Packages

package v0

Type Members

  1. final class AUC extends GeneratedMessage 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

    Annotations
    @Generated()
  2. trait AUCOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  3. final class AUCPrecisionRecall extends GeneratedMessage 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

    Annotations
    @Generated()
  4. trait AUCPrecisionRecallOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  5. final class AllowlistDeriver extends GeneratedMessage with AllowlistDeriverOrBuilder

    Protobuf type tensorflow.metadata.v0.AllowlistDeriver

    Protobuf type tensorflow.metadata.v0.AllowlistDeriver

    Annotations
    @Generated()
  6. trait AllowlistDeriverOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  7. final class Annotation extends GeneratedMessage 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

    Annotations
    @Generated()
  8. trait AnnotationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  9. final class Anomalies extends GeneratedMessage 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

    Annotations
    @Generated()
  10. trait AnomaliesOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  11. final class AnomaliesOuterClass extends GeneratedFile
    Annotations
    @Generated()
  12. final class AnomalyInfo extends GeneratedMessage with AnomalyInfoOrBuilder

    Message to represent information about an individual anomaly.


    Message to represent information about an individual anomaly.

    Protobuf type tensorflow.metadata.v0.AnomalyInfo

    Annotations
    @Generated()
  13. trait AnomalyInfoOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  14. final class ArgmaxTopK extends GeneratedMessage with ArgmaxTopKOrBuilder

    Protobuf type tensorflow.metadata.v0.ArgmaxTopK

    Protobuf type tensorflow.metadata.v0.ArgmaxTopK

    Annotations
    @Generated()
  15. trait ArgmaxTopKOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  16. final class BinaryAccuracy extends GeneratedMessage 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

    Annotations
    @Generated()
  17. trait BinaryAccuracyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  18. final class BinaryClassification extends GeneratedMessage 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

    Annotations
    @Generated()
  19. trait BinaryClassificationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  20. final class BinaryCrossEntropy extends GeneratedMessage 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

    Annotations
    @Generated()
  21. trait BinaryCrossEntropyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  22. final class BlockUtility extends GeneratedMessage with BlockUtilityOrBuilder

    DEPRECATED


    DEPRECATED

    Protobuf type tensorflow.metadata.v0.BlockUtility

    Annotations
    @Generated()
  23. trait BlockUtilityOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  24. final class BoolDomain extends GeneratedMessage 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

    Annotations
    @Generated()
  25. trait BoolDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  26. final class BytesStatistics extends GeneratedMessage with BytesStatisticsOrBuilder

    Statistics for a bytes feature in a dataset.


    Statistics for a bytes feature in a dataset.

    Protobuf type tensorflow.metadata.v0.BytesStatistics

    Annotations
    @Generated()
  27. trait BytesStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  28. final class CategoricalAccuracy extends GeneratedMessage 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

    Annotations
    @Generated()
  29. trait CategoricalAccuracyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  30. final class CategoricalCrossEntropy extends GeneratedMessage 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

    Annotations
    @Generated()
  31. trait CategoricalCrossEntropyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  32. final class CategoricalCrossStatistics extends GeneratedMessage with CategoricalCrossStatisticsOrBuilder

    Protobuf type tensorflow.metadata.v0.CategoricalCrossStatistics

    Protobuf type tensorflow.metadata.v0.CategoricalCrossStatistics

    Annotations
    @Generated()
  33. trait CategoricalCrossStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  34. final class ChangedRegion extends GeneratedMessage 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

    Annotations
    @Generated()
  35. trait ChangedRegionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  36. final class CommonStatistics extends GeneratedMessage 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. For nested features with N nested levels (N > 1), the
statistics counting number of values will rely on the innermost level.


    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. For nested features with N nested levels (N > 1), the
statistics counting number of values will rely on the innermost level.

    Protobuf type tensorflow.metadata.v0.CommonStatistics

    Annotations
    @Generated()
  37. trait CommonStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  38. final class Cosine extends GeneratedMessage 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

    Annotations
    @Generated()
  39. trait CosineOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  40. final class CrossFeatureStatistics extends GeneratedMessage with CrossFeatureStatisticsOrBuilder

    NextID: 8


    NextID: 8

    Protobuf type tensorflow.metadata.v0.CrossFeatureStatistics

    Annotations
    @Generated()
  41. trait CrossFeatureStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  42. final class CustomMetric extends GeneratedMessage 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

    Annotations
    @Generated()
  43. trait CustomMetricOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  44. final class CustomStatistic extends GeneratedMessage 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

    Annotations
    @Generated()
  45. trait CustomStatisticOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  46. final class DatasetConstraints extends GeneratedMessage with DatasetConstraintsOrBuilder

    Constraints on the entire dataset.


