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/*
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/automl/v1beta1/tables.proto
// Protobuf Java Version: 3.25.5
package com.google.cloud.automl.v1beta1;
/**
*
*
*
* Metadata for a dataset used for AutoML Tables.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.TablesDatasetMetadata}
*/
public final class TablesDatasetMetadata extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.TablesDatasetMetadata)
TablesDatasetMetadataOrBuilder {
private static final long serialVersionUID = 0L;
// Use TablesDatasetMetadata.newBuilder() to construct.
private TablesDatasetMetadata(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private TablesDatasetMetadata() {
primaryTableSpecId_ = "";
targetColumnSpecId_ = "";
weightColumnSpecId_ = "";
mlUseColumnSpecId_ = "";
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new TablesDatasetMetadata();
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.Tables
.internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_descriptor;
}
@SuppressWarnings({"rawtypes"})
@java.lang.Override
protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(
int number) {
switch (number) {
case 6:
return internalGetTargetColumnCorrelations();
default:
throw new RuntimeException("Invalid map field number: " + number);
}
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1beta1.Tables
.internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1beta1.TablesDatasetMetadata.class,
com.google.cloud.automl.v1beta1.TablesDatasetMetadata.Builder.class);
}
private int bitField0_;
public static final int PRIMARY_TABLE_SPEC_ID_FIELD_NUMBER = 1;
@SuppressWarnings("serial")
private volatile java.lang.Object primaryTableSpecId_ = "";
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @return The primaryTableSpecId.
*/
@java.lang.Override
public java.lang.String getPrimaryTableSpecId() {
java.lang.Object ref = primaryTableSpecId_;
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();
primaryTableSpecId_ = s;
return s;
}
}
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @return The bytes for primaryTableSpecId.
*/
@java.lang.Override
public com.google.protobuf.ByteString getPrimaryTableSpecIdBytes() {
java.lang.Object ref = primaryTableSpecId_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
primaryTableSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int TARGET_COLUMN_SPEC_ID_FIELD_NUMBER = 2;
@SuppressWarnings("serial")
private volatile java.lang.Object targetColumnSpecId_ = "";
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @return The targetColumnSpecId.
*/
@java.lang.Override
public java.lang.String getTargetColumnSpecId() {
java.lang.Object ref = targetColumnSpecId_;
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();
targetColumnSpecId_ = s;
return s;
}
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @return The bytes for targetColumnSpecId.
*/
@java.lang.Override
public com.google.protobuf.ByteString getTargetColumnSpecIdBytes() {
java.lang.Object ref = targetColumnSpecId_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
targetColumnSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int WEIGHT_COLUMN_SPEC_ID_FIELD_NUMBER = 3;
@SuppressWarnings("serial")
private volatile java.lang.Object weightColumnSpecId_ = "";
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @return The weightColumnSpecId.
*/
@java.lang.Override
public java.lang.String getWeightColumnSpecId() {
java.lang.Object ref = weightColumnSpecId_;
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();
weightColumnSpecId_ = s;
return s;
}
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @return The bytes for weightColumnSpecId.
*/
@java.lang.Override
public com.google.protobuf.ByteString getWeightColumnSpecIdBytes() {
java.lang.Object ref = weightColumnSpecId_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
weightColumnSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int ML_USE_COLUMN_SPEC_ID_FIELD_NUMBER = 4;
@SuppressWarnings("serial")
private volatile java.lang.Object mlUseColumnSpecId_ = "";
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @return The mlUseColumnSpecId.
*/
@java.lang.Override
public java.lang.String getMlUseColumnSpecId() {
java.lang.Object ref = mlUseColumnSpecId_;
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();
mlUseColumnSpecId_ = s;
return s;
}
}
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @return The bytes for mlUseColumnSpecId.
