From 3338e5d816a8ace4ac02c1be34f6fb9715737a16 Mon Sep 17 00:00:00 2001
From: Zhenyu Tan <tanzheny@google.com>
Date: Wed, 5 Aug 2020 13:33:32 -0700
Subject: [PATCH] fixit for feature column VocabularyListFeatureColumnTest,
IndicatorColumn, IdentityColumn and WeightCategoricalColumn.
PiperOrigin-RevId: 325090178
Change-Id: I427fae37945eeed690b99bc959e7757dea7086ed
---
.../feature_column/feature_column_test.py | 891 +++++++++---------
1 file changed, 437 insertions(+), 454 deletions(-)
diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py
index 3a38f86b95f..755df6060d3 100644
--- a/tensorflow/python/feature_column/feature_column_test.py
+++ b/tensorflow/python/feature_column/feature_column_test.py
@@ -3827,7 +3827,6 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
'aaa': parsing_ops.VarLenFeature(dtypes.int64)
}, column._parse_example_spec)
- @test_util.run_deprecated_v1
def test_all_constructor_args(self):
column = fc._categorical_column_with_vocabulary_list(
key='aaa',
@@ -3839,7 +3838,6 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
'aaa': parsing_ops.VarLenFeature(dtypes.int32)
}, column._parse_example_spec)
- @test_util.run_deprecated_v1
def test_deep_copy(self):
original = fc._categorical_column_with_vocabulary_list(
key='aaa', vocabulary_list=(12, 24, 36), dtype=dtypes.int32)
@@ -3974,41 +3972,41 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
dense_shape=[1, 2]),
features['aaa'].eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors(self):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=('marlo', 'skywalker', 'omar'),
- dense_shape=(2, 2))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((2, -1, 0), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=('marlo', 'skywalker', 'omar'),
+ dense_shape=(2, 2))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((2, -1, 0), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_transform_feature(self):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=('marlo', 'skywalker', 'omar'),
- dense_shape=(2, 2))
- id_tensor = _transform_features({'aaa': inputs}, [column])[column]
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((2, -1, 0), dtype=np.int64),
- dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=('marlo', 'skywalker', 'omar'),
+ dense_shape=(2, 2))
+ id_tensor = _transform_features({'aaa': inputs}, [column])[column]
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((2, -1, 0), dtype=np.int64),
+ dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
def test_get_sparse_tensors_weight_collections(self):
column = fc._categorical_column_with_vocabulary_list(
@@ -4026,134 +4024,129 @@ class VocabularyListCategoricalColumnTest(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):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
- id_weight_pair = column._get_sparse_tensors(
- _LazyBuilder({
- 'aaa': (('marlo', ''), ('skywalker', 'omar'))
- }))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=np.array((2, -1, 0), dtype=np.int64),
- dense_shape=(2, 2)),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+ id_weight_pair = column._get_sparse_tensors(
+ _LazyBuilder({'aaa': (('marlo', ''), ('skywalker', 'omar'))}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=np.array((2, -1, 0), dtype=np.int64),
+ dense_shape=(2, 2)), id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors_default_value_in_vocabulary(self):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa',
- vocabulary_list=('omar', 'stringer', 'marlo'),
- default_value=2)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=('marlo', 'skywalker', 'omar'),
- dense_shape=(2, 2))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((2, 2, 0), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa',
+ vocabulary_list=('omar', 'stringer', 'marlo'),
+ default_value=2)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=('marlo', 'skywalker', 'omar'),
+ dense_shape=(2, 2))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((2, 2, 0), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors_with_oov_buckets(self):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa',
- vocabulary_list=('omar', 'stringer', 'marlo'),
- num_oov_buckets=100)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1), (1, 2)),
- values=('marlo', 'skywalker', 'omar', 'heisenberg'),
- dense_shape=(2, 3))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((2, 33, 0, 62), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa',
+ vocabulary_list=('omar', 'stringer', 'marlo'),
+ num_oov_buckets=100)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1), (1, 2)),
+ values=('marlo', 'skywalker', 'omar', 'heisenberg'),
+ dense_shape=(2, 3))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((2, 33, 0, 62), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors_int32(self):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa',
- vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
- dtype=dtypes.int32)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1), (2, 2)),
