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
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