evidently.metrics.classification_performance

Submodules

base_classification_metric module

class ThresholdClassificationMetric(probas_threshold: Optional[float], k: Optional[Union[float, int]])

Bases: Metric[TResult], ABC

Attributes:

k : Optional[Union[float, int]]

probas_threshold : Optional[float]

Methods:

get_target_prediction_data(data: DataFrame, column_mapping: ColumnMapping)

class_balance_metric module

class ClassificationClassBalance()

Bases: Metric[ClassificationClassBalanceResult]

Methods:

calculate(data: InputData)

class ClassificationClassBalanceRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationClassBalance)

render_json(obj: ClassificationClassBalance)

class ClassificationClassBalanceResult(plot_data: Dict[str, int])

Bases: object

Attributes:

plot_data : Dict[str, int]

class_separation_metric module

class ClassificationClassSeparationPlot()

Bases: Metric[ClassificationClassSeparationPlotResults]

Methods:

calculate(data: InputData)

class ClassificationClassSeparationPlotRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationClassSeparationPlot)

render_json(obj: ClassificationClassSeparationPlot)

class ClassificationClassSeparationPlotResults(target_name: str, current_plot: Optional[pandas.core.frame.DataFrame] = None, reference_plot: Optional[pandas.core.frame.DataFrame] = None)

Bases: object

Attributes:

current_plot : Optional[DataFrame] = None

reference_plot : Optional[DataFrame] = None

target_name : str

classification_dummy_metric module

class ClassificationDummyMetric(probas_threshold: Optional[float] = None, k: Optional[Union[float, int]] = None)

Bases: ThresholdClassificationMetric[ClassificationDummyMetricResults]

Attributes:

quality_metric : ClassificationQualityMetric

Methods:

calculate(data: InputData)

correction_for_threshold(dummy_results: DatasetClassificationQuality, threshold: float, target: Series, labels: list, probas_shape: tuple)

class ClassificationDummyMetricRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationDummyMetric)

render_json(obj: ClassificationDummyMetric)

class ClassificationDummyMetricResults(dummy: DatasetClassificationQuality, by_reference_dummy: Optional[DatasetClassificationQuality], model_quality: Optional[DatasetClassificationQuality], metrics_matrix: dict)

Bases: object

Attributes:

by_reference_dummy : Optional[DatasetClassificationQuality]

dummy : DatasetClassificationQuality

metrics_matrix : dict

model_quality : Optional[DatasetClassificationQuality]

classification_quality_metric module

class ClassificationQualityMetric(probas_threshold: Optional[float] = None, k: Optional[Union[float, int]] = None)

Bases: ThresholdClassificationMetric[ClassificationQualityMetricResult]

Attributes:

confusion_matrix_metric : ClassificationConfusionMatrix

Methods:

calculate(data: InputData)

class ClassificationQualityMetricRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationQualityMetric)

render_json(obj: ClassificationQualityMetric)

class ClassificationQualityMetricResult(current: DatasetClassificationQuality, reference: Optional[DatasetClassificationQuality], target_name: str)

Bases: object

Attributes:

current : DatasetClassificationQuality

reference : Optional[DatasetClassificationQuality]

target_name : str

confusion_matrix_metric module

class ClassificationConfusionMatrix(probas_threshold: Optional[float] = None, k: Optional[Union[float, int]] = None)

Bases: ThresholdClassificationMetric[ClassificationConfusionMatrixResult]

Attributes:

k : Optional[Union[float, int]]

probas_threshold : Optional[float]

Methods:

calculate(data: InputData)

class ClassificationConfusionMatrixRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationConfusionMatrix)

render_json(obj: ClassificationConfusionMatrix)

class ClassificationConfusionMatrixResult(current_matrix: ConfusionMatrix, reference_matrix: Optional[ConfusionMatrix])

Bases: object

Attributes:

current_matrix : ConfusionMatrix

reference_matrix : Optional[ConfusionMatrix]

pr_curve_metric module

class ClassificationPRCurve()

Bases: Metric[ClassificationPRCurveResults]

