Interface ClassificationEvaluationMetricsOrBuilder (2.3.15)

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public interface ClassificationEvaluationMetricsOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getAnnotationSpecId(int index)

public abstract String getAnnotationSpecId(int index)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
String

The annotationSpecId at the given index.

getAnnotationSpecIdBytes(int index)

public abstract ByteString getAnnotationSpecIdBytes(int index)

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Parameter
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
ByteString

The bytes of the annotationSpecId at the given index.

getAnnotationSpecIdCount()

public abstract int getAnnotationSpecIdCount()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
TypeDescription
int

The count of annotationSpecId.

getAnnotationSpecIdList()

public abstract List<String> getAnnotationSpecIdList()

Output only. The annotation spec ids used for this evaluation.

repeated string annotation_spec_id = 5;

Returns
TypeDescription
List<String>

A list containing the annotationSpecId.

getAuPrc()

public abstract float getAuPrc()

Output only. The Area Under Precision-Recall Curve metric. Micro-averaged for the overall evaluation.

float au_prc = 1;

Returns
TypeDescription
float

The auPrc.

getAuRoc()

public abstract float getAuRoc()

Output only. The Area Under Receiver Operating Characteristic curve metric. Micro-averaged for the overall evaluation.

float au_roc = 6;

Returns
TypeDescription
float

The auRoc.

getConfidenceMetricsEntry(int index)

public abstract ClassificationEvaluationMetrics.ConfidenceMetricsEntry getConfidenceMetricsEntry(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationEvaluationMetrics.ConfidenceMetricsEntry

getConfidenceMetricsEntryCount()

public abstract int getConfidenceMetricsEntryCount()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
int

getConfidenceMetricsEntryList()

public abstract List<ClassificationEvaluationMetrics.ConfidenceMetricsEntry> getConfidenceMetricsEntryList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
List<ConfidenceMetricsEntry>

getConfidenceMetricsEntryOrBuilder(int index)

public abstract ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder getConfidenceMetricsEntryOrBuilder(int index)

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Parameter
NameDescription
indexint
Returns
TypeDescription
ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder

getConfidenceMetricsEntryOrBuilderList()

public abstract List<? extends ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder> getConfidenceMetricsEntryOrBuilderList()

Output only. Metrics for each confidence_threshold in 0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and position_threshold = INT32_MAX_VALUE. ROC and precision-recall curves, and other aggregated metrics are derived from them. The confidence metrics entries may also be supplied for additional values of position_threshold, but from these no aggregated metrics are computed.

repeated .google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;

Returns
TypeDescription
List<? extends com.google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder>

getConfusionMatrix()

public abstract ClassificationEvaluationMetrics.ConfusionMatrix getConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
ClassificationEvaluationMetrics.ConfusionMatrix

The confusionMatrix.

getConfusionMatrixOrBuilder()

public abstract ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder getConfusionMatrixOrBuilder()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder

getLogLoss()

public abstract float getLogLoss()

Output only. The Log Loss metric.

float log_loss = 7;

Returns
TypeDescription
float

The logLoss.

hasConfusionMatrix()

public abstract boolean hasConfusionMatrix()

Output only. Confusion matrix of the evaluation. Only set for MULTICLASS classification problems where number of labels is no more than 10. Only set for model level evaluation, not for evaluation per label.

.google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;

Returns
TypeDescription
boolean

Whether the confusionMatrix field is set.