def train_rls():
X_train, Y_train, X_test, Y_test = load_newsgroups()
#CGRLS does not support multi-output learning, so we train
#one classifier for the first column of Y. Multi-class learning
#would be implemented by training one CGRLS for each column, and
#taking the argmax of class predictions.
predictions = []
rls = CGRankRLS(X_train, Y_train[:,0], regparam= 100.0)
P = rls.predict(X_test)
perf = auc(Y_test[:,0], P)
print("auc for task 1 %f" %perf)
def train_rls():
X_train, Y_train, X_test, Y_test = load_newsgroups()
#CGRLS does not support multi-output learning, so we train
#one classifier for the first column of Y. Multi-class learning
#would be implemented by training one CGRLS for each column, and
#taking the argmax of class predictions.
predictions = []
rls = CGRankRLS(X_train, Y_train[:, 0], regparam=100.0)
P = rls.predict(X_test)
perf = auc(Y_test[:, 0], P)
print("auc for task 1 %f" % perf)
