQ6jBkIf23Olep+QGUNrMAZJZii3G1GWr4dOlXh9UUxsa658phSP4uVKTIrXv+NlIybFo3i5HnMgxFMqlRhTrIK60Y9qdwhWrRrqtmumnl4/McYQlxmibUl9LC5DBgPPAUqBh1qhhEkvwKRRQKtSQKPkoVEKUCt4cBwHURSxGcBQpxlDeQGekIimQATN/hjqAyLUbTcE1IrUtfHmeKHj/edyftfTbctaom+z2SAIAhoaGro83tDQcMR18i6Xq0fPb0/yDxw4gOXLl3cZcV++fDnef/99uN3ujsefeeYZLF26FC+99FK3tQHIkUXjEpoDMRRYNBjhMELBDdwe6CS7HEYNNEoBuxr8qPeFYTdoUrZuv69CsTjcoRjy9CqU2/SwG9RUXZ8QQggh/ZZKwaPSZYJK4LG3OQi98sjXNXFJRkySO5L6uMzAgUGp4BMj9FoBJo0GOpUCGqUAjZKHWiH0eNBGKfCwG9WwG9UI50twh2Jo9EXgDsXQLEahUytgVCugEHLjujBXZC3RV6lUOO6447Bs2bKONfqyLGPZsmVYsGBBtz8zZcoULFu2rMsa/aVLl2LKlCkdf29P8nft2oXPPvsM+fn5XV4jFAoBAHi+6xeB53nIMiWpyQjF4vCERZTlazHMboRKwUMU6TMk6WPSKDG2yIw9jQFUu8MwaZQwZLGFoyQzNAeiEHigwmFASZ4upXeWCSGEEEKyReA5DLUboFTw2FnrAZBYoijGWMfUe3CJ56kVPLQqAQ6jGnr19wm9RilAmcIEXKsSoFVpUWDWwB+Nwx2Moc4bQXMwBsYYjGoldGqhSyHBwSqrU/dvuOEGXHbZZZg0aRImT56Mxx9/HMFgsKMK/6WXXoqioiLcf//9AIBrr70W06ZNw6OPPoq5c+fi9ddfx5o1a/Dcc88BSCT5P/nJT7B27Vq8//77kCSpY/1+Xl4eVCoVpkyZAqvVissuuwx33nkntFot/vGPf2Dfvn2YO3dudj6IfsgbFhERJYxwGFCar+/zNGpCekqtEFDpMkGnErCnOYioKCG/j+v2e8MXFhGIJZYRlOfrYdWrMr4NhBBCCCHpxPMcyvL14JmMtXuBcCwOtVoNh0kFk0aVGJ1vS+pVAp+xGY0cx8GkUcKkUaLIooU3LKIlEEOjP4I6rwilkPh3jXLwDsBkNdG/8MIL0dTUhDvvvBP19fWYOHEilixZ0lFwr6qqqsvI+9SpU/Hqq6/i9ttvx6233orhw4dj8eLFGDt2LACgpqYG7777LgBg4sSJXWJ99tlnOPXUU2Gz2bBkyRLcdtttOO200yCKIsaMGYN33nkHEyZMyMwb7+daAok2g6MKTSg0a2iKMsk4gedQbjNAp1ZgZ0MAdd5wStbt94QoyWgKRKFVChjlMqHQoqWpYoQQQggZ0FzmRFHv48ryoNeqwefQIJ9C4JFvUCPfoEZpvg6ekIgGXwTuYAwtwSi0SgWMGkVKZxb0B1kvxrdgwYIjTtVfsWLFYY9dcMEFuOCCC7p9fnl5OXrSLXDSpEn4+OOPk9pO0l5ZPwqNSkCl0wi7MfOjqIR0lsl1+4wxeEIiInEJBWYNhtj0MGpyt1ALIYQQQkiqaVRCTiX5h9IoBbjMAlxmDQJtU/vrfZGOgskGtQIGjWJQTO3PeqJP+gdJZqj3hZFvUGOE0wizlhIckhsysW4/IkpoCURh0ioxzmWG06jJ6ZMcIYQQQshgZ1ArYFArvp/aH4yiwRdFvTcMgecTbfxUAzcdHrjvjKRMLC6jKRCBy6TBCJdxQO8QpH9K17r9Li3zbHqU5euhVQ3etV6EEEIIIf0Nz3Ow6lWw6lUozdPDE4qh0R9FSyAKd0iERsEPyFmalLGRH5RoGyaiNE+HCocxZ9qZEXKoVK/bD0YTXSXy9EoMsRlgM6ioHgUhhBBCSD+mUvBwmDRwmDQdeU69NwxPOIbIAOseRok+OSJfWERIjKPCoccQm4Eq65N+oa/r9uOSjJZgDAqew3CHAcV5WmqZRwghhBAywOhUian7BSYN/JE4mn1BbK7O9lalDiX6pFstgSgYGEa6TCi2amkkk/QrvV237w2LCERFOE2JYnsWHbXMI4QQQggZyHieg1mnhE6px+Zsb0wKUaJPumCMoSkQhVrBY4TLBIdRk+1NIqRXklm3L0oymvxR6NQCxhSaUGCmlnmEEEIIIaT/okSfdJBkhgZfBGadEiNdRhrNJP1ed+v2bbrvD3udW+YVWbUoy9cNyGIshBBCCCFkcKFEnwBIjGg2+iNwmjQY4TRCn+L2ZIRkU+d1+w3eEIBEyzx3OAazTokRLgscRjW1zCOEEEIIIQMCZXME4ZiE1lAUJXk6VDgMVHiMDEjt6/Z38gx79wG+iIghdhNK83TUMo8QQgghhAwolOgPcoFIHP6oiAq7AeU2Pa1LJgOaWiFguMOIvQDGFVvgNOuo0CQhhBBCCBlwKNEfxNzBGEQmY6TLiJI8SnjI4NDeJjJfr6LvPCGEEEIIGZAo0R+EGGNo8kehVPAYW2CG00SV9QkhhBBCCCFkoKBEf5CRZIYGfwRmrRKVTiOseqqsTwghhBBCCCEDCSX6g0h7ZX27UY1KlwkGqqxPCCGEEEIIIQMOZXqDRCwuwxuNoMiiw3CnARolVRknhBBCCCGEkIGIEv1BIiRKGOYwY4hdDyVV1ieEEEIIIYSQAYsS/QEvUVW80mlEud0Anqcq44QQQgghhBAykNHQ7gCnUiR+xSV5OkryCSGEEEIIIWQQoESfEEIIIYQQQggZQCjRJ4QQQgghhBBCBhBK9AkhhBBCCCGEkAGEEn1CCCGEEEIIIWQAoUSfEEIIIYQQQggZQCjRJ4QQQgghhBBCBhBK9AkhhBBCCCGEkAGEEn1CCCGEEEIIIWQAUWR7A/orxhgAwOfzZXlLfpgoigiFQvD5fFAqlRSvH8akeP07XjZiUjyKl+sxKV7/jpeNmBSP4uV6TIrXv+NlK2ZvtOef7fnokVCi30t+vx8AUFJSkuUtIYQQQgghhBAymPj9fpjN5iP+O8eOdiuAdEuWZdTW1sJoNILjuGxvzhH5fD6UlJSguroaJpOJ4vXDmBSvf8fLRkyKR/FyPSbF69/xshGT4lG8XI9J8fp3vGzF7A3GGPx+PwoLC8HzR16JTyP6vcTzPIqLi7O9GT1mMpky+oUd6PGyEZPi9e942YhJ8ShersekeP07XjZiUjyKl+sxKV7/jpetmMn6oZH8dlSMjxBCCCGEEEIIGUAo0SeEEEIIIYQQQgYQSvQHOLVajbvuugtqtZri9dOYFK9/x8tGTIpH8XI9JsXr3/GyEZPiUbxcj0nx+ne8bMVMJyrGRwghhBBCCCGEDCA0ok8IIYQQQgghhAwglOgTQgghhBBCCCEDCCX6hBBCCCGEEELIAEKJPiGEEEIIIYQQMoBQoj9Aff7555g3bx4KCwvBcRwWL16c1nj3338/jj/+eBiNRjgcDpxzzjnYsWNH2uL97W9/w/jx42EymWAymTBlyhR89NFHaYt3qAceeAAcx+G6665Ly+vffffd4Diuy5+RI0emJVZnNTU1+NnPfob8/HxotVqMGzcOa9asSUus8vLyw94jx3G45ppr0hJPkiTccccdGDJkCLRaLYYNG4Z7770X6axH6vf7cd1116GsrAxarRZTp07Ft99+m5LXPto+zhjDnXfeiYKCAmi1WsyYMQO7du1Ka8y33noLM2fORH5+PjiOw/r169MWTxRF3HTTTRg3bhz0ej0KCwtx6aWXora2Ni3xgMR+OXLkSOj1elitVsyYMQOrV69OW7zOfv3rX4PjODz++ONpi3f55Zcftj/Onj07bfEAYNu2bTjrrLNgNpuh1+tx/PHHo6qqKm0xuzvmcByHhx9+OC3xAoEAFixYgOLiYmi1WowePRrPPvtsL9/d0eM1NDTg8ssvR2FhIXQ6HWbPnt2n/b4n5/ZIJIJrrrkG+fn5MBgMOP/889HQ0JC2eM899xxOPfVUmEwmcBwHj8fT27d31Hitra343e9+h8rKSmi1WpSWluL3v/89vF5v2t7fr371KwwbNgxarRZ2ux1nn302tm/fnrZ47RhjOOOMM/p8zdiTmKeeeuph++Cvf/3rtMUDgFWrVuG0006DXq+HyWTCKaecgnA4nPJ4+/fvP+Jx5s0330zL+6uvr8fPf/5zuFwu6PV6HHvssfjvf/+bdKyextuzZw/OPfdc2O12mEwmzJ8/v9f7PHD0a/pUHmN6Ei+Vx5hso0R/gAoGg5gwYQKefvrpjMRbuXIlrrnmGnz99ddYunQpRFHEzJkzEQwG0xKvuLgYDzzwAL777jusWbMGp512Gs4++2xs2bIlLfE6+/bbb/H3v/8d48ePT2ucMWPGoK6uruPPl19+mdZ4brcbJ510EpRKJT766CNs3boVjz76KKxWa1riffvtt13e39KlSwEAF1xwQVriPfjgg/jb3/6Gp556Ctu2bcODDz6Ihx56CE8++WRa4gHAL37xCyxduhQvv/wyNm3ahJkzZ2LGjBmoqanp82sfbR9/6KGH8Ne//hXPPvssVq9eDb1ej1mzZiESiaQtZjAYxMknn4wHH3yw1zF6Gi8UCmHt2rW44447sHbtWrz11lvYsWMHzjrrrLTEA4ARI0bgqaeewqZNm/Dll1+ivLwcM2fORFNTU1ritXv77bfx9ddfo7CwsFdxkok3e/bsLvvla6+9lrZ4e/bswcknn4yRI0dixYoV2LhxI+644w5oNJq0xez83urq6vDCCy+A4zicf/75aYl3ww03YMmSJfjXv/6Fbdu24brrrsOCBQvw7rvvpjweYwznnHMO9u7di3feeQfr1q1DWVkZZsyY0etzcU/O7ddffz3ee+89vPnmm1i5ciVqa2tx3nnnpS1eKBTC7Nmzceutt/YqRjLxamtrUVtbi0ceeQSbN2/Giy++iCVLluCqq65K2/s77rjjsGjRImzbtg0ff/wxGGOYOXMmJElKS7x2jz/+ODiO69X76k3Mq6++usu++NBDD6Ut3qpVqzB79mzMnDkT33zzDb799lssWLAAPJ98GnS0eCUlJYcdZ+655x4YDAacccYZaXl/l156KXbs2IF3330XmzZtwnnnnYf58+dj3bp1KY8XDAYxc+ZMcByH5cuX43//+x9isRjmzZsHWZaTjgcc/Zo+lceYnsRL5TEm6xgZ8ACwt99+O6MxGxsbGQC2cuXKjMW0Wq3s//7v/9Iaw+/3s+HDh7OlS5eyadOmsWuvvTYtce666y42YcKEtLz2kdx0003s5JNPzmjMzq699lo2bNgwJstyWl5/7ty57Morr+zy2HnnnccuueSStMQLhUJMEAT2/vvvd3n82GOPZbfddltKYx26j8uyzFwuF3v44Yc7HvN4PEytVrPXXnstLTE727dvHwPA1q1bl5JYR4vX7ptvvmEA2IEDBzISz+v1MgDs008/TVu8gwcPsqKiIrZ582ZWVlbG/vKXv/Q51pHiXXbZZezss89Oyev3JN6FF17Ifvazn6Ul3pFiHurss89mp512WtrijRkzhv3xj3/s8liqjgGHxtuxYwcDwDZv3tzxmCRJzG63s3/84x99jsfY4ed2j8fDlEole/PNNzues23bNgaArVq1KuXxOvvss88YAOZ2u/scpyfx2v373/9mKpWKiaKYkXgbNmxgANju3bvTFm/dunWsqKiI1dXVpfyasbuY6bx+6i7eCSecwG6//faMxTvUxIkTD7v+SGU8vV7P/vnPf3Z5Xl5eXkr2+0Pjffzxx4zneeb1ejue4/F4GMdxbOnSpX2O1679mj7dx5hD43WWjmNMptGIPkmL9mlteXl5aY8lSRJef/11BINBTJkyJa2xrrnmGsydOxczZsxIaxwA2LVrFwoLCzF06FBccsklfZrO2hPvvvsuJk2ahAsuuAAOhwPHHHMM/vGPf6Q1ZrtYLIZ//etfuPLKK1MyotCdqVOnYtmyZdi5cycAYMOGDfjyyy97dYe9J+LxOCRJOmx0UqvVpn12xr59+1BfX9/le2o2m3HCCSdg1apVaY2dTV6vFxzHwWKxpD1WLBbDc889B7PZjAkTJqQlhizL+PnPf46FCxdizJgxaYlxqBUrVsDhcKCyshK/+c1v0NLSkpY4sizjgw8+wIgRIzBr1iw4HA6ccMIJaV9m1llDQwM++OCDXo/O9sTUqVPx7rvvoqamBowxfPbZZ9i5cydmzpyZ8ljRaBQAuhxzeJ6HWq1O2THn0HP7d999B1EUuxxrRo4cidLS0pQcazJ5LdHTeF6vFyaTCQqFIu3xgsEgFi1ahCFDhqCkpCQt8UKhEC6++GI8/fTTcLlcfY7Rk5gA8Morr8Bms2Hs2LG45ZZbEAqF0hKvsbERq1evhsPhwNSpU+F0OjFt2rS07ROH+u6777B+/fqUHWe6izd16lS88cYbaG1thSzLeP311xGJRHDqqaemPF40GgXHcVCr1R3P0Wg04Hk+JZ/podf06T7GZDKHyIps32kg6YcMj+hLksTmzp3LTjrppLTG2bhxI9Pr9UwQBGY2m9kHH3yQ1nivvfYaGzt2LAuHw4yx9N6R/vDDD9m///1vtmHDBrZkyRI2ZcoUVlpaynw+X1riMcaYWq1marWa3XLLLWzt2rXs73//O9NoNOzFF19MW8x2b7zxBhMEgdXU1KQthiRJ7KabbmIcxzGFQsE4jmN//vOf0xaPMcamTJnCpk2bxmpqalg8Hmcvv/wy43mejRgxIqVxDt3H//e//zEArLa2tsvzLrjgAjZ//vy0xOwsGyP64XCYHXvsseziiy9Oa7z33nuP6fV6xnEcKywsZN98803a4v35z39mp59+escsl3SP6L/22mvsnXfeYRs3bmRvv/02GzVqFDv++ONZPB5Pebz2kUOdTscee+wxtm7dOnb//fczjuPYihUr+hyvu5iHevDBB5nVau04pqcjXiQSYZdeeikDwBQKBVOpVOyll15KS7xYLMZKS0vZBRdcwFpbW1k0GmUPPPAAA8BmzpzZ53jdndtfeeUVplKpDnvu8ccfz2688caUx+ss1aNtPbl2aWpqYqWlpezWW29Na7ynn36a6fV6BoBVVlamZDT/SPF++ctfsquuuqrj76m8ZjxSzL///e9syZIlbOPGjexf//oXKyoqYueee25a4q1atYoBYHl5eeyFF15ga9euZddddx1TqVRs586dKY93qN/85jds1KhRfYpztHhut5vNnDmz4zhjMpnYxx9/nJZ4jY2NzGQysWuvvZYFg0EWCATYggULGAD2y1/+stexjnRNn65jTE9yiIEwok+J/iCQ6UT/17/+NSsrK2PV1dVpjRONRtmuXbvYmjVr2M0338xsNhvbsmVLWmJVVVUxh8PBNmzY0PFYOhP9Q7ndbmYymdK6NEGpVLIpU6Z0eex3v/sdO/HEE9MWs93MmTPZmWeemdYYr732GisuLmavvfYa27hxI/vnP//J8vLy0nojY/fu3eyUU05hAJggCOz4449nl1xyCRs5cmRK4wz2RD8Wi7F58+axY445pst0wnTECwQCbNeuXWzVqlXsyiuvZOXl5ayhoSHl8dasWcOcTmeXm1/pTvQPtWfPnrQtTaipqWEA2EUXXdTlefPmzWM//elP+xyvu5iHqqysZAsWLEhJrCPFe/jhh9mIESPYu+++yzZs2MCefPJJZjAYUjLFtbt4a9asYRMmTOg45syaNYudccYZbPbs2X2O1925PZ2J/tGuJVJ9EX60eF6vl02ePJnNnj2bxWKxtMbzeDxs586dbOXKlWzevHns2GOP7fMNqe7ivfPOO6yiooL5/f6Ox1J5zdjT68Fly5alZHlCd/Haz4e33HJLl+eOGzeO3XzzzSmP11koFGJms5k98sgjfYpztHgLFixgkydPZp9++ilbv349u/vuu5nZbGYbN25MS7yPP/6YDR06lHEcxwRBYD/72c/Ysccey37961/3OtaRrunTdYzpSQ5BiT7pFzKZ6F9zzTWsuLiY7d27NyPxOps+fXqf7ib+kLfffrvjwqn9D4COg1wqRryOZtKkSX0+Kf2Q0tLSLnf1GWPsmWeeYYWFhWmLyRhj+/fvZzzPs8WLF6c1TnFxMXvqqae6PHbvvfeyysrKtMZlLJEctifd8+fPZ3PmzEnp6x+6j7cnaIcm2qeccgr7/e9/n5aYnWUy0Y/FYuycc85h48ePZ83NzWmPd6iKioqUzAw5NN5f/vKXjuNL52MOz/OsrKws5fGOxGazsWeffTbl8aLRKFMoFOzee+/t8rwbb7yRTZ06tc/xuovZ2eeff84AsPXr16ckVnfxQqEQUyqVh9XpuOqqq9isWbNSHq8zj8fDGhsbGWOMTZ48mf32t7/tU6wjndvbE7RDL4RLS0vZY489lvJ4naXyIvxo8Xw+H5syZQqbPn16SmaAJHOtFI1GmU6nY6+++mrK41177bVHPM5Mmzat1/F+KGZ3AoEAA8CWLFmS8nh79+5lANjLL7/c5fH58+f3aQZYT97fP//5T6ZUKjv2xb44Urzdu3cfVpuDscQ18a9+9auUx+usqampY/9zOp3soYce6nW8Q7Vf06frGHOkeJ0NhESf1uiTlGCMYcGCBXj77bexfPlyDBkyJOPbIMtyxxrFVJs+fTo2bdqE9evXd/yZNGkSLrnkEqxfvx6CIKQlbrtAIIA9e/agoKAgbTFOOumkw1qo7Ny5E2VlZWmLCQCLFi2Cw+HA3Llz0xonFAodVmFXEIReV4lNhl6vR0FBAdxuNz7++GOcffbZaY03ZMgQuFwuLFu2rOMxn8+H1atXD6g1aKIoYv78+di1axc+/fRT5OfnZ3wb0nXc+fnPf46NGzd2OeYUFhZi4cKF+Pjjj1MerzsHDx5ES0tLWo47KpUKxx9/fFaOOQDw/PPP47jjjktbfQUg8f0URTErxx2z2Qy73Y5du3ZhzZo1vT7mHO3cftxxx0GpVHY51uzYsQNVVVW9OtZk+lqiJ/F8Ph9mzpwJlUqFd999t09dIXrz/lhiUK5Xx5mjxbv55psPO84AwF/+8hcsWrQo6Xg9idmd9ri9OdYcLV55eTkKCwtTdqxJ5v09//zzOOuss2C325OO09N47bUNUnWcSeb92Ww2WCwWLF++HI2NjX3qenOo9nNrqo8xR4s30PS9kgjJSYFAALt37+74+759+7B+/Xrk5eWhtLQ05fGuueYavPrqq3jnnXdgNBpRX18PIHGxodVqUx7vlltuwRlnnIHS0lL4/X68+uqrWLFiRdougI1GI8aOHdvlMb1ej/z8/MMeT4U//OEPmDdvHsrKylBbW4u77roLgiDgoosuSnmsdtdffz2mTp2KP//5z5g/fz6++eYbPPfcc3juuefSFlOWZSxatAiXXXZZSgob/ZB58+bhvvvuQ2lpKcaMGYN169bhsccew5VXXpm2mO2tkSorK7F7924sXLgQI0eOxBVXXNHn1z7aPn7dddfhT3/6E4YPH44hQ4bgjjvuQGFhIc4555y0xWxtbUVVVVVHL/v2CyuXy9WrIk8/FK+goAA/+clPsHbtWrz//vuQJKnjuJOXlweVSpXSePn5+bjvvvtw1llnoaCgAM3NzXj66adRU1PT65aQR/s8D71xoVQq4XK5UFlZmfJ4eXl5uOeee3D++efD5XJhz549uPHGG1FRUYFZs2al5f0tXLgQF154IU455RT8+Mc/xpIlS/Dee+9hxYoVvYrXk5hAInF788038eijj/Y6Tk/jTZs2DQsXLoRWq0VZWRlWrlyJf/7zn3jsscfSEu/NN9+E3W5HaWkpNm3ahGuvvRbnnHNOr4v/He3cbjabcdVVV+GGG25AXl4eTCYTfve732HKlCk48cQTUx4PSPQMr6+v7/gcNm3aBKPRiNLS0qSL9h0tXnuSHwqF8K9//Qs+nw8+nw8AYLfbk77Jf7R4e/fuxRtvvIGZM2fCbrfj4MGDeOCBB6DVajFnzpykYvUk3pGOzaWlpb2+yXK0mHv27MGrr76KOXPmID8/Hxs3bsT111+PU045pVdti48Wj+M4LFy4EHfddRcmTJiAiRMn4qWXXsL27dvxn//8J+Xx2u3evRuff/45Pvzww6RjJBNv5MiRqKiowK9+9Ss88sgjyM/Px+LFi7F06VK8//77aXl/ixYtwqhRo2C327Fq1Spce+21uP7663t9bvqha/pUH2OOFg9I7TEm67IxjYCkX/t0k0P/XHbZZWmJ110sAGzRokVpiXfllVeysrIyplKpmN1uZ9OnT2effPJJWmIdSTrX6F944YWsoKCAqVQqVlRUxC688MKUFOM5mvfee4+NHTuWqdVqNnLkSPbcc8+lNd7HH3/MALAdO3akNQ5jiamX1157LSstLWUajYYNHTqU3