bluecast.tests.test_cast_cv_regression

Module Contents

Classes

CustomLRModel
MyCustomLastMilePreprocessing	This class is an entry point for last mile computations before model training or tuning. It is an abstract class
CustomModel

Functions

synthetic_train_test_data(→ Tuple[pandas.DataFrame, ...)
synthetic_calibration_data(→ pandas.DataFrame)
test_blueprint_cv_xgboost(synthetic_train_test_data, ...)	Test that tests the BlueCast cv class
test_bluecast_cv_fit_eval_with_custom_model()
test_bluecast_cv_with_custom_objects()

bluecast.tests.test_cast_cv_regression.synthetic_train_test_data() → Tuple[pandas.DataFrame, pandas.DataFrame]

bluecast.tests.test_cast_cv_regression.synthetic_calibration_data() → pandas.DataFrame

bluecast.tests.test_cast_cv_regression.test_blueprint_cv_xgboost(synthetic_train_test_data, synthetic_calibration_data)

Test that tests the BlueCast cv class

class bluecast.tests.test_cast_cv_regression.CustomLRModel

Bases: bluecast.ml_modelling.base_classes.BaseClassMlRegressionModel

fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) → None

predict(df: pandas.DataFrame) → numpy.ndarray

class bluecast.tests.test_cast_cv_regression.MyCustomLastMilePreprocessing(trained_patterns: Any | None = None)

Bases: bluecast.preprocessing.custom.CustomPreprocessing

This class is an entry point for last mile computations before model training or tuning. It is an abstract class and must be extended by the user. For fit_transform x_train and y_train are passed. For transform x_test and y_test are passed in the BlueCast pipeline. Use prediction_mode = False to skip processing the missing targets. :param trained_patterns: Optional. Can we used to save anything from training to be loaded and used in transform. If more placeholders are needed, use a dictionary.

custom_function(df: pandas.DataFrame) → pandas.DataFrame

fit_transform(df: pandas.DataFrame, target: pandas.Series) → Tuple[pandas.DataFrame, pandas.Series]

transform(df: pandas.DataFrame, target: pandas.Series | None = None, predicton_mode: bool = False) → Tuple[pandas.DataFrame, pandas.Series | None]

Use prediction mode to not process the missing target during inference.

class bluecast.tests.test_cast_cv_regression.CustomModel

Bases: bluecast.ml_modelling.base_classes.BaseClassMlRegressionModel

fit(x_train: pandas.DataFrame, x_test: pandas.DataFrame, y_train: pandas.Series, y_test: pandas.Series) → None

predict(df: pandas.DataFrame) → numpy.ndarray

bluecast.tests.test_cast_cv_regression.test_bluecast_cv_fit_eval_with_custom_model()

bluecast.tests.test_cast_cv_regression.test_bluecast_cv_with_custom_objects()