def test_LeaveOneSubGroupOut(self):
cv = cross_validation.LeaveOneSubGroupOut(verbose=2)
# if only one subgroup
tempG = np.copy(g)
tempG[np.where(y == 5)] = 1
self.assertRaises(Exception, cv.get_n_splits, X, y, tempG)
# if all is ok
cv = cross_validation.LeaveOneSubGroupOut(verbose=2)
y_vl = np.array([])
for tr, vl in cv.split(X, y, g):
y_vl = np.concatenate((y_vl, vl))
assert (not np.unique(np.in1d([1, 2], [3, 4]))[0])
assert (np.all(
np.unique(np.asarray(y_vl), return_counts=True)[1] == 1))
list_files = cv.save_to_vector(vector,
'Class',
group='uniquefid',
out_vector='/tmp/cv_g.gpkg')
assert (len(list_files) == cv.get_n_splits(X, y, g))
group = 'uniquefid'
X, y, g = extract_ROI(raster, vector, field, group)
##############################################################################
# Initialize Random-Forest
# ---------------------------
classifier = RandomForestClassifier(random_state=12, n_jobs=1)
##############################################################################
# Create list of different CV
# ---------------------------
CVs = [
cross_validation.RandomStratifiedKFold(n_splits=2),
cross_validation.LeavePSubGroupOut(valid_size=0.5),
cross_validation.LeaveOneSubGroupOut(),
StratifiedKFold(n_splits=2, shuffle=True) #from sklearn
]
kappas = []
for cv in CVs:
SL = SuperLearner(classifier=classifier,
param_grid=dict(n_estimators=[50, 100]),
n_jobs=1)
SL.fit(X, y, group=g, cv=cv)
print('Kappa for ' + str(type(cv).__name__))
cvKappa = []
for stats in SL.get_stats_from_cv(confusion_matrix=False, kappa=True):
print(stats['kappa'])