XbbbSwajaYt5htvvMGGDh3KVCoVc7lc7JprrmEejyclr320fVyWZXbHHXcwp9PJ1Go1mz59ep8/56PFXLRoUbf/ftddd6U8XvvygO7+fPbZZymPFw6H2bnnnssKCwuZSqViBQUF7KyzzupTMb5kj9N9XaP/Q/FCoRCbOXMms9vtTKlUsrKyMnb11Vez+vr6tMRr9/zzz7OKigqm0WjYhAkT+ryEpycx//73vzOtVpuSffFo8erq6tjll1/OCgsLmUajYZWVlezRRx/tdRvRo8V74oknWHFxMVMqlay0tJTdfvvtfTrG9eTcHg6H2W9/+1tmtVqZTqdj5557Lqurq0tbvLvuuitl1xtHi3ekzxsA27dvX8rj1dTUsDPOOIM5HA6mVCpZcXExu/jii9n27duTjtWTeEf6mb4s9zxazKqqKnbKKaewvLw8plarWUVFBVu4cGGv66v09D3ef//9rLi4mOl0OjZlyhT2xRdfpDXeLbfcwkpKSpgkSb2Kk0y8nTt3svPOO485HA6m0+nY+PHjD2u3l8p4N910E3M6nUypVLLhw4f36ZjG2NGv6VN5jOlJvFQeY7KNY4wxEEIIIYQQQgghZECgNfqEEEIIIYQQQsgAQok+IYQQQgghhBAygFCiTwghhBBCCCGEDCCU6BNCCCGEEEIIIQMIJfqEEEIIIYQQQsgAQok+IYQQQgghhBAygFCiTwghhBBCCCGEDCCU6BNCCCGEEEIIIQMIJfqEEELIAHHqqafiuuuuy/ZmdHjuuedQUlICnufx+OOPZ3tzkrJixQpwHAePx5PtTSGEEEKSRok+IYQQQlLO5/NhwYIFuOmmm1BTU4Nf/vKX2d6kI8q1GySEEEJIXymyvQGEEEIIGXiqqqogiiLmzp2LgoKCbG8OIYQQMqjQiD4hhBDSDwWDQVx66aUwGAwoKCjAo48+2uXfX375ZUyaNAlGoxEulwsXX3wxGhsbAQCMMVRUVOCRRx7p8jPr168Hx3HYvXv3UeNXVVXh7LPPhsFggMlkwvz589HQ0AAAePHFFzFu3DgAwNChQ8FxHPbv3/+Dr3f33Xdj4sSJeOGFF1BaWgqDwYDf/va3kCQJDz30EFwuFxwOB+67774eb0fn13355ZdRXl4Os9mMn/70p/D7/QCAyy+/HCtXrsQTTzwBjuMO29bvvvsOkyZNgk6nw9SpU7Fjx46jfjaEEEJItlGiTwghhPRDCxcuxMqVK/HOO+/gk08+wYoVK7B27dqOfxdFEffeey82bNiAxYsXY//+/bj88ssBABzH4corr8SiRYu6vOaiRYtwyimnoKKi4gdjy7KMs88+G62trVi5ciWWLl2KvXv34sILLwQAXHjhhfj0008BAN988w3q6upQUlJy1Pe0Z88efPTRR1iyZAlee+01PP/885g7dy4OHjyIlStX4sEHH8Ttt9+O1atX92g7Or/u4sWL8f777+P999/HypUr8cADDwAAnnjiCUyZMgVXX3016urqDtvW2267DY8++ijWrFkDhUKBK6+88qjvgxBCCMk6RgghhJB+xe/3M5VKxf797393PNbS0sK0Wi279tpru/2Zb7/9lgFgfr+fMcZYTU0NEwSBrV69mjHGWCwWYzabjb344otHjf/JJ58wQRBYVVVVx2NbtmxhANg333zDGGNs3bp1DADbt29fj97TXXfdxXQ6HfP5fB2PzZo1i5WXlzNJkjoeq6ysZPfff3+Pt6O71124cCE74YQTOv4+bdq0wz63zz77jAFgn376acdjH3zwAQPAwuFwj94TIYQQki00ok8IIYT0M3v27EEsFsMJJ5zQ8VheXh4qKys7/v7dd99h3rx5KC0thdFoxLRp0wAkproDQGFhIebOnYsXXngBAPDee+8hGo3iggsuOGr8bdu2oaSkpMvI9+jRo2GxWLBt27Zev6/y8nIYjcaOvzudTowePRo8z3d5rH0JQk+349DXLSgo6HiNoxk/fnyXnwPQ458lhBBCsoUSfUIIIWSACQaDmDVrFkwmE1555RV8++23ePvttwEAsVis43m/+MUv8PrrryMcDmPRokW48MILodPpsrXZUCqVXf7OcVy3j8my3OfX7elrdP5ZjuMAIOn4hBBCSKZRok8IIYT0M8OGDYNSqexYqw4AbrcbO3fuBABs374dLS0teOCBB/CjH/0II0eO7HYUes6cOdDr9fjb3/6GJUuW9Hj9+ahRo1BdXY3q6uqOx7Zu3QqPx4PRo0f38d31XKq2Q6VSQZKkdGwiIYQQkhWU6BNCCCH9jMFgwFVXXYWFCxdi+fLl2Lx5My6//PKOKe6lpaVQqVR48sknsXfvXrz77ru49957D3sdQRBw+eWX45ZbbsHw4cMxZcqUHsWfMWMGxo0bh0suuQRr167FN998g0svvRTTpk3DpEmTUvpeM7Ed5eXlWL16Nfbv34/m5mYasSeEENLvUaJPCCGE9EMPP/wwfvSjH2HevHmYMWMGTj75ZBx33HEAALvdjhdffBFvvvkmRo8ejQceeOCwVnrtrrrqKsRiMVxxxRU9js1xHN555x1YrVaccsopmDFjBoYOHYo33ngjJe8t09vxhz/8AYIgYPTo0bDb7R11DAghhJD+imOMsWxvBCGEEEKy44svvsD06dNRXV0Np9OZ7c0hhBBCSApQok8IIYQMQtFoFE1NTbjsssvgcrnwyiuvZHuTCCGEEJIiNHWfEEIIGYRee+01lJWVwePx4KGHHuryb6+88goMBkO3f8aMGdOreGPGjDnia9JNBkIIISS1aESfEEIIIV34/X40NDR0+29KpRJlZWVJv+aBAwcgimK3/+Z0Orv0uSeEEEJI31CiTwghhBBCCCGEDCA0dZ8QQgghhBBCCBlAKNEnhBBCCCGEEEIGEEr0CSGEEEIIIYSQAYQSfUIIIYQQQgghZAChRJ8QQgghhBBCCBlAKNEnhBBCCCGEEEIGEEr0CSGEEEIIIYSQAeT/A4bsovg00pdwAAAAAElFTkSuQmCC\n"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"# Target distribution by day_of_week\nplt.figure(figsize=(8, 3))\nplt.title('Target distribution by day of week')\ng = sns.lineplot(data=plot_data, x='day_of_week', y='target')\ng.set(xticks=np.arange(1, 8, 1))\nplt.grid()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:17.864533Z","iopub.execute_input":"2025-11-21T13:05:17.864763Z","iopub.status.idle":"2025-11-21T13:05:28.423919Z","shell.execute_reply.started":"2025-11-21T13:05:17.864746Z","shell.execute_reply":"2025-11-21T13:05:28.423095Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x300 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"# Free memory\ndel plot_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:28.426156Z","iopub.execute_input":"2025-11-21T13:05:28.426378Z","iopub.status.idle":"2025-11-21T13:05:28.430356Z","shell.execute_reply.started":"2025-11-21T13:05:28.42636Z","shell.execute_reply":"2025-11-21T13:05:28.429591Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"# Check the weeks with the highest missing values ratio\ndf_missing = X.groupby('WEEK_NUM').case_id.count().reset_index()\ndf_missing = df_missing.rename(columns={'case_id': 'Count'})\n\nfor week in X.WEEK_NUM.unique():\n    missing = X[X.WEEK_NUM.eq(week)].isna().sum().sum()\n    size = X[X.WEEK_NUM.eq(week)].size\n    df_missing.loc[df_missing.WEEK_NUM.eq(week), 'Missing_ratio'] = missing / size\n\ndf_missing.sort_values('Missing_ratio', ascending=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:28.431151Z","iopub.execute_input":"2025-11-21T13:05:28.431488Z","iopub.status.idle":"2025-11-21T13:05:34.096004Z","shell.execute_reply.started":"2025-11-21T13:05:28.431464Z","shell.execute_reply":"2025-11-21T13:05:34.095323Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"    WEEK_NUM  Count  Missing_ratio\n0          0  16735       0.649385\n2          2  17476       0.536394\n1          1  18841       0.535349\n3          3  16108       0.532419\n4          4  14309       0.527747\n..       ...    ...            ...\n84        84   9721       0.389028\n83        83   7295       0.387152\n82        82   5423       0.383946\n81        81   6743       0.383068\n62        62  17164       0.382641\n\n[92 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>WEEK_NUM</th>\n      <th>Count</th>\n      <th>Missing_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>16735</td>\n      <td>0.649385</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>17476</td>\n      <td>0.536394</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>18841</td>\n      <td>0.535349</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>16108</td>\n      <td>0.532419</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>14309</td>\n      <td>0.527747</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>84</td>\n      <td>9721</td>\n      <td>0.389028</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>83</td>\n      <td>7295</td>\n      <td>0.387152</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>82</td>\n      <td>5423</td>\n      <td>0.383946</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>81</td>\n      <td>6743</td>\n      <td>0.383068</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>62</td>\n      <td>17164</td>\n      <td>0.382641</td>\n    </tr>\n  </tbody>\n</table>\n<p>92 rows × 3 columns</p>\n</div>"},"metadata":{}}],"execution_count":15},{"cell_type":"markdown","source":"* week 0 has very high missing ratio comparing to other weeks","metadata":{}},{"cell_type":"code","source":"# Check which weeks has the most maximum values by columns\nmax_df = X.select_dtypes(np.number).groupby('WEEK_NUM').max()\nmax_week_list = []\nfor col in max_df.columns:\n    max_week = max_df.sort_values(col, ascending=False).iloc[0].name\n    max_week_list.append(max_week)\nmax_week_df = pd.DataFrame(max_week_list)\nmax_week_df.value_counts()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:34.097096Z","iopub.execute_input":"2025-11-21T13:05:34.097408Z","iopub.status.idle":"2025-11-21T13:05:36.922055Z","shell.execute_reply.started":"2025-11-21T13:05:34.097376Z","shell.execute_reply":"2025-11-21T13:05:36.921322Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"0 \n91    63\n87    13\n75    10\n1      8\n61     8\n      ..\n22     1\n24     1\n25     1\n27     1\n80     1\nName: count, Length: 75, dtype: int64"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"* weeks 0 and 91 have very much maximum values comparing to other weeks","metadata":{}},{"cell_type":"markdown","source":"#### Define pipeline transformers","metadata":{}},{"cell_type":"code","source":"class TimeFeatTransformer(BaseEstimator, TransformerMixin):\n    \"\"\"\n    Transformer to create time related features\n    \"\"\"\n    def fit(self, df, y=None):\n        self.ref_cols = ['birth_259D_mean', 'date_decision']\n        self.original_cols = [col for col in df.columns \n                              if col not in self.ref_cols]\n        return self\n    \n    def transform(self, df):\n        # Create time delta features\n        for col in self.original_cols:\n            delta_col_0 = f'delta_{col}_{self.ref_cols[0]}'\n            df[delta_col_0] = abs(df[col] - df[self.ref_cols[0]]).dt.days\n\n            delta_col_1 = f'delta_{col}_{self.ref_cols[1]}'\n            df[delta_col_1] = abs(df[col] - df[self.ref_cols[1]]).dt.days\n            \n        delta_col_0_1 = f'delta_{self.ref_cols[1]}_{self.ref_cols[0]}'      \n        df[delta_col_0_1] = abs(df[self.ref_cols[1]] - df[self.ref_cols[0]]).dt.days\n        \n        # Drop used cols \n        df = df.drop(self.ref_cols + self.original_cols, axis=1)\n        \n        self.delta_cols = df.columns.to_list()\n        \n        return df\n   \n    def get_feature_names_out(self, input_features=None):\n        return self.delta_cols\n\nclass NumFeatTransformer(BaseEstimator, TransformerMixin):\n    \"\"\"\n    Transformer to create numeric related features\n    \"\"\"\n    def fit(self, df, y=None):\n        self.ref_cols = ['birth_year', 'decision_year']\n        self.year_cols = []\n        for col in df.columns:\n            if 'year' in col and 'T' in col:\n                self.year_cols.append(col)\n        return self\n    \n    def transform(self, df):\n        # Create year delta features\n        for col in self.year_cols:\n            delta_col_0 = f'delta_{col}_{self.ref_cols[0]}'\n            df[delta_col_0] = abs(df[col] - df[self.ref_cols[0]])\n\n            delta_col_1 = f'delta_{col}_{self.ref_cols[1]}'\n            df[delta_col_1] = abs(df[col] - df[self.ref_cols[1]])\n\n        delta_col_0_1 = f'delta_{self.ref_cols[1]}_{self.ref_cols[0]}'   \n        df[delta_col_0_1] = abs(df[self.ref_cols[1]] - df[self.ref_cols[0]])\n        \n        df = df.drop(self.ref_cols + self.year_cols, axis=1)\n        \n        self.all_cols = df.columns.to_list()\n\n        return df.astype(float)\n   \n    def get_feature_names_out(self, input_features=None):\n        return self.all_cols\n\nclass BadColsDropTransformer(BaseEstimator, TransformerMixin): \n    \"\"\"\n    Transformer to drop unuseful columns\n    \"\"\"\n    def fit(self, df, y=None):\n        # Columns with many missing values \n        self.missing_values = df.isna().mean().sort_values(ascending=False)\n        self.cols_to_drop = set(\n            