def test_fit():
with dask.config.set(scheduler="single-threaded"):
sgd = SGDClassifier(max_iter=5, tol=1e-3)
sgd = fit(sgd, X, Y, classes=np.array([-1, 0, 1]))
sol = sgd.predict(z)
result = predict(sgd, Z)
assert result.chunks == ((2, 2), )
assert result.compute().tolist() == sol.tolist()
def test_dataframes():
df = pd.DataFrame({"x": range(10), "y": [0, 1] * 5})
ddf = dd.from_pandas(df, npartitions=2)
with dask.config.set(scheduler="single-threaded"):
sgd = SGDClassifier(max_iter=5, tol=1e-3)
sgd = fit(sgd, ddf[["x"]], ddf.y, classes=[0, 1])
sol = sgd.predict(df[["x"]])
result = predict(sgd, ddf[["x"]])
da.utils.assert_eq(sol, result)
def test_fit_shuffle_blocks():
N = 10
X = da.from_array(1 + np.arange(N).reshape(-1, 1), chunks=1)
y = da.from_array(np.ones(N), chunks=1)
classes = [0, 1]
sgd = SGDClassifier(max_iter=5,
random_state=0,
fit_intercept=False,
shuffle=False,
tol=1e-3)
sgd1 = fit(clone(sgd), X, y, random_state=0, classes=classes)
sgd2 = fit(clone(sgd), X, y, random_state=42, classes=classes)
assert len(sgd1.coef_) == len(sgd2.coef_) == 1
assert not np.allclose(sgd1.coef_, sgd2.coef_)
X, y = make_classification(random_state=0, chunks=20)
sgd_a = fit(clone(sgd),
X,
y,
random_state=0,
classes=classes,
shuffle_blocks=False)
sgd_b = fit(clone(sgd),
X,
y,
random_state=42,
classes=classes,
shuffle_blocks=False)
assert np.allclose(sgd_a.coef_, sgd_b.coef_)
with pytest.raises(ValueError, match="cannot be used to seed"):
fit(
sgd,
X,
y,
classes=np.array([-1, 0, 1]),
shuffle_blocks=True,
random_state=da.random.RandomState(42),
)
def test_no_compute():
sgd = SGDClassifier(max_iter=5, tol=1e-3)
result = fit(sgd, X, Y, classes=np.array([-1, 0, 1]), compute=False)
assert isinstance(result, Delayed)
def test_no_partial_fit_raises():
X, y = make_classification(chunks=50)
with pytest.raises(ValueError, match="RandomForestClassifier"):
fit(RandomForestClassifier(), X, y)
def test_bag():
x = db.from_sequence(range(10), npartitions=2)
vect = dask_ml.feature_extraction.text.HashingVectorizer()
vect = fit(vect, x, None)
y = vect.transform(x)
assert y.shape[1] == vect.n_features