def test_compute(): a = value(1) + 5 b = a + 1 c = a + 2 assert compute(b, c) == (7, 8) assert compute(b) == (7, ) assert compute([a, b], c) == ([6, 7], 8)
def test_compute(): a = value(1) + 5 b = a + 1 c = a + 2 assert compute(b, c) == (7, 8) assert compute(b) == (7,) assert compute([a, b], c) == ([6, 7], 8)
("svm", LinearSVC())]) # X, y = make_blobs() categories = [ 'alt.atheism', 'talk.religion.misc', ] data_train = fetch_20newsgroups(subset='train', categories=categories) data_test = fetch_20newsgroups(subset='test', categories=categories) X_train, y_train = data_train.data, data_train.target X_test, y_test = data_test.data, data_test.target for fdr in [0.05, 0.01, 0.1, 0.2]: for C in np.logspace(-3, 2, 3): pipeline.set_params(select_fdr__alpha=fdr, svm__C=C) pipeline.fit(X_train, y_train) results.append(pipeline.score(X_test, y_test)) """ from dask.diagnostics import ProgressBar ProgressBar().register() """ from dask.imperative import compute, value value(results).visualize('dask.pdf') results2 = compute(results, get=get_sync) print results2