def test_invalid_iterable_error():
with pytest.raises(ValueError):
x = [(3, 4, 5)]
COO.from_iter(x)
with pytest.raises(ValueError):
x = [((2.3, 4.5), 3.2)]
COO.from_iter(x)
def asformat(self, format):
"""
Convert this sparse array to a given format.
Parameters
----------
format : str
A format string.
Returns
-------
out : SparseArray
The converted array.
Raises
------
NotImplementedError
If the format isn't supported.
"""
if format == "dok" or format is DOK:
return self
from ._coo import COO
if format == "coo" or format is COO:
return COO.from_iter(
self.data,
shape=self.shape,
fill_value=self.fill_value,
dtype=self.dtype,
)
raise NotImplementedError("The given format is not supported.")
def asformat(self, format, **kwargs):
"""
Convert this sparse array to a given format.
Parameters
----------
format : str
A format string.
Returns
-------
out : SparseArray
The converted array.
Raises
------
NotImplementedError
If the format isn't supported.
"""
from sparse._utils import convert_format
format = convert_format(format)
if format == "dok":
return self
if format == "coo":
from ._coo import COO
if len(kwargs) != 0:
raise ValueError(f"Extra kwargs found: {kwargs}")
return COO.from_iter(
self.data,
shape=self.shape,
fill_value=self.fill_value,
dtype=self.dtype,
)
return self.asformat("coo").asformat(format, **kwargs)
def confusion_matrix(dataset: LabeledDataset,
model: TopicModel,
n_topic: Optional[int] = None) -> da.array:
labels = dataset.get_labels_vect()
predicted = model.get_labels()
L = labels.shape[0]
P = predicted.shape[0]
if L != P:
raise ValueError("Mismatching in document number")
if n_topic:
shape = (np.max(labels) + 1, n_topic + 1)
else:
shape = (
np.max(labels) + 1,
np.max(predicted) + 1,
)
return COO.from_iter(
(np.ones_like(labels, dtype=np.dtype("u1")), (labels, predicted)),
shape=shape,
fill_value=0,
dtype=np.dtype(int),
)
