def eval_band_cv(X, y, times=10):
"""
:param X:
:param y:
:param times: n times k-fold cv
:return: knn/svm/elm=>(OA+std, Kappa+std)
"""
p = Processor()
img_ = maxabs_scale(X)
estimator = [
KNN(n_neighbors=5),
SVC(C=1e4, kernel='rbf', gamma=1.),
ELM_Classifier(200)
]
estimator_pre, y_test_all = [[], [], []], []
for i in range(times): # repeat N times K-fold CV
skf = StratifiedKFold(n_splits=3, shuffle=True)
for train_index, test_index in skf.split(img_, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
y_test_all.append(y_test)
for c in range(3):
estimator[c].fit(X_train, y_train)
estimator_pre[c].append(estimator[c].predict(X_test))
clf = ['knn', 'svm', 'elm']
score = []
for z in range(3):
ca, oa, aa, kappa = p.save_res_4kfolds_cv(estimator_pre[z],
y_test_all,
file_name=clf[z] +
'score.npz',
verbose=True)
score.append([oa, kappa])
return score
def eval_feature_cv(self,
X,
y,
times=3,
test_size=0.95,
random_state=None):
print(X.shape)
# X = normalize(X)
p = Processor()
estimator = [KNN(n_neighbors=5), SVC(C=1e6, kernel='rbf')]
estimator_pre, y_test_all = [[], []], []
for i in range(times): # repeat N times K-fold CV
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=test_size,
random_state=random_state,
shuffle=True,
stratify=y)
# train_index, test_index = p_Cora.stratified_train_test_index(y, test_size)
# X_train, X_test = X[train_index], X[test_index]
# y_train, y_test = y[train_index], y[test_index]
y_test_all.append(y_test)
for c in range(len(estimator)):
estimator[c].fit(X_train, y_train)
y_pre = estimator[c].predict(X_test)
estimator_pre[c].append(y_pre)
# score_Cora = []
score_dic = {
'knn': {
'ca': [],
'oa': [],
'aa': [],
'kappa': []
},
'svm': {
'ca': [],
'oa': [],
'aa': [],
'kappa': []
}
}
key_ = ['knn', 'svm']
for z in range(len(estimator)):
ca, oa, aa, kappa = p.save_res_4kfolds_cv(estimator_pre[z],
y_test_all,
file_name=None,
verbose=False)
# score_Cora.append([oa, kappa, aa, ca])
score_dic[key_[z]]['ca'] = ca
score_dic[key_[z]]['oa'] = oa
score_dic[key_[z]]['aa'] = aa
score_dic[key_[z]]['kappa'] = kappa
return score_dic
def eval_band_cv(X, y, times=10, test_size=0.95):
p = Processor()
estimator = [KNN(n_neighbors=3), SVC(C=1e5, kernel='rbf', gamma=1.)]
estimator_pre, y_test_all = [[], []], []
for i in range(times): # repeat N times K-fold CV
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=test_size,
random_state=None,
shuffle=True,
stratify=y)
# skf = StratifiedKFold(n_splits=20, shuffle=True)
# for test_index, train_index in skf.split(img_correct, gt_correct):
# X_train, X_test = img_correct[train_index], img_correct[test_index]
# y_train, y_test = gt_correct[train_index], gt_correct[test_index]
y_test_all.append(y_test)
for c in range(len(estimator)):
estimator[c].fit(X_train, y_train)
y_pre = estimator[c].predict(X_test)
estimator_pre[c].append(y_pre)
# score = []
score_dic = {
'knn': {
'ca': [],
'oa': [],
'aa': [],
'kappa': []
},
'svm': {
'ca': [],
'oa': [],
'aa': [],
'kappa': []
}
}
key_ = ['knn', 'svm']
for z in range(len(estimator)):
ca, oa, aa, kappa = p.save_res_4kfolds_cv(estimator_pre[z],
y_test_all,
file_name=None,
verbose=False)
# score.append([oa, kappa, aa, ca])
score_dic[key_[z]]['ca'] = ca
score_dic[key_[z]]['oa'] = oa
score_dic[key_[z]]['aa'] = aa
score_dic[key_[z]]['kappa'] = kappa
return score_dic
all_aa, all_oa, all_kappa, all_ca = [], [], [], []
skf = StratifiedKFold(n_splits=3, random_state=55, shuffle=True)
for j in range(X.shape[0]):
X_ = X[j]
y_pres = []
y_tests = []
for i in range(10):
for train_index, test_index in skf.split(X_, y): # [index]
X_train, X_test = X_[train_index], X_[test_index] # [index]
y_train, y_test = y[train_index], y[test_index]
elm = BaseELM(500, C=1e8)
y_predicted = elm.fit(X_train, y_train).predict(X_test)
y_pres.append(y_predicted)
y_tests.append(y_test)
ca, oa, aa, kappa = p.save_res_4kfolds_cv(np.asarray(y_pres),
np.asarray(y_tests),
file_name=None,
verbose=True)
all_oa.append(oa)
all_aa.append(aa)
all_kappa.append(kappa)
all_ca.append(ca)
np.savez('./experimental_results/Acc_comparison/IndianPines-Box_plot_data.npz',
ca=all_ca,
oa=all_oa,
aa=all_aa,
kappa=all_kappa)
print('DONE')
