bluecast.blueprints.cast_cv_regression

Module Contents

Classes

BlueCastCVRegression

Wrapper to train and predict multiple blueCast instances.
class bluecast.blueprints.cast_cv_regression.BlueCastCVRegression(class_problem: Literal[regression] = 'regression', cat_columns: List[str | float | int] | None = None, stratifier: Any | None = None, conf_training: bluecast.config.training_config.TrainingConfig | None = None, conf_xgboost: bluecast.config.training_config.XgboostTuneParamsRegressionConfig | None = None, conf_params_xgboost: bluecast.config.training_config.XgboostRegressionFinalParamConfig | None = None, experiment_tracker: bluecast.experimentation.tracking.ExperimentTracker | None = None, custom_in_fold_preprocessor: bluecast.preprocessing.custom.CustomPreprocessing | None = None, custom_last_mile_computation: bluecast.preprocessing.custom.CustomPreprocessing | None = None, custom_preprocessor: bluecast.preprocessing.custom.CustomPreprocessing | None = None, custom_feature_selector: bluecast.preprocessing.feature_selection.BoostaRootaWrapper | bluecast.preprocessing.custom.CustomPreprocessing | None = None, ml_model: bluecast.ml_modelling.xgboost.XgboostModel | Any | None = None, single_fold_eval_metric_func: bluecast.evaluation.eval_metrics.RegressionEvalWrapper | None = None)

Wrapper to train and predict multiple blueCast instances.

Check the BlueCast class documentation for additional parameter details. A custom splitter can be provided.

Parameters:

  • :class_problem – Takes a string containing the class problem type. At the moment “regression” only.

  • :target_column – Takes a string containing the name of the target column.

  • :cat_columns – Takes a list of strings containing the names of the categorical columns. If not provided, BlueCast will infer these automatically.

  • :date_columns – Takes a list of strings containing the names of the date columns. If not provided, BlueCast will infer these automatically.

  • :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.

  • :ml_model – Takes an instance of a XgboostModelRegression 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.

  • 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).

  • 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.

  • 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.

  • experiment_tracker – Takes an instance of an ExperimentTracker class. If not provided this will be initialized automatically.

  • 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.

prepare_data(df: pandas.DataFrame, target: str) → Tuple[pandas.DataFrame, pandas.Series]
show_oof_scores(metric: str = 'RMSE') → Tuple[float, float]

Show out of fold scores.

When calling BlueCastCVRegression’s fit_eval function multiple BlueCastRegression instances are called and each of them predicts on unseen/oof data.

This function collects these scores and return mean and average of them.

Parameters:

metric – String indicating which metric shall be returned.

Returns:

Tuple with (mean, std) of oof scores

fit(df: pandas.DataFrame, target_col: str) → None

Fit multiple BlueCastRegression instances on different data splits.

Input df is expected the target column.

fit_eval(df: pandas.DataFrame, target_col: str) → Tuple[float, float]

Fit multiple BlueCastRegression instances on different data splits.

Input df is expected the target column. Evaluation is executed on out-of-fold dataset in each split. :param df: Pandas DataFrame that includes the target column :param target_col: String indicating the name of the target column :returns Tuple of (oof_mean, oof_std) with scores on unseen data during eval

predict(df: pandas.DataFrame, return_sub_models_preds: bool = False, save_shap_values: bool = False, mean_type: Literal[arithmetic, median, geometric, harmonic] = 'arithmetic') → pandas.DataFrame | pandas.Series

Predict on unseen data using multiple trained BlueCastRegression instances.

Parameters:

  • df – Pandas DataFrame with unseen data

  • return_sub_models_preds – If true will return a DataFrame with the predictions of each model stored in separate columns.

  • save_shap_values – If True, calculates and saves shap values, so they can be used to plot waterfall plots for selected rows o demand.

  • mean_type – String indicating the type of mean to be used to blend the predictions of the sub models. Possible values are ‘arithmetic’, ‘geometric’ and ‘harmonic’ (default=’arithmetic’).

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. Used is the mean prediction of all sub models. :param: x_calibration: Pandas DataFrame without target column, that has not been seen by the model during

training.

Parameters:

y_calibration – Pandas Series holding the target value, hat has not been seen by the model during training.

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.