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Add support for validating that number of unique values is within a user-specified range.
PiperOrigin-RevId: 315969158
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‎RELEASE.md

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@@ -15,6 +15,8 @@
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limit on number of examples.
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* Fix bug in display_anomalies that prevented dataset-level anomalies from
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being displayed.
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* Trigger anomalies when a feature has a number of unique values that does not
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conform to the specified minimum/maximum.
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## Known Issues
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‎g3doc/anomalies.md

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@@ -193,6 +193,35 @@ condition(s) under which each anomaly type is detected.
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- Anomaly type not detected in TFDV
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- `FEATURE_TYPE_HIGH_UNIQUE`
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- Schema Fields:
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- `feature.unique_constraints.max`
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- Statistics Fields:
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- `feature.string_stats.unique`
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- Detection Condition:
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- `feature.string_stats.unique` > `feature.unique_constraints.max`
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- `FEATURE_TYPE_LOW_UNIQUE`
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- Schema Fields:
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- `feature.unique_constraints.min`
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- Statistics Fields:
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- `feature.string_stats.unique`
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- Detection Condition:
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- `feature.string_stats.unique` < `feature.unique_constraints.min`
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- `FEATURE_TYPE_NO_UNIQUE`
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- Schema Fields:
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- `feature.unique_constraints`
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- Statistics Fields:
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- `feature.string_stats.unique`
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- Detection Condition:
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- `feature.unique_constraints` specified but no
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`feature.string_stats.unique` present (as is the case where the
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feature is not a string or categorical)
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- `FLOAT_TYPE_BIG_FLOAT`
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- Schema Fields:

‎tensorflow_data_validation/anomalies/feature_util.cc

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@@ -320,5 +320,42 @@ std::vector<Description> UpdatePresence(
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return descriptions;
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}
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std::vector<Description> UpdateUniqueConstraints(
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const FeatureStatsView& feature_stats_view,
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tensorflow::metadata::v0::Feature* feature) {
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std::vector<Description> descriptions;
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const absl::optional<int> num_unique = feature_stats_view.GetNumUnique();
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if (num_unique) {
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if (num_unique < feature->unique_constraints().min()) {
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descriptions.push_back(
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{tensorflow::metadata::v0::AnomalyInfo::FEATURE_TYPE_LOW_UNIQUE,
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"Low number of unique values",
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absl::StrCat(
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"Expected at least ", feature->unique_constraints().min(),
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" unique values but found only ", num_unique.value(), ".")});
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feature->mutable_unique_constraints()->set_min(num_unique.value());
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}
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if (num_unique > feature->unique_constraints().max()) {
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descriptions.push_back(
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{tensorflow::metadata::v0::AnomalyInfo::FEATURE_TYPE_HIGH_UNIQUE,
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"High number of unique values",
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absl::StrCat("Expected no more than ",
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feature->unique_constraints().max(),
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" unique values but found ", num_unique.value(), ".")});
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feature->mutable_unique_constraints()->set_max(num_unique.value());
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}
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} else {
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descriptions.push_back(
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{tensorflow::metadata::v0::AnomalyInfo::FEATURE_TYPE_NO_UNIQUE,
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"No unique values",
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absl::StrCat(
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"UniqueConstraints specified for the feature, but unique values "
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"were not counted (i.e., feature is not string or "
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"categorical).")});
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feature->clear_unique_constraints();
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}
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return descriptions;
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}
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} // namespace data_validation
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} // namespace tensorflow

‎tensorflow_data_validation/anomalies/feature_util.h

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@@ -107,6 +107,12 @@ double GetMaxOffDomain(const tensorflow::metadata::v0::DistributionConstraints&
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// Clear the domain of the feature.
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void ClearDomain(tensorflow::metadata::v0::Feature* feature);
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// Updates the UniqueConstraints specified for the feature.
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std::vector<Description> UpdateUniqueConstraints(
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const FeatureStatsView& feature_stats_view,
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tensorflow::metadata::v0::Feature* feature);
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} // namespace data_validation
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} // namespace tensorflow
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#endif // TENSORFLOW_DATA_VALIDATION_ANOMALIES_FEATURE_UTIL_H_