Methods:

calculate(data: InputData)

calculate_metrics(target_data: Series, prediction: PredictionData)

class ClassificationPRCurveRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationPRCurve)

render_json(obj: ClassificationPRCurve)

class ClassificationPRCurveResults(current_pr_curve: Optional[dict] = None, reference_pr_curve: Optional[dict] = None)

Bases: object

Attributes:

current_pr_curve : Optional[dict] = None

reference_pr_curve : Optional[dict] = None

pr_table_metric module

class ClassificationPRTable()

Bases: Metric[ClassificationPRTableResults]

Methods:

calculate(data: InputData)

calculate_metrics(target_data: Series, prediction: PredictionData)

class ClassificationPRTableRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationPRTable)

render_json(obj: ClassificationPRTable)

class ClassificationPRTableResults(current_pr_table: Optional[dict] = None, reference_pr_table: Optional[dict] = None)

Bases: object

Attributes:

current_pr_table : Optional[dict] = None

reference_pr_table : Optional[dict] = None

probability_distribution_metric module

class ClassificationProbDistribution()

Bases: Metric[ClassificationProbDistributionResults]

Methods:

calculate(data: InputData)

static get_distribution(dataset: DataFrame, target_name: str, prediction_labels: Iterable)

class ClassificationProbDistributionRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationProbDistribution)

render_json(obj: ClassificationProbDistribution)

class ClassificationProbDistributionResults(current_distribution: Optional[Dict[str, list]], reference_distribution: Optional[Dict[str, list]])

Bases: object

Attributes:

current_distribution : Optional[Dict[str, list]]

reference_distribution : Optional[Dict[str, list]]

quality_by_class_metric module

class ClassificationQualityByClass(probas_threshold: Optional[float] = None, k: Optional[Union[float, int]] = None)

Bases: ThresholdClassificationMetric[ClassificationQualityByClassResult]

Attributes:

k : Optional[Union[float, int]]

probas_threshold : Optional[float]

Methods:

calculate(data: InputData)

class ClassificationQualityByClassRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationQualityByClass)

render_json(obj: ClassificationQualityByClass)

class ClassificationQualityByClassResult(columns: DatasetColumns, current_metrics: dict, current_roc_aucs: Optional[list], reference_metrics: Optional[dict], reference_roc_aucs: Optional[dict])

Bases: object

Attributes:

columns : DatasetColumns

current_metrics : dict

current_roc_aucs : Optional[list]

reference_metrics : Optional[dict]

reference_roc_aucs : Optional[dict]

quality_by_feature_table module

class ClassificationQualityByFeatureTable(columns: Optional[List[str]] = None)

Bases: Metric[ClassificationQualityByFeatureTableResults]

Attributes:

columns : Optional[List[str]]

Methods:

calculate(data: InputData)

class ClassificationQualityByFeatureTableRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationQualityByFeatureTable)

render_json(obj: ClassificationQualityByFeatureTable)

class ClassificationQualityByFeatureTableResults(current_plot_data: pandas.core.frame.DataFrame, reference_plot_data: Optional[pandas.core.frame.DataFrame], target_name: str, curr_predictions: PredictionData, ref_predictions: Optional[PredictionData], columns: List[str])

Bases: object

Attributes:

columns : List[str]

curr_predictions : PredictionData

current_plot_data : DataFrame

ref_predictions : Optional[PredictionData]

reference_plot_data : Optional[DataFrame]

target_name : str

roc_curve_metric module

class ClassificationRocCurve()

Bases: Metric[ClassificationRocCurveResults]

Methods:

calculate(data: InputData)

calculate_metrics(target_data: Series, prediction: PredictionData)

class ClassificationRocCurveRenderer(color_options: Optional[ColorOptions] = None)

Bases: MetricRenderer

Attributes:

color_options : ColorOptions

Methods:

render_html(obj: ClassificationRocCurve)

render_json(obj: ClassificationRocCurve)

class ClassificationRocCurveResults(current_roc_curve: Optional[dict] = None, reference_roc_curve: Optional[dict] = None)

Bases: object

Attributes:

current_roc_curve : Optional[dict] = None

reference_roc_curve : Optional[dict] = None