public final class AutoMlTablesInputs extends GeneratedMessageV3 implements AutoMlTablesInputsOrBuilder
Protobuf type
google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
public static final int ADDITIONAL_EXPERIMENTS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int DISABLE_EARLY_STOPPING_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int EXPORT_EVALUATED_DATA_ITEMS_CONFIG_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int OPTIMIZATION_OBJECTIVE_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int OPTIMIZATION_OBJECTIVE_PRECISION_VALUE_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int OPTIMIZATION_OBJECTIVE_RECALL_VALUE_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int PREDICTION_TYPE_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TARGET_COLUMN_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int TRANSFORMATIONS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
public static final int WEIGHT_COLUMN_NAME_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
public static AutoMlTablesInputs getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static AutoMlTablesInputs.Builder newBuilder()
public static AutoMlTablesInputs.Builder newBuilder(AutoMlTablesInputs prototype)
public static AutoMlTablesInputs parseDelimitedFrom(InputStream input)
public static AutoMlTablesInputs parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static AutoMlTablesInputs parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
public static AutoMlTablesInputs parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static AutoMlTablesInputs parseFrom(ByteString data)
public static AutoMlTablesInputs parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static AutoMlTablesInputs parseFrom(CodedInputStream input)
public static AutoMlTablesInputs parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static AutoMlTablesInputs parseFrom(InputStream input)
public static AutoMlTablesInputs parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static AutoMlTablesInputs parseFrom(ByteBuffer data)
public static AutoMlTablesInputs parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<AutoMlTablesInputs> parser()
Methods
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
Overrides
public String getAdditionalExperiments(int index)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Parameter |
| Name |
Description |
index |
int
The index of the element to return.
|
| Returns |
| Type |
Description |
String |
The additionalExperiments at the given index.
|
public ByteString getAdditionalExperimentsBytes(int index)
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Parameter |
| Name |
Description |
index |
int
The index of the value to return.
|
| Returns |
| Type |
Description |
ByteString |
The bytes of the additionalExperiments at the given index.
|
public int getAdditionalExperimentsCount()
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
| Returns |
| Type |
Description |
int |
The count of additionalExperiments.
|
public ProtocolStringList getAdditionalExperimentsList()
Additional experiment flags for the Tables training pipeline.
repeated string additional_experiments = 11;
public AutoMlTablesInputs.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()
public AutoMlTablesInputs getDefaultInstanceForType()
public boolean getDisableEarlyStopping()
Use the entire training budget. This disables the early stopping feature.
By default, the early stopping feature is enabled, which means that AutoML
Tables might stop training before the entire training budget has been used.
bool disable_early_stopping = 8;
| Returns |
| Type |
Description |
boolean |
The disableEarlyStopping.
|
public ExportEvaluatedDataItemsConfig getExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table. If
this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
public ExportEvaluatedDataItemsConfigOrBuilder getExportEvaluatedDataItemsConfigOrBuilder()
Configuration for exporting test set predictions to a BigQuery table. If
this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
public String getOptimizationObjective()
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;
| Returns |
| Type |
Description |
String |
The optimizationObjective.
|
public ByteString getOptimizationObjectiveBytes()
Objective function the model is optimizing towards. The training process
creates a model that maximizes/minimizes the value of the objective
function over the validation set.
The supported optimization objectives depend on the prediction type.
If the field is not set, a default objective function is used.
classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver
operating characteristic (ROC) curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified
recall value.
"maximize-recall-at-precision" - Maximize recall for a specified
precision value.
classification (multi-class):
"minimize-log-loss" (default) - Minimize log loss.
regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
string optimization_objective = 4;
| Returns |
| Type |
Description |
ByteString |
The bytes for optimizationObjective.
|
public float getOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision".
Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;
| Returns |
| Type |
Description |
float |
The optimizationObjectivePrecisionValue.
|
public float getOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall".
Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;
| Returns |
| Type |
Description |
float |
The optimizationObjectiveRecallValue.
|
public Parser<AutoMlTablesInputs> getParserForType()
Overrides
public String getPredictionType()
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;
| Returns |
| Type |
Description |
String |
The predictionType.
|
public ByteString getPredictionTypeBytes()
The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is
picked for each row.
"regression" - Predict a value based on its relation to other values.