    Constraints on the entire dataset.

    Protobuf type tensorflow.metadata.v0.DatasetConstraints

    Annotations
    @Generated()
  47. trait DatasetConstraintsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  48. final class DatasetFeatureStatistics extends GeneratedMessage with DatasetFeatureStatisticsOrBuilder

    The feature statistics for a single dataset.


    The feature statistics for a single dataset.

    Protobuf type tensorflow.metadata.v0.DatasetFeatureStatistics

    Annotations
    @Generated()
  49. final class DatasetFeatureStatisticsList extends GeneratedMessage 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

    Annotations
    @Generated()
  50. trait DatasetFeatureStatisticsListOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  51. trait DatasetFeatureStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  52. final class DerivedFeature extends GeneratedFile
    Annotations
    @Generated()
  53. final class DerivedFeatureConfig extends GeneratedMessage 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

    Annotations
    @Generated()
  54. trait DerivedFeatureConfigOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  55. final class DerivedFeatureSource extends GeneratedMessage 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

    Annotations
    @Generated()
  56. trait DerivedFeatureSourceOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  57. final class DiffRegion extends GeneratedMessage 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

    Annotations
    @Generated()
  58. trait DiffRegionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  59. final class DistributionConstraints extends GeneratedMessage 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

    Annotations
    @Generated()
  60. trait DistributionConstraintsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  61. final class DriftSkewInfo extends GeneratedMessage 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

    Annotations
    @Generated()
  62. trait DriftSkewInfoOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  63. final class DynamicClassSpec extends GeneratedMessage 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

    Annotations
    @Generated()
  64. trait DynamicClassSpecOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  65. final class FalseNegativeRateAtThreshold extends GeneratedMessage with FalseNegativeRateAtThresholdOrBuilder

    Protobuf type tensorflow.metadata.v0.FalseNegativeRateAtThreshold

    Protobuf type tensorflow.metadata.v0.FalseNegativeRateAtThreshold

    Annotations
    @Generated()
  66. trait FalseNegativeRateAtThresholdOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  67. final class FalsePositiveRateAtThreshold extends GeneratedMessage with FalsePositiveRateAtThresholdOrBuilder

    Protobuf type tensorflow.metadata.v0.FalsePositiveRateAtThreshold

    Protobuf type tensorflow.metadata.v0.FalsePositiveRateAtThreshold

    Annotations
    @Generated()
  68. trait FalsePositiveRateAtThresholdOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  69. final class Feature extends GeneratedMessage 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

    Annotations
    @Generated()
  70. final class FeatureComparator extends GeneratedMessage with FeatureComparatorOrBuilder

    Protobuf type tensorflow.metadata.v0.FeatureComparator

    Protobuf type tensorflow.metadata.v0.FeatureComparator

    Annotations
    @Generated()
  71. trait FeatureComparatorOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  72. final class FeatureCoverageConstraints extends GeneratedMessage with FeatureCoverageConstraintsOrBuilder

    Encodes vocabulary coverage constraints.


    Encodes vocabulary coverage constraints.

    Protobuf type tensorflow.metadata.v0.FeatureCoverageConstraints

    Annotations
    @Generated()
  73. trait FeatureCoverageConstraintsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  74. final class FeatureNameStatistics extends GeneratedMessage 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

    Annotations
    @Generated()
  75. trait FeatureNameStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  76. trait FeatureOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  77. final class FeaturePresence extends GeneratedMessage 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

    Annotations
    @Generated()
  78. trait FeaturePresenceOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  79. final class FeaturePresenceWithinGroup extends GeneratedMessage 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

    Annotations
    @Generated()
  80. trait FeaturePresenceWithinGroupOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  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

    Annotations
    @Generated()
  82. final class FixedShape extends GeneratedMessage 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 for fully defined shapes. The FixedShape message cannot represent
unknown dimensions or an unknown rank.


    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 for fully defined shapes. The FixedShape message cannot represent
unknown dimensions or an unknown rank.

