def test_dense_input_wrong_shape_fails(self):
x = np.array([[3, 2, 1], [5, 4, 4]], dtype=np.int32)
weights = np.array([[3, 2], [5, 4], [4, 3]])
# Note: Eager mode and graph mode throw different errors here. Graph mode
# will fail with a ValueError from the shape checking logic, while Eager
# will fail with an InvalidArgumentError from the kernel itself.
if context.executing_eagerly():
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"must have the same shape"):
self.evaluate(bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
else:
with self.assertRaisesRegexp(ValueError, "both shapes must be equal"):
self.evaluate(bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def call(self, inputs, count_weights=None):
if isinstance(inputs, (list, np.ndarray)):
inputs = ops.convert_to_tensor_v2(inputs)
if inputs.shape.rank == 1:
inputs = array_ops.expand_dims(inputs, 1)
if count_weights is not None and self._output_mode != COUNT:
raise ValueError(
"count_weights is not used in `output_mode='tf-idf'`, "
"or `output_mode='binary'`. Please pass a single input.")
self._called = True
if self._max_tokens is None:
out_depth = K.get_value(self.num_elements)
if out_depth == 0:
raise RuntimeError(
"If you construct a `CategoryEncoding` layer with "
"`max_tokens=None`, you need to call `adapt()` "
"on it before using it")
else:
out_depth = self._max_tokens
if self._output_mode == TFIDF:
# If the input is a sparse tensor, we densify it with the default value of
# -1. Because -1 is ignored by one_hot, this effectively drops the non-set
# positions from the output encoding.
if self._sparse:
raise ValueError("`sparse=True` with `output_mode=tfidf` "
"is not supported.")
if isinstance(inputs, sparse_tensor.SparseTensor):
inputs = sparse_ops.sparse_tensor_to_dense(inputs,
default_value=-1)
one_hot_data = array_ops.one_hot(inputs, depth=out_depth)
counts = math_ops.reduce_sum(one_hot_data, axis=1)
tf_idf_data = math_ops.multiply(counts, self.tf_idf_weights)
tf_idf_data.set_shape(tensor_shape.TensorShape((None, out_depth)))
return tf_idf_data
binary_output = (self._output_mode == BINARY)
if self._sparse:
result = bincount_ops.sparse_bincount(inputs,
weights=count_weights,
minlength=out_depth,
axis=-1,
binary_output=binary_output)
result = math_ops.cast(result, K.floatx())
batch_size = array_ops.shape(result)[0]
result = sparse_tensor.SparseTensor(
indices=result.indices,
values=result.values,
dense_shape=[batch_size, out_depth])
return result
else:
result = bincount_ops.bincount(inputs,
weights=count_weights,
minlength=out_depth,
dtype=K.floatx(),
axis=-1,
binary_output=binary_output)
result.set_shape(tensor_shape.TensorShape((None, out_depth)))
return result
def test_dense_input_ragged_weights_fails(self):
x = np.array([[3, 2, 1], [5, 4, 4]], dtype=np.int32)
weights = ragged_factory_ops.constant([[6, 0.5, 2], [14],
[10, 0.25, 5, 3]])
with self.assertRaisesRegex(ValueError, "must be a tf.Tensor"):
self.evaluate(
bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def call(self, inputs):
self._called = True
if self._max_tokens is None:
out_depth = K.get_value(self.num_elements)
else:
out_depth = self._max_tokens
if self._output_mode == TFIDF:
# If the input is a sparse tensor, we densify it with the default value of
# -1. Because -1 is ignored by one_hot, this effectively drops the non-set
# positions from the output encoding.
if isinstance(inputs, sparse_tensor.SparseTensor):
inputs = sparse_ops.sparse_tensor_to_dense(inputs, default_value=-1)
one_hot_data = array_ops.one_hot(inputs, depth=out_depth)
counts = math_ops.reduce_sum(one_hot_data, axis=1)
tf_idf_data = math_ops.multiply(counts, self.tf_idf_weights)
tf_idf_data.set_shape(tensor_shape.TensorShape((None, out_depth)))
return tf_idf_data
binary_output = (self._output_mode == BINARY)
if self._sparse:
return bincount_ops.sparse_bincount(
inputs, minlength=out_depth, axis=-1, binary_output=binary_output)
else:
result = bincount_ops.bincount(
inputs,
minlength=out_depth,
dtype=dtypes.int64,
axis=-1,
binary_output=binary_output)
result.set_shape(tensor_shape.TensorShape((None, out_depth)))
return result
def test_sparse_input_too_many_indices_fails(self):
x = sparse_ops.from_dense(
np.array([[3, 0, 1, 0], [0, 0, 0, 0], [5, 0, 4, 4]], dtype=np.int32))
weights = sparse_ops.from_dense(
np.array([[3, 1, 1, 0], [0, 0, 0, 0], [5, 0, 4, 4]], dtype=np.int32))
with self.assertRaisesIncompatibleShapesError():
self.evaluate(bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def test_sparse_input_wrong_indices_fails(self):
x = sparse_ops.from_dense(
np.array([[3, 0, 1, 0], [0, 0, 0, 0], [5, 0, 4, 4]], dtype=np.int32))
weights = sparse_ops.from_dense(
np.array([[3, 1, 0, 0], [0, 0, 0, 0], [5, 0, 4, 4]], dtype=np.int32))
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"must have the same indices"):
self.evaluate(bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def test_ragged_input_different_shape_fails(self):
x = ragged_factory_ops.constant([[6, 1, 2], [14], [10, 1, 5, 3]])
weights = ragged_factory_ops.constant([[6, 0.5, 2], [],
[10, 0.25, 5, 3]])
with self.assertRaisesRegex(errors.InvalidArgumentError,
"must have the same row splits"):
self.evaluate(
bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def test_ragged_input_sparse_weights_fails(self):
x = ragged_factory_ops.constant([[6, 1, 2], [14], [10, 1, 5, 3]])
weights = sparse_ops.from_dense(
np.array([[3, 0, 1, 0], [0, 0, 0, 0], [5, 0, 4, 4]],
dtype=np.int32))
with self.assertRaisesRegex(ValueError, "must be a RaggedTensor"):
self.evaluate(
bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def test_sparse_input_dense_weights_fails(self):
x = sparse_ops.from_dense(
np.array([[3, 0, 1, 0], [0, 0, 0, 0], [5, 0, 4, 4]],
dtype=np.int32))
weights = np.array([[3, 2, 1], [5, 4, 4]], dtype=np.int32)
with self.assertRaisesRegex(ValueError, "must be a SparseTensor"):
self.evaluate(
bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
def sparse_bincount(inputs, out_depth, multi_hot_output, count_weights=None):
"""Apply binary or count encoding to an input and return a sparse tensor."""
