Type Members

1.  final class AUC extends GeneratedMessageV3 with AUCOrBuilder

   Area under curve for the ROC-curve.
   https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC
   

   Area under curve for the ROC-curve.
   https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC
   

   Protobuf type tensorflow.metadata.v0.AUC

2.  trait AUCOrBuilder extends MessageOrBuilder
3.  final class AUCPrecisionRecall extends GeneratedMessageV3 with AUCPrecisionRecallOrBuilder

   Area under curve for the precision-recall-curve.
   https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC
   

   Area under curve for the precision-recall-curve.
   https://www.tensorflow.org/api_docs/python/tf/keras/metrics/AUC
   

   Protobuf type tensorflow.metadata.v0.AUCPrecisionRecall

4.  trait AUCPrecisionRecallOrBuilder extends MessageOrBuilder
5.  final class AllowlistDeriver extends GeneratedMessageV3 with AllowlistDeriverOrBuilder

   Protobuf type tensorflow.metadata.v0.AllowlistDeriver

6.  trait AllowlistDeriverOrBuilder extends MessageOrBuilder
7.  final class Annotation extends GeneratedMessageV3 with AnnotationOrBuilder

   Additional information about the schema or about a feature.
   

   Additional information about the schema or about a feature.
   

   Protobuf type tensorflow.metadata.v0.Annotation

8.  trait AnnotationOrBuilder extends MessageOrBuilder
9.  final class Anomalies extends GeneratedMessageV3 with AnomaliesOrBuilder

   Message to represent the anomalies, which describe the mismatches (if any)
   between the stats and the schema.
   

   Message to represent the anomalies, which describe the mismatches (if any)
   between the stats and the schema.
   

   Protobuf type tensorflow.metadata.v0.Anomalies

10.  trait AnomaliesOrBuilder extends MessageOrBuilder
11.  final class AnomaliesOuterClass extends AnyRef
12.  final class AnomalyInfo extends GeneratedMessageV3 with AnomalyInfoOrBuilder

    Message to represent information about an individual anomaly.
    

    Message to represent information about an individual anomaly.
    

    Protobuf type tensorflow.metadata.v0.AnomalyInfo

13.  trait AnomalyInfoOrBuilder extends MessageOrBuilder
14.  final class ArgmaxTopK extends GeneratedMessageV3 with ArgmaxTopKOrBuilder

    Protobuf type tensorflow.metadata.v0.ArgmaxTopK

15.  trait ArgmaxTopKOrBuilder extends MessageOrBuilder
16.  final class BinaryAccuracy extends GeneratedMessageV3 with BinaryAccuracyOrBuilder

    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/binary_accuracy
    

    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/binary_accuracy
    

    Protobuf type tensorflow.metadata.v0.BinaryAccuracy

17.  trait BinaryAccuracyOrBuilder extends MessageOrBuilder
18.  final class BinaryClassification extends GeneratedMessageV3 with BinaryClassificationOrBuilder

    Configuration for a binary classification task.
    The output is one of two possible class labels, encoded as the same type
    as the label column.
    BinaryClassification is the same as MultiClassClassification with
    n_classes = 2.
    

    Configuration for a binary classification task.
    The output is one of two possible class labels, encoded as the same type
    as the label column.
    BinaryClassification is the same as MultiClassClassification with
    n_classes = 2.
    

    Protobuf type tensorflow.metadata.v0.BinaryClassification

19.  trait BinaryClassificationOrBuilder extends MessageOrBuilder
20.  final class BinaryCrossEntropy extends GeneratedMessageV3 with BinaryCrossEntropyOrBuilder

    Binary cross entropy as a metric is equal to the negative log likelihood
    (see logistic regression).
    In addition, when used to solve a binary classification task, binary cross
    entropy implies that the binary label will maximize binary accuracy.
    binary_crossentropy(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/binary_crossentropy
    

    Binary cross entropy as a metric is equal to the negative log likelihood
    (see logistic regression).
    In addition, when used to solve a binary classification task, binary cross
    entropy implies that the binary label will maximize binary accuracy.
    binary_crossentropy(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/binary_crossentropy
    

    Protobuf type tensorflow.metadata.v0.BinaryCrossEntropy

21.  trait BinaryCrossEntropyOrBuilder extends MessageOrBuilder
22.  final class BlockUtility extends GeneratedMessageV3 with BlockUtilityOrBuilder

    DEPRECATED
    

    DEPRECATED
    

    Protobuf type tensorflow.metadata.v0.BlockUtility

23.  trait BlockUtilityOrBuilder extends MessageOrBuilder
24.  final class BoolDomain extends GeneratedMessageV3 with BoolDomainOrBuilder

    Encodes information about the domain of a boolean attribute that encodes its
    TRUE/FALSE values as strings, or 0=false, 1=true.
    Note that FeatureType could be either INT or BYTES.
    

    Encodes information about the domain of a boolean attribute that encodes its
    TRUE/FALSE values as strings, or 0=false, 1=true.
    Note that FeatureType could be either INT or BYTES.
    

    Protobuf type tensorflow.metadata.v0.BoolDomain

25.  trait BoolDomainOrBuilder extends MessageOrBuilder
26.  final class BytesStatistics extends GeneratedMessageV3 with BytesStatisticsOrBuilder

    Statistics for a bytes feature in a dataset.
    

    Statistics for a bytes feature in a dataset.
    

    Protobuf type tensorflow.metadata.v0.BytesStatistics

27.  trait BytesStatisticsOrBuilder extends MessageOrBuilder
28.  final class CategoricalAccuracy extends GeneratedMessageV3 with CategoricalAccuracyOrBuilder

    categorical_accuracy(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_accuracy
    

    categorical_accuracy(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_accuracy
    

    Protobuf type tensorflow.metadata.v0.CategoricalAccuracy

29.  trait CategoricalAccuracyOrBuilder extends MessageOrBuilder
30.  final class CategoricalCrossEntropy extends GeneratedMessageV3 with CategoricalCrossEntropyOrBuilder

    categorical_crossentropy(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_crossentropy
    

    categorical_crossentropy(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/categorical_crossentropy
    

    Protobuf type tensorflow.metadata.v0.CategoricalCrossEntropy

31.  trait CategoricalCrossEntropyOrBuilder extends MessageOrBuilder
32.  final class CategoricalCrossStatistics extends GeneratedMessageV3 with CategoricalCrossStatisticsOrBuilder

    Protobuf type tensorflow.metadata.v0.CategoricalCrossStatistics

33.  trait CategoricalCrossStatisticsOrBuilder extends MessageOrBuilder
34.  final class ChangedRegion extends GeneratedMessageV3 with ChangedRegionOrBuilder

    Describes a chunk that represents changes in both artifacts over the same
    number of lines.
    

    Describes a chunk that represents changes in both artifacts over the same
    number of lines.
    

    Protobuf type tensorflow.metadata.v0.ChangedRegion

35.  trait ChangedRegionOrBuilder extends MessageOrBuilder
36.  final class CommonStatistics extends GeneratedMessageV3 with CommonStatisticsOrBuilder

    Common statistics for all feature types. Statistics counting number of values
    (i.e., min_num_values, max_num_values, avg_num_values, and tot_num_values)
    include NaNs.
    

    Common statistics for all feature types. Statistics counting number of values
    (i.e., min_num_values, max_num_values, avg_num_values, and tot_num_values)
    include NaNs.
    