self.missing_values[self.missing_values.gt(0.95)].index\n        )\n        # Columns with one higly dominant value\n        for col in df.columns:\n            if (df[col].value_counts(normalize=True) > 0.95).any():\n                self.cols_to_drop.add(col)\n                \n        # Columns with identical values  \n        for col1 in df.columns[:-1]:\n            if col1 not in self.cols_to_drop:\n                for col2 in df.columns[df.columns.get_loc(col1) + 1:]:\n                    if df[col1].equals(df[col2]):\n                        self.cols_to_drop.add(col2)\n        return self\n        \n    def transform(self, df):\n        return df.drop(list(self.cols_to_drop), axis=1)\n    \n    def get_feature_names_out(self, input_features=None):\n        return [col for col in input_features \n                if col not in self.cols_to_drop]\n    \nclass HighCorrDropTransformer(BaseEstimator, TransformerMixin):\n    \"\"\"\n    Transformer to drop highly correlated numerical columns\n    \"\"\"\n    def fit(self, df, y=None):\n        self.corr_matrix = df.corr()\n        self.cols_to_drop = set()\n        for col1 in self.corr_matrix.columns:\n            for col2 in self.corr_matrix.columns:\n                if col1 != col2:\n                    # Check for high correlation\n                    if abs(self.corr_matrix.loc[col1, col2]) >= 0.90:\n                        # Check which column has more missing values\n                        if df[col1].isna().sum() > df[col2].isna().sum():\n                            self.cols_to_drop.add(col1)\n                        else:\n                            self.cols_to_drop.add(col2) \n        return self\n    \n    def transform(self, df):\n        return df.drop(list(self.cols_to_drop), axis=1)\n    \n    def get_feature_names_out(self, input_features=None):\n        return [col for col in input_features if col not in self.cols_to_drop]\n        \nclass LowFreqTransformer(BaseEstimator, TransformerMixin):\n    \"\"\"\n    Transformer to process categorical, boolean and object columns\n    Fill missing and convert infrequent values\n    \"\"\"\n    def fit(self, df, y=None):\n        self.original_cols = df.columns\n        self.frequencies = {}\n        self.threshold = {}\n        for col in df.columns:\n            self.frequencies[col] = df[col].value_counts(normalize=True, \n                                                         ascending=True)\n            self.threshold[col] = self.frequencies[col][\n                (self.frequencies[col].cumsum() > 0.05).idxmax()\n                ]\n        return self\n    \n    def transform(self, df):\n        for col in self.original_cols:\n            df[col] = df[col].astype(str)\n            \n            infrequent_mask = (df[col].isin(\n                self.frequencies[col].index[\n                    self.frequencies[col] < self.threshold[col]\n                ]))\n            # Convert low frequency categoricals to 'infrequent'\n            df.loc[infrequent_mask, col] = 'infrequent'\n        return df\n    \n    def get_feature_names_out(self, input_features=None):\n        return input_features","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:36.922986Z","iopub.execute_input":"2025-11-21T13:05:36.923222Z","iopub.status.idle":"2025-11-21T13:05:36.940993Z","shell.execute_reply.started":"2025-11-21T13:05:36.923196Z","shell.execute_reply":"2025-11-21T13:05:36.94038Z"}},"outputs":[],"execution_count":17},{"cell_type":"markdown","source":"#### Processing pipeline","metadata":{}},{"cell_type":"code","source":"# Drop outlier weeks after analysis\nX = X[~X.WEEK_NUM.eq(0)]\n\n# Drop day 335 as an outlier, the only date without any target=1\nX = X[~X['date_decision'].dt.dayofyear.eq(335)]\n\n# Separate target\ny = X.pop('target')\n\n# Keep a copy of WEEK_NUM for group splitting in modeling\nweek_num = X.WEEK_NUM\n\n# Drop unuseful and duplicate columns\ncols_to_drop = [\n    'case_id', 'MONTH', 'WEEK_NUM', 'birthdate_574D', 'dateofbirth_337D',  \n]\nX = X.drop(cols_to_drop, axis=1)\n\n# Keep the structure of X to match it later with X_test \nX_structure = X.drop(X.index)\n\nX.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:36.941801Z","iopub.execute_input":"2025-11-21T13:05:36.941986Z","iopub.status.idle":"2025-11-21T13:05:43.735776Z","shell.execute_reply.started":"2025-11-21T13:05:36.941971Z","shell.execute_reply":"2025-11-21T13:05:43.734948Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 1509873 entries, 100 to 1526658\nColumns: 485 entries, actualdpdtolerance_344P to decision_day_of_week\ndtypes: Int8(1), bool(1), category(113), datetime64[ms](56), float32(297), int16(3), int8(2), object(12)\nmemory usage: 2.7+ GB\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"# Separate columns by type\ndate_cols, num_cols, cat_cols = cols_types(X)\ncat_unique = X[cat_cols].nunique()\nlow_card_cols = list(cat_unique.index[cat_unique.le(12)])\nmed_card_cols = list(cat_unique.index[cat_unique.gt(12) & cat_unique.le(200)])\nhigh_card_cols = list(cat_unique.index[cat_unique.gt(200)])\n\n# Pipeline to process date columns\ndate_pipeline = make_pipeline(\n    TimeFeatTransformer(),\n    BadColsDropTransformer(),\n    HighCorrDropTransformer(),\n    PowerTransformer(copy=False),\n    )\n# Pipeline to process numerical columns\nnum_pipeline = make_pipeline(\n    NumFeatTransformer(),\n    BadColsDropTransformer(),\n    HighCorrDropTransformer(),\n    PowerTransformer(copy=False),\n    )\n# Pipeline to process low cardinality columns\nlow_card_pipeline = make_pipeline(\n    BadColsDropTransformer(),\n    LowFreqTransformer(),\n    OneHotEncoder(\n        dtype=np.int8, drop='if_binary', sparse_output=False,\n        min_frequency=0.02, handle_unknown='infrequent_if_exist'),\n    )\n# Pipeline to process medium cardinality columns\nmed_card_pipeline = make_pipeline(\n    BadColsDropTransformer(),\n    LowFreqTransformer(),\n    OrdinalEncoder(handle_unknown='use_encoded_value',\n                   unknown_value=np.nan,\n                   dtype=np.float32),\n    )\n# Pipeline to process high cardinality columns\nhigh_card_pipeline = make_pipeline(\n    BadColsDropTransformer(),\n    LowFreqTransformer(),\n    TargetEncoder(target_type='binary', smooth='auto', shuffle=True),\n    PowerTransformer(copy=False),\n    )\n# Define column transformer\nprocessor = make_column_transformer(\n    (date_pipeline, date_cols),\n    (num_pipeline, num_cols),\n    (low_card_pipeline, low_card_cols),\n    (med_card_pipeline, med_card_cols),\n    (high_card_pipeline, high_card_cols),\n    verbose=True,\n    )\nprocessor","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:43.736726Z","iopub.execute_input":"2025-11-21T13:05:43.737352Z","iopub.status.idle":"2025-11-21T13:05:45.180805Z","shell.execute_reply.started":"2025-11-21T13:05:43.73732Z","shell.execute_reply":"2025-11-21T13:05:45.180072Z"}},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"ColumnTransformer(transformers=[('pipeline-1',\n                                 Pipeline(steps=[('timefeattransformer',\n                                                  TimeFeatTransformer()),\n                                                 ('badcolsdroptransformer',\n                                                  BadColsDropTransformer()),\n                                                 ('highcorrdroptransformer',\n                                                  HighCorrDropTransformer()),\n                                                 ('powertransformer',\n                                                  PowerTransformer(copy=False))]),\n                                 ['date_decision', 'datefirstoffer_1144D',\n                                  'datelastinstal40dpd_247D',\n                                  'datelastunpaid_3...\n                                  'district_544M_mode', 'profession_152M_mode',\n                                  'contaddr_district_15M_mode',\n                                  'contaddr_zipcode_807M_mode',\n                                  'empladdr_district_926M_mode',\n                                  'empladdr_zipcode_114M_mode',\n                                  'registaddr_district_1083M_mode',\n                                  'registaddr_zipcode_184M_mode',\n                                  'name_4527232M_mode', 'name_4917606M_mode',\n                                  'employername_160M_mode',\n                                  'addres_district_368M_mode_mode',\n                                  'addres_zip_823M_mode_mode'])],\n                  verbose=True)","text/html":"<style>#sk-container-id-1 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-1 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n  padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n                                 Pipeline(steps=[(&#x27;timefeattransformer&#x27;,\n                                                  TimeFeatTransformer()),\n                                                 (&#x27;badcolsdroptransformer&#x27;,\n                                                  BadColsDropTransformer()),\n                                                 (&#x27;highcorrdroptransformer&#x27;,\n                                                  HighCorrDropTransformer()),\n                                                 (&#x27;powertransformer&#x27;,\n                                                  PowerTransformer(copy=False))]),\n                                 [&#x27;date_decision&#x27;, &#x27;datefirstoffer_1144D&#x27;,\n                                  &#x27;datelastinstal40dpd_247D&#x27;,\n                                  &#x27;datelastunpaid_3...