    Protobuf type tensorflow.metadata.v0.FixedShape

    Annotations
    @Generated()
  83. trait FixedShapeOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  84. final class FloatDomain extends GeneratedMessage 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

    Annotations
    @Generated()
  85. trait FloatDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  86. final class HiddenRegion extends GeneratedMessage 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

    Annotations
    @Generated()
  87. trait HiddenRegionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  88. final class Hinge extends GeneratedMessage 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

    Annotations
    @Generated()
  89. trait HingeOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  90. final class Histogram extends GeneratedMessage 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

    Annotations
    @Generated()
  91. trait HistogramOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  92. final class HistogramSelection extends GeneratedMessage with HistogramSelectionOrBuilder

    Protobuf type tensorflow.metadata.v0.HistogramSelection

    Protobuf type tensorflow.metadata.v0.HistogramSelection

    Annotations
    @Generated()
  93. trait HistogramSelectionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  94. final class ImageDomain extends GeneratedMessage with ImageDomainOrBuilder

    Image data.


    Image data.

    Protobuf type tensorflow.metadata.v0.ImageDomain

    Annotations
    @Generated()
  95. trait ImageDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  96. final class ImageQualityDeriver extends GeneratedMessage with ImageQualityDeriverOrBuilder

    Protobuf type tensorflow.metadata.v0.ImageQualityDeriver

    Protobuf type tensorflow.metadata.v0.ImageQualityDeriver

    Annotations
    @Generated()
  97. trait ImageQualityDeriverOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  98. final class InfinityNorm extends GeneratedMessage 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

    Annotations
    @Generated()
  99. trait InfinityNormOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  100. final class IntDomain extends GeneratedMessage 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

    Annotations
    @Generated()
  101. trait IntDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  102. final class JensenShannonDivergence extends GeneratedMessage 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

    Annotations
    @Generated()
  103. trait JensenShannonDivergenceOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  104. final class KullbackLeiblerDivergence extends GeneratedMessage 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

    Annotations
    @Generated()
  105. trait KullbackLeiblerDivergenceOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  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

    Annotations
    @Generated()
  107. final class LiftSeries extends GeneratedMessage 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

    Annotations
    @Generated()
  108. trait LiftSeriesOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  109. final class LiftStatistics extends GeneratedMessage with LiftStatisticsOrBuilder

    Protobuf type tensorflow.metadata.v0.LiftStatistics

    Protobuf type tensorflow.metadata.v0.LiftStatistics

    Annotations
    @Generated()
  110. trait LiftStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  111. final class LogisticRegression extends GeneratedMessage 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

    Annotations
    @Generated()
  112. trait LogisticRegressionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  113. final class MIDDomain extends GeneratedMessage 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

    Annotations
    @Generated()
  114. trait MIDDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  115. final class MaximumMeanDiscrepancy extends GeneratedMessage 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

    Annotations
    @Generated()
  116. trait MaximumMeanDiscrepancyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  117. final class MeanAbsoluteError extends GeneratedMessage 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

    Annotations
    @Generated()
  118. trait MeanAbsoluteErrorOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  119. final class MeanAbsolutePercentageError extends GeneratedMessage 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

    Annotations
    @Generated()
  120. trait MeanAbsolutePercentageErrorOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  121. final class MeanReciprocalRank extends GeneratedMessage with MeanReciprocalRankOrBuilder

    Protobuf type tensorflow.metadata.v0.MeanReciprocalRank

    Protobuf type tensorflow.metadata.v0.MeanReciprocalRank

    Annotations
    @Generated()
  122. trait MeanReciprocalRankOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  123. final class MeanSquaredError extends GeneratedMessage 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

    Annotations
    @Generated()
  124. trait MeanSquaredErrorOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  125. final class MeanSquaredLogarithmicError extends GeneratedMessage 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

    Annotations
    @Generated()
  126. trait MeanSquaredLogarithmicErrorOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  127. final class MetaOptimizationTarget extends GeneratedMessage 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

    Annotations
    @Generated()
  128. trait MetaOptimizationTargetOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  129. final class Metric extends GeneratedFile
    Annotations
    @Generated()
  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

    Annotations
    @Generated()
  131. final class MicroAUC extends GeneratedMessage 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

    Annotations
    @Generated()
  132. trait MicroAUCOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  133. final class MultiClassClassification extends GeneratedMessage 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

    Annotations
    @Generated()
  134. trait MultiClassClassificationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  135. final class MultiDimensionalRegression extends GeneratedMessage with MultiDimensionalRegressionOrBuilder

    A multi-dimensional regression task.
Similar to OneDimensionalRegression, MultiDimensionalRegression predicts
continuous real numbers. However instead of predicting a single scalar value
per example, we predict a fixed dimensional vector of values. By default the
range is any float -inf to inf, but specific sub-types (e.g. probability)
define more narrow ranges.