- values=np.array((11, 100, 30, 22), dtype=np.int32),
- dense_shape=(3, 3))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((2, -1, 0, 4), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa',
+ vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+ dtype=dtypes.int32)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+ values=np.array((11, 100, 30, 22), dtype=np.int32),
+ dense_shape=(3, 3))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((2, -1, 0, 4), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors_int32_dense_input(self):
- default_value = -100
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa',
- vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
- dtype=dtypes.int32,
- default_value=default_value)
- id_weight_pair = column._get_sparse_tensors(
- _LazyBuilder({
- 'aaa':
- np.array(
- ((11, -1, -1), (100, 30, -1), (-1, -1, 22)), dtype=np.int32)
- }))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1), (2, 2)),
- values=np.array((2, default_value, 0, 4), dtype=np.int64),
- dense_shape=(3, 3)),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ default_value = -100
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa',
+ vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+ dtype=dtypes.int32,
+ default_value=default_value)
+ id_weight_pair = column._get_sparse_tensors(
+ _LazyBuilder({
+ 'aaa':
+ np.array(((11, -1, -1), (100, 30, -1), (-1, -1, 22)),
+ dtype=np.int32)
+ }))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+ values=np.array((2, default_value, 0, 4), dtype=np.int64),
+ dense_shape=(3, 3)), id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors_int32_with_oov_buckets(self):
- column = fc._categorical_column_with_vocabulary_list(
- key='aaa',
- vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
- dtype=dtypes.int32,
- num_oov_buckets=100)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1), (2, 2)),
- values=(11, 100, 30, 22),
- dense_shape=(3, 3))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((2, 60, 0, 4), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_vocabulary_list(
+ key='aaa',
+ vocabulary_list=np.array((30, 35, 11, 23, 22), dtype=np.int32),
+ dtype=dtypes.int32,
+ num_oov_buckets=100)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1), (2, 2)),
+ values=(11, 100, 30, 22),
+ dense_shape=(3, 3))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((2, 60, 0, 4), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_linear_model(self):
wire_column = fc._categorical_column_with_vocabulary_list(
key='aaa',
@@ -4179,7 +4172,6 @@ class VocabularyListCategoricalColumnTest(test.TestCase):
# 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
self.assertAllClose(((3.,), (5.,)), self.evaluate(predictions))
- @test_util.run_deprecated_v1
def test_keras_linear_model(self):
wire_column = fc._categorical_column_with_vocabulary_list(
key='aaa',
@@ -4223,7 +4215,6 @@ class IdentityCategoricalColumnTest(test.TestCase):
with self.assertRaisesRegex(ValueError, 'key must be a string.'):
fc._categorical_column_with_identity(key=('aaa',), num_buckets=3)
- @test_util.run_deprecated_v1
def test_deep_copy(self):
original = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
for column in (original, copy.deepcopy(original)):
@@ -4282,39 +4273,39 @@ class IdentityCategoricalColumnTest(test.TestCase):
dense_shape=[1, 2]),
features['aaa'].eval())
- @test_util.run_deprecated_v1
def test_get_sparse_tensors(self):
- column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(0, 1, 0),
- dense_shape=(2, 2))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((0, 1, 0), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(0, 1, 0),
+ dense_shape=(2, 2))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((0, 1, 0), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
- @test_util.run_deprecated_v1
def test_transform_feature(self):
- column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(0, 1, 0),
- dense_shape=(2, 2))
- id_tensor = _transform_features({'aaa': inputs}, [column])[column]
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((0, 1, 0), dtype=np.int64),
- dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(0, 1, 0),
+ dense_shape=(2, 2))
+ id_tensor = _transform_features({'aaa': inputs}, [column])[column]
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((0, 1, 0), dtype=np.int64),
+ dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
def test_get_sparse_tensors_weight_collections(self):
column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
@@ -4331,139 +4322,139 @@ class IdentityCategoricalColumnTest(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):
- column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
- id_weight_pair = column._get_sparse_tensors(
- _LazyBuilder({
- 'aaa': ((0, -1), (1, 0))
- }))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=np.array((0, 1, 0), dtype=np.int64),
- dense_shape=(2, 2)),
- id_weight_pair.id_tensor.eval())
-
- @test_util.run_deprecated_v1
- def test_get_sparse_tensors_with_inputs_too_big(self):
- # Inputs.
