View source on GitHub
|
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.
Other Members | |
|---|---|
| NO_PREDICTED_CLASS_ID |
-1
|