def CastToFloat(tensor):
if tensor.dtype == dtypes.string:
return tensor_forest_ops.reinterpret_string_to_float(tensor)
elif tensor.dtype.is_integer:
return math_ops.to_float(tensor)
else:
return tensor
def CastToFloat(tensor):
if tensor.dtype == dtypes.string:
return tensor_forest_ops.reinterpret_string_to_float(tensor)
elif tensor.dtype.is_integer:
return math_ops.to_float(tensor)
else:
return tensor
def ParseDataTensorOrDict(data):
"""Return a tensor to use for input data.
The incoming features can be a dict where keys are the string names of the
columns, which we turn into a single 2-D tensor.
Args:
data: `Tensor` or `dict` of `Tensor` objects.
Returns:
A 2-D tensor for input to tensor_forest, a keys tensor for the
tf.Examples if they exist, and a list of the type of each column
(e.g. continuous float, categorical).
"""
if isinstance(data, dict):
# If there's at least one sparse tensor, everything has to be sparse.
is_sparse = False
for v in data.values():
if isinstance(v, sparse_tensor.SparseTensor):
is_sparse = True
break
categorical_types = (dtypes.string, dtypes.int32, dtypes.int64)
data_spec = [constants.DATA_CATEGORICAL if
data[k].dtype in categorical_types else
constants.DATA_FLOAT for k in sorted(data.keys())]
data_spec = [constants.DATA_FLOAT] + data_spec
features = []
for k in sorted(data.keys()):
if data[k].dtype == dtypes.string:
features.append(tensor_forest_ops.reinterpret_string_to_float(data[k]))
elif data[k].dtype.is_integer:
features.append(math_ops.to_float(data[k]))
else:
features.append(data[k])
if is_sparse:
return sparse_ops.sparse_concat(1, features), data_spec
else:
return array_ops.concat_v2(features, 1), data_spec
else:
return (data, [constants.DATA_FLOAT])