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 testModel(self):
train_labels = np.random.random((10))
test_labels = np.random.random((10))
train_features = np.random.random((10,100))
test_features = np.random.random((10,100))
kwargs = {}
kwargs["train_labels"] = train_labels
kwargs["train_features"] = train_features
kwargs["regparam"] = 1
learner = RLS.createLearner(**kwargs)
learner.train()
model = learner.getModel()
print
#print 'Ten data points, single label '
model = mod.LinearModel(np.random.random((100)))
self.all_pred_cases(model)
model = mod.LinearModel(np.random.random((100, 2)))
self.all_pred_cases(model)
#model = mod.LinearModel(np.random.random((1, 2)))
#self.all_pred_cases(model)
kwargs["kernel"] = "GaussianKernel"
train_labels = np.random.random((10))
kwargs["train_labels"] = train_labels
learner = RLS.createLearner(**kwargs)
learner.train()
model = learner.getModel()
self.all_pred_cases(model)
kwargs["kernel"] = "GaussianKernel"
train_labels = np.random.random((10,2))
kwargs["train_labels"] = train_labels
learner = RLS.createLearner(**kwargs)
learner.train()
model = learner.getModel()
self.all_pred_cases(model)
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))
from rlscore.measure import auc
from rlscore.learner.rls import LOOCV
from rlscore.utilities.grid_search import grid_search
train_labels = np.loadtxt("./examples/data/class_train.labels")
test_labels = np.loadtxt("./examples/data/class_test.labels")
train_features = read_sparse("./examples/data/class_train.features")
test_features = read_sparse("./examples/data/class_test.features")
kwargs = {}
kwargs["train_labels"] = train_labels
kwargs["train_features"] = train_features
kwargs["regparam"] = 1
kwargs["coef0"] = 1
kwargs["degree"] = 3
kwargs["gamma"] = 2
kwargs["kernel"] = "PolynomialKernel"
learner = RLS.createLearner(**kwargs)
learner.train()
kwargs = {}
kwargs["learner"] = learner
kwargs["measure"] = auc
crossvalidator = LOOCV(**kwargs)
grid = [2**i for i in range(-10, 11)]
learner, perfs = grid_search(crossvalidator, grid)
for i in range(len(grid)):
print "parameter %f cv_performance %f" % (grid[i], perfs[i])
model = learner.getModel()
P = model.predict(test_features)
test_perf = auc(test_labels, P)
print "test set performance: %f" % test_perf
from rlscore.reader import read_sparse
from rlscore.reader import read_sparse
from rlscore.measure import auc
from rlscore.learner.rls import NfoldCV
from rlscore.utilities.grid_search import grid_search
train_labels = np.loadtxt("./examples/data/class_train.labels")
test_labels = np.loadtxt("./examples/data/class_test.labels")
folds = read_folds("./examples/data/folds.txt")
train_features = read_sparse("./examples/data/class_train.features")
test_features = read_sparse("./examples/data/class_test.features")
kwargs = {}
kwargs["train_labels"] = train_labels
kwargs["train_features"] = train_features
kwargs["regparam"] = 1
learner = RLS.createLearner(**kwargs)
learner.train()
kwargs = {}
kwargs["learner"] = learner
kwargs["folds"] = folds
kwargs["measure"] = auc
crossvalidator = NfoldCV(**kwargs)
grid = [2 ** i for i in range(-10, 11)]
learner, perfs = grid_search(crossvalidator, grid)
for i in range(len(grid)):
print "parameter %f cv_performance %f" % (grid[i], perfs[i])
model = learner.getModel()
P = model.predict(test_features)
test_perf = auc(test_labels, P)
print "test set performance: %f" % test_perf
def test_two_step_rls(self):
regparam1 = 0.001
regparam2 = 10
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 linear two-step RLS with data-matrices
params = {}
params["regparam1"] = regparam1
params["regparam2"] = regparam2
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train
linear_two_step_learner = TwoStepRLS.createLearner(**params)
