tables.rb in google-cloud-automl-0.2.0

- old
+ new

@@ -25,15 +25,18 @@
         #   @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
-        #       Furthermore, if the type is CATEGORY , then only up to
-        #       100 unique values may exist in that column across all rows.
         #
+        #     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
@@ -72,15 +75,16 @@
         #     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]
-        #     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.
+        #     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] target_column_spec
         #   @return [Google::Cloud::AutoML::V1beta1::ColumnSpec]
@@ -105,16 +109,20 @@
         #     {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.
+        #
+        #     * 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.
@@ -138,22 +146,10 @@
         #
         #     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).
-        #
-        #     FORECASTING:
-        #       "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
-        #       "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
-        # @!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] tables_model_column_info
         #   @return [Array<Google::Cloud::AutoML::V1beta1::TablesModelColumnInfo>]
         #     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
@@ -204,13 +200,15 @@
         #   @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.
+        #
+        #     * 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<Google::Cloud::AutoML::V1beta1::TablesModelColumnInfo>]
         #     Output only. Auxiliary information for each of the model's
         #
         #     {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#input_feature_column_specs input_feature_column_specs}
@@ -234,12 +232,10 @@
         #   @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):
+        #     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.
         #
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