From a73b5ce940b3eeb5e322463e42c862e46c49a58e Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Mon, 3 Aug 2020 16:58:43 -0700 Subject: [PATCH] partial fixit for feature_columns_test PiperOrigin-RevId: 324713509 Change-Id: Ie1b69ed70ac787d8782f48fa2f9831c9bd622a17 --- .../feature_column/feature_column_test.py | 51 ++++++------------- 1 file changed, 15 insertions(+), 36 deletions(-) diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py index 2ea7face467..d6d4d2eb1a1 100644 --- a/tensorflow/python/feature_column/feature_column_test.py +++ b/tensorflow/python/feature_column/feature_column_test.py @@ -171,7 +171,6 @@ class LazyColumnTest(test.TestCase): TypeError, '"key" must be either a "str" or "_FeatureColumn".'): builder.get(NotAFeatureColumn()) - @test_util.run_deprecated_v1 def test_expand_dim_rank_1_sparse_tensor_empty_batch(self): # empty 1-D sparse tensor: builder = _LazyBuilder(features={'a': sparse_tensor.SparseTensor( @@ -179,7 +178,7 @@ class LazyColumnTest(test.TestCase): dense_shape=[0], values=np.array([]))}) with self.cached_session(): - spv = builder.get('a').eval() + spv = builder.get('a') self.assertAllEqual(np.array([0, 1], dtype=np.int64), spv.dense_shape) self.assertAllEqual( np.reshape(np.array([], dtype=np.int64), (0, 2)), spv.indices) @@ -187,7 +186,6 @@ class LazyColumnTest(test.TestCase): class NumericColumnTest(test.TestCase): - @test_util.run_deprecated_v1 def test_defaults(self): a = fc._numeric_column('aaa') self.assertEqual('aaa', a.key) @@ -266,7 +264,6 @@ class NumericColumnTest(test.TestCase): 'aaa': parsing_ops.FixedLenFeature((2, 3), dtype=dtypes.int32) }, a._parse_example_spec) - @test_util.run_deprecated_v1 def test_parse_example_no_default_value(self): price = fc._numeric_column('price', shape=[2]) data = example_pb2.Example(features=feature_pb2.Features( @@ -309,7 +306,6 @@ class NumericColumnTest(test.TestCase): with self.assertRaisesRegex(TypeError, 'must be a callable'): fc._numeric_column('price', normalizer_fn='NotACallable') - @test_util.run_deprecated_v1 def test_normalizer_fn_transform_feature(self): def _increment_two(input_tensor): @@ -328,7 +324,7 @@ class NumericColumnTest(test.TestCase): price = fc._numeric_column('price', shape=[2], normalizer_fn=_increment_two) builder = _LazyBuilder({'price': [[1., 2.], [5., 6.]]}) - self.assertEqual(builder.get(price), price._get_dense_tensor(builder)) + self.assertAllClose(builder.get(price), price._get_dense_tensor(builder)) def test_sparse_tensor_not_supported(self): price = fc._numeric_column('price') @@ -340,7 +336,6 @@ class NumericColumnTest(test.TestCase): with self.assertRaisesRegex(ValueError, 'must be a Tensor'): price._transform_feature(builder) - @test_util.run_deprecated_v1 def test_deep_copy(self): a = fc._numeric_column('aaa', shape=[1, 2], default_value=[[3., 2.]]) a_copy = copy.deepcopy(a) @@ -353,7 +348,6 @@ class NumericColumnTest(test.TestCase): 'aaa', shape=[1, 2], default_value=np.array([[3., 2.]])) self.assertEqual(a.default_value, ((3., 2.),)) - @test_util.run_deprecated_v1 def test_linear_model(self): price = fc._numeric_column('price') with ops.Graph().as_default(): @@ -368,7 +362,6 @@ class NumericColumnTest(test.TestCase): sess.run(price_var.assign([[10.]])) self.assertAllClose([[10.], [50.]], self.evaluate(predictions)) - @test_util.run_deprecated_v1 def test_keras_linear_model(self): price = fc._numeric_column('price') with ops.Graph().as_default(): @@ -465,8 +458,8 @@ class BucketizedColumnTest(test.TestCase): 'price': [[-1., 1.], [5., 6.]] }, [bucketized_price]) with _initialized_session(): - self.assertAllEqual([[0, 1], [3, 4]], - transformed_tensor[bucketized_price].eval()) + self.assertAllClose([[0, 1], [3, 4]], + transformed_tensor[bucketized_price]) def test_get_dense_tensor_one_input_value(self): """Tests _get_dense_tensor() for input with