def _tensor_to_sparse_feature_column(dense_tensor):
"""Returns SparseFeatureColumn for the input dense_tensor."""
ignore_value = 0.0
sparse_indices = array_ops.where(math_ops.not_equal(
dense_tensor, math_ops.cast(ignore_value, dense_tensor.dtype)))
sparse_values = array_ops.gather_nd(dense_tensor, sparse_indices)
# TODO(sibyl-Aix6ihai, sibyl-vie3Poto): Makes this efficient, as now SDCA supports
# very sparse features with weights and not weights.
return sdca_ops.SparseFeatureColumn(
array_ops.reshape(
array_ops.split(1, 2, sparse_indices)[0], [-1]),
array_ops.reshape(
array_ops.split(1, 2, sparse_indices)[1], [-1]),
array_ops.reshape(
math_ops.to_float(sparse_values), [-1]))
def _training_examples_and_variables():
"""Returns dictionaries for training examples and variables."""
batch_size = targets.get_shape()[0]
# Iterate over all feature columns and create appropriate lists for dense
# and sparse features as well as dense and sparse weights (variables) for
# SDCA.
# TODO(sibyl-vie3Poto): Reshape variables stored as values in column_to_variables
# dict as 1-dimensional tensors.
dense_features, sparse_features, sparse_feature_with_values = [], [], []
dense_feature_weights = []
sparse_feature_weights, sparse_feature_with_values_weights = [], []
# pylint: disable=protected-access
for column in sorted(columns_to_variables.keys(), key=lambda x: x.key):
transformed_tensor = features[column]
if isinstance(column, layers.feature_column._RealValuedColumn):
# A real-valued column corresponds to a dense feature in SDCA. A
# transformed tensor corresponding to a RealValuedColumn has rank 2
# (its shape is typically [batch_size, column.dimension]) and so it
# can be passed to SDCA as is.
dense_features.append(transformed_tensor)
# For real valued columns, the variables list contains exactly one
# element.
dense_feature_weights.append(columns_to_variables[column][0])
elif isinstance(column, layers.feature_column._BucketizedColumn):
# A bucketized column corresponds to a sparse feature in SDCA. The
# bucketized feature is "sparsified" for SDCA by converting it to a
# SparseFeatureColumn respresenting the one-hot encoding of the
# bucketized feature.
dense_bucket_tensor = layers.input_from_feature_columns(
{column: transformed_tensor}, [column])
sparse_feature_column = _tensor_to_sparse_feature_column(
dense_bucket_tensor)
sparse_feature_with_values.append(sparse_feature_column)
# For bucketized columns, the variables list contains exactly one
# element.
sparse_feature_with_values_weights.append(
columns_to_variables[column][0])
elif isinstance(column, (layers.feature_column._CrossedColumn,
layers.feature_column._SparseColumn)):
sparse_features.append(sdca_ops.SparseFeatureColumn(
array_ops.reshape(
array_ops.split(1, 2, transformed_tensor.indices)[0], [-1]),
array_ops.reshape(transformed_tensor.values, [-1]), None))
sparse_feature_weights.append(columns_to_variables[column][0])
elif isinstance(column, layers.feature_column._WeightedSparseColumn):
id_tensor = column.id_tensor(transformed_tensor)
weight_tensor = column.weight_tensor(transformed_tensor)
sparse_feature_with_values.append(sdca_ops.SparseFeatureColumn(
array_ops.reshape(
array_ops.split(1, 2, id_tensor.indices)[0], [-1]),
array_ops.reshape(id_tensor.values, [-1]), array_ops.reshape(
weight_tensor.values, [-1])))
sparse_feature_with_values_weights.append(
columns_to_variables[column][0])
else:
raise ValueError('SDCAOptimizer does not support column type %s.' %
type(column).__name__)
# pylint: enable=protected-access
example_weights = array_ops.reshape(
features[weight_column_name],
shape=[-1]) if weight_column_name else array_ops.ones([batch_size])
example_ids = features[self._example_id_column]
sparse_feature_with_values.extend(sparse_features)
sparse_feature_with_values_weights.extend(sparse_feature_weights)
examples = dict(sparse_features=sparse_feature_with_values,
dense_features=dense_features,
example_labels=math_ops.to_float(array_ops.reshape(
targets, shape=[-1])),
example_weights=example_weights,
example_ids=example_ids)
sdca_variables = dict(
sparse_features_weights=sparse_feature_with_values_weights,
dense_features_weights=dense_feature_weights)
return examples, sdca_variables
def _training_examples_and_variables():
"""Returns dictionaries for training examples and variables."""