    Protobuf type tensorflow.metadata.v0.CommonStatistics

37.  trait CommonStatisticsOrBuilder extends MessageOrBuilder
38.  final class Cosine extends GeneratedMessageV3 with CosineOrBuilder

    cosine(...)
    cosine_proximity(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/cosine_proximity
    DEPRECATED
    

    cosine(...)
    cosine_proximity(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/cosine_proximity
    DEPRECATED
    

    Protobuf type tensorflow.metadata.v0.Cosine

39.  trait CosineOrBuilder extends MessageOrBuilder
40.  final class CrossFeatureStatistics extends GeneratedMessageV3 with CrossFeatureStatisticsOrBuilder

    NextID: 8
    

    NextID: 8
    

    Protobuf type tensorflow.metadata.v0.CrossFeatureStatistics

41.  trait CrossFeatureStatisticsOrBuilder extends MessageOrBuilder
42.  final class CustomMetric extends GeneratedMessageV3 with CustomMetricOrBuilder

    A custom metric.
    Prefer using or adding an explicit metric message
    and only use this generic message as a last resort.
    NEXT_TAG: 4
    

    A custom metric.
    Prefer using or adding an explicit metric message
    and only use this generic message as a last resort.
    NEXT_TAG: 4
    

    Protobuf type tensorflow.metadata.v0.CustomMetric

43.  trait CustomMetricOrBuilder extends MessageOrBuilder
44.  final class CustomStatistic extends GeneratedMessageV3 with CustomStatisticOrBuilder

    Stores the name and value of any custom statistic. The value can be a string,
    double, or histogram.
    

    Stores the name and value of any custom statistic. The value can be a string,
    double, or histogram.
    

    Protobuf type tensorflow.metadata.v0.CustomStatistic

45.  trait CustomStatisticOrBuilder extends MessageOrBuilder
46.  final class DatasetConstraints extends GeneratedMessageV3 with DatasetConstraintsOrBuilder

    Constraints on the entire dataset.
    

    Constraints on the entire dataset.
    

    Protobuf type tensorflow.metadata.v0.DatasetConstraints

47.  trait DatasetConstraintsOrBuilder extends MessageOrBuilder
48.  final class DatasetFeatureStatistics extends GeneratedMessageV3 with DatasetFeatureStatisticsOrBuilder

    The feature statistics for a single dataset.
    

    The feature statistics for a single dataset.
    

    Protobuf type tensorflow.metadata.v0.DatasetFeatureStatistics

49.  final class DatasetFeatureStatisticsList extends GeneratedMessageV3 with DatasetFeatureStatisticsListOrBuilder

    A list of features statistics for different datasets. If you wish to compare
    different datasets using this list, then the DatasetFeatureStatistics
    entries should all contain the same list of features.
    LINT.IfChange
    

    A list of features statistics for different datasets. If you wish to compare
    different datasets using this list, then the DatasetFeatureStatistics
    entries should all contain the same list of features.
    LINT.IfChange
    

    Protobuf type tensorflow.metadata.v0.DatasetFeatureStatisticsList

50.  trait DatasetFeatureStatisticsListOrBuilder extends MessageOrBuilder
51.  trait DatasetFeatureStatisticsOrBuilder extends MessageOrBuilder
52.  final class DerivedFeature extends AnyRef
53.  final class DerivedFeatureConfig extends GeneratedMessageV3 with DerivedFeatureConfigOrBuilder

    Stores configuration for a variety of canned feature derivers.
    TODO(b/227478330): Consider validating config in merge_util.cc.
    

    Stores configuration for a variety of canned feature derivers.
    TODO(b/227478330): Consider validating config in merge_util.cc.
    

    Protobuf type tensorflow.metadata.v0.DerivedFeatureConfig

54.  trait DerivedFeatureConfigOrBuilder extends MessageOrBuilder
55.  final class DerivedFeatureSource extends GeneratedMessageV3 with DerivedFeatureSourceOrBuilder

    DerivedFeatureSource tracks information about the source of a derived
    feature. Derived features are computed from ordinary features for the
    purposes of statistics collection and validation, but do not exist in the
    dataset.
    Experimental and subject to change.
    LINT.IfChange
    

    DerivedFeatureSource tracks information about the source of a derived
    feature. Derived features are computed from ordinary features for the
    purposes of statistics collection and validation, but do not exist in the
    dataset.
    Experimental and subject to change.
    LINT.IfChange
    

    Protobuf type tensorflow.metadata.v0.DerivedFeatureSource

56.  trait DerivedFeatureSourceOrBuilder extends MessageOrBuilder
57.  final class DiffRegion extends GeneratedMessageV3 with DiffRegionOrBuilder

    Describes a region in the comparison between two text artifacts. Note that
    a region also contains the contents of the two artifacts that correspond to
    the region.
    

    Describes a region in the comparison between two text artifacts. Note that
    a region also contains the contents of the two artifacts that correspond to
    the region.
    

    Protobuf type tensorflow.metadata.v0.DiffRegion

58.  trait DiffRegionOrBuilder extends MessageOrBuilder
59.  final class DistributionConstraints extends GeneratedMessageV3 with DistributionConstraintsOrBuilder

    Models constraints on the distribution of a feature's values.
    TODO(martinz): replace min_domain_mass with max_off_domain (but slowly).
    

    Models constraints on the distribution of a feature's values.
    TODO(martinz): replace min_domain_mass with max_off_domain (but slowly).
    

    Protobuf type tensorflow.metadata.v0.DistributionConstraints

60.  trait DistributionConstraintsOrBuilder extends MessageOrBuilder
61.  final class DriftSkewInfo extends GeneratedMessageV3 with DriftSkewInfoOrBuilder

    Message to contain the result of the drift/skew measurements for a feature.
    

    Message to contain the result of the drift/skew measurements for a feature.
    

    Protobuf type tensorflow.metadata.v0.DriftSkewInfo

62.  trait DriftSkewInfoOrBuilder extends MessageOrBuilder
63.  final class DynamicClassSpec extends GeneratedMessageV3 with DynamicClassSpecOrBuilder

    Specifies a dynamic multiclass/multi-label problem where the number of label
     classes is inferred from the data.
    

    Specifies a dynamic multiclass/multi-label problem where the number of label
     classes is inferred from the data.
    

    Protobuf type tensorflow.metadata.v0.DynamicClassSpec

64.  trait DynamicClassSpecOrBuilder extends MessageOrBuilder
65.  final class FalseNegativeRateAtThreshold extends GeneratedMessageV3 with FalseNegativeRateAtThresholdOrBuilder

    Protobuf type tensorflow.metadata.v0.FalseNegativeRateAtThreshold

66.  trait FalseNegativeRateAtThresholdOrBuilder extends MessageOrBuilder
67.  final class FalsePositiveRateAtThreshold extends GeneratedMessageV3 with FalsePositiveRateAtThresholdOrBuilder

    Protobuf type tensorflow.metadata.v0.FalsePositiveRateAtThreshold

68.  trait FalsePositiveRateAtThresholdOrBuilder extends MessageOrBuilder
69.  final class Feature extends GeneratedMessageV3 with FeatureOrBuilder

    Describes schema-level information about a specific feature.
    NextID: 36
    

    Describes schema-level information about a specific feature.
    NextID: 36
    

    Protobuf type tensorflow.metadata.v0.Feature

70.  final class FeatureComparator extends GeneratedMessageV3 with FeatureComparatorOrBuilder

    Protobuf type tensorflow.metadata.v0.FeatureComparator

71.  trait FeatureComparatorOrBuilder extends MessageOrBuilder
72.  final class FeatureCoverageConstraints extends GeneratedMessageV3 with FeatureCoverageConstraintsOrBuilder

    Encodes vocabulary coverage constraints.
    

    Encodes vocabulary coverage constraints.
    

