* Type of the classification problem. ** * Protobuf enum {@code google.cloud.automl.v1beta1.ClassificationType} */ public enum ClassificationType implements com.google.protobuf.ProtocolMessageEnum { /** * * *
* An un-set value of this enum.
*
*
* CLASSIFICATION_TYPE_UNSPECIFIED = 0;
*/
CLASSIFICATION_TYPE_UNSPECIFIED(0),
/**
*
*
*
* At most one label is allowed per example.
*
*
* MULTICLASS = 1;
*/
MULTICLASS(1),
/**
*
*
*
* Multiple labels are allowed for one example.
*
*
* MULTILABEL = 2;
*/
MULTILABEL(2),
UNRECOGNIZED(-1),
;
/**
*
*
*
* An un-set value of this enum.
*
*
* CLASSIFICATION_TYPE_UNSPECIFIED = 0;
*/
public static final int CLASSIFICATION_TYPE_UNSPECIFIED_VALUE = 0;
/**
*
*
*
* At most one label is allowed per example.
*
*
* MULTICLASS = 1;
*/
public static final int MULTICLASS_VALUE = 1;
/**
*
*
*
* Multiple labels are allowed for one example.
*
*
* MULTILABEL = 2;
*/
public static final int MULTILABEL_VALUE = 2;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @param value The numeric wire value of the corresponding enum entry.
* @return The enum associated with the given numeric wire value.
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static ClassificationType valueOf(int value) {
return forNumber(value);
}
/**
* @param value The numeric wire value of the corresponding enum entry.
* @return The enum associated with the given numeric wire value.
*/
public static ClassificationType forNumber(int value) {
switch (value) {
case 0:
return CLASSIFICATION_TYPE_UNSPECIFIED;
case 1:
return MULTICLASS;
case 2:
return MULTILABEL;
default:
return null;
}
}
public static com.google.protobuf.Internal.EnumLiteMap
* Output only. A confidence estimate between 0.0 and 1.0. A higher value
* means greater confidence that the annotation is positive. If a user
* approves an annotation as negative or positive, the score value remains
* unchanged. If a user creates an annotation, the score is 0 for negative or
* 1 for positive.
*
*
* float score = 1;
*
* @return The score.
*/
float getScore();
}
/**
*
*
* * Contains annotation details specific to classification. ** * Protobuf type {@code google.cloud.automl.v1beta1.ClassificationAnnotation} */ public static final class ClassificationAnnotation extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.ClassificationAnnotation) ClassificationAnnotationOrBuilder { private static final long serialVersionUID = 0L; // Use ClassificationAnnotation.newBuilder() to construct. private ClassificationAnnotation(com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private ClassificationAnnotation() {} @java.lang.Override @SuppressWarnings({"unused"}) protected java.lang.Object newInstance(UnusedPrivateParameter unused) { return new ClassificationAnnotation(); } @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { return com.google.cloud.automl.v1beta1.ClassificationProto .internal_static_google_cloud_automl_v1beta1_ClassificationAnnotation_descriptor; } @java.lang.Override protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return com.google.cloud.automl.v1beta1.ClassificationProto .internal_static_google_cloud_automl_v1beta1_ClassificationAnnotation_fieldAccessorTable .ensureFieldAccessorsInitialized( com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation.class, com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation.Builder .class); } public static final int SCORE_FIELD_NUMBER = 1; private float score_ = 0F; /** * * *
* Output only. A confidence estimate between 0.0 and 1.0. A higher value
* means greater confidence that the annotation is positive. If a user
* approves an annotation as negative or positive, the score value remains
* unchanged. If a user creates an annotation, the score is 0 for negative or
* 1 for positive.
*
*
* float score = 1;
*
* @return The score.
*/
@java.lang.Override
public float getScore() {
return score_;
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
if (java.lang.Float.floatToRawIntBits(score_) != 0) {
output.writeFloat(1, score_);
}
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (java.lang.Float.floatToRawIntBits(score_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(1, score_);
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj
instanceof
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation other =
(com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation) obj;
if (java.lang.Float.floatToIntBits(getScore())
!= java.lang.Float.floatToIntBits(other.getScore())) return false;
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + SCORE_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getScore());
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* Contains annotation details specific to classification.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.ClassificationAnnotation}
*/
public static final class Builder
extends com.google.protobuf.GeneratedMessageV3.Builder
* Output only. A confidence estimate between 0.0 and 1.0. A higher value
* means greater confidence that the annotation is positive. If a user
* approves an annotation as negative or positive, the score value remains
* unchanged. If a user creates an annotation, the score is 0 for negative or
* 1 for positive.
*
*
* float score = 1;
*
* @return The score.
*/
@java.lang.Override
public float getScore() {
return score_;
}
/**
*
*
*
* Output only. A confidence estimate between 0.0 and 1.0. A higher value
* means greater confidence that the annotation is positive. If a user
* approves an annotation as negative or positive, the score value remains
* unchanged. If a user creates an annotation, the score is 0 for negative or
* 1 for positive.
*
*
* float score = 1;
*
* @param value The score to set.
* @return This builder for chaining.
*/
public Builder setScore(float value) {
score_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Output only. A confidence estimate between 0.0 and 1.0. A higher value
* means greater confidence that the annotation is positive. If a user
* approves an annotation as negative or positive, the score value remains
* unchanged. If a user creates an annotation, the score is 0 for negative or
* 1 for positive.
*
*
* float score = 1;
*
* @return This builder for chaining.
*/
public Builder clearScore() {
bitField0_ = (bitField0_ & ~0x00000001);
score_ = 0F;
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.ClassificationAnnotation)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationAnnotation)
private static final com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationAnnotation
DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE =
new com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation();
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return The type.
*/
java.lang.String getType();
/**
*
*
*
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return The bytes for type.
*/
com.google.protobuf.ByteString getTypeBytes();
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*
* @return Whether the classificationAnnotation field is set.
*/
boolean hasClassificationAnnotation();
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*
* @return The classificationAnnotation.
*/
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
getClassificationAnnotation();
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotationOrBuilder
getClassificationAnnotationOrBuilder();
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*
* @return Whether the timeSegment field is set.
*/
boolean hasTimeSegment();
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*
* @return The timeSegment.
*/
com.google.cloud.automl.v1beta1.TimeSegment getTimeSegment();
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
com.google.cloud.automl.v1beta1.TimeSegmentOrBuilder getTimeSegmentOrBuilder();
}
/**
*
*
* * Contains annotation details specific to video classification. ** * Protobuf type {@code google.cloud.automl.v1beta1.VideoClassificationAnnotation} */ public static final class VideoClassificationAnnotation extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.VideoClassificationAnnotation) VideoClassificationAnnotationOrBuilder { private static final long serialVersionUID = 0L; // Use VideoClassificationAnnotation.newBuilder() to construct. private VideoClassificationAnnotation( com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private VideoClassificationAnnotation() { type_ = ""; } @java.lang.Override @SuppressWarnings({"unused"}) protected java.lang.Object newInstance(UnusedPrivateParameter unused) { return new VideoClassificationAnnotation(); } @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { return com.google.cloud.automl.v1beta1.ClassificationProto .internal_static_google_cloud_automl_v1beta1_VideoClassificationAnnotation_descriptor; } @java.lang.Override protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return com.google.cloud.automl.v1beta1.ClassificationProto .internal_static_google_cloud_automl_v1beta1_VideoClassificationAnnotation_fieldAccessorTable .ensureFieldAccessorsInitialized( com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation .class, com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation .Builder.class); } public static final int TYPE_FIELD_NUMBER = 1; @SuppressWarnings("serial") private volatile java.lang.Object type_ = ""; /** * * *
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return The type.
*/
@java.lang.Override
public java.lang.String getType() {
java.lang.Object ref = type_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
type_ = s;
return s;
}
}
/**
*
*
*
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return The bytes for type.
*/
@java.lang.Override
public com.google.protobuf.ByteString getTypeBytes() {
java.lang.Object ref = type_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
type_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int CLASSIFICATION_ANNOTATION_FIELD_NUMBER = 2;
private com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
classificationAnnotation_;
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*
* @return Whether the classificationAnnotation field is set.
*/
@java.lang.Override
public boolean hasClassificationAnnotation() {
return classificationAnnotation_ != null;
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*
* @return The classificationAnnotation.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
getClassificationAnnotation() {
return classificationAnnotation_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
.getDefaultInstance()
: classificationAnnotation_;
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotationOrBuilder
getClassificationAnnotationOrBuilder() {
return classificationAnnotation_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
.getDefaultInstance()
: classificationAnnotation_;
}
public static final int TIME_SEGMENT_FIELD_NUMBER = 3;
private com.google.cloud.automl.v1beta1.TimeSegment timeSegment_;
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*
* @return Whether the timeSegment field is set.
*/
@java.lang.Override
public boolean hasTimeSegment() {
return timeSegment_ != null;
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*
* @return The timeSegment.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.TimeSegment getTimeSegment() {
return timeSegment_ == null
? com.google.cloud.automl.v1beta1.TimeSegment.getDefaultInstance()
: timeSegment_;
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.TimeSegmentOrBuilder getTimeSegmentOrBuilder() {
return timeSegment_ == null
? com.google.cloud.automl.v1beta1.TimeSegment.getDefaultInstance()
: timeSegment_;
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(type_)) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 1, type_);
}
if (classificationAnnotation_ != null) {
output.writeMessage(2, getClassificationAnnotation());
}
if (timeSegment_ != null) {
output.writeMessage(3, getTimeSegment());
}
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(type_)) {
size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, type_);
}
if (classificationAnnotation_ != null) {
size +=
com.google.protobuf.CodedOutputStream.computeMessageSize(
2, getClassificationAnnotation());
}
if (timeSegment_ != null) {
size += com.google.protobuf.CodedOutputStream.computeMessageSize(3, getTimeSegment());
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj
instanceof
com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation other =
(com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation) obj;
if (!getType().equals(other.getType())) return false;
if (hasClassificationAnnotation() != other.hasClassificationAnnotation()) return false;
if (hasClassificationAnnotation()) {
if (!getClassificationAnnotation().equals(other.getClassificationAnnotation()))
return false;
}
if (hasTimeSegment() != other.hasTimeSegment()) return false;
if (hasTimeSegment()) {
if (!getTimeSegment().equals(other.getTimeSegment())) return false;
}
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + TYPE_FIELD_NUMBER;
hash = (53 * hash) + getType().hashCode();
if (hasClassificationAnnotation()) {
hash = (37 * hash) + CLASSIFICATION_ANNOTATION_FIELD_NUMBER;
hash = (53 * hash) + getClassificationAnnotation().hashCode();
}
if (hasTimeSegment()) {
hash = (37 * hash) + TIME_SEGMENT_FIELD_NUMBER;
hash = (53 * hash) + getTimeSegment().hashCode();
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* Contains annotation details specific to video classification.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.VideoClassificationAnnotation}
*/
public static final class Builder
extends com.google.protobuf.GeneratedMessageV3.Builder
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return The type.
*/
public java.lang.String getType() {
java.lang.Object ref = type_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
type_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return The bytes for type.
*/
public com.google.protobuf.ByteString getTypeBytes() {
java.lang.Object ref = type_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
type_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @param value The type to set.
