def predict(self,
K1pred=None,
K2pred=None,
row_inds_K1pred=None,
row_inds_K2pred=None,
pko=None):
"""Computes predictions for test examples.
Parameters
----------
K1pred : {array-like, list of equally shaped array-likes}, shape = [n_samples1, n_train_pairs]
the first part of the test data matrix
K2pred : {array-like, list of equally shaped array-likes}, shape = [n_samples2, n_train_pairs]
the second part of the test data matrix
row_inds_K1pred : list of indices, shape = [n_test_pairs], optional
maps rows of K1pred to vector of predictions P. If not supplied, predictions are computed for all possible test pair combinations.
row_inds_K2pred : list of indices, shape = [n_test_pairs], optional
maps rows of K2pred to vector of predictions P. If not supplied, predictions are computed for all possible test pair combinations.
Returns
----------
P : array, shape = [n_test_pairs] or [n_samples1*n_samples2]
predictions, either ordered according to the supplied row indices, or if no such are supplied by default
prediction for (K1[i], K2[j]) maps to P[i + j*n_samples1].
"""
if pko == None:
pko = pairwise_kernel_operator.PairwiseKernelOperator(
K1pred, K2pred, row_inds_K1pred, row_inds_K2pred,
self.row_inds_K1training, self.row_inds_K2training,
self.weights)
return pko.mv(self.A)
'''
def predict(self,
X1pred,
X2pred,
inds_X1pred=None,
inds_X2pred=None,
pko=None):
"""Computes predictions for test examples.
Parameters
----------
X1pred : array-like, shape = [n_samples1, n_features1]
the first part of the test data matrix
X2pred : array-like, shape = [n_samples2, n_features2]
the second part of the test data matrix
inds_X1pred : list of indices, shape = [n_test_pairs], optional
maps rows of X1pred to vector of predictions P. If not supplied, predictions are computed for all possible test pair combinations.
inds_X2pred : list of indices, shape = [n_test_pairs], optional
maps rows of X2pred to vector of predictions P. If not supplied, predictions are computed for all possible test pair combinations.
Returns
----------
P : array, shape = [n_test_pairs] or [n_samples1*n_samples2]
predictions, either ordered according to the supplied row indices, or if no such are supplied by default
prediction for (X1[i], X2[j]) maps to P[i + j*n_samples1].
"""
if pko == None:
pko = pairwise_kernel_operator.PairwiseKernelOperator(
X1pred, X2pred, inds_X1pred, inds_X2pred, None, None,
self.weights)
return pko.matvec(self.W)
def __init__(self, **kwargs):
self.Y = kwargs["Y"]
#self.Y = array_tools.as_2d_array(Y)
self.trained = False
if "regparam" in kwargs:
self.regparam = kwargs["regparam"]
else:
self.regparam = 0.
regparam = self.regparam
if CALLBACK_FUNCTION in kwargs:
self.callbackfun = kwargs[CALLBACK_FUNCTION]
else:
self.callbackfun = None
if "compute_risk" in kwargs:
self.compute_risk = kwargs["compute_risk"]
else:
self.compute_risk = False
if 'K1' in kwargs or 'pko' in kwargs:
if 'pko' in kwargs:
pko = kwargs['pko']
else:
self.input1_inds = np.array(kwargs["label_row_inds"],
dtype=np.int32)
self.input2_inds = np.array(kwargs["label_col_inds"],
dtype=np.int32)
K1 = kwargs['K1']
K2 = kwargs['K2']
if 'weights' in kwargs: weights = kwargs['weights']
else: weights = None
pko = pairwise_kernel_operator.PairwiseKernelOperator(
K1, K2, self.input1_inds, self.input2_inds,
self.input1_inds, self.input2_inds, weights)
self.pko = pko
if 'maxiter' in kwargs: maxiter = int(kwargs['maxiter'])
else: maxiter = None
Y = np.array(self.Y).ravel(order='F')
self.bestloss = float("inf")
def mv(v):
return pko.matvec(v) + regparam * v
def mvr(v):
raise Exception('This function should not be called!')
def cgcb(v):
if self.compute_risk:
P = sampled_kronecker_products.sampled_vec_trick(
v, K2, K1, self.input2_inds, self.input1_inds,
self.input2_inds, self.input1_inds)
z = (Y - P)
Ka = sampled_kronecker_products.sampled_vec_trick(
v, K2, K1, self.input2_inds, self.input1_inds,
self.input2_inds, self.input1_inds)
loss = (np.dot(z, z) + regparam * np.dot(v, Ka))
print("loss", 0.5 * loss)
if loss < self.bestloss:
self.A = v.copy()
self.bestloss = loss
else:
self.A = v
if not self.callbackfun is None:
#self.predictor = KernelPairwisePredictor(self.A, self.input1_inds, self.input2_inds, self.pko.weights)
self.callbackfun.callback(self)
G = LinearOperator((self.Y.shape[0], self.Y.shape[0]),
matvec=mv,
rmatvec=mvr,
dtype=np.float64)
self.A = minres(G,
self.Y,
maxiter=maxiter,
callback=cgcb,
tol=1e-20)[0]
self.predictor = KernelPairwisePredictor(
self.A, self.pko.original_col_inds_K1,
self.pko.original_col_inds_K2, self.pko.weights)
if not self.callbackfun is None:
self.callbackfun.finished(self)
else:
self.input1_inds = np.array(kwargs["label_row_inds"],
dtype=np.int32)
self.input2_inds = np.array(kwargs["label_col_inds"],
dtype=np.int32)
X1 = kwargs['X1']
X2 = kwargs['X2']
self.X1, self.X2 = X1, X2
if 'maxiter' in kwargs: maxiter = int(kwargs['maxiter'])
else: maxiter = None
if 'weights' in kwargs: weights = kwargs['weights']
else: weights = None
Y = np.array(self.Y).ravel(order='F')
self.bestloss = float("inf")
def mv(v):
v_after = pko.matvec(v)
v_after = pko.rmatvec(v_after) + regparam * v
return v_after
def cgcb(v):
if self.compute_risk:
P = sampled_kronecker_products.sampled_vec_trick(
v, X2, X1, self.input2_inds, self.input1_inds)
z = (Y - P)
loss = (np.dot(z, z) + regparam * np.dot(v, v))
if loss < self.bestloss:
self.W = v.copy().reshape(pko.shape, order='F')
self.bestloss = loss
else:
self.W = v
if not self.callbackfun is None:
self.predictor = LinearPairwisePredictor(self.W)
self.callbackfun.callback(self)
v_init = np.array(self.Y).reshape(self.Y.shape[0])
pko = pairwise_kernel_operator.PairwiseKernelOperator(
X1, X