    Protobuf type tensorflow.metadata.v0.FeatureCoverageConstraints

73.  trait FeatureCoverageConstraintsOrBuilder extends MessageOrBuilder
74.  final class FeatureNameStatistics extends GeneratedMessageV3 with FeatureNameStatisticsOrBuilder

    The complete set of statistics for a given feature name for a dataset.
    NextID: 11
    

    The complete set of statistics for a given feature name for a dataset.
    NextID: 11
    

    Protobuf type tensorflow.metadata.v0.FeatureNameStatistics

75.  trait FeatureNameStatisticsOrBuilder extends MessageOrBuilder
76.  trait FeatureOrBuilder extends MessageOrBuilder
77.  final class FeaturePresence extends GeneratedMessageV3 with FeaturePresenceOrBuilder

    Describes constraints on the presence of the feature in the data.
    

    Describes constraints on the presence of the feature in the data.
    

    Protobuf type tensorflow.metadata.v0.FeaturePresence

78.  trait FeaturePresenceOrBuilder extends MessageOrBuilder
79.  final class FeaturePresenceWithinGroup extends GeneratedMessageV3 with FeaturePresenceWithinGroupOrBuilder

    Records constraints on the presence of a feature inside a "group" context
    (e.g., .presence inside a group of features that define a sequence).
    

    Records constraints on the presence of a feature inside a "group" context
    (e.g., .presence inside a group of features that define a sequence).
    

    Protobuf type tensorflow.metadata.v0.FeaturePresenceWithinGroup

80.  trait FeaturePresenceWithinGroupOrBuilder extends MessageOrBuilder
81.  sealed final class FeatureType extends Enum[FeatureType] with ProtocolMessageEnum

    Describes the physical representation of a feature.
    It may be different than the logical representation, which
    is represented as a Domain.
    

    Describes the physical representation of a feature.
    It may be different than the logical representation, which
    is represented as a Domain.
    

    Protobuf enum tensorflow.metadata.v0.FeatureType

82.  final class FixedShape extends GeneratedMessageV3 with FixedShapeOrBuilder

    Specifies a fixed shape for the feature's values. The immediate implication
    is that each feature has a fixed number of values. Moreover, these values
    can be parsed in a multi-dimensional tensor using the specified axis sizes.
    The FixedShape defines a lexicographical ordering of the data. For instance,
    if there is a FixedShape {
      dim {size:3} dim {size:2}
    }
    then tensor[0][0]=field[0]
    then tensor[0][1]=field[1]
    then tensor[1][0]=field[2]
    then tensor[1][1]=field[3]
    then tensor[2][0]=field[4]
    then tensor[2][1]=field[5]
    
    The FixedShape message is identical with the TensorFlow TensorShape proto
    message.
    

    Specifies a fixed shape for the feature's values. The immediate implication
    is that each feature has a fixed number of values. Moreover, these values
    can be parsed in a multi-dimensional tensor using the specified axis sizes.
    The FixedShape defines a lexicographical ordering of the data. For instance,
    if there is a FixedShape {
      dim {size:3} dim {size:2}
    }
    then tensor[0][0]=field[0]
    then tensor[0][1]=field[1]
    then tensor[1][0]=field[2]
    then tensor[1][1]=field[3]
    then tensor[2][0]=field[4]
    then tensor[2][1]=field[5]
    
    The FixedShape message is identical with the TensorFlow TensorShape proto
    message.
    

    Protobuf type tensorflow.metadata.v0.FixedShape

83.  trait FixedShapeOrBuilder extends MessageOrBuilder
84.  final class FloatDomain extends GeneratedMessageV3 with FloatDomainOrBuilder

    Encodes information for domains of float values.
    Note that FeatureType could be either INT or BYTES.
    

    Encodes information for domains of float values.
    Note that FeatureType could be either INT or BYTES.
    

    Protobuf type tensorflow.metadata.v0.FloatDomain

85.  trait FloatDomainOrBuilder extends MessageOrBuilder
86.  final class HiddenRegion extends GeneratedMessageV3 with HiddenRegionOrBuilder

    A chunk that represents identical lines, whose contents are hidden.
    

    A chunk that represents identical lines, whose contents are hidden.
    

    Protobuf type tensorflow.metadata.v0.HiddenRegion

87.  trait HiddenRegionOrBuilder extends MessageOrBuilder
88.  final class Hinge extends GeneratedMessageV3 with HingeOrBuilder

    Linear Hinge Loss
    hinge(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/hinge
    DEPRECATED
    

    Linear Hinge Loss
    hinge(...)
    https://www.tensorflow.org/api_docs/python/tf/keras/metrics/hinge
    DEPRECATED
    

    Protobuf type tensorflow.metadata.v0.Hinge

89.  trait HingeOrBuilder extends MessageOrBuilder
90.  final class Histogram extends GeneratedMessageV3 with HistogramOrBuilder

    The data used to create a histogram of a numeric feature for a dataset.
    

    The data used to create a histogram of a numeric feature for a dataset.
    

    Protobuf type tensorflow.metadata.v0.Histogram

91.  trait HistogramOrBuilder extends MessageOrBuilder
92.  final class HistogramSelection extends GeneratedMessageV3 with HistogramSelectionOrBuilder

    Protobuf type tensorflow.metadata.v0.HistogramSelection

93.  trait HistogramSelectionOrBuilder extends MessageOrBuilder
94.  final class ImageDomain extends GeneratedMessageV3 with ImageDomainOrBuilder

    Image data.
    

    Image data.
    

    Protobuf type tensorflow.metadata.v0.ImageDomain

95.  trait ImageDomainOrBuilder extends MessageOrBuilder
96.  final class ImageQualityDeriver extends GeneratedMessageV3 with ImageQualityDeriverOrBuilder

    Protobuf type tensorflow.metadata.v0.ImageQualityDeriver

97.  trait ImageQualityDeriverOrBuilder extends MessageOrBuilder
98.  final class InfinityNorm extends GeneratedMessageV3 with InfinityNormOrBuilder

    Checks that the L-infinity norm is below a certain threshold between the
    two discrete distributions. Since this is applied to a FeatureNameStatistics,
    it only considers the top k.
    L_infty(p,q) = max_i |p_i-q_i|
    

    Checks that the L-infinity norm is below a certain threshold between the
    two discrete distributions. Since this is applied to a FeatureNameStatistics,
    it only considers the top k.
    L_infty(p,q) = max_i |p_i-q_i|
    

    Protobuf type tensorflow.metadata.v0.InfinityNorm

99.  trait InfinityNormOrBuilder extends MessageOrBuilder
100.  final class IntDomain extends GeneratedMessageV3 with IntDomainOrBuilder

     Encodes information for domains of integer values.
     Note that FeatureType could be either INT or BYTES.
     

     Encodes information for domains of integer values.
     Note that FeatureType could be either INT or BYTES.
     

     Protobuf type tensorflow.metadata.v0.IntDomain

101.  trait IntDomainOrBuilder extends MessageOrBuilder
102.  final class JensenShannonDivergence extends GeneratedMessageV3 with JensenShannonDivergenceOrBuilder

     Checks that the approximate Jensen-Shannon Divergence is below a certain
     threshold between the two distributions.
     

     Checks that the approximate Jensen-Shannon Divergence is below a certain
     threshold between the two distributions.
     

     Protobuf type tensorflow.metadata.v0.JensenShannonDivergence

103.  trait JensenShannonDivergenceOrBuilder extends MessageOrBuilder
104.  final class KullbackLeiblerDivergence extends GeneratedMessageV3 with KullbackLeiblerDivergenceOrBuilder

     kld(...)
     kullback_leibler_divergence(...)
     KLD(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/kullback_leibler_divergence
     DEPRECATED
     

     kld(...)
     kullback_leibler_divergence(...)
     KLD(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/kullback_leibler_divergence
     DEPRECATED
     

     Protobuf type tensorflow.metadata.v0.KullbackLeiblerDivergence

105.  trait KullbackLeiblerDivergenceOrBuilder extends MessageOrBuilder
106.  sealed final class LifecycleStage extends Enum[LifecycleStage] with ProtocolMessageEnum

     LifecycleStage. Only UNKNOWN_STAGE, BETA, PRODUCTION, and VALIDATION_DERIVED
     features are actually validated.
     PLANNED, ALPHA, DISABLED, and DEBUG are treated as DEPRECATED.
     