* @return This builder for chaining.
*/
public Builder setType(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
type_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @return This builder for chaining.
*/
public Builder clearType() {
type_ = getDefaultInstance().getType();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
*
*
* Output only. Expresses the type of video classification. Possible values:
* * `segment` - Classification done on a specified by user
* time segment of a video. AnnotationSpec is answered to be present
* in that time segment, if it is present in any part of it. The video
* ML model evaluations are done only for this type of classification.
* * `shot`- Shot-level classification.
* AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
* * `1s_interval` - AutoML Video Intelligence returns labels and their
* confidence scores for each second of the entire segment of the video
* that user specified in the request configuration.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no
* metrics provided to describe that quality.
*
*
* string type = 1;
*
* @param value The bytes for type to set.
* @return This builder for chaining.
*/
public Builder setTypeBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
type_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
private com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
classificationAnnotation_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotationOrBuilder>
classificationAnnotationBuilder_;
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*
* @return Whether the classificationAnnotation field is set.
*/
public boolean hasClassificationAnnotation() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*
* @return The classificationAnnotation.
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
getClassificationAnnotation() {
if (classificationAnnotationBuilder_ == null) {
return classificationAnnotation_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
.getDefaultInstance()
: classificationAnnotation_;
} else {
return classificationAnnotationBuilder_.getMessage();
}
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
public Builder setClassificationAnnotation(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation value) {
if (classificationAnnotationBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
classificationAnnotation_ = value;
} else {
classificationAnnotationBuilder_.setMessage(value);
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
public Builder setClassificationAnnotation(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation.Builder
builderForValue) {
if (classificationAnnotationBuilder_ == null) {
classificationAnnotation_ = builderForValue.build();
} else {
classificationAnnotationBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
public Builder mergeClassificationAnnotation(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation value) {
if (classificationAnnotationBuilder_ == null) {
if (((bitField0_ & 0x00000002) != 0)
&& classificationAnnotation_ != null
&& classificationAnnotation_
!= com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
.getDefaultInstance()) {
getClassificationAnnotationBuilder().mergeFrom(value);
} else {
classificationAnnotation_ = value;
}
} else {
classificationAnnotationBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
public Builder clearClassificationAnnotation() {
bitField0_ = (bitField0_ & ~0x00000002);
classificationAnnotation_ = null;
if (classificationAnnotationBuilder_ != null) {
classificationAnnotationBuilder_.dispose();
classificationAnnotationBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation.Builder
getClassificationAnnotationBuilder() {
bitField0_ |= 0x00000002;
onChanged();
return getClassificationAnnotationFieldBuilder().getBuilder();
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotationOrBuilder
getClassificationAnnotationOrBuilder() {
if (classificationAnnotationBuilder_ != null) {
return classificationAnnotationBuilder_.getMessageOrBuilder();
} else {
return classificationAnnotation_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
.getDefaultInstance()
: classificationAnnotation_;
}
}
/**
*
*
*
* Output only . The classification details of this annotation.
*
*
* .google.cloud.automl.v1beta1.ClassificationAnnotation classification_annotation = 2;
*
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotationOrBuilder>
getClassificationAnnotationFieldBuilder() {
if (classificationAnnotationBuilder_ == null) {
classificationAnnotationBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationAnnotation
.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationAnnotationOrBuilder>(
getClassificationAnnotation(), getParentForChildren(), isClean());
classificationAnnotation_ = null;
}
return classificationAnnotationBuilder_;
}
private com.google.cloud.automl.v1beta1.TimeSegment timeSegment_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.TimeSegment,
com.google.cloud.automl.v1beta1.TimeSegment.Builder,
com.google.cloud.automl.v1beta1.TimeSegmentOrBuilder>
timeSegmentBuilder_;
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*
* @return Whether the timeSegment field is set.
*/
public boolean hasTimeSegment() {
return ((bitField0_ & 0x00000004) != 0);
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*
* @return The timeSegment.
*/
public com.google.cloud.automl.v1beta1.TimeSegment getTimeSegment() {
if (timeSegmentBuilder_ == null) {
return timeSegment_ == null
? com.google.cloud.automl.v1beta1.TimeSegment.getDefaultInstance()
: timeSegment_;
} else {
return timeSegmentBuilder_.getMessage();
}
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
public Builder setTimeSegment(com.google.cloud.automl.v1beta1.TimeSegment value) {
if (timeSegmentBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
timeSegment_ = value;
} else {
timeSegmentBuilder_.setMessage(value);
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
public Builder setTimeSegment(
com.google.cloud.automl.v1beta1.TimeSegment.Builder builderForValue) {
if (timeSegmentBuilder_ == null) {
timeSegment_ = builderForValue.build();
} else {
timeSegmentBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
public Builder mergeTimeSegment(com.google.cloud.automl.v1beta1.TimeSegment value) {
if (timeSegmentBuilder_ == null) {
if (((bitField0_ & 0x00000004) != 0)
&& timeSegment_ != null
&& timeSegment_ != com.google.cloud.automl.v1beta1.TimeSegment.getDefaultInstance()) {
getTimeSegmentBuilder().mergeFrom(value);
} else {
timeSegment_ = value;
}
} else {
timeSegmentBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
public Builder clearTimeSegment() {
bitField0_ = (bitField0_ & ~0x00000004);
timeSegment_ = null;
if (timeSegmentBuilder_ != null) {
timeSegmentBuilder_.dispose();
timeSegmentBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
public com.google.cloud.automl.v1beta1.TimeSegment.Builder getTimeSegmentBuilder() {
bitField0_ |= 0x00000004;
onChanged();
return getTimeSegmentFieldBuilder().getBuilder();
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
public com.google.cloud.automl.v1beta1.TimeSegmentOrBuilder getTimeSegmentOrBuilder() {
if (timeSegmentBuilder_ != null) {
return timeSegmentBuilder_.getMessageOrBuilder();
} else {
return timeSegment_ == null
? com.google.cloud.automl.v1beta1.TimeSegment.getDefaultInstance()
: timeSegment_;
}
}
/**
*
*
*
* Output only . The time segment of the video to which the
* annotation applies.
*
*
* .google.cloud.automl.v1beta1.TimeSegment time_segment = 3;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.TimeSegment,
com.google.cloud.automl.v1beta1.TimeSegment.Builder,
com.google.cloud.automl.v1beta1.TimeSegmentOrBuilder>
getTimeSegmentFieldBuilder() {
if (timeSegmentBuilder_ == null) {
timeSegmentBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.TimeSegment,
com.google.cloud.automl.v1beta1.TimeSegment.Builder,
com.google.cloud.automl.v1beta1.TimeSegmentOrBuilder>(
getTimeSegment(), getParentForChildren(), isClean());
timeSegment_ = null;
}
return timeSegmentBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.VideoClassificationAnnotation)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.VideoClassificationAnnotation)
private static final com.google.cloud.automl.v1beta1.ClassificationProto
.VideoClassificationAnnotation
DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE =
new com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation();
}
public static com.google.cloud.automl.v1beta1.ClassificationProto.VideoClassificationAnnotation
getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
* Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
* for the overall evaluation.
*
*
* float au_prc = 1;
*
* @return The auPrc.
*/
float getAuPrc();
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric based on priors.
* Micro-averaged for the overall evaluation.
* Deprecated.
*
*
* float base_au_prc = 2 [deprecated = true];
*
* @deprecated google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is
* deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188
* @return The baseAuPrc.
*/
@java.lang.Deprecated
float getBaseAuPrc();
/**
*
*
*
* Output only. The Area Under Receiver Operating Characteristic curve metric.
* Micro-averaged for the overall evaluation.
*
*
* float au_roc = 6;
*
* @return The auRoc.
*/
float getAuRoc();
/**
*
*
*
* Output only. The Log Loss metric.
*
*
* float log_loss = 7;
*
* @return The logLoss.
*/
float getLogLoss();
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
com.google.cloud.automl.v1beta1.ClassificationProto.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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
java.util.List<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto.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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntryOrBuilder
getConfidenceMetricsEntryOrBuilder(int index);
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*
* @return Whether the confusionMatrix field is set.
*/
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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*
* @return The confusionMatrix.
*/
com.google.cloud.automl.v1beta1.ClassificationProto.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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrixOrBuilder
getConfusionMatrixOrBuilder();
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return A list containing the annotationSpecId.
*/
java.util.List
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return The count of annotationSpecId.
*/
int getAnnotationSpecIdCount();
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index of the element to return.
* @return The annotationSpecId at the given index.
*/
java.lang.String getAnnotationSpecId(int index);
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index of the value to return.
* @return The bytes of the annotationSpecId at the given index.
*/
com.google.protobuf.ByteString getAnnotationSpecIdBytes(int index);
}
/**
*
*
* * Model evaluation metrics for classification problems. * Note: For Video Classification this metrics only describe quality of the * Video Classification predictions of "segment_classification" type. ** * Protobuf type {@code google.cloud.automl.v1beta1.ClassificationEvaluationMetrics} */ public static final class ClassificationEvaluationMetrics extends com.google.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics) ClassificationEvaluationMetricsOrBuilder { private static final long serialVersionUID = 0L; // Use ClassificationEvaluationMetrics.newBuilder() to construct. private ClassificationEvaluationMetrics( com.google.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private ClassificationEvaluationMetrics() { confidenceMetricsEntry_ = java.util.Collections.emptyList(); annotationSpecId_ = com.google.protobuf.LazyStringArrayList.EMPTY; } @java.lang.Override @SuppressWarnings({"unused"}) protected java.lang.Object newInstance(UnusedPrivateParameter unused) { return new ClassificationEvaluationMetrics(); } @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { return com.google.cloud.automl.v1beta1.ClassificationProto .internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_descriptor; } @java.lang.Override protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return com.google.cloud.automl.v1beta1.ClassificationProto .internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_fieldAccessorTable .ensureFieldAccessorsInitialized( com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics .class, com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics .Builder.class); } public interface ConfidenceMetricsEntryOrBuilder extends // @@protoc_insertion_point(interface_extends:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry) com.google.protobuf.MessageOrBuilder { /** * * *
* Output only. Metrics are computed with an assumption that the model
* never returns predictions with score lower than this value.
*
*
* float confidence_threshold = 1;
*
* @return The confidenceThreshold.
*/
float getConfidenceThreshold();
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* always returns at most this many predictions (ordered by their score,
* descendingly), but they all still need to meet the confidence_threshold.
*
*
* int32 position_threshold = 14;
*
* @return The positionThreshold.
*/
int getPositionThreshold();
/**
*
*
*
* Output only. Recall (True Positive Rate) for the given confidence
* threshold.
*
*
* float recall = 2;
*
* @return The recall.
*/
float getRecall();
/**
*
*
*
* Output only. Precision for the given confidence threshold.
*
*
* float precision = 3;
*
* @return The precision.
*/
float getPrecision();
/**
*
*
*
* Output only. False Positive Rate for the given confidence threshold.
*
*
* float false_positive_rate = 8;
*
* @return The falsePositiveRate.
*/
float getFalsePositiveRate();
/**
*
*
*
* Output only. The harmonic mean of recall and precision.