shape=[1].""" @@ -539,7 +532,6 @@ class BucketizedColumnTest(test.TestCase): with self.assertRaisesRegex(ValueError, 'must be a Tensor'): bucketized_price._transform_feature(builder) - @test_util.run_deprecated_v1 def test_deep_copy(self): a = fc._numeric_column('aaa', shape=[2]) a_bucketized = fc._bucketized_column(a, boundaries=[0, 1]) @@ -667,7 +659,6 @@ class BucketizedColumnTest(test.TestCase): class HashedCategoricalColumnTest(test.TestCase): - @test_util.run_deprecated_v1 def test_defaults(self): a = fc._categorical_column_with_hash_bucket('aaa', 10) self.assertEqual('aaa', a.name) @@ -695,7 +686,6 @@ class HashedCategoricalColumnTest(test.TestCase): with self.assertRaisesRegex(ValueError, 'dtype must be string or integer'): fc._categorical_column_with_hash_bucket('aaa', 10, dtype=dtypes.float32) - @test_util.run_deprecated_v1 def test_deep_copy(self): original = fc._categorical_column_with_hash_bucket('aaa', 10) for column in (original, copy.deepcopy(original)): @@ -735,10 +725,8 @@ class HashedCategoricalColumnTest(test.TestCase): sparse_tensor.SparseTensorValue( indices=[[0, 0], [0, 1]], values=np.array([b'omar', b'stringer'], dtype=np.object_), - dense_shape=[1, 2]), - features['aaa'].eval()) + dense_shape=[1, 2]), features['aaa'].eval()) - @test_util.run_deprecated_v1 def test_strings_should_be_hashed(self): hashed_sparse = fc._categorical_column_with_hash_bucket('wire', 10) wire_tensor = sparse_tensor.SparseTensor( @@ -753,7 +741,7 @@ class HashedCategoricalColumnTest(test.TestCase): self.assertEqual(dtypes.int64, output.values.dtype) self.assertAllEqual(expected_values, output.values) self.assertAllEqual(wire_tensor.indices, output.indices) - self.assertAllEqual(wire_tensor.dense_shape, output.dense_shape.eval()) + self.assertAllEqual(wire_tensor.dense_shape, output.dense_shape) def test_tensor_dtype_should_be_string_or_integer(self): string_fc = fc._categorical_column_with_hash_bucket( @@ -793,7 +781,6 @@ class HashedCategoricalColumnTest(test.TestCase): with self.assertRaisesRegex(ValueError, 'dtype must be compatible'): builder.get(hashed_sparse) - @test_util.run_deprecated_v1 def test_ints_should_be_hashed(self): hashed_sparse = fc._categorical_column_with_hash_bucket( 'wire', 10, dtype=dtypes.int64) @@ -852,7 +839,6 @@ class HashedCategoricalColumnTest(test.TestCase): ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) self.assertCountEqual([], ops.get_collection('my_weights')) - @test_util.run_deprecated_v1 def test_get_sparse_tensors_dense_input(self): hashed_sparse = fc._categorical_column_with_hash_bucket('wire', 10) builder = _LazyBuilder({'wire': (('omar', ''), ('stringer', 'marlo'))}) @@ -860,7 +846,6 @@ class HashedCategoricalColumnTest(test.TestCase): self.assertIsNone(id_weight_pair.weight_tensor) self.assertEqual(builder.get(hashed_sparse), id_weight_pair.id_tensor) - @test_util.run_deprecated_v1 def test_linear_model(self): wire_column = fc._categorical_column_with_hash_bucket('wire', 4) self.assertEqual(4, wire_column._num_buckets) @@ -878,12 +863,11 @@ class HashedCategoricalColumnTest(test.TestCase): self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), self.evaluate(wire_var)) self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions)) - wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval() + self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,)))) # 'marlo' -> 3: wire_var[3] = 4 # 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6 self.assertAllClose(((4.,), (6.,)), self.evaluate(predictions)) - @test_util.run_deprecated_v1 def test_keras_linear_model(self): wire_column = fc._categorical_column_with_hash_bucket('wire', 4) self.assertEqual(4, wire_column._num_buckets) @@ -902,7 +886,7 @@ class