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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;
public interface TablesModelMetadataOrBuilder
extends
// @@protoc_insertion_point(interface_extends:google.cloud.automl.v1beta1.TablesModelMetadata)
com.google.protobuf.MessageOrBuilder {
/**
*
*
*
* Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
* Must be between 0 and 1, inclusive.
*
*
* float optimization_objective_recall_value = 17;
*
* @return Whether the optimizationObjectiveRecallValue field is set.
*/
boolean hasOptimizationObjectiveRecallValue();
/**
*
*
*
* Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
* Must be between 0 and 1, inclusive.
*
*
* float optimization_objective_recall_value = 17;
*
* @return The optimizationObjectiveRecallValue.
*/
float getOptimizationObjectiveRecallValue();
/**
*
*
*
* Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
* Must be between 0 and 1, inclusive.
*
*
* float optimization_objective_precision_value = 18;
*
* @return Whether the optimizationObjectivePrecisionValue field is set.
*/
boolean hasOptimizationObjectivePrecisionValue();
/**
*
*
*
* Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
* Must be between 0 and 1, inclusive.
*
*
* float optimization_objective_precision_value = 18;
*
* @return The optimizationObjectivePrecisionValue.
*/
float getOptimizationObjectivePrecisionValue();
/**
*
*
*
* 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;
*
* @return Whether the targetColumnSpec field is set.
*/
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;
*
* @return The targetColumnSpec.
*/
com.google.cloud.automl.v1beta1.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;
*/
com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getTargetColumnSpecOrBuilder();
/**
*
*
*
* 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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
* as well as, according to dataset's state upon model creation,
*
* [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
* and
*
* [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
* 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;
*/
java.util.List 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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
* as well as, according to dataset's state upon model creation,
*
* [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
* and
*
* [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
* 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;
*/
com.google.cloud.automl.v1beta1.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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
* as well as, according to dataset's state upon model creation,
*
* [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
* and
*
* [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
* 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;
*/
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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
* as well as, according to dataset's state upon model creation,
*
* [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
* and
*
* [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
* 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;
*/
java.util.List
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][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
* as well as, according to dataset's state upon model creation,
*
* [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
* and
*
* [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
* 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;
*/
com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder(
int index);
/**
*
*
*
* 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;
*
* @return The optimizationObjective.
*/
java.lang.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;
*
* @return The bytes for optimizationObjective.
*/
com.google.protobuf.ByteString getOptimizationObjectiveBytes();
/**
*
*
*
* 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;
*
*/
java.util.List
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;
*
*/
com.google.cloud.automl.v1beta1.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;
*
*/
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;
*
*/
java.util.List
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;
*
*/
com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder getTablesModelColumnInfoOrBuilder(
int index);
/**
*
*
*
* 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;
*
* @return The trainBudgetMilliNodeHours.
*/
long getTrainBudgetMilliNodeHours();
/**
*
*
*
* 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;
*
* @return The trainCostMilliNodeHours.
*/
long getTrainCostMilliNodeHours();
/**
*
*
*
* 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;
*
* @return The disableEarlyStopping.
*/
boolean getDisableEarlyStopping();
com.google.cloud.automl.v1beta1.TablesModelMetadata.AdditionalOptimizationObjectiveConfigCase
getAdditionalOptimizationObjectiveConfigCase();
}