*
*
* float f1_score = 4;
*
* @return The f1Score.
*/
float getF1Score();
/**
*
*
*
* Output only. The Recall (True Positive Rate) when only considering the
* label that has the highest prediction score and not below the confidence
* threshold for each example.
*
*
* float recall_at1 = 5;
*
* @return The recallAt1.
*/
float getRecallAt1();
/**
*
*
*
* Output only. The precision when only considering the label that has the
* highest prediction score and not below the confidence threshold for each
* example.
*
*
* float precision_at1 = 6;
*
* @return The precisionAt1.
*/
float getPrecisionAt1();
/**
*
*
*
* Output only. The False Positive Rate when only considering the label that
* has the highest prediction score and not below the confidence threshold
* for each example.
*
*
* float false_positive_rate_at1 = 9;
*
* @return The falsePositiveRateAt1.
*/
float getFalsePositiveRateAt1();
/**
*
*
*
* Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
*
*
* float f1_score_at1 = 7;
*
* @return The f1ScoreAt1.
*/
float getF1ScoreAt1();
/**
*
*
*
* Output only. The number of model created labels that match a ground truth
* label.
*
*
* int64 true_positive_count = 10;
*
* @return The truePositiveCount.
*/
long getTruePositiveCount();
/**
*
*
*
* Output only. The number of model created labels that do not match a
* ground truth label.
*
*
* int64 false_positive_count = 11;
*
* @return The falsePositiveCount.
*/
long getFalsePositiveCount();
/**
*
*
*
* Output only. The number of ground truth labels that are not matched
* by a model created label.
*
*
* int64 false_negative_count = 12;
*
* @return The falseNegativeCount.
*/
long getFalseNegativeCount();
/**
*
*
*
* Output only. The number of labels that were not created by the model,
* but if they would, they would not match a ground truth label.
*
*
* int64 true_negative_count = 13;
*
* @return The trueNegativeCount.
*/
long getTrueNegativeCount();
}
/**
*
*
*
* Metrics for a single confidence threshold.
*
*
* Protobuf type {@code
* google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry}
*/
public static final class ConfidenceMetricsEntry extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry)
ConfidenceMetricsEntryOrBuilder {
private static final long serialVersionUID = 0L;
// Use ConfidenceMetricsEntry.newBuilder() to construct.
private ConfidenceMetricsEntry(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private ConfidenceMetricsEntry() {}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new ConfidenceMetricsEntry();
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet getUnknownFields() {
return this.unknownFields;
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.ClassificationProto
.internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_ConfidenceMetricsEntry_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1beta1.ClassificationProto
.internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_ConfidenceMetricsEntry_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.class,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder.class);
}
public static final int CONFIDENCE_THRESHOLD_FIELD_NUMBER = 1;
private float confidenceThreshold_ = 0F;
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* never returns predictions with score lower than this value.
*
*
* float confidence_threshold = 1;
*
* @return The confidenceThreshold.
*/
@java.lang.Override
public float getConfidenceThreshold() {
return confidenceThreshold_;
}
public static final int POSITION_THRESHOLD_FIELD_NUMBER = 14;
private int positionThreshold_ = 0;
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* always returns at most this many predictions (ordered by their score,
* descendingly), but they all still need to meet the confidence_threshold.
*
*
* int32 position_threshold = 14;
*
* @return The positionThreshold.
*/
@java.lang.Override
public int getPositionThreshold() {
return positionThreshold_;
}
public static final int RECALL_FIELD_NUMBER = 2;
private float recall_ = 0F;
/**
*
*
*
* Output only. Recall (True Positive Rate) for the given confidence
* threshold.
*
*
* float recall = 2;
*
* @return The recall.
*/
@java.lang.Override
public float getRecall() {
return recall_;
}
public static final int PRECISION_FIELD_NUMBER = 3;
private float precision_ = 0F;
/**
*
*
*
* Output only. Precision for the given confidence threshold.
*
*
* float precision = 3;
*
* @return The precision.
*/
@java.lang.Override
public float getPrecision() {
return precision_;
}
public static final int FALSE_POSITIVE_RATE_FIELD_NUMBER = 8;
private float falsePositiveRate_ = 0F;
/**
*
*
*
* Output only. False Positive Rate for the given confidence threshold.
*
*
* float false_positive_rate = 8;
*
* @return The falsePositiveRate.
*/
@java.lang.Override
public float getFalsePositiveRate() {
return falsePositiveRate_;
}
public static final int F1_SCORE_FIELD_NUMBER = 4;
private float f1Score_ = 0F;
/**
*
*
*
* Output only. The harmonic mean of recall and precision.
*
*
* float f1_score = 4;
*
* @return The f1Score.
*/
@java.lang.Override
public float getF1Score() {
return f1Score_;
}
public static final int RECALL_AT1_FIELD_NUMBER = 5;
private float recallAt1_ = 0F;
/**
*
*
*
* Output only. The Recall (True Positive Rate) when only considering the
* label that has the highest prediction score and not below the confidence
* threshold for each example.
*
*
* float recall_at1 = 5;
*
* @return The recallAt1.
*/
@java.lang.Override
public float getRecallAt1() {
return recallAt1_;
}
public static final int PRECISION_AT1_FIELD_NUMBER = 6;
private float precisionAt1_ = 0F;
/**
*
*
*
* Output only. The precision when only considering the label that has the
* highest prediction score and not below the confidence threshold for each
* example.
*
*
* float precision_at1 = 6;
*
* @return The precisionAt1.
*/
@java.lang.Override
public float getPrecisionAt1() {
return precisionAt1_;
}
public static final int FALSE_POSITIVE_RATE_AT1_FIELD_NUMBER = 9;
private float falsePositiveRateAt1_ = 0F;
/**
*
*
*
* Output only. The False Positive Rate when only considering the label that
* has the highest prediction score and not below the confidence threshold
* for each example.
*
*
* float false_positive_rate_at1 = 9;
*
* @return The falsePositiveRateAt1.
*/
@java.lang.Override
public float getFalsePositiveRateAt1() {
return falsePositiveRateAt1_;
}
public static final int F1_SCORE_AT1_FIELD_NUMBER = 7;
private float f1ScoreAt1_ = 0F;
/**
*
*
*
* Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
*
*
* float f1_score_at1 = 7;
*
* @return The f1ScoreAt1.
*/
@java.lang.Override
public float getF1ScoreAt1() {
return f1ScoreAt1_;
}
public static final int TRUE_POSITIVE_COUNT_FIELD_NUMBER = 10;
private long truePositiveCount_ = 0L;
/**
*
*
*
* Output only. The number of model created labels that match a ground truth
* label.
*
*
* int64 true_positive_count = 10;
*
* @return The truePositiveCount.
*/
@java.lang.Override
public long getTruePositiveCount() {
return truePositiveCount_;
}
public static final int FALSE_POSITIVE_COUNT_FIELD_NUMBER = 11;
private long falsePositiveCount_ = 0L;
/**
*
*
*
* Output only. The number of model created labels that do not match a
* ground truth label.
*
*
* int64 false_positive_count = 11;
*
* @return The falsePositiveCount.
*/
@java.lang.Override
public long getFalsePositiveCount() {
return falsePositiveCount_;
}
public static final int FALSE_NEGATIVE_COUNT_FIELD_NUMBER = 12;
private long falseNegativeCount_ = 0L;
/**
*
*
*
* Output only. The number of ground truth labels that are not matched
* by a model created label.
*
*
* int64 false_negative_count = 12;
*
* @return The falseNegativeCount.
*/
@java.lang.Override
public long getFalseNegativeCount() {
return falseNegativeCount_;
}
public static final int TRUE_NEGATIVE_COUNT_FIELD_NUMBER = 13;
private long trueNegativeCount_ = 0L;
/**
*
*
*
* Output only. The number of labels that were not created by the model,
* but if they would, they would not match a ground truth label.
*
*
* int64 true_negative_count = 13;
*
* @return The trueNegativeCount.