- vocabulary_size = 2
- sparse_input = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)), values=(2, 1, 0), dense_shape=(2, 2))
-
- # Embedding variable.
- embedding_dimension = 2
- embedding_values = (
- (1., 2.), # id 0
- (3., 5.), # id 1
- )
-
- def _initializer(shape, dtype, partition_info=None):
- del shape, dtype, partition_info
- return embedding_values
-
- # Build columns.
- categorical_column = fc._categorical_column_with_identity(
- key='aaa', num_buckets=vocabulary_size)
- embedding_column = fc._embedding_column(
- categorical_column,
- dimension=embedding_dimension,
- initializer=_initializer)
-
- # Provide sparse input and get dense result.
- embedding_lookup = embedding_column._get_dense_tensor(
- _LazyBuilder({'aaa': sparse_input}))
-
- with _initialized_session():
- with self.assertRaisesRegex(errors.OpError,
- r'indices\[0\] .* 2 .* \[0, 2\)'):
- self.evaluate(embedding_lookup)
-
- @test_util.run_deprecated_v1
- def test_get_sparse_tensors_with_inputs_too_small(self):
- # Inputs.
- vocabulary_size = 2
- sparse_input = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (0, 0), (1, 1), (1, 2)),
- values=(-9, 0, -6, 1),
- dense_shape=(2, 4))
-
- # Embedding variable.
- embedding_dimension = 2
- embedding_values = (
- (1., 2.), # id 0
- (3., 5.), # id 1
- )
-
- def _initializer(shape, dtype, partition_info=None):
- del shape, dtype, partition_info
- return embedding_values
-
- # Build columns.
- categorical_column = fc._categorical_column_with_identity(
- key='aaa', num_buckets=vocabulary_size)
- embedding_column = fc._embedding_column(
- categorical_column,
- dimension=embedding_dimension,
- initializer=_initializer)
-
- # Provide sparse input and get dense result.
- embedding_lookup = embedding_column._get_dense_tensor(
- _LazyBuilder({'aaa': sparse_input}))
- expected_lookups = ((1., 2.), (3., 5))
- with _initialized_session():
- self.assertAllEqual(expected_lookups, self.evaluate(embedding_lookup))
-
- @test_util.run_deprecated_v1
- def test_get_sparse_tensors_with_default_value(self):
- column = fc._categorical_column_with_identity(
- key='aaa', num_buckets=4, default_value=3)
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(1, -1, 99),
- dense_shape=(2, 2))
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array((1, 3, 3), dtype=np.int64),
- dense_shape=inputs.dense_shape),
- id_weight_pair.id_tensor.eval())
-
- @test_util.run_deprecated_v1
- def test_get_sparse_tensors_with_default_value_and_placeholder_inputs(self):
- column = fc._categorical_column_with_identity(
- key='aaa', num_buckets=4, default_value=3)
- input_indices = array_ops.placeholder(dtype=dtypes.int64)
- input_values = array_ops.placeholder(dtype=dtypes.int32)
- input_shape = array_ops.placeholder(dtype=dtypes.int64)
- inputs = sparse_tensor.SparseTensorValue(
- indices=input_indices,
- values=input_values,
- dense_shape=input_shape)
- id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
- self.assertIsNone(id_weight_pair.weight_tensor)
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=np.array(((0, 0), (1, 0), (1, 1)), dtype=np.int64),
- values=np.array((1, 3, 3), dtype=np.int64),
- dense_shape=np.array((2, 2), dtype=np.int64)),
- id_weight_pair.id_tensor.eval(feed_dict={
- input_indices: ((0, 0), (1, 0), (1, 1)),
- input_values: (1, -1, 99),
- input_shape: (2, 2),
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
+ id_weight_pair = column._get_sparse_tensors(
+ _LazyBuilder({
+ 'aaa': ((0, -1), (1, 0))
}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=np.array((0, 1, 0), dtype=np.int64),
+ dense_shape=(2, 2)),
+ id_weight_pair.id_tensor.eval())
+
+ def test_get_sparse_tensors_with_inputs_too_big(self):
+ with ops.Graph().as_default():
+ # Inputs.
+ vocabulary_size = 2
+ sparse_input = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)), values=(2, 1, 0),
+ dense_shape=(2, 2))
+
+ # Embedding variable.
