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Init module for TensorFlow Model Analysis metrics.
Classes
class AUC: Approximates the AUC (Area under the curve) of the ROC or PR curves.
class AUCCurve: An enumeration.
class AUCPrecisionRecall: Alias for AUC(curve='PR').
class AUCSummationMethod: An enumeration.
class AttributionsMetric: Base type for attribution metrics.
class BalancedAccuracy: Balanced accuracy (BA).
class BinaryAccuracy: Calculates how often predictions match binary labels.
class BinaryCrossEntropy: Calculates the binary cross entropy.
class BooleanFlipRates: FlipeRate computes the rate at which predicitons between models switch.
class COCOAveragePrecision: Confusion matrix at thresholds.
class COCOAverageRecall: Average recall metric for object detection.
class COCOMeanAveragePrecision: Mean average precision for object detections.
class COCOMeanAverageRecall: Mean Average recall metric for object detection.
class Calibration: Calibration.
class CalibrationPlot: Calibration plot.
class CategoricalCrossEntropy: Calculates the categorical cross entropy.
class CoefficientOfDiscrimination: Coefficient of discrimination metric.
class ConfusionMatrixAtThresholds: Confusion matrix at thresholds.
class ConfusionMatrixPlot: Confusion matrix plot.
class DerivedMetricComputation: DerivedMetricComputation derives its result from other computations.
class DiagnosticOddsRatio: Diagnostic odds ratio (DOR).
class ExactMatch: Exact Match Metric.
class ExampleCount: Example count.
class F1Score: F1 score.
class FN: Alias for FalseNegatives.
class FNR: Alias for MissRate.
class FP: Alias for FalsePositives.
class FPR: Alias for FallOut.
class FallOut: Fall-out (FPR).
class FalseDiscoveryRate: False discovery rate (FDR).
class FalseNegatives: Calculates the number of false negatives.
class FalseOmissionRate: False omission rate (FOR).
class FalsePositives: Calculates the number of false positives.
class FowlkesMallowsIndex: Fowlkes-Mallows index (FM).
class Informedness: Informedness or bookmaker informedness (BM).
class Markedness: Markedness (MK) or deltaP.
class MatthewsCorrelationCoefficient: Matthews corrrelation coefficient (MCC).
class MaxRecall: Computes the max recall of the predictions with respect to the labels.
class MeanAbsoluteAttributions: Mean aboslute attributions metric.
class MeanAbsoluteError: Calculates the mean of absolute error between labels and predictions.
class MeanAbsolutePercentageError: Calculates the mean of absolute percentage error.
class MeanAttributions: Mean attributions metric.
class MeanLabel: Mean label.
class MeanPrediction: Mean prediction.
class MeanSquaredError: Calculates the mean of squared error between labels and predictions.
class MeanSquaredLogarithmicError: Calculates the mean of squared logarithmic error.
class Metric: Metric wraps a set of metric computations.
class MetricComputation: MetricComputation represents one or more metric computations.
class MetricKey: A MetricKey uniquely identifies a metric.
class MinLabelPosition: Min label position metric.
class MissRate: Miss rate (FNR).
class MultiClassConfusionMatrixAtThresholds: Multi-class confusion matrix metrics at thresholds.
class MultiClassConfusionMatrixPlot: Multi-class confusion matrix plot.
class MultiLabelConfusionMatrixPlot: Multi-label confusion matrix.
class NDCG: NDCG (normalized discounted cumulative gain) metric.
class NPV: Alias for NegativePredictiveValue.
class NegativeLikelihoodRatio: Negative likelihood ratio (LR-).
class NegativePredictiveValue: Negative predictive value (NPV).
class ObjectDetectionConfusionMatrixPlot: Object Detection Confusion matrix plot.
class ObjectDetectionMaxRecall: Computes the max recall of the predictions with respect to the labels.
class ObjectDetectionPrecision: Computes the precision of the predictions with respect to the labels.
class ObjectDetectionPrecisionAtRecall: Computes best precision where recall is >= specified value.
class ObjectDetectionRecall: Computes the recall of the predictions with respect to the labels.
class ObjectDetectionThresholdAtRecall: Computes maximum threshold where recall is >= specified value.
class PPV: Alias for Precision.
class PlotKey: A PlotKey is a metric key that uniquely identifies a plot.
class PositiveLikelihoodRatio: Positive likelihood ratio (LR+).
class Precision: Computes the precision of the predictions with respect to the labels.
class PrecisionAtRecall: Computes best precision where recall is >= specified value.
class Preprocessor: Preprocessor wrapper for preprocessing data in the metric computation.
class Prevalence: Prevalence.
class PrevalenceThreshold: Prevalence threshold (PT).
class QueryStatistics: Query statistic metrics.
class Recall: Computes the recall of the predictions with respect to the labels.
class RecallAtPrecision: Computes best recall where precision is >= specified value.
class RelativeCoefficientOfDiscrimination: Relative coefficient of discrimination metric.
class ScoreDistributionPlot: Score distribution plot.
class SensitivityAtSpecificity: Computes best sensitivity where specificity is >= specified value.
class SetMatchPrecision: Computes precision for sets of labels and predictions.
class SetMatchRecall: Computes recall for sets of labels and predictions.
class Specificity: Specificity (TNR) or selectivity.
class SpecificityAtSensitivity: Computes best specificity where sensitivity is >= specified value.
class SquaredPearsonCorrelation: Squared pearson correlation (r^2) metric.
class StandardMetricInputs: Standard inputs used by most metric computations.
class SubKey: A SubKey identifies a sub-types of metrics and plots.
class SymmetricPredictionDifference: PredictionDifference computes the avg pointwise diff between models.
class TN: Alias for TrueNegatives.
class TNR: Alias for Specificity.
class TP: Alias for TruePositives.
class TPR: Alias for Recall.
class ThreatScore: Threat score or critical success index (TS or CSI).
class TotalAbsoluteAttributions: Total absolute attributions metric.
class TotalAttributions: Total attributions metric.
class TrueNegatives: Calculates the number of true negatives.
class TruePositives: Calculates the number of true positives.
class WeightedExampleCount: Weighted example count (deprecated - use ExampleCount).
Functions
FeaturePreprocessor(...): Returns preprocessor for including features in StandardMetricInputs.
default_binary_classification_specs(...): Returns default metric specs for binary classification problems.
default_multi_class_classification_specs(...): Returns default metric specs for multi-class classification problems.
default_regression_specs(...): Returns default metric specs for for regression problems.
has_attributions_metrics(...): Returns true if any of the metrics_specs have attributions metrics.
merge_per_key_computations(...): Wraps create_computations_fn to be called separately for each key.
metric_thresholds_from_metrics_specs(...): Returns thresholds associated with given metrics specs.
specs_from_metrics(...): Returns specs for tf.keras.metrics/losses or tfma.metrics classes.
to_label_prediction_example_weight(...): Yields label, prediction, and example weights for use in calculations.
to_standard_metric_inputs(...): Verifies extract keys and converts extracts to StandardMetricInputs.
Type Aliases
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| NO_PREDICTED_CLASS_ID |
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