     LifecycleStage. Only UNKNOWN_STAGE, BETA, PRODUCTION, and VALIDATION_DERIVED
     features are actually validated.
     PLANNED, ALPHA, DISABLED, and DEBUG are treated as DEPRECATED.
     

     Protobuf enum tensorflow.metadata.v0.LifecycleStage

107.  final class LiftSeries extends GeneratedMessageV3 with LiftSeriesOrBuilder

     Container for lift information for a specific y-value.
     

     Container for lift information for a specific y-value.
     

     Protobuf type tensorflow.metadata.v0.LiftSeries

108.  trait LiftSeriesOrBuilder extends MessageOrBuilder
109.  final class LiftStatistics extends GeneratedMessageV3 with LiftStatisticsOrBuilder

     Protobuf type tensorflow.metadata.v0.LiftStatistics

110.  trait LiftStatisticsOrBuilder extends MessageOrBuilder
111.  final class LogisticRegression extends GeneratedMessageV3 with LogisticRegressionOrBuilder

     AKA the negative log likelihood or log loss.
     Given a label y\in {0,1} and a predicted probability p in [0,1]:
     -yln(p)-(1-y)ln(1-p)
     TODO(martinz): if this is interpreted the same as binary_cross_entropy,
     we may need to revisit the semantics.
     DEPRECATED
     

     AKA the negative log likelihood or log loss.
     Given a label y\in {0,1} and a predicted probability p in [0,1]:
     -yln(p)-(1-y)ln(1-p)
     TODO(martinz): if this is interpreted the same as binary_cross_entropy,
     we may need to revisit the semantics.
     DEPRECATED
     

     Protobuf type tensorflow.metadata.v0.LogisticRegression

112.  trait LogisticRegressionOrBuilder extends MessageOrBuilder
113.  final class MIDDomain extends GeneratedMessageV3 with MIDDomainOrBuilder

     Knowledge graph ID, see: https://www.wikidata.org/wiki/Property:P646
     

     Knowledge graph ID, see: https://www.wikidata.org/wiki/Property:P646
     

     Protobuf type tensorflow.metadata.v0.MIDDomain

114.  trait MIDDomainOrBuilder extends MessageOrBuilder
115.  final class MaximumMeanDiscrepancy extends GeneratedMessageV3 with MaximumMeanDiscrepancyOrBuilder

     https://www.tensorflow.org/responsible_ai/model_remediation/api_docs/python/model_remediation/min_diff/losses/MMDLoss
     

     https://www.tensorflow.org/responsible_ai/model_remediation/api_docs/python/model_remediation/min_diff/losses/MMDLoss
     

     Protobuf type tensorflow.metadata.v0.MaximumMeanDiscrepancy

116.  trait MaximumMeanDiscrepancyOrBuilder extends MessageOrBuilder
117.  final class MeanAbsoluteError extends GeneratedMessageV3 with MeanAbsoluteErrorOrBuilder

     MAE(...)
     mae(...)
     mean_absolute_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_absolute_error
     

     MAE(...)
     mae(...)
     mean_absolute_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_absolute_error
     

     Protobuf type tensorflow.metadata.v0.MeanAbsoluteError

118.  trait MeanAbsoluteErrorOrBuilder extends MessageOrBuilder
119.  final class MeanAbsolutePercentageError extends GeneratedMessageV3 with MeanAbsolutePercentageErrorOrBuilder

     MAPE(...)
     mape(...)
     mean_absolute_percentage_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_absolute_percentage_error
     

     MAPE(...)
     mape(...)
     mean_absolute_percentage_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_absolute_percentage_error
     

     Protobuf type tensorflow.metadata.v0.MeanAbsolutePercentageError

120.  trait MeanAbsolutePercentageErrorOrBuilder extends MessageOrBuilder
121.  final class MeanReciprocalRank extends GeneratedMessageV3 with MeanReciprocalRankOrBuilder

     Protobuf type tensorflow.metadata.v0.MeanReciprocalRank

122.  trait MeanReciprocalRankOrBuilder extends MessageOrBuilder
123.  final class MeanSquaredError extends GeneratedMessageV3 with MeanSquaredErrorOrBuilder

     MSE(...)
     mse(...)
     mean_squared_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_error
     

     MSE(...)
     mse(...)
     mean_squared_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_error
     

     Protobuf type tensorflow.metadata.v0.MeanSquaredError

124.  trait MeanSquaredErrorOrBuilder extends MessageOrBuilder
125.  final class MeanSquaredLogarithmicError extends GeneratedMessageV3 with MeanSquaredLogarithmicErrorOrBuilder

     msle(...)
     MSLE(...)
     mean_squared_logarithmic_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error
     

     msle(...)
     MSLE(...)
     mean_squared_logarithmic_error(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_logarithmic_error
     

     Protobuf type tensorflow.metadata.v0.MeanSquaredLogarithmicError

126.  trait MeanSquaredLogarithmicErrorOrBuilder extends MessageOrBuilder
127.  final class MetaOptimizationTarget extends GeneratedMessageV3 with MetaOptimizationTargetOrBuilder

     The high-level objectives described by this problem statement. These
     objectives provide a basis for ranking models and can be optimized by a meta
     optimizer (e.g. a grid search over hyperparameters). A solution provider may
     also directly use the meta optimization targets to heuristically select
     losses, etc without any meta-optimization process. If not specified, the
     high-level meta optimization target is inferred from the task. These
     objectives do not need to be differentiable, as the solution provider may use
     proxy function to optimize model weights. Target definitions include tasks,
     metrics, and any weighted combination of them.
     

     The high-level objectives described by this problem statement. These
     objectives provide a basis for ranking models and can be optimized by a meta
     optimizer (e.g. a grid search over hyperparameters). A solution provider may
     also directly use the meta optimization targets to heuristically select
     losses, etc without any meta-optimization process. If not specified, the
     high-level meta optimization target is inferred from the task. These
     objectives do not need to be differentiable, as the solution provider may use
     proxy function to optimize model weights. Target definitions include tasks,
     metrics, and any weighted combination of them.
     

     Protobuf type tensorflow.metadata.v0.MetaOptimizationTarget

128.  trait MetaOptimizationTargetOrBuilder extends MessageOrBuilder
129.  final class Metric extends AnyRef
130.  sealed final class MetricType extends Enum[MetricType] with ProtocolMessageEnum

     Metric type indicates which direction of a real-valued metric is "better".
     For most message types, this is invariant. For custom message types,
     is_maximized == true is like MAXIMIZE, and otherwise MINIMIZE.
     

     Metric type indicates which direction of a real-valued metric is "better".
     For most message types, this is invariant. For custom message types,
     is_maximized == true is like MAXIMIZE, and otherwise MINIMIZE.
     