\n                                  &#x27;district_544M_mode&#x27;, &#x27;profession_152M_mode&#x27;,\n                                  &#x27;contaddr_district_15M_mode&#x27;,\n                                  &#x27;contaddr_zipcode_807M_mode&#x27;,\n                                  &#x27;empladdr_district_926M_mode&#x27;,\n                                  &#x27;empladdr_zipcode_114M_mode&#x27;,\n                                  &#x27;registaddr_district_1083M_mode&#x27;,\n                                  &#x27;registaddr_zipcode_184M_mode&#x27;,\n                                  &#x27;name_4527232M_mode&#x27;, &#x27;name_4917606M_mode&#x27;,\n                                  &#x27;employername_160M_mode&#x27;,\n                                  &#x27;addres_district_368M_mode_mode&#x27;,\n                                  &#x27;addres_zip_823M_mode_mode&#x27;])],\n                  verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;pipeline-1&#x27;,\n                                 Pipeline(steps=[(&#x27;timefeattransformer&#x27;,\n                                                  TimeFeatTransformer()),\n                                                 (&#x27;badcolsdroptransformer&#x27;,\n                                                  BadColsDropTransformer()),\n                                                 (&#x27;highcorrdroptransformer&#x27;,\n                                                  HighCorrDropTransformer()),\n                                                 (&#x27;powertransformer&#x27;,\n                                                  PowerTransformer(copy=False))]),\n                                 [&#x27;date_decision&#x27;, &#x27;datefirstoffer_1144D&#x27;,\n                                  &#x27;datelastinstal40dpd_247D&#x27;,\n                                  &#x27;datelastunpaid_3...\n                                  &#x27;district_544M_mode&#x27;, &#x27;profession_152M_mode&#x27;,\n                                  &#x27;contaddr_district_15M_mode&#x27;,\n                                  &#x27;contaddr_zipcode_807M_mode&#x27;,\n                                  &#x27;empladdr_district_926M_mode&#x27;,\n                                  &#x27;empladdr_zipcode_114M_mode&#x27;,\n                                  &#x27;registaddr_district_1083M_mode&#x27;,\n                                  &#x27;registaddr_zipcode_184M_mode&#x27;,\n                                  &#x27;name_4527232M_mode&#x27;, &#x27;name_4917606M_mode&#x27;,\n                                  &#x27;employername_160M_mode&#x27;,\n                                  &#x27;addres_district_368M_mode_mode&#x27;,\n                                  &#x27;addres_zip_823M_mode_mode&#x27;])],\n                  verbose=True)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-1</label><div class=\"sk-toggleable__content \"><pre>[&#x27;date_decision&#x27;, &#x27;datefirstoffer_1144D&#x27;, &#x27;datelastinstal40dpd_247D&#x27;, &#x27;datelastunpaid_3546854D&#x27;, &#x27;dtlastpmtallstes_4499206D&#x27;, &#x27;firstclxcampaign_1125D&#x27;, &#x27;firstdatedue_489D&#x27;, &#x27;lastactivateddate_801D&#x27;, &#x27;lastapplicationdate_877D&#x27;, &#x27;lastapprdate_640D&#x27;, &#x27;lastdelinqdate_224D&#x27;, &#x27;lastrejectdate_50D&#x27;, &#x27;lastrepayingdate_696D&#x27;, &#x27;maxdpdinstldate_3546855D&#x27;, &#x27;payvacationpostpone_4187118D&#x27;, &#x27;validfrom_1069D&#x27;, &#x27;assignmentdate_238D&#x27;, &#x27;assignmentdate_4527235D&#x27;, &#x27;assignmentdate_4955616D&#x27;, &#x27;dateofbirth_342D&#x27;, &#x27;responsedate_1012D&#x27;, &#x27;responsedate_4527233D&#x27;, &#x27;responsedate_4917613D&#x27;, &#x27;dateofcredend_289D_mean&#x27;, &#x27;dateofcredend_353D_mean&#x27;, &#x27;dateofcredstart_181D_mean&#x27;, &#x27;dateofcredstart_739D_mean&#x27;, &#x27;dateofrealrepmt_138D_mean&#x27;, &#x27;lastupdate_1112D_mean&#x27;, &#x27;lastupdate_388D_mean&#x27;, &#x27;numberofoverdueinstlmaxdat_148D_mean&#x27;, &#x27;numberofoverdueinstlmaxdat_641D_mean&#x27;, &#x27;overdueamountmax2date_1002D_mean&#x27;, &#x27;overdueamountmax2date_1142D_mean&#x27;, &#x27;refreshdate_3813885D_mean&#x27;, &#x27;contractdate_551D_mean&#x27;, &#x27;contractmaturitydate_151D_mean&#x27;, &#x27;lastupdate_260D_mean&#x27;, &#x27;approvaldate_319D_mean&#x27;, &#x27;creationdate_885D_mean&#x27;, &#x27;dateactivated_425D_mean&#x27;, &#x27;dtlastpmt_581D_mean&#x27;, &#x27;dtlastpmtallstes_3545839D_mean&#x27;, &#x27;employedfrom_700D_mean&#x27;, &#x27;firstnonzeroinstldate_307D_mean&#x27;, &#x27;openingdate_857D_mean&#x27;, &#x27;contractenddate_991D_mean&#x27;, &#x27;openingdate_313D_mean&#x27;, &#x27;birth_259D_mean&#x27;, &#x27;birthdate_87D_mean&#x27;, &#x27;empl_employedfrom_271D_mean&#x27;, &#x27;recorddate_4527225D_mean&#x27;, &#x27;deductiondate_4917603D_mean&#x27;, &#x27;processingdate_168D_mean&#x27;, &#x27;pmts_date_1107D_mean_mean&#x27;, &#x27;empls_employedfrom_796D_mean_mean&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">TimeFeatTransformer</label><div class=\"sk-toggleable__content \"><pre>TimeFeatTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">BadColsDropTransformer</label><div class=\"sk-toggleable__content \"><pre>BadColsDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">HighCorrDropTransformer</label><div class=\"sk-toggleable__content \"><pre>HighCorrDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;PowerTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>PowerTransformer(copy=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-2</label><div class=\"sk-toggleable__content \"><pre>[&#x27;actualdpdtolerance_344P&#x27;, &#x27;amtinstpaidbefduel24m_4187115A&#x27;, &#x27;annuity_780A&#x27;, &#x27;annuitynextmonth_57A&#x27;, &#x27;applicationcnt_361L&#x27;, &#x27;applications30d_658L&#x27;, &#x27;applicationscnt_1086L&#x27;, &#x27;applicationscnt_464L&#x27;, &#x27;applicationscnt_629L&#x27;, &#x27;applicationscnt_867L&#x27;, &#x27;avgdbddpdlast24m_3658932P&#x27;, &#x27;avgdbddpdlast3m_4187120P&#x27;, &#x27;avgdbdtollast24m_4525197P&#x27;, &#x27;avgdpdtolclosure24_3658938P&#x27;, &#x27;avginstallast24m_3658937A&#x27;, &#x27;avglnamtstart24m_4525187A&#x27;, &#x27;avgmaxdpdlast9m_3716943P&#x27;, &#x27;avgoutstandbalancel6m_4187114A&#x27;, &#x27;avgpmtlast12m_4525200A&#x27;, &#x27;clientscnt12m_3712952L&#x27;, &#x27;clientscnt3m_3712950L&#x27;, &#x27;clientscnt6m_3712949L&#x27;, &#x27;clientscnt_100L&#x27;, &#x27;clientscnt_1022L&#x27;, &#x27;clientscnt_1071L&#x27;, &#x27;clientscnt_1130L&#x27;, &#x27;clientscnt_136L&#x27;, &#x27;clientscnt_157L&#x27;, &#x27;clientscnt_257L&#x27;, &#x27;clientscnt_304L&#x27;, &#x27;clientscnt_360L&#x27;, &#x27;clientscnt_493L&#x27;, &#x27;clientscnt_533L&#x27;, &#x27;clientscnt_887L&#x27;, &#x27;clientscnt_946L&#x27;, &#x27;cntincpaycont9m_3716944L&#x27;, &#x27;cntpmts24_3658933L&#x27;, &#x27;commnoinclast6m_3546845L&#x27;, &#x27;credamount_770A&#x27;, &#x27;currdebt_22A&#x27;, &#x27;currdebtcredtyperange_828A&#x27;, &#x27;daysoverduetolerancedd_3976961L&#x27;, &#x27;deferredmnthsnum_166L&#x27;, &#x27;disbursedcredamount_1113A&#x27;, &#x27;downpmt_116A&#x27;, &#x27;eir_270L&#x27;, &#x27;homephncnt_628L&#x27;, &#x27;inittransactionamount_650A&#x27;, &#x27;interestrate_311L&#x27;, &#x27;interestrategrace_34L&#x27;, &#x27;lastapprcredamount_781A&#x27;, &#x27;lastdependentsnum_448L&#x27;, &#x27;lastotherinc_902A&#x27;, &#x27;lastotherlnsexpense_631A&#x27;, &#x27;lastrejectcredamount_222A&#x27;, &#x27;maininc_215A&#x27;, &#x27;mastercontrelectronic_519L&#x27;, &#x27;mastercontrexist_109L&#x27;, &#x27;maxannuity_159A&#x27;, &#x27;maxannuity_4075009A&#x27;, &#x27;maxdbddpdlast1m_3658939P&#x27;, &#x27;maxdbddpdtollast12m_3658940P&#x27;, &#x27;maxdbddpdtollast6m_4187119P&#x27;, &#x27;maxdebt4_972A&#x27;, &#x27;maxdpdfrom6mto36m_3546853P&#x27;, &#x27;maxdpdinstlnum_3546846P&#x27;, &#x27;maxdpdlast12m_727P&#x27;, &#x27;maxdpdlast24m_143P&#x27;, &#x27;maxdpdlast3m_392P&#x27;, &#x27;maxdpdlast6m_474P&#x27;, &#x27;maxdpdlast9m_1059P&#x27;, &#x27;maxdpdtolerance_374P&#x27;, &#x27;maxinstallast24m_3658928A&#x27;, &#x27;maxlnamtstart6m_4525199A&#x27;, &#x27;maxoutstandbalancel12m_4187113A&#x27;, &#x27;maxpmtlast3m_4525190A&#x27;, &#x27;mindbddpdlast24m_3658935P&#x27;, &#x27;mindbdtollast24m_4525191P&#x27;, &#x27;mobilephncnt_593L&#x27;, &#x27;monthsannuity_845L&#x27;, &#x27;numactivecreds_622L&#x27;, &#x27;numactivecredschannel_414L&#x27;, &#x27;numactiverelcontr_750L&#x27;, &#x27;numcontrs3months_479L&#x27;, &#x27;numincomingpmts_3546848L&#x27;, &#x27;numinstlallpaidearly3d_817L&#x27;, &#x27;numinstls_657L&#x27;, &#x27;numinstlsallpaid_934L&#x27;, &#x27;numinstlswithdpd10_728L&#x27;, &#x27;numinstlswithdpd5_4187116L&#x27;, &#x27;numinstlswithoutdpd_562L&#x27;, &#x27;numinstmatpaidtearly2d_4499204L&#x27;, &#x27;numinstpaid_4499208L&#x27;, &#x27;numinstpaidearly3d_3546850L&#x27;, &#x27;numinstpaidearly3dest_4493216L&#x27;, &#x27;numinstpaidearly5d_1087L&#x27;, &#x27;numinstpaidearly5dest_4493211L&#x27;, &#x27;numinstpaidearly5dobd_4499205L&#x27;, &#x27;numinstpaidearly_338L&#x27;, &#x27;numinstpaidearlyest_4493214L&#x27;, &#x27;numinstpaidlastcontr_4325080L&#x27;, &#x27;numinstpaidlate1d_3546852L&#x27;, &#x27;numinstregularpaid_973L&#x27;, &#x27;numinstregularpaidest_4493210L&#x27;, &#x27;numinsttopaygr_769L&#x27;, &#x27;numinsttopaygrest_4493213L&#x27;, &#x27;numinstunpaidmax_3546851L&#x27;, &#x27;numinstunpaidmaxest_4493212L&#x27;, &#x27;numnotactivated_1143L&#x27;, &#x27;numpmtchanneldd_318L&#x27;, &#x27;numrejects9m_859L&#x27;, &#x27;pctinstlsallpaidearl3d_427L&#x27;, &#x27;pctinstlsallpaidlat10d_839L&#x27;, &#x27;pctinstlsallpaidlate1d_3546856L&#x27;, &#x27;pctinstlsallpaidlate4d_3546849L&#x27;, &#x27;pctinstlsallpaidlate6d_3546844L&#x27;, &#x27;pmtnum_254L&#x27;, &#x27;posfpd10lastmonth_333P&#x27;, &#x27;posfpd30lastmonth_3976960P&#x27;, &#x27;posfstqpd30lastmonth_3976962P&#x27;, &#x27;price_1097A&#x27;, &#x27;sellerplacecnt_915L&#x27;, &#x27;sellerplacescnt_216L&#x27;, &#x27;sumoutstandtotal_3546847A&#x27;, &#x27;sumoutstandtotalest_4493215A&#x27;, &#x27;totaldebt_9A&#x27;, &#x27;totalsettled_863A&#x27;, &#x27;totinstallast1m_4525188A&#x27;, &#x27;contractssum_5085716L&#x27;, &#x27;days120_123L&#x27;, &#x27;days180_256L&#x27;, &#x27;days30_165L&#x27;, &#x27;days360_512L&#x27;, &#x27;days90_310L&#x27;, &#x27;firstquarter_103L&#x27;, &#x27;for3years_128L&#x27;, &#x27;for3years_504L&#x27;, &#x27;for3years_584L&#x27;, &#x27;formonth_118L&#x27;, &#x27;formonth_206L&#x27;, &#x27;formonth_535L&#x27;, &#x27;forquarter_1017L&#x27;, &#x27;forquarter_462L&#x27;, &#x27;forquarter_634L&#x27;, &#x27;fortoday_1092L&#x27;, &#x27;forweek_1077L&#x27;, &#x27;forweek_528L&#x27;, &#x27;forweek_601L&#x27;, &#x27;foryear_618L&#x27;, &#x27;foryear_818L&#x27;, &#x27;foryear_850L&#x27;, &#x27;fourthquarter_440L&#x27;, &#x27;numberofqueries_373L&#x27;, &#x27;pmtaverage_3A&#x27;, &#x27;pmtaverage_4527227A&#x27;, &#x27;pmtaverage_4955615A&#x27;, &#x27;pmtcount_4527229L&#x27;, &#x27;pmtcount_4955617L&#x27;, &#x27;pmtcount_693L&#x27;, &#x27;pmtscount_423L&#x27;, &#x27;pmtssum_45A&#x27;, &#x27;riskassesment_940T&#x27;, &#x27;secondquarter_766L&#x27;, &#x27;thirdquarter_1082L&#x27;, &#x27;annualeffectiverate_199L_count&#x27;, &#x27;annualeffectiverate_199L_mean&#x27;, &#x27;annualeffectiverate_63L_mean&#x27;, &#x27;contractsum_5085717L_mean&#x27;, &#x27;credlmt_230A_mean&#x27;, &#x27;credlmt_935A_mean&#x27;, &#x27;debtoutstand_525A_mean&#x27;, &#x27;debtoverdue_47A_mean&#x27;, &#x27;dpdmax_139P_mean&#x27;, &#x27;dpdmax_757P_mean&#x27;, &#x27;dpdmaxdateyear_596T_mean&#x27;, &#x27;dpdmaxdateyear_896T_mean&#x27;, &#x27;instlamount_768A_mean&#x27;, &#x27;instlamount_852A_mean&#x27;, &#x27;interestrate_508L_mean&#x27;, &#x27;monthlyinstlamount_332A_mean&#x27;, &#x27;monthlyinstlamount_674A_mean&#x27;, &#x27;nominalrate_281L_mean&#x27;, &#x27;nominalrate_498L_mean&#x27;, &#x27;numberofcontrsvalue_258L_mean&#x27;, &#x27;numberofcontrsvalue_358L_mean&#x27;, &#x27;numberofinstls_229L_mean&#x27;, &#x27;numberofinstls_320L_mean&#x27;, &#x27;numberofoutstandinstls_520L_mean&#x27;, &#x27;numberofoutstandinstls_59L_mean&#x27;, &#x27;numberofoverdueinstlmax_1039L_mean&#x27;, &#x27;numberofoverdueinstlmax_1151L_mean&#x27;, &#x27;numberofoverdueinstls_725L_mean&#x27;, &#x27;numberofoverdueinstls_834L_mean&#x27;, &#x27;outstandingamount_354A_mean&#x27;, &#x27;outstandingamount_362A_mean&#x27;, &#x27;overdueamount_31A_mean&#x27;, &#x27;overdueamount_659A_mean&#x27;, &#x27;overdueamountmax2_14A_mean&#x27;, &#x27;overdueamountmax2_398A_mean&#x27;, &#x27;overdueamountmax_155A_mean&#x27;, &#x27;overdueamountmax_35A_mean&#x27;, &#x27;overdueamountmaxdateyear_2T_mean&#x27;, &#x27;overdueamountmaxdateyear_994T_mean&#x27;, &#x27;periodicityofpmts_1102L_mean&#x27;, &#x27;periodicityofpmts_837L_mean&#x27;, &#x27;prolongationcount_1120L_mean&#x27;, &#x27;prolongationcount_599L_mean&#x27;, &#x27;residualamount_488A_mean&#x27;, &#x27;residualamount_856A_mean&#x27;, &#x27;totalamount_6A_mean&#x27;, &#x27;totalamount_996A_mean&#x27;, &#x27;totaldebtoverduevalue_178A_mean&#x27;, &#x27;totaldebtoverduevalue_718A_mean&#x27;, &#x27;totaloutstanddebtvalue_39A_mean&#x27;, &#x27;totaloutstanddebtvalue_668A_mean&#x27;, &#x27;amount_1115A_count&#x27;, &#x27;amount_1115A_mean&#x27;, &#x27;credlmt_1052A_mean&#x27;, &#x27;credlmt_228A_mean&#x27;, &#x27;credlmt_3940954A_mean&#x27;, &#x27;credquantity_1099L_mean&#x27;, &#x27;credquantity_984L_mean&#x27;, &#x27;debtpastduevalue_732A_mean&#x27;, &#x27;debtvalue_227A_mean&#x27;, &#x27;dpd_550P_mean&#x27;, &#x27;dpd_733P_mean&#x27;, &#x27;dpdmax_851P_mean&#x27;, &#x27;dpdmaxdateyear_742T_mean&#x27;, &#x27;installmentamount_644A_mean&#x27;, &#x27;installmentamount_833A_mean&#x27;, &#x27;instlamount_892A_mean&#x27;, &#x27;interesteffectiverate_369L_mean&#x27;, &#x27;interestrateyearly_538L_mean&#x27;, &#x27;maxdebtpduevalodued_3940955A_mean&#x27;, &#x27;numberofinstls_810L_mean&#x27;, &#x27;overdueamountmax_950A_mean&#x27;, &#x27;overdueamountmaxdateyear_432T_mean&#x27;, &#x27;pmtdaysoverdue_1135P_mean&#x27;, &#x27;pmtnumpending_403L_mean&#x27;, &#x27;residualamount_1093A_mean&#x27;, &#x27;residualamount_127A_mean&#x27;, &#x27;residualamount_3940956A_mean&#x27;, &#x27;totalamount_503A_mean&#x27;, &#x27;totalamount_881A_mean&#x27;, &#x27;actualdpd_943P_count&#x27;, &#x27;actualdpd_943P_mean&#x27;, &#x27;annuity_853A_mean&#x27;, &#x27;byoccupationinc_3656910L_mean&#x27;, &#x27;childnum_21L_mean&#x27;, &#x27;credacc_actualbalance_314A_mean&#x27;, &#x27;credacc_credlmt_575A_mean&#x27;, &#x27;credacc_maxhisbal_375A_mean&#x27;, &#x27;credacc_minhisbal_90A_mean&#x27;, &#x27;credacc_transactions_402L_mean&#x27;, &#x27;credamount_590A_mean&#x27;, &#x27;currdebt_94A_mean&#x27;, &#x27;downpmt_134A_mean&#x27;, &#x27;mainoccupationinc_437A_mean&#x27;, &#x27;maxdpdtolerance_577P_mean&#x27;, &#x27;outstandingdebt_522A_mean&#x27;, &#x27;pmtnum_8L_mean&#x27;, &#x27;revolvingaccount_394A_mean&#x27;, &#x27;tenor_203L_mean&#x27;, &#x27;last180dayaveragebalance_704A_count&#x27;, &#x27;last180dayaveragebalance_704A_mean&#x27;, &#x27;last180dayturnover_1134A_mean&#x27;, &#x27;last30dayturnover_651A_mean&#x27;, &#x27;amount_416A_count&#x27;, &#x27;amount_416A_mean&#x27;, &#x27;amtdebitincoming_4809443A_count&#x27;, &#x27;amtdebitincoming_4809443A_mean&#x27;, &#x27;amtdebitoutgoing_4809440A_mean&#x27;, &#x27;amtdepositbalance_4809441A_mean&#x27;, &#x27;amtdepositincoming_4809444A_mean&#x27;, &#x27;amtdepositoutgoing_4809442A_mean&#x27;, &#x27;birth_259D_count&#x27;, &#x27;childnum_185L_mean&#x27;, &#x27;mainoccupationinc_384A_mean&#x27;, &#x27;amount_4527230A_count&#x27;, &#x27;amount_4527230A_mean&#x27;, &#x27;amount_4917619A_count&#x27;, &#x27;amount_4917619A_mean&#x27;, &#x27;employername_160M_count&#x27;, &#x27;pmtamount_36A_mean&#x27;, &#x27;collater_typofvalofguarant_298M_mode_count&#x27;, &#x27;collater_valueofguarantee_1124L_mean_mean&#x27;, &#x27;collater_valueofguarantee_876L_mean_mean&#x27;, &#x27;pmts_dpd_1073P_mean_mean&#x27;, &#x27;pmts_dpd_303P_mean_mean&#x27;, &#x27;pmts_overdue_1140A_mean_mean&#x27;, &#x27;pmts_overdue_1152A_mean_mean&#x27;, &#x27;pmts_year_1139T_mean_mean&#x27;, &#x27;pmts_year_507T_mean_mean&#x27;, &#x27;pmts_date_1107D_mean_count&#x27;, &#x27;pmts_dpdvalue_108P_mean_mean&#x27;, &#x27;pmts_pmtsoverdue_635A_mean_mean&#x27;, &#x27;cacccardblochreas_147M_mode_count&#x27;, &#x27;addres_district_368M_mode_count&#x27;, &#x27;birth_year&#x27;, &#x27;decision_year&#x27;, &#x27;decision_day_of_month&#x27;, &#x27;decision_day_of_year&#x27;, &#x27;decision_week_of_year&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">NumFeatTransformer</label><div class=\"sk-toggleable__content \"><pre>NumFeatTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">BadColsDropTransformer</label><div class=\"sk-toggleable__content \"><pre>BadColsDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">HighCorrDropTransformer</label><div class=\"sk-toggleable__content \"><pre>HighCorrDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;PowerTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>PowerTransformer(copy=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-3</label><div class=\"sk-toggleable__content \"><pre>[&#x27;bankacctype_710L&#x27;, &#x27;cardtype_51L&#x27;, &#x27;credtype_322L&#x27;, &#x27;disbursementtype_67L&#x27;, &#x27;equalitydataagreement_891L&#x27;, &#x27;equalityempfrom_62L&#x27;, &#x27;inittransactioncode_186L&#x27;, &#x27;isbidproduct_1095L&#x27;, &#x27;isbidproductrequest_292L&#x27;, &#x27;isdebitcard_729L&#x27;, &#x27;lastst_736L&#x27;, &#x27;opencred_647L&#x27;, &#x27;paytype1st_925L&#x27;, &#x27;paytype_783L&#x27;, &#x27;twobodfilling_608L&#x27;, &#x27;typesuite_864L&#x27;, &#x27;description_5085714M&#x27;, &#x27;education_1103M&#x27;, &#x27;education_88M&#x27;, &#x27;maritalst_385M&#x27;, &#x27;maritalst_893M&#x27;, &#x27;requesttype_4525192L&#x27;, &#x27;classificationofcontr_13M_mode&#x27;, &#x27;contractst_545M_mode&#x27;, &#x27;description_351M_mode&#x27;, &#x27;dpdmaxdatemonth_442T_mode&#x27;, &#x27;dpdmaxdatemonth_89T_mode&#x27;, &#x27;overdueamountmaxdatemonth_284T_mode&#x27;, &#x27;overdueamountmaxdatemonth_365T_mode&#x27;, &#x27;purposeofcred_426M_mode&#x27;, &#x27;subjectrole_182M_mode&#x27;, &#x27;subjectrole_93M_mode&#x27;, &#x27;classificationofcontr_1114M_mode&#x27;, &#x27;dpdmaxdatemonth_804T_mode&#x27;, &#x27;overdueamountmaxdatemonth_494T_mode&#x27;, &#x27;periodicityofpmts_997L_mode&#x27;, &#x27;periodicityofpmts_997M_mode&#x27;, &#x27;pmtmethod_731M_mode&#x27;, &#x27;subjectrole_326M_mode&#x27;, &#x27;subjectrole_43M_mode&#x27;, &#x27;credacc_status_367L_mode&#x27;, &#x27;credtype_587L_mode&#x27;, &#x27;education_1138M_mode&#x27;, &#x27;familystate_726L_mode&#x27;, &#x27;inittransactioncode_279L_mode&#x27;, &#x27;isbidproduct_390L_mode&#x27;, &#x27;isdebitcard_527L_mode&#x27;, &#x27;postype_4733339M_mode&#x27;, &#x27;rejectreasonclient_4145042M_mode&#x27;, &#x27;status_219L_mode&#x27;, &#x27;contaddr_matchlist_1032L_mode&#x27;, &#x27;contaddr_smempladdr_334L_mode&#x27;, &#x27;education_927M_mode&#x27;, &#x27;empl_employedtotal_800L_mode&#x27;, &#x27;familystate_447L_mode&#x27;, &#x27;gender_992L_mode&#x27;, &#x27;housetype_905L_mode&#x27;, &#x27;housingtype_772L_mode&#x27;, &#x27;incometype_1044T_mode&#x27;, &#x27;isreference_387L_mode&#x27;, &#x27;language1_981M_mode&#x27;, &#x27;maritalst_703L_mode&#x27;, &#x27;personindex_1023L_mode&#x27;, &#x27;persontype_1072L_mode&#x27;, &#x27;persontype_792L_mode&#x27;, &#x27;relationshiptoclient_415T_mode&#x27;, &#x27;relationshiptoclient_642T_mode&#x27;, &#x27;remitter_829L_mode&#x27;, &#x27;role_1084L_mode&#x27;, &#x27;role_993L_mode&#x27;, &#x27;safeguarantyflag_411L_mode&#x27;, &#x27;sex_738L_mode&#x27;, &#x27;type_25L_mode&#x27;, &#x27;collater_typofvalofguarant_298M_mode_mode&#x27;, &#x27;collater_typofvalofguarant_407M_mode_mode&#x27;, &#x27;collaterals_typeofguarante_359M_mode_mode&#x27;, &#x27;collaterals_typeofguarante_669M_mode_mode&#x27;, &#x27;pmts_month_158T_mode_mode&#x27;, &#x27;pmts_month_706T_mode_mode&#x27;, &#x27;subjectroles_name_541M_mode_mode&#x27;, &#x27;subjectroles_name_838M_mode_mode&#x27;, &#x27;cacccardblochreas_147M_mode_mode&#x27;, &#x27;conts_type_509L_mode_mode&#x27;, &#x27;credacc_cards_status_52L_mode_mode&#x27;, &#x27;addres_role_871L_mode_mode&#x27;, &#x27;conts_role_79M_mode_mode&#x27;, &#x27;empls_economicalst_849M_mode_mode&#x27;, &#x27;empls_employer_name_740M_mode_mode&#x27;, &#x27;relatedpersons_role_762T_mode_mode&#x27;, &#x27;decision_quarter&#x27;, &#x27;decision_month_of_year&#x27;, &#x27;decision_day_of_week&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">BadColsDropTransformer</label><div class=\"sk-toggleable__content \"><pre>BadColsDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">LowFreqTransformer</label><div class=\"sk-toggleable__content \"><pre>LowFreqTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(drop=&#x27;if_binary&#x27;, dtype=&lt;class &#x27;numpy.int8&#x27;&gt;,\n              handle_unknown=&#x27;infrequent_if_exist&#x27;, min_frequency=0.02,\n              sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-4</label><div class=\"sk-toggleable__content \"><pre>[&#x27;lastapprcommoditycat_1041M&#x27;, &#x27;lastcancelreason_561M&#x27;, &#x27;lastrejectcommoditycat_161M&#x27;, &#x27;lastrejectcommodtypec_5251769M&#x27;, &#x27;lastrejectreason_759M&#x27;, &#x27;lastrejectreasonclient_4145040M&#x27;, &#x27;riskassesment_302T&#x27;, &#x27;classificationofcontr_400M_mode&#x27;, &#x27;contractst_964M_mode&#x27;, &#x27;financialinstitution_382M_mode&#x27;, &#x27;financialinstitution_591M_mode&#x27;, &#x27;purposeofcred_874M_mode&#x27;, &#x27;contractst_516M_mode&#x27;, &#x27;contracttype_653M_mode&#x27;, &#x27;credor_3940957M_mode&#x27;, &#x27;purposeofcred_722M_mode&#x27;, &#x27;cancelreason_3545846M_mode&#x27;, &#x27;rejectreason_755M_mode&#x27;, &#x27;empl_industry_691L_mode&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">BadColsDropTransformer</label><div class=\"sk-toggleable__content \"><pre>BadColsDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">LowFreqTransformer</label><div class=\"sk-toggleable__content \"><pre>LowFreqTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder(dtype=&lt;class &#x27;numpy.float32&#x27;&gt;,\n               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=nan)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">pipeline-5</label><div class=\"sk-toggleable__content \"><pre>[&#x27;lastapprcommoditytypec_5251766M&#x27;, &#x27;previouscontdistrict_112M&#x27;, &#x27;district_544M_mode&#x27;, &#x27;profession_152M_mode&#x27;, &#x27;contaddr_district_15M_mode&#x27;, &#x27;contaddr_zipcode_807M_mode&#x27;, &#x27;empladdr_district_926M_mode&#x27;, &#x27;empladdr_zipcode_114M_mode&#x27;, &#x27;registaddr_district_1083M_mode&#x27;, &#x27;registaddr_zipcode_184M_mode&#x27;, &#x27;name_4527232M_mode&#x27;, &#x27;name_4917606M_mode&#x27;, &#x27;employername_160M_mode&#x27;, &#x27;addres_district_368M_mode_mode&#x27;, &#x27;addres_zip_823M_mode_mode&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">BadColsDropTransformer</label><div class=\"sk-toggleable__content \"><pre>BadColsDropTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">LowFreqTransformer</label><div class=\"sk-toggleable__content \"><pre>LowFreqTransformer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;TargetEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.TargetEncoder.html\">?<span>Documentation for TargetEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>TargetEncoder(target_type=&#x27;binary&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;PowerTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.PowerTransformer.html\">?<span>Documentation for PowerTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>PowerTransformer(copy=False)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"%%time \n# Train / validation split\nX_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, stratify=y)\ndel X, y\n\n# Process data\nX_train = pd.DataFrame(processor.fit_transform(X_train, y_train), \n                       columns=processor.get_feature_names_out(),\n                       index=X_train.index)\nX_val = pd.DataFrame(processor.transform(X_val), \n                     columns=processor.get_feature_names_out(),\n                     index=X_val.index)   \n\n# Free memory\nX_train = downcast(X_train)\nX_val = downcast(X_val)  \n\n# Convert med_card_cols to category to pass them unencoded to the model \nenc_med_card_cols = []\nfor col in med_card_cols:\n    if 'pipeline-4__' + col in X_train.columns:\n        enc_med_card_cols.append('pipeline-4__' + col)\n\nX_train[enc_med_card_cols] = X_train[enc_med_card_cols].astype('str').astype('category')\nX_val[enc_med_card_cols] = X_val[enc_med_card_cols].astype('str').astype('category')\n\nX_train.info()","metadata":{"scrolled":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:05:45.181437Z","iopub.execute_input":"2025-11-21T13:05:45.181651Z","iopub.status.idle":"2025-11-21T13:19:10.68038Z","shell.execute_reply.started":"2025-11-21T13:05:45.181634Z","shell.execute_reply":"2025-11-21T13:19:10.679653Z"}},"outputs":[{"name":"stdout","text":"[ColumnTransformer] .... (1 of 5) Processing pipeline-1, total= 2.0min\n[ColumnTransformer] .... (2 of 5) Processing pipeline-2, total= 8.9min\n[ColumnTransformer] .... (3 of 5) Processing pipeline-3, total=  53.4s\n[ColumnTransformer] .... (4 of 5) Processing pipeline-4, total=   5.5s\n[ColumnTransformer] .... (5 of 5) Processing pipeline-5, total=  12.3s\n<class 'pandas.core.frame.DataFrame'>\nIndex: 1207898 entries, 322840 to 257869\nColumns: 423 entries, pipeline-1__delta_datefirstoffer_1144D_date_decision to pipeline-5__employername_160M_mode\ndtypes: category(12), float32(411)\nmemory usage: 1.9 GB\nCPU times: user 14min 27s, sys: 13.1 s, total: 14min 40s\nWall time: 13min 25s\n","output_type":"stream"}],"execution_count":20},{"cell_type":"markdown","source":"#### Optuna tuning","metadata":{}},{"cell_type":"code","source":"def create_model(model_type, params):\n    '''\n    Create model instance\n    '''\n    models = {'lgb': LGBMClassifier, \n              'xgb': XGBClassifier, \n              'cb': CatBoostClassifier,\n             }\n    return models[model_type](**params)\n    \nclass StabilityMetric:\n    \"\"\"\n    Stability metric for model optimization during training\n    \"\"\"\n    def __init__(self, model_type, week_num, X_val):\n        self.model_type = model_type\n        self.X_val = X_val\n        self.week_num = week_num       \n\n    def metric_func(self, y_true, y_pred):\n        gini_in_time = []\n        weeks_to_score = self.week_num[self.X_val.index].reset_index(drop=True)\n        \n        for week in weeks_to_score.unique():\n            week_idx = weeks_to_score.eq(week)\n            gini = 2 * roc_auc_score(y_true[week_idx], y_pred[week_idx]) - 1\n            gini_in_time.append(gini)\n            \n        w_fallingrate = 88.0\n        w_resstd = -0.5\n        x = np.arange(len(gini_in_time))\n        y = np.array(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(y)\n        stability_score = avg_gini + w_fallingrate * min(0, a) + w_resstd * res_std\n        \n        if self.model_type == 'lgb':\n            is_higher_better = True\n            return 'stability_score', stability_score, is_higher_better\n        else:\n            return 'stability_score', stability_score\n\ndef model_objective(trial, model_type, X_train, y_train, X_val, y_val, \n                    cat_cols, week_num, device='gpu'):\n    \"\"\"\n    Objective function for hyperparameter tuning\n    \"\"\" \n    # Target ratio\n    y_ratio = np.sum(y_train == 0) / np.sum(y_train == 1)\n    \n    # Define hyperparameter search space\n    if model_type == 'lgb':\n        tune_params = {      \n            'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),\n            'num_leaves': trial.suggest_int('num_leaves', 8, 256),\n            'min_child_samples': trial.suggest_int('min_child_samples', 5, 200),\n            'reg_alpha': trial.suggest_float('reg_alpha', 1e-8, 10.0, log=True),\n            'reg_lambda': trial.suggest_float('reg_lambda', 1e-8, 10.0, log=True),\n            'colsample_bytree': trial.suggest_float('colsample_bytree', 0.4, 1.0),\n            'subsample': trial.suggest_float('subsample', 0.5, 1.0),\n            'subsample_freq': trial.suggest_int('subsample_freq', 0, 10),\n            'scale_pos_weight': trial.suggest_float('scale_pos_weight', 1.0, y_ratio*2, log=True),\n\n            'n_estimators': 3000,\n            'objective': 'binary',\n            'metric': 'None',\n            'verbosity': -1,\n            'device': device,\n            'max_bin': 255,\n            'n_jobs': -1,\n            }\n    elif model_type == 'xgb':\n        tune_params = { \n            'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),\n            'max_depth': trial.suggest_int('max_depth', 3, 15),\n            'min_child_weight': trial.suggest_int('min_child_weight', 1, 15),\n            'gamma': trial.suggest_float('gamma', 1e-8, 1.0, log=True),\n            'subsample': trial.suggest_float('subsample', 0.5, 1.0),\n            'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),\n            'reg_lambda': trial.suggest_float('reg_lambda', 1e-8, 10.0, log=True),\n            'reg_alpha': trial.suggest_float('reg_alpha', 1e-8, 10.0, log=True),\n            'scale_pos_weight': trial.suggest_float('scale_pos_weight', 0.3, y_ratio*3, log=True),\n\n            'n_estimators': 3000,\n            'early_stopping_rounds': 50,\n            'objective': 'binary:logistic',\n            'enable_categorical': True,\n            'verbosity': 0, \n            'device': 'cuda' if device == 'gpu' else 'cpu',\n            'n_jobs': -1,\n            } \n    elif model_type == 'cb':\n        tune_params = {\n            'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),\n            'depth': trial.suggest_int('depth', 4, 10),\n            'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 1e-8, 10.0, log=True),\n            'random_strength': trial.suggest_float('random_strength', 1e-8, 10.0, log=True),\n            'bagging_temperature': trial.suggest_float('bagging_temperature', 0.0, 1.0),\n            'subsample': trial.suggest_float('subsample', 0.5, 1.0),\n            'rsm': trial.suggest_float('rsm', 0.5, 1.0),\n            'scale_pos_weight': trial.suggest_float('scale_pos_weight', 0.3, y_ratio*3, log=True),\n\n            'iterations': 3000,\n            'early_stopping_rounds': 50,\n            'cat_features': cat_cols, \n            'objective': 'Logloss', \n            'task_type': device.upper(),\n            'border_count': 128 if device == 'gpu' else 254,\n            'verbose': 0, \n            'thread_count': -1,\n        } \n    stability_metric = StabilityMetric(model_type, week_num, X_val)    \n    model = create_model(model_type, tune_params)\n\n    if model_type == 'lgb':\n        model.fit(X_train, y_train, \n                  eval_set=[(X_val, y_val)],\n                  eval_metric=stability_metric.metric_func,\n                  callbacks=[early_stopping(50)],\n                 ) \n        y_pred = model.predict_proba(X_val)[:, 1]\n        _, stability_score, _ = stability_metric.metric_func(\n            np.array(y_val), np.array(y_pred)\n        )\n    else:\n        model.fit(X_train, y_train, eval_set=[(X_val, y_val)])\n        y_pred = model.predict_proba(X_val)[:, 1]\n        _, stability_score = stability_metric.metric_func(\n            np.array(y_val), np.array(y_pred)\n        )\n    return