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])