def test_lm(fit_intercept):
X, y = make_regression(n_samples=100, n_features=5, chunksize=10)
lr = LinearRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
if fit_intercept:
assert lr.intercept_ is not None
def test_lm(fit_intercept, is_sparse, is_cupy):
X, y = make_regression(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 = LinearRegression(fit_intercept=fit_intercept)
lr.fit(X, y)
lr.predict(X)
if fit_intercept:
assert lr.intercept_ is not None
def glm_example(): client = Client(processes=False, threads_per_worker=4, n_workers=1, memory_limit='2GB') X, y = make_regression(n_samples=200000, n_features=100, n_informative=5, chunksize=10000) X, y = dask.persist(X, y) b = dask_glm.algorithms.admm(X, y, max_iter=5) print(b) b = dask_glm.algorithms.proximal_grad(X, y, max_iter=5) print(b) family = dask_glm.families.Poisson() regularizer = dask_glm.regularizers.ElasticNet() b = dask_glm.algorithms.proximal_grad( X, y, max_iter=5, family=family, regularizer=regularizer, ) print(b)