def main():
X1_train, X2_train, Y_train, X1_test, X2_test, Y_test = davis_data.setting4_split(
)
learner = KronRLS(X1=X1_train, X2=X2_train, Y=Y_train, regparam=2.**30)
predictor = learner.predictor
print predictor.W
#Predict labels for all X1_test - X2_test combinations)
#Order: column-major: [(X1[0], X2[0]), (X1[1], X2[0])...]
P = predictor.predict(X1_test, X2_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(X1_test, X2_test, x1_ind, x2_ind)
print("three first predictions again: " + str(P2))
print("Number of coefficients %d x %d" % predictor.W.shape)
def test_conditional_ranking(self):
regparam = 0.001
K_train1, K_train2, Y_train, K_test1, K_test2, Y_test, X_train1, X_train2, X_test1, X_test2 = self.generate_xortask()
train_rows, train_columns = Y_train.shape
trainlabelcount = train_rows * train_columns
K_Kron_train_x = np.kron(K_train2, K_train1)
#Train linear Conditional Ranking Kronecker RLS
params = {}
params["X1"] = X_train1
params["X2"] = X_train2
params["Y"] = Y_train
params["regparam"] = regparam
linear_kron_condrank_learner = KronRLS(**params)
linear_kron_condrank_learner.solve_linear_conditional_ranking(regparam)
#Train an ordinary RankRLS for reference
params = {}
params["X"] = K_Kron_train_x
params["kernel"] = "PrecomputedKernel"
params["Y"] = Y_train.reshape((trainlabelcount, 1), order = 'F')
qids = []
for j in range(Y_train.shape[1]):
for i in range(Y_train.shape[0]):
qids.append(i)
params["qids"] = qids
rankrls_learner = QueryRankRLS(**params)
rankrls_learner.solve(regparam)
K_test_x = np.kron(K_test2, K_test1)
ordrankrls_testpred = rankrls_learner.predict(K_test_x)
condrank_testpred = linear_kron_condrank_learner.predict(X_test1, X_test2)
#print('')
print('\n\nMeanabsdiff: conditional ranking vs rankrls ' + str(np.mean(np.abs(condrank_testpred - ordrankrls_testpred))) + '\n')
np.testing.assert_almost_equal(condrank_testpred, ordrankrls_testpred)
def test_kron_rls(self):
regparam = 0.001
K_train1, K_train2, Y_train, K_test1, K_test2, Y_test, X_train1, X_train2, X_test1, X_test2 = self.generate_xortask()
Y_train = Y_train.ravel(order = 'F')
Y_test = Y_test.ravel(order = 'F')
train_rows, train_columns = K_train1.shape[0], K_train2.shape[0]
test_rows, test_columns = K_test1.shape[0], K_test2.shape[0]
trainlabelcount = train_rows * train_columns
#Train linear Kronecker RLS with data-matrices
params = {}
params["regparam"] = regparam
params["X1"] = X_train1
params["X2"] = X_train2
params["Y"] = Y_train
linear_kron_learner = KronRLS(**params)
linear_kron_testpred = linear_kron_learner.predict(X_test1, X_test2).reshape((test_rows, test_columns), order = 'F')
#Train kernel Kronecker RLS with pre-computed kernel matrices
params = {}
params["regparam"] = regparam
params["K1"] = K_train1
params["K2"] = K_train2
params["Y"] = Y_train
kernel_kron_learner = KronRLS(**params)
kernel_kron_testpred = kernel_kron_learner.predict(K_test1, K_test2).reshape((test_rows, test_columns), order = 'F')
#Train an ordinary RLS regressor for reference
K_Kron_train_x = np.kron(K_train2, K_train1)
params = {}
params["X"] = K_Kron_train_x
params["kernel"] = "PrecomputedKernel"
params["Y"] = Y_train.reshape(trainlabelcount, 1, order = 'F')
ordrls_learner = RLS(**params)
ordrls_learner.solve(regparam)
K_Kron_test_x = np.kron(K_test2, K_test1)
ordrls_testpred = ordrls_learner.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape((test_rows, test_columns), order = 'F')
print('')
print('Prediction: linear KronRLS, kernel KronRLS, ordinary RLS')
print('[0, 0] ' + str(linear_kron_testpred[0, 0]) + ' ' + str(kernel_kron_testpred[0, 0]) + ' ' + str(ordrls_testpred[0, 0]))
print('[0, 1] ' + str(linear_kron_testpred[0, 1]) + ' ' + str(kernel_kron_testpred[0, 1]) + ' ' + str(ordrls_testpred[0, 1]))
print('[1, 0] ' + str(linear_kron_testpred[1, 0]) + ' ' + str(kernel_kron_testpred[1, 0]) + ' ' + str(ordrls_testpred[1, 0]))
print('Meanabsdiff: linear KronRLS - ordinary RLS, kernel KronRLS - ordinary RLS')
print(str(np.mean(np.abs(linear_kron_testpred - ordrls_testpred))) + ' ' + str(np.mean(np.abs(kernel_kron_testpred - ordrls_testpred))))
np.testing.assert_almost_equal(linear_kron_testpred, ordrls_testpred)
np.testing.assert_almost_equal(kernel_kron_testpred, ordrls_testpred)
print('')
ordrls_loopred = ordrls_learner.leave_one_out().reshape((train_rows, train_columns), order = 'F')
linear_kron_loopred = linear_kron_learner.in_sample_loo().reshape((train_rows, train_columns), order = 'F')
kernel_kron_loopred = kernel_kron_learner.in_sample_loo().reshape((train_rows, train_columns), order = 'F')
print('In-sample LOO: linear KronRLS, kernel KronRLS, ordinary RLS')
print('[0, 0] ' + str(linear_kron_loopred[0, 0]) + ' ' + str(kernel_kron_loopred[0, 0]) + ' ' + str(ordrls_loopred[0, 0]))
print('[0, 1] ' + str(linear_kron_loopred[0, 1]) + ' ' + str(kernel_kron_loopred[0, 1]) + ' ' + str(ordrls_loopred[0, 1]))
print('[1, 0] ' + str(linear_kron_loopred[1, 0]) + ' ' + str(kernel_kron_loopred[1, 0]) + ' ' + str(ordrls_loopred[1, 0]))
print('Meanabsdiff: linear KronRLS - ordinary RLS, kernel KronRLS - ordinary RLS')
print(str(np.mean(np.abs(linear_kron_loopred - ordrls_loopred))) + ' ' + str(np.mean(np.abs(kernel_kron_loopred - ordrls_loopred))))
np.testing.assert_almost_equal(linear_kron_loopred, ordrls_loopred)
np.testing.assert_almost_equal(kernel_kron_loopred, ordrls_loopred)
