config.py

from sklearn.metrics import r2_score

class Config():

    def __init__(self):
        pass

    target = 'demand'
    split_local_test = False

    not_include_features = [
        target,
        'date'
    ]

    cat_features = [
        'product_id',
        'product_application',
        'product_marketing_name',
        'product_main_family',
        'planning_method_latest'
    ]

    scorer = r2_score
    model_type = 'CATBOOST'
    fold = 5
    fold_models_directory = 'models/date_models_test'
    fold_input_directory = 'maps/date_models_test'

    catboost_params = {
        'learning_rate': 0.03,
        'objective':'RMSE',
        'depth': 5,
        'early_stopping_rounds':200,
        'iterations': 2000,
        'use_best_model': True,
    #     'eval_metric': CatBoostEvalMetricSMAPE(),
        'eval_metric': 'R2',
        'random_state': 42,
        'allow_writing_files': False,
        'thread_count':-1
    }

    # deployment
    MAIN_TITLE = 'Infineon Product Demand Forecasting System'
    SUB_TITLE = 'Data Analytics in Applications'
    ICON_PATH = 'images/infineon-icon-1.png'

    FORECAST_START_DATE = '01-11-2023'
    FORECAST_END_DATE = '01-07-2024'

    FORECAST_BUTTON_TEXT = 'Predict'

    LINE_PLOT_SELECTBOX_TEXT = 'Filter at product level'
    BAR_PLOT_SELECTBOX_TEXT = 'Filter at category level'
    
    SAVE_CHECKBOX_TEXT = 'Save predictions'
    SAVE_BUTTON_TEXT = 'Apply'
    SAVE_BUTTON_SUCCESS_TEXT = 'File successfully saved'