def test_dense_feature_with_training_arg(self):
price1 = tf.feature_column.numeric_column("price1", shape=2)
price2 = tf.feature_column.numeric_column("price2")
# Monkey patch the second numeric column to simulate a column that has
# different behavior by mode.
def training_aware_get_dense_tensor(
transformation_cache, state_manager, training=None
):
return transformation_cache.get(
price2, state_manager, training=training
)
def training_aware_transform_feature(
transformation_cache, state_manager, training=None
):
input_tensor = transformation_cache.get(
price2.key, state_manager, training=training
)
if training:
return input_tensor * 10.0
else:
return input_tensor * 20.0
price2.get_dense_tensor = training_aware_get_dense_tensor
price2.transform_feature = training_aware_transform_feature
with tf.Graph().as_default():
features = {
"price1": [[1.0, 2.0], [5.0, 6.0]],
"price2": [[3.0], [4.0]],
}
train_mode = df.DenseFeatures([price1, price2])(
features, training=True
)
predict_mode = df.DenseFeatures([price1, price2])(
features, training=False
)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose(
[[1.0, 2.0, 30.0], [5.0, 6.0, 40.0]], self.evaluate(train_mode)
)
self.assertAllClose(
[[1.0, 2.0, 60.0], [5.0, 6.0, 80.0]],
self.evaluate(predict_mode),
)
def test_does_not_support_dict_columns(self):
with self.assertRaisesRegex(
ValueError, "Expected feature_columns to be iterable, found dict."
):
df.DenseFeatures(
feature_columns={"a": tf.feature_column.numeric_column("a")}
)(features={"a": [[0]]})
def test_column_order(self):
price_a = tf.feature_column.numeric_column('price_a')
price_b = tf.feature_column.numeric_column('price_b')
with tf.Graph().as_default():
features = {
'price_a': [[1.]],
'price_b': [[3.]],
}
net1 = df.DenseFeatures([price_a, price_b])(features)
net2 = df.DenseFeatures([price_b, price_a])(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[1., 3.]], self.evaluate(net1))
self.assertAllClose([[1., 3.]], self.evaluate(net2))
def test_linear_model_with_feature_column(self):
vocab_list = ["alpha", "beta", "gamma"]
vocab_val = [0.4, 0.6, 0.9]
data = np.random.choice(vocab_list, size=256)
y = np.zeros_like(data, dtype=np.float32)
for vocab, val in zip(vocab_list, vocab_val):
indices = np.where(data == vocab)
y[indices] = val + np.random.uniform(
low=-0.01, high=0.01, size=indices[0].shape
)
cat_column = tf.feature_column.categorical_column_with_vocabulary_list(
key="symbol", vocabulary_list=vocab_list
)
ind_column = tf.feature_column.indicator_column(cat_column)
dense_feature_layer = dense_features_v2.DenseFeatures([ind_column])
linear_model = linear.LinearModel(
use_bias=False, kernel_initializer="zeros"
)
combined = sequential.Sequential([dense_feature_layer, linear_model])
opt = gradient_descent.SGD(learning_rate=0.1)
combined.compile(opt, "mse", [])
combined.fit(x={"symbol": data}, y=y, batch_size=32, epochs=10)
self.assertAllClose(
[[0.4], [0.6], [0.9]],
combined.layers[1].dense_layers[0].kernel.numpy(),
atol=0.01,
)
def test_with_1d_unknown_shape_sparse_tensor(self):
embedding_values = (
(1., 2.), # id 0
(6., 7.), # id 1
(11., 12.) # id 2
)
def _initializer(shape, dtype, partition_info=None):
del shape, dtype, partition_info
return embedding_values
# price has 1 dimension in dense_features
price = tf.feature_column.numeric_column('price')
# one_hot_body_style has 3 dims in dense_features.
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
one_hot_body_style = tf.feature_column.indicator_column(body_style)
# embedded_body_style has 5 dims in dense_features.
country = tf.feature_column.categorical_column_with_vocabulary_list(
'country', vocabulary_list=['US', 'JP', 'CA'])
embedded_country = tf.feature_column.embedding_column(
country, dimension=2, initializer=_initializer)
# Provides 1-dim tensor and dense tensor.
with tf.Graph().as_default():
features = {
'price': tf.compat.v1.placeholder(tf.float32),
'body-style': tf.compat.v1.sparse_placeholder(tf.string),
# This is dense tensor for the categorical_column.
'country': tf.compat.v1.placeholder(tf.string),
}
self.assertIsNone(features['price'].shape.ndims)
self.assertIsNone(features['body-style'].get_shape().ndims)
self.assertIsNone(features['country'].shape.ndims)
price_data = np.array([11., 12.])
