Deprecated external use of tfma.slicer.SingleSliceSpec (tfma.SlicingSpec should be used).

PiperOrigin-RevId: 317321141

Author: mdreves

RELEASE.md

@@ -38,6 +38,8 @@
     load_eval_results.
 *   Fix typo in jupyter widgets breaking TimeSeriesView and PlotViewer.
 *   Add `tfma.slicer.stringify_slice_key()`.
+*   Deprecated external use of tfma.slicer.SingleSliceSpec (tfma.SlicingSpec
+    should be used instead).
 
 ## Breaking changes
 

tensorflow_model_analysis/api/model_eval_lib.py

@@ -405,20 +405,22 @@ def default_extractors(  # pylint: disable=invalid-name
   Raises:
     NotImplementedError: If eval_config contains mixed serving and eval models.
   """
+  if slice_spec and eval_config:
+    raise ValueError('slice_spec is deprecated, only use eval_config')
   if eval_config is not None:
     eval_config = _update_eval_config_with_defaults(eval_config,
                                                     eval_shared_model)
-    slice_spec = [
-        slicer.SingleSliceSpec(spec=spec) for spec in eval_config.slicing_specs
-    ]
 
   if _is_legacy_eval(eval_shared_model, eval_config):
     # Backwards compatibility for previous add_metrics_callbacks implementation.
+    if not eval_config and slice_spec:
+      eval_config = config.EvalConfig(
+          slicing_specs=[s.to_proto() for s in slice_spec])
     return [
         predict_extractor.PredictExtractor(
             eval_shared_model, materialize=materialize),
         slice_key_extractor.SliceKeyExtractor(
-            slice_spec, materialize=materialize)
+            eval_config=eval_config, materialize=materialize)
     ]
   elif eval_shared_model:
     model_types = _model_types(eval_shared_model)
@@ -435,7 +437,7 @@ def default_extractors(  # pylint: disable=invalid-name
           tflite_predict_extractor.TFLitePredictExtractor(
               eval_config=eval_config, eval_shared_model=eval_shared_model),
           slice_key_extractor.SliceKeyExtractor(
-              slice_spec, materialize=materialize)
+              eval_config=eval_config, materialize=materialize)
       ]
     elif constants.TF_LITE in model_types:
       raise NotImplementedError(
@@ -451,7 +453,7 @@ def default_extractors(  # pylint: disable=invalid-name
               materialize=materialize,
               eval_config=eval_config),
           slice_key_extractor.SliceKeyExtractor(
-              slice_spec, materialize=materialize)
+              eval_config=eval_config, materialize=materialize)
       ]
     elif (eval_config and constants.TF_ESTIMATOR in model_types and
           any(eval_constants.EVAL_TAG in m.model_loader.tags
@@ -470,15 +472,15 @@ def default_extractors(  # pylint: disable=invalid-name
                 tensor_adapter_config=tensor_adapter_config),
             unbatch_extractor.UnbatchExtractor(),
             slice_key_extractor.SliceKeyExtractor(
-                slice_spec, materialize=materialize)
+                eval_config=eval_config, materialize=materialize)
         ]
       else:
         return [
             input_extractor.InputExtractor(eval_config=eval_config),
             predict_extractor_v2.PredictExtractor(
                 eval_config=eval_config, eval_shared_model=eval_shared_model),
             slice_key_extractor.SliceKeyExtractor(
-                slice_spec, materialize=materialize)
+                eval_config=eval_config, materialize=materialize)
         ]
   else:
     if enable_batched_extractors:
@@ -487,13 +489,13 @@ def default_extractors(  # pylint: disable=invalid-name
               eval_config=eval_config),
           unbatch_extractor.UnbatchExtractor(),
           slice_key_extractor.SliceKeyExtractor(
-              slice_spec, materialize=materialize)
+              eval_config=eval_config, materialize=materialize)
       ]
     else:
       return [
           input_extractor.InputExtractor(eval_config=eval_config),
           slice_key_extractor.SliceKeyExtractor(
-              slice_spec, materialize=materialize)
+              eval_config=eval_config, materialize=materialize)
       ]
 
 
@@ -1117,6 +1119,7 @@ def single_model_analysis(
     model_location: Text,
     data_location: Text,
     output_path: Text = None,
+    eval_config: Optional[config.EvalConfig] = None,
     slice_spec: Optional[List[slicer.SingleSliceSpec]] = None
 ) -> view_types.EvalResult:
   """Run model analysis for a single model on a single data set.
@@ -1130,7 +1133,8 @@ def single_model_analysis(
     data_location: The location of the data files.
     output_path: The directory to output metrics and results to. If None, we use
       a temporary directory.
-    slice_spec: A list of tfma.slicer.SingleSliceSpec.
+    eval_config: Eval config.
+    slice_spec: Deprecated (use EvalConfig).
 
