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Init module for TensorFlow Model Analysis metrics.
Classes
class AttributionsMetric
: Base type for attribution metrics.
class BalancedAccuracy
: Balanced accuracy (BA).
class Calibration
: Calibration.
class CalibrationPlot
: Calibration plot.
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 ExampleCount
: Example count.
class F1Score
: F1 score.
class FallOut
: Fall-out (FPR).
class FalseDiscoveryRate
: False discovery rate (FDR).
class FalseOmissionRate
: False omission rate (FOR).
class FeaturePreprocessor
: Preprocessor for copying features to the standard metric inputs.
class FowlkesMallowsIndex
: Fowlkes-Mallows index (FM).
class Informedness
: Informedness or bookmaker informedness (BM).
class Markedness
: Markedness (MK) or deltaP.
class MatthewsCorrelationCoefficent
: Matthews corrrelation coefficient (MCC).
class MeanAbsoluteAttributions
: Mean aboslute attributions metric.
class MeanAttributions
: Mean attributions metric.
class MeanLabel
: Mean label.
class MeanPrediction
: Mean prediction.
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 NegativeLikelihoodRatio
: Negative likelihood ratio (LR-).
class NegativePredictiveValue
: Negative predictive value (NPV).
class PlotKey
: A PlotKey is a metric key that uniquely identifies a plot.
class PositiveLikelihoodRatio
: Positive likelihood ratio (LR+).
class Prevalence
: Prevalence.
class PrevalenceThreshold
: Prevalence threshold (PT).
class QueryStatistics
: Query statistic metrics.
class RelativeCoefficientOfDiscrimination
: Relative coefficient of discrimination metric.
class Specificity
: Specificity (TNR) or selectivity.
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 ThreatScore
: Threat score or critical success index (TS or CSI).
class TotalAbsoluteAttributions
: Total absolute attributions metric.
class TotalAttributions
: Total attributions metric.
class WeightedExampleCount
: Weighted example count.
Functions
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.
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(...)
: Filters and converts extracts to StandardMetricInputs.
Type Aliases
MetricComputations
: The central part of internal API.
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