logml.metrics.registry.regression

Classes

BaseRegressionMetric(**kwargs)	Base class for regression metrics.
MAE(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html
MAPE(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html
MSLE(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html
R2Score(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html
RMSE(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html

class logml.metrics.registry.regression.BaseRegressionMetric(**kwargs)

Bases: logml.metrics.base.BaseMetric

Base class for regression metrics.

TASK: logml.common.ModelingTask = 'regression'

get_value(y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, class_labels: Optional[list] = None, **kwargs)

See paret description.

class logml.metrics.registry.regression.MAE(**kwargs)

Bases: logml.metrics.registry.regression.BaseRegressionMetric

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html

LABEL: str = 'mae'

get_value(y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, class_labels: Optional[list] = None, **kwargs)

See paret description.

class logml.metrics.registry.regression.RMSE(**kwargs)

Bases: logml.metrics.registry.regression.BaseRegressionMetric

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html

LABEL: str = 'rmse'

get_value(y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, class_labels: Optional[list] = None, **kwargs)

See paret description.

class logml.metrics.registry.regression.MSLE(**kwargs)

Bases: logml.metrics.registry.regression.BaseRegressionMetric

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_log_error.html

LABEL: str = 'msle'

is_applicable(objective: logml.common.ModelingTask, y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, return_loss: Optional[bool] = None, return_score: Optional[bool] = None, **kwargs) → bool

Checks if the metric is applible for the modeling problem at hand.

get_value(y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, class_labels: Optional[list] = None, **kwargs)

See paret description.

class logml.metrics.registry.regression.MAPE(**kwargs)

Bases: logml.metrics.registry.regression.BaseRegressionMetric

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html

LABEL: str = 'mape'

get_value(y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, class_labels: Optional[list] = None, **kwargs)

See paret description.

class logml.metrics.registry.regression.R2Score(**kwargs)

Bases: logml.metrics.registry.regression.BaseRegressionMetric

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html

LABEL: str = 'r2'

get_value(y_true: Optional[numpy.ndarray] = None, y_pred: Optional[numpy.ndarray] = None, y_pred_proba: Optional[numpy.ndarray] = None, class_labels: Optional[list] = None, **kwargs)

See paret description.