linear_two_step_learner.train()
linear_two_step_model = linear_two_step_learner.getModel()
linear_two_step_testpred = linear_two_step_model.predictWithDataMatrices(X_test1, X_test2)
#Train kernel two-step RLS with pre-computed kernel matrices
params = {}
params["regparam1"] = regparam1
params["regparam2"] = regparam2
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train
kernel_two_step_learner = TwoStepRLS.createLearner(**params)
kernel_two_step_learner.train()
kernel_two_step_model = kernel_two_step_learner.getModel()
kernel_two_step_testpred = kernel_two_step_model.predictWithKernelMatrices(K_test1, K_test2)
#Train ordinary RLS in two steps for a reference
params = {}
params["regparam"] = regparam2
params["kernel_matrix"] = K_train2
params["train_labels"] = Y_train.T
ordinary_rls_first_step = RLS.createLearner(**params)
ordinary_rls_first_step.train()
firststeploo = ordinary_rls_first_step.computeLOO().T
params = {}
params["regparam"] = regparam1
params["kernel_matrix"] = K_train1
params["train_labels"] = firststeploo
ordinary_rls_second_step = RLS.createLearner(**params)
ordinary_rls_second_step.train()
secondsteploo = ordinary_rls_second_step.computeLOO()
#print 'Basic RLS', secondsteploo[0, 0]
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 linear_kron_testpred[0, 0], kernel_two_step_testpred[0, 0]
#print linear_kron_testpred[0, 1], kernel_two_step_testpred[0, 1]
#print linear_kron_testpred[1, 0], kernel_two_step_testpred[1, 0]
linear_twostepoutofsampleloo = linear_two_step_learner.computeLOO()
kernel_twostepoutofsampleloo = kernel_two_step_learner.computeLOO()
#Train linear two-step RLS without out-of-sample rows or columns for [0,0]
params = {}
params["regparam1"] = regparam1
params["regparam2"] = regparam2
params["xmatrix1"] = X_train1[range(1, X_train1.shape[0])]
params["xmatrix2"] = X_train2[range(1, X_train2.shape[0])]
params["train_labels"] = Y_train[np.ix_(range(1, Y_train.shape[0]), range(1, Y_train.shape[1]))]
linear_kron_learner = TwoStepRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred_00 = linear_kron_model.predictWithDataMatrices(X_train1[0], X_train2[0])
#Train linear two-step RLS without out-of-sample rows or columns for [2,4]
params = {}
params["regparam1"] = regparam1
params["regparam2"] = regparam2
params["xmatrix1"] = X_train1[[0, 1] + range(3, K_train1.shape[0])]
params["xmatrix2"] = X_train2[[0, 1, 2, 3] + range(5, K_train2.shape[0])]
params["train_labels"] = Y_train[np.ix_([0, 1] + range(3, K_train1.shape[0]), [0, 1, 2, 3] + range(5, K_train2.shape[0]))]
linear_kron_learner = TwoStepRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred_24 = linear_kron_model.predictWithDataMatrices(X_train1[2], X_train2[4])
#Train kernel two-step RLS without out-of-sample rows or columns for [0,0]
params = {}
params["regparam1"] = regparam1
params["regparam2"] = regparam2
params["kmatrix1"] = K_train1[np.ix_(range(1, K_train1.shape[0]), range(1, K_train1.shape[1]))]
params["kmatrix2"] = K_train2[np.ix_(range(1, K_train2.shape[0]), range(1, K_train2.shape[1]))]
params["train_labels"] = Y_train[np.ix_(range(1, Y_train.shape[0]), range(1, Y_train.shape[1]))]
kernel_kron_learner = TwoStepRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
kernel_kron_testpred_00 = kernel_kron_model.predictWithKernelMatrices(K_train1[range(1, K_train1.shape[0]), 0], K_train2[0, range(1, K_train2.shape[0])])
#Train kernel two-step RLS without out-of-sample rows or columns for [2,4]
params = {}
params["regparam1"] = regparam1
params["regparam2"] = regparam2
params["kmatrix1"] = K_train1[np.ix_([0, 1] + range(3, K_train1.shape[0]), [0, 1] + range(3, K_train1.shape[0]))]