*/
@java.lang.Override
public com.google.protobuf.ByteString getMlUseColumnSpecIdBytes() {
java.lang.Object ref = mlUseColumnSpecId_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
mlUseColumnSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int TARGET_COLUMN_CORRELATIONS_FIELD_NUMBER = 6;
private static final class TargetColumnCorrelationsDefaultEntryHolder {
static final com.google.protobuf.MapEntry<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
defaultEntry =
com.google.protobuf.MapEntry
.
newDefaultInstance(
com.google.cloud.automl.v1beta1.Tables
.internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_TargetColumnCorrelationsEntry_descriptor,
com.google.protobuf.WireFormat.FieldType.STRING,
"",
com.google.protobuf.WireFormat.FieldType.MESSAGE,
com.google.cloud.automl.v1beta1.CorrelationStats.getDefaultInstance());
}
@SuppressWarnings("serial")
private com.google.protobuf.MapField<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
targetColumnCorrelations_;
private com.google.protobuf.MapField<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
internalGetTargetColumnCorrelations() {
if (targetColumnCorrelations_ == null) {
return com.google.protobuf.MapField.emptyMapField(
TargetColumnCorrelationsDefaultEntryHolder.defaultEntry);
}
return targetColumnCorrelations_;
}
public int getTargetColumnCorrelationsCount() {
return internalGetTargetColumnCorrelations().getMap().size();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public boolean containsTargetColumnCorrelations(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
return internalGetTargetColumnCorrelations().getMap().containsKey(key);
}
/** Use {@link #getTargetColumnCorrelationsMap()} instead. */
@java.lang.Override
@java.lang.Deprecated
public java.util.Map
getTargetColumnCorrelations() {
return getTargetColumnCorrelationsMap();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public java.util.Map
getTargetColumnCorrelationsMap() {
return internalGetTargetColumnCorrelations().getMap();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public /* nullable */ com.google.cloud.automl.v1beta1.CorrelationStats
getTargetColumnCorrelationsOrDefault(
java.lang.String key,
/* nullable */
com.google.cloud.automl.v1beta1.CorrelationStats defaultValue) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map =
internalGetTargetColumnCorrelations().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.CorrelationStats getTargetColumnCorrelationsOrThrow(
java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map =
internalGetTargetColumnCorrelations().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public static final int STATS_UPDATE_TIME_FIELD_NUMBER = 7;
private com.google.protobuf.Timestamp statsUpdateTime_;
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*
* @return Whether the statsUpdateTime field is set.
*/
@java.lang.Override
public boolean hasStatsUpdateTime() {
return ((bitField0_ & 0x00000001) != 0);
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*
* @return The statsUpdateTime.
*/
@java.lang.Override
public com.google.protobuf.Timestamp getStatsUpdateTime() {
return statsUpdateTime_ == null
? com.google.protobuf.Timestamp.getDefaultInstance()
: statsUpdateTime_;
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
@java.lang.Override
public com.google.protobuf.TimestampOrBuilder getStatsUpdateTimeOrBuilder() {
return statsUpdateTime_ == null
? com.google.protobuf.Timestamp.getDefaultInstance()
: statsUpdateTime_;
}
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(primaryTableSpecId_)) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 1, primaryTableSpecId_);
}
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(targetColumnSpecId_)) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 2, targetColumnSpecId_);
}
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(weightColumnSpecId_)) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 3, weightColumnSpecId_);
}
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(mlUseColumnSpecId_)) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 4, mlUseColumnSpecId_);
}
com.google.protobuf.GeneratedMessageV3.serializeStringMapTo(
output,
internalGetTargetColumnCorrelations(),
TargetColumnCorrelationsDefaultEntryHolder.defaultEntry,
6);
if (((bitField0_ & 0x00000001) != 0)) {
output.writeMessage(7, getStatsUpdateTime());
}
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(primaryTableSpecId_)) {
size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, primaryTableSpecId_);
}
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(targetColumnSpecId_)) {
size += com.google.protobuf.GeneratedMessageV3.computeStringSize(2, targetColumnSpecId_);
}
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(weightColumnSpecId_)) {
size += com.google.protobuf.GeneratedMessageV3.computeStringSize(3, weightColumnSpecId_);
}
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(mlUseColumnSpecId_)) {
size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, mlUseColumnSpecId_);
}
for (java.util.Map.Entry
entry : internalGetTargetColumnCorrelations().getMap().entrySet()) {
com.google.protobuf.MapEntry<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
targetColumnCorrelations__ =
TargetColumnCorrelationsDefaultEntryHolder.defaultEntry
.newBuilderForType()
.setKey(entry.getKey())
.setValue(entry.getValue())
.build();
size +=
com.google.protobuf.CodedOutputStream.computeMessageSize(6, targetColumnCorrelations__);
}
if (((bitField0_ & 0x00000001) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeMessageSize(7, getStatsUpdateTime());
}
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.TablesDatasetMetadata)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.TablesDatasetMetadata other =
(com.google.cloud.automl.v1beta1.TablesDatasetMetadata) obj;
if (!getPrimaryTableSpecId().equals(other.getPrimaryTableSpecId())) return false;
if (!getTargetColumnSpecId().equals(other.getTargetColumnSpecId())) return false;
if (!getWeightColumnSpecId().equals(other.getWeightColumnSpecId())) return false;
if (!getMlUseColumnSpecId().equals(other.getMlUseColumnSpecId())) return false;
if (!internalGetTargetColumnCorrelations().equals(other.internalGetTargetColumnCorrelations()))
return false;
if (hasStatsUpdateTime() != other.hasStatsUpdateTime()) return false;
if (hasStatsUpdateTime()) {
if (!getStatsUpdateTime().equals(other.getStatsUpdateTime())) 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) + PRIMARY_TABLE_SPEC_ID_FIELD_NUMBER;
hash = (53 * hash) + getPrimaryTableSpecId().hashCode();
