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
.internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_descriptor;
}
@SuppressWarnings({"rawtypes"})
protected com.google.protobuf.MapField internalGetMapField(int number) {
switch (number) {
case 6:
return internalGetTargetColumnCorrelations();
default:
throw new RuntimeException("Invalid map field number: " + number);
}
}
@SuppressWarnings({"rawtypes"})
protected com.google.protobuf.MapField internalGetMutableMapField(int number) {
switch (number) {
case 6:
return internalGetMutableTargetColumnCorrelations();
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);
}
// Construct using com.google.cloud.automl.v1beta1.TablesDatasetMetadata.newBuilder()
private Builder() {}
private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
}
@java.lang.Override
public Builder clear() {
super.clear();
bitField0_ = 0;
primaryTableSpecId_ = "";
targetColumnSpecId_ = "";
weightColumnSpecId_ = "";
mlUseColumnSpecId_ = "";
internalGetMutableTargetColumnCorrelations().clear();
statsUpdateTime_ = null;
if (statsUpdateTimeBuilder_ != null) {
statsUpdateTimeBuilder_.dispose();
statsUpdateTimeBuilder_ = null;
}
return this;
}
@java.lang.Override
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
return com.google.cloud.automl.v1beta1.Tables
.internal_static_google_cloud_automl_v1beta1_TablesDatasetMetadata_descriptor;
}
@java.lang.Override
public com.google.cloud.automl.v1beta1.TablesDatasetMetadata getDefaultInstanceForType() {
return com.google.cloud.automl.v1beta1.TablesDatasetMetadata.getDefaultInstance();
}
@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;
}
@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);
}
onBuilt();
return result;
}
private void buildPartial0(com.google.cloud.automl.v1beta1.TablesDatasetMetadata result) {
int from_bitField0_ = bitField0_;
if (((from_bitField0_ & 0x00000001) != 0)) {
result.primaryTableSpecId_ = primaryTableSpecId_;
}
if (((from_bitField0_ & 0x00000002) != 0)) {
result.targetColumnSpecId_ = targetColumnSpecId_;
}
if (((from_bitField0_ & 0x00000004) != 0)) {
result.weightColumnSpecId_ = weightColumnSpecId_;
}
if (((from_bitField0_ & 0x00000008) != 0)) {
result.mlUseColumnSpecId_ = mlUseColumnSpecId_;
}
if (((from_bitField0_ & 0x00000010) != 0)) {
result.targetColumnCorrelations_ = internalGetTargetColumnCorrelations();
result.targetColumnCorrelations_.makeImmutable();
}
if (((from_bitField0_ & 0x00000020) != 0)) {
result.statsUpdateTime_ =
statsUpdateTimeBuilder_ == null ? statsUpdateTime_ : statsUpdateTimeBuilder_.build();
}
}
@java.lang.Override
public Builder clone() {
return super.clone();
}
@java.lang.Override
public Builder setField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
return super.setField(field, value);
}
@java.lang.Override
public Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field) {
return super.clearField(field);
}
@java.lang.Override
public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) {
return super.clearOneof(oneof);
}
@java.lang.Override
public Builder setRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) {
return super.setRepeatedField(field, index, value);
}
@java.lang.Override
public Builder addRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
return super.addRepeatedField(field, value);
}
@java.lang.Override
public Builder mergeFrom(com.google.protobuf.Message other) {
if (other instanceof com.google.cloud.automl.v1beta1.TablesDatasetMetadata) {
return mergeFrom((com.google.cloud.automl.v1beta1.TablesDatasetMetadata) other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(com.google.cloud.automl.v1beta1.TablesDatasetMetadata other) {
if (other == com.google.cloud.automl.v1beta1.TablesDatasetMetadata.getDefaultInstance())
return this;
if (!other.getPrimaryTableSpecId().isEmpty()) {
primaryTableSpecId_ = other.primaryTableSpecId_;
bitField0_ |= 0x00000001;
onChanged();
}
if (!other.getTargetColumnSpecId().isEmpty()) {
targetColumnSpecId_ = other.targetColumnSpecId_;
bitField0_ |= 0x00000002;
onChanged();
}
if (!other.getWeightColumnSpecId().isEmpty()) {
weightColumnSpecId_ = other.weightColumnSpecId_;
bitField0_ |= 0x00000004;
onChanged();
}
if (!other.getMlUseColumnSpecId().isEmpty()) {
mlUseColumnSpecId_ = other.mlUseColumnSpecId_;
bitField0_ |= 0x00000008;
onChanged();
}
internalGetMutableTargetColumnCorrelations()
.mergeFrom(other.internalGetTargetColumnCorrelations());
bitField0_ |= 0x00000010;
if (other.hasStatsUpdateTime()) {
mergeStatsUpdateTime(other.getStatsUpdateTime());
}
this.mergeUnknownFields(other.getUnknownFields());
onChanged();
return this;
}
@java.lang.Override
public final boolean isInitialized() {
return true;
}
@java.lang.Override
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 10:
{
primaryTableSpecId_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000001;
break;
} // case 10
case 18:
{
targetColumnSpecId_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000002;
break;
} // case 18
case 26:
{
weightColumnSpecId_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000004;
break;
} // case 26
case 34:
{
mlUseColumnSpecId_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000008;
break;
} // case 34
case 50:
{
com.google.protobuf.MapEntry<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
targetColumnCorrelations__ =
input.readMessage(
TargetColumnCorrelationsDefaultEntryHolder.defaultEntry
.getParserForType(),
extensionRegistry);
internalGetMutableTargetColumnCorrelations()
.getMutableMap()
.put(
targetColumnCorrelations__.getKey(), targetColumnCorrelations__.getValue());
bitField0_ |= 0x00000010;
break;
} // case 50
case 58:
{
input.readMessage(getStatsUpdateTimeFieldBuilder().getBuilder(), extensionRegistry);
bitField0_ |= 0x00000020;
break;
} // case 58
default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
}
break;
} // default:
} // switch (tag)
} // while (!done)
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.unwrapIOException();
} finally {
onChanged();
} // finally
return this;
}
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 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_;
}
private com.google.protobuf.MapField<
java.lang.String, com.google.cloud.automl.v1beta1.CorrelationStats>
internalGetMutableTargetColumnCorrelations() {
if (targetColumnCorrelations_ == null) {
targetColumnCorrelations_ =
com.google.protobuf.MapField.newMapField(
TargetColumnCorrelationsDefaultEntryHolder.defaultEntry);
}
if (!targetColumnCorrelations_.isMutable()) {
targetColumnCorrelations_ = targetColumnCorrelations_.copy();
}
bitField0_ |= 0x00000010;
onChanged();
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 Builder clearTargetColumnCorrelations() {
bitField0_ = (bitField0_ & ~0x00000010);
internalGetMutableTargetColumnCorrelations().getMutableMap().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().getMutableMap().remove(key);
return this;
}
/** Use alternate mutation accessors instead. */
@java.lang.Deprecated
public java.util.Map
getMutableTargetColumnCorrelations() {
bitField0_ |= 0x00000010;
return internalGetMutableTargetColumnCorrelations().getMutableMap();
}
/**
*
*
*
* 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().getMutableMap().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) {
internalGetMutableTargetColumnCorrelations().getMutableMap().putAll(values);
bitField0_ |= 0x00000010;
return this;
}
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);
}
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;
}
}