+ embedding_dimension = 2
+ embedding_values = (
+ (1., 2.), # id 0
+ (3., 5.), # id 1
+ )
+
+ def _initializer(shape, dtype, partition_info=None):
+ del shape, dtype, partition_info
+ return embedding_values
+
+ # Build columns.
+ categorical_column = fc._categorical_column_with_identity(
+ key='aaa', num_buckets=vocabulary_size)
+ embedding_column = fc._embedding_column(
+ categorical_column,
+ dimension=embedding_dimension,
+ initializer=_initializer)
+
+ # Provide sparse input and get dense result.
+ embedding_lookup = embedding_column._get_dense_tensor(
+ _LazyBuilder({'aaa': sparse_input}))
+
+ with _initialized_session():
+ with self.assertRaisesRegex(errors.OpError,
+ r'indices\[0\] .* 2 .* \[0, 2\)'):
+ self.evaluate(embedding_lookup)
+
+ def test_get_sparse_tensors_with_inputs_too_small(self):
+ with ops.Graph().as_default():
+ # Inputs.
+ vocabulary_size = 2
+ sparse_input = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (0, 0), (1, 1), (1, 2)),
+ values=(-9, 0, -6, 1),
+ dense_shape=(2, 4))
+
+ # Embedding variable.
+ embedding_dimension = 2
+ embedding_values = (
+ (1., 2.), # id 0
+ (3., 5.), # id 1
+ )
+
+ def _initializer(shape, dtype, partition_info=None):
+ del shape, dtype, partition_info
+ return embedding_values
+
+ # Build columns.
+ categorical_column = fc._categorical_column_with_identity(
+ key='aaa', num_buckets=vocabulary_size)
+ embedding_column = fc._embedding_column(
+ categorical_column,
+ dimension=embedding_dimension,
+ initializer=_initializer)
+
+ # Provide sparse input and get dense result.
+ embedding_lookup = embedding_column._get_dense_tensor(
+ _LazyBuilder({'aaa': sparse_input}))
+ expected_lookups = ((1., 2.), (3., 5))
+ with _initialized_session():
+ self.assertAllEqual(expected_lookups, self.evaluate(embedding_lookup))
+
+ def test_get_sparse_tensors_with_default_value(self):
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_identity(
+ key='aaa', num_buckets=4, default_value=3)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(1, -1, 99),
+ dense_shape=(2, 2))
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array((1, 3, 3), dtype=np.int64),
+ dense_shape=inputs.dense_shape),
+ id_weight_pair.id_tensor.eval())
+
+ def test_get_sparse_tensors_with_default_value_and_placeholder_inputs(self):
+ with ops.Graph().as_default():
+ column = fc._categorical_column_with_identity(
+ key='aaa', num_buckets=4, default_value=3)
+ input_indices = array_ops.placeholder(dtype=dtypes.int64)
+ input_values = array_ops.placeholder(dtype=dtypes.int32)
+ input_shape = array_ops.placeholder(dtype=dtypes.int64)
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=input_indices,
+ values=input_values,
+ dense_shape=input_shape)
+ id_weight_pair = column._get_sparse_tensors(_LazyBuilder({'aaa': inputs}))
+ self.assertIsNone(id_weight_pair.weight_tensor)
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=np.array(((0, 0), (1, 0), (1, 1)), dtype=np.int64),
+ values=np.array((1, 3, 3), dtype=np.int64),
+ dense_shape=np.array((2, 2), dtype=np.int64)),
+ id_weight_pair.id_tensor.eval(feed_dict={
+ input_indices: ((0, 0), (1, 0), (1, 1)),
+ input_values: (1, -1, 99),
+ input_shape: (2, 2),
+ }))
- @test_util.run_deprecated_v1
def test_linear_model(self):
column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
self.assertEqual(3, column._num_buckets)
@@ -4485,7 +4476,6 @@ class IdentityCategoricalColumnTest(test.TestCase):
# weight_var[2] + weight_var[1] = 3+2 = 5
self.assertAllClose(((1.,), (5.,)), self.evaluate(predictions))
- @test_util.run_deprecated_v1
def test_keras_linear_model(self):
column = fc._categorical_column_with_identity(key='aaa', num_buckets=3)