     Protobuf enum tensorflow.metadata.v0.MetricType

131.  final class MicroAUC extends GeneratedMessageV3 with MicroAUCOrBuilder

     Area under ROC-curve calculated globally for MultiClassClassification (model
     predicts a single label) or MultiLabelClassification (model predicts class
     probabilities). The area is calculated by treating the entire set of data as
     an aggregate result, and computing a single metric rather than k metrics
     (one for each target label) that get averaged together. For example, the FPR
     and TPR at a given point on the AUC curve for k targer labels are:
       FPR = (FP1 + FP2 + ... + FPk) / ((FP1 + FP2 + ... + FPk) +
                                        (TN1 + TN2 + ... + TNk))
       TPR = (TP1 + TP2 + ... +TPk) / ((TP1 + TP2 + ... + TPk) +
                                       (FN1 + FN2 + ... + FNk))
     

     Area under ROC-curve calculated globally for MultiClassClassification (model
     predicts a single label) or MultiLabelClassification (model predicts class
     probabilities). The area is calculated by treating the entire set of data as
     an aggregate result, and computing a single metric rather than k metrics
     (one for each target label) that get averaged together. For example, the FPR
     and TPR at a given point on the AUC curve for k targer labels are:
       FPR = (FP1 + FP2 + ... + FPk) / ((FP1 + FP2 + ... + FPk) +
                                        (TN1 + TN2 + ... + TNk))
       TPR = (TP1 + TP2 + ... +TPk) / ((TP1 + TP2 + ... + TPk) +
                                       (FN1 + FN2 + ... + FNk))
     

     Protobuf type tensorflow.metadata.v0.MicroAUC

132.  trait MicroAUCOrBuilder extends MessageOrBuilder
133.  final class MultiClassClassification extends GeneratedMessageV3 with MultiClassClassificationOrBuilder

     Configuration for a multi-class classification task.
     In this problem type, there are n_classes possible label values, and the
     model predicts a single label.
     The output is one of the class labels, out of n_classes possible classes.
     The output type will correspond to the label column type.
     

     Configuration for a multi-class classification task.
     In this problem type, there are n_classes possible label values, and the
     model predicts a single label.
     The output is one of the class labels, out of n_classes possible classes.
     The output type will correspond to the label column type.
     

     Protobuf type tensorflow.metadata.v0.MultiClassClassification

134.  trait MultiClassClassificationOrBuilder extends MessageOrBuilder
135.  final class MultiLabelClassification extends GeneratedMessageV3 with MultiLabelClassificationOrBuilder

     Configuration for a multi-label classification task.
     In this problem type there are n_classes unique possible label values
     overall. There can be from zero up to n_classes unique labels per example.
     The output, which is of type real number, is class probabilities associated
     with each class. It will be of n_classes dimension for each example, if
     n_classes is specified. Otherwise, the dimension will be set to the number
     of unique class labels that are dynamically inferred from the data based on
     dynamic_class_spec.
     

     Configuration for a multi-label classification task.
     In this problem type there are n_classes unique possible label values
     overall. There can be from zero up to n_classes unique labels per example.
     The output, which is of type real number, is class probabilities associated
     with each class. It will be of n_classes dimension for each example, if
     n_classes is specified. Otherwise, the dimension will be set to the number
     of unique class labels that are dynamically inferred from the data based on
     dynamic_class_spec.
     

     Protobuf type tensorflow.metadata.v0.MultiLabelClassification

136.  trait MultiLabelClassificationOrBuilder extends MessageOrBuilder
137.  final class MultilabelCrossEntropy extends GeneratedMessageV3 with MultilabelCrossEntropyOrBuilder

     Cross entropy for MultiLabelClassification where each target and
     prediction is the probabily of belonging to that class independent of other
     classes.
     

     Cross entropy for MultiLabelClassification where each target and
     prediction is the probabily of belonging to that class independent of other
     classes.
     

     Protobuf type tensorflow.metadata.v0.MultilabelCrossEntropy

138.  trait MultilabelCrossEntropyOrBuilder extends MessageOrBuilder
139.  final class NaturalLanguageDomain extends GeneratedMessageV3 with NaturalLanguageDomainOrBuilder

     Natural language text.
     

     Natural language text.
     

     Protobuf type tensorflow.metadata.v0.NaturalLanguageDomain

140.  trait NaturalLanguageDomainOrBuilder extends MessageOrBuilder
141.  final class NaturalLanguageStatistics extends GeneratedMessageV3 with NaturalLanguageStatisticsOrBuilder

     Statistics for a feature containing a NL domain.
     

     Statistics for a feature containing a NL domain.
     

     Protobuf type tensorflow.metadata.v0.NaturalLanguageStatistics

142.  trait NaturalLanguageStatisticsOrBuilder extends MessageOrBuilder
143.  final class NormalizedAbsoluteDifference extends GeneratedMessageV3 with NormalizedAbsoluteDifferenceOrBuilder

     Checks that the absolute count difference relative to the total count of both
     datasets is small. This metric is appropriate for comparing datasets that
     are expected to have similar absolute counts, and not necessarily just
     similar distributions.
     Computed as max_i | x_i - y_i |  / sum_i(x_i + y_i) for aligned datasets
     x and y. Results will be in the interval [0.0, 1.0] so sensible bounds should
     be in the interval [0.0, 1.0).
     

     Checks that the absolute count difference relative to the total count of both
     datasets is small. This metric is appropriate for comparing datasets that
     are expected to have similar absolute counts, and not necessarily just
     similar distributions.
     Computed as max_i | x_i - y_i |  / sum_i(x_i + y_i) for aligned datasets
     x and y. Results will be in the interval [0.0, 1.0] so sensible bounds should
     be in the interval [0.0, 1.0).
     

     Protobuf type tensorflow.metadata.v0.NormalizedAbsoluteDifference

144.  trait NormalizedAbsoluteDifferenceOrBuilder extends MessageOrBuilder
145.  final class NumericCrossStatistics extends GeneratedMessageV3 with NumericCrossStatisticsOrBuilder

     Protobuf type tensorflow.metadata.v0.NumericCrossStatistics

146.  trait NumericCrossStatisticsOrBuilder extends MessageOrBuilder
147.  final class NumericStatistics extends GeneratedMessageV3 with NumericStatisticsOrBuilder

     Statistics for a numeric feature in a dataset.
     

     Statistics for a numeric feature in a dataset.
     

     Protobuf type tensorflow.metadata.v0.NumericStatistics

148.  trait NumericStatisticsOrBuilder extends MessageOrBuilder
149.  final class NumericValueComparator extends GeneratedMessageV3 with NumericValueComparatorOrBuilder

     Checks that the ratio of the current value to the previous value is not below
     the min_fraction_threshold or above the max_fraction_threshold. That is,
     previous value * min_fraction_threshold <= current value <=
     previous value * max_fraction_threshold.
     To specify that the value cannot change, set both min_fraction_threshold and
     max_fraction_threshold to 1.0.
     

     Checks that the ratio of the current value to the previous value is not below
     the min_fraction_threshold or above the max_fraction_threshold. That is,
     previous value * min_fraction_threshold <= current value <=
     previous value * max_fraction_threshold.
     To specify that the value cannot change, set both min_fraction_threshold and
     max_fraction_threshold to 1.0.
     

     Protobuf type tensorflow.metadata.v0.NumericValueComparator

150.  trait NumericValueComparatorOrBuilder extends MessageOrBuilder
151.  final class OneDimensionalRegression extends GeneratedMessageV3 with OneDimensionalRegressionOrBuilder

     A one-dimensional regression task.
     The output is a single real number, whose range is dependent upon the
     objective.
     

     A one-dimensional regression task.
     The output is a single real number, whose range is dependent upon the
     objective.
     

     Protobuf type tensorflow.metadata.v0.OneDimensionalRegression

152.  trait OneDimensionalRegressionOrBuilder extends MessageOrBuilder
153.  final class OneSideRegion extends GeneratedMessageV3 with OneSideRegionOrBuilder

     Describes a chunk that applies to only one of the two artifacts.
     

     Describes a chunk that applies to only one of the two artifacts.
     

