def asarray(x: Any, dtype: Optional[str] = None) -> np.ndarray:
"""Convert `x` to interger Numpy array.
Parameters:
----------
x: Tensor, Scipy sparse matrix,
Numpy array-like, etc.
Returns:
----------
Integer Numpy array with dtype or `graphgallery.intx()`
"""
if dtype is None:
dtype = gg.intx()
if gf.is_tensor(x, backend="tensorflow"):
if x.dtype != dtype:
return tf.cast(x, dtype=dtype)
else:
return x
if gf.is_tensor(x, backend="torch"):
if x.dtype != dtype:
return x.to(getattr(torch, dtype))
else:
return x
if gg.is_intscalar(x):
x = np.asarray([x], dtype=dtype)
elif gg.is_listlike(x) or (isinstance(x, np.ndarray) and x.dtype != "O"):
x = np.asarray(x, dtype=dtype)
else:
raise ValueError(
f"Invalid input which should be either array-like or integer scalar, but got {type(x)}."
)
return x
def astensor(x, *, dtype=None, device=None, escape=None):
try:
if x is None or (escape is not None and isinstance(x, escape)):
return x
except TypeError:
raise TypeError(f"argument 'escape' must be a type or tuple of types.")
if dtype is None:
dtype = gf.infer_type(x)
if isinstance(dtype, (np.dtype, str)):
dtype = data_type_dict().get(str(dtype), dtype)
elif not isinstance(dtype, torch.dtype):
raise TypeError(
f"argument 'dtype' must be torch.dtype, np.dtype or str, but got {type(dtype)}."
)
if is_tensor(x):
tensor = x.to(dtype)
elif gf.is_tensor(x, backend='tensorflow'):
return astensor(gf.tensoras(x),
dtype=dtype,
device=device,
escape=escape)
elif sp.isspmatrix(x):
if gg.backend() == "dgl_torch":
import dgl
tensor = dgl.from_scipy(x, idtype=getattr(torch, gg.intx()))
elif gg.backend() == "pyg":
edge_index, edge_weight = gf.sparse_adj_to_edge(x)
return (astensor(edge_index,
dtype=gg.intx(),
device=device,
escape=escape),
astensor(edge_weight,
dtype=gg.floatx(),
device=device,
escape=escape))
else:
tensor = sparse_adj_to_sparse_tensor(x, dtype=dtype)
elif any((isinstance(x, (np.ndarray, np.matrix)), gg.is_listlike(x),
gg.is_scalar(x))):
tensor = torch.tensor(x, dtype=dtype, device=device)
else:
raise TypeError(
f"Invalid type of inputs. 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, escape=None):
try:
if x is None or (escape is not None and isinstance(x, escape)):
return x
except TypeError:
raise TypeError(f"argument 'escape' must be a type or tuple of types.")
if dtype is None:
dtype = gf.infer_type(x)
elif isinstance(dtype, tf.dtypes.DType):
dtype = dtype.name
elif isinstance(dtype, (np.dtype, str)):
dtype = str(dtype)
else:
raise TypeError(
f"argument 'dtype' must be tf.dtypes.DType, np.dtype or str, but got {type(dtype)}."
)
with tf.device(device):
if is_tensor(x):
if x.dtype != dtype:
return tf.cast(x, dtype=dtype)
return tf.identity(x)
elif gf.is_tensor(x, backend='torch'):
return astensor(gf.tensoras(x),
dtype=dtype,
device=device,
escape=escape)
elif sp.isspmatrix(x):
if gg.backend() == "dgl_tf":
import dgl
return dgl.from_scipy(x, idtype=getattr(tf,
gg.intx())).to(device)
else:
return sparse_adj_to_sparse_tensor(x, dtype=dtype)
elif any((isinstance(x, (np.ndarray, np.matrix)), gg.is_listlike(x),
gg.is_scalar(x))):
return tf.convert_to_tensor(x, dtype=dtype)
else:
raise TypeError(
f"Invalid type of inputs. Allowed data type(Tensor, SparseTensor, Numpy array, Scipy sparse matrix, None), but got {type(x)}."
)