self.assertEqual(3, column._num_buckets)
@@ -4637,7 +4627,6 @@ class IndicatorColumnTest(test.TestCase):
with self.cached_session():
self.assertAllEqual([[0., 1., 1., 0.]], self.evaluate(output))
- @test_util.run_deprecated_v1
def test_deep_copy(self):
a = fc._categorical_column_with_hash_bucket('a', 4)
column = fc._indicator_column(a)
@@ -4670,66 +4659,66 @@ class IndicatorColumnTest(test.TestCase):
dense_shape=[1, 2]),
features['aaa'].eval())
- @test_util.run_deprecated_v1
def test_transform(self):
- a = fc._categorical_column_with_vocabulary_list(
- key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
- a_indicator = fc._indicator_column(a)
- features = {
- 'aaa': sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=('marlo', 'skywalker', 'omar'),
- dense_shape=(2, 2))
- }
- indicator_tensor = _transform_features(features, [a_indicator])[a_indicator]
- with _initialized_session():
- self.assertAllEqual([[0, 0, 1], [1, 0, 0]],
- self.evaluate(indicator_tensor))
+ with ops.Graph().as_default():
+ a = fc._categorical_column_with_vocabulary_list(
+ key='aaa', vocabulary_list=('omar', 'stringer', 'marlo'))
+ a_indicator = fc._indicator_column(a)
+ features = {
+ 'aaa': sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=('marlo', 'skywalker', 'omar'),
+ dense_shape=(2, 2))
+ }
+ indicator_tensor = _transform_features(features,
+ [a_indicator])[a_indicator]
+ with _initialized_session():
+ self.assertAllEqual([[0, 0, 1], [1, 0, 0]],
+ self.evaluate(indicator_tensor))
- @test_util.run_deprecated_v1
def test_transform_with_weighted_column(self):
- # Github issue 12557
- ids = fc._categorical_column_with_vocabulary_list(
- key='ids', vocabulary_list=('a', 'b', 'c'))
- weights = fc._weighted_categorical_column(ids, 'weights')
- indicator = fc._indicator_column(weights)
- features = {
- 'ids': constant_op.constant([['c', 'b', 'a', 'c']]),
- 'weights': constant_op.constant([[2., 4., 6., 1.]])
- }
- indicator_tensor = _transform_features(features, [indicator])[indicator]
- with _initialized_session():
- self.assertAllEqual([[6., 4., 3.]], self.evaluate(indicator_tensor))
+ with ops.Graph().as_default():
+ # Github issue 12557
+ ids = fc._categorical_column_with_vocabulary_list(
+ key='ids', vocabulary_list=('a', 'b', 'c'))
+ weights = fc._weighted_categorical_column(ids, 'weights')
+ indicator = fc._indicator_column(weights)
+ features = {
+ 'ids': constant_op.constant([['c', 'b', 'a', 'c']]),
+ 'weights': constant_op.constant([[2., 4., 6., 1.]])
+ }
+ indicator_tensor = _transform_features(features, [indicator])[indicator]
+ with _initialized_session():
+ self.assertAllEqual([[6., 4., 3.]], self.evaluate(indicator_tensor))
- @test_util.run_deprecated_v1
def test_transform_with_missing_value_in_weighted_column(self):
- # Github issue 12583
- ids = fc._categorical_column_with_vocabulary_list(
- key='ids', vocabulary_list=('a', 'b', 'c'))
- weights = fc._weighted_categorical_column(ids, 'weights')
- indicator = fc._indicator_column(weights)
- features = {
- 'ids': constant_op.constant([['c', 'b', 'unknown']]),
- 'weights': constant_op.constant([[2., 4., 6.]])
- }
- indicator_tensor = _transform_features(features, [indicator])[indicator]
- with _initialized_session():
- self.assertAllEqual([[0., 4., 2.]], self.evaluate(indicator_tensor))
+ with ops.Graph().as_default():
+ # Github issue 12583
+ ids = fc._categorical_column_with_vocabulary_list(
+ key='ids', vocabulary_list=('a', 'b', 'c'))
+ weights = fc._weighted_categorical_column(ids, 'weights')
+ indicator = fc._indicator_column(weights)
+ features = {
+ 'ids': constant_op.constant([['c', 'b', 'unknown']]),
+ 'weights': constant_op.constant([[2., 4., 6.]])
