1 /* 2 * Copyright 2020 Google LLC 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * https://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 // Generated by the protocol buffer compiler. DO NOT EDIT! 17 // source: google/cloud/automl/v1beta1/tables.proto 18 19 package com.google.cloud.automl.v1beta1; 20 21 public interface TablesModelMetadataOrBuilder 22 extends 23 // @@protoc_insertion_point(interface_extends:google.cloud.automl.v1beta1.TablesModelMetadata) 24 com.google.protobuf.MessageOrBuilder { 25 26 /** 27 * 28 * 29 * <pre> 30 * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". 31 * Must be between 0 and 1, inclusive. 32 * </pre> 33 * 34 * <code>float optimization_objective_recall_value = 17;</code> 35 * 36 * @return Whether the optimizationObjectiveRecallValue field is set. 37 */ hasOptimizationObjectiveRecallValue()38 boolean hasOptimizationObjectiveRecallValue(); 39 /** 40 * 41 * 42 * <pre> 43 * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". 44 * Must be between 0 and 1, inclusive. 45 * </pre> 46 * 47 * <code>float optimization_objective_recall_value = 17;</code> 48 * 49 * @return The optimizationObjectiveRecallValue. 50 */ getOptimizationObjectiveRecallValue()51 float getOptimizationObjectiveRecallValue(); 52 53 /** 54 * 55 * 56 * <pre> 57 * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". 58 * Must be between 0 and 1, inclusive. 59 * </pre> 60 * 61 * <code>float optimization_objective_precision_value = 18;</code> 62 * 63 * @return Whether the optimizationObjectivePrecisionValue field is set. 64 */ hasOptimizationObjectivePrecisionValue()65 boolean hasOptimizationObjectivePrecisionValue(); 66 /** 67 * 68 * 69 * <pre> 70 * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". 71 * Must be between 0 and 1, inclusive. 72 * </pre> 73 * 74 * <code>float optimization_objective_precision_value = 18;</code> 75 * 76 * @return The optimizationObjectivePrecisionValue. 77 */ getOptimizationObjectivePrecisionValue()78 float getOptimizationObjectivePrecisionValue(); 79 80 /** 81 * 82 * 83 * <pre> 84 * Column spec of the dataset's primary table's column the model is 85 * predicting. Snapshotted when model creation started. 86 * Only 3 fields are used: 87 * name - May be set on CreateModel, if it's not then the ColumnSpec 88 * corresponding to the current target_column_spec_id of the dataset 89 * the model is trained from is used. 90 * If neither is set, CreateModel will error. 91 * display_name - Output only. 92 * data_type - Output only. 93 * </pre> 94 * 95 * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code> 96 * 97 * @return Whether the targetColumnSpec field is set. 98 */ hasTargetColumnSpec()99 boolean hasTargetColumnSpec(); 100 /** 101 * 102 * 103 * <pre> 104 * Column spec of the dataset's primary table's column the model is 105 * predicting. Snapshotted when model creation started. 106 * Only 3 fields are used: 107 * name - May be set on CreateModel, if it's not then the ColumnSpec 108 * corresponding to the current target_column_spec_id of the dataset 109 * the model is trained from is used. 110 * If neither is set, CreateModel will error. 111 * display_name - Output only. 112 * data_type - Output only. 113 * </pre> 114 * 115 * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code> 116 * 117 * @return The targetColumnSpec. 118 */ getTargetColumnSpec()119 com.google.cloud.automl.v1beta1.ColumnSpec getTargetColumnSpec(); 120 /** 121 * 122 * 123 * <pre> 124 * Column spec of the dataset's primary table's column the model is 125 * predicting. Snapshotted when model creation started. 126 * Only 3 fields are used: 127 * name - May be set on CreateModel, if it's not then the ColumnSpec 128 * corresponding to the current target_column_spec_id of the dataset 129 * the model is trained from is used. 130 * If neither is set, CreateModel will error. 131 * display_name - Output only. 132 * data_type - Output only. 133 * </pre> 134 * 135 * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code> 136 */ getTargetColumnSpecOrBuilder()137 com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getTargetColumnSpecOrBuilder(); 138 139 /** 140 * 141 * 142 * <pre> 143 * Column specs of the dataset's primary table's columns, on which 144 * the model is trained and which are used as the input for predictions. 145 * The 146 * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] 147 * as well as, according to dataset's state upon model creation, 148 * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], 149 * and 150 * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] 151 * must never be included here. 152 * Only 3 fields are used: 153 * * name - May be set on CreateModel, if set only the columns specified are 154 * used, otherwise all primary table's columns (except the ones listed 155 * above) are used for the training and prediction input. 156 * * display_name - Output only. 157 * * data_type - Output only. 158 * </pre> 159 * 160 * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code> 161 */ getInputFeatureColumnSpecsList()162 java.util.List<com.google.cloud.automl.v1beta1.ColumnSpec> getInputFeatureColumnSpecsList(); 163 /** 164 * 165 * 166 * <pre> 167 * Column specs of the dataset's primary table's columns, on which 168 * the model is trained and which are used as the input for predictions. 169 * The 170 * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] 171 * as well as, according to dataset's state upon model creation, 172 * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], 173 * and 174 * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] 175 * must never be included here. 176 * Only 3 fields are used: 177 * * name - May be set on CreateModel, if set only the columns specified are 178 * used, otherwise all primary table's columns (except the ones listed 179 * above) are used for the training and prediction input. 180 * * display_name - Output only. 181 * * data_type - Output only. 182 * </pre> 183 * 184 * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code> 185 */ getInputFeatureColumnSpecs(int index)186 com.google.cloud.automl.v1beta1.ColumnSpec getInputFeatureColumnSpecs(int index); 187 /** 188 * 189 * 190 * <pre> 191 * Column specs of the dataset's primary table's columns, on which 192 * the model is trained and which are used as the input for predictions. 193 * The 194 * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] 195 * as well as, according to dataset's state upon model creation, 196 * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], 197 * and 198 * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] 199 * must never be included here. 200 * Only 3 fields are used: 201 * * name - May be set on CreateModel, if set only the columns specified are 202 * used, otherwise all primary table's columns (except the ones listed 203 * above) are used for the training and prediction input. 204 * * display_name - Output only. 205 * * data_type - Output only. 206 * </pre> 207 * 208 * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code> 209 */ getInputFeatureColumnSpecsCount()210 int getInputFeatureColumnSpecsCount(); 211 /** 212 * 213 * 214 * <pre> 215 * Column specs of the dataset's primary table's columns, on which 216 * the model is trained and which are used as the input for predictions. 217 * The 218 * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] 219 * as well as, according to dataset's state upon model creation, 220 * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], 221 * and 222 * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] 223 * must never be included here. 224 * Only 3 fields are used: 225 * * name - May be set on CreateModel, if set only the columns specified are 226 * used, otherwise all primary table's columns (except the ones listed 227 * above) are used for the training and prediction input. 228 * * display_name - Output only. 229 * * data_type - Output only. 230 * </pre> 231 * 232 * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code> 233 */ 234 java.util.List<? extends com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder> getInputFeatureColumnSpecsOrBuilderList()235 getInputFeatureColumnSpecsOrBuilderList(); 236 /** 237 * 238 * 239 * <pre> 240 * Column specs of the dataset's primary table's columns, on which 241 * the model is trained and which are used as the input for predictions. 242 * The 243 * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec] 244 * as well as, according to dataset's state upon model creation, 245 * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id], 246 * and 247 * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id] 248 * must never be included here. 249 * Only 3 fields are used: 250 * * name - May be set on CreateModel, if set only the columns specified are 251 * used, otherwise all primary table's columns (except the ones listed 252 * above) are used for the training and prediction input. 253 * * display_name - Output only. 254 * * data_type - Output only. 255 * </pre> 256 * 257 * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code> 258 */ getInputFeatureColumnSpecsOrBuilder( int index)259 com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder( 260 int index); 261 262 /** 263 * 264 * 265 * <pre> 266 * Objective function the model is optimizing towards. The training process 267 * creates a model that maximizes/minimizes the value of the objective 268 * function over the validation set. 269 * The supported optimization objectives depend on the prediction type. 270 * If the field is not set, a default objective function is used. 271 * CLASSIFICATION_BINARY: 272 * "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver 273 * operating characteristic (ROC) curve. 274 * "MINIMIZE_LOG_LOSS" - Minimize log loss. 275 * "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve. 276 * "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified 277 * recall value. 278 * "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified 279 * precision value. 280 * CLASSIFICATION_MULTI_CLASS : 281 * "MINIMIZE_LOG_LOSS" (default) - Minimize log loss. 282 * REGRESSION: 283 * "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE). 284 * "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). 285 * "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE). 286 * </pre> 287 * 288 * <code>string optimization_objective = 4;</code> 289 * 290 * @return The optimizationObjective. 291 */ getOptimizationObjective()292 java.lang.String getOptimizationObjective(); 293 /** 294 * 295 * 296 * <pre> 297 * Objective function the model is optimizing towards. The training process 298 * creates a model that maximizes/minimizes the value of the objective 299 * function over the validation set. 300 * The supported optimization objectives depend on the prediction type. 301 * If the field is not set, a default objective function is used. 302 * CLASSIFICATION_BINARY: 303 * "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver 304 * operating characteristic (ROC) curve. 305 * "MINIMIZE_LOG_LOSS" - Minimize log loss. 306 * "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve. 307 * "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified 308 * recall value. 309 * "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified 310 * precision value. 311 * CLASSIFICATION_MULTI_CLASS : 312 * "MINIMIZE_LOG_LOSS" (default) - Minimize log loss. 313 * REGRESSION: 314 * "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE). 315 * "MINIMIZE_MAE" - Minimize mean-absolute error (MAE). 316 * "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE). 317 * </pre> 318 * 319 * <code>string optimization_objective = 4;</code> 320 * 321 * @return The bytes for optimizationObjective. 322 */ getOptimizationObjectiveBytes()323 com.google.protobuf.ByteString getOptimizationObjectiveBytes(); 324 325 /** 326 * 327 * 328 * <pre> 329 * Output only. Auxiliary information for each of the 330 * input_feature_column_specs with respect to this particular model. 331 * </pre> 332 * 333 * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5; 334 * </code> 335 */ 336 java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo> getTablesModelColumnInfoList()337 getTablesModelColumnInfoList(); 338 /** 339 * 340 * 341 * <pre> 342 * Output only. Auxiliary information for each of the 343 * input_feature_column_specs with respect to this particular model. 344 * </pre> 345 * 346 * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5; 347 * </code> 348 */ getTablesModelColumnInfo(int index)349 com.google.cloud.automl.v1beta1.TablesModelColumnInfo getTablesModelColumnInfo(int index); 350 /** 351 * 352 * 353 * <pre> 354 * Output only. Auxiliary information for each of the 355 * input_feature_column_specs with respect to this particular model. 356 * </pre> 357 * 358 * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5; 359 * </code> 360 */ getTablesModelColumnInfoCount()361 int getTablesModelColumnInfoCount(); 362 /** 363 * 364 * 365 * <pre> 366 * Output only. Auxiliary information for each of the 367 * input_feature_column_specs with respect to this particular model. 368 * </pre> 369 * 370 * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5; 371 * </code> 372 */ 373 java.util.List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder> getTablesModelColumnInfoOrBuilderList()374 getTablesModelColumnInfoOrBuilderList(); 375 /** 376 * 377 * 378 * <pre> 379 * Output only. Auxiliary information for each of the 380 * input_feature_column_specs with respect to this particular model. 381 * </pre> 382 * 383 * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5; 384 * </code> 385 */ getTablesModelColumnInfoOrBuilder( int index)386 com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder getTablesModelColumnInfoOrBuilder( 387 int index); 388 389 /** 390 * 391 * 392 * <pre> 393 * Required. The train budget of creating this model, expressed in milli node 394 * hours i.e. 1,000 value in this field means 1 node hour. 395 * The training cost of the model will not exceed this budget. The final cost 396 * will be attempted to be close to the budget, though may end up being (even) 397 * noticeably smaller - at the backend's discretion. This especially may 398 * happen when further model training ceases to provide any improvements. 399 * If the budget is set to a value known to be insufficient to train a 400 * model for the given dataset, the training won't be attempted and 401 * will error. 402 * The train budget must be between 1,000 and 72,000 milli node hours, 403 * inclusive. 404 * </pre> 405 * 406 * <code>int64 train_budget_milli_node_hours = 6;</code> 407 * 408 * @return The trainBudgetMilliNodeHours. 409 */ getTrainBudgetMilliNodeHours()410 long getTrainBudgetMilliNodeHours(); 411 412 /** 413 * 414 * 415 * <pre> 416 * Output only. The actual training cost of the model, expressed in milli 417 * node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed 418 * to not exceed the train budget. 419 * </pre> 420 * 421 * <code>int64 train_cost_milli_node_hours = 7;</code> 422 * 423 * @return The trainCostMilliNodeHours. 424 */ getTrainCostMilliNodeHours()425 long getTrainCostMilliNodeHours(); 426 427 /** 428 * 429 * 430 * <pre> 431 * Use the entire training budget. This disables the early stopping feature. 432 * By default, the early stopping feature is enabled, which means that AutoML 433 * Tables might stop training before the entire training budget has been used. 434 * </pre> 435 * 436 * <code>bool disable_early_stopping = 12;</code> 437 * 438 * @return The disableEarlyStopping. 439 */ getDisableEarlyStopping()440 boolean getDisableEarlyStopping(); 441 442 public com.google.cloud.automl.v1beta1.TablesModelMetadata 443 .AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase()444 getAdditionalOptimizationObjectiveConfigCase(); 445 } 446