result = bincount_ops.sparse_bincount(inputs,
weights=count_weights,
minlength=out_depth,
maxlength=out_depth,
axis=-1,
binary_output=multi_hot_output)
result = math_ops.cast(result, backend.floatx())
batch_size = array_ops.shape(result)[0]
result = sparse_tensor.SparseTensor(indices=result.indices,
values=result.values,
dense_shape=[batch_size, out_depth])
return result
def test_dense_input(self,
x,
expected_indices,
expected_values,
expected_shape,
minlength=None,
maxlength=None,
binary_output=False,
weights=None,
axis=-1):
y = bincount_ops.sparse_bincount(x,
weights=weights,
minlength=minlength,
maxlength=maxlength,
binary_output=binary_output,
axis=axis)
self.assertAllEqual(expected_indices, y.indices)
self.assertAllEqual(expected_values, y.values)
self.assertAllEqual(expected_shape, y.dense_shape)
def call(self, inputs, count_weights=None):
if count_weights is not None and self._output_mode != COUNT:
raise ValueError(
"count_weights is not used in `output_mode='tf-idf'`, "
"or `output_mode='binary'`. Please pass a single input.")
self._called = True
if self._max_tokens is None:
out_depth = K.get_value(self.num_elements)
else:
out_depth = self._max_tokens
if self._output_mode == TFIDF:
# If the input is a sparse tensor, we densify it with the default value of
# -1. Because -1 is ignored by one_hot, this effectively drops the non-set
# positions from the output encoding.
if isinstance(inputs, sparse_tensor.SparseTensor):
inputs = sparse_ops.sparse_tensor_to_dense(inputs,
default_value=-1)
one_hot_data = array_ops.one_hot(inputs, depth=out_depth)
counts = math_ops.reduce_sum(one_hot_data, axis=1)
tf_idf_data = math_ops.multiply(counts, self.tf_idf_weights)
tf_idf_data.set_shape(tensor_shape.TensorShape((None, out_depth)))
return tf_idf_data
binary_output = (self._output_mode == BINARY)
if self._sparse:
result = bincount_ops.sparse_bincount(inputs,
weights=count_weights,
minlength=out_depth,
axis=-1,
binary_output=binary_output)
return math_ops.cast(result, K.floatx())
else:
result = bincount_ops.bincount(inputs,
weights=count_weights,
minlength=out_depth,
dtype=K.floatx(),
axis=-1,
binary_output=binary_output)
result.set_shape(tensor_shape.TensorShape((None, out_depth)))
return result
def test_ragged_input(self,
x,
expected_indices,
expected_values,
expected_shape,
maxlength=None,
minlength=None,
binary_output=False,
weights=None,
axis=-1):
x_ragged = ragged_factory_ops.constant(x)
w = ragged_factory_ops.constant(weights) if weights is not None else None
y = bincount_ops.sparse_bincount(
x_ragged,
weights=w,
minlength=minlength,
maxlength=maxlength,
binary_output=binary_output,
axis=axis)
self.assertAllEqual(expected_indices, y.indices)
self.assertAllEqual(expected_values, y.values)
self.assertAllEqual(expected_shape, y.dense_shape)
def test_sparse_input(self,
x,
expected_indices,
expected_values,
expected_shape,
maxlength=None,
minlength=None,
binary_output=False,
weights=None,
axis=-1):
x_sparse = sparse_ops.from_dense(x)
w_sparse = sparse_ops.from_dense(weights) if weights is not None else None
y = bincount_ops.sparse_bincount(
x_sparse,
weights=w_sparse,
minlength=minlength,
maxlength=maxlength,
binary_output=binary_output,
axis=axis)
self.assertAllEqual(expected_indices, y.indices)
self.assertAllEqual(expected_values, y.values)
self.assertAllEqual(expected_shape, y.dense_shape)