params["kmatrix2"] = K_train2[np.ix_([0, 1, 2, 3] + range(5, K_train2.shape[0]), [0, 1, 2, 3] + range(5, K_train2.shape[0]))]
params["train_labels"] = Y_train[np.ix_([0, 1] + range(3, Y_train.shape[0]), [0, 1, 2, 3] + range(5, Y_train.shape[1]))]
kernel_kron_learner = TwoStepRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
#print K_train1[range(1, K_train1.shape[0]), 0].shape, K_train2[0, range(1, K_train2.shape[0])].shape
kernel_kron_testpred_24 = kernel_kron_model.predictWithKernelMatrices(K_train1[[0, 1] + range(3, K_train1.shape[0]), 2], K_train2[4, [0, 1, 2, 3] + range(5, K_train2.shape[0])])
print Y_train.shape, secondsteploo.shape, kernel_twostepoutofsampleloo.shape
print secondsteploo[0, 0], linear_kron_testpred_00, kernel_kron_testpred_00, linear_twostepoutofsampleloo[0, 0], kernel_twostepoutofsampleloo[0, 0]
print secondsteploo[2, 4], linear_kron_testpred_24, kernel_kron_testpred_24, linear_twostepoutofsampleloo[2, 4], kernel_twostepoutofsampleloo[2, 4]
print
#print 'Two-step RLS LOO', twostepoutofsampleloo[2, 4]
#print np.mean(np.abs(linear_kron_testpred - ordrls_testpred)), np.mean(np.abs(kernel_kron_testpred - ordrls_testpred))
#Create symmetric data
K_train1, K_train2, Y_train, K_test1, K_test2, Y_test, X_train1, X_train2, X_test1, X_test2 \
= self.generate_xortask(
trainpos1 = 6,
trainneg1 = 7,
trainpos2 = 6,
trainneg2 = 7,
testpos1 = 26,
testneg1 = 27,
testpos2 = 25,
testneg2 = 25
)
K_train1 = K_train2
K_test1 = K_test2
Y_train = 0.5 * (Y_train + Y_train.T)
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 symmetric kernel two-step RLS with pre-computed kernel matrices
params = {}
params["regparam1"] = regparam2
params["regparam2"] = regparam2
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train
kernel_two_step_learner = TwoStepRLS.createLearner(**params)
kernel_two_step_learner.train()
kernel_two_step_model = kernel_two_step_learner.getModel()
kernel_two_step_testpred = kernel_two_step_model.predictWithKernelMatrices(K_test1, K_test2)
#Train two-step RLS without out-of-sample rows or columns
rowind, colind = 2, 4
trainrowinds = range(K_train1.shape[0])
trainrowinds.remove(rowind)
trainrowinds.remove(colind)
traincolinds = range(K_train2.shape[0])
traincolinds.remove(rowind)
traincolinds.remove(colind)
params = {}
params["regparam1"] = regparam2
params["regparam2"] = regparam2
params["kmatrix1"] = K_train1[np.ix_(trainrowinds, trainrowinds)]
params["kmatrix2"] = K_train2[np.ix_(traincolinds, traincolinds)]
params["train_labels"] = Y_train[np.ix_(trainrowinds, traincolinds)]
kernel_kron_learner = TwoStepRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
#kernel_kron_testpred = kernel_kron_model.predictWithKernelMatrices(K_train1[np.ix_([rowind, colind], trainrowinds)], K_train2[np.ix_([rowind, colind], traincolinds)])
kernel_kron_testpred = kernel_kron_model.predictWithKernelMatrices(K_train1[np.ix_([rowind], trainrowinds)], K_train2[np.ix_([colind], traincolinds)])
print kernel_kron_testpred
fcsho = kernel_two_step_learner.compute_symmetric_double_LOO()
print fcsho[2, 4]
def test_cg_kron_rls(self):
regparam = 0.0001
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
rowstimescols = rows * columns
indmatrix = np.mat(range(rowstimescols)).T.reshape(rows, columns)
label_row_inds = [
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 1, 2, 2, 2,
2, 3, 4, 5, 6, 6, 7, 9
]
label_col_inds = [
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 1, 0, 1, 1, 2, 2,
2, 3, 4, 4, 4, 5, 5, 12
]
Y_train_nonzeros = []
Y_alt = []
B = np.mat(np.zeros((len(label_row_inds), rowstimescols)))
for ind in range(len(label_row_inds)):
i, j = label_row_inds[ind], label_col_inds[ind]
Y_train_nonzeros.append(Y_train[i, j])
Y_alt.append(Y_train[i, j])
#B[ind, i * columns + j] = 1.