batch_size = targets.get_shape()[0]
# Iterate over all feature columns and create appropriate lists for dense
# and sparse features as well as dense and sparse weights (variables) for
# SDCA.
# TODO(sibyl-vie3Poto): Reshape variables stored as values in column_to_variables
# dict as 1-dimensional tensors.
dense_features, sparse_features, sparse_feature_with_values = [], [], []
dense_feature_weights = []
sparse_feature_weights, sparse_feature_with_values_weights = [], []
# pylint: disable=protected-access
for column in sorted(set(linear_feature_columns),
key=lambda x: x.key):
transformed_tensor = features[column]
if isinstance(column, layers.feature_column._RealValuedColumn):
# A real-valued column corresponds to a dense feature in SDCA.
if column.dimension != 1:
raise ValueError(
"Invalid column dimension %d for column %s. SDCAOptimizer "
"supports only 1-dimensional dense feature columns."
% (column.dimension, column.name))
# TODO(sibyl-Aix6ihai, sibyl-vie3Poto): SDCA supports efficient dense representation.
# Perhaps concat dense features for efficiency.
dense_features.append(
array_ops.reshape(transformed_tensor, shape=[-1, 1]))
# For real valued columns, the variables list contains exactly one
# element.
dense_feature_weights.append(
columns_to_variables[column][0])
elif isinstance(column,
layers.feature_column._BucketizedColumn):
# A bucketized column corresponds to a sparse feature in SDCA. The
# bucketized feature is "sparsified" for SDCA by converting it to a
# SparseFeatureColumn respresenting the one-hot encoding of the
# bucketized feature.
dense_bucket_tensor = column.to_dnn_input_layer(
transformed_tensor)
sparse_feature_column = _tensor_to_sparse_feature_column(
dense_bucket_tensor)
sparse_feature_with_values.append(sparse_feature_column)
# For bucketized columns, the variables list contains exactly one
# element.
sparse_feature_with_values_weights.append(
columns_to_variables[column][0])
elif isinstance(column, (layers.feature_column._CrossedColumn,
layers.feature_column._SparseColumn)):
sparse_features.append(
sdca_ops.SparseFeatureColumn(
array_ops.reshape(
array_ops.split(1, 2,
transformed_tensor.indices)[0],
[-1]),
array_ops.reshape(transformed_tensor.values, [-1]),
None))
sparse_feature_weights.append(
columns_to_variables[column][0])
elif isinstance(column,
layers.feature_column._WeightedSparseColumn):
id_tensor = column.id_tensor(transformed_tensor)
weight_tensor = column.weight_tensor(transformed_tensor)
sparse_feature_with_values.append(
sdca_ops.SparseFeatureColumn(
array_ops.reshape(
array_ops.split(1, 2,
id_tensor.indices)[0], [-1]),
array_ops.reshape(id_tensor.values, [-1]),
array_ops.reshape(weight_tensor.values, [-1])))
sparse_feature_with_values_weights.append(
columns_to_variables[column][0])
else:
raise ValueError(
"SDCAOptimizer does not support column type %s." %
type(column).__name__)
# pylint: enable=protected-access
example_weights = array_ops.reshape(
features[weight_column_name], shape=[
-1
]) if weight_column_name else array_ops.ones([batch_size])
example_ids = features[self._example_id_column]
sparse_feature_with_values.extend(sparse_features)
sparse_feature_with_values_weights.extend(sparse_feature_weights)
examples = dict(sparse_features=sparse_feature_with_values,
dense_features=dense_features,
example_labels=math_ops.to_float(
array_ops.reshape(targets, shape=[-1])),
example_weights=example_weights,
example_ids=example_ids)
sdca_variables = dict(
sparse_features_weights=sparse_feature_with_values_weights,
dense_features_weights=dense_feature_weights)
return examples, sdca_variables
