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