B[ind, j * rows + i] = 1.
#print B
Y_train_nonzeros = np.mat(Y_train_nonzeros).T
#Y_train_nonzeros = B * Y_train.reshape(rowstimescols, 1)
#Train linear Kronecker RLS
params = {}
params["regparam"] = regparam
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
linear_kron_learner = CGKronRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred = linear_kron_model.predictWithDataMatricesAlt(
X_test1, X_test2).reshape(X_test1.shape[0],
X_test2.shape[0],
order='F')
#Train kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
kernel_kron_learner = CGKronRLS.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_train2, K_train1)
params = {}
params["kmatrix"] = B * K_Kron_train_x * B.T
params[
"train_labels"] = Y_train_nonzeros #B*(B.T * Y_train_nonzeros).reshape(rows, columns).reshape(rowstimescols, 1) # #Y_train.reshape(rowstimescols, 1)
ordrls_learner = RLS.createLearner(**params)
ordrls_learner.solve(regparam)
ordrls_model = ordrls_learner.getModel()
K_Kron_test_x = np.kron(K_test2, K_test1) * B.T
ordrls_testpred = ordrls_model.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape(Y_test.shape[0],
Y_test.shape[1],
order='F')
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_cg_kron_rls(self):
regparam = 0.0001
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
rowstimescols = rows * columns
indmatrix = np.mat(range(rowstimescols)).T.reshape(rows, columns)
label_row_inds = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,0,1,1,2,2,2,2,3,4,5,6,6,7,9]
label_col_inds = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,1,0,1,1,2,2,2,3,4,4,4,5,5,12]
Y_train_nonzeros = []
Y_alt = []
B = np.mat(np.zeros((len(label_row_inds), rowstimescols)))
for ind in range(len(label_row_inds)):
i, j = label_row_inds[ind], label_col_inds[ind]
Y_train_nonzeros.append(Y_train[i, j])
Y_alt.append(Y_train[i, j])
#B[ind, i * columns + j] = 1.
B[ind, j * rows + i] = 1.