hash = (37 * hash) + TARGET_COLUMN_SPEC_ID_FIELD_NUMBER;
hash = (53 * hash) + getTargetColumnSpecId().hashCode();
hash = (37 * hash) + WEIGHT_COLUMN_SPEC_ID_FIELD_NUMBER;
hash = (53 * hash) + getWeightColumnSpecId().hashCode();
hash = (37 * hash) + ML_USE_COLUMN_SPEC_ID_FIELD_NUMBER;
hash = (53 * hash) + getMlUseColumnSpecId().hashCode();
if (!internalGetTargetColumnCorrelations().getMap().isEmpty()) {
hash = (37 * hash) + TARGET_COLUMN_CORRELATIONS_FIELD_NUMBER;
hash = (53 * hash) + internalGetTargetColumnCorrelations().hashCode();
}
if (hasStatsUpdateTime()) {
hash = (37 * hash) + STATS_UPDATE_TIME_FIELD_NUMBER;
hash = (53 * hash) + getStatsUpdateTime().hashCode();
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata parseFrom(
java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata 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.TablesDatasetMetadata parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata 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.TablesDatasetMetadata parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata 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.TablesDatasetMetadata parseFrom(
java.io.InputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
}
public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata parseFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
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return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
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@java.lang.Override
public Builder newBuilderForType() {
return newBuilder();
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public static Builder newBuilder() {
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public static Builder newBuilder(
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return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
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@java.lang.Override
public Builder toBuilder() {
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@java.lang.Override
protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
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/**
*
*
*
* Metadata for a dataset used for AutoML Tables.
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.TablesDatasetMetadata}
*/
public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder
implements
// @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.TablesDatasetMetadata)
com.google.cloud.automl.v1beta1.TablesDatasetMetadataOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.Tables
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@SuppressWarnings({"rawtypes"})
protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(
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switch (number) {
case 6:
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default:
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@SuppressWarnings({"rawtypes"})
protected com.google.protobuf.MapFieldReflectionAccessor internalGetMutableMapFieldReflection(
int number) {
switch (number) {
case 6:
return internalGetMutableTargetColumnCorrelations();
default:
throw new RuntimeException("Invalid map field number: " + number);
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// Construct using com.google.cloud.automl.v1beta1.TablesDatasetMetadata.newBuilder()
private Builder() {
maybeForceBuilderInitialization();
}
private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
maybeForceBuilderInitialization();
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private void maybeForceBuilderInitialization() {
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@java.lang.Override
public Builder clear() {
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bitField0_ = 0;
primaryTableSpecId_ = "";
targetColumnSpecId_ = "";
weightColumnSpecId_ = "";
mlUseColumnSpecId_ = "";
internalGetMutableTargetColumnCorrelations().clear();
statsUpdateTime_ = null;
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@java.lang.Override
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@java.lang.Override
public com.google.cloud.automl.v1beta1.TablesDatasetMetadata build() {
com.google.cloud.automl.v1beta1.TablesDatasetMetadata result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
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@java.lang.Override
public com.google.cloud.automl.v1beta1.TablesDatasetMetadata buildPartial() {
com.google.cloud.automl.v1beta1.TablesDatasetMetadata result =
new com.google.cloud.automl.v1beta1.TablesDatasetMetadata(this);
if (bitField0_ != 0) {
buildPartial0(result);
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onBuilt();
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private void buildPartial0(com.google.cloud.automl.v1beta1.TablesDatasetMetadata result) {
int from_bitField0_ = bitField0_;
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result.primaryTableSpecId_ = primaryTableSpecId_;
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@java.lang.Override
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if (other instanceof com.google.cloud.automl.v1beta1.TablesDatasetMetadata) {
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super.mergeFrom(other);
return this;
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public Builder mergeFrom(com.google.cloud.automl.v1beta1.TablesDatasetMetadata other) {
if (other == com.google.cloud.automl.v1beta1.TablesDatasetMetadata.getDefaultInstance())
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if (!other.getPrimaryTableSpecId().isEmpty()) {
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public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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case 18:
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targetColumnSpecId_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000002;
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} // case 18
case 26:
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weightColumnSpecId_ = input.readStringRequireUtf8();
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case 34:
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mlUseColumnSpecId_ = input.readStringRequireUtf8();
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com.google.protobuf.MapEntry<
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private int bitField0_;
private java.lang.Object primaryTableSpecId_ = "";
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @return The primaryTableSpecId.
