public final class TablesModelMetadata extends GeneratedMessageV3 implements TablesModelMetadataOrBuilder
Model metadata specific to AutoML Tables.
Protobuf type google.cloud.automl.v1beta1.TablesModelMetadata
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
Static Fields
public static final int DISABLE_EARLY_STOPPING_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
public static final int INPUT_FEATURE_COLUMN_SPECS_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 TABLES_MODEL_COLUMN_INFO_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
public static final int TARGET_COLUMN_SPEC_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 TRAIN_COST_MILLI_NODE_HOURS_FIELD_NUMBER
| Field Value |
|---|
| Type | Description |
int | |
Static Methods
public static TablesModelMetadata getDefaultInstance()
public static final Descriptors.Descriptor getDescriptor()
public static TablesModelMetadata.Builder newBuilder()
public static TablesModelMetadata.Builder newBuilder(TablesModelMetadata prototype)
public static TablesModelMetadata parseDelimitedFrom(InputStream input)
public static TablesModelMetadata parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static TablesModelMetadata parseFrom(byte[] data)
| Parameter |
|---|
| Name | Description |
data | byte[]
|
public static TablesModelMetadata parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static TablesModelMetadata parseFrom(ByteString data)
public static TablesModelMetadata parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static TablesModelMetadata parseFrom(CodedInputStream input)
public static TablesModelMetadata parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static TablesModelMetadata parseFrom(InputStream input)
public static TablesModelMetadata parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
public static TablesModelMetadata parseFrom(ByteBuffer data)
public static TablesModelMetadata parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static Parser<TablesModelMetadata> parser()
Methods
public boolean equals(Object obj)
| Parameter |
|---|
| Name | Description |
obj | Object
|
Overrides
public TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()
public TablesModelMetadata 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 = 12;
| Returns |
|---|
| Type | Description |
boolean | The disableEarlyStopping.
|
public ColumnSpec getInputFeatureColumnSpecs(int index)
Column specs of the dataset's primary table's columns, on which
the model is trained and which are used as the input for predictions.
The
target_column
as well as, according to dataset's state upon model creation,
weight_column,
and
ml_use_column
must never be included here.
Only 3 fields are used:
- name - May be set on CreateModel, if set only the columns specified are
used, otherwise all primary table's columns (except the ones listed
above) are used for the training and prediction input.
- display_name - Output only.
- data_type - Output only.
repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
| Parameter |
|---|
| Name | Description |
index | int
|
public int getInputFeatureColumnSpecsCount()
Column specs of the dataset's primary table's columns, on which
the model is trained and which are used as the input for predictions.
The
target_column
as well as, according to dataset's state upon model creation,
weight_column,
and
ml_use_column
must never be included here.
Only 3 fields are used:
- name - May be set on CreateModel, if set only the columns specified are
used, otherwise all primary table's columns (except the ones listed
above) are used for the training and prediction input.
- display_name - Output only.
- data_type - Output only.
repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
| Returns |
|---|
| Type | Description |
int | |
public List<ColumnSpec> getInputFeatureColumnSpecsList()
Column specs of the dataset's primary table's columns, on which
the model is trained and which are used as the input for predictions.
The
target_column
as well as, according to dataset's state upon model creation,
weight_column,
and
ml_use_column
must never be included here.
Only 3 fields are used:
- name - May be set on CreateModel, if set only the columns specified are
used, otherwise all primary table's columns (except the ones listed
above) are used for the training and prediction input.
- display_name - Output only.
- data_type - Output only.
repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
public ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder(int index)
Column specs of the dataset's primary table's columns, on which
the model is trained and which are used as the input for predictions.
The
target_column
as well as, according to dataset's state upon model creation,
weight_column,
and
ml_use_column
must never be included here.
Only 3 fields are used:
- name - May be set on CreateModel, if set only the columns specified are
used, otherwise all primary table's columns (except the ones listed
above) are used for the training and prediction input.
- display_name - Output only.
- data_type - Output only.
repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
| Parameter |
|---|
| Name | Description |
index | int
|
public List<? extends ColumnSpecOrBuilder> getInputFeatureColumnSpecsOrBuilderList()
Column specs of the dataset's primary table's columns, on which
the model is trained and which are used as the input for predictions.
The
target_column
as well as, according to dataset's state upon model creation,
weight_column,
and
ml_use_column
must never be included here.
Only 3 fields are used:
- name - May be set on CreateModel, if set only the columns specified are
used, otherwise all primary table's columns (except the ones listed
above) are used for the training and prediction input.
- display_name - Output only.
- data_type - Output only.
repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;
| Returns |
|---|
| Type | Description |
List<? extends com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder> | |
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 = 18;
| 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 = 17;
| Returns |
|---|
| Type | Description |
float | The optimizationObjectiveRecallValue.
|
public Parser<TablesModelMetadata> getParserForType()
Overrides
public int getSerializedSize()
| Returns |
|---|
| Type | Description |
int | |
Overrides
public TablesModelColumnInfo getTablesModelColumnInfo(int index)
Output only. Auxiliary information for each of the
input_feature_column_specs with respect to this particular model.
repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
| Parameter |
|---|
| Name | Description |
index | int
|
public int getTablesModelColumnInfoCount()
Output only. Auxiliary information for each of the
input_feature_column_specs with respect to this particular model.
repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
| Returns |
|---|
| Type | Description |
int | |
public List<TablesModelColumnInfo> getTablesModelColumnInfoList()
Output only. Auxiliary information for each of the
input_feature_column_specs with respect to this particular model.
repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
public TablesModelColumnInfoOrBuilder getTablesModelColumnInfoOrBuilder(int index)
Output only. Auxiliary information for each of the
input_feature_column_specs with respect to this particular model.
repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
| Parameter |
|---|
| Name | Description |
index | int
|
public List<? extends TablesModelColumnInfoOrBuilder> getTablesModelColumnInfoOrBuilderList()
Output only. Auxiliary information for each of the
input_feature_column_specs with respect to this particular model.
repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
| Returns |
|---|
| Type | Description |
List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder> | |
public ColumnSpec getTargetColumnSpec()
Column spec of the dataset's primary table's column the model is
predicting. Snapshotted when model creation started.
Only 3 fields are used:
name - May be set on CreateModel, if it's not then the ColumnSpec
corresponding to the current target_column_spec_id of the dataset
the model is trained from is used.
If neither is set, CreateModel will error.
display_name - Output only.
data_type - Output only.
.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;
| Returns |
|---|
| Type | Description |
ColumnSpec | The targetColumnSpec.
|
public ColumnSpecOrBuilder getTargetColumnSpecOrBuilder()
Column spec of the dataset's primary table's column the model is
predicting. Snapshotted when model creation started.
Only 3 fields are used:
name - May be set on CreateModel, if it's not then the ColumnSpec
corresponding to the current target_column_spec_id of the dataset
the model is trained from is used.
If neither is set, CreateModel will error.
display_name - Output only.
data_type - Output only.
.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;
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 = 6;
| Returns |
|---|
| Type | Description |
long | The trainBudgetMilliNodeHours.
|
public long getTrainCostMilliNodeHours()
Output only. The actual training cost of the model, expressed in milli
node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
to not exceed the train budget.
int64 train_cost_milli_node_hours = 7;
| Returns |
|---|
| Type | Description |
long | The trainCostMilliNodeHours.
|
public final UnknownFieldSet getUnknownFields()
Overrides
public boolean hasOptimizationObjectivePrecisionValue()
Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
Must be between 0 and 1, inclusive.
float optimization_objective_precision_value = 18;
| 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 = 17;
| Returns |
|---|
| Type | Description |
boolean | Whether the optimizationObjectiveRecallValue field is set.
|
public boolean hasTargetColumnSpec()
Column spec of the dataset's primary table's column the model is
predicting. Snapshotted when model creation started.
Only 3 fields are used:
name - May be set on CreateModel, if it's not then the ColumnSpec
corresponding to the current target_column_spec_id of the dataset
the model is trained from is used.
If neither is set, CreateModel will error.
display_name - Output only.
data_type - Output only.
.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;
| Returns |
|---|
| Type | Description |
boolean | Whether the targetColumnSpec field is set.
|
| Returns |
|---|
| Type | Description |
int | |
Overrides
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
public final boolean isInitialized()
Overrides
public TablesModelMetadata.Builder newBuilderForType()
protected TablesModelMetadata.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Overrides
public TablesModelMetadata.Builder toBuilder()
public void writeTo(CodedOutputStream output)
Overrides