HashedCategoricalColumnTest(test.TestCase): self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), self.evaluate(wire_var)) self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions)) - wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval() + self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,)))) # 'marlo' -> 3: wire_var[3] = 4 # 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6 self.assertAllClose(((4.,), (6.,)), self.evaluate(predictions)) @@ -990,7 +974,6 @@ class CrossedColumnTest(test.TestCase): crossed = fc._crossed_column([b, 'c'], 15) self.assertEqual(15, crossed._num_buckets) - @test_util.run_deprecated_v1 def test_deep_copy(self): a = fc._numeric_column('a', dtype=dtypes.int32) b = fc._bucketized_column(a, boundaries=[0, 1]) @@ -1001,7 +984,6 @@ class CrossedColumnTest(test.TestCase): self.assertEqual(15, crossed2_copy.hash_bucket_size) self.assertEqual(5, crossed2_copy.hash_key) - @test_util.run_deprecated_v1 def test_parse_example(self): price = fc._numeric_column('price', shape=[2]) bucketized_price = fc._bucketized_column(price, boundaries=[0, 50]) @@ -1044,7 +1026,7 @@ class CrossedColumnTest(test.TestCase): } outputs = _transform_features(features, [price_cross_wire]) output = outputs[price_cross_wire] - with self.cached_session() as sess: + with self.cached_session(): output_val = self.evaluate(output) self.assertAllEqual( [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]], output_val.indices) @@ -1052,7 +1034,6 @@ class CrossedColumnTest(test.TestCase): self.assertIn(val, list(range(hash_bucket_size))) self.assertAllEqual([2, 4], output_val.dense_shape) - @test_util.run_deprecated_v1 def test_get_sparse_tensors(self): a = fc._numeric_column('a', dtype=dtypes.int32, shape=(2,)) b = fc._bucketized_column(a, boundaries=(0, 1)) @@ -1120,7 +1101,6 @@ class CrossedColumnTest(test.TestCase): self.assertAllEqual(expected_values, id_tensor_eval.values) self.assertAllEqual((2, 4), id_tensor_eval.dense_shape) - @test_util.run_deprecated_v1 def test_linear_model(self): """Tests linear_model. @@ -1139,15 +1119,15 @@ class CrossedColumnTest(test.TestCase): }, (crossed,)) bias = get_linear_model_bias() crossed_var = get_linear_model_column_var(crossed) - with _initialized_session() as sess: + with _initialized_session(): self.assertAllClose((0.,), self.evaluate(bias)) self.assertAllClose(((0.,), (0.,), (0.,), (0.,), (0.,)), self.evaluate(crossed_var)) self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions)) - sess.run(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,)))) + self.evaluate(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,)))) # Expected ids after cross = (1, 0, 1, 3, 4, 2) self.assertAllClose(((3.,), (14.,)), self.evaluate(predictions)) - sess.run(bias.assign((.1,))) + self.evaluate(bias.assign((.1,))) self.assertAllClose(((3.1,), (14.1,)), self.evaluate(predictions)) def test_linear_model_with_weights(self): @@ -1202,7 +1182,6 @@ class CrossedColumnTest(test.TestCase): dense_shape=(2, 2)), }, (crossed,)) - @test_util.run_deprecated_v1 def test_keras_linear_model(self): """Tests _LinearModel. @@ -1223,15 +1202,15 @@ class CrossedColumnTest(test.TestCase): }, (crossed,)) bias = get_linear_model_bias() crossed_var = get_linear_model_column_var(crossed) - with _initialized_session() as sess: + with _initialized_session(): self.assertAllClose((0.,), self.evaluate(bias)) self.assertAllClose(((0.,), (0.,), (0.,), (0.,), (0.,)), self.evaluate(crossed_var)) self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions)) - sess.run(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,)))) + self.evaluate(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,)))) # Expected ids after cross = (1, 0, 1, 3, 4, 2) self.assertAllClose(((3.,), (14.,)), self.evaluate(predictions)) - sess.run(bias.assign((.1,))) + self.evaluate(bias.assign((.1,))) self.assertAllClose(((3.1,), (14.1,)), self.evaluate(predictions)) def test_keras_linear_model_with_weights(self):