// Copyright 2019 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.
// Note: this file is purely for documentation. Any contents are not expected
// to be loaded as the JS file.
/**
* Metadata for a dataset used for AutoML Tables.
*
* @property {string} primaryTableSpecId
* Output only. The table_spec_id of the primary table of this dataset.
*
* @property {string} targetColumnSpecId
* 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.
*
* @property {string} weightColumnSpecId
* 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.
*
* @property {string} mlUseColumnSpecId
* 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.
*
* @property {Object.<string, Object>} targetColumnCorrelations
* Output only. Correlations between
*
* TablesDatasetMetadata.target_column_spec_id,
* and other columns of the
*
* 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.
*
* @property {Object} statsUpdateTime
* 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.
*
* This object should have the same structure as [Timestamp]{@link google.protobuf.Timestamp}
*
* @typedef TablesDatasetMetadata
* @memberof google.cloud.automl.v1beta1
* @see [google.cloud.automl.v1beta1.TablesDatasetMetadata definition in proto format]{@link https://github.com/googleapis/googleapis/blob/master/google/cloud/automl/v1beta1/tables.proto}
*/
const TablesDatasetMetadata = {
// This is for documentation. Actual contents will be loaded by gRPC.
};
/**
* Model metadata specific to AutoML Tables.
*
* @property {Object} targetColumnSpec
* 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.
*
* This object should have the same structure as [ColumnSpec]{@link google.cloud.automl.v1beta1.ColumnSpec}
*
* @property {Object[]} inputFeatureColumnSpecs
* 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.
*
* This object should have the same structure as [ColumnSpec]{@link google.cloud.automl.v1beta1.ColumnSpec}
*
* @property {string} optimizationObjective
* 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).
*
* @property {Object[]} tablesModelColumnInfo
* Output only. Auxiliary information for each of the
* input_feature_column_specs with respect to this particular model.
*
* This object should have the same structure as [TablesModelColumnInfo]{@link google.cloud.automl.v1beta1.TablesModelColumnInfo}
*
* @property {number} trainBudgetMilliNodeHours
* 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.
*
* @property {number} trainCostMilliNodeHours
* 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.
*
* @property {boolean} disableEarlyStopping
* 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.
*
* @typedef TablesModelMetadata
* @memberof google.cloud.automl.v1beta1
* @see [google.cloud.automl.v1beta1.TablesModelMetadata definition in proto format]{@link https://github.com/googleapis/googleapis/blob/master/google/cloud/automl/v1beta1/tables.proto}
*/
const TablesModelMetadata = {
// This is for documentation. Actual contents will be loaded by gRPC.
};
/**
* Contains annotation details specific to Tables.
*
* @property {number} score
* Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher
* value means greater confidence in the returned value.
* For
*
* target_column_spec
* of FLOAT64 data type the score is not populated.
*
* @property {Object} predictionInterval
* Output only. Only populated when
*
* target_column_spec
* has FLOAT64 data type. An interval in which the exactly correct target
* value has 95% chance to be in.
*
* This object should have the same structure as [DoubleRange]{@link google.cloud.automl.v1beta1.DoubleRange}
*
* @property {Object} value
* The predicted value of the row's
*
* 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.
*
* This object should have the same structure as [Value]{@link google.protobuf.Value}
*
* @property {Object[]} tablesModelColumnInfo
* Output only. Auxiliary information for each of the model's
*
* input_feature_column_specs
* with respect to this particular prediction.
* If no other fields than
*
* column_spec_name
* and
*
* column_display_name
* would be populated, then this whole field is not.
*
* This object should have the same structure as [TablesModelColumnInfo]{@link google.cloud.automl.v1beta1.TablesModelColumnInfo}
*
* @typedef TablesAnnotation
* @memberof google.cloud.automl.v1beta1
* @see [google.cloud.automl.v1beta1.TablesAnnotation definition in proto format]{@link https://github.com/googleapis/googleapis/blob/master/google/cloud/automl/v1beta1/tables.proto}
*/
const TablesAnnotation = {
// This is for documentation. Actual contents will be loaded by gRPC.
};
/**
* An information specific to given column and Tables Model, in context
* of the Model and the predictions created by it.
*
* @property {string} columnSpecName
* Output only. The name of the ColumnSpec describing the column. Not
* populated when this proto is outputted to BigQuery.
*
* @property {string} columnDisplayName
* Output only. The display name of the column (same as the display_name of
* its ColumnSpec).
*
* @property {number} featureImportance
* 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 is set) or Batch
* Predict (populated iff
* feature_importance
* param is set):
* Measurement of how impactful for the prediction returned for the given row
* the value in this column was. A value between 0 and 1, higher means larger
* impact. These values are normalized - for all input feature columns of a
* single predicted row they add to 1.
*
* @typedef TablesModelColumnInfo
* @memberof google.cloud.automl.v1beta1
* @see [google.cloud.automl.v1beta1.TablesModelColumnInfo definition in proto format]{@link https://github.com/googleapis/googleapis/blob/master/google/cloud/automl/v1beta1/tables.proto}
*/
const TablesModelColumnInfo = {
// This is for documentation. Actual contents will be loaded by gRPC.
};