def test_deprecated():
expected = (r"'PartialSGDClassifier' is deprecated. Use "
r"'dask_ml.wrappers.Incremental.*SGDClassifier.*"
r"instead.")
with pytest.warns(FutureWarning, match=expected):
lm.PartialSGDClassifier(classes=[0, 1])
def test_lazy(xy_classification):
X, y = xy_classification
sgd = lm.PartialSGDClassifier(classes=[0, 1], max_iter=5, tol=1e-3)
r = sgd.fit(X, y, compute=False)
assert isinstance(r, Delayed)
result = r.compute()
assert isinstance(result, lm_.SGDClassifier)
def test_basic(self, single_chunk_classification):
X, y = single_chunk_classification
a = lm.PartialSGDClassifier(classes=[0, 1],
random_state=0,
max_iter=1000,
tol=1e-3)
b = lm_.SGDClassifier(random_state=0, max_iter=1000, tol=1e-3)
a.fit(X, y)
b.partial_fit(*dask.compute(X, y), classes=[0, 1])
assert_estimator_equal(a, b, exclude=exclude)
def test_numpy_arrays(self, single_chunk_classification):
# fit with dask arrays, test with numpy arrays
X, y = single_chunk_classification
a = lm.PartialSGDClassifier(classes=[0, 1],
random_state=0,
max_iter=1000,
tol=1e-3)
a.fit(X, y)
X = X.compute()
y = y.compute()
a.predict(X)
a.score(X, y)
