nannyml.performance_calculation.metrics.binary_classification module

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationAUROC(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Area under Receiver Operating Curve metric.

Creates a new AUROC instance.

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationAccuracy(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Accuracy metric.

Creates a new Accuracy instance.

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationBusinessValue(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, business_value_matrix: Union[List, numpy.ndarray], normalize_business_value: Optional[str] = None, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Business Value metric.

Creates a new Business Value instance.

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationConfusionMatrix(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, normalize_confusion_matrix: Optional[str] = None, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Confusion Matrix metric.

Creates a new Confusion Matrix instance.

fit(reference_data: pandas.core.frame.DataFrame, chunker: nannyml.chunk.Chunker)[source]

Fits a Metric on reference data.

Parameters

reference_data (pd.DataFrame) – The reference data used for fitting. Must have target data available.
chunker (Chunker) – The Chunker used to split the reference data into chunks. This value is provided by the calling PerformanceCalculator.

get_chunk_record(chunk_data: pandas.core.frame.DataFrame) → Dict[source]: Returns a DataFrame containing the performance metrics for a given chunk.

get_false_neg_info(chunk_data: pandas.core.frame.DataFrame) → Dict[source]

get_false_pos_info(chunk_data: pandas.core.frame.DataFrame) → Dict[source]

get_true_neg_info(chunk_data: pandas.core.frame.DataFrame) → Dict[source]

get_true_pos_info(chunk_data: pandas.core.frame.DataFrame) → Dict[source]

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationF1(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

F1 score metric.

Creates a new F1 instance.

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationPrecision(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Precision metric.

Creates a new Precision instance.

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationRecall(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Recall metric, also known as ‘sensitivity’.

Creates a new Recall instance.

class nannyml.performance_calculation.metrics.binary_classification.BinaryClassificationSpecificity(y_true: str, y_pred: str, threshold: nannyml.thresholds.Threshold, y_pred_proba: Optional[str] = None, **kwargs)[source]

Bases: nannyml.performance_calculation.metrics.base.Metric

Specificity metric.

Creates a new F1 instance.