body_style_data = tf.compat.v1.SparseTensorValue(
indices=((0, ), (1, )),
values=('sedan', 'hardtop'),
dense_shape=(2, ))
country_data = np.array([['US'], ['CA']])
net = df.DenseFeatures(
[price, one_hot_body_style, embedded_country])(features)
self.assertEqual(1 + 3 + 2, net.shape[1])
with _initialized_session() as sess:
# Each row is formed by concatenating `embedded_body_style`,
# `one_hot_body_style`, and `price` in order.
self.assertAllEqual(
[[0., 0., 1., 1., 2., 11.], [1., 0., 0., 11., 12., 12.]],
sess.run(net,
feed_dict={
features['price']: price_data,
features['body-style']: body_style_data,
features['country']: country_data
}))
def test_should_be_dense_column(self):
with self.assertRaisesRegex(ValueError, 'must be a .*DenseColumn'):
df.DenseFeatures(feature_columns=[
tf.feature_column.categorical_column_with_hash_bucket(
'wire_cast', 4)
])(features={
'a': [[0]]
})
def test_raises_if_shape_mismatch(self):
price = tf.feature_column.numeric_column('price', shape=2)
with tf.Graph().as_default():
features = {'price': [[1.], [5.]]}
with self.assertRaisesRegex(
Exception,
r'Cannot reshape a tensor with 2 elements to shape \[2,2\]'
):
df.DenseFeatures([price])(features)
def test_raises_if_duplicate_name(self):
with self.assertRaisesRegex(
ValueError, 'Duplicate feature column name found for columns'):
df.DenseFeatures(feature_columns=[
tf.feature_column.numeric_column('a'),
tf.feature_column.numeric_column('a')
])(features={
'a': [[0]]
})
def test_with_1d_sparse_tensor(self):
embedding_values = (
(1., 2., 3., 4., 5.), # id 0
(6., 7., 8., 9., 10.), # id 1
(11., 12., 13., 14., 15.) # id 2
)
def _initializer(shape, dtype, partition_info=None):
del shape, dtype, partition_info
return embedding_values
# price has 1 dimension in dense_features
price = tf.feature_column.numeric_column('price')
# one_hot_body_style has 3 dims in dense_features.
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
'body-style', vocabulary_list=['hardtop', 'wagon', 'sedan'])
one_hot_body_style = tf.feature_column.indicator_column(body_style)
# embedded_body_style has 5 dims in dense_features.
country = tf.feature_column.categorical_column_with_vocabulary_list(
'country', vocabulary_list=['US', 'JP', 'CA'])
embedded_country = tf.feature_column.embedding_column(
country, dimension=5, initializer=_initializer)
with tf.Graph().as_default():
# Provides 1-dim tensor and dense tensor.
features = {
'price':
tf.constant([
11.,
12.,
]),
'body-style':
tf.SparseTensor(indices=((0, ), (1, )),
values=('sedan', 'hardtop'),
dense_shape=(2, )),
# This is dense tensor for the categorical_column.
'country':
tf.constant(['CA', 'US']),
}
self.assertEqual(1, features['price'].shape.ndims)
self.assertEqual(1,
features['body-style'].dense_shape.get_shape()[0])
self.assertEqual(1, features['country'].shape.ndims)
net = df.DenseFeatures(
[price, one_hot_body_style, embedded_country])(features)
self.assertEqual(1 + 3 + 5, net.shape[1])
with _initialized_session() as sess:
# Each row is formed by concatenating `embedded_body_style`,
# `one_hot_body_style`, and `price` in order.
self.assertAllEqual(
[[0., 0., 1., 11., 12., 13., 14., 15., 11.],
[1., 0., 0., 1., 2., 3., 4., 5., 12.]], sess.run(net))
def test_reshaping(self):
price = tf.feature_column.numeric_column('price', shape=[1, 2])
with tf.Graph().as_default():
features = {'price': [[[1., 2.]], [[5., 6.]]]}
net = df.DenseFeatures([price])(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[1., 2.], [5., 6.]], self.evaluate(net))
def test_bare_column(self):
with tf.Graph().as_default():
features = features = {'a': [0.]}
net = df.DenseFeatures(
tf.feature_column.numeric_column('a'))(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[0.]], self.evaluate(net))
def test_multi_dimension(self):
price = tf.feature_column.numeric_column("price", shape=2)
with tf.Graph().as_default():
features = {"price": [[1.0, 2.0], [5.0, 6.0]]}
net = df.DenseFeatures([price])(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[1.0, 2.0], [5.0, 6.0]], self.evaluate(net))
def test_column_generator(self):
with tf.Graph().as_default():
features = features = {'a': [0.], 'b': [1.]}
columns = (tf.feature_column.numeric_column(key)
for key in features)
net = df.DenseFeatures(columns)(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[0., 1.]], self.evaluate(net))
def test_fails_for_categorical_column(self):
animal = tf.feature_column.categorical_column_with_identity(
'animal', num_buckets=4)
with tf.Graph().as_default():
features = {
'animal':
tf.SparseTensor(indices=[[0, 0], [0, 1]],
values=[1, 2],
dense_shape=[1, 2])
}
with self.assertRaisesRegex(Exception, 'must be a .*DenseColumn'):
df.DenseFeatures([animal])(features)
def test_multi_column(self):
price1 = tf.feature_column.numeric_column('price1', shape=2)
price2 = tf.feature_column.numeric_column('price2')
with tf.Graph().as_default():
features = {'price1': [[1., 2.], [5., 6.]], 'price2': [[3.], [4.]]}
net = df.DenseFeatures([price1, price2])(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[1., 2., 3.], [5., 6., 4.]],
self.evaluate(net))
def test_static_batch_size_mismatch(self):
price1 = tf.feature_column.numeric_column('price1')
price2 = tf.feature_column.numeric_column('price2')
with tf.Graph().as_default():
features = {
'price1': [[1.], [5.], [7.]], # batchsize = 3
'price2': [[3.], [4.]] # batchsize = 2
}
with self.assertRaisesRegex(
ValueError,
r'Batch size \(first dimension\) of each feature must be same.'
): # pylint: disable=anomalous-backslash-in-string
df.DenseFeatures([price1, price2])(features)
def test_static_batch_size_mismatch(self):
price1 = tf.feature_column.numeric_column("price1")
price2 = tf.feature_column.numeric_column("price2")
with tf.Graph().as_default():
features = {
"price1": [[1.0], [5.0], [7.0]], # batchsize = 3
"price2": [[3.0], [4.0]], # batchsize = 2
}
with self.assertRaisesRegex(
ValueError,
r"Batch size \(first dimension\) of each feature must be same.",
):
df.DenseFeatures([price1, price2])(features)
def test_multiple_layers_with_same_embedding_column(self):
some_sparse_column = (
tf.feature_column.categorical_column_with_hash_bucket(
"sparse_feature", hash_bucket_size=5
)
)
some_embedding_column = tf.feature_column.embedding_column(
some_sparse_column, dimension=10
)
with tf.Graph().as_default():
features = {
"sparse_feature": [["a"], ["x"]],
}
all_cols = [some_embedding_column]
df.DenseFeatures(all_cols)(features)
df.DenseFeatures(all_cols)(features)
# Make sure that 2 variables get created in this case.
self.assertEqual(
2,
len(
tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.GLOBAL_VARIABLES
)
),
)
expected_var_names = [
"dense_features/sparse_feature_embedding/embedding_weights:0",
"dense_features_1/sparse_feature_embedding/embedding_weights:0",
]
self.assertItemsEqual(
expected_var_names,
[
v.name
for v in tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.GLOBAL_VARIABLES
)
],
)
def test_with_rank_0_feature(self):
# price has 1 dimension in dense_features
price = tf.feature_column.numeric_column('price')
features = {
'price': tf.constant(0),
}
self.assertEqual(0, features['price'].shape.ndims)
# Static rank 0 should fail
with self.assertRaisesRegex(ValueError,
'Feature .* cannot have rank 0'):
df.DenseFeatures([price])(features)
with tf.Graph().as_default():
# Dynamic rank 0 should fail
features = {
'price': tf.compat.v1.placeholder(tf.float32),
}
net = df.DenseFeatures([price])(features)
self.assertEqual(1, net.shape[1])
with _initialized_session() as sess:
with self.assertRaisesOpError('Feature .* cannot have rank 0'):
sess.run(net, feed_dict={features['price']: np.array(1)})
def test_multi_column(self):
price1 = tf.feature_column.numeric_column("price1", shape=2)
price2 = tf.feature_column.numeric_column("price2")
with tf.Graph().as_default():
features = {
"price1": [[1.0, 2.0], [5.0, 6.0]],
"price2": [[3.0], [4.0]],
}
net = df.DenseFeatures([price1, price2])(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose(
[[1.0, 2.0, 3.0], [5.0, 6.0, 4.0]], self.evaluate(net)
)
def test_subset_of_static_batch_size_mismatch(self):
price1 = tf.feature_column.numeric_column('price1')
price2 = tf.feature_column.numeric_column('price2')
price3 = tf.feature_column.numeric_column('price3')
with tf.Graph().as_default():
features = {
'price1':
tf.compat.v1.placeholder(dtype=tf.int64), # batchsize = 3
'price2': [[3.], [4.]], # batchsize = 2
'price3': [[3.], [4.], [5.]] # batchsize = 3
}
with self.assertRaisesRegex(
ValueError,
r'Batch size \(first dimension\) of each feature must be same.'
