The ML.TFDV_VALIDATE function

This document describes the ML.TFDV_VALIDATE function, which you can use to compare the statistics for training and serving data, or two sets of serving data, in order to identify anomalous differences between the two data sets. Calling this function provides the same behavior as calling the TensorFlow validate_statistics API . You can use the data output by this function for model monitoring .

Syntax

 ML 
 . 
 TFDV_VALIDATE 
 ( 
  
 base_statistics 
 , 
  
 study_statistics 
  
 [, 
  
 detection_type 
 ] 
  
 [, 
  
 categorical_default_threshold 
 ] 
  
 [, 
  
 categorical_metric_type 
 ] 
  
 [, 
  
 numerical_default_threshold 
 ] 
  
 [, 
  
 numerical_metric_type 
 ] 
  
 [, 
  
 thresholds 
 ] 
 ) 

Arguments

ML.TFDV_VALIDATE takes the following arguments:

• base_statistics : the statistics of the training or serving data that you want to use as the baseline for comparison. This must be a TensorFlow DatasetFeatureStatisticsList protocol buffer in JSON format. You can generate a protocol buffer in the correct format by running the ML.TFDV_DESCRIBE function , or you can load it from outside of BigQuery.
• study_statistics : the statistics of the training or serving data that you want to compare to the baseline. This must be a TensorFlow DatasetFeatureStatisticsList protocol buffer in JSON format. You can generate a protocol buffer in the correct format by running the ML.TFDV_DESCRIBE function, or you can load it from outside of BigQuery.
• detection_type : a STRING value that specifies the type of comparison that you want to make. Valid values are as follows:
  • SKEW : returns the data skew, which represents the statistical variation between training and serving data.
  • DRIFT : returns the data drift, which represents the statistical variation between two different sets of serving data.
• categorical_default_threshold : a FLOAT64 value that specifies the custom threshold to use for anomaly detection for categorical and ARRAY<categorical> features. The value must be in the range [0, 1) . The default value is 0.3 .
• categorical_metric_type : a STRING value that specifies the metric used to compare statistics for categorical and ARRAY<categorical> features. Valid values are as follows:
  • L_INFTY : use L-infinity distance . This value is the default.
  • JENSEN_SHANNON_DIVERGENCE : use Jensen–Shannon divergence .
• numerical_default_threshold : a FLOAT64 value that specifies the custom threshold to use for anomaly detection for numerical, ARRAY<numerical> , and ARRAY<STRUCT<INT64, numerical>> features. The value must be in the range [0, 1) . The default value is 0.3 .
• numerical_metric_type : a STRING value that specifies the metric used to compare statistics for numerical, ARRAY<numerical> , and ARRAY<STRUCT<INT64, numerical>> features. The only valid value is JENSEN_SHANNON_DIVERGENCE .
• thresholds : an ARRAY<STRUCT<STRING, FLOAT64>> value that specifies the anomaly detection thresholds for one or more columns for which you don't want to use the default threshold. The STRING value in the struct specifies the column name, and the FLOAT64 value specifies the threshold. The FLOAT64 value must be in the range [0, 1) . For example, [('col_a', 0.1), ('col_b', 0.8)] .

ML.TFDV_VALIDATE uses positional arguments, so if you specify an optional argument, you must also specify all arguments prior to that argument. For more information on argument types, see Named arguments .

Output

ML.TFDV_VALIDATE returns a TensorFlow Anomalies protocol buffer in JSON format.

Examples

The following example returns the skew between training and serving data and also sets custom anomaly detection thresholds for two of the feature columns:

 DECLARE 
  
 stats1 
  
 JSON 
 ; 
 DECLARE 
  
 stats2 
  
 JSON 
 ; 
 SET 
  
 stats1 
  
 = 
  
 ( 
 SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 TFDV_DESCRIBE 
 ( 
 TABLE 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 training 
 ` 
 )); 
 SET 
  
 stats2 
  
 = 
  
 ( 
 SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 TFDV_DESCRIBE 
 ( 
 TABLE 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 serving 
 ` 
 )); 
 SELECT 
  
 ML 
 . 
 TFDV_VALIDATE 
 ( 
  
 stats1 
 , 
  
 stats2 
 , 
  
 'SKEW' 
 , 
  
 . 
 3 
 , 
  
 'L_INFTY' 
 , 
  
 . 
 3 
 , 
  
 'JENSEN_SHANNON_DIVERGENCE' 
 , 
  
 [( 
 'feature1' 
 , 
  
 0 
 . 
 2 
 ), 
  
 ( 
 'feature2' 
 , 
  
 0 
 . 
 5 
 )] 
 ); 
 INSERT 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 serve_stats 
 ` 
  
 ( 
 t 
 , 
  
 dataset_feature_statistics_list 
 ) 
 SELECT 
  
 CURRENT_TIMESTAMP 
 () 
  
 AS 
  
 t 
 , 
  
 stats1 
 ; 

The following example returns the drift between two sets of serving data:

 SELECT 
  
 ML 
 . 
 TFDV_VALIDATE 
 ( 
  
 ( 
 SELECT 
  
 dataset_feature_statistics_list 
  
 FROM 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 servingJan24 
 ` 
 ), 
  
 ( 
 SELECT 
  
 * 
  
 FROM 
  
 ML 
 . 
 TFDV_DESCRIBE 
 ( 
 TABLE 
  
 ` 
 myproject 
 . 
 mydataset 
 . 
 serving 
 ` 
 )), 
  
 'DRIFT' 
 ); 

Limitations

The ML.TFDV_VALIDATE function doesn't conduct schema validation.

ML.TFDV_VALIDATE handles type mismatch as follows:

• If you specify JENSEN_SHANNON_DIVERGENCE for the categorical_default_threshold or numerical_default_threshold argument, the feature isn't included in the final anomaly report.
• If you specify L_INFTY for the categorical_default_threshold argument, the function outputs the computed feature distance as expected.