def test_randomholdoutsplit_default_iter():
h_iter = RandomHoldoutSplit(valid_size=0.3, random_seed=123)
cnt = 0
for train_ind, valid_ind in h_iter.split(X, y):
cnt += 1
assert cnt == 1
expect_train_ind = np.array([60, 16, 88, 130, 6, 149, 7, 139, 83, 4,
147, 111, 108, 11, 44, 36, 31, 35, 40, 119,
107, 24, 103, 87, 10, 82, 29, 106, 104,
90, 118, 132, 129, 84, 85, 43, 74, 94,
73, 62, 69, 133, 52, 23, 1, 134, 96, 14, 50,
128, 8, 140, 0, 18, 55, 67, 75, 120, 78,
141, 126, 15, 5, 109, 102, 114, 3, 142, 26,
79, 71, 136, 137, 21, 48, 47, 70, 92,
124, 53, 121, 131, 54, 59, 81, 98, 49, 30,
99, 51, 112, 113, 27, 46, 97, 123, 89, 145,
95, 13, 58, 41, 12, 20, 148])
expect_valid_ind = np.array([72, 125, 80, 86, 117, 17, 33, 68, 2, 28, 122,
19, 116, 22, 91, 9, 146, 101, 38, 39, 37, 42,
25, 65, 32, 56, 115, 34, 76, 143, 144, 100,
138, 57, 93, 61, 127, 77, 110, 45, 135,
64, 66, 105, 63])
assert (train_ind == expect_train_ind).all()
assert (valid_ind == expect_valid_ind).all()
def test_randomholdoutsplit_in_grid():
params = {'n_neighbors': [1, 2, 3, 4, 5]}
grid = GridSearchCV(KNeighborsClassifier(),
param_grid=params,
cv=RandomHoldoutSplit(valid_size=0.3, random_seed=123))
grid.fit(X, y)
assert grid.n_splits_ == 1
def test_randomholdoutsplit_in_grid():
params = {'n_neighbors': [1, 2, 3, 4, 5]}
if Version(sklearn_version) < '0.24.1':
grid = GridSearchCV(KNeighborsClassifier(),
iid=False,
param_grid=params,
cv=RandomHoldoutSplit(valid_size=0.3,
random_seed=123))
else:
grid = GridSearchCV(KNeighborsClassifier(),
param_grid=params,
cv=RandomHoldoutSplit(valid_size=0.3,
random_seed=123))
grid.fit(X, y)
assert grid.n_splits_ == 1
def test_randomholdoutsplit_in_sfs():
h_iter = RandomHoldoutSplit(valid_size=0.3, random_seed=123)
knn = KNeighborsClassifier(n_neighbors=4)
sfs1 = SFS(knn,
k_features=3,
forward=True,
floating=False,
verbose=2,
scoring='accuracy',
cv=h_iter)
sfs1 = sfs1.fit(X, y)
d = sfs1.get_metric_dict()
assert d[1]['cv_scores'].shape[0] == 1