    A multi-dimensional regression task.
Similar to OneDimensionalRegression, MultiDimensionalRegression predicts
continuous real numbers. However instead of predicting a single scalar value
per example, we predict a fixed dimensional vector of values. By default the
range is any float -inf to inf, but specific sub-types (e.g. probability)
define more narrow ranges.

    Protobuf type tensorflow.metadata.v0.MultiDimensionalRegression

    Annotations
    @Generated()
  136. trait MultiDimensionalRegressionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  137. final class MultiLabelClassification extends GeneratedMessage 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

    Annotations
    @Generated()
  138. trait MultiLabelClassificationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  139. final class MultilabelCrossEntropy extends GeneratedMessage 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

    Annotations
    @Generated()
  140. trait MultilabelCrossEntropyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  141. final class NaturalLanguageDomain extends GeneratedMessage with NaturalLanguageDomainOrBuilder

    Natural language text.


    Natural language text.

    Protobuf type tensorflow.metadata.v0.NaturalLanguageDomain

    Annotations
    @Generated()
  142. trait NaturalLanguageDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  143. final class NaturalLanguageStatistics extends GeneratedMessage with NaturalLanguageStatisticsOrBuilder

    Statistics for a feature containing a NL domain.


    Statistics for a feature containing a NL domain.

    Protobuf type tensorflow.metadata.v0.NaturalLanguageStatistics

    Annotations
    @Generated()
  144. trait NaturalLanguageStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  145. final class NormalizedAbsoluteDifference extends GeneratedMessage 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

    Annotations
    @Generated()
  146. trait NormalizedAbsoluteDifferenceOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  147. final class NumericCrossStatistics extends GeneratedMessage with NumericCrossStatisticsOrBuilder

    Protobuf type tensorflow.metadata.v0.NumericCrossStatistics

    Protobuf type tensorflow.metadata.v0.NumericCrossStatistics

    Annotations
    @Generated()
  148. trait NumericCrossStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  149. final class NumericStatistics extends GeneratedMessage with NumericStatisticsOrBuilder

    Statistics for a numeric feature in a dataset.


    Statistics for a numeric feature in a dataset.

    Protobuf type tensorflow.metadata.v0.NumericStatistics

    Annotations
    @Generated()
  150. trait NumericStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  151. final class NumericValueComparator extends GeneratedMessage 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

    Annotations
    @Generated()
  152. trait NumericValueComparatorOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  153. final class OneDimensionalRegression extends GeneratedMessage 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

    Annotations
    @Generated()
  154. trait OneDimensionalRegressionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  155. final class OneSideRegion extends GeneratedMessage 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

    Annotations
    @Generated()
  156. trait OneSideRegionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  157. final class Path extends GeneratedMessage 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

    Annotations
    @Generated()
  158. trait PathOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  159. final class PathOuterClass extends GeneratedFile
    Annotations
    @Generated()
  160. final class PerformanceMetric extends GeneratedMessage 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

    Annotations
    @Generated()
  161. trait PerformanceMetricOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  162. final class Poisson extends GeneratedMessage with PoissonOrBuilder

    poisson(...)
DEPRECATED


    poisson(...)
DEPRECATED

    Protobuf type tensorflow.metadata.v0.Poisson

    Annotations
    @Generated()
  163. trait PoissonOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  164. final class PrecisionAtK extends GeneratedMessage with PrecisionAtKOrBuilder

    Protobuf type tensorflow.metadata.v0.PrecisionAtK

    Protobuf type tensorflow.metadata.v0.PrecisionAtK

    Annotations
    @Generated()
  165. trait PrecisionAtKOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  166. final class PrecisionAtRecall extends GeneratedMessage 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

    Annotations
    @Generated()
  167. trait PrecisionAtRecallOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  168. final class PredictionMean extends GeneratedMessage with PredictionMeanOrBuilder

    The mean of the prediction across the dataset.


    The mean of the prediction across the dataset.

