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)