def cife():
before = datetime.datetime.now()
result = CIFE.cife(data, labels, mode="index", n_selected_features=treshold)
after = datetime.datetime.now()
print("CIFE")
print(len(result))
print("cas: " + str(after - before))
print('\n')
if len(result) < len(header):
transform_and_save(result, "CIFE")
def main():
# load data
mat = scipy.io.loadmat('../data/colon.mat')
X = mat['X'] # data
X = X.astype(float)
y = mat['Y'] # label
y = y[:, 0]
n_samples, n_features = X.shape # number of samples and number of features
# split data into 10 folds
ss = cross_validation.KFold(n_samples, n_folds=10, shuffle=True)
# perform evaluation on classification task
num_fea = 10 # number of selected features
clf = svm.LinearSVC() # linear SVM
correct = 0
for train, test in ss:
# obtain the index of each feature on the training set
idx = CIFE.cife(X[train], y[train], n_selected_features=num_fea)
# obtain the dataset on the selected features
features = X[:, idx[0:num_fea]]
# train a classification model with the selected features on the training dataset
clf.fit(features[train], y[train])
# predict the class labels of test data
y_predict = clf.predict(features[test])
# obtain the classification accuracy on the test data
acc = accuracy_score(y[test], y_predict)
correct = correct + acc
# output the average classification accuracy over all 10 folds
print 'Accuracy:', float(correct) / 10
def compute_cife_score(train_features, train_labels):
# no score available for CIFE extraction
idx, score, _ = CIFE.cife(train_features,
train_labels,
n_selected_features=10)
return score, idx
def CIFE_FS(X_train, y_train):
F, J_CMI, MIfy = CIFE.cife(X_train, y_train, n_selected_features=num_fea)
return (F)
sheet_test.write(r, c, roc_auc_score(Y_test, Y_pred))
r = r + 1
c = c + 1
r = 0
MV_sel = []
MV_sel.append(('MIM', MIM.mim(X_train, Y_train, n_selected_features=num_fea)))
print('MIM')
MV_sel.append(('MIFS', MIFS.mifs(X_train, Y_train,
n_selected_features=num_fea)))
print('MIFS')
MV_sel.append(('MRMR', MRMR.mrmr(X_train, Y_train,
n_selected_features=num_fea)))
print('MRMR')
MV_sel.append(('CIFE', CIFE.cife(X_train, Y_train,
n_selected_features=num_fea)))
print('CIFE')
MV_sel.append(('JMI', JMI.jmi(X_train, Y_train, n_selected_features=num_fea)))
print('JMI')
MV_sel.append(('CMIM', CMIM.cmim(X_train, Y_train,
n_selected_features=num_fea)))
print('CMIM')
MV_sel.append(('ICAP', ICAP.icap(X_train, Y_train,
n_selected_features=num_fea)))
print('ICAP')
MV_sel.append(('DISR', DISR.disr(X_train, Y_train,
n_selected_features=num_fea)))
for name, model in models:
for kind, idx in MV_sel:
#print(idx[0:num_fea][0])
for i in range(n_selected_features):
idx.append(pval[i][0])
return idx
# MULTIVARIATE FEATURE SELECTION X CLASSIFICATION (10 fold CV)
# print('BEFORE')
MV_sel = []
MV_sel.append(('WLCX', WLCX(X, Y, n_selected_features=num_fea)))
print('WLCX')
MV_sel.append(('MIFS', MIFS.mifs(X, Y, n_selected_features=num_fea)))
print('MIFS')
MV_sel.append(('MRMR', MRMR.mrmr(X, Y, n_selected_features=num_fea)))
print('MRMR')
MV_sel.append(('CIFE', CIFE.cife(X, Y, n_selected_features=num_fea)))
print('CIFE')
MV_sel.append(('JMI', JMI.jmi(X, Y, n_selected_features=num_fea)))
print('JMI')
MV_sel.append(('CMIM', CMIM.cmim(X, Y, n_selected_features=num_fea)))
print('CMIM')
MV_sel.append(('ICAP', ICAP.icap(X, Y, n_selected_features=num_fea)))
print('ICAP')
MV_sel.append(('DISR', DISR.disr(X, Y, n_selected_features=num_fea)))
for name, model in models:
for kind, idx in MV_sel:
# X_sel = X[:, idx[0:num_fea]]
# X_test_ = X_test[:,idx[0:num_fea]]
X_train_ = X_train[:, idx[0:num_fea]]
# X_validation_ = X_validation[:, idx[0:num_fea]]
# X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X_sel, Y, test_size=validation_size, random_state=seed)
resultsdf_test_balacc.boxplot()
plt.ylim([0, 1])
plt.title("SVC, Test balanced accuracy")
plt.xlabel("Class")
plt.ylabel("Balanced accuracy")
plt.savefig("/media/yannick/MANAGE/BraTS20/results/svc_cing_test_balacc.png")
plt.show()
# CIFE
X_inp = cind_filtered.drop(columns=["ID", "Survival_days"])
y_inp = cind_filtered["Survival_days"]
# normalize
# X_inp_norm =
selidx, selscore, _ = CIFE.cife(X_inp.values, y_inp.values, n_selected_features=5)
selidx_mifs, selscore_mifs, _ = MIFS.mifs(X_inp.values, y_inp.values, n_selected_features=5)
X_cife5 = X_inp.iloc[:, selidx]
X_mifs5 = X_inp.iloc[:, selidx]
clf = SVC(kernel='rbf')
# scoring = ['accuracy', 'balanced_accuracy', 'average_precision', 'recall', 'f1', 'roc_auc']
scoring = ['accuracy', 'balanced_accuracy']
n_cvruns = 50
resultsdat = np.zeros((n_cvruns, 3+1))
resultsdf_train = pd.DataFrame(data=resultsdat, columns=["Run", "STS", "MTS", "LTS"])
resultsdf_train["Run"] = ["run_" + str(elem) for elem in np.arange(n_cvruns)]
resultsdf_train.set_index("Run", inplace=True)
def CIFE_ours(train, test, K):
idx, _, _ = CIFE.cife(train[0], train[1], n_selected_features=K)
return idx[0:K]