    Protobuf type tensorflow.metadata.v0.PredictionMean

    Annotations
    @Generated()
  169. trait PredictionMeanOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  170. final class PresenceAndValencyStatistics extends GeneratedMessage 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

    Annotations
    @Generated()
  171. trait PresenceAndValencyStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  172. final class ProblemStatement extends GeneratedMessage with ProblemStatementOrBuilder

    Protobuf type tensorflow.metadata.v0.ProblemStatement

    Protobuf type tensorflow.metadata.v0.ProblemStatement

    Annotations
    @Generated()
  173. trait ProblemStatementOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  174. final class ProblemStatementOuterClass extends GeneratedFile
    Annotations
    @Generated()
  175. final class RankHistogram extends GeneratedMessage 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

    Annotations
    @Generated()
  176. trait RankHistogramOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  177. final class RecallAtPrecision extends GeneratedMessage 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

    Annotations
    @Generated()
  178. trait RecallAtPrecisionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  179. final class ReduceOp extends GeneratedMessage with ReduceOpOrBuilder

    Protobuf type tensorflow.metadata.v0.ReduceOp

    Protobuf type tensorflow.metadata.v0.ReduceOp

    Annotations
    @Generated()
  180. trait ReduceOpOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  181. final class Schema extends GeneratedMessage with SchemaOrBuilder

    
Message to represent schema information.
NextID: 15


    
Message to represent schema information.
NextID: 15

    Protobuf type tensorflow.metadata.v0.Schema

    Annotations
    @Generated()
  182. trait SchemaOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  183. final class SchemaOuterClass extends GeneratedFile
    Annotations
    @Generated()
  184. final class SensitivityAtSpecificity extends GeneratedMessage 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

    Annotations
    @Generated()
  185. trait SensitivityAtSpecificityOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  186. final class SequenceLengthConstraints extends GeneratedMessage with SequenceLengthConstraintsOrBuilder

    Encodes constraints on sequence lengths.


    Encodes constraints on sequence lengths.

    Protobuf type tensorflow.metadata.v0.SequenceLengthConstraints

    Annotations
    @Generated()
  187. trait SequenceLengthConstraintsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  188. final class SequenceMetadata extends GeneratedMessage with SequenceMetadataOrBuilder

    Protobuf type tensorflow.metadata.v0.SequenceMetadata

    Protobuf type tensorflow.metadata.v0.SequenceMetadata

    Annotations
    @Generated()
  189. trait SequenceMetadataOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  190. final class SequenceValueConstraints extends GeneratedMessage with SequenceValueConstraintsOrBuilder

    Encodes constraints on specific values in sequences.


    Encodes constraints on specific values in sequences.

    Protobuf type tensorflow.metadata.v0.SequenceValueConstraints

    Annotations
    @Generated()
  191. trait SequenceValueConstraintsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  192. final class SliceSql extends GeneratedMessage with SliceSqlOrBuilder

    Protobuf type tensorflow.metadata.v0.SliceSql

    Protobuf type tensorflow.metadata.v0.SliceSql

    Annotations
    @Generated()
  193. trait SliceSqlOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  194. sealed final class SliceValueTypes extends Enum[SliceValueTypes] with ProtocolMessageEnum

    Protobuf enum tensorflow.metadata.v0.SliceValueTypes

    Protobuf enum tensorflow.metadata.v0.SliceValueTypes

    Annotations
    @Generated()
  195. final class SparseFeature extends GeneratedMessage 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

    Annotations
    @Generated()
  196. trait SparseFeatureOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  197. final class SparseTopKCategoricalAccuracy extends GeneratedMessage 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

    Annotations
    @Generated()
  198. trait SparseTopKCategoricalAccuracyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  199. final class SpecificityAtSensitivity extends GeneratedMessage 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

    Annotations
    @Generated()
  200. trait SpecificityAtSensitivityOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  201. final class SquaredHinge extends GeneratedMessage 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

    Annotations
    @Generated()
  202. trait SquaredHingeOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  203. final class Statistics extends GeneratedFile
    Annotations
    @Generated()
  204. final class StringDomain extends GeneratedMessage with StringDomainOrBuilder

    Encodes information for domains of string values.


    Encodes information for domains of string values.

    Protobuf type tensorflow.metadata.v0.StringDomain

    Annotations
    @Generated()
  205. trait StringDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  206. final class StringStatistics extends GeneratedMessage with StringStatisticsOrBuilder

    Statistics for a string feature in a dataset.


    Statistics for a string feature in a dataset.