   Returns:
     An EvalResult that can be used with the TFMA visualization functions.
@@ -1141,8 +1145,11 @@ def single_model_analysis(
   if not tf.io.gfile.exists(output_path):
     tf.io.gfile.makedirs(output_path)
 
-  eval_config = config.EvalConfig(
-      slicing_specs=[s.to_proto() for s in slice_spec])
+  if slice_spec and eval_config:
+    raise ValueError('slice_spec is deprecated, only use eval_config')
+  if slice_spec:
+    eval_config = config.EvalConfig(
+        slicing_specs=[s.to_proto() for s in slice_spec])
 
   return run_model_analysis(
       eval_config=eval_config,

tensorflow_model_analysis/api/model_eval_lib_test.py

@@ -50,7 +50,6 @@
 from tensorflow_model_analysis.post_export_metrics import metric_keys
 from tensorflow_model_analysis.post_export_metrics import post_export_metrics
 from tensorflow_model_analysis.proto import validation_result_pb2
-from tensorflow_model_analysis.slicer import slicer_lib as slicer
 from tensorflow_model_analysis.view import view_types
 
 from google.protobuf import text_format
@@ -230,14 +229,14 @@ def testRunModelAnalysisExtraFieldsPlusFeatureExtraction(self):
     eval_config = config.EvalConfig(slicing_specs=slicing_specs)
     eval_shared_model = model_eval_lib.default_eval_shared_model(
         eval_saved_model_path=model_location, example_weight_key='age')
-    slice_spec = [slicer.SingleSliceSpec(spec=slicing_specs[0])]
     extractors_with_feature_extraction = [
         predict_extractor.PredictExtractor(
             eval_shared_model, desired_batch_size=3, materialize=False),
         feature_extractor.FeatureExtractor(
             extract_source=constants.INPUT_KEY,
             extract_dest=constants.FEATURES_PREDICTIONS_LABELS_KEY),
-        slice_key_extractor.SliceKeyExtractor(slice_spec, materialize=False)
+        slice_key_extractor.SliceKeyExtractor(
+            eval_config=eval_config, materialize=False)
     ]
     eval_result = model_eval_lib.run_model_analysis(
         eval_config=eval_config,
@@ -1171,10 +1170,13 @@ def testMultipleModelAnalysis(self):
         self._makeExample(age=5.0, language='chinese', label=1.0)
     ]
     data_location = self._writeTFExamplesToTFRecords(examples)
+    eval_config = config.EvalConfig(slicing_specs=[
+        config.SlicingSpec(feature_values={'language': 'english'})
+    ])
     eval_results = model_eval_lib.multiple_model_analysis(
         [model_location_1, model_location_2],
         data_location,
-        slice_spec=[slicer.SingleSliceSpec(features=[('language', 'english')])])
+        eval_config=eval_config)
     # We only check some of the metrics to ensure that the end-to-end
     # pipeline works.
     self.assertLen(eval_results._results, 2)
@@ -1213,9 +1215,12 @@ def testMultipleDataAnalysis(self):
     ])
     data_location_2 = self._writeTFExamplesToTFRecords(
         [self._makeExample(age=4.0, language='english', label=1.0)])
+    eval_config = config.EvalConfig(slicing_specs=[
+        config.SlicingSpec(feature_values={'language': 'english'})
+    ])
     eval_results = model_eval_lib.multiple_data_analysis(
         model_location, [data_location_1, data_location_2],
-        slice_spec=[slicer.SingleSliceSpec(features=[('language', 'english')])])
+        eval_config=eval_config)
     self.assertLen(eval_results._results, 2)
     # We only check some of the metrics to ensure that the end-to-end
     # pipeline works.