     Protobuf type tensorflow.metadata.v0.OneSideRegion

154.  trait OneSideRegionOrBuilder extends MessageOrBuilder
155.  final class Path extends GeneratedMessageV3 with PathOrBuilder

     A path is a more general substitute for the name of a field or feature that
     can be used for flat examples as well as structured data. For example, if
     we had data in a protocol buffer:
     message Person {
       int age = 1;
       optional string gender = 2;
       repeated Person parent = 3;
     }
     Thus, here the path {step:["parent", "age"]} in statistics would refer to the
     age of a parent, and {step:["parent", "parent", "age"]} would refer to the
     age of a grandparent. This allows us to distinguish between the statistics
     of parents' ages and grandparents' ages. In general, repeated messages are
     to be preferred to linked lists of arbitrary length.
     For SequenceExample, if we have a feature list "foo", this is represented
     by {step:["##SEQUENCE##", "foo"]}.
     

     A path is a more general substitute for the name of a field or feature that
     can be used for flat examples as well as structured data. For example, if
     we had data in a protocol buffer:
     message Person {
       int age = 1;
       optional string gender = 2;
       repeated Person parent = 3;
     }
     Thus, here the path {step:["parent", "age"]} in statistics would refer to the
     age of a parent, and {step:["parent", "parent", "age"]} would refer to the
     age of a grandparent. This allows us to distinguish between the statistics
     of parents' ages and grandparents' ages. In general, repeated messages are
     to be preferred to linked lists of arbitrary length.
     For SequenceExample, if we have a feature list "foo", this is represented
     by {step:["##SEQUENCE##", "foo"]}.
     

     Protobuf type tensorflow.metadata.v0.Path

156.  trait PathOrBuilder extends MessageOrBuilder
157.  final class PathOuterClass extends AnyRef
158.  final class PerformanceMetric extends GeneratedMessageV3 with PerformanceMetricOrBuilder

     Performance metrics measure the quality of a model. They need not be
     differentiable.
     

     Performance metrics measure the quality of a model. They need not be
     differentiable.
     

     Protobuf type tensorflow.metadata.v0.PerformanceMetric

159.  trait PerformanceMetricOrBuilder extends MessageOrBuilder
160.  final class Poisson extends GeneratedMessageV3 with PoissonOrBuilder

     poisson(...)
     DEPRECATED
     

     poisson(...)
     DEPRECATED
     

     Protobuf type tensorflow.metadata.v0.Poisson

161.  trait PoissonOrBuilder extends MessageOrBuilder
162.  final class PrecisionAtK extends GeneratedMessageV3 with PrecisionAtKOrBuilder

     Protobuf type tensorflow.metadata.v0.PrecisionAtK

163.  trait PrecisionAtKOrBuilder extends MessageOrBuilder
164.  final class PrecisionAtRecall extends GeneratedMessageV3 with PrecisionAtRecallOrBuilder

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/PrecisionAtRecall
     

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/PrecisionAtRecall
     

     Protobuf type tensorflow.metadata.v0.PrecisionAtRecall

165.  trait PrecisionAtRecallOrBuilder extends MessageOrBuilder
166.  final class PredictionMean extends GeneratedMessageV3 with PredictionMeanOrBuilder

     The mean of the prediction across the dataset.
     

     The mean of the prediction across the dataset.
     

     Protobuf type tensorflow.metadata.v0.PredictionMean

167.  trait PredictionMeanOrBuilder extends MessageOrBuilder
168.  final class PresenceAndValencyStatistics extends GeneratedMessageV3 with PresenceAndValencyStatisticsOrBuilder

     Statistics about the presence and valency of feature values. Feature values
     could be nested lists. A feature in tf.Examples or other "flat" datasets has
     values of nest level 1 -- they are lists of primitives. A nest level N
     (N > 1) feature value is a list of lists of nest level (N - 1).
     This proto can be used to describe the presence and valency of values at each
     level.
     

     Statistics about the presence and valency of feature values. Feature values
     could be nested lists. A feature in tf.Examples or other "flat" datasets has
     values of nest level 1 -- they are lists of primitives. A nest level N
     (N > 1) feature value is a list of lists of nest level (N - 1).
     This proto can be used to describe the presence and valency of values at each
     level.
     

     Protobuf type tensorflow.metadata.v0.PresenceAndValencyStatistics

169.  trait PresenceAndValencyStatisticsOrBuilder extends MessageOrBuilder
170.  final class ProblemStatement extends GeneratedMessageV3 with ProblemStatementOrBuilder

     Protobuf type tensorflow.metadata.v0.ProblemStatement

171.  trait ProblemStatementOrBuilder extends MessageOrBuilder
172.  final class ProblemStatementOuterClass extends AnyRef
173.  final class RankHistogram extends GeneratedMessageV3 with RankHistogramOrBuilder

     The data used to create a rank histogram of a non-numeric feature of a
     dataset. The rank of a value in a feature can be used as a measure of how
     commonly the value is found in the entire dataset. With bucket sizes of one,
     this becomes a distribution function of all feature values.
     

     The data used to create a rank histogram of a non-numeric feature of a
     dataset. The rank of a value in a feature can be used as a measure of how
     commonly the value is found in the entire dataset. With bucket sizes of one,
     this becomes a distribution function of all feature values.
     

     Protobuf type tensorflow.metadata.v0.RankHistogram

174.  trait RankHistogramOrBuilder extends MessageOrBuilder
175.  final class RecallAtPrecision extends GeneratedMessageV3 with RecallAtPrecisionOrBuilder

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RecallAtPrecision
     

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RecallAtPrecision
     

     Protobuf type tensorflow.metadata.v0.RecallAtPrecision

176.  trait RecallAtPrecisionOrBuilder extends MessageOrBuilder
177.  final class ReduceOp extends GeneratedMessageV3 with ReduceOpOrBuilder

     Protobuf type tensorflow.metadata.v0.ReduceOp

178.  trait ReduceOpOrBuilder extends MessageOrBuilder
179.  final class Schema extends GeneratedMessageV3 with SchemaOrBuilder

     
     Message to represent schema information.
     NextID: 15
     

     
     Message to represent schema information.
     NextID: 15
     

     Protobuf type tensorflow.metadata.v0.Schema

180.  trait SchemaOrBuilder extends MessageOrBuilder
181.  final class SchemaOuterClass extends AnyRef
182.  final class SensitivityAtSpecificity extends GeneratedMessageV3 with SensitivityAtSpecificityOrBuilder

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SensitivityAtSpecificity
     

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SensitivityAtSpecificity
     

     Protobuf type tensorflow.metadata.v0.SensitivityAtSpecificity

183.  trait SensitivityAtSpecificityOrBuilder extends MessageOrBuilder
184.  final class SequenceLengthConstraints extends GeneratedMessageV3 with SequenceLengthConstraintsOrBuilder

     Encodes constraints on sequence lengths.
     

     Encodes constraints on sequence lengths.
     

     Protobuf type tensorflow.metadata.v0.SequenceLengthConstraints

185.  trait SequenceLengthConstraintsOrBuilder extends MessageOrBuilder
186.  final class SequenceMetadata extends GeneratedMessageV3 with SequenceMetadataOrBuilder

     Protobuf type tensorflow.metadata.v0.SequenceMetadata

187.  trait SequenceMetadataOrBuilder extends MessageOrBuilder
188.  final class SequenceValueConstraints extends GeneratedMessageV3 with SequenceValueConstraintsOrBuilder

     Encodes constraints on specific values in sequences.
     

     Encodes constraints on specific values in sequences.
     

     Protobuf type tensorflow.metadata.v0.SequenceValueConstraints

189.  trait SequenceValueConstraintsOrBuilder extends MessageOrBuilder
190.  final class SliceSql extends GeneratedMessageV3 with SliceSqlOrBuilder

     Protobuf type tensorflow.metadata.v0.SliceSql

191.  trait SliceSqlOrBuilder extends MessageOrBuilder
192.  sealed final class SliceValueTypes extends Enum[SliceValueTypes] with ProtocolMessageEnum

     Protobuf enum tensorflow.metadata.v0.SliceValueTypes

193.  final class SparseFeature extends GeneratedMessageV3 with SparseFeatureOrBuilder

     A sparse feature represents a sparse tensor that is encoded with a
     combination of raw features, namely index features and a value feature. Each
     index feature defines a list of indices in a different dimension.
     