+ }
+ indicator_tensor = _transform_features(features, [indicator])[indicator]
+ with _initialized_session():
+ self.assertAllEqual([[0., 4., 2.]], self.evaluate(indicator_tensor))
- @test_util.run_deprecated_v1
def test_transform_with_missing_value_in_categorical_column(self):
- # Github issue 12583
- ids = fc._categorical_column_with_vocabulary_list(
- key='ids', vocabulary_list=('a', 'b', 'c'))
- indicator = fc._indicator_column(ids)
- features = {
- 'ids': constant_op.constant([['c', 'b', 'unknown']]),
- }
- indicator_tensor = _transform_features(features, [indicator])[indicator]
- with _initialized_session():
- self.assertAllEqual([[0., 1., 1.]], self.evaluate(indicator_tensor))
+ with ops.Graph().as_default():
+ # Github issue 12583
+ ids = fc._categorical_column_with_vocabulary_list(
+ key='ids', vocabulary_list=('a', 'b', 'c'))
+ indicator = fc._indicator_column(ids)
+ features = {
+ 'ids': constant_op.constant([['c', 'b', 'unknown']]),
+ }
+ indicator_tensor = _transform_features(features, [indicator])[indicator]
+ with _initialized_session():
+ self.assertAllEqual([[0., 1., 1.]], self.evaluate(indicator_tensor))
- @test_util.run_deprecated_v1
def test_linear_model(self):
animal = fc._indicator_column(
fc._categorical_column_with_identity('animal', num_buckets=4))
@@ -4749,7 +4738,6 @@ class IndicatorColumnTest(test.TestCase):
weight_var.assign([[1.], [2.], [3.], [4.]]).eval()
self.assertAllClose([[2. + 3.]], self.evaluate(predictions))
- @test_util.run_deprecated_v1
def test_keras_linear_model(self):
animal = fc._indicator_column(
fc._categorical_column_with_identity('animal', num_buckets=4))
@@ -4769,7 +4757,6 @@ class IndicatorColumnTest(test.TestCase):
weight_var.assign([[1.], [2.], [3.], [4.]]).eval()
self.assertAllClose([[2. + 3.]], self.evaluate(predictions))
- @test_util.run_deprecated_v1
def test_input_layer(self):
animal = fc._indicator_column(
fc._categorical_column_with_identity('animal', num_buckets=4))
@@ -6279,7 +6266,6 @@ class SharedEmbeddingColumnTest(test.TestCase, parameterized.TestCase):
class WeightedCategoricalColumnTest(test.TestCase):
- @test_util.run_deprecated_v1
def test_defaults(self):
column = fc._weighted_categorical_column(
categorical_column=fc._categorical_column_with_identity(
@@ -6293,7 +6279,6 @@ class WeightedCategoricalColumnTest(test.TestCase):
'values': parsing_ops.VarLenFeature(dtypes.float32)
}, column._parse_example_spec)
- @test_util.run_deprecated_v1
def test_deep_copy(self):
"""Tests deepcopy of categorical_column_with_hash_bucket."""
original = fc._weighted_categorical_column(
@@ -6392,95 +6377,94 @@ class WeightedCategoricalColumnTest(test.TestCase):
dense_shape=[1, 2]),
features['weights'].eval())
- @test_util.run_deprecated_v1
def test_transform_features(self):
- column = fc._weighted_categorical_column(
- categorical_column=fc._categorical_column_with_identity(
- key='ids', num_buckets=3),
- weight_feature_key='values')
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(0, 1, 0),
- dense_shape=(2, 2))
- weights = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(0.5, 1.0, 0.1),
- dense_shape=(2, 2))
- id_tensor, weight_tensor = _transform_features({
- 'ids': inputs,
- 'values': weights,
- }, (column,))[column]
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array(inputs.values, dtype=np.int64),
- dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=weights.indices,
- values=np.array(weights.values, dtype=np.float32),
- dense_shape=weights.dense_shape), self.evaluate(weight_tensor))
+ with ops.Graph().as_default():
+ column = fc._weighted_categorical_column(
+ categorical_column=fc._categorical_column_with_identity(
+ key='ids', num_buckets=3),
+ weight_feature_key='values')
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(0, 1, 0),
+ dense_shape=(2, 2))
+ weights = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(0.5, 1.0, 0.1),
+ dense_shape=(2, 2))
+ id_tensor, weight_tensor = _transform_features({
+ 'ids': inputs,
+ 'values': weights,
+ }, (column,))[column]
