Add a workaround for the removal of top-level TFMA attributes (#7637)

nikelite · web-flow · commit 58fa4a8b2985 · 2024-11-22T10:13:07.000+09:00
* Add a workaround for the removal of top-level TFMA attributes in TFMA 0.47.0
diff --git a/tfx/components/model_validator/executor.py b/tfx/components/model_validator/executor.py
@@ -28,6 +28,12 @@
 from tfx.utils import io_utils
 from tfx.utils import path_utils
 
+try:
+  # Try to access EvalResult from tfma directly
+  _EvalResult = tfma.EvalResult
+except AttributeError:
+  # If tfma doesn't have EvalResult, use the one from view_types
+  from tensorflow_model_analysis.view.view_types import EvalResult as _EvalResult
 
 class Executor(base_beam_executor.BaseBeamExecutor):
   """DEPRECATED: Please use `Evaluator` instead.
@@ -51,13 +57,13 @@ class Executor(base_beam_executor.BaseBeamExecutor):
   """
 
   # TODO(jyzhao): customized threshold support.
-  def _pass_threshold(self, eval_result: tfma.EvalResult) -> bool:
+  def _pass_threshold(self, eval_result: _EvalResult) -> bool:
     """Check threshold."""
     return True
 
   # TODO(jyzhao): customized validation support.
-  def _compare_eval_result(self, current_model_eval_result: tfma.EvalResult,
-                           blessed_model_eval_result: tfma.EvalResult) -> bool:
+  def _compare_eval_result(self, current_model_eval_result: _EvalResult,
+                           blessed_model_eval_result: _EvalResult) -> bool:
     """Compare accuracy of all metrics and return true if current is better or equal."""
     for current_metric, blessed_metric in zip(
         current_model_eval_result.slicing_metrics,
diff --git a/tfx/components/testdata/module_file/evaluator_module.py b/tfx/components/testdata/module_file/evaluator_module.py
@@ -19,9 +19,24 @@
 from tfx_bsl.tfxio import tensor_adapter
 
 
+try:
+  # Try to access EvalSharedModel from tfma directly
+  _EvalSharedModel = tfma.EvalSharedModel
+except AttributeError:
+  # If tfma doesn't have EvalSharedModel, use the one from api.types
+  from tensorflow_model_analysis.api.types import EvalSharedModel as _EvalSharedModel
+
+try:
+  # Try to access MaybeMultipleEvalSharedModels from tfma directly
+  _MaybeMultipleEvalSharedModels = tfma.MaybeMultipleEvalSharedModels
+except AttributeError:
+  # If tfma doesn't have MaybeMultipleEvalSharedModels, use the one from api.types
+  from tensorflow_model_analysis.api.types import MaybeMultipleEvalSharedModels as _MaybeMultipleEvalSharedModels
+
+
 def custom_eval_shared_model(eval_saved_model_path: str, model_name: str,
                              eval_config: tfma.EvalConfig,
-                             **kwargs: Dict[str, Any]) -> tfma.EvalSharedModel:
+                             **kwargs: Dict[str, Any]) -> _EvalSharedModel:
   return tfma.default_eval_shared_model(
       eval_saved_model_path=eval_saved_model_path,
       model_name=model_name,
@@ -30,7 +45,7 @@ def custom_eval_shared_model(eval_saved_model_path: str, model_name: str,
 
 
 def custom_extractors(
-    eval_shared_model: tfma.MaybeMultipleEvalSharedModels,
+    eval_shared_model: _MaybeMultipleEvalSharedModels,
     eval_config: tfma.EvalConfig,
     tensor_adapter_config: tensor_adapter.TensorAdapterConfig,
 ) -> List[tfma.extractors.Extractor]:
diff --git a/tfx/examples/penguin/experimental/sklearn_predict_extractor.py b/tfx/examples/penguin/experimental/sklearn_predict_extractor.py
@@ -25,9 +25,16 @@
 
 _PREDICT_EXTRACTOR_STAGE_NAME = 'SklearnPredict'
 
+try:
+  # Try to access EvalSharedModel from tfma directly
+  _EvalSharedModel = tfma.EvalSharedModel
+except AttributeError:
+  # If tfma doesn't have EvalSharedModel, use the one from api.types
+  from tensorflow_model_analysis.api.types import EvalSharedModel as _EvalSharedModel
+
 
 def _make_sklearn_predict_extractor(
-    eval_shared_model: tfma.EvalSharedModel,) -> tfma.extractors.Extractor:
+    eval_shared_model: _EvalSharedModel,) -> tfma.extractors.Extractor:
   """Creates an extractor for performing predictions using a scikit-learn model.
 
   The extractor's PTransform loads and runs the serving pickle against
@@ -54,7 +61,7 @@ def _make_sklearn_predict_extractor(
 class _TFMAPredictionDoFn(tfma.utils.DoFnWithModels):
   """A DoFn that loads the models and predicts."""
 
-  def __init__(self, eval_shared_models: Dict[str, tfma.EvalSharedModel]):
+  def __init__(self, eval_shared_models: Dict[str, _EvalSharedModel]):
     super().__init__({k: v.model_loader for k, v in eval_shared_models.items()})
 
   def setup(self):
@@ -116,7 +123,7 @@ def process(self, elem: tfma.Extracts) -> Iterable[tfma.Extracts]:
 @beam.typehints.with_output_types(tfma.Extracts)
 def _ExtractPredictions(  # pylint: disable=invalid-name
     extracts: beam.pvalue.PCollection,
-    eval_shared_models: Dict[str, tfma.EvalSharedModel],
+    eval_shared_models: Dict[str, _EvalSharedModel],
 ) -> beam.pvalue.PCollection:
   """A PTransform that adds predictions and possibly other tensors to extracts.
 
@@ -139,7 +146,7 @@ def _custom_model_loader_fn(model_path: str):
 # TFX Evaluator will call the following functions.
 def custom_eval_shared_model(
     eval_saved_model_path, model_name, eval_config,
-    **kwargs) -> tfma.EvalSharedModel:
+    **kwargs) -> _EvalSharedModel:
   """Returns a single custom EvalSharedModel."""
   model_path = os.path.join(eval_saved_model_path, 'model.pkl')
   return tfma.default_eval_shared_model(
diff --git a/tfx/experimental/pipeline_testing/executor_verifier_utils.py b/tfx/experimental/pipeline_testing/executor_verifier_utils.py
@@ -33,6 +33,14 @@
 from tensorflow_metadata.proto.v0 import anomalies_pb2
 
 
+try:
+  # Try to access EvalResult from tfma directly
+  _EvalResult = tfma.EvalResult
+except AttributeError:
+  # If tfma doesn't have EvalResult, use the one from view_types
+  from tensorflow_model_analysis.view.view_types import EvalResult as _EvalResult
+
+
 def compare_dirs(dir1: str, dir2: str):
   """Recursively compares contents of the two directories.
 
@@ -159,7 +167,7 @@ def verify_file_dir(output_uri: str,
 
 
 def _group_metric_by_slice(
-    eval_result: tfma.EvalResult) -> Dict[str, Dict[str, float]]:
+    eval_result: _EvalResult) -> Dict[str, Dict[str, float]]:
   """Returns a dictionary holding metric values for every slice.
 
   Args:
