Remove duplicate filter_schema function.

dgf/src/api/transform.py

@@ -29,7 +29,6 @@
 from dgf.src.transform.normalize import SoftQuantileNormalizer
 from dgf.src.transform.normalize import SinusoidTimedeltaNormalizer
 
-from dgf.src.transform.extract import filter_schema
 from dgf.src.transform.extract import filter_graph
 from dgf.src.transform.extract import drop_edge_features
 

dgf/src/transform/BUILD

@@ -185,6 +185,7 @@ py_test(
     srcs = ["extract_test.py"],
     deps = [
         ":extract",
+        ":schema",
         # absl/testing:absltest dep,
         "//dgf/src/data:in_memory_graph",
         "//dgf/src/data:schema",

dgf/src/transform/extract.py

@@ -15,43 +15,12 @@
 """Operations on a schema."""
 
 import copy
-from typing import List, Tuple
+from typing import Tuple
 from dgf.src.data import in_memory_graph as in_memory_graph_lib
 from dgf.src.data import schema as schema_lib
 from dgf.src.transform import schema as schema_transform_lib
 
 
-def filter_schema(
-    src: schema_lib.GraphSchema, selected_features: List[str]
-) -> schema_lib.GraphSchema:
-  """Creates a new schema with a subset of the features.
-
-  The other parts of the schema are not modified.
-
-  Args:
-    src: The source schema to extract.
-    selected_features: A list of feature names to include in the new schema.
-
-  Returns:
-    The extracted schema.
-  """
-  extracted_schema = copy.deepcopy(src)
-
-  for node_name in extracted_schema.node_sets:
-    node_schema = extracted_schema.node_sets[node_name]
-    node_schema.features = {
-        k: v for k, v in node_schema.features.items() if k in selected_features
-    }
-
-  for edge_name in extracted_schema.edge_sets:
-    edge_schema = extracted_schema.edge_sets[edge_name]
-    edge_schema.features = {
-        k: v for k, v in edge_schema.features.items() if k in selected_features
-    }
-
-  return extracted_schema
-
-
 def filter_graph(
     graph: in_memory_graph_lib.InMemoryGraph,
     schema: schema_lib.GraphSchema,

dgf/src/transform/extract_test.py

@@ -19,31 +19,22 @@
 from dgf.src.data import in_memory_graph as in_memory_graph_lib
 from dgf.src.data import schema as schema_lib
 from dgf.src.transform import extract as extract_lib
+from dgf.src.transform import schema as schema_transform_lib
 from dgf.src.util import gen_test_graph
 from dgf.src.util import test_util
 
 
 class FilterTest(absltest.TestCase):
 
-  def test_filter_schema(self):
-    schema = gen_test_graph.generate_schema()
-    selected_features = ["f1", "f3"]
-    extracted_schema = extract_lib.filter_schema(schema, selected_features)
-    expected_extracted_schema = copy.deepcopy(schema)
-    del expected_extracted_schema.node_sets["n1"].features["f2"]
-    del expected_extracted_schema.node_sets["n2"].features["f4"]
-    del expected_extracted_schema.node_sets["n2"].features["f5"]
-    del expected_extracted_schema.node_sets["n2"].features["f6"]
-    test_util.assert_are_equal(
-        self,
-        extracted_schema,
-        expected_extracted_schema,
-    )
-
   def test_filter_graph(self):
     graph = gen_test_graph.generate_in_memory_graph(variable_length=False)
     full_schema = gen_test_graph.generate_schema(variable_length=False)
-    extracted_schema = extract_lib.filter_schema(full_schema, ["f1", "f3"])
+    extracted_schema = schema_transform_lib.filter_schema(
+        full_schema,
+        schema_lib.GraphSchemaFilter(
+            feature_fn=lambda name, _: name in ["f1", "f3"]
+        ),
+    )
     extracted_graph = extract_lib.filter_graph(graph, extracted_schema)
     expected_extracted_graph = copy.deepcopy(graph)
     del expected_extracted_graph.node_sets["n1"].features["f2"]