*/
@java.lang.Override
public long getTrueNegativeCount() {
return trueNegativeCount_;
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
if (java.lang.Float.floatToRawIntBits(confidenceThreshold_) != 0) {
output.writeFloat(1, confidenceThreshold_);
}
if (java.lang.Float.floatToRawIntBits(recall_) != 0) {
output.writeFloat(2, recall_);
}
if (java.lang.Float.floatToRawIntBits(precision_) != 0) {
output.writeFloat(3, precision_);
}
if (java.lang.Float.floatToRawIntBits(f1Score_) != 0) {
output.writeFloat(4, f1Score_);
}
if (java.lang.Float.floatToRawIntBits(recallAt1_) != 0) {
output.writeFloat(5, recallAt1_);
}
if (java.lang.Float.floatToRawIntBits(precisionAt1_) != 0) {
output.writeFloat(6, precisionAt1_);
}
if (java.lang.Float.floatToRawIntBits(f1ScoreAt1_) != 0) {
output.writeFloat(7, f1ScoreAt1_);
}
if (java.lang.Float.floatToRawIntBits(falsePositiveRate_) != 0) {
output.writeFloat(8, falsePositiveRate_);
}
if (java.lang.Float.floatToRawIntBits(falsePositiveRateAt1_) != 0) {
output.writeFloat(9, falsePositiveRateAt1_);
}
if (truePositiveCount_ != 0L) {
output.writeInt64(10, truePositiveCount_);
}
if (falsePositiveCount_ != 0L) {
output.writeInt64(11, falsePositiveCount_);
}
if (falseNegativeCount_ != 0L) {
output.writeInt64(12, falseNegativeCount_);
}
if (trueNegativeCount_ != 0L) {
output.writeInt64(13, trueNegativeCount_);
}
if (positionThreshold_ != 0) {
output.writeInt32(14, positionThreshold_);
}
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (java.lang.Float.floatToRawIntBits(confidenceThreshold_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(1, confidenceThreshold_);
}
if (java.lang.Float.floatToRawIntBits(recall_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(2, recall_);
}
if (java.lang.Float.floatToRawIntBits(precision_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(3, precision_);
}
if (java.lang.Float.floatToRawIntBits(f1Score_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(4, f1Score_);
}
if (java.lang.Float.floatToRawIntBits(recallAt1_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(5, recallAt1_);
}
if (java.lang.Float.floatToRawIntBits(precisionAt1_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(6, precisionAt1_);
}
if (java.lang.Float.floatToRawIntBits(f1ScoreAt1_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(7, f1ScoreAt1_);
}
if (java.lang.Float.floatToRawIntBits(falsePositiveRate_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(8, falsePositiveRate_);
}
if (java.lang.Float.floatToRawIntBits(falsePositiveRateAt1_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(9, falsePositiveRateAt1_);
}
if (truePositiveCount_ != 0L) {
size += com.google.protobuf.CodedOutputStream.computeInt64Size(10, truePositiveCount_);
}
if (falsePositiveCount_ != 0L) {
size += com.google.protobuf.CodedOutputStream.computeInt64Size(11, falsePositiveCount_);
}
if (falseNegativeCount_ != 0L) {
size += com.google.protobuf.CodedOutputStream.computeInt64Size(12, falseNegativeCount_);
}
if (trueNegativeCount_ != 0L) {
size += com.google.protobuf.CodedOutputStream.computeInt64Size(13, trueNegativeCount_);
}
if (positionThreshold_ != 0) {
size += com.google.protobuf.CodedOutputStream.computeInt32Size(14, positionThreshold_);
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj
instanceof
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
other =
(com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry)
obj;
if (java.lang.Float.floatToIntBits(getConfidenceThreshold())
!= java.lang.Float.floatToIntBits(other.getConfidenceThreshold())) return false;
if (getPositionThreshold() != other.getPositionThreshold()) return false;
if (java.lang.Float.floatToIntBits(getRecall())
!= java.lang.Float.floatToIntBits(other.getRecall())) return false;
if (java.lang.Float.floatToIntBits(getPrecision())
!= java.lang.Float.floatToIntBits(other.getPrecision())) return false;
if (java.lang.Float.floatToIntBits(getFalsePositiveRate())
!= java.lang.Float.floatToIntBits(other.getFalsePositiveRate())) return false;
if (java.lang.Float.floatToIntBits(getF1Score())
!= java.lang.Float.floatToIntBits(other.getF1Score())) return false;
if (java.lang.Float.floatToIntBits(getRecallAt1())
!= java.lang.Float.floatToIntBits(other.getRecallAt1())) return false;
if (java.lang.Float.floatToIntBits(getPrecisionAt1())
!= java.lang.Float.floatToIntBits(other.getPrecisionAt1())) return false;
if (java.lang.Float.floatToIntBits(getFalsePositiveRateAt1())
!= java.lang.Float.floatToIntBits(other.getFalsePositiveRateAt1())) return false;
if (java.lang.Float.floatToIntBits(getF1ScoreAt1())
!= java.lang.Float.floatToIntBits(other.getF1ScoreAt1())) return false;
if (getTruePositiveCount() != other.getTruePositiveCount()) return false;
if (getFalsePositiveCount() != other.getFalsePositiveCount()) return false;
if (getFalseNegativeCount() != other.getFalseNegativeCount()) return false;
if (getTrueNegativeCount() != other.getTrueNegativeCount()) return false;
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + CONFIDENCE_THRESHOLD_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getConfidenceThreshold());
hash = (37 * hash) + POSITION_THRESHOLD_FIELD_NUMBER;
hash = (53 * hash) + getPositionThreshold();
hash = (37 * hash) + RECALL_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getRecall());
hash = (37 * hash) + PRECISION_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getPrecision());
hash = (37 * hash) + FALSE_POSITIVE_RATE_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getFalsePositiveRate());
hash = (37 * hash) + F1_SCORE_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getF1Score());
hash = (37 * hash) + RECALL_AT1_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getRecallAt1());
hash = (37 * hash) + PRECISION_AT1_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getPrecisionAt1());
hash = (37 * hash) + FALSE_POSITIVE_RATE_AT1_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getFalsePositiveRateAt1());
hash = (37 * hash) + F1_SCORE_AT1_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getF1ScoreAt1());
hash = (37 * hash) + TRUE_POSITIVE_COUNT_FIELD_NUMBER;
hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getTruePositiveCount());
hash = (37 * hash) + FALSE_POSITIVE_COUNT_FIELD_NUMBER;
hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getFalsePositiveCount());
hash = (37 * hash) + FALSE_NEGATIVE_COUNT_FIELD_NUMBER;
hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getFalseNegativeCount());
hash = (37 * hash) + TRUE_NEGATIVE_COUNT_FIELD_NUMBER;
hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getTrueNegativeCount());
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* Metrics for a single confidence threshold.
*
*
* Protobuf type {@code
* google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry}
*/
public static final class Builder
extends com.google.protobuf.GeneratedMessageV3.Builder
* Output only. Metrics are computed with an assumption that the model
* never returns predictions with score lower than this value.
*
*
* float confidence_threshold = 1;
*
* @return The confidenceThreshold.
*/
@java.lang.Override
public float getConfidenceThreshold() {
return confidenceThreshold_;
}
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* never returns predictions with score lower than this value.
*
*
* float confidence_threshold = 1;
*
* @param value The confidenceThreshold to set.
* @return This builder for chaining.
*/
public Builder setConfidenceThreshold(float value) {
confidenceThreshold_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* never returns predictions with score lower than this value.
*
*
* float confidence_threshold = 1;
*
* @return This builder for chaining.
*/
public Builder clearConfidenceThreshold() {
bitField0_ = (bitField0_ & ~0x00000001);
confidenceThreshold_ = 0F;
onChanged();
return this;
}
private int positionThreshold_;
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* always returns at most this many predictions (ordered by their score,
* descendingly), but they all still need to meet the confidence_threshold.
*
*
* int32 position_threshold = 14;
*
* @return The positionThreshold.
*/
@java.lang.Override
public int getPositionThreshold() {
return positionThreshold_;
}
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* always returns at most this many predictions (ordered by their score,
* descendingly), but they all still need to meet the confidence_threshold.
*
*
* int32 position_threshold = 14;
*
* @param value The positionThreshold to set.
* @return This builder for chaining.
*/
public Builder setPositionThreshold(int value) {
positionThreshold_ = value;
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Output only. Metrics are computed with an assumption that the model
* always returns at most this many predictions (ordered by their score,
* descendingly), but they all still need to meet the confidence_threshold.
*
*
* int32 position_threshold = 14;
*
* @return This builder for chaining.
*/
public Builder clearPositionThreshold() {
bitField0_ = (bitField0_ & ~0x00000002);
positionThreshold_ = 0;
onChanged();
return this;
}
private float recall_;
/**
*
*
*
* Output only. Recall (True Positive Rate) for the given confidence
* threshold.
*
*
* float recall = 2;
*
* @return The recall.
*/
@java.lang.Override
public float getRecall() {
return recall_;
}
/**
*
*
*
* Output only. Recall (True Positive Rate) for the given confidence
* threshold.
*
*
* float recall = 2;
*
* @param value The recall to set.
* @return This builder for chaining.
*/
public Builder setRecall(float value) {
recall_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Output only. Recall (True Positive Rate) for the given confidence
* threshold.
*
*
* float recall = 2;
*
* @return This builder for chaining.
*/
public Builder clearRecall() {
bitField0_ = (bitField0_ & ~0x00000004);
recall_ = 0F;
onChanged();
return this;
}
private float precision_;
/**
*
*
*
* Output only. Precision for the given confidence threshold.
*
*
* float precision = 3;
*
* @return The precision.
*/
@java.lang.Override
public float getPrecision() {
return precision_;
}
/**
*
*
*
* Output only. Precision for the given confidence threshold.
*
*
* float precision = 3;
*
* @param value The precision to set.
* @return This builder for chaining.
*/
public Builder setPrecision(float value) {
precision_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
/**
*
*
*
* Output only. Precision for the given confidence threshold.
*
*
* float precision = 3;
*
* @return This builder for chaining.
*/
public Builder clearPrecision() {
bitField0_ = (bitField0_ & ~0x00000008);
precision_ = 0F;
onChanged();
return this;
}
private float falsePositiveRate_;
/**
*
*
*
* Output only. False Positive Rate for the given confidence threshold.
*
*
* float false_positive_rate = 8;
*
* @return The falsePositiveRate.
*/
@java.lang.Override
public float getFalsePositiveRate() {
return falsePositiveRate_;
}
/**
*
*
*
* Output only. False Positive Rate for the given confidence threshold.
*
*
* float false_positive_rate = 8;
*
* @param value The falsePositiveRate to set.
* @return This builder for chaining.
*/
public Builder setFalsePositiveRate(float value) {
falsePositiveRate_ = value;
bitField0_ |= 0x00000010;
onChanged();
return this;
}
/**
*
*
*
* Output only. False Positive Rate for the given confidence threshold.
*
*
* float false_positive_rate = 8;
*
* @return This builder for chaining.
*/
public Builder clearFalsePositiveRate() {
bitField0_ = (bitField0_ & ~0x00000010);
falsePositiveRate_ = 0F;
onChanged();
return this;
}
private float f1Score_;
/**
*
*
*
* Output only. The harmonic mean of recall and precision.
*
*
* float f1_score = 4;
*
* @return The f1Score.
*/
@java.lang.Override
public float getF1Score() {
return f1Score_;
}
/**
*
*
*
* Output only. The harmonic mean of recall and precision.
*
*
* float f1_score = 4;
*
* @param value The f1Score to set.
* @return This builder for chaining.
*/
public Builder setF1Score(float value) {
f1Score_ = value;
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* Output only. The harmonic mean of recall and precision.
*
*
* float f1_score = 4;
*
* @return This builder for chaining.
*/
public Builder clearF1Score() {
bitField0_ = (bitField0_ & ~0x00000020);
f1Score_ = 0F;
onChanged();
return this;
}
private float recallAt1_;
/**
*
*
*
* Output only. The Recall (True Positive Rate) when only considering the
* label that has the highest prediction score and not below the confidence
* threshold for each example.
*
*
* float recall_at1 = 5;
*
* @return The recallAt1.
*/
@java.lang.Override
public float getRecallAt1() {
return recallAt1_;
}
/**
*
*
*
* Output only. The Recall (True Positive Rate) when only considering the
* label that has the highest prediction score and not below the confidence
* threshold for each example.
*
*
* float recall_at1 = 5;
*
* @param value The recallAt1 to set.
* @return This builder for chaining.
*/
public Builder setRecallAt1(float value) {
recallAt1_ = value;
bitField0_ |= 0x00000040;
onChanged();
return this;
}
/**
*
*
*
* Output only. The Recall (True Positive Rate) when only considering the
* label that has the highest prediction score and not below the confidence
* threshold for each example.
*
*
* float recall_at1 = 5;
*
* @return This builder for chaining.
*/
public Builder clearRecallAt1() {
bitField0_ = (bitField0_ & ~0x00000040);
recallAt1_ = 0F;
onChanged();
return this;
}
private float precisionAt1_;
/**
*
*
*
* Output only. The precision when only considering the label that has the
* highest prediction score and not below the confidence threshold for each
* example.
*
*
* float precision_at1 = 6;
*
* @return The precisionAt1.
*/
@java.lang.Override
public float getPrecisionAt1() {
return precisionAt1_;
}
/**
*
*
*
* Output only. The precision when only considering the label that has the
* highest prediction score and not below the confidence threshold for each
* example.
*
*
* float precision_at1 = 6;
*
* @param value The precisionAt1 to set.
* @return This builder for chaining.
*/
public Builder setPrecisionAt1(float value) {
precisionAt1_ = value;
bitField0_ |= 0x00000080;
onChanged();
return this;
}
/**
*
*
*
* Output only. The precision when only considering the label that has the
* highest prediction score and not below the confidence threshold for each
* example.
*
*
* float precision_at1 = 6;
*
* @return This builder for chaining.
*/
public Builder clearPrecisionAt1() {
bitField0_ = (bitField0_ & ~0x00000080);
precisionAt1_ = 0F;
onChanged();
return this;
}
private float falsePositiveRateAt1_;
/**
*
*
*
* Output only. The False Positive Rate when only considering the label that
* has the highest prediction score and not below the confidence threshold
* for each example.
*
*
* float false_positive_rate_at1 = 9;
*
* @return The falsePositiveRateAt1.
