[GitHub] [flink-ml] yunfengzhou-hub commented on a diff in pull request #133: [FLINK-28601] Add Transformer for FeatureHasher

[GitBox <gi...@apache.org>]

yunfengzhou-hub commented on code in PR #133:
URL: https://github.com/apache/flink-ml/pull/133#discussion_r931707197


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flink-ml-python/pyflink/ml/lib/feature/tests/test_feature_hasher.py:
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@@ -0,0 +1,77 @@
+################################################################################
+#  Licensed to the Apache Software Foundation (ASF) under one
+#  or more contributor license agreements.  See the NOTICE file
+#  distributed with this work for additional information
+#  regarding copyright ownership.  The ASF licenses this file
+#  to you under the Apache License, Version 2.0 (the
+#  "License"); you may not use this file except in compliance
+#  with the License.  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+import os
+
+from pyflink.common import Types
+
+from pyflink.ml.core.linalg import Vectors
+from pyflink.ml.lib.feature.featurehasher import FeatureHasher
+from pyflink.ml.tests.test_utils import PyFlinkMLTestCase
+
+
+class FeatureHasherTest(PyFlinkMLTestCase):
+    def setUp(self):
+        super(FeatureHasherTest, self).setUp()
+        self.input_data_table = self.t_env.from_data_stream(
+            self.env.from_collection([
+                (0, 'a', 1.0, True),
+                (1, 'c', 1.0, False)
+            ],
+                type_info=Types.ROW_NAMED(
+                    ['id', 'f0', 'f1', 'f2'],
+                    [Types.INT(), Types.STRING(), Types.DOUBLE(), Types.BOOLEAN()])))
+
+        self.expected_output_data_1 = Vectors.sparse(1000, [607, 635, 913], [1.0, 1.0, 1.0])
+        self.expected_output_data_2 = Vectors.sparse(1000, [242, 869, 913], [1.0, 1.0, 1.0])
+
+    def test_param(self):
+        feature_hasher = FeatureHasher()
+
+        self.assertEqual('output', feature_hasher.output_col)
+        self.assertEqual(262144, feature_hasher.num_features)
+
+        feature_hasher.set_input_cols('f0', 'f1', 'f2') \
+            .set_categorical_cols('f0', 'f2') \
+            .set_output_col('vec') \
+            .set_num_features(1000)
+
+        self.assertEqual(('f0', 'f1', 'f2'), feature_hasher.input_cols)
+        self.assertEqual(('f0', 'f2'), feature_hasher.categorical_cols)
+        self.assertEqual(1000, feature_hasher.num_features)
+        self.assertEqual('vec', feature_hasher.output_col)
+
+    def test_save_load_transform(self):
+        feature_hasher = FeatureHasher() \
+            .set_input_cols('f0', 'f1', 'f2') \
+            .set_categorical_cols('f0', 'f2') \
+            .set_output_col('vec') \
+            .set_num_features(1000)
+
+        path = os.path.join(self.temp_dir, 'test_save_load_transform_feature_hasher')
+        feature_hasher.save(path)
+        feature_hasher = FeatureHasher.load(self.t_env, path)
+
+        output_table = feature_hasher.transform(self.input_data_table)[0]
+        actual_outputs = [(result[0], result[4]) for result in
+                          self.t_env.to_data_stream(output_table).execute_and_collect()]
+
+        for actual_output in actual_outputs:

Review Comment:
   Sorry for my ambiguity. I mean when `actual_outputs` is empty.



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