): # pylint: disable=anomalous-backslash-in-string
df.DenseFeatures([price1, price2, price3])(features)
def test_runtime_batch_size_matches(self):
price1 = tf.feature_column.numeric_column('price1')
price2 = tf.feature_column.numeric_column('price2')
with tf.Graph().as_default():
features = {
'price1':
tf.compat.v1.placeholder(dtype=tf.int64), # batchsize = 2
'price2':
tf.compat.v1.placeholder(dtype=tf.int64), # batchsize = 2
}
net = df.DenseFeatures([price1, price2])(features)
with _initialized_session() as sess:
sess.run(net,
feed_dict={
features['price1']: [[1.], [5.]],
features['price2']: [[1.], [5.]],
})
def test_runtime_batch_size_mismatch(self):
price1 = tf.feature_column.numeric_column('price1')
price2 = tf.feature_column.numeric_column('price2')
with tf.Graph().as_default():
features = {
'price1':
tf.compat.v1.placeholder(dtype=tf.int64), # batchsize = 3
'price2': [[3.], [4.]] # batchsize = 2
}
net = df.DenseFeatures([price1, price2])(features)
with _initialized_session() as sess:
with self.assertRaisesRegex(
tf.errors.OpError,
'Dimensions of inputs should match'):
sess.run(
net,
feed_dict={features['price1']: [[1.], [5.], [7.]]})
def test_subset_of_static_batch_size_mismatch(self):
price1 = tf.feature_column.numeric_column("price1")
price2 = tf.feature_column.numeric_column("price2")
price3 = tf.feature_column.numeric_column("price3")
with tf.Graph().as_default():
features = {
"price1": tf.compat.v1.placeholder(
dtype=tf.int64
), # batchsize = 3
"price2": [[3.0], [4.0]], # batchsize = 2
"price3": [[3.0], [4.0], [5.0]], # batchsize = 3
}
with self.assertRaisesRegex(
ValueError,
r"Batch size \(first dimension\) of each feature must be same.",
):
df.DenseFeatures([price1, price2, price3])(features)
def test_cols_to_output_tensors(self):
price1 = tf.feature_column.numeric_column('price1', shape=2)
price2 = tf.feature_column.numeric_column('price2')
with tf.Graph().as_default():
cols_dict = {}
features = {'price1': [[1., 2.], [5., 6.]], 'price2': [[3.], [4.]]}
dense_features = df.DenseFeatures([price1, price2])
net = dense_features(features, cols_dict)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose([[1., 2.], [5., 6.]],
self.evaluate(cols_dict[price1]))
self.assertAllClose([[3.], [4.]], self.evaluate(cols_dict[price2]))
self.assertAllClose([[1., 2., 3.], [5., 6., 4.]],
self.evaluate(net))
def test_feature_column_dense_features_gradient(self):
sparse_input = tf.SparseTensor(
indices=((0, 0), (1, 0), (2, 0)),
values=(0, 1, 2),
dense_shape=(3, 3),
)
# Create feature columns (categorical and embedding).
categorical_column = tf.feature_column.categorical_column_with_identity(
key="a", num_buckets=3
)
embedding_dimension = 2
def _embedding_column_initializer(shape, dtype, partition_info=None):
del shape # unused
del dtype # unused
del partition_info # unused
embedding_values = ((1, 0), (0, 1), (1, 1)) # id 0 # id 1 # id 2
return embedding_values
embedding_column = tf.feature_column.embedding_column(
categorical_column,
dimension=embedding_dimension,
initializer=_embedding_column_initializer,
)
dense_features = df.DenseFeatures([embedding_column])
features = {"a": sparse_input}
def scale_matrix():
matrix = dense_features(features)