*/
@java.lang.Override
public float getFalsePositiveRateAt1() {
return falsePositiveRateAt1_;
}
/**
*
*
*
* Output only. The False Positive Rate when only considering the label that
* has the highest prediction score and not below the confidence threshold
* for each example.
*
*
* float false_positive_rate_at1 = 9;
*
* @param value The falsePositiveRateAt1 to set.
* @return This builder for chaining.
*/
public Builder setFalsePositiveRateAt1(float value) {
falsePositiveRateAt1_ = value;
bitField0_ |= 0x00000100;
onChanged();
return this;
}
/**
*
*
*
* Output only. The False Positive Rate when only considering the label that
* has the highest prediction score and not below the confidence threshold
* for each example.
*
*
* float false_positive_rate_at1 = 9;
*
* @return This builder for chaining.
*/
public Builder clearFalsePositiveRateAt1() {
bitField0_ = (bitField0_ & ~0x00000100);
falsePositiveRateAt1_ = 0F;
onChanged();
return this;
}
private float f1ScoreAt1_;
/**
*
*
*
* Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
*
*
* float f1_score_at1 = 7;
*
* @return The f1ScoreAt1.
*/
@java.lang.Override
public float getF1ScoreAt1() {
return f1ScoreAt1_;
}
/**
*
*
*
* Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
*
*
* float f1_score_at1 = 7;
*
* @param value The f1ScoreAt1 to set.
* @return This builder for chaining.
*/
public Builder setF1ScoreAt1(float value) {
f1ScoreAt1_ = value;
bitField0_ |= 0x00000200;
onChanged();
return this;
}
/**
*
*
*
* Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1].
*
*
* float f1_score_at1 = 7;
*
* @return This builder for chaining.
*/
public Builder clearF1ScoreAt1() {
bitField0_ = (bitField0_ & ~0x00000200);
f1ScoreAt1_ = 0F;
onChanged();
return this;
}
private long truePositiveCount_;
/**
*
*
*
* Output only. The number of model created labels that match a ground truth
* label.
*
*
* int64 true_positive_count = 10;
*
* @return The truePositiveCount.
*/
@java.lang.Override
public long getTruePositiveCount() {
return truePositiveCount_;
}
/**
*
*
*
* Output only. The number of model created labels that match a ground truth
* label.
*
*
* int64 true_positive_count = 10;
*
* @param value The truePositiveCount to set.
* @return This builder for chaining.
*/
public Builder setTruePositiveCount(long value) {
truePositiveCount_ = value;
bitField0_ |= 0x00000400;
onChanged();
return this;
}
/**
*
*
*
* Output only. The number of model created labels that match a ground truth
* label.
*
*
* int64 true_positive_count = 10;
*
* @return This builder for chaining.
*/
public Builder clearTruePositiveCount() {
bitField0_ = (bitField0_ & ~0x00000400);
truePositiveCount_ = 0L;
onChanged();
return this;
}
private long falsePositiveCount_;
/**
*
*
*
* Output only. The number of model created labels that do not match a
* ground truth label.
*
*
* int64 false_positive_count = 11;
*
* @return The falsePositiveCount.
*/
@java.lang.Override
public long getFalsePositiveCount() {
return falsePositiveCount_;
}
/**
*
*
*
* Output only. The number of model created labels that do not match a
* ground truth label.
*
*
* int64 false_positive_count = 11;
*
* @param value The falsePositiveCount to set.
* @return This builder for chaining.
*/
public Builder setFalsePositiveCount(long value) {
falsePositiveCount_ = value;
bitField0_ |= 0x00000800;
onChanged();
return this;
}
/**
*
*
*
* Output only. The number of model created labels that do not match a
* ground truth label.
*
*
* int64 false_positive_count = 11;
*
* @return This builder for chaining.
*/
public Builder clearFalsePositiveCount() {
bitField0_ = (bitField0_ & ~0x00000800);
falsePositiveCount_ = 0L;
onChanged();
return this;
}
private long falseNegativeCount_;
/**
*
*
*
* Output only. The number of ground truth labels that are not matched
* by a model created label.
*
*
* int64 false_negative_count = 12;
*
* @return The falseNegativeCount.
*/
@java.lang.Override
public long getFalseNegativeCount() {
return falseNegativeCount_;
}
/**
*
*
*
* Output only. The number of ground truth labels that are not matched
* by a model created label.
*
*
* int64 false_negative_count = 12;
*
* @param value The falseNegativeCount to set.
* @return This builder for chaining.
*/
public Builder setFalseNegativeCount(long value) {
falseNegativeCount_ = value;
bitField0_ |= 0x00001000;
onChanged();
return this;
}
/**
*
*
*
* Output only. The number of ground truth labels that are not matched
* by a model created label.
*
*
* int64 false_negative_count = 12;
*
* @return This builder for chaining.
*/
public Builder clearFalseNegativeCount() {
bitField0_ = (bitField0_ & ~0x00001000);
falseNegativeCount_ = 0L;
onChanged();
return this;
}
private long trueNegativeCount_;
/**
*
*
*
* Output only. The number of labels that were not created by the model,
* but if they would, they would not match a ground truth label.
*
*
* int64 true_negative_count = 13;
*
* @return The trueNegativeCount.
*/
@java.lang.Override
public long getTrueNegativeCount() {
return trueNegativeCount_;
}
/**
*
*
*
* Output only. The number of labels that were not created by the model,
* but if they would, they would not match a ground truth label.
*
*
* int64 true_negative_count = 13;
*
* @param value The trueNegativeCount to set.
* @return This builder for chaining.
*/
public Builder setTrueNegativeCount(long value) {
trueNegativeCount_ = value;
bitField0_ |= 0x00002000;
onChanged();
return this;
}
/**
*
*
*
* Output only. The number of labels that were not created by the model,
* but if they would, they would not match a ground truth label.
*
*
* int64 true_negative_count = 13;
*
* @return This builder for chaining.
*/
public Builder clearTrueNegativeCount() {
bitField0_ = (bitField0_ & ~0x00002000);
trueNegativeCount_ = 0L;
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry)
private static final com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE =
new com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry();
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return A list containing the annotationSpecId.
*/
java.util.List
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return The count of annotationSpecId.
*/
int getAnnotationSpecIdCount();
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index of the element to return.
* @return The annotationSpecId at the given index.
*/
java.lang.String getAnnotationSpecId(int index);
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index of the value to return.
* @return The bytes of the annotationSpecId at the given index.
*/
com.google.protobuf.ByteString getAnnotationSpecIdBytes(int index);
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return A list containing the displayName.
*/
java.util.List
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return The count of displayName.
*/
int getDisplayNameCount();
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index of the element to return.
* @return The displayName at the given index.
*/
java.lang.String getDisplayName(int index);
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index of the value to return.
* @return The bytes of the displayName at the given index.
*/
com.google.protobuf.ByteString getDisplayNameBytes(int index);
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row>
getRowList();
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
getRow(int index);
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
int getRowCount();
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
java.util.List<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.RowOrBuilder>
getRowOrBuilderList();
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.RowOrBuilder
getRowOrBuilder(int index);
}
/**
*
*
*
* Confusion matrix of the model running the classification.
*
*
* Protobuf type {@code
* google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix}
*/
public static final class ConfusionMatrix extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix)
ConfusionMatrixOrBuilder {
private static final long serialVersionUID = 0L;
// Use ConfusionMatrix.newBuilder() to construct.
private ConfusionMatrix(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private ConfusionMatrix() {
annotationSpecId_ = com.google.protobuf.LazyStringArrayList.EMPTY;
displayName_ = com.google.protobuf.LazyStringArrayList.EMPTY;
row_ = java.util.Collections.emptyList();
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new ConfusionMatrix();
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet getUnknownFields() {
return this.unknownFields;
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.ClassificationProto
.internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_ConfusionMatrix_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1beta1.ClassificationProto
.internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_ConfusionMatrix_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.class,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Builder.class);
}
public interface RowOrBuilder
extends
// @@protoc_insertion_point(interface_extends:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row)
com.google.protobuf.MessageOrBuilder {
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return A list containing the exampleCount.
*/
java.util.List
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return The count of exampleCount.
*/
int getExampleCountCount();
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @param index The index of the element to return.
* @return The exampleCount at the given index.
*/
int getExampleCount(int index);
}
/**
*
*
*
* Output only. A row in the confusion matrix.
*
*
* Protobuf type {@code
* google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row}
*/
public static final class Row extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row)
RowOrBuilder {
private static final long serialVersionUID = 0L;
// Use Row.newBuilder() to construct.
private Row(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private Row() {
exampleCount_ = emptyIntList();
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new Row();
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet getUnknownFields() {
return this.unknownFields;
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.ClassificationProto
.internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_ConfusionMatrix_Row_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1beta1.ClassificationProto
.internal_static_google_cloud_automl_v1beta1_ClassificationEvaluationMetrics_ConfusionMatrix_Row_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row.class,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row.Builder.class);
}
public static final int EXAMPLE_COUNT_FIELD_NUMBER = 1;
@SuppressWarnings("serial")
private com.google.protobuf.Internal.IntList exampleCount_;
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return A list containing the exampleCount.
*/
@java.lang.Override
public java.util.List
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return The count of exampleCount.
*/
public int getExampleCountCount() {
return exampleCount_.size();
}
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @param index The index of the element to return.
* @return The exampleCount at the given index.
*/
public int getExampleCount(int index) {
return exampleCount_.getInt(index);
}
private int exampleCountMemoizedSerializedSize = -1;
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
getSerializedSize();
if (getExampleCountList().size() > 0) {
output.writeUInt32NoTag(10);
output.writeUInt32NoTag(exampleCountMemoizedSerializedSize);
}
for (int i = 0; i < exampleCount_.size(); i++) {
output.writeInt32NoTag(exampleCount_.getInt(i));
}
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
{
int dataSize = 0;
for (int i = 0; i < exampleCount_.size(); i++) {
dataSize +=
com.google.protobuf.CodedOutputStream.computeInt32SizeNoTag(
exampleCount_.getInt(i));
}
size += dataSize;
if (!getExampleCountList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream.computeInt32SizeNoTag(dataSize);
}
exampleCountMemoizedSerializedSize = dataSize;
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj
instanceof
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
other =
(com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row)
obj;
if (!getExampleCountList().equals(other.getExampleCountList())) return false;
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
if (getExampleCountCount() > 0) {
hash = (37 * hash) + EXAMPLE_COUNT_FIELD_NUMBER;
hash = (53 * hash) + getExampleCountList().hashCode();
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(
java.nio.ByteBuffer data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* Output only. A row in the confusion matrix.
*
*
* Protobuf type {@code
* google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row}
*/
public static final class Builder
extends com.google.protobuf.GeneratedMessageV3.Builder
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return A list containing the exampleCount.
*/
public java.util.List
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return The count of exampleCount.
*/
public int getExampleCountCount() {
return exampleCount_.size();
}
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @param index The index of the element to return.
* @return The exampleCount at the given index.
*/
public int getExampleCount(int index) {
return exampleCount_.getInt(index);
}
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @param index The index to set the value at.
* @param value The exampleCount to set.
* @return This builder for chaining.
