partial fixit for feature_columns_test
PiperOrigin-RevId: 324713509 Change-Id: Ie1b69ed70ac787d8782f48fa2f9831c9bd622a17
This commit is contained in:
Zhenyu Tan
TensorFlower Gardener
parent
5666240e19
commit
a73b5ce940
@ -171,7 +171,6 @@ class LazyColumnTest(test.TestCase):
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TypeError, '"key" must be either a "str" or "_FeatureColumn".'):
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builder.get(NotAFeatureColumn())
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@test_util.run_deprecated_v1
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def test_expand_dim_rank_1_sparse_tensor_empty_batch(self):
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# empty 1-D sparse tensor:
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builder = _LazyBuilder(features={'a': sparse_tensor.SparseTensor(
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@ -179,7 +178,7 @@ class LazyColumnTest(test.TestCase):
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dense_shape=[0],
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values=np.array([]))})
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with self.cached_session():
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spv = builder.get('a').eval()
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spv = builder.get('a')
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self.assertAllEqual(np.array([0, 1], dtype=np.int64), spv.dense_shape)
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self.assertAllEqual(
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np.reshape(np.array([], dtype=np.int64), (0, 2)), spv.indices)
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@ -187,7 +186,6 @@ class LazyColumnTest(test.TestCase):
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class NumericColumnTest(test.TestCase):
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@test_util.run_deprecated_v1
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def test_defaults(self):
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a = fc._numeric_column('aaa')
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self.assertEqual('aaa', a.key)
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@ -266,7 +264,6 @@ class NumericColumnTest(test.TestCase):
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'aaa': parsing_ops.FixedLenFeature((2, 3), dtype=dtypes.int32)
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}, a._parse_example_spec)
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@test_util.run_deprecated_v1
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def test_parse_example_no_default_value(self):
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price = fc._numeric_column('price', shape=[2])
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data = example_pb2.Example(features=feature_pb2.Features(
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@ -309,7 +306,6 @@ class NumericColumnTest(test.TestCase):
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with self.assertRaisesRegex(TypeError, 'must be a callable'):
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fc._numeric_column('price', normalizer_fn='NotACallable')
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@test_util.run_deprecated_v1
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def test_normalizer_fn_transform_feature(self):
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def _increment_two(input_tensor):
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@ -328,7 +324,7 @@ class NumericColumnTest(test.TestCase):
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price = fc._numeric_column('price', shape=[2], normalizer_fn=_increment_two)
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builder = _LazyBuilder({'price': [[1., 2.], [5., 6.]]})
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self.assertEqual(builder.get(price), price._get_dense_tensor(builder))
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self.assertAllClose(builder.get(price), price._get_dense_tensor(builder))
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def test_sparse_tensor_not_supported(self):
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price = fc._numeric_column('price')
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@ -340,7 +336,6 @@ class NumericColumnTest(test.TestCase):
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with self.assertRaisesRegex(ValueError, 'must be a Tensor'):
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price._transform_feature(builder)
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@test_util.run_deprecated_v1
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def test_deep_copy(self):
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a = fc._numeric_column('aaa', shape=[1, 2], default_value=[[3., 2.]])
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a_copy = copy.deepcopy(a)
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@ -353,7 +348,6 @@ class NumericColumnTest(test.TestCase):
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'aaa', shape=[1, 2], default_value=np.array([[3., 2.]]))
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self.assertEqual(a.default_value, ((3., 2.),))
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@test_util.run_deprecated_v1
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def test_linear_model(self):
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price = fc._numeric_column('price')
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with ops.Graph().as_default():
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@ -368,7 +362,6 @@ class NumericColumnTest(test.TestCase):
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sess.run(price_var.assign([[10.]]))
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self.assertAllClose([[10.], [50.]], self.evaluate(predictions))
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@test_util.run_deprecated_v1
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def test_keras_linear_model(self):
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price = fc._numeric_column('price')
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with ops.Graph().as_default():
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@ -465,8 +458,8 @@ class BucketizedColumnTest(test.TestCase):
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'price': [[-1., 1.], [5., 6.]]
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}, [bucketized_price])
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with _initialized_session():
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self.assertAllEqual([[0, 1], [3, 4]],
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transformed_tensor[bucketized_price].eval())
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self.assertAllClose([[0, 1], [3, 4]],
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transformed_tensor[bucketized_price])
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def test_get_dense_tensor_one_input_value(self):
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"""Tests _get_dense_tensor() for input with shape=[1]."""