return 2 * matrix
# Sanity check: Verify that scale_matrix returns the correct output.
self.assertAllEqual([[2, 0], [0, 2], [2, 2]], scale_matrix())
# Check that the returned gradient is correct.
grad_function = backprop.implicit_grad(scale_matrix)
grads_and_vars = grad_function()
indexed_slice = grads_and_vars[0][0]
gradient = grads_and_vars[0][0].values
self.assertAllEqual([0, 1, 2], indexed_slice.indices)
self.assertAllEqual([[2, 2], [2, 2], [2, 2]], gradient)
def test_compute_output_shape(self):
price1 = tf.feature_column.numeric_column('price1', shape=2)
price2 = tf.feature_column.numeric_column('price2', shape=4)
with tf.Graph().as_default():
features = {
'price1': [[1., 2.], [5., 6.]],
'price2': [[3., 4., 5., 6.], [7., 8., 9., 10.]]
}
dense_features = df.DenseFeatures([price1, price2])
self.assertEqual((None, 6),
dense_features.compute_output_shape((None, )))
net = dense_features(features)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.tables_initializer())
self.assertAllClose(
[[1., 2., 3., 4., 5., 6.], [5., 6., 7., 8., 9., 10.]],
self.evaluate(net))
def test_reuses_variables(self):
sparse_input = tf.SparseTensor(
indices=((0, 0), (1, 0), (2, 0)),
values=(0, 1, 2),
dense_shape=(3, 3),
)
# Create feature columns (categorical and embedding).
categorical_column = tf.feature_column.categorical_column_with_identity(
key="a", num_buckets=3
)
embedding_dimension = 2
def _embedding_column_initializer(shape, dtype, partition_info=None):
del shape # unused
del dtype # unused
del partition_info # unused
embedding_values = ((1, 0), (0, 1), (1, 1)) # id 0 # id 1 # id 2
return embedding_values
embedding_column = tf.feature_column.embedding_column(
categorical_column,
dimension=embedding_dimension,
initializer=_embedding_column_initializer,
)
dense_features = df.DenseFeatures([embedding_column])
features = {"a": sparse_input}
inputs = dense_features(features)
variables = dense_features.variables
# Sanity check: test that the inputs are correct.
self.assertAllEqual([[1, 0], [0, 1], [1, 1]], inputs)
# Check that only one variable was created.
self.assertEqual(1, len(variables))
# Check that invoking dense_features on the same features does not
# create additional variables
_ = dense_features(features)
self.assertEqual(1, len(variables))
self.assertIs(variables[0], dense_features.variables[0])
def test_runtime_batch_size_mismatch(self):
price1 = tf.feature_column.numeric_column("price1")
price2 = tf.feature_column.numeric_column("price2")
with tf.Graph().as_default():
features = {
"price1": tf.compat.v1.placeholder(
dtype=tf.int64
), # batchsize = 3
"price2": [[3.0], [4.0]], # batchsize = 2
}
net = df.DenseFeatures([price1, price2])(features)
with _initialized_session() as sess:
with self.assertRaisesRegex(
tf.errors.OpError,
"Dimension 0 in both shapes must be equal|"
"Dimensions of inputs should match",
):
sess.run(
net,
feed_dict={features["price1"]: [[1.0], [5.0], [7.0]]},
)
def test_wide_deep_model_with_single_feature_column(self):
vocab_list = ['alpha', 'beta', 'gamma']
vocab_val = [0.4, 0.6, 0.9]
data = np.random.choice(vocab_list, size=256)
y = np.zeros_like(data, dtype=np.float32)
for vocab, val in zip(vocab_list, vocab_val):
indices = np.where(data == vocab)
y[indices] = val + np.random.uniform(
low=-0.01, high=0.01, size=indices[0].shape)
cat_column = tf.feature_column.categorical_column_with_vocabulary_list(
key='symbol', vocabulary_list=vocab_list)
ind_column = tf.feature_column.indicator_column(cat_column)
dense_feature_layer = dense_features_v2.DenseFeatures([ind_column])
linear_model = linear.LinearModel(use_bias=False,
kernel_initializer='zeros')
dnn_model = sequential.Sequential([core.Dense(units=1)])
wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
combined = sequential.Sequential(
[dense_feature_layer, wide_deep_model])
opt = gradient_descent.SGD(learning_rate=0.1)
combined.compile(opt,
'mse', [],
run_eagerly=testing_utils.should_run_eagerly())
combined.fit(x={'symbol': data}, y=y, batch_size=32, epochs=10)
