def augment_attr(attr_matrix: np.ndarray, N: int,
fill_weight: Union[float, list, np.ndarray] =0.):
"""Augment a specified attribute matrix.
Examples
----------
>>> augment_attr(attr_matrix, 10, fill_weight=1.0)
>>> augment_attr(attr_matrix, 10, fill_weight=attr_matrix[-1])
Parameters
----------
attr_matrix: shape [n_nodes, n_nodes].
A Scipy sparse adjacency matrix.
N: number of added nodes.
node ids [n_nodes, ..., n_nodes+N-1].
fill_weight: float or 1D array.
+ float scalar: the weight for the augmented matrix
+ 1D array: repeated N times to augment the matrix.
"""
if is_scalar_like(fill_weight):
M = np.zeros([N, attr_matrix.shape[1]], dtype=attr_matrix.dtype) + fill_weight
elif isinstance(fill_weight, (list, np.ndarray)):
fill_weight = fill_weight.astype(attr_matrix.dtype, copy=False)
M = np.tile(fill_weight, N).reshape([N, -1])
else:
raise ValueError(f"Unrecognized input: {fill_weight}.")
augmented_attr = np.vstack([attr_matrix, M])
return augmented_attr
def cal_outpus(func: Callable, args: List, kwargs: Dict,
type_check: bool = True):
if is_list_like(args) and not is_scalar_like(args[0]):
if type_check:
assert_same_type(*args)
return tuple(cal_outpus(func, arg, kwargs, type_check=type_check) for arg in args)
return func(args, **kwargs)
def astensor(x, dtype=None, device=None):
"""Convert input matrices to Tensor or SparseTensor.
Parameters:
----------
x: tf.Tensor, tf.Variable, Scipy sparse matrix,
Numpy array-like, etc.
dtype: The type of Tensor `x`, if not specified,
it will automatically using appropriate data type.
See `graphgallery.infer_type`.
device (:class:`torch.device`, optional): the desired device of returned tensor.
Default: if ``None``, uses the current device for the default tensor type
(see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.
Returns:
----------
Tensor or SparseTensor with dtype:
1. `graphgallery.floatx()` if `x` is floating
2. `graphgallery.intx() ` if `x` is integer
3. `'bool'` if `x` is bool.
"""
if x is None:
return x
if dtype is None:
dtype = infer_type(x)
elif isinstance(dtype, str):
...
# TODO
elif isinstance(dtype, torch.dtype):
dtype = str(dtype).split('.')[-1]
else:
raise TypeError(
f"argument 'dtype' must be torch.dtype or str, not {type(dtype).__name__}."
)
if is_th_tensor(x):
tensor = x.to(getattr(torch, dtype))
elif sp.isspmatrix(x):
tensor = sparse_adj_to_sparse_tensor(x, dtype=dtype)
elif isinstance(
x,
(np.ndarray, np.matrix)) or is_list_like(x) or is_scalar_like(x):
tensor = torch.tensor(x, dtype=getattr(torch, dtype), device=device)
else:
raise TypeError(
f'Invalid type of inputs data. Allowed data type `(Tensor, SparseTensor, Numpy array, Scipy sparse tensor, None)`, but got {type(x)}.'
)
return tensor.to(device)
def astensor(x, *, dtype=None, device=None):
"""Convert input matrices to Tensor or SparseTensor.
Parameters:
----------
x: any python object
dtype: The type of Tensor `x`, if not specified,
it will automatically use appropriate data type.
See `graphgallery.infer_type`.
device (:class:`tf.device`, optional): the desired device of returned tensor.
Default: if ``None``, uses the current device for the default tensor type.
Returns:
----------
Tensor(s) or SparseTensor(s) with dtype, if dtype is `None`:
1. `graphgallery.floatx()` if `x` is floating
2. `graphgallery.intx() ` if `x` is integer
3. `'bool'` if `x` is bool.
"""
if x is None:
return x
if dtype is None:
dtype = infer_type(x)
elif isinstance(dtype, str):
...
# TODO
elif isinstance(dtype, tf.dtypes.DType):
dtype = dtype.name
else:
raise TypeError(
f"argument 'dtype' must be tensorflow.dtypes.DType or str, not {type(dtype).__name__}."
)
with tf.device(device):
if is_tensor(x, kind="T"):
if x.dtype != dtype:
x = tf.cast(x, dtype=dtype)
return x
elif sp.isspmatrix(x):
return sparse_adj_to_sparse_tensor(x, dtype=dtype)
elif isinstance(
x,
(np.ndarray, np.matrix)) or is_list_like(x) or is_scalar_like(x):
return tf.convert_to_tensor(x, dtype=dtype)
else:
raise TypeError(
f'Invalid type of inputs data. Allowed data type `(Tensor, SparseTensor, Numpy array, Scipy sparse tensor, None)`, but got {type(x)}.'
)