def test_big(fit_intercept, is_sparse):
with dask.config.set(scheduler='synchronous'):
X, y = make_classification(is_sparse=is_sparse)
lr = LogisticRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
lr.predict_proba(X)
if fit_intercept:
assert lr.intercept_ is not None
def test_fit(fit_intercept, is_sparse):
X, y = make_classification(n_samples=100,
n_features=5,
chunksize=10,
is_sparse=is_sparse)
lr = LogisticRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
lr.predict_proba(X)
def test_big(fit_intercept):
import dask
dask.set_options(get=dask.get)
X, y = make_classification()
lr = LogisticRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
lr.predict_proba(X)
if fit_intercept:
assert lr.intercept_ is not None
def test_big(fit_intercept, is_sparse, is_cupy):
with dask.config.set(scheduler='synchronous'):
X, y = make_classification(is_sparse=is_sparse)
if is_cupy and not is_sparse:
cupy = pytest.importorskip('cupy')
X, y = to_dask_cupy_array_xy(X, y, cupy)
lr = LogisticRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
lr.predict_proba(X)
if fit_intercept:
assert lr.intercept_ is not None
def test_fit(fit_intercept, is_sparse, is_cupy):
X, y = make_classification(n_samples=100,
n_features=5,
chunksize=10,
is_sparse=is_sparse)
if is_cupy and not is_sparse:
cupy = pytest.importorskip('cupy')
X, y = to_dask_cupy_array_xy(X, y, cupy)
lr = LogisticRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
lr.predict_proba(X)