     A sparse feature represents a sparse tensor that is encoded with a
     combination of raw features, namely index features and a value feature. Each
     index feature defines a list of indices in a different dimension.
     

     Protobuf type tensorflow.metadata.v0.SparseFeature

194.  trait SparseFeatureOrBuilder extends MessageOrBuilder
195.  final class SparseTopKCategoricalAccuracy extends GeneratedMessageV3 with SparseTopKCategoricalAccuracyOrBuilder

     sparse_top_k_categorical_accuracy(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_top_k_categorical_accuracy
     DEPRECATED
     

     sparse_top_k_categorical_accuracy(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/sparse_top_k_categorical_accuracy
     DEPRECATED
     

     Protobuf type tensorflow.metadata.v0.SparseTopKCategoricalAccuracy

196.  trait SparseTopKCategoricalAccuracyOrBuilder extends MessageOrBuilder
197.  final class SpecificityAtSensitivity extends GeneratedMessageV3 with SpecificityAtSensitivityOrBuilder

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SpecificityAtSensitivity
     

     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/SpecificityAtSensitivity
     

     Protobuf type tensorflow.metadata.v0.SpecificityAtSensitivity

198.  trait SpecificityAtSensitivityOrBuilder extends MessageOrBuilder
199.  final class SquaredHinge extends GeneratedMessageV3 with SquaredHingeOrBuilder

     squared_hinge(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/squared_hinge
     DEPRECATED
     

     squared_hinge(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/squared_hinge
     DEPRECATED
     

     Protobuf type tensorflow.metadata.v0.SquaredHinge

200.  trait SquaredHingeOrBuilder extends MessageOrBuilder
201.  final class Statistics extends AnyRef
202.  final class StringDomain extends GeneratedMessageV3 with StringDomainOrBuilder

     Encodes information for domains of string values.
     

     Encodes information for domains of string values.
     

     Protobuf type tensorflow.metadata.v0.StringDomain

203.  trait StringDomainOrBuilder extends MessageOrBuilder
204.  final class StringStatistics extends GeneratedMessageV3 with StringStatisticsOrBuilder

     Statistics for a string feature in a dataset.
     

     Statistics for a string feature in a dataset.
     

     Protobuf type tensorflow.metadata.v0.StringStatistics

205.  trait StringStatisticsOrBuilder extends MessageOrBuilder
206.  final class StructDomain extends GeneratedMessageV3 with StructDomainOrBuilder

     Domain for a recursive struct.
     NOTE: If a feature with a StructDomain is deprecated, then all the
     child features (features and sparse_features of the StructDomain) are also
     considered to be deprecated.  Similarly child features can only be in
     environments of the parent feature.
     

     Domain for a recursive struct.
     NOTE: If a feature with a StructDomain is deprecated, then all the
     child features (features and sparse_features of the StructDomain) are also
     considered to be deprecated.  Similarly child features can only be in
     environments of the parent feature.
     

     Protobuf type tensorflow.metadata.v0.StructDomain

207.  trait StructDomainOrBuilder extends MessageOrBuilder
208.  final class StructStatistics extends GeneratedMessageV3 with StructStatisticsOrBuilder

     Protobuf type tensorflow.metadata.v0.StructStatistics

209.  trait StructStatisticsOrBuilder extends MessageOrBuilder
210.  final class Task extends GeneratedMessageV3 with TaskOrBuilder

     Describes a single task in a model and all its properties.
     A task corresponds to a single output of the model.
     Multiple tasks in the same problem statement correspond to different outputs
     of the model.
     

     Describes a single task in a model and all its properties.
     A task corresponds to a single output of the model.
     Multiple tasks in the same problem statement correspond to different outputs
     of the model.
     

     Protobuf type tensorflow.metadata.v0.Task

211.  trait TaskOrBuilder extends MessageOrBuilder
212.  sealed final class TaskType extends Enum[TaskType] with ProtocolMessageEnum

     Protobuf enum tensorflow.metadata.v0.TaskType

213.  final class TensorRepresentation extends GeneratedMessageV3 with TensorRepresentationOrBuilder

     A TensorRepresentation captures the intent for converting columns in a
     dataset to TensorFlow Tensors (or more generally, tf.CompositeTensors).
     Note that one tf.CompositeTensor may consist of data from multiple columns,
     for example, a N-dimensional tf.SparseTensor may need N + 1 columns to
     provide the sparse indices and values.
     Note that the "column name" that a TensorRepresentation needs is a
     string, not a Path -- it means that the column name identifies a top-level
     Feature in the schema (i.e. you cannot specify a Feature nested in a STRUCT
     Feature).
     

     A TensorRepresentation captures the intent for converting columns in a
     dataset to TensorFlow Tensors (or more generally, tf.CompositeTensors).
     Note that one tf.CompositeTensor may consist of data from multiple columns,
     for example, a N-dimensional tf.SparseTensor may need N + 1 columns to
     provide the sparse indices and values.
     Note that the "column name" that a TensorRepresentation needs is a
     string, not a Path -- it means that the column name identifies a top-level
     Feature in the schema (i.e. you cannot specify a Feature nested in a STRUCT
     Feature).
     

     Protobuf type tensorflow.metadata.v0.TensorRepresentation

214.  final class TensorRepresentationGroup extends GeneratedMessageV3 with TensorRepresentationGroupOrBuilder

     A TensorRepresentationGroup is a collection of TensorRepresentations with
     names. These names may serve as identifiers when converting the dataset
     to a collection of Tensors or tf.CompositeTensors.
     For example, given the following group:
     {
       key: "dense_tensor"
       tensor_representation {
         dense_tensor {
           column_name: "univalent_feature"
           shape {
             dim {
               size: 1
             }
           }
           default_value {
             float_value: 0
           }
         }
       }
     }
     {
       key: "varlen_sparse_tensor"
       tensor_representation {
         varlen_sparse_tensor {
           column_name: "multivalent_feature"
         }
       }
     }
     
     Then the schema is expected to have feature "univalent_feature" and
     "multivalent_feature", and when a batch of data is converted to Tensors using
     this TensorRepresentationGroup, the result may be the following dict:
     {
       "dense_tensor": tf.Tensor(...),
       "varlen_sparse_tensor": tf.SparseTensor(...),
     }
     

     A TensorRepresentationGroup is a collection of TensorRepresentations with
     names. These names may serve as identifiers when converting the dataset
     to a collection of Tensors or tf.CompositeTensors.
     For example, given the following group:
     {
       key: "dense_tensor"
       tensor_representation {
         dense_tensor {
           column_name: "univalent_feature"
           shape {
             dim {
               size: 1
             }
           }
           default_value {
             float_value: 0
           }
         }
       }
     }
     {
       key: "varlen_sparse_tensor"
       tensor_representation {
         varlen_sparse_tensor {
           column_name: "multivalent_feature"
         }
       }
     }
     
     Then the schema is expected to have feature "univalent_feature" and
     "multivalent_feature", and when a batch of data is converted to Tensors using
     this TensorRepresentationGroup, the result may be the following dict:
     {
       "dense_tensor": tf.Tensor(...),
       "varlen_sparse_tensor": tf.SparseTensor(...),
     }
     

     Protobuf type tensorflow.metadata.v0.TensorRepresentationGroup

215.  trait TensorRepresentationGroupOrBuilder extends MessageOrBuilder
216.  trait TensorRepresentationOrBuilder extends MessageOrBuilder
217.  final class TimeDomain extends GeneratedMessageV3 with TimeDomainOrBuilder

     Time or date representation.
     