*/
public java.lang.String getPrimaryTableSpecId() {
java.lang.Object ref = primaryTableSpecId_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
primaryTableSpecId_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @return The bytes for primaryTableSpecId.
*/
public com.google.protobuf.ByteString getPrimaryTableSpecIdBytes() {
java.lang.Object ref = primaryTableSpecId_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
primaryTableSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @param value The primaryTableSpecId to set.
* @return This builder for chaining.
*/
public Builder setPrimaryTableSpecId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
primaryTableSpecId_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @return This builder for chaining.
*/
public Builder clearPrimaryTableSpecId() {
primaryTableSpecId_ = getDefaultInstance().getPrimaryTableSpecId();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
*
*
* Output only. The table_spec_id of the primary table of this dataset.
*
*
* string primary_table_spec_id = 1;
*
* @param value The bytes for primaryTableSpecId to set.
* @return This builder for chaining.
*/
public Builder setPrimaryTableSpecIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
primaryTableSpecId_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
private java.lang.Object targetColumnSpecId_ = "";
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @return The targetColumnSpecId.
*/
public java.lang.String getTargetColumnSpecId() {
java.lang.Object ref = targetColumnSpecId_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
targetColumnSpecId_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @return The bytes for targetColumnSpecId.
*/
public com.google.protobuf.ByteString getTargetColumnSpecIdBytes() {
java.lang.Object ref = targetColumnSpecId_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
targetColumnSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @param value The targetColumnSpecId to set.
* @return This builder for chaining.
*/
public Builder setTargetColumnSpecId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
targetColumnSpecId_ = value;
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @return This builder for chaining.
*/
public Builder clearTargetColumnSpecId() {
targetColumnSpecId_ = getDefaultInstance().getTargetColumnSpecId();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
return this;
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* training & prediction target.
* This column must be non-nullable and have one of following data types
* (otherwise model creation will error):
*
* * CATEGORY
*
* * FLOAT64
*
* If the type is CATEGORY , only up to
* 100 unique values may exist in that column across all rows.
*
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string target_column_spec_id = 2;
*
* @param value The bytes for targetColumnSpecId to set.
* @return This builder for chaining.
*/
public Builder setTargetColumnSpecIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
targetColumnSpecId_ = value;
bitField0_ |= 0x00000002;
onChanged();
return this;
}
private java.lang.Object weightColumnSpecId_ = "";
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @return The weightColumnSpecId.
*/
public java.lang.String getWeightColumnSpecId() {
java.lang.Object ref = weightColumnSpecId_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
weightColumnSpecId_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @return The bytes for weightColumnSpecId.
*/
public com.google.protobuf.ByteString getWeightColumnSpecIdBytes() {
java.lang.Object ref = weightColumnSpecId_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
weightColumnSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @param value The weightColumnSpecId to set.
* @return This builder for chaining.
*/
public Builder setWeightColumnSpecId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
weightColumnSpecId_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @return This builder for chaining.
*/
public Builder clearWeightColumnSpecId() {
weightColumnSpecId_ = getDefaultInstance().getWeightColumnSpecId();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
/**
*
*
*
* column_spec_id of the primary table's column that should be used as the
* weight column, i.e. the higher the value the more important the row will be
* during model training.
* Required type: FLOAT64.
