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(
)
rows, columns = Y_train.shape
#print K_train1.shape, K_train2.shape, K_test1.shape, K_test2.shape, rows, columns
trainlabelcount = rows * columns
indmatrix = np.mat(range(trainlabelcount)).T.reshape(rows, columns)
#Train linear Kronecker RLS with data-matrices
params = {}
params["regparam"] = regparam
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train
linear_kron_learner = KronRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred = linear_kron_model.predictWithDataMatrices(
X_test1, X_test2)
#Train kernel Kronecker RLS with pre-computed kernel matrices
params = {}
params["regparam"] = regparam
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train
kernel_kron_learner = KronRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
kernel_kron_testpred = kernel_kron_model.predictWithKernelMatrices(
K_test1, K_test2)
#Train an ordinary RLS regressor for reference
K_Kron_train_x = np.kron(K_train1, K_train2)
params = {}
params["kernel_matrix"] = K_Kron_train_x
params["train_labels"] = Y_train.reshape(trainlabelcount, 1)
ordrls_learner = RLS.createLearner(**params)
ordrls_learner.solve(regparam)
ordrls_model = ordrls_learner.getModel()
K_Kron_test_x = np.kron(K_test1, K_test2)
ordrls_testpred = ordrls_model.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape(Y_test.shape[0],
Y_test.shape[1])
print
print type(linear_kron_testpred), type(kernel_kron_testpred), type(
ordrls_testpred)
print linear_kron_testpred[0, 0], kernel_kron_testpred[
0, 0], ordrls_testpred[0, 0]
print linear_kron_testpred[0, 1], kernel_kron_testpred[
0, 1], ordrls_testpred[0, 1]
print linear_kron_testpred[1, 0], kernel_kron_testpred[
1, 0], ordrls_testpred[1, 0]
print np.mean(np.abs(linear_kron_testpred - ordrls_testpred)), np.mean(
np.abs(kernel_kron_testpred - ordrls_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()
rows, columns = Y_train.shape
#print K_train1.shape, K_train2.shape, K_test1.shape, K_test2.shape, rows, columns
trainlabelcount = rows * columns
indmatrix = np.mat(range(trainlabelcount)).T.reshape(rows, columns)
#Train linear Kronecker RLS with data-matrices
params = {}
params["regparam"] = regparam
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train
linear_kron_learner = KronRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred = linear_kron_model.predictWithDataMatrices(X_test1, X_test2)
#Train kernel Kronecker RLS with pre-computed kernel matrices
params = {}
params["regparam"] = regparam
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train
kernel_kron_learner = KronRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
kernel_kron_testpred = kernel_kron_model.predictWithKernelMatrices(K_test1, K_test2)
#Train an ordinary RLS regressor for reference
K_Kron_train_x = np.kron(K_train1, K_train2)
params = {}
params["kernel_matrix"] = K_Kron_train_x
params["train_labels"] = Y_train.reshape(trainlabelcount, 1)
ordrls_learner = RLS.createLearner(**params)
ordrls_learner.solve(regparam)
ordrls_model = ordrls_learner.getModel()
K_Kron_test_x = np.kron(K_test1, K_test2)
ordrls_testpred = ordrls_model.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape(Y_test.shape[0], Y_test.shape[1])
print
print type(linear_kron_testpred), type(kernel_kron_testpred), type(ordrls_testpred)
print linear_kron_testpred[0, 0], kernel_kron_testpred[0, 0], ordrls_testpred[0, 0]
print linear_kron_testpred[0, 1], kernel_kron_testpred[0, 1], ordrls_testpred[0, 1]
print linear_kron_testpred[1, 0], kernel_kron_testpred[1, 0], ordrls_testpred[1, 0]
print np.mean(np.abs(linear_kron_testpred - ordrls_testpred)), np.mean(np.abs(kernel_kron_testpred - ordrls_testpred))
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(
)
rows, columns = Y_train.shape
trainlabelcount = rows * columns
indmatrix = np.mat(range(trainlabelcount)).T.reshape(rows, columns)
K_Kron_train_x = np.kron(K_train1, K_train2)
K_test_x = np.kron(K_test1, K_test2)
#Train linear Conditional Ranking Kronecker RLS
params = {}
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train
params["regparam"] = regparam
linear_kron_condrank_learner = KronRLS.createLearner(**params)
linear_kron_condrank_learner.solve_linear_conditional_ranking(regparam)
condrank_model = linear_kron_condrank_learner.getModel()
#Train an ordinary RankRLS for reference
params = {}
params["kernel_matrix"] = K_Kron_train_x
params["train_labels"] = Y_train.reshape(trainlabelcount, 1)
params["train_qids"] = [
range(i * Y_train.shape[1], (i + 1) * Y_train.shape[1])
for i in range(Y_train.shape[0])
]
rankrls_learner = LabelRankRLS.createLearner(**params)
rankrls_learner.solve(regparam)
rankrls_model = rankrls_learner.getModel()
K_test_x = np.kron(K_test1, K_test2)
ordrankrls_testpred = rankrls_model.predict(K_test_x).reshape(
Y_test.shape[0], Y_test.shape[1])
condrank_testpred = condrank_model.predictWithDataMatrices(
X_test1, X_test2)
print
#print condrank_testpred.ravel().shape, Y_test.ravel().shape, ordrankrls_testpred.ravel().shape, Y_test.ravel().shape
print 'TEST cond vs rankrls', np.mean(
np.abs(condrank_testpred - ordrankrls_testpred))
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()
rows, columns = Y_train.shape
trainlabelcount = rows * columns
indmatrix = np.mat(range(trainlabelcount)).T.reshape(rows, columns)
K_Kron_train_x = np.kron(K_train1, K_train2)
K_test_x = np.kron(K_test1, K_test2)
#Train linear Conditional Ranking Kronecker RLS
params = {}
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train
params["regparam"] = regparam
linear_kron_condrank_learner = KronRLS.createLearner(**params)
linear_kron_condrank_learner.solve_linear_conditional_ranking(regparam)
condrank_model = linear_kron_condrank_learner.getModel()
#Train an ordinary RankRLS for reference
params = {}
params["kernel_matrix"] = K_Kron_train_x
params["train_labels"] = Y_train.reshape(trainlabelcount, 1)
params["train_qids"] = [range(i*Y_train.shape[1], (i+1)*Y_train.shape[1]) for i in range(Y_train.shape[0])]
rankrls_learner = LabelRankRLS.createLearner(**params)
rankrls_learner.solve(regparam)
rankrls_model = rankrls_learner.getModel()
K_test_x = np.kron(K_test1, K_test2)
ordrankrls_testpred = rankrls_model.predict(K_test_x).reshape(Y_test.shape[0], Y_test.shape[1])
condrank_testpred = condrank_model.predictWithDataMatrices(X_test1, X_test2)
print
#print condrank_testpred.ravel().shape, Y_test.ravel().shape, ordrankrls_testpred.ravel().shape, Y_test.ravel().shape
print 'TEST cond vs rankrls', np.mean(np.abs(condrank_testpred - ordrankrls_testpred))