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@ -539,7 +532,6 @@ class BucketizedColumnTest(test.TestCase):
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with self.assertRaisesRegex(ValueError, 'must be a Tensor'):
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bucketized_price._transform_feature(builder)
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@test_util.run_deprecated_v1
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def test_deep_copy(self):
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a = fc._numeric_column('aaa', shape=[2])
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a_bucketized = fc._bucketized_column(a, boundaries=[0, 1])
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@ -667,7 +659,6 @@ class BucketizedColumnTest(test.TestCase):
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class HashedCategoricalColumnTest(test.TestCase):
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@test_util.run_deprecated_v1
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def test_defaults(self):
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a = fc._categorical_column_with_hash_bucket('aaa', 10)
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self.assertEqual('aaa', a.name)
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@ -695,7 +686,6 @@ class HashedCategoricalColumnTest(test.TestCase):
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with self.assertRaisesRegex(ValueError, 'dtype must be string or integer'):
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fc._categorical_column_with_hash_bucket('aaa', 10, dtype=dtypes.float32)
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@test_util.run_deprecated_v1
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def test_deep_copy(self):
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original = fc._categorical_column_with_hash_bucket('aaa', 10)
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for column in (original, copy.deepcopy(original)):
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@ -735,10 +725,8 @@ class HashedCategoricalColumnTest(test.TestCase):
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sparse_tensor.SparseTensorValue(
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indices=[[0, 0], [0, 1]],
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values=np.array([b'omar', b'stringer'], dtype=np.object_),
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dense_shape=[1, 2]),
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features['aaa'].eval())
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dense_shape=[1, 2]), features['aaa'].eval())
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@test_util.run_deprecated_v1
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def test_strings_should_be_hashed(self):
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hashed_sparse = fc._categorical_column_with_hash_bucket('wire', 10)
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wire_tensor = sparse_tensor.SparseTensor(
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@ -753,7 +741,7 @@ class HashedCategoricalColumnTest(test.TestCase):
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self.assertEqual(dtypes.int64, output.values.dtype)
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self.assertAllEqual(expected_values, output.values)
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self.assertAllEqual(wire_tensor.indices, output.indices)
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self.assertAllEqual(wire_tensor.dense_shape, output.dense_shape.eval())
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self.assertAllEqual(wire_tensor.dense_shape, output.dense_shape)
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def test_tensor_dtype_should_be_string_or_integer(self):
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string_fc = fc._categorical_column_with_hash_bucket(
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@ -793,7 +781,6 @@ class HashedCategoricalColumnTest(test.TestCase):
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with self.assertRaisesRegex(ValueError, 'dtype must be compatible'):
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builder.get(hashed_sparse)
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@test_util.run_deprecated_v1
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def test_ints_should_be_hashed(self):
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hashed_sparse = fc._categorical_column_with_hash_bucket(
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'wire', 10, dtype=dtypes.int64)
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@ -852,7 +839,6 @@ class HashedCategoricalColumnTest(test.TestCase):
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ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES))
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self.assertCountEqual([], ops.get_collection('my_weights'))
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@test_util.run_deprecated_v1
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def test_get_sparse_tensors_dense_input(self):
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hashed_sparse = fc._categorical_column_with_hash_bucket('wire', 10)
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builder = _LazyBuilder({'wire': (('omar', ''), ('stringer', 'marlo'))})
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@ -860,7 +846,6 @@ class HashedCategoricalColumnTest(test.TestCase):
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self.assertIsNone(id_weight_pair.weight_tensor)
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self.assertEqual(builder.get(hashed_sparse), id_weight_pair.id_tensor)
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@test_util.run_deprecated_v1
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def test_linear_model(self):
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wire_column = fc._categorical_column_with_hash_bucket('wire', 4)
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self.assertEqual(4, wire_column._num_buckets)
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@ -878,12 +863,11 @@ class HashedCategoricalColumnTest(test.TestCase):
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self.assertAllClose(((0.,), (0.,), (0.,), (0.,)),
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self.evaluate(wire_var))
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self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
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wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
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self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,))))
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# 'marlo' -> 3: wire_var[3] = 4
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# 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6
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self.assertAllClose(((4.,), (6.,)), self.evaluate(predictions))
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@test_util.run_deprecated_v1
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def test_keras_linear_model(self):
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wire_column = fc._categorical_column_with_hash_bucket('wire', 4)
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self.assertEqual(4, wire_column._num_buckets)
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@ -902,7 +886,7 @@ class HashedCategoricalColumnTest(test.TestCase):
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self.assertAllClose(((0.,), (0.,), (0.,), (0.,)),
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self.evaluate(wire_var))
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self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
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wire_var.assign(((1.,), (2.,), (3.,), (4.,))).eval()