* Allowed values: 0 to 10000, inclusive on both ends; 0 means the row is
* ignored for training.
* If not set all rows are assumed to have equal weight of 1.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string weight_column_spec_id = 3;
*
* @param value The bytes for weightColumnSpecId to set.
* @return This builder for chaining.
*/
public Builder setWeightColumnSpecIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
weightColumnSpecId_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
private java.lang.Object mlUseColumnSpecId_ = "";
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @return The mlUseColumnSpecId.
*/
public java.lang.String getMlUseColumnSpecId() {
java.lang.Object ref = mlUseColumnSpecId_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
mlUseColumnSpecId_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @return The bytes for mlUseColumnSpecId.
*/
public com.google.protobuf.ByteString getMlUseColumnSpecIdBytes() {
java.lang.Object ref = mlUseColumnSpecId_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
mlUseColumnSpecId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @param value The mlUseColumnSpecId to set.
* @return This builder for chaining.
*/
public Builder setMlUseColumnSpecId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
mlUseColumnSpecId_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @return This builder for chaining.
*/
public Builder clearMlUseColumnSpecId() {
mlUseColumnSpecId_ = getDefaultInstance().getMlUseColumnSpecId();
bitField0_ = (bitField0_ & ~0x00000008);
onChanged();
return this;
}
/**
*
*
*
* column_spec_id of the primary table column which specifies a possible ML
* use of the row, i.e. the column will be used to split the rows into TRAIN,
* VALIDATE and TEST sets.
* Required type: STRING.
* This column, if set, must either have all of `TRAIN`, `VALIDATE`, `TEST`
* among its values, or only have `TEST`, `UNASSIGNED` values. In the latter
* case the rows with `UNASSIGNED` value will be assigned by AutoML. Note
* that if a given ml use distribution makes it impossible to create a "good"
* model, that call will error describing the issue.
* If both this column_spec_id and primary table's time_column_spec_id are not
* set, then all rows are treated as `UNASSIGNED`.
* NOTE: Updates of this field will instantly affect any other users
* concurrently working with the dataset.
*
*
* string ml_use_column_spec_id = 4;
*
* @param value The bytes for mlUseColumnSpecId to set.
* @return This builder for chaining.
*/
public Builder setMlUseColumnSpecIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
mlUseColumnSpecId_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
private static final class TargetColumnCorrelationsConverter
implements com.google.protobuf.MapFieldBuilder.Converter<
java.lang.String,
com.google.cloud.automl.v1beta1.CorrelationStatsOrBuilder,
com.google.cloud.automl.v1beta1.CorrelationStats> {
@java.lang.Override
public com.google.cloud.automl.v1beta1.CorrelationStats build(
com.google.cloud.automl.v1beta1.CorrelationStatsOrBuilder val) {
if (val instanceof com.google.cloud.automl.v1beta1.CorrelationStats) {
return (com.google.cloud.automl.v1beta1.CorrelationStats) val;
}
return ((com.google.cloud.automl.v1beta1.CorrelationStats.Builder) val).build();
}
@java.lang.Override
public com.google.protobuf.MapEntry<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
defaultEntry() {
return TargetColumnCorrelationsDefaultEntryHolder.defaultEntry;
}
};
private static final TargetColumnCorrelationsConverter targetColumnCorrelationsConverter =
new TargetColumnCorrelationsConverter();
private com.google.protobuf.MapFieldBuilder<
java.lang.String,
com.google.cloud.automl.v1beta1.CorrelationStatsOrBuilder,
com.google.cloud.automl.v1beta1.CorrelationStats,
com.google.cloud.automl.v1beta1.CorrelationStats.Builder>
targetColumnCorrelations_;
private com.google.protobuf.MapFieldBuilder<
java.lang.String,
com.google.cloud.automl.v1beta1.CorrelationStatsOrBuilder,
com.google.cloud.automl.v1beta1.CorrelationStats,
com.google.cloud.automl.v1beta1.CorrelationStats.Builder>
internalGetTargetColumnCorrelations() {
if (targetColumnCorrelations_ == null) {
return new com.google.protobuf.MapFieldBuilder<>(targetColumnCorrelationsConverter);
}
return targetColumnCorrelations_;
}
private com.google.protobuf.MapFieldBuilder<
java.lang.String,
com.google.cloud.automl.v1beta1.CorrelationStatsOrBuilder,
com.google.cloud.automl.v1beta1.CorrelationStats,
com.google.cloud.automl.v1beta1.CorrelationStats.Builder>
internalGetMutableTargetColumnCorrelations() {
if (targetColumnCorrelations_ == null) {
targetColumnCorrelations_ =
new com.google.protobuf.MapFieldBuilder<>(targetColumnCorrelationsConverter);
}
