def test_compute():
a = delayed(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 = delayed(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_nout():
func = delayed(lambda x: (x, -x), nout=2, pure=True)
x = func(1)
assert len(x) == 2
a, b = x
assert compute(a, b) == (1, -1)
assert a._length is None
assert b._length is None
pytest.raises(TypeError, lambda: len(a))
pytest.raises(TypeError, lambda: list(a))
pytest.raises(ValueError, lambda: delayed(add, nout=-1))
pytest.raises(ValueError, lambda: delayed(add, nout=True))
func = delayed(add, nout=None)
a = func(1)
assert a._length is None
pytest.raises(TypeError, lambda: list(a))
pytest.raises(TypeError, lambda: len(a))
func = delayed(lambda x: (x, ), nout=1, pure=True)
x = func(1)
assert len(x) == 1
a, = x
assert a.compute() == 1
assert a._length is None
pytest.raises(TypeError, lambda: len(a))
func = delayed(lambda x: tuple(), nout=0, pure=True)
x = func(1)
assert len(x) == 0
assert x.compute() == tuple()
def test_nout():
func = delayed(lambda x: (x, -x), nout=2, pure=True)
x = func(1)
assert len(x) == 2
a, b = x
assert compute(a, b) == (1, -1)
assert a._length is None
assert b._length is None
pytest.raises(TypeError, lambda: len(a))
pytest.raises(TypeError, lambda: list(a))
pytest.raises(ValueError, lambda: delayed(add, nout=-1))
pytest.raises(ValueError, lambda: delayed(add, nout=True))
func = delayed(add, nout=None)
a = func(1)
assert a._length is None
pytest.raises(TypeError, lambda: list(a))
pytest.raises(TypeError, lambda: len(a))
func = delayed(lambda x: (x,), nout=1, pure=True)
x = func(1)
assert len(x) == 1
a, = x
assert a.compute() == 1
assert a._length is None
pytest.raises(TypeError, lambda: len(a))
func = delayed(lambda x: tuple(), nout=0, pure=True)
x = func(1)
assert len(x) == 0
assert x.compute() == tuple()
def _fit(self, X, y, parameter_iterable):
estimator = from_sklearn(self.estimator)
self.scorer_ = check_scoring(estimator, scoring=self.scoring)
cv = check_cv(self.cv, X, y, classifier=is_classifier(estimator))
n_folds = len(cv)
X, y = check_X_y(X, y)
tups = []
parameters = []
train_test_sets = list(cv.split(X, y))
for params in parameter_iterable:
est = estimator.set_params(**params)
for X_train, y_train, X_test, y_test in train_test_sets:
fit = est.fit(X_train, y_train, **self.fit_params)
tups.append(score_and_n(self.scorer_, fit, X_test, y_test))
parameters.append(params)
# Compute results
get = self.get or _globals['get'] or threaded.get
scores, n_samples = zip(*compute(tups, get=get)[0])
# Extract grid_scores and best parameters
grid_scores = get_grid_scores(scores, parameters, n_samples, n_folds,
self.iid)
best = get_best(grid_scores)
# Update attributes
self.grid_scores_ = grid_scores
self.best_params_ = best.parameters
self.best_score_ = best.mean_validation_score
# Refit if needed
if self.refit:
self.best_estimator_ = (estimator.set_params(
**best.parameters).fit(X, y,
**self.fit_params).compute(get=get))
return self
