def extract_features(features, feature_columns):
"""Extracts columns from a dictionary of features.
Args:
features: `dict` of `Tensor` objects.
feature_columns: A list of feature_columns.
Returns:
Seven values:
- A list of all feature column names.
- A list of dense floats.
- A list of sparse float feature indices.
- A list of sparse float feature values.
- A list of sparse float feature shapes.
- A list of sparse int feature indices.
- A list of sparse int feature values.
- A list of sparse int feature shapes.
Raises:
ValueError: if features is not valid.
"""
if not features:
raise ValueError("Features dictionary must be specified.")
# Make a shallow copy of features to ensure downstream usage
# is unaffected by modifications in the model function.
features = copy.copy(features)
if feature_columns:
scope = "gbdt"
with variable_scope.variable_scope(scope):
feature_columns = list(feature_columns)
transformed_features = {}
for fc in feature_columns:
# pylint: disable=protected-access
if isinstance(fc, feature_column_lib._EmbeddingColumn):
# pylint: enable=protected-access
transformed_features[fc.name] = fc_core.input_layer(
features, [fc], weight_collections=[scope])
else:
result = feature_column_ops.transform_features(
features, [fc])
if len(result) > 1:
raise ValueError(
"Unexpected number of output features")
transformed_features[fc.name] = result[list(
result.keys())[0]]
features = transformed_features
dense_float_names = []
dense_floats = []
sparse_float_names = []
sparse_float_indices = []
sparse_float_values = []
sparse_float_shapes = []
sparse_int_names = []
sparse_int_indices = []
sparse_int_values = []
sparse_int_shapes = []
for key in sorted(features.keys()):
tensor = features[key]
if isinstance(tensor, sparse_tensor.SparseTensor):
if tensor.values.dtype == dtypes.float32:
sparse_float_names.append(key)
sparse_float_indices.append(tensor.indices)
sparse_float_values.append(tensor.values)
sparse_float_shapes.append(tensor.dense_shape)
elif tensor.values.dtype == dtypes.int64:
sparse_int_names.append(key)
sparse_int_indices.append(tensor.indices)
sparse_int_values.append(tensor.values)
sparse_int_shapes.append(tensor.dense_shape)
else:
raise ValueError(
"Unsupported sparse feature %s with dtype %s." %
(tensor.indices.name, tensor.dtype))
else:
if tensor.dtype == dtypes.float32:
if len(tensor.shape) > 1 and tensor.shape[1] > 1:
unstacked = array_ops.unstack(tensor, axis=1)
for i in range(len(unstacked)):
dense_float_names.append(_FEATURE_NAME_TEMPLATE %
(key, i))
dense_floats.append(
array_ops.reshape(unstacked[i], [-1, 1]))
else:
dense_float_names.append(key)
dense_floats.append(tensor)
else:
raise ValueError(
"Unsupported dense feature %s with dtype %s." %
(tensor.name, tensor.dtype))
# Feature columns are logically organized into incrementing slots starting
# from dense floats, then sparse floats then sparse ints.
fc_names = (dense_float_names + sparse_float_names + sparse_int_names)
return (fc_names, dense_floats, sparse_float_indices, sparse_float_values,
sparse_float_shapes, sparse_int_indices, sparse_int_values,
sparse_int_shapes)
def extract_features(features, feature_columns):
"""Extracts columns from a dictionary of features.
Args:
features: `dict` of `Tensor` objects.
feature_columns: A list of feature_columns.
Returns:
Seven values:
- A list of all feature column names.
- A list of dense floats.
- A list of sparse float feature indices.
- A list of sparse float feature values.
- A list of sparse float feature shapes.
- A list of sparse int feature indices.
- A list of sparse int feature values.
- A list of sparse int feature shapes.
Raises:
ValueError: if features is not valid.
"""
if not features:
raise ValueError("Features dictionary must be specified.")
# Make a shallow copy of features to ensure downstream usage
# is unaffected by modifications in the model function.
features = copy.copy(features)
if feature_columns:
scope = "gbdt"
with variable_scope.variable_scope(scope):
feature_columns = list(feature_columns)
transformed_features = {}
for fc in feature_columns:
# pylint: disable=protected-access
if isinstance(fc, feature_column_lib._EmbeddingColumn):
# pylint: enable=protected-access
transformed_features[fc.name] = fc_core.input_layer(
features, [fc],
weight_collections=[scope])
else:
result = feature_column_ops.transform_features(features, [fc])
if len(result) > 1:
raise ValueError("Unexpected number of output features")
transformed_features[fc.name] = result[list(result.keys())[0]]
features = transformed_features
dense_float_names = []
dense_floats = []
sparse_float_names = []
sparse_float_indices = []
sparse_float_values = []
sparse_float_shapes = []
sparse_int_names = []
sparse_int_indices = []
sparse_int_values = []
sparse_int_shapes = []
for key in sorted(features.keys()):
tensor = features[key]
if isinstance(tensor, sparse_tensor.SparseTensor):
if tensor.values.dtype == dtypes.float32:
sparse_float_names.append(key)
sparse_float_indices.append(tensor.indices)
sparse_float_values.append(tensor.values)
sparse_float_shapes.append(tensor.dense_shape)
elif tensor.values.dtype == dtypes.int64:
sparse_int_names.append(key)
sparse_int_indices.append(tensor.indices)
sparse_int_values.append(tensor.values)
sparse_int_shapes.append(tensor.dense_shape)
else:
raise ValueError("Unsupported sparse feature %s with dtype %s." %
(tensor.indices.name, tensor.dtype))
else:
if tensor.dtype == dtypes.float32:
if len(tensor.shape) > 1 and tensor.shape[1] > 1:
unstacked = array_ops.unstack(tensor, axis=1)
for i in xrange(len(unstacked)):
dense_float_names.append(_FEATURE_NAME_TEMPLATE % (key, i))
dense_floats.append(array_ops.reshape(unstacked[i], [-1, 1]))
else:
dense_float_names.append(key)
dense_floats.append(tensor)
else:
raise ValueError("Unsupported dense feature %s with dtype %s." %
(tensor.name, tensor.dtype))
# Feature columns are logically organized into incrementing slots starting
# from dense floats, then sparse floats then sparse ints.
fc_names = (dense_float_names + sparse_float_names + sparse_int_names)
return (fc_names, dense_floats, sparse_float_indices, sparse_float_values,
sparse_float_shapes, sparse_int_indices, sparse_int_values,
sparse_int_shapes)