    Protobuf type tensorflow.metadata.v0.StringStatistics

    Annotations
    @Generated()
  207. trait StringStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  208. final class StructDomain extends GeneratedMessage 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

    Annotations
    @Generated()
  209. trait StructDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  210. final class StructStatistics extends GeneratedMessage with StructStatisticsOrBuilder

    Protobuf type tensorflow.metadata.v0.StructStatistics

    Protobuf type tensorflow.metadata.v0.StructStatistics

    Annotations
    @Generated()
  211. trait StructStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  212. final class Task extends GeneratedMessage 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

    Annotations
    @Generated()
  213. trait TaskOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  214. sealed final class TaskType extends Enum[TaskType] with ProtocolMessageEnum

    Protobuf enum tensorflow.metadata.v0.TaskType

    Protobuf enum tensorflow.metadata.v0.TaskType

    Annotations
    @Generated()
  215. final class TensorRepresentation extends GeneratedMessage 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

    Annotations
    @Generated()
  216. final class TensorRepresentationGroup extends GeneratedMessage 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

    Annotations
    @Generated()
  217. trait TensorRepresentationGroupOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  218. trait TensorRepresentationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  219. final class TextGeneration extends GeneratedMessage with TextGenerationOrBuilder

    Configuration for a text generation task where the model should predict
a sequence of natural language text.


    Configuration for a text generation task where the model should predict
a sequence of natural language text.

    Protobuf type tensorflow.metadata.v0.TextGeneration

    Annotations
    @Generated()
  220. trait TextGenerationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  221. final class TimeDomain extends GeneratedMessage with TimeDomainOrBuilder

    Time or date representation.


    Time or date representation.

    Protobuf type tensorflow.metadata.v0.TimeDomain

    Annotations
    @Generated()
  222. trait TimeDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  223. final class TimeOfDayDomain extends GeneratedMessage with TimeOfDayDomainOrBuilder

    Time of day, without a particular date.


    Time of day, without a particular date.

    Protobuf type tensorflow.metadata.v0.TimeOfDayDomain

    Annotations
    @Generated()
  224. trait TimeOfDayDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  225. final class TopKCategoricalAccuracy extends GeneratedMessage 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

    Annotations
    @Generated()
  226. trait TopKCategoricalAccuracyOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  227. final class TopKClassification extends GeneratedMessage 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

    Annotations
    @Generated()
  228. trait TopKClassificationOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  229. final class Type extends GeneratedMessage 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

    Annotations
    @Generated()
  230. trait TypeOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  231. final class URLDomain extends GeneratedMessage 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

    Annotations
    @Generated()
  232. trait URLDomainOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  233. final class UnchangedRegion extends GeneratedMessage 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

    Annotations
    @Generated()
  234. trait UnchangedRegionOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  235. final class UniqueConstraints extends GeneratedMessage 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

    Annotations
    @Generated()
  236. trait UniqueConstraintsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  237. final class ValueCount extends GeneratedMessage 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

    Annotations
    @Generated()
  238. final class ValueCountList extends GeneratedMessage with ValueCountListOrBuilder

    Protobuf type tensorflow.metadata.v0.ValueCountList

    Protobuf type tensorflow.metadata.v0.ValueCountList

    Annotations
    @Generated()
  239. trait ValueCountListOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  240. trait ValueCountOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  241. final class WeightedCommonStatistics extends GeneratedMessage 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. For nested features with N nested levels (N > 1), the
statistics counting number of values will rely on the innermost level.


    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. For nested features with N nested levels (N > 1), the
statistics counting number of values will rely on the innermost level.

    Protobuf type tensorflow.metadata.v0.WeightedCommonStatistics

    Annotations
    @Generated()
  242. trait WeightedCommonStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  243. final class WeightedFeature extends GeneratedMessage 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

    Annotations
    @Generated()
  244. trait WeightedFeatureOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  245. final class WeightedNaturalLanguageStatistics extends GeneratedMessage 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

    Annotations
    @Generated()
  246. trait WeightedNaturalLanguageStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  247. final class WeightedNumericStatistics extends GeneratedMessage 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

    Annotations
    @Generated()
  248. trait WeightedNumericStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()
  249. final class WeightedStringStatistics extends GeneratedMessage 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

    Annotations
    @Generated()
  250. trait WeightedStringStatisticsOrBuilder extends MessageOrBuilder
    Annotations
    @Generated()

Ungrouped