bitField0_ |= 0x00000010;
onChanged();
return targetColumnCorrelations_;
}
public int getTargetColumnCorrelationsCount() {
return internalGetTargetColumnCorrelations().ensureBuilderMap().size();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public boolean containsTargetColumnCorrelations(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
return internalGetTargetColumnCorrelations().ensureBuilderMap().containsKey(key);
}
/** Use {@link #getTargetColumnCorrelationsMap()} instead. */
@java.lang.Override
@java.lang.Deprecated
public java.util.Map
getTargetColumnCorrelations() {
return getTargetColumnCorrelationsMap();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public java.util.Map
getTargetColumnCorrelationsMap() {
return internalGetTargetColumnCorrelations().getImmutableMap();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public /* nullable */ com.google.cloud.automl.v1beta1.CorrelationStats
getTargetColumnCorrelationsOrDefault(
java.lang.String key,
/* nullable */
com.google.cloud.automl.v1beta1.CorrelationStats defaultValue) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map
map = internalGetMutableTargetColumnCorrelations().ensureBuilderMap();
return map.containsKey(key)
? targetColumnCorrelationsConverter.build(map.get(key))
: defaultValue;
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.CorrelationStats getTargetColumnCorrelationsOrThrow(
java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map
map = internalGetMutableTargetColumnCorrelations().ensureBuilderMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return targetColumnCorrelationsConverter.build(map.get(key));
}
public Builder clearTargetColumnCorrelations() {
bitField0_ = (bitField0_ & ~0x00000010);
internalGetMutableTargetColumnCorrelations().clear();
return this;
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
public Builder removeTargetColumnCorrelations(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
internalGetMutableTargetColumnCorrelations().ensureBuilderMap().remove(key);
return this;
}
/** Use alternate mutation accessors instead. */
@java.lang.Deprecated
public java.util.Map
getMutableTargetColumnCorrelations() {
bitField0_ |= 0x00000010;
return internalGetMutableTargetColumnCorrelations().ensureMessageMap();
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
public Builder putTargetColumnCorrelations(
java.lang.String key, com.google.cloud.automl.v1beta1.CorrelationStats value) {
if (key == null) {
throw new NullPointerException("map key");
}
if (value == null) {
throw new NullPointerException("map value");
}
internalGetMutableTargetColumnCorrelations().ensureBuilderMap().put(key, value);
bitField0_ |= 0x00000010;
return this;
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
public Builder putAllTargetColumnCorrelations(
java.util.Map values) {
for (java.util.Map.Entry
e : values.entrySet()) {
if (e.getKey() == null || e.getValue() == null) {
throw new NullPointerException();
}
}
internalGetMutableTargetColumnCorrelations().ensureBuilderMap().putAll(values);
bitField0_ |= 0x00000010;
return this;
}
/**
*
*
*
* Output only. Correlations between
*
* [TablesDatasetMetadata.target_column_spec_id][google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id],
* and other columns of the
*
* [TablesDatasetMetadataprimary_table][google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id].
* Only set if the target column is set. Mapping from other column spec id to
* its CorrelationStats with the target column.
* This field may be stale, see the stats_update_time field for
* for the timestamp at which these stats were last updated.
*
*
*
* map<string, .google.cloud.automl.v1beta1.CorrelationStats> target_column_correlations = 6;
*
*/
public com.google.cloud.automl.v1beta1.CorrelationStats.Builder
putTargetColumnCorrelationsBuilderIfAbsent(java.lang.String key) {
java.util.Map
builderMap = internalGetMutableTargetColumnCorrelations().ensureBuilderMap();
com.google.cloud.automl.v1beta1.CorrelationStatsOrBuilder entry = builderMap.get(key);
if (entry == null) {
entry = com.google.cloud.automl.v1beta1.CorrelationStats.newBuilder();
builderMap.put(key, entry);
}
if (entry instanceof com.google.cloud.automl.v1beta1.CorrelationStats) {
entry = ((com.google.cloud.automl.v1beta1.CorrelationStats) entry).toBuilder();
builderMap.put(key, entry);
}
return (com.google.cloud.automl.v1beta1.CorrelationStats.Builder) entry;
}
private com.google.protobuf.Timestamp statsUpdateTime_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.protobuf.Timestamp,
com.google.protobuf.Timestamp.Builder,
com.google.protobuf.TimestampOrBuilder>
statsUpdateTimeBuilder_;
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*
* @return Whether the statsUpdateTime field is set.