*/
public Builder setExampleCount(int index, int value) {
ensureExampleCountIsMutable();
exampleCount_.setInt(index, value);
onChanged();
return this;
}
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @param value The exampleCount to add.
* @return This builder for chaining.
*/
public Builder addExampleCount(int value) {
ensureExampleCountIsMutable();
exampleCount_.addInt(value);
onChanged();
return this;
}
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @param values The exampleCount to add.
* @return This builder for chaining.
*/
public Builder addAllExampleCount(
java.lang.Iterable values) {
ensureExampleCountIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(values, exampleCount_);
onChanged();
return this;
}
/**
*
*
*
* Output only. Value of the specific cell in the confusion matrix.
* The number of values each row has (i.e. the length of the row) is equal
* to the length of the `annotation_spec_id` field or, if that one is not
* populated, length of the [display_name][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name] field.
*
*
* repeated int32 example_count = 1;
*
* @return This builder for chaining.
*/
public Builder clearExampleCount() {
exampleCount_ = emptyIntList();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row)
private static final com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE =
new com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row();
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row
getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return A list containing the annotationSpecId.
*/
public com.google.protobuf.ProtocolStringList getAnnotationSpecIdList() {
return annotationSpecId_;
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return The count of annotationSpecId.
*/
public int getAnnotationSpecIdCount() {
return annotationSpecId_.size();
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index of the element to return.
* @return The annotationSpecId at the given index.
*/
public java.lang.String getAnnotationSpecId(int index) {
return annotationSpecId_.get(index);
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index of the value to return.
* @return The bytes of the annotationSpecId at the given index.
*/
public com.google.protobuf.ByteString getAnnotationSpecIdBytes(int index) {
return annotationSpecId_.getByteString(index);
}
public static final int DISPLAY_NAME_FIELD_NUMBER = 3;
@SuppressWarnings("serial")
private com.google.protobuf.LazyStringList displayName_;
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return A list containing the displayName.
*/
public com.google.protobuf.ProtocolStringList getDisplayNameList() {
return displayName_;
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return The count of displayName.
*/
public int getDisplayNameCount() {
return displayName_.size();
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index of the element to return.
* @return The displayName at the given index.
*/
public java.lang.String getDisplayName(int index) {
return displayName_.get(index);
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index of the value to return.
* @return The bytes of the displayName at the given index.
*/
public com.google.protobuf.ByteString getDisplayNameBytes(int index) {
return displayName_.getByteString(index);
}
public static final int ROW_FIELD_NUMBER = 2;
@SuppressWarnings("serial")
private java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row>
row_;
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
@java.lang.Override
public java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row>
getRowList() {
return row_;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
@java.lang.Override
public java.util.List<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.RowOrBuilder>
getRowOrBuilderList() {
return row_;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
@java.lang.Override
public int getRowCount() {
return row_.size();
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
getRow(int index) {
return row_.get(index);
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.RowOrBuilder
getRowOrBuilder(int index) {
return row_.get(index);
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
for (int i = 0; i < annotationSpecId_.size(); i++) {
com.google.protobuf.GeneratedMessageV3.writeString(
output, 1, annotationSpecId_.getRaw(i));
}
for (int i = 0; i < row_.size(); i++) {
output.writeMessage(2, row_.get(i));
}
for (int i = 0; i < displayName_.size(); i++) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 3, displayName_.getRaw(i));
}
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
{
int dataSize = 0;
for (int i = 0; i < annotationSpecId_.size(); i++) {
dataSize += computeStringSizeNoTag(annotationSpecId_.getRaw(i));
}
size += dataSize;
size += 1 * getAnnotationSpecIdList().size();
}
for (int i = 0; i < row_.size(); i++) {
size += com.google.protobuf.CodedOutputStream.computeMessageSize(2, row_.get(i));
}
{
int dataSize = 0;
for (int i = 0; i < displayName_.size(); i++) {
dataSize += computeStringSizeNoTag(displayName_.getRaw(i));
}
size += dataSize;
size += 1 * getDisplayNameList().size();
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj
instanceof
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
other =
(com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix)
obj;
if (!getAnnotationSpecIdList().equals(other.getAnnotationSpecIdList())) return false;
if (!getDisplayNameList().equals(other.getDisplayNameList())) return false;
if (!getRowList().equals(other.getRowList())) return false;
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
if (getAnnotationSpecIdCount() > 0) {
hash = (37 * hash) + ANNOTATION_SPEC_ID_FIELD_NUMBER;
hash = (53 * hash) + getAnnotationSpecIdList().hashCode();
}
if (getDisplayNameCount() > 0) {
hash = (37 * hash) + DISPLAY_NAME_FIELD_NUMBER;
hash = (53 * hash) + getDisplayNameList().hashCode();
}
if (getRowCount() > 0) {
hash = (37 * hash) + ROW_FIELD_NUMBER;
hash = (53 * hash) + getRowList().hashCode();
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* Confusion matrix of the model running the classification.
*
*
* Protobuf type {@code
* google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix}
*/
public static final class Builder
extends com.google.protobuf.GeneratedMessageV3.Builder
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return A list containing the annotationSpecId.
*/
public com.google.protobuf.ProtocolStringList getAnnotationSpecIdList() {
return annotationSpecId_.getUnmodifiableView();
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return The count of annotationSpecId.
*/
public int getAnnotationSpecIdCount() {
return annotationSpecId_.size();
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index of the element to return.
* @return The annotationSpecId at the given index.
*/
public java.lang.String getAnnotationSpecId(int index) {
return annotationSpecId_.get(index);
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index of the value to return.
* @return The bytes of the annotationSpecId at the given index.
*/
public com.google.protobuf.ByteString getAnnotationSpecIdBytes(int index) {
return annotationSpecId_.getByteString(index);
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param index The index to set the value at.
* @param value The annotationSpecId to set.
* @return This builder for chaining.
*/
public Builder setAnnotationSpecId(int index, java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureAnnotationSpecIdIsMutable();
annotationSpecId_.set(index, value);
onChanged();
return this;
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param value The annotationSpecId to add.
* @return This builder for chaining.
*/
public Builder addAnnotationSpecId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureAnnotationSpecIdIsMutable();
annotationSpecId_.add(value);
onChanged();
return this;
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param values The annotationSpecId to add.
* @return This builder for chaining.
*/
public Builder addAllAnnotationSpecId(java.lang.Iterable
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @return This builder for chaining.
*/
public Builder clearAnnotationSpecId() {
annotationSpecId_ = com.google.protobuf.LazyStringArrayList.EMPTY;
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
*
*
* Output only. IDs of the annotation specs used in the confusion matrix.
* For Tables CLASSIFICATION
* [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]
* only list of [annotation_spec_display_name-s][] is populated.
*
*
* repeated string annotation_spec_id = 1;
*
* @param value The bytes of the annotationSpecId to add.
* @return This builder for chaining.
*/
public Builder addAnnotationSpecIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
ensureAnnotationSpecIdIsMutable();
annotationSpecId_.add(value);
onChanged();
return this;
}
private com.google.protobuf.LazyStringList displayName_ =
com.google.protobuf.LazyStringArrayList.EMPTY;
private void ensureDisplayNameIsMutable() {
if (!((bitField0_ & 0x00000002) != 0)) {
displayName_ = new com.google.protobuf.LazyStringArrayList(displayName_);
bitField0_ |= 0x00000002;
}
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return A list containing the displayName.
*/
public com.google.protobuf.ProtocolStringList getDisplayNameList() {
return displayName_.getUnmodifiableView();
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return The count of displayName.
*/
public int getDisplayNameCount() {
return displayName_.size();
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index of the element to return.
* @return The displayName at the given index.
*/
public java.lang.String getDisplayName(int index) {
return displayName_.get(index);
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index of the value to return.
* @return The bytes of the displayName at the given index.
*/
public com.google.protobuf.ByteString getDisplayNameBytes(int index) {
return displayName_.getByteString(index);
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param index The index to set the value at.
* @param value The displayName to set.
* @return This builder for chaining.
*/
public Builder setDisplayName(int index, java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureDisplayNameIsMutable();
displayName_.set(index, value);
onChanged();
return this;
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param value The displayName to add.
* @return This builder for chaining.
*/
public Builder addDisplayName(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureDisplayNameIsMutable();
displayName_.add(value);
onChanged();
return this;
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param values The displayName to add.
* @return This builder for chaining.
*/
public Builder addAllDisplayName(java.lang.Iterable
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @return This builder for chaining.
*/
public Builder clearDisplayName() {
displayName_ = com.google.protobuf.LazyStringArrayList.EMPTY;
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
return this;
}
/**
*
*
*
* Output only. Display name of the annotation specs used in the confusion
* matrix, as they were at the moment of the evaluation. For Tables
* CLASSIFICATION
* [prediction_type-s][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type],
* distinct values of the target column at the moment of the model
* evaluation are populated here.
*
*
* repeated string display_name = 3;
*
* @param value The bytes of the displayName to add.
* @return This builder for chaining.
*/
public Builder addDisplayNameBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
ensureDisplayNameIsMutable();
displayName_.add(value);
onChanged();
return this;
}
private java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row>
row_ = java.util.Collections.emptyList();
private void ensureRowIsMutable() {
if (!((bitField0_ & 0x00000004) != 0)) {
row_ =
new java.util.ArrayList<
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row>(row_);
bitField0_ |= 0x00000004;
}
}
private com.google.protobuf.RepeatedFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.RowOrBuilder>
rowBuilder_;
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row>
getRowList() {
if (rowBuilder_ == null) {
return java.util.Collections.unmodifiableList(row_);
} else {
return rowBuilder_.getMessageList();
}
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public int getRowCount() {
if (rowBuilder_ == null) {
return row_.size();
} else {
return rowBuilder_.getCount();
}
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
getRow(int index) {
if (rowBuilder_ == null) {
return row_.get(index);
} else {
return rowBuilder_.getMessage(index);
}
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder setRow(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
value) {
if (rowBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureRowIsMutable();
row_.set(index, value);
onChanged();
} else {
rowBuilder_.setMessage(index, value);
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder setRow(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder
builderForValue) {
if (rowBuilder_ == null) {
ensureRowIsMutable();
row_.set(index, builderForValue.build());
onChanged();
} else {
rowBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder addRow(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
value) {
if (rowBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureRowIsMutable();
row_.add(value);
onChanged();
} else {
rowBuilder_.addMessage(value);
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder addRow(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row
value) {
if (rowBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureRowIsMutable();
row_.add(index, value);
onChanged();
} else {
rowBuilder_.addMessage(index, value);
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder addRow(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder
builderForValue) {
if (rowBuilder_ == null) {
ensureRowIsMutable();
row_.add(builderForValue.build());
onChanged();
} else {
rowBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder addRow(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder
builderForValue) {
if (rowBuilder_ == null) {
ensureRowIsMutable();
row_.add(index, builderForValue.build());
onChanged();
} else {
rowBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder addAllRow(
java.lang.Iterable<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row>
values) {
if (rowBuilder_ == null) {
ensureRowIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(values, row_);
onChanged();
} else {
rowBuilder_.addAllMessages(values);
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder clearRow() {
if (rowBuilder_ == null) {
row_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
} else {
rowBuilder_.clear();
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public Builder removeRow(int index) {
if (rowBuilder_ == null) {
ensureRowIsMutable();
row_.remove(index);
onChanged();
} else {
rowBuilder_.remove(index);
}
return this;
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder
getRowBuilder(int index) {
return getRowFieldBuilder().getBuilder(index);
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.RowOrBuilder
getRowOrBuilder(int index) {
if (rowBuilder_ == null) {
return row_.get(index);
} else {
return rowBuilder_.getMessageOrBuilder(index);
}
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public java.util.List<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.RowOrBuilder>
getRowOrBuilderList() {
if (rowBuilder_ != null) {
return rowBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(row_);
}
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder
addRowBuilder() {
return getRowFieldBuilder()
.addBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row.getDefaultInstance());
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder
addRowBuilder(int index) {
return getRowFieldBuilder()
.addBuilder(
index,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row.getDefaultInstance());
}
/**
*
*
*
* Output only. Rows in the confusion matrix. The number of rows is equal to
* the size of `annotation_spec_id`.
