:py:mod:`bluecast.blueprints.cast_regression` ============================================= .. py:module:: bluecast.blueprints.cast_regression .. autoapi-nested-parse:: Run fully configured classification blueprint. Customization via class attributes is possible. Configs can be instantiated and provided to change Xgboost training. Default hyperparameter search space is relatively light-weight to speed up the prototyping. Can deal with binary and multi-class classification problems. Hyperparameter tuning can be switched off or even strengthened via cross-validation. This behaviour can be controlled via the config class attributes from config.training_config module. Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: bluecast.blueprints.cast_regression.BlueCastRegression .. py:class:: BlueCastRegression(class_problem: Literal[regression], cat_columns: Optional[List[Union[str, float, int]]] = None, date_columns: Optional[List[Union[str, float, int]]] = None, time_split_column: Optional[str] = None, ml_model: Optional[Union[bluecast.ml_modelling.catboost_regression.CatboostModelRegression, Any]] = None, custom_in_fold_preprocessor: Optional[bluecast.preprocessing.custom.CustomPreprocessing] = None, custom_last_mile_computation: Optional[bluecast.preprocessing.custom.CustomPreprocessing] = None, custom_preprocessor: Optional[bluecast.preprocessing.custom.CustomPreprocessing] = None, custom_feature_selector: Optional[Union[bluecast.preprocessing.feature_selection.BoostaRootaWrapper, bluecast.preprocessing.custom.CustomPreprocessing]] = None, conf_training: Optional[bluecast.config.training_config.TrainingConfig] = None, conf_xgboost: Optional[Union[bluecast.config.training_config.XgboostTuneParamsRegressionConfig, bluecast.config.training_config.CatboostTuneParamsRegressionConfig]] = None, conf_params_xgboost: Optional[Union[bluecast.config.training_config.XgboostRegressionFinalParamConfig, bluecast.config.training_config.CatboostRegressionFinalParamConfig]] = None, experiment_tracker: Optional[bluecast.experimentation.tracking.ExperimentTracker] = None, single_fold_eval_metric_func: Optional[bluecast.evaluation.eval_metrics.RegressionEvalWrapper] = None) Run fully configured classification blueprint. Customization via class attributes is possible. Configs can be instantiated and provided to change Xgboost training. Default hyperparameter search space is relatively light-weight to speed up the prototyping. :param :class_problem: Takes a string containing the class problem type. At the moment "regression" only. :param :target_column: Takes a string containing the name of the target column. :param :cat_columns: Takes a list of strings containing the names of the categorical columns. If not provided, BlueCast will infer these automatically. :param :date_columns: Takes a list of strings containing the names of the date columns. If not provided, BlueCast will infer these automatically. :param :time_split_column: Takes a string containing the name of the time split column. If not provided, BlueCast will not split the data by time or order, but do a random split instead. :param :ml_model: Takes an instance of a CatboostModelRegression class. If not provided, BlueCast will instantiate one. This is an API to pass any model class. Inherit the baseclass from ml_modelling.base_model.BaseModel. :param custom_in_fold_preprocessor: Takes an instance of a CustomPreprocessing class. Allows users to eeecute preprocessing after the train test split within cv folds. This will be executed only if precise_cv_tuning in the conf_Training is True. Custom ML models need to implement this themselves. This step is only useful when the proprocessing step has a high chance of overfitting otherwise (i.e: oversampling techniques). :param custom_preprocessor: Takes an instance of a CustomPreprocessing class. Allows users to inject custom preprocessing steps which take place right after the train test spit. :param custom_last_mile_computation: Takes an instance of a CustomPreprocessing class. Allows users to inject custom preprocessing steps which take place right before the model training. :param experiment_tracker: Takes an instance of an ExperimentTracker class. If not provided this will be initialized automatically. :param single_fold_eval_metric_func: Takes a function which calculates the evaluation metric for a single fold. Default is mean_squared_error. This function is used to calculate the evaluation metric for each fold during hyperparameter tuning when hyperparameter_tuning_rounds = 1 (default). Lower must be better. .. py:method:: initial_checks(df: pandas.DataFrame) -> None .. py:method:: fit(df: pandas.DataFrame, target_col: str) -> None Train a full ML pipeline. .. py:method:: fit_eval(df: pandas.DataFrame, df_eval: pandas.DataFrame, target_eval: pandas.Series, target_col: str) -> Dict[str, Any] Train a full ML pipeline and evaluate on a holdout set. This is a convenience function to train and evaluate on a holdout set. It is recommended to use this for model exploration. On production the simple fit() function should be used. :param :df: Takes a pandas DataFrame containing the training data and the targets. :param :df_eval: Takes a pandas DataFrame containing the evaluation data, but not the targets. :param :target_eval: Takes a pandas Series containing the evaluation targets. :param :target_col: Takes a string containing the name of the target column inside the training data df. .. py:method:: transform_new_data(df: pandas.DataFrame) -> pandas.DataFrame Transform new data according to preprocessing pipeline. .. py:method:: predict(df: pandas.DataFrame, save_shap_values: bool = False) -> numpy.ndarray Predict on unseen data. Return the predicted probabilities and the predicted classes: y_probs, y_classes = predict(df) :param df: Pandas DataFrame with unseen data :param save_shap_values: If True, calculates and saves shap values, so they can be used to plot waterfall plots for selected rows o demand. .. py:method:: calibrate(x_calibration: pandas.DataFrame, y_calibration: pandas.Series, **kwargs) -> None Calibrate the model. Via this function the nonconformity measures are taken and used to predict prediction intervals vis the predict_interval function. :param: x_calibration: Pandas DataFrame without target column, that has not been seen by the model during training. :param y_calibration: Pandas Series holding the target value, hat has not been seen by the model during training. .. py:method:: predict_interval(df: pandas.DataFrame, alphas: List[float]) -> pandas.DataFrame Create prediction intervals based on a certain confidence levels. Conformal prediction guarantees, that the correct value is present in the prediction band with a probability of 1 - alpha. :param df: Pandas DataFrame holding unseen data :param alphas: List of floats indicating the desired confidence levels. :returns: A Pandas DataFrame with sorted columns 'alpha_XX_low' (alpha) and 'alpha_XX_high' (1 - alpha) for each alpha in the provided list of alphas. To obtain the mean prediction call the 'predict' method.