def test_run_predict():
"""[Parallel | Wrapper] test predict with auxiliary"""
lr, tr = EstimatorContainer().get_learner('stack', False, True)
group = Group(learners=lr, transformers=tr, dtype=np.float64)
_run(group, 'fit', X, y)
A = _run(group, 'predict', X)
np.testing.assert_array_equal(A, P)
def test_clone():
"""[Parallel | Learner] Testing cloning"""
lr = Learner(OLS(), indexer=data.indexer, name='lr')
l = clone(lr)
l.set_indexer(data.indexer)
assert not l.__fitted__
F = _run(l, 'fit', X, y, return_preds=True)
H = _run(lr, 'fit', X, y, return_preds=True, refit=False)
np.testing.assert_array_equal(F, H)
def test_data():
"""[Parallel | Learner] Test data attribute"""
lr = Learner(OLS(), indexer=data.indexer, name='lr')
lr.scorer = scorer
_run(lr, 'fit', X, y, return_preds=True)
assert lr.raw_data
assert isinstance(lr.raw_data, list)
assert isinstance(lr.data, dict)
assert lr.data.__repr__()
assert 'score' in lr.data.__repr__()
def test_collect():
"""[Parallel | Learner] Testing multiple collections"""
lr = Learner(OLS(), indexer=data.indexer, name='lr')
lr.__no_output__ = False
a = _run(lr, 'fit', X, y, return_preds=True)
b = _run(lr, 'fit', X, y, refit=False, return_preds=True)
c = _run(lr, 'transform', X, y, return_preds=True)
d = _run(lr, 'transform', X, y)
np.testing.assert_array_equal(a, b)
np.testing.assert_array_equal(a, c)
np.testing.assert_array_equal(a, d)
def run(cls, job, eval=True):
"""Run a test"""
if job == 'fit':
lr, _ = est.get_learner(cls, True, False)
lr.dtype = np.float64
else:
lr = run(cls, 'fit', False)
data = Data(cls, True, False, True)
X, y = data.get_data((25, 4), 3)
if job in ['fit', 'transform']:
(F, wf), _ = data.ground_truth(X, y, data.indexer.partitions)
else:
_, (F, wf) = data.ground_truth(X, y, data.indexer.partitions)
args = {'fit': [X, y], 'transform': [X], 'predict': [X]}[job]
P = _run(lr, job, *args, return_preds=True)
if not eval:
return lr
np.testing.assert_array_equal(P, F)
if job in ['fit', 'transform']:
lrs = lr.sublearners
else:
lrs = lr.learner
w = [obj.estimator.coef_ for obj in lrs]
np.testing.assert_array_equal(w, wf)
def test_transformer():
"""[Parallel | Transform] test run transformer as estimator"""
_, tr = est.get_learner('stack', True, True)
data = Data('stack', True, True, True)
X, y = data.get_data((25, 4), 3)
F = _run(tr, 'fit', X, y, return_preds=True)
H = _run(tr, 'transform', X, y, return_preds=True)
P = _run(tr, 'predict', X, y, return_preds=True)
Z, _ = tr.estimator.fit_transform(X)
G = _run(tr, 'fit', X, y, return_preds=True, refit=False)
np.testing.assert_array_equal(H, F)
np.testing.assert_array_equal(Z, P)
np.testing.assert_array_equal(G, F)
def test_fit():
"""[Parallel | Learner] Testing fit flags"""
lr = Learner(OLS(), indexer=data.indexer, name='lr')
assert not lr.__fitted__
_run(lr, 'fit', X, y)
assert lr.__fitted__