#print B
Y_train_nonzeros = np.mat(Y_train_nonzeros).T
#Y_train_nonzeros = B * Y_train.reshape(rowstimescols, 1)
#Train linear Kronecker RLS
params = {}
params["regparam"] = regparam
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
linear_kron_learner = CGKronRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred = linear_kron_model.predictWithDataMatricesAlt(X_test1, X_test2).reshape(X_test1.shape[0], X_test2.shape[0], order = 'F')
#Train kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
kernel_kron_learner = CGKronRLS.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_train2, K_train1)
params = {}
params["kmatrix"] = B * K_Kron_train_x * B.T
params["train_labels"] = Y_train_nonzeros#B*(B.T * Y_train_nonzeros).reshape(rows, columns).reshape(rowstimescols, 1) # #Y_train.reshape(rowstimescols, 1)
ordrls_learner = RLS.createLearner(**params)
ordrls_learner.solve(regparam)
ordrls_model = ordrls_learner.getModel()
K_Kron_test_x = np.kron(K_test2, K_test1) * B.T
ordrls_testpred = ordrls_model.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape(Y_test.shape[0], Y_test.shape[1], order = 'F')
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_cg_kron_rls(self):
regparam = 0.0001
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
rowstimescols = rows * columns
indmatrix = np.mat(range(rowstimescols)).T.reshape(rows, columns)
#label_row_inds = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,0,1,1,2,2,2,2,3,4,5,6,6,7,9]
#label_col_inds = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,1,0,1,1,2,2,2,3,4,4,4,5,5,12]
pointrange = np.arange(rows, columns)
label_row_inds, label_col_inds = np.unravel_index(
pointrange, (rows, columns))
Y_train_nonzeros = []
Y_alt = []
B = np.mat(np.zeros((len(label_row_inds), rowstimescols)))
for ind in range(len(label_row_inds)):
i, j = label_row_inds[ind], label_col_inds[ind]
Y_train_nonzeros.append(Y_train[i, j])
Y_alt.append(Y_train[i, j])
#B[ind, i * columns + j] = 1.
B[ind, j * rows + i] = 1.
#print B
Y_train_nonzeros = np.mat(Y_train_nonzeros).T
#Y_train_nonzeros = B * Y_train.reshape(rowstimescols, 1)
#Train an ordinary RLS regressor for reference
K_Kron_train_x = np.kron(K_train2, K_train1)
params = {}
params["kernel_matrix"] = B * K_Kron_train_x * B.T
params[
"train_labels"] = Y_train_nonzeros #B*(B.T * Y_train_nonzeros).reshape(rows, columns).reshape(rowstimescols, 1) # #Y_train.reshape(rowstimescols, 1)
ordrls_learner = RLS.createLearner(**params)
ordrls_learner.solve(regparam)
ordrls_model = ordrls_learner.getModel()
K_Kron_test_x = np.kron(K_test2, K_test1) * B.T
ordrls_testpred = ordrls_model.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape(Y_test.shape[0],
Y_test.shape[1],
order='F')
#Train linear Kronecker RLS
class TestCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
print self.round
def finished(self, learner):
print 'finished'
params = {}
params["regparam"] = regparam
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
tcb = TestCallback()
params['callback'] = tcb
linear_kron_learner = CGKronRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred = linear_kron_model.predictWithDataMatrices(
X_test1, X_test2).reshape(X_test1.shape[0],
X_test2.shape[0],
order='F')
#params["warm_start"] = linear_kron_learner.W
#linear_kron_learner = CGKronRLS.createLearner(**params)
#linear_kron_learner.train()
#linear_kron_model = linear_kron_learner.getModel()
#linear_kron_testpred = linear_kron_model.predictWithDataMatricesAlt(X_test1, X_test2).reshape(X_test1.shape[0], X_test2.shape[0], order = 'F')
#Train kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
class KernelCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
tp = KernelPairwiseModel(
learner.A, learner.label_row_inds,
learner.label_col_inds).predictWithKernelMatrices(
K_test1, K_test2)
print self.round, np.mean(np.abs(tp - ordrls_testpred))
def finished(self, learner):
print 'finished'
tcb = KernelCallback()
params['callback'] = tcb
kernel_kron_learner = CGKronRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
kernel_kron_testpred = kernel_kron_model.predictWithKernelMatrices(
K_test1, K_test2)
kernel_kron_testpred_alt = kernel_kron_model.predictWithKernelMatrices(
K_test1, K_test2, row_inds=[0, 0, 1], col_inds=[0, 1, 0])
print linear_kron_testpred[0, 0], kernel_kron_testpred[
0, 0], kernel_kron_testpred_alt[0], ordrls_testpred[0, 0]
print linear_kron_testpred[0, 1], kernel_kron_testpred[
0, 1], kernel_kron_testpred_alt[1], ordrls_testpred[0, 1]
print linear_kron_testpred[1, 0], kernel_kron_testpred[
1, 0], kernel_kron_testpred_alt[2], 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_cg_kron_rls(self):
regparam = 0.0001
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
rowstimescols = rows * columns
indmatrix = np.mat(range(rowstimescols)).T.reshape(rows, columns)
#label_row_inds = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,0,1,1,2,2,2,2,3,4,5,6,6,7,9]
#label_col_inds = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,0,1,0,1,1,2,2,2,3,4,4,4,5,5,12]
pointrange = np.arange(rows, columns)
label_row_inds, label_col_inds = np.unravel_index(pointrange, (rows, columns))
Y_train_nonzeros = []
Y_alt = []
B = np.mat(np.zeros((len(label_row_inds), rowstimescols)))
for ind in range(len(label_row_inds)):
i, j = label_row_inds[ind], label_col_inds[ind]
Y_train_nonzeros.append(Y_train[i, j])
Y_alt.append(Y_train[i, j])
#B[ind, i * columns + j] = 1.