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array(inputs.values, dtype=np.int64),
+ dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=weights.indices,
+ values=np.array(weights.values, dtype=np.float32),
+ dense_shape=weights.dense_shape), self.evaluate(weight_tensor))
- @test_util.run_deprecated_v1
def test_transform_features_dense_input(self):
- column = fc._weighted_categorical_column(
- categorical_column=fc._categorical_column_with_identity(
- key='ids', num_buckets=3),
- weight_feature_key='values')
- weights = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(0.5, 1.0, 0.1),
- dense_shape=(2, 2))
- id_tensor, weight_tensor = _transform_features({
- 'ids': ((0, -1), (1, 0)),
- 'values': weights,
- }, (column,))[column]
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=np.array((0, 1, 0), dtype=np.int64),
- dense_shape=(2, 2)), self.evaluate(id_tensor))
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=weights.indices,
- values=np.array(weights.values, dtype=np.float32),
- dense_shape=weights.dense_shape), self.evaluate(weight_tensor))
+ with ops.Graph().as_default():
+ column = fc._weighted_categorical_column(
+ categorical_column=fc._categorical_column_with_identity(
+ key='ids', num_buckets=3),
+ weight_feature_key='values')
+ weights = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(0.5, 1.0, 0.1),
+ dense_shape=(2, 2))
+ id_tensor, weight_tensor = _transform_features({
+ 'ids': ((0, -1), (1, 0)),
+ 'values': weights,
+ }, (column,))[column]
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=np.array((0, 1, 0), dtype=np.int64),
+ dense_shape=(2, 2)), self.evaluate(id_tensor))
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=weights.indices,
+ values=np.array(weights.values, dtype=np.float32),
+ dense_shape=weights.dense_shape), self.evaluate(weight_tensor))
- @test_util.run_deprecated_v1
def test_transform_features_dense_weights(self):
- column = fc._weighted_categorical_column(
- categorical_column=fc._categorical_column_with_identity(
- key='ids', num_buckets=3),
- weight_feature_key='values')
- inputs = sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=(2, 1, 0),
- dense_shape=(2, 2))
- id_tensor, weight_tensor = _transform_features({
- 'ids': inputs,
- 'values': ((.5, 0.), (1., .1)),
- }, (column,))[column]
- with _initialized_session():
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=inputs.indices,
- values=np.array(inputs.values, dtype=np.int64),
- dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
- _assert_sparse_tensor_value(
- self,
- sparse_tensor.SparseTensorValue(
- indices=((0, 0), (1, 0), (1, 1)),
- values=np.array((.5, 1., .1), dtype=np.float32),
- dense_shape=(2, 2)), self.evaluate(weight_tensor))
+ with ops.Graph().as_default():
+ column = fc._weighted_categorical_column(
+ categorical_column=fc._categorical_column_with_identity(
+ key='ids', num_buckets=3),
+ weight_feature_key='values')
+ inputs = sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=(2, 1, 0),
+ dense_shape=(2, 2))
+ id_tensor, weight_tensor = _transform_features({
+ 'ids': inputs,
+ 'values': ((.5, 0.), (1., .1)),
+ }, (column,))[column]
+ with _initialized_session():
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=inputs.indices,
+ values=np.array(inputs.values, dtype=np.int64),
+ dense_shape=inputs.dense_shape), self.evaluate(id_tensor))
+ _assert_sparse_tensor_value(
+ self,
+ sparse_tensor.SparseTensorValue(
+ indices=((0, 0), (1, 0), (1, 1)),
+ values=np.array((.5, 1., .1), dtype=np.float32),
+ dense_shape=(2, 2)), self.evaluate(weight_tensor))
- @test_util.run_deprecated_v1
def test_keras_linear_model(self):
column = fc._weighted_categorical_column(
categorical_column=fc._categorical_column_with_identity(
@@ -6583,7 +6567,6 @@ class WeightedCategoricalColumnTest(test.TestCase):
# = 3*1 + 2*.1 = 3+.2 = 3.2
self.assertAllClose(((.5,), (3.2,)), self.evaluate(predictions))
- @test_util.run_deprecated_v1
def test_linear_model(self):
column = fc._weighted_categorical_column(
categorical_column=fc._categorical_column_with_identity(