X = X[:,attribute_included].reshape(-1,1)
attributeNames = attributeNames[attribute_included]
N, M = X.shape
C = len(classNames)
# K-fold crossvalidation
K = 2
CV = StratifiedKFold(K, shuffle=True)
k=0
for train_index, test_index in CV.split(X,y):
print(train_index)
# extract training and test set for current CV fold
X_train, y_train = X[train_index,:], y[train_index]
X_test, y_test = X[test_index,:], y[test_index]
logit_classifier = LogisticRegression()
logit_classifier.fit(X_train, y_train)
y_test_est = logit_classifier.predict(X_test).T
p = logit_classifier.predict_proba(X_test)[:,1].T
figure(k)
rocplot(p,y_test)
figure(k+1)
confmatplot(y_test,y_test_est)
k+=2
show()
X = X[:,attribute_included]
attributeNames = attributeNames[attribute_included]
N, M = X.shape
C = len(classNames)
# K-fold crossvalidation
K = 2
CV = cross_validation.StratifiedKFold(y.A.ravel().tolist(),K)
k=0
for train_index, test_index in CV:
# extract training and test set for current CV fold
X_train, y_train = X[train_index,:], y[train_index,:]
X_test, y_test = X[test_index,:], y[test_index,:]
logit_classifier = LogisticRegression()
logit_classifier.fit(X_train, y_train.A.ravel())
y_test_est = np.mat(logit_classifier.predict(X_test)).T
p = np.mat(logit_classifier.predict_proba(X_test)[:,1]).T
figure(k)
rocplot(p,y_test)
figure(k+1)
confmatplot(y_test,y_test_est)
k+=2
show()
