def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.settingB_split()
learner = KronRLS(X1 = X1_train, X2 = X2_train, Y = Y_train)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting2_split()
learner = KronRLS(X1 = X1_train, X2 = X2_train, Y = Y_train)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(X1_test, X2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1, X2, Y = davis_data.load_davis()
Y = Y.ravel(order='F')
learner = KronRLS(X1 = X1, X2 = X2, Y = Y)
log_regparams = range(15, 35)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.in_sample_loo()
perf = cindex(Y, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def fit(self, Y, user_features, item_features, reg):
self.Y = np.array(Y.toarray(), dtype=float)
self.m, self.n = Y.shape
self.Y = self.Y.ravel(order='F')
self.user_features = user_features
self.item_features = item_features
X_users = np.array(user_features.toarray(), dtype=float)
X_items = np.array(item_features.toarray(), dtype=float)
self.learner = KronRLS(X1=X_users, X2=X_items, Y=self.Y, regparam=reg)
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.settingD_split()
kernel1 = GaussianKernel(X1_train, gamma=0.01)
kernel2 = GaussianKernel(X2_train, gamma=10**-9)
K1_train = kernel1.getKM(X1_train)
K1_test = kernel1.getKM(X1_test)
K2_train = kernel2.getKM(X2_train)
K2_test = kernel2.getKM(X2_test)
learner = KronRLS(K1 = K1_train, K2 = K2_train, Y = Y_train)
log_regparams = range(-15, 15)
for log_regparam in log_regparams:
learner.solve(2.**log_regparam)
P = learner.predict(K1_test, K2_test)
perf = cindex(Y_test, P)
print("regparam 2**%d, cindex %f" %(log_regparam, perf))
def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split()
kernel1 = GaussianKernel(X1_train, gamma=0.01)
kernel2 = GaussianKernel(X2_train, gamma=10**-9)
K1_train = kernel1.getKM(X1_train)
K1_test = kernel1.getKM(X1_test)
K2_train = kernel2.getKM(X2_train)
K2_test = kernel2.getKM(X2_test)
learner = KronRLS(K1 = K1_train, K2 = K2_train, Y = Y_train, regparam=2**-5)
predictor = learner.predictor
P = predictor.predict(K1_test, K2_test)
print("Number of predictions: %d" %P.shape)
print("three first predictions: " +str(P[:3]))
x1_ind = [0,1,2]
x2_ind = [0,0,0]
P2 = predictor.predict(K1_test, K2_test, x1_ind, x2_ind)
print("three first predictions again: " +str(P2))
print("Number of coefficients %d" %predictor.A.shape)
Y = np.random.randn(size**2)
return X1, X2, Y
if __name__=="__main__":
#trains Kronecker RLS for different sample sizes
#comparing CPU time and verifying that the learned
#dual coefficients are same for both methods
regparam = 1.0
for size in [10, 20, 40, 60, 80, 100, 500, 1000, 2000, 4000, 6000]:
X1, X2, y = random_data(size, 100)
kernel1 = GaussianKernel(X1, gamma=0.01)
K1 = kernel1.getKM(X1)
kernel2 = GaussianKernel(X2, gamma=0.01)
K2 = kernel2.getKM(X2)
start = time.clock()
rls = KronRLS(K1=K1, K2=K2, Y=y, regparam=regparam)
dur = time.clock() - start
print("RLScore pairs: %d, CPU time: %f" %(size**2, dur))
#forming full Kronecker product kernel matrix becomes fast
#unfeasible
if size <=100:
K = np.kron(K2, K1)
start = time.clock()
ridge = KernelRidge(alpha=regparam, kernel="precomputed")
ridge.fit(K, y)
dur = time.clock() - start
print("sklearn pairs: %d, CPU time: %f" %(size**2, dur))
sklearn_coef = ridge.dual_coef_
core_coef = rls.predictor.A.reshape(K1.shape[0], K2.shape[0]).T.ravel()
print("Are the coefficients same: %r" %np.allclose(sklearn_coef, core_coef))
else:
class QuestionsXTags():
def fit(self, Y, user_features, item_features, reg):
self.Y = np.array(Y.toarray(), dtype=float)
self.m, self.n = Y.shape
self.Y = self.Y.ravel(order='F')
self.user_features = user_features
self.item_features = item_features
X_users = np.array(user_features.toarray(), dtype=float)
X_items = np.array(item_features.toarray(), dtype=float)
self.learner = KronRLS(X1=X_users, X2=X_items, Y=self.Y, regparam=reg)
def predict(self, row):
x = self.user_features.getrow(row)
W = self.learner.predictor.W
X = sp.kron(self.item_features, x)
predictions = X.dot(W)
return (predictions)
def predict_online(self, x):
#x = x.reshape(1,-1)
W = self.learner.predictor.W
X = sp.kron(self.item_features, x)
predictions = X.dot(W)
return (predictions)
def predict1(self):
m, n = self.m, self.n
X_users = np.array(self.user_features.toarray(), dtype=float)
X_items = np.array(self.item_features.toarray(), dtype=float)
predictions = self.learner.predict(X_users, X_items).reshape((m, n),
order='F')
return (predictions)
def predict2(self, item_features):
m, n = self.m, self.n
t, k = item_features.shape
X_users = np.array(self.user_features.toarray(), dtype=float)
predictions = self.learner.predict(X_users, item_features).reshape(
(m, t), order='F')
return (predictions)
def predict3(self, user_features):
m, n = self.m, self.n
t, k = user_features.shape
X_items = np.array(self.item_features.toarray(), dtype=float)
predictions = self.learner.predict(user_features, X_items).reshape(
(t, n), order='F')
return (predictions)
def predict4(self, user_features, item_features):
t, k = user_features.shape
s, k = item_features.shape
predictions = self.learner.predict(user_features,
item_features).reshape((t, s),
order='F')
return (predictions)