*/
public boolean hasStatsUpdateTime() {
return ((bitField0_ & 0x00000020) != 0);
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*
* @return The statsUpdateTime.
*/
public com.google.protobuf.Timestamp getStatsUpdateTime() {
if (statsUpdateTimeBuilder_ == null) {
return statsUpdateTime_ == null
? com.google.protobuf.Timestamp.getDefaultInstance()
: statsUpdateTime_;
} else {
return statsUpdateTimeBuilder_.getMessage();
}
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
public Builder setStatsUpdateTime(com.google.protobuf.Timestamp value) {
if (statsUpdateTimeBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
statsUpdateTime_ = value;
} else {
statsUpdateTimeBuilder_.setMessage(value);
}
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
public Builder setStatsUpdateTime(com.google.protobuf.Timestamp.Builder builderForValue) {
if (statsUpdateTimeBuilder_ == null) {
statsUpdateTime_ = builderForValue.build();
} else {
statsUpdateTimeBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
public Builder mergeStatsUpdateTime(com.google.protobuf.Timestamp value) {
if (statsUpdateTimeBuilder_ == null) {
if (((bitField0_ & 0x00000020) != 0)
&& statsUpdateTime_ != null
&& statsUpdateTime_ != com.google.protobuf.Timestamp.getDefaultInstance()) {
getStatsUpdateTimeBuilder().mergeFrom(value);
} else {
statsUpdateTime_ = value;
}
} else {
statsUpdateTimeBuilder_.mergeFrom(value);
}
if (statsUpdateTime_ != null) {
bitField0_ |= 0x00000020;
onChanged();
}
return this;
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
public Builder clearStatsUpdateTime() {
bitField0_ = (bitField0_ & ~0x00000020);
statsUpdateTime_ = null;
if (statsUpdateTimeBuilder_ != null) {
statsUpdateTimeBuilder_.dispose();
statsUpdateTimeBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
public com.google.protobuf.Timestamp.Builder getStatsUpdateTimeBuilder() {
bitField0_ |= 0x00000020;
onChanged();
return getStatsUpdateTimeFieldBuilder().getBuilder();
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
public com.google.protobuf.TimestampOrBuilder getStatsUpdateTimeOrBuilder() {
if (statsUpdateTimeBuilder_ != null) {
return statsUpdateTimeBuilder_.getMessageOrBuilder();
} else {
return statsUpdateTime_ == null
? com.google.protobuf.Timestamp.getDefaultInstance()
: statsUpdateTime_;
}
}
/**
*
*
*
* Output only. The most recent timestamp when target_column_correlations
* field and all descendant ColumnSpec.data_stats and
* ColumnSpec.top_correlated_columns fields were last (re-)generated. Any
* changes that happened to the dataset afterwards are not reflected in these
* fields values. The regeneration happens in the background on a best effort
* basis.
*
*
* .google.protobuf.Timestamp stats_update_time = 7;
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.protobuf.Timestamp,
com.google.protobuf.Timestamp.Builder,
com.google.protobuf.TimestampOrBuilder>
getStatsUpdateTimeFieldBuilder() {
if (statsUpdateTimeBuilder_ == null) {
statsUpdateTimeBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.protobuf.Timestamp,
com.google.protobuf.Timestamp.Builder,
com.google.protobuf.TimestampOrBuilder>(
getStatsUpdateTime(), getParentForChildren(), isClean());
statsUpdateTime_ = null;
}
return statsUpdateTimeBuilder_;
}
@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.TablesDatasetMetadata)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesDatasetMetadata)
private static final com.google.cloud.automl.v1beta1.TablesDatasetMetadata DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.TablesDatasetMetadata();
}
public static com.google.cloud.automl.v1beta1.TablesDatasetMetadata getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public TablesDatasetMetadata parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.cloud.automl.v1beta1.TablesDatasetMetadata getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}