Migrate min_label_position.py to not depend on estimator related libr…

…aries.

PiperOrigin-RevId: 649238064

tensorflow_model_analysis/evaluators/query_metrics/min_label_position.py

@@ -23,10 +23,10 @@
 from typing import Any, Dict, Iterable, NamedTuple
 
 import apache_beam as beam
-from tensorflow_model_analysis.eval_saved_model import constants as eval_saved_model_constants
-from tensorflow_model_analysis.eval_saved_model import util as eval_saved_model_util
+from tensorflow_model_analysis import constants
 from tensorflow_model_analysis.evaluators.query_metrics import query_types
 from tensorflow_model_analysis.post_export_metrics import metric_keys
+from tensorflow_model_analysis.utils import util
 
 _State = NamedTuple('_State', [('min_pos_sum', float), ('weight_sum', float)])
 
@@ -70,8 +70,7 @@ def __init__(self, label_key: str, weight_key: str):
       # If label_key is set to the empty string, the user is telling us
       # that their Estimator returns a labels Tensor rather than a
       # dictionary. Set the key to the magic key we use in that case.
-      self._label_key = eval_saved_model_util.default_dict_key(
-          eval_saved_model_constants.LABELS_NAME)
+      self._label_key = util.default_dict_key(constants.LABELS_KEY)
     else:
       self._label_key = label_key
     self._weight_key = weight_key

tensorflow_model_analysis/utils/util.py

@@ -43,6 +43,11 @@
 VALUES_SUFFIX = 'values'
 
 
+def default_dict_key(prefix: str) -> str:
+  """Returns the default key to use with a dict associated with given prefix."""
+  return KEY_SEPARATOR + prefix
+
+
 def is_sparse_or_ragged_tensor_value(tensor: Any) -> bool:
   """Returns true if sparse or ragged tensor."""
   return (isinstance(tensor, types.SparseTensorValue) or
@@ -838,10 +843,14 @@ def merge_lists(target: types.Extracts) -> types.Extracts:
           ) from e
       return {k: merge_lists(v) for k, v in target.items()}
     elif (
-        target and
-        np.any([isinstance(t, tf.compat.v1.SparseTensorValue) for t in target])
+        target
+        and np.any(
+            [isinstance(t, tf.compat.v1.SparseTensorValue) for t in target]
+        )
         or np.any(
-            [isinstance(target[0], types.SparseTensorValue) for t in target])):
+            [isinstance(target[0], types.SparseTensorValue) for _ in target]
+        )
+    ):
       t = tf.compat.v1.sparse_concat(
           0,
           [tf.sparse.expand_dims(to_tensorflow_tensor(t), 0) for t in target],