* `row[i].example_count[j]` is the number of examples that have ground
* truth of the `annotation_spec_id[i]` and are predicted as
* `annotation_spec_id[j]` by the model being evaluated.
*
*
*
* repeated .google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row row = 2;
*
*/
public java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder>
getRowBuilderList() {
return getRowFieldBuilder().getBuilderList();
}
private com.google.protobuf.RepeatedFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Row.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.RowOrBuilder>
getRowFieldBuilder() {
if (rowBuilder_ == null) {
rowBuilder_ =
new com.google.protobuf.RepeatedFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Row.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.RowOrBuilder>(
row_, ((bitField0_ & 0x00000004) != 0), getParentForChildren(), isClean());
row_ = null;
}
return rowBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix)
private static final com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE =
new com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix();
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix
getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
* Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
* for the overall evaluation.
*
*
* float au_prc = 1;
*
* @return The auPrc.
*/
@java.lang.Override
public float getAuPrc() {
return auPrc_;
}
public static final int BASE_AU_PRC_FIELD_NUMBER = 2;
private float baseAuPrc_ = 0F;
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric based on priors.
* Micro-averaged for the overall evaluation.
* Deprecated.
*
*
* float base_au_prc = 2 [deprecated = true];
*
* @deprecated google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is
* deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188
* @return The baseAuPrc.
*/
@java.lang.Override
@java.lang.Deprecated
public float getBaseAuPrc() {
return baseAuPrc_;
}
public static final int AU_ROC_FIELD_NUMBER = 6;
private float auRoc_ = 0F;
/**
*
*
*
* Output only. The Area Under Receiver Operating Characteristic curve metric.
* Micro-averaged for the overall evaluation.
*
*
* float au_roc = 6;
*
* @return The auRoc.
*/
@java.lang.Override
public float getAuRoc() {
return auRoc_;
}
public static final int LOG_LOSS_FIELD_NUMBER = 7;
private float logLoss_ = 0F;
/**
*
*
*
* Output only. The Log Loss metric.
*
*
* float log_loss = 7;
*
* @return The logLoss.
*/
@java.lang.Override
public float getLogLoss() {
return logLoss_;
}
public static final int CONFIDENCE_METRICS_ENTRY_FIELD_NUMBER = 3;
@SuppressWarnings("serial")
private java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry>
confidenceMetricsEntry_;
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
@java.lang.Override
public java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry>
getConfidenceMetricsEntryList() {
return confidenceMetricsEntry_;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
@java.lang.Override
public java.util.List<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntryOrBuilder>
getConfidenceMetricsEntryOrBuilderList() {
return confidenceMetricsEntry_;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
@java.lang.Override
public int getConfidenceMetricsEntryCount() {
return confidenceMetricsEntry_.size();
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
getConfidenceMetricsEntry(int index) {
return confidenceMetricsEntry_.get(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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntryOrBuilder
getConfidenceMetricsEntryOrBuilder(int index) {
return confidenceMetricsEntry_.get(index);
}
public static final int CONFUSION_MATRIX_FIELD_NUMBER = 4;
private com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
confusionMatrix_;
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*
* @return Whether the confusionMatrix field is set.
*/
@java.lang.Override
public boolean hasConfusionMatrix() {
return confusionMatrix_ != null;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*
* @return The confusionMatrix.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
getConfusionMatrix() {
return confusionMatrix_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.getDefaultInstance()
: confusionMatrix_;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrixOrBuilder
getConfusionMatrixOrBuilder() {
return confusionMatrix_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.getDefaultInstance()
: confusionMatrix_;
}
public static final int ANNOTATION_SPEC_ID_FIELD_NUMBER = 5;
@SuppressWarnings("serial")
private com.google.protobuf.LazyStringList annotationSpecId_;
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return A list containing the annotationSpecId.
*/
public com.google.protobuf.ProtocolStringList getAnnotationSpecIdList() {
return annotationSpecId_;
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return The count of annotationSpecId.
*/
public int getAnnotationSpecIdCount() {
return annotationSpecId_.size();
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index of the element to return.
* @return The annotationSpecId at the given index.
*/
public java.lang.String getAnnotationSpecId(int index) {
return annotationSpecId_.get(index);
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index of the value to return.
* @return The bytes of the annotationSpecId at the given index.
*/
public com.google.protobuf.ByteString getAnnotationSpecIdBytes(int index) {
return annotationSpecId_.getByteString(index);
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
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output.writeFloat(2, baseAuPrc_);
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for (int i = 0; i < confidenceMetricsEntry_.size(); i++) {
output.writeMessage(3, confidenceMetricsEntry_.get(i));
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if (confusionMatrix_ != null) {
output.writeMessage(4, getConfusionMatrix());
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for (int i = 0; i < annotationSpecId_.size(); i++) {
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if (java.lang.Float.floatToRawIntBits(auRoc_) != 0) {
output.writeFloat(6, auRoc_);
}
if (java.lang.Float.floatToRawIntBits(logLoss_) != 0) {
output.writeFloat(7, logLoss_);
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getUnknownFields().writeTo(output);
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@java.lang.Override
public int getSerializedSize() {
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3, confidenceMetricsEntry_.get(i));
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if (confusionMatrix_ != null) {
size += com.google.protobuf.CodedOutputStream.computeMessageSize(4, getConfusionMatrix());
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{
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if (java.lang.Float.floatToRawIntBits(logLoss_) != 0) {
size += com.google.protobuf.CodedOutputStream.computeFloatSize(7, logLoss_);
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size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj
instanceof
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics other =
(com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics) obj;
if (java.lang.Float.floatToIntBits(getAuPrc())
!= java.lang.Float.floatToIntBits(other.getAuPrc())) return false;
if (java.lang.Float.floatToIntBits(getBaseAuPrc())
!= java.lang.Float.floatToIntBits(other.getBaseAuPrc())) return false;
if (java.lang.Float.floatToIntBits(getAuRoc())
!= java.lang.Float.floatToIntBits(other.getAuRoc())) return false;
if (java.lang.Float.floatToIntBits(getLogLoss())
!= java.lang.Float.floatToIntBits(other.getLogLoss())) return false;
if (!getConfidenceMetricsEntryList().equals(other.getConfidenceMetricsEntryList()))
return false;
if (hasConfusionMatrix() != other.hasConfusionMatrix()) return false;
if (hasConfusionMatrix()) {
if (!getConfusionMatrix().equals(other.getConfusionMatrix())) return false;
}
if (!getAnnotationSpecIdList().equals(other.getAnnotationSpecIdList())) return false;
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + AU_PRC_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getAuPrc());
hash = (37 * hash) + BASE_AU_PRC_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getBaseAuPrc());
hash = (37 * hash) + AU_ROC_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getAuRoc());
hash = (37 * hash) + LOG_LOSS_FIELD_NUMBER;
hash = (53 * hash) + java.lang.Float.floatToIntBits(getLogLoss());
if (getConfidenceMetricsEntryCount() > 0) {
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hash = (53 * hash) + getConfidenceMetricsEntryList().hashCode();
}
if (hasConfusionMatrix()) {
hash = (37 * hash) + CONFUSION_MATRIX_FIELD_NUMBER;
hash = (53 * hash) + getConfusionMatrix().hashCode();
}
if (getAnnotationSpecIdCount() > 0) {
hash = (37 * hash) + ANNOTATION_SPEC_ID_FIELD_NUMBER;
hash = (53 * hash) + getAnnotationSpecIdList().hashCode();
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(
java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
PARSER, input, extensionRegistry);
}
@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
*
*
* Model evaluation metrics for classification problems.
* Note: For Video Classification this metrics only describe quality of the
* Video Classification predictions of "segment_classification" type.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.ClassificationEvaluationMetrics}
*/
public static final class Builder
extends com.google.protobuf.GeneratedMessageV3.Builder
* Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
* for the overall evaluation.
*
*
* float au_prc = 1;
*
* @return The auPrc.
*/
@java.lang.Override
public float getAuPrc() {
return auPrc_;
}
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
* for the overall evaluation.
*
*
* float au_prc = 1;
*
* @param value The auPrc to set.
* @return This builder for chaining.
*/
public Builder setAuPrc(float value) {
auPrc_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric. Micro-averaged
* for the overall evaluation.
*
*
* float au_prc = 1;
*
* @return This builder for chaining.
*/
public Builder clearAuPrc() {
bitField0_ = (bitField0_ & ~0x00000001);
auPrc_ = 0F;
onChanged();
return this;
}
private float baseAuPrc_;
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric based on priors.
* Micro-averaged for the overall evaluation.
* Deprecated.
*
*
* float base_au_prc = 2 [deprecated = true];
*
* @deprecated google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is
* deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188
* @return The baseAuPrc.
*/
@java.lang.Override
@java.lang.Deprecated
public float getBaseAuPrc() {
return baseAuPrc_;
}
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric based on priors.
* Micro-averaged for the overall evaluation.
* Deprecated.
*
*
* float base_au_prc = 2 [deprecated = true];
*
* @deprecated google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is
* deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188
* @param value The baseAuPrc to set.
* @return This builder for chaining.
*/
@java.lang.Deprecated
public Builder setBaseAuPrc(float value) {
baseAuPrc_ = value;
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Output only. The Area Under Precision-Recall Curve metric based on priors.
* Micro-averaged for the overall evaluation.
* Deprecated.
*
*
* float base_au_prc = 2 [deprecated = true];
*
* @deprecated google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc is
* deprecated. See google/cloud/automl/v1beta1/classification.proto;l=188
* @return This builder for chaining.
*/
@java.lang.Deprecated
public Builder clearBaseAuPrc() {
bitField0_ = (bitField0_ & ~0x00000002);
baseAuPrc_ = 0F;
onChanged();
return this;
}
private float auRoc_;
/**
*
*
*
* Output only. The Area Under Receiver Operating Characteristic curve metric.
* Micro-averaged for the overall evaluation.
*
*
* float au_roc = 6;
*
* @return The auRoc.
*/
@java.lang.Override
public float getAuRoc() {
return auRoc_;
}
/**
*
*
*
* Output only. The Area Under Receiver Operating Characteristic curve metric.
* Micro-averaged for the overall evaluation.
*
*
* float au_roc = 6;
*
* @param value The auRoc to set.
* @return This builder for chaining.