stability_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:25:14.006253Z","iopub.execute_input":"2025-11-21T13:25:14.007011Z","iopub.status.idle":"2025-11-21T13:25:14.023049Z","shell.execute_reply.started":"2025-11-21T13:25:14.006981Z","shell.execute_reply":"2025-11-21T13:25:14.022258Z"}},"outputs":[],"execution_count":25},{"cell_type":"code","source":"%%time\n# Optuna study\noptuna_model = None # Set to 'lgb', 'xgb', 'cb', or None to skip\n\nif optuna_model != None:\n    # Define objective function with closure\n    def objective(trial):\n        return model_objective(\n            trial, optuna_model, X_train, y_train, X_val, y_val, \n            enc_med_card_cols, week_num, device,\n        )\n    # Optuna study\n    sampler = optuna.samplers.TPESampler(multivariate=True, seed=42)\n    study = optuna.create_study(direction='maximize', sampler=sampler)\n    study.optimize(objective, n_trials=50, timeout=60*60*12)  # 12 hours timeout\n\n    # Show best results\n    trial = study.best_trial\n    completed_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]\n    print(f'Number of completed trials: {len(completed_trials)}')\n    print(f'Best score: {trial.value:.3f}')\n    print(f'Best params: {trial.params}')","metadata":{"scrolled":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:25:20.70134Z","iopub.execute_input":"2025-11-21T13:25:20.701751Z","iopub.status.idle":"2025-11-21T13:29:29.815507Z","shell.execute_reply.started":"2025-11-21T13:25:20.701725Z","shell.execute_reply":"2025-11-21T13:29:29.814906Z"}},"outputs":[{"name":"stderr","text":"[I 2025-11-21 13:25:20,705] A new study created in memory with name: no-name-fa86e16b-238e-498d-b4d0-a5177da1db3c\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n1 warning generated.\n","output_type":"stream"},{"name":"stdout","text":"Training until validation scores don't improve for 50 rounds\nEarly stopping, best iteration is:\n[292]\tvalid_0's stability_score: 0.677452\n","output_type":"stream"},{"name":"stderr","text":"[I 2025-11-21 13:29:07,330] Trial 0 finished with value: 0.6774524381563042 and parameters: {'learning_rate': 0.03574712922600244, 'num_leaves': 244, 'min_child_samples': 148, 'reg_alpha': 0.0024430162614261413, 'reg_lambda': 2.5361081166471375e-07, 'colsample_bytree': 0.49359671220172163, 'subsample': 0.5290418060840998, 'subsample_freq': 9, 'scale_pos_weight': 11.88776889599969}. Best is trial 0 with value: 0.6774524381563042.\n","output_type":"stream"},{"name":"stdout","text":"Training until validation scores don't improve for 50 rounds\n","output_type":"stream"},{"name":"stderr","text":"[W 2025-11-21 13:29:29,768] Trial 1 failed with parameters: {'learning_rate': 0.11114989443094977, 'num_leaves': 13, 'min_child_samples': 195, 'reg_alpha': 0.31044435499483225, 'reg_lambda': 8.148018307012941e-07, 'colsample_bytree': 0.5090949803242604, 'subsample': 0.5917022549267169, 'subsample_freq': 3, 'scale_pos_weight': 8.680250354268889} because of the following error: KeyboardInterrupt().\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/dist-packages/optuna/study/_optimize.py\", line 201, in _run_trial\n    value_or_values = func(trial)\n                      ^^^^^^^^^^^\n  File \"<timed exec>\", line 7, in objective\n  File \"/tmp/ipykernel_136/1769191995.py\", line 120, in model_objective\n    model.fit(X_train, y_train,\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/sklearn.py\", line 1560, in fit\n    super().fit(\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/sklearn.py\", line 1049, in fit\n    self._Booster = train(\n                    ^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/engine.py\", line 329, in train\n    evaluation_result_list.extend(booster.eval_valid(feval))\n                                  ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/basic.py\", line 4442, in eval_valid\n    return [\n           ^\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/basic.py\", line 4445, in <listcomp>\n    for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)\n                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/basic.py\", line 5206, in __inner_eval\n    feval_ret = eval_function(self.__inner_predict(data_idx), cur_data)\n                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/lightgbm/sklearn.py\", line 306, in __call__\n    return self.func(labels, preds)  # type: ignore[call-arg]\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/tmp/ipykernel_136/1769191995.py\", line 27, in metric_func\n    gini = 2 * roc_auc_score(y_true[week_idx], y_pred[week_idx]) - 1\n               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/sklearn/utils/_param_validation.py\", line 213, in wrapper\n    return func(*args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_ranking.py\", line 640, in roc_auc_score\n    return _average_binary_score(\n           ^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_base.py\", line 75, in _average_binary_score\n    return binary_metric(y_true, y_score, sample_weight=sample_weight)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_ranking.py\", line 381, in _binary_roc_auc_score\n    if len(np.unique(y_true)) != 2:\n           ^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/numpy/lib/arraysetops.py\", line 274, in unique\n    ret = _unique1d(ar, return_index, return_inverse, return_counts,\n         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.11/dist-packages/numpy/lib/arraysetops.py\", line 336, in _unique1d\n    ar.sort()\nKeyboardInterrupt\n[W 2025-11-21 13:29:29,779] Trial 1 failed with value None.\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/optuna/study/study.py\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m    488\u001b[0m                 \u001b[0mIf\u001b[0m \u001b[0mnested\u001b[0m \u001b[0minvocation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthis\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0moccurs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         \"\"\"\n\u001b[0;32m--> 490\u001b[0;31m         _optimize(\n\u001b[0m\u001b[1;32m    491\u001b[0m             \u001b[0mstudy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             _optimize_sequential(\n\u001b[0m\u001b[1;32m     64\u001b[0m                 \u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m                 \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_optimize_sequential\u001b[0;34m(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m             \u001b[0mfrozen_trial_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_run_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstudy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;31m# The following line mitigates memory problems that can be occurred in some\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/optuna/study/_optimize.py\u001b[0m in \u001b[0;36m_run_trial\u001b[0;34m(study, func, catch)\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     ):\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mfunc_err\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m     \u001b[0;32mreturn\u001b[0m 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     \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36mroc_auc_score\u001b[0;34m(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    639\u001b[0m         \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m 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 \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    382\u001b[0m         raise ValueError(\n\u001b[1;32m    383\u001b[0m             \u001b[0;34m\"Only one class present in y_true. 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\u001b[0mar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}],"execution_count":26},{"cell_type":"markdown","source":"#### Models' best parameters","metadata":{}},{"cell_type":"code","source":"# LGBM best parameters\nlgbm_params = {\n    'num_leaves': 214, \n    'min_data_in_leaf': 1831, \n    'learning_rate': 0.018016752095308213, \n    'lambda_l1': 0.039542276491157664, \n    'lambda_l2': 0.0028839017821387612, \n    'feature_fraction': 0.8606139936996, \n    'bagging_fraction': 0.6505054297397137,\n    'bagging_freq': 4,\n    'scale_pos_weight': 30.72,\n\n    'n_estimators': 3000,\n    'objective': 'binary',\n    'metric': 'None', # stability metric is used as eval_metric\n    'verbosity': -1,\n    'device': device,\n    'max_bin': 255,\n    'n_jobs': -1,\n    }\n# XGB best parameters\nxgb_params = {\n    'learning_rate': 0.02563139404535397, \n    'max_depth': 17, \n    'min_child_weight': 1446, \n    'max_delta_setp': 0,\n    'subsample': 0.8564298564731834, \n    'colsample_bytree': 0.9235898601649664, \n    'reg_lambda': 1.1289608466365559e-08, \n    'reg_alpha': 4.911518931389214e-07, \n    'gamma': 0.00035869889925144383, \n    'scale_pos_weight': 30.72,\n\n    'n_estimators': 3000,\n    'early_stopping_rounds': 50,\n    'objective': 'binary:logistic',\n    'enable_categorical': True,\n    'verbosity': 0, \n    'device': 'cuda' if device == 'gpu' else 'cpu',\n    'n_jobs': -1,\n    }\n# CatBoost best parameters\ncb_params = { \n    'learning_rate': 0.08, \n    'l2_leaf_reg': 57.87508612048416, \n    'bagging_temperature': 0.737099486243173, \n    'random_strength': 0.0017723437301297412, \n    'depth': 6, \n    'min_data_in_leaf': 66,\n    'scale_pos_weight': 30.72,\n\n    'iterations': 2000,\n    'early_stopping_rounds': 50,\n    'cat_features': enc_med_card_cols, \n    'objective': 'Logloss', \n    'task_type': device.upper(),\n    'border_count': 128 if device == 'gpu' else 254,\n    'verbose': 0, \n    'thread_count': -1,\n    }","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:29:36.042459Z","iopub.execute_input":"2025-11-21T13:29:36.043259Z","iopub.status.idle":"2025-11-21T13:29:36.049614Z","shell.execute_reply.started":"2025-11-21T13:29:36.043234Z","shell.execute_reply":"2025-11-21T13:29:36.048843Z"}},"outputs":[],"execution_count":27},{"cell_type":"markdown","source":"#### Create model","metadata":{}},{"cell_type":"code","source":"def train_model(X_train, y_train, X_val, y_val, model_type, params, week_num):\n    \"\"\"\n    Train a specific model type with predefined parameters\n    \"\"\"\n    stability_metric = StabilityMetric(model_type, week_num, X_val) \n    model = create_model(model_type, params)\n\n    if model_type == 'lgb':\n        model.fit(X_train, y_train, \n                  