     Time or date representation.
     

     Protobuf type tensorflow.metadata.v0.TimeDomain

218.  trait TimeDomainOrBuilder extends MessageOrBuilder
219.  final class TimeOfDayDomain extends GeneratedMessageV3 with TimeOfDayDomainOrBuilder

     Time of day, without a particular date.
     

     Time of day, without a particular date.
     

     Protobuf type tensorflow.metadata.v0.TimeOfDayDomain

220.  trait TimeOfDayDomainOrBuilder extends MessageOrBuilder
221.  final class TopKCategoricalAccuracy extends GeneratedMessageV3 with TopKCategoricalAccuracyOrBuilder

     top_k_categorical_accuracy(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/top_k_categorical_accuracy
     

     top_k_categorical_accuracy(...)
     https://www.tensorflow.org/api_docs/python/tf/keras/metrics/top_k_categorical_accuracy
     

     Protobuf type tensorflow.metadata.v0.TopKCategoricalAccuracy

222.  trait TopKCategoricalAccuracyOrBuilder extends MessageOrBuilder
223.  final class TopKClassification extends GeneratedMessageV3 with TopKClassificationOrBuilder

     Configuration for a top-K classification task.
     In this problem type, there are n_classes possible label values, and the
     model predicts n_predicted_labels labels.
     The output is a sequence of n_predicted_labels labels, out of n_classes
     possible classes. The order of the predicted output labels is determined
     by the predictions_order field.
     (*) MultiClassClassification is the same as TopKClassification with
         n_predicted_labels = 1.
     (*) TopKClassification does NOT mean multi-class multi-label classification:
         e.g., the output contains a sequence of labels, all coming from the same
         label column in the data.
     

     Configuration for a top-K classification task.
     In this problem type, there are n_classes possible label values, and the
     model predicts n_predicted_labels labels.
     The output is a sequence of n_predicted_labels labels, out of n_classes
     possible classes. The order of the predicted output labels is determined
     by the predictions_order field.
     (*) MultiClassClassification is the same as TopKClassification with
         n_predicted_labels = 1.
     (*) TopKClassification does NOT mean multi-class multi-label classification:
         e.g., the output contains a sequence of labels, all coming from the same
         label column in the data.
     

     Protobuf type tensorflow.metadata.v0.TopKClassification

224.  trait TopKClassificationOrBuilder extends MessageOrBuilder
225.  final class Type extends GeneratedMessageV3 with TypeOrBuilder

     The type of a head or meta-objective. Specifies the label, weight,
     and output type of the head.
     TODO(martinz): add logistic regression.
     

     The type of a head or meta-objective. Specifies the label, weight,
     and output type of the head.
     TODO(martinz): add logistic regression.
     

     Protobuf type tensorflow.metadata.v0.Type

226.  trait TypeOrBuilder extends MessageOrBuilder
227.  final class URLDomain extends GeneratedMessageV3 with URLDomainOrBuilder

     A URL, see: https://en.wikipedia.org/wiki/URL
     

     A URL, see: https://en.wikipedia.org/wiki/URL
     

     Protobuf type tensorflow.metadata.v0.URLDomain

228.  trait URLDomainOrBuilder extends MessageOrBuilder
229.  final class UnchangedRegion extends GeneratedMessageV3 with UnchangedRegionOrBuilder

     Describes a chunk that is the same in the two artifacts.
     

     Describes a chunk that is the same in the two artifacts.
     

     Protobuf type tensorflow.metadata.v0.UnchangedRegion

230.  trait UnchangedRegionOrBuilder extends MessageOrBuilder
231.  final class UniqueConstraints extends GeneratedMessageV3 with UniqueConstraintsOrBuilder

     Checks that the number of unique values is greater than or equal to the min,
     and less than or equal to the max.
     

     Checks that the number of unique values is greater than or equal to the min,
     and less than or equal to the max.
     

     Protobuf type tensorflow.metadata.v0.UniqueConstraints

232.  trait UniqueConstraintsOrBuilder extends MessageOrBuilder
233.  final class ValueCount extends GeneratedMessageV3 with ValueCountOrBuilder

     Limits on maximum and minimum number of values in a
     single example (when the feature is present). Use this when the minimum
     value count can be different than the maximum value count. Otherwise prefer
     FixedShape.
     

     Limits on maximum and minimum number of values in a
     single example (when the feature is present). Use this when the minimum
     value count can be different than the maximum value count. Otherwise prefer
     FixedShape.
     

     Protobuf type tensorflow.metadata.v0.ValueCount

234.  final class ValueCountList extends GeneratedMessageV3 with ValueCountListOrBuilder

     Protobuf type tensorflow.metadata.v0.ValueCountList

235.  trait ValueCountListOrBuilder extends MessageOrBuilder
236.  trait ValueCountOrBuilder extends MessageOrBuilder
237.  final class WeightedCommonStatistics extends GeneratedMessageV3 with WeightedCommonStatisticsOrBuilder

     Common weighted statistics for all feature types. Statistics counting number
     of values (i.e., avg_num_values and tot_num_values) include NaNs.
     If the weighted column is missing, then this counts as a weight of 1
     for that example.
     

     Common weighted statistics for all feature types. Statistics counting number
     of values (i.e., avg_num_values and tot_num_values) include NaNs.
     If the weighted column is missing, then this counts as a weight of 1
     for that example.
     

     Protobuf type tensorflow.metadata.v0.WeightedCommonStatistics

238.  trait WeightedCommonStatisticsOrBuilder extends MessageOrBuilder
239.  final class WeightedFeature extends GeneratedMessageV3 with WeightedFeatureOrBuilder

     Represents a weighted feature that is encoded as a combination of raw base
     features. The `weight_feature` should be a float feature with identical
     shape as the `feature`. This is useful for representing weights associated
     with categorical tokens (e.g. a TFIDF weight associated with each token).
     TODO(b/142122960): Handle WeightedCategorical end to end in TFX (validation,
     TFX Unit Testing, etc)
     

     Represents a weighted feature that is encoded as a combination of raw base
     features. The `weight_feature` should be a float feature with identical
     shape as the `feature`. This is useful for representing weights associated
     with categorical tokens (e.g. a TFIDF weight associated with each token).
     TODO(b/142122960): Handle WeightedCategorical end to end in TFX (validation,
     TFX Unit Testing, etc)
     

     Protobuf type tensorflow.metadata.v0.WeightedFeature

240.  trait WeightedFeatureOrBuilder extends MessageOrBuilder
241.  final class WeightedNaturalLanguageStatistics extends GeneratedMessageV3 with WeightedNaturalLanguageStatisticsOrBuilder

     Statistics for a weighted feature with an NL domain.
     

     Statistics for a weighted feature with an NL domain.
     

     Protobuf type tensorflow.metadata.v0.WeightedNaturalLanguageStatistics

242.  trait WeightedNaturalLanguageStatisticsOrBuilder extends MessageOrBuilder
243.  final class WeightedNumericStatistics extends GeneratedMessageV3 with WeightedNumericStatisticsOrBuilder

     Statistics for a weighted numeric feature in a dataset.
     

     Statistics for a weighted numeric feature in a dataset.
     

     Protobuf type tensorflow.metadata.v0.WeightedNumericStatistics

244.  trait WeightedNumericStatisticsOrBuilder extends MessageOrBuilder
245.  final class WeightedStringStatistics extends GeneratedMessageV3 with WeightedStringStatisticsOrBuilder

     Statistics for a weighted string feature in a dataset.
     

     Statistics for a weighted string feature in a dataset.
     

     Protobuf type tensorflow.metadata.v0.WeightedStringStatistics

246.  trait WeightedStringStatisticsOrBuilder extends MessageOrBuilder