‎tensorflow_data_validation/anomalies/feature_util_test.cc

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@@ -273,6 +273,194 @@ TEST(FeatureUtilTest,
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EXPECT_EQ(actual_descriptions.size(), 0);
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}
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TEST(FeatureUtilTest, UpdateUniqueConstraintsNoChange) {
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DatasetFeatureStatistics statistics =
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ParseTextProtoOrDie<DatasetFeatureStatistics>(R"(
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features: {
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name: "categorical_feature"
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type: INT
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string_stats {
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common_stats: {
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num_missing: 0
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num_non_missing: 2
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min_num_values: 1
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max_num_values: 1
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}
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unique: 5
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}
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},
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features: {
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name: "string_feature"
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type: STRING
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string_stats {
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common_stats: {
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num_missing: 0
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num_non_missing: 2
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min_num_values: 1
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max_num_values: 1
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}
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unique: 1
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}
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})");
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DatasetStatsView stats_view(statistics);
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const FeatureStatsView categorical_feature_stats_view =
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stats_view.GetByPath(Path({"categorical_feature"})).value();
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const FeatureStatsView string_feature_stats_view =
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stats_view.GetByPath(Path({"string_feature"})).value();
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Feature original_categorical_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(R"(
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name: "categorical_feature"
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type: INT
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int_domain { is_categorical: true }
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unique_constraints { min: 1 max: 5 })");
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Feature categorical_feature;
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categorical_feature.CopyFrom(original_categorical_feature);
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Feature original_string_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(
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R"(name: "string_feature"
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type: BYTES
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unique_constraints { min: 1 max: 5 })");
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Feature string_feature;
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string_feature.CopyFrom(original_string_feature);
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std::vector<Description> actual_categorical_descriptions =
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UpdateUniqueConstraints(categorical_feature_stats_view,
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&categorical_feature);
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std::vector<Description> actual_string_descriptions =
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UpdateUniqueConstraints(string_feature_stats_view, &string_feature);
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// The feature is not changed, and no anomalies are generated.
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EXPECT_THAT(categorical_feature, EqualsProto(original_categorical_feature));
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EXPECT_EQ(actual_categorical_descriptions.size(), 0);
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EXPECT_THAT(string_feature, EqualsProto(original_string_feature));
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EXPECT_EQ(actual_string_descriptions.size(), 0);
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}
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TEST(FeatureUtilTest, UpdateUniqueConstraintsNumUniquesOutsideRange) {
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DatasetFeatureStatistics statistics =
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ParseTextProtoOrDie<DatasetFeatureStatistics>(R"(
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features: {
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name: "categorical_feature"
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type: INT
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string_stats {
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common_stats: {
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num_missing: 0
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num_non_missing: 2
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min_num_values: 1
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max_num_values: 1
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}
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unique: 5
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}
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},
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features: {
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name: "string_feature"
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type: STRING
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string_stats {
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common_stats: {
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num_missing: 0
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num_non_missing: 2
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min_num_values: 1
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max_num_values: 1
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}
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unique: 1
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}
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})");
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DatasetStatsView stats_view(statistics);
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const FeatureStatsView categorical_feature_stats_view =
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stats_view.GetByPath(Path({"categorical_feature"})).value();
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const FeatureStatsView string_feature_stats_view =
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stats_view.GetByPath(Path({"string_feature"})).value();
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Feature categorical_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(R"(
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name: "categorical_feature"
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type: INT
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int_domain { is_categorical: true }
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unique_constraints { min: 2 max: 2 })");
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Feature string_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(
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R"(name: "string_feature"
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type: BYTES
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unique_constraints { min: 2 max: 2 })");
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// The number of unique values for the categorical feature is higher than the
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// original unique_constraints.max for that feature, so expect that the max
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// will be updated.
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Feature expected_categorical_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(R"(
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name: "categorical_feature"
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type: INT
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int_domain { is_categorical: true }
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unique_constraints { min: 2 max: 5 })");
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// The number of unique values for the string feature is lower than the
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// original unique_constraints.min for that feature, so expect that the
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// min will be updated.
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Feature expected_string_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(
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R"(name: "string_feature"
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type: BYTES
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unique_constraints { min: 1 max: 2 })");
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std::vector<Description> actual_categorical_descriptions =
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UpdateUniqueConstraints(categorical_feature_stats_view,
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&categorical_feature);
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std::vector<Description> actual_string_descriptions =
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UpdateUniqueConstraints(string_feature_stats_view, &string_feature);
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EXPECT_THAT(categorical_feature, EqualsProto(expected_categorical_feature));
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EXPECT_EQ(actual_categorical_descriptions.size(), 1);
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EXPECT_EQ(actual_categorical_descriptions.at(0).long_description,
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"Expected no more than 2 unique values but found 5.");
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EXPECT_THAT(string_feature, EqualsProto(expected_string_feature));
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EXPECT_EQ(actual_string_descriptions.size(), 1);
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EXPECT_EQ(actual_string_descriptions.at(0).long_description,
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"Expected at least 2 unique values but found only 1.");
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}
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TEST(FeatureUtilTest, UpdateUniqueConstraintsNotStringOrCategorical) {
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DatasetFeatureStatistics statistics =
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ParseTextProtoOrDie<DatasetFeatureStatistics>(R"(
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features: {
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name: "numeric_feature"
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type: INT
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num_stats {
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common_stats: {
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num_missing: 0
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num_non_missing: 6
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min_num_values: 1
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max_num_values: 1
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}
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}
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})");
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DatasetStatsView stats_view(statistics);
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const FeatureStatsView numeric_feature_stats_view =
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stats_view.GetByPath(Path({"numeric_feature"})).value();
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Feature numeric_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(R"(
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name: "numeric_feature"
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type: INT
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unique_constraints { min: 5 max: 5 })");
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Feature expected_numeric_feature =
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ParseTextProtoOrDie<tensorflow::metadata::v0::Feature>(R"(
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name: "numeric_feature"
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type: INT)");
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std::vector<Description> actual_numeric_descriptions =
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UpdateUniqueConstraints(numeric_feature_stats_view, &numeric_feature);
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// The unique_constraints are cleared, and an anomaly is generated.
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EXPECT_THAT(numeric_feature, EqualsProto(expected_numeric_feature));
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EXPECT_EQ(actual_numeric_descriptions.size(), 1);
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EXPECT_EQ(actual_numeric_descriptions.at(0).long_description,
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"UniqueConstraints specified for the feature, but unique values "
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"were not counted (i.e., feature is not string or categorical).");
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}
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// Confirm that the result of calling DeprecateFeature on a feature is
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// recognized as by FeatureIsDeprecated.
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TEST(FeatureTypeTest, DeprecateConsistency) {

‎tensorflow_data_validation/anomalies/schema.cc

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@@ -1059,6 +1059,10 @@ std::vector<Description> Schema::UpdateFeatureInternal(
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DCHECK(false);
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}
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if (feature->has_unique_constraints()) {
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add_to_descriptions(UpdateUniqueConstraints(view, feature));
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}
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const std::vector<FeatureComparatorType> all_comparator_types = {
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FeatureComparatorType::DRIFT, FeatureComparatorType::SKEW};
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// Handle comparators here.

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