*/
public Builder setAuRoc(float value) {
auRoc_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Output only. The Area Under Receiver Operating Characteristic curve metric.
* Micro-averaged for the overall evaluation.
*
*
* float au_roc = 6;
*
* @return This builder for chaining.
*/
public Builder clearAuRoc() {
bitField0_ = (bitField0_ & ~0x00000004);
auRoc_ = 0F;
onChanged();
return this;
}
private float logLoss_;
/**
*
*
*
* Output only. The Log Loss metric.
*
*
* float log_loss = 7;
*
* @return The logLoss.
*/
@java.lang.Override
public float getLogLoss() {
return logLoss_;
}
/**
*
*
*
* Output only. The Log Loss metric.
*
*
* float log_loss = 7;
*
* @param value The logLoss to set.
* @return This builder for chaining.
*/
public Builder setLogLoss(float value) {
logLoss_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
/**
*
*
*
* Output only. The Log Loss metric.
*
*
* float log_loss = 7;
*
* @return This builder for chaining.
*/
public Builder clearLogLoss() {
bitField0_ = (bitField0_ & ~0x00000008);
logLoss_ = 0F;
onChanged();
return this;
}
private java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry>
confidenceMetricsEntry_ = java.util.Collections.emptyList();
private void ensureConfidenceMetricsEntryIsMutable() {
if (!((bitField0_ & 0x00000010) != 0)) {
confidenceMetricsEntry_ =
new java.util.ArrayList<
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry>(
confidenceMetricsEntry_);
bitField0_ |= 0x00000010;
}
}
private com.google.protobuf.RepeatedFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntryOrBuilder>
confidenceMetricsEntryBuilder_;
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry>
getConfidenceMetricsEntryList() {
if (confidenceMetricsEntryBuilder_ == null) {
return java.util.Collections.unmodifiableList(confidenceMetricsEntry_);
} else {
return confidenceMetricsEntryBuilder_.getMessageList();
}
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public int getConfidenceMetricsEntryCount() {
if (confidenceMetricsEntryBuilder_ == null) {
return confidenceMetricsEntry_.size();
} else {
return confidenceMetricsEntryBuilder_.getCount();
}
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
getConfidenceMetricsEntry(int index) {
if (confidenceMetricsEntryBuilder_ == null) {
return confidenceMetricsEntry_.get(index);
} else {
return confidenceMetricsEntryBuilder_.getMessage(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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder setConfidenceMetricsEntry(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
value) {
if (confidenceMetricsEntryBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.set(index, value);
onChanged();
} else {
confidenceMetricsEntryBuilder_.setMessage(index, value);
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder setConfidenceMetricsEntry(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder
builderForValue) {
if (confidenceMetricsEntryBuilder_ == null) {
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.set(index, builderForValue.build());
onChanged();
} else {
confidenceMetricsEntryBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder addConfidenceMetricsEntry(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
value) {
if (confidenceMetricsEntryBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.add(value);
onChanged();
} else {
confidenceMetricsEntryBuilder_.addMessage(value);
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder addConfidenceMetricsEntry(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry
value) {
if (confidenceMetricsEntryBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.add(index, value);
onChanged();
} else {
confidenceMetricsEntryBuilder_.addMessage(index, value);
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder addConfidenceMetricsEntry(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder
builderForValue) {
if (confidenceMetricsEntryBuilder_ == null) {
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.add(builderForValue.build());
onChanged();
} else {
confidenceMetricsEntryBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder addConfidenceMetricsEntry(
int index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder
builderForValue) {
if (confidenceMetricsEntryBuilder_ == null) {
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.add(index, builderForValue.build());
onChanged();
} else {
confidenceMetricsEntryBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder addAllConfidenceMetricsEntry(
java.lang.Iterable<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry>
values) {
if (confidenceMetricsEntryBuilder_ == null) {
ensureConfidenceMetricsEntryIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(values, confidenceMetricsEntry_);
onChanged();
} else {
confidenceMetricsEntryBuilder_.addAllMessages(values);
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder clearConfidenceMetricsEntry() {
if (confidenceMetricsEntryBuilder_ == null) {
confidenceMetricsEntry_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000010);
onChanged();
} else {
confidenceMetricsEntryBuilder_.clear();
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public Builder removeConfidenceMetricsEntry(int index) {
if (confidenceMetricsEntryBuilder_ == null) {
ensureConfidenceMetricsEntryIsMutable();
confidenceMetricsEntry_.remove(index);
onChanged();
} else {
confidenceMetricsEntryBuilder_.remove(index);
}
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder
getConfidenceMetricsEntryBuilder(int index) {
return getConfidenceMetricsEntryFieldBuilder().getBuilder(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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntryOrBuilder
getConfidenceMetricsEntryOrBuilder(int index) {
if (confidenceMetricsEntryBuilder_ == null) {
return confidenceMetricsEntry_.get(index);
} else {
return confidenceMetricsEntryBuilder_.getMessageOrBuilder(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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public java.util.List<
? extends
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder>
getConfidenceMetricsEntryOrBuilderList() {
if (confidenceMetricsEntryBuilder_ != null) {
return confidenceMetricsEntryBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(confidenceMetricsEntry_);
}
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder
addConfidenceMetricsEntryBuilder() {
return getConfidenceMetricsEntryFieldBuilder()
.addBuilder(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.getDefaultInstance());
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder
addConfidenceMetricsEntryBuilder(int index) {
return getConfidenceMetricsEntryFieldBuilder()
.addBuilder(
index,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.getDefaultInstance());
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry confidence_metrics_entry = 3;
*
*/
public java.util.List<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder>
getConfidenceMetricsEntryBuilderList() {
return getConfidenceMetricsEntryFieldBuilder().getBuilderList();
}
private com.google.protobuf.RepeatedFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntry.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfidenceMetricsEntryOrBuilder>
getConfidenceMetricsEntryFieldBuilder() {
if (confidenceMetricsEntryBuilder_ == null) {
confidenceMetricsEntryBuilder_ =
new com.google.protobuf.RepeatedFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder>(
confidenceMetricsEntry_,
((bitField0_ & 0x00000010) != 0),
getParentForChildren(),
isClean());
confidenceMetricsEntry_ = null;
}
return confidenceMetricsEntryBuilder_;
}
private com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
confusionMatrix_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrixOrBuilder>
confusionMatrixBuilder_;
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*
* @return Whether the confusionMatrix field is set.
*/
public boolean hasConfusionMatrix() {
return ((bitField0_ & 0x00000020) != 0);
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*
* @return The confusionMatrix.
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
getConfusionMatrix() {
if (confusionMatrixBuilder_ == null) {
return confusionMatrix_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.getDefaultInstance()
: confusionMatrix_;
} else {
return confusionMatrixBuilder_.getMessage();
}
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
public Builder setConfusionMatrix(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
value) {
if (confusionMatrixBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
confusionMatrix_ = value;
} else {
confusionMatrixBuilder_.setMessage(value);
}
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
public Builder setConfusionMatrix(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Builder
builderForValue) {
if (confusionMatrixBuilder_ == null) {
confusionMatrix_ = builderForValue.build();
} else {
confusionMatrixBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
public Builder mergeConfusionMatrix(
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix
value) {
if (confusionMatrixBuilder_ == null) {
if (((bitField0_ & 0x00000020) != 0)
&& confusionMatrix_ != null
&& confusionMatrix_
!= com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.getDefaultInstance()) {
getConfusionMatrixBuilder().mergeFrom(value);
} else {
confusionMatrix_ = value;
}
} else {
confusionMatrixBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
public Builder clearConfusionMatrix() {
bitField0_ = (bitField0_ & ~0x00000020);
confusionMatrix_ = null;
if (confusionMatrixBuilder_ != null) {
confusionMatrixBuilder_.dispose();
confusionMatrixBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Builder
getConfusionMatrixBuilder() {
bitField0_ |= 0x00000020;
onChanged();
return getConfusionMatrixFieldBuilder().getBuilder();
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
public com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrixOrBuilder
getConfusionMatrixOrBuilder() {
if (confusionMatrixBuilder_ != null) {
return confusionMatrixBuilder_.getMessageOrBuilder();
} else {
return confusionMatrix_ == null
? com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.getDefaultInstance()
: confusionMatrix_;
}
}
/**
*
*
*
* 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.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix confusion_matrix = 4;
*
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrix.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics
.ConfusionMatrixOrBuilder>
getConfusionMatrixFieldBuilder() {
if (confusionMatrixBuilder_ == null) {
confusionMatrixBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrix.Builder,
com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder>(
getConfusionMatrix(), getParentForChildren(), isClean());
confusionMatrix_ = null;
}
return confusionMatrixBuilder_;
}
private com.google.protobuf.LazyStringList annotationSpecId_ =
com.google.protobuf.LazyStringArrayList.EMPTY;
private void ensureAnnotationSpecIdIsMutable() {
if (!((bitField0_ & 0x00000040) != 0)) {
annotationSpecId_ = new com.google.protobuf.LazyStringArrayList(annotationSpecId_);
bitField0_ |= 0x00000040;
}
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return A list containing the annotationSpecId.
*/
public com.google.protobuf.ProtocolStringList getAnnotationSpecIdList() {
return annotationSpecId_.getUnmodifiableView();
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return The count of annotationSpecId.
*/
public int getAnnotationSpecIdCount() {
return annotationSpecId_.size();
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index of the element to return.
* @return The annotationSpecId at the given index.
*/
public java.lang.String getAnnotationSpecId(int index) {
return annotationSpecId_.get(index);
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index of the value to return.
* @return The bytes of the annotationSpecId at the given index.
*/
public com.google.protobuf.ByteString getAnnotationSpecIdBytes(int index) {
return annotationSpecId_.getByteString(index);
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param index The index to set the value at.
* @param value The annotationSpecId to set.
* @return This builder for chaining.
*/
public Builder setAnnotationSpecId(int index, java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureAnnotationSpecIdIsMutable();
annotationSpecId_.set(index, value);
onChanged();
return this;
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param value The annotationSpecId to add.
* @return This builder for chaining.
*/
public Builder addAnnotationSpecId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureAnnotationSpecIdIsMutable();
annotationSpecId_.add(value);
onChanged();
return this;
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param values The annotationSpecId to add.
* @return This builder for chaining.
*/
public Builder addAllAnnotationSpecId(java.lang.Iterable
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @return This builder for chaining.
*/
public Builder clearAnnotationSpecId() {
annotationSpecId_ = com.google.protobuf.LazyStringArrayList.EMPTY;
bitField0_ = (bitField0_ & ~0x00000040);
onChanged();
return this;
}
/**
*
*
*
* Output only. The annotation spec ids used for this evaluation.
*
*
* repeated string annotation_spec_id = 5;
*
* @param value The bytes of the annotationSpecId to add.
* @return This builder for chaining.
*/
public Builder addAnnotationSpecIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
ensureAnnotationSpecIdIsMutable();
annotationSpecId_.add(value);
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics)
private static final com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE =
new com.google.cloud.automl.v1beta1.ClassificationProto.ClassificationEvaluationMetrics();
}
public static com.google.cloud.automl.v1beta1.ClassificationProto
.ClassificationEvaluationMetrics
getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser