# Copyright 2020 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. module Google module Cloud module AutoML module V1beta1 # Metadata for a dataset used for AutoML Tables. # @!attribute [rw] primary_table_spec_id # @return [String] # Output only. The table_spec_id of the primary table of this dataset. # @!attribute [rw] target_column_spec_id # @return [String] # 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. # @!attribute [rw] weight_column_spec_id # @return [String] # 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. # @!attribute [rw] ml_use_column_spec_id # @return [String] # 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. # @!attribute [rw] target_column_correlations # @return [Hash{String => Google::Cloud::AutoML::V1beta1::CorrelationStats}] # Output only. Correlations between # # {Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#target_column_spec_id TablesDatasetMetadata#target_column_spec_id}, # and other columns of the # # {Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#primary_table_spec_id TablesDatasetMetadataprimary_table}. # 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. # @!attribute [rw] stats_update_time # @return [Google::Protobuf::Timestamp] # 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. class TablesDatasetMetadata; end # Model metadata specific to AutoML Tables. # @!attribute [rw] optimization_objective_recall_value # @return [Float] # Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL". # Must be between 0 and 1, inclusive. # @!attribute [rw] optimization_objective_precision_value # @return [Float] # Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION". # Must be between 0 and 1, inclusive. # @!attribute [rw] target_column_spec # @return [Google::Cloud::AutoML::V1beta1::ColumnSpec] # 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. # @!attribute [rw] input_feature_column_specs # @return [Array] # 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 # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column} # as well as, according to dataset's state upon model creation, # # {Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#weight_column_spec_id weight_column}, # and # # {Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#ml_use_column_spec_id 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. # @!attribute [rw] optimization_objective # @return [String] # 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). # @!attribute [rw] tables_model_column_info # @return [Array] # Output only. Auxiliary information for each of the # input_feature_column_specs with respect to this particular model. # @!attribute [rw] train_budget_milli_node_hours # @return [Integer] # 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. # @!attribute [rw] train_cost_milli_node_hours # @return [Integer] # 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. # @!attribute [rw] disable_early_stopping # @return [true, false] # 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. class TablesModelMetadata; end # Contains annotation details specific to Tables. # @!attribute [rw] score # @return [Float] # Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher # value means greater confidence in the returned value. # For # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_spec} # of FLOAT64 data type the score is not populated. # @!attribute [rw] prediction_interval # @return [Google::Cloud::AutoML::V1beta1::DoubleRange] # Output only. Only populated when # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_spec} # has FLOAT64 data type. An interval in which the exactly correct target # value has 95% chance to be in. # @!attribute [rw] value # @return [Google::Protobuf::Value] # The predicted value of the row's # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column}. # The value depends on the column's DataType: # # * CATEGORY - the predicted (with the above confidence `score`) CATEGORY # value. # # * FLOAT64 - the predicted (with above `prediction_interval`) FLOAT64 value. # @!attribute [rw] tables_model_column_info # @return [Array] # Output only. Auxiliary information for each of the model's # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#input_feature_column_specs input_feature_column_specs} # with respect to this particular prediction. # If no other fields than # # {Google::Cloud::AutoML::V1beta1::TablesModelColumnInfo#column_spec_name column_spec_name} # and # # {Google::Cloud::AutoML::V1beta1::TablesModelColumnInfo#column_display_name column_display_name} # would be populated, then this whole field is not. class TablesAnnotation; end # An information specific to given column and Tables Model, in context # of the Model and the predictions created by it. # @!attribute [rw] column_spec_name # @return [String] # Output only. The name of the ColumnSpec describing the column. Not # populated when this proto is outputted to BigQuery. # @!attribute [rw] column_display_name # @return [String] # Output only. The display name of the column (same as the display_name of # its ColumnSpec). # @!attribute [rw] feature_importance # @return [Float] # Output only. When given as part of a Model (always populated): # Measurement of how much model predictions correctness on the TEST data # depend on values in this column. A value between 0 and 1, higher means # higher influence. These values are normalized - for all input feature # columns of a given model they add to 1. # # When given back by Predict (populated iff # [feature_importance # param][google.cloud.automl.v1beta1.PredictRequest.params] is set) or Batch # Predict (populated iff # {Google::Cloud::AutoML::V1beta1::PredictRequest#params feature_importance} # param is set): # Measurement of how impactful for the prediction returned for the given row # the value in this column was. Specifically, the feature importance # specifies the marginal contribution that the feature made to the prediction # score compared to the baseline score. These values are computed using the # Sampled Shapley method. class TablesModelColumnInfo; end end end end end