This type is available only to columns that contain
semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
string prediction_type = 1;
| Returns |
| Type |
Description |
ByteString |
The bytes for predictionType.
|
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
Overrides
public String getTargetColumn()
The column name of the target column that the model is to predict.
string target_column = 2;
| Returns |
| Type |
Description |
String |
The targetColumn.
|
public ByteString getTargetColumnBytes()
The column name of the target column that the model is to predict.
string target_column = 2;
| Returns |
| Type |
Description |
ByteString |
The bytes for targetColumn.
|
public long getTrainBudgetMilliNodeHours()
Required. The train budget of creating this model, expressed in milli node
hours i.e. 1,000 value in this field means 1 node hour.
The training cost of the model will not exceed this budget. The final cost
will be attempted to be close to the budget, though may end up being (even)
noticeably smaller - at the backend's discretion. This especially may
happen when further model training ceases to provide any improvements.
If the budget is set to a value known to be insufficient to train a
model for the given dataset, the training won't be attempted and
will error.
The train budget must be between 1,000 and 72,000 milli node hours,
inclusive.
int64 train_budget_milli_node_hours = 7;
| Returns |
| Type |
Description |
long |
The trainBudgetMilliNodeHours.
|
public AutoMlTablesInputs.Transformation getTransformations(int index)
Each transformation will apply transform function to given input column.
And the result will be used for training.
When creating transformation for BigQuery Struct column, the column should
be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Parameter |
| Name |
Description |
index |
int
|
public int getTransformationsCount()
Each transformation will apply transform function to given input column.
And the result will be used for training.
When creating transformation for BigQuery Struct column, the column should
be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Returns |
| Type |
Description |
int |
|
public List<AutoMlTablesInputs.Transformation> getTransformationsList()
Each transformation will apply transform function to given input column.
And the result will be used for training.
When creating transformation for BigQuery Struct column, the column should
be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
public AutoMlTablesInputs.TransformationOrBuilder getTransformationsOrBuilder(int index)
Each transformation will apply transform function to given input column.
And the result will be used for training.
When creating transformation for BigQuery Struct column, the column should
be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Parameter |
| Name |
Description |
index |
int
|
public List<? extends AutoMlTablesInputs.TransformationOrBuilder> getTransformationsOrBuilderList()
Each transformation will apply transform function to given input column.
And the result will be used for training.
When creating transformation for BigQuery Struct column, the column should
be flattened using "." as the delimiter.
repeated .google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation transformations = 3;
| Returns |
| Type |
Description |
List<? extends com.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.AutoMlTablesInputs.TransformationOrBuilder> |
|
public String getWeightColumnName()
Column name that should be used as the weight column.
Higher values in this column give more importance to the row
during model training. The column must have numeric values between 0 and
10000 inclusively; 0 means the row is ignored for training. If weight
column field is not set, then all rows are assumed to have equal weight
of 1.
string weight_column_name = 9;
| Returns |
| Type |
Description |
String |
The weightColumnName.
|
public ByteString getWeightColumnNameBytes()
Column name that should be used as the weight column.
Higher values in this column give more importance to the row
during model training. The column must have numeric values between 0 and
10000 inclusively; 0 means the row is ignored for training. If weight
column field is not set, then all rows are assumed to have equal weight
of 1.
string weight_column_name = 9;
| Returns |
| Type |
Description |
ByteString |
The bytes for weightColumnName.
|
public boolean hasExportEvaluatedDataItemsConfig()
Configuration for exporting test set predictions to a BigQuery table. If
this configuration is absent, then the export is not performed.
.google.cloud.aiplatform.v1beta1.schema.trainingjob.definition.ExportEvaluatedDataItemsConfig export_evaluated_data_items_config = 10;
| Returns |
| Type |
Description |
boolean |
Whether the exportEvaluatedDataItemsConfig field is set.
|
public boolean hasOptimizationObjectivePrecisionValue()
Required when optimization_objective is "maximize-recall-at-precision".
Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 6;
| Returns |
| Type |
Description |
boolean |
Whether the optimizationObjectivePrecisionValue field is set.
|
public boolean hasOptimizationObjectiveRecallValue()
Required when optimization_objective is "maximize-precision-at-recall".
Must be between 0 and 1, inclusive.
float optimization_objective_recall_value = 5;
| Returns |
| Type |
Description |
boolean |
Whether the optimizationObjectiveRecallValue field is set.
|
| Returns |
| Type |
Description |
int |
|
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public AutoMlTablesInputs.Builder newBuilderForType()
protected AutoMlTablesInputs.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
public AutoMlTablesInputs.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