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self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,))))
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# 'marlo' -> 3: wire_var[3] = 4
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# 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6
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self.assertAllClose(((4.,), (6.,)), self.evaluate(predictions))
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@ -990,7 +974,6 @@ class CrossedColumnTest(test.TestCase):
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crossed = fc._crossed_column([b, 'c'], 15)
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self.assertEqual(15, crossed._num_buckets)
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@test_util.run_deprecated_v1
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def test_deep_copy(self):
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a = fc._numeric_column('a', dtype=dtypes.int32)
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b = fc._bucketized_column(a, boundaries=[0, 1])
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@ -1001,7 +984,6 @@ class CrossedColumnTest(test.TestCase):
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self.assertEqual(15, crossed2_copy.hash_bucket_size)
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self.assertEqual(5, crossed2_copy.hash_key)
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@test_util.run_deprecated_v1
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def test_parse_example(self):
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price = fc._numeric_column('price', shape=[2])
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bucketized_price = fc._bucketized_column(price, boundaries=[0, 50])
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@ -1044,7 +1026,7 @@ class CrossedColumnTest(test.TestCase):
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}
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outputs = _transform_features(features, [price_cross_wire])
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output = outputs[price_cross_wire]
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with self.cached_session() as sess:
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with self.cached_session():
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output_val = self.evaluate(output)
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self.assertAllEqual(
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[[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]], output_val.indices)
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@ -1052,7 +1034,6 @@ class CrossedColumnTest(test.TestCase):
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self.assertIn(val, list(range(hash_bucket_size)))
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self.assertAllEqual([2, 4], output_val.dense_shape)
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@test_util.run_deprecated_v1
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def test_get_sparse_tensors(self):
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a = fc._numeric_column('a', dtype=dtypes.int32, shape=(2,))
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b = fc._bucketized_column(a, boundaries=(0, 1))
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@ -1120,7 +1101,6 @@ class CrossedColumnTest(test.TestCase):
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self.assertAllEqual(expected_values, id_tensor_eval.values)
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self.assertAllEqual((2, 4), id_tensor_eval.dense_shape)
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@test_util.run_deprecated_v1
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def test_linear_model(self):
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"""Tests linear_model.
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@ -1139,15 +1119,15 @@ class CrossedColumnTest(test.TestCase):
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}, (crossed,))
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bias = get_linear_model_bias()
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crossed_var = get_linear_model_column_var(crossed)
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with _initialized_session() as sess:
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with _initialized_session():
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self.assertAllClose((0.,), self.evaluate(bias))
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self.assertAllClose(((0.,), (0.,), (0.,), (0.,), (0.,)),
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self.evaluate(crossed_var))
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self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
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sess.run(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,))))
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self.evaluate(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,))))
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# Expected ids after cross = (1, 0, 1, 3, 4, 2)
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self.assertAllClose(((3.,), (14.,)), self.evaluate(predictions))
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sess.run(bias.assign((.1,)))
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self.evaluate(bias.assign((.1,)))
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self.assertAllClose(((3.1,), (14.1,)), self.evaluate(predictions))
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def test_linear_model_with_weights(self):
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@ -1202,7 +1182,6 @@ class CrossedColumnTest(test.TestCase):
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dense_shape=(2, 2)),
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}, (crossed,))
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@test_util.run_deprecated_v1
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def test_keras_linear_model(self):
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"""Tests _LinearModel.
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@ -1223,15 +1202,15 @@ class CrossedColumnTest(test.TestCase):
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}, (crossed,))
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bias = get_linear_model_bias()
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crossed_var = get_linear_model_column_var(crossed)
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with _initialized_session() as sess:
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with _initialized_session():
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self.assertAllClose((0.,), self.evaluate(bias))
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self.assertAllClose(((0.,), (0.,), (0.,), (0.,), (0.,)),
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self.evaluate(crossed_var))
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self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
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sess.run(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,))))
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self.evaluate(crossed_var.assign(((1.,), (2.,), (3.,), (4.,), (5.,))))
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# Expected ids after cross = (1, 0, 1, 3, 4, 2)
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self.assertAllClose(((3.,), (14.,)), self.evaluate(predictions))
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sess.run(bias.assign((.1,)))
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self.evaluate(bias.assign((.1,)))
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self.assertAllClose(((3.1,), (14.1,)), self.evaluate(predictions))
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def test_keras_linear_model_with_weights(self):
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