B[ind, j * rows + i] = 1.
#print B
Y_train_nonzeros = np.mat(Y_train_nonzeros).T
#Y_train_nonzeros = B * Y_train.reshape(rowstimescols, 1)
#Train an ordinary RLS regressor for reference
K_Kron_train_x = np.kron(K_train2, K_train1)
params = {}
params["kernel_matrix"] = B * K_Kron_train_x * B.T
params["train_labels"] = Y_train_nonzeros#B*(B.T * Y_train_nonzeros).reshape(rows, columns).reshape(rowstimescols, 1) # #Y_train.reshape(rowstimescols, 1)
ordrls_learner = RLS.createLearner(**params)
ordrls_learner.solve(regparam)
ordrls_model = ordrls_learner.getModel()
K_Kron_test_x = np.kron(K_test2, K_test1) * B.T
ordrls_testpred = ordrls_model.predict(K_Kron_test_x)
ordrls_testpred = ordrls_testpred.reshape(Y_test.shape[0], Y_test.shape[1], order = 'F')
#Train linear Kronecker RLS
class TestCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
print self.round
def finished(self, learner):
print 'finished'
params = {}
params["regparam"] = regparam
params["xmatrix1"] = X_train1
params["xmatrix2"] = X_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
tcb = TestCallback()
params['callback'] = tcb
linear_kron_learner = CGKronRLS.createLearner(**params)
linear_kron_learner.train()
linear_kron_model = linear_kron_learner.getModel()
linear_kron_testpred = linear_kron_model.predictWithDataMatrices(X_test1, X_test2).reshape(X_test1.shape[0], X_test2.shape[0], order = 'F')
#params["warm_start"] = linear_kron_learner.W
#linear_kron_learner = CGKronRLS.createLearner(**params)
#linear_kron_learner.train()
#linear_kron_model = linear_kron_learner.getModel()
#linear_kron_testpred = linear_kron_model.predictWithDataMatricesAlt(X_test1, X_test2).reshape(X_test1.shape[0], X_test2.shape[0], order = 'F')
#Train kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["kmatrix1"] = K_train1
params["kmatrix2"] = K_train2
params["train_labels"] = Y_train_nonzeros
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
class KernelCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
tp = KernelPairwiseModel(learner.A, learner.label_row_inds, learner.label_col_inds).predictWithKernelMatrices(K_test1, K_test2)
print self.round, np.mean(np.abs(tp - ordrls_testpred))
def finished(self, learner):
print 'finished'
tcb = KernelCallback()
params['callback'] = tcb
kernel_kron_learner = CGKronRLS.createLearner(**params)
kernel_kron_learner.train()
kernel_kron_model = kernel_kron_learner.getModel()
kernel_kron_testpred = kernel_kron_model.predictWithKernelMatrices(K_test1, K_test2)
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))
