logml.metrics.registry.classification

Classes

Accuracy(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
BaseClassificationMetric(**kwargs)	Base class for classification metrics (from 0 to 1).
F1Score(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
Jaccard(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html
LogLoss(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html
PRCurve(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
Precision(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html
Recall(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html
RocAUC(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html
RocCurve(**kwargs)	https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html

class logml.metrics.registry.classification.BaseClassificationMetric(**kwargs)

Bases: logml.metrics.base.BaseMetric

Base class for classification metrics (from 0 to 1).

TASK: logml.common.ModelingTask = 'classification'

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 parent

class logml.metrics.registry.classification.Accuracy(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

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

LABEL: str = 'accuracy'

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)

Get metric value

class logml.metrics.registry.classification.F1Score(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html

LABEL: str = 'f1'

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)

Get metric value

class logml.metrics.registry.classification.Jaccard(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

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

LABEL: str = 'jaccard'

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)

Get metric value

class logml.metrics.registry.classification.LogLoss(**kwargs)

Bases: logml.metrics.base.BaseMetric

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

TASK: logml.common.ModelingTask = 'classification'

LABEL: str = 'logloss'

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)

Get metric value

class logml.metrics.registry.classification.Precision(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

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

LABEL: str = 'precision'

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)

Get metric value

class logml.metrics.registry.classification.Recall(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

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

LABEL: str = 'recall'

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)

Get metric value

class logml.metrics.registry.classification.RocAUC(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

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

LABEL: str = 'rocauc'

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)

Get metric value

class logml.metrics.registry.classification.RocCurve(**kwargs)

Bases: logml.metrics.registry.classification.BaseClassificationMetric

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

LABEL: str = 'roc-curve'

REQUIRED: bool = False

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 parent

get_loss(**kwargs)

Loss is not supported.

get_score(**kwargs)

Loss is not supported.

class logml.metrics.registry.classification.PRCurve(**kwargs)

Bases: logml.metrics.registry.classification.RocCurve

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

LABEL: str = 'pr-curve'