eval_set=[(X_val, y_val)],\n                  eval_metric=stability_metric.metric_func,\n                  callbacks=[early_stopping(50)],\n                 ) \n    else:\n        model.fit(X_train, y_train, eval_set=[(X_val, y_val)])\n        \n    return model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:29:36.761036Z","iopub.execute_input":"2025-11-21T13:29:36.761288Z","iopub.status.idle":"2025-11-21T13:29:36.766458Z","shell.execute_reply.started":"2025-11-21T13:29:36.761273Z","shell.execute_reply":"2025-11-21T13:29:36.7656Z"}},"outputs":[],"execution_count":28},{"cell_type":"code","source":"%%time\n\n# Train model\nX_train, y_train = shuffle(X_train, y_train)\nlgbm_model = train_model(X_train, y_train, X_val, y_val, 'lgb', lgbm_params, week_num)","metadata":{"scrolled":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:29:59.414396Z","iopub.execute_input":"2025-11-21T13:29:59.415095Z","iopub.status.idle":"2025-11-21T13:38:10.349472Z","shell.execute_reply.started":"2025-11-21T13:29:59.415069Z","shell.execute_reply":"2025-11-21T13:38:10.348626Z"}},"outputs":[{"name":"stdout","text":"Training until validation scores don't improve for 50 rounds\nEarly stopping, best iteration is:\n[824]\tvalid_0's stability_score: 0.686429\nCPU times: user 20min 51s, sys: 5.45 s, total: 20min 56s\nWall time: 8min 10s\n","output_type":"stream"}],"execution_count":32},{"cell_type":"code","source":"# Plot LGBM model features importance\nplot_importance(lgbm_model, \n                importance_type='gain', \n                max_num_features=20, \n                height=0.5,\n                grid=False,\n                precision=0,\n                )\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:38:10.351186Z","iopub.execute_input":"2025-11-21T13:38:10.351389Z","iopub.status.idle":"2025-11-21T13:38:10.655036Z","shell.execute_reply.started":"2025-11-21T13:38:10.351372Z","shell.execute_reply":"2025-11-21T13:38:10.654205Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":33},{"cell_type":"markdown","source":"#### Read and prepare test set","metadata":{}},{"cell_type":"code","source":"# Free memory\ndel X_train, X_val, y_train, y_val\n\n# Read, preprocess and merge all the test files together\nX_test = read_prepare_all(test_path, files_dict)\n\n# Match X_test columns with X columns\nfor col in X_structure.columns:\n    if col not in X_test.columns:\n        X_test[col] = X_structure[col]\n        print(f'{col} added to X_test')\n        \nX_test = X_test[X_structure.columns]\n\nX_test.info()","metadata":{"scrolled":true,"_kg_hide-output":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:38:10.655861Z","iopub.execute_input":"2025-11-21T13:38:10.656082Z","iopub.status.idle":"2025-11-21T13:38:11.981928Z","shell.execute_reply.started":"2025-11-21T13:38:10.656065Z","shell.execute_reply":"2025-11-21T13:38:11.981252Z"}},"outputs":[{"name":"stdout","text":"Chunk 0 added to list\nbase created\n### Start read static_0\nChunk 0 added to list\nChunk 1 added to list\nChunk 2 added to list\n=== static_0 merged to df_all\n### Start read static_cb_0\nChunk 0 added to list\n=== static_cb_0 merged to df_all\n### Start read credit_bureau_a_1\nDepth1 aggregation finished\nChunk 0 added to list\nDepth1 aggregation finished\nChunk 1 added to list\nDepth1 aggregation finished\nChunk 2 added to list\nDepth1 aggregation finished\nChunk 3 added to list\nDepth1 aggregation finished\nChunk 4 added to list\n=== credit_bureau_a_1 merged to df_all\n### Start read credit_bureau_b_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== credit_bureau_b_1 merged to df_all\n### Start read applprev_1\nDepth1 aggregation finished\nChunk 0 added to list\nDepth1 aggregation finished\nChunk 1 added to list\nDepth1 aggregation finished\nChunk 2 added to list\n=== applprev_1 merged to df_all\n### Start read debitcard_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== debitcard_1 merged to df_all\n### Start read deposit_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== deposit_1 merged to df_all\n### Start read other_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== other_1 merged to df_all\n### Start read person_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== person_1 merged to df_all\n### Start read tax_registry_a_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== tax_registry_a_1 merged to df_all\n### Start read tax_registry_b_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== tax_registry_b_1 merged to df_all\n### Start read tax_registry_c_1\nDepth1 aggregation finished\nChunk 0 added to list\n=== tax_registry_c_1 merged to df_all\n### Start read credit_bureau_a_2\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 0 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 1 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 2 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 3 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 4 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 5 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 6 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 7 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 8 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 9 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 10 added to list\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 11 added to list\n=== credit_bureau_a_2 merged to df_all\n### Start read credit_bureau_b_2\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 0 added to list\n=== credit_bureau_b_2 merged to df_all\n### Start read applprev_2\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 0 added to list\n=== applprev_2 merged to df_all\n### Start read person_2\nDepth2 aggregation finished\nDepth1 aggregation finished\nChunk 0 added to list\n=== person_2 merged to df_all\ndf converted to pandas\npmtamount_36A_mean added to X_test\npmts_date_1107D_mean_mean added to X_test\nempls_employedfrom_796D_mean_mean added to X_test\nperiodicityofpmts_997L_mode added to X_test\nempl_employedtotal_800L_mode added to X_test\nempl_industry_691L_mode added to X_test\nemployername_160M_mode added to X_test\ncredacc_cards_status_52L_mode_mode added to X_test\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 10 entries, 0 to 9\nColumns: 485 entries, actualdpdtolerance_344P to decision_day_of_week\ndtypes: Int8(1), bool(2), category(124), datetime64[ms](56), float32(299), int16(2), int8(1)\nmemory usage: 4.9 MB\n","output_type":"stream"}],"execution_count":34},{"cell_type":"markdown","source":"#### Predict and submit","metadata":{}},{"cell_type":"code","source":"def predict_proba_in_batches(X_test, final_model, batch_size=10000):\n    \"\"\"\n    Process the test set and predict in batches\n    \"\"\"\n    num_samples = len(X_test)\n    num_batches = (num_samples // batch_size) + 1\n    y_pred = []\n    \n    for batch_idx in range(num_batches):\n        start_idx = batch_idx * batch_size\n        end_idx = min((batch_idx + 1) * batch_size, num_samples)\n        X_batch = X_test.iloc[start_idx:end_idx]\n        X_batch = pd.DataFrame(\n            processor.transform(X_batch), \n            columns=processor.get_feature_names_out(),\n            index=X_batch.index)\n        X_batch[enc_med_card_cols] = (X_batch[enc_med_card_cols]\n                                      .astype(str).astype('category'))\n        batch_preds = []\n        \n        for model_type, model in final_model.items():\n            if 'single' in model_type:\n                batch_model_preds = model.predict_proba(X_batch)[:, 1]\n\n            elif 'ensemble' in model_type: \n                # Predict the average from a list of estimators\n                batch_model_preds = mean_predict_proba(\n                    X_batch, estimators=model)\n                \n            batch_preds.append(batch_model_preds)\n            \n        batch_preds = np.vstack(batch_preds)\n        y_pred.append(batch_preds) \n        \n    # The average from all predictions\n    y_pred = np.hstack(y_pred)\n    y_pred = np.mean(y_pred, axis=0) \n        \n    return y_pred","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:38:11.983457Z","iopub.execute_input":"2025-11-21T13:38:11.98367Z","iopub.status.idle":"2025-11-21T13:38:11.989859Z","shell.execute_reply.started":"2025-11-21T13:38:11.983653Z","shell.execute_reply":"2025-11-21T13:38:11.988992Z"}},"outputs":[],"execution_count":35},{"cell_type":"code","source":"# Predict\nfinal_model = {\n    'single': lgbm_model,\n#     'single': xgb_model,\n#     'ensemble': lgbm_bag_week_model,\n}\ny_pred = predict_proba_in_batches(X_test, final_model, batch_size=10000)\n\n# Submit\nsubmit = pd.read_csv(main_path + 'sample_submission.csv')\nsubmit.score = y_pred\n\nsubmit.to_csv('submission.csv', index=False)\n\nsubmit","metadata":{"scrolled":true,"trusted":true,"execution":{"iopub.status.busy":"2025-11-21T13:38:11.990534Z","iopub.execute_input":"2025-11-21T13:38:11.990765Z","iopub.status.idle":"2025-11-21T13:38:12.62102Z","shell.execute_reply.started":"2025-11-21T13:38:11.990747Z","shell.execute_reply":"2025-11-21T13:38:12.620227Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"   case_id     score\n0    57543  0.063732\n1    57549  0.408924\n2    57551  0.009432\n3    57552  0.252517\n4    57569  0.630620\n5    57630  0.035381\n6    57631  0.132236\n7    57632  0.020213\n8    57633  0.226313\n9    57634  0.182521","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>case_id</th>\n      <th>score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57543</td>\n      <td>0.063732</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57549</td>\n      <td>0.408924</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>57551</td>\n      <td>0.009432</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>57552</td>\n      <td>0.252517</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57569</td>\n      <td>0.630620</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>57630</td>\n      <td>0.035381</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>57631</td>\n      <td>0.132236</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>57632</td>\n      <td>0.020213</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>57633</td>\n      <td>0.226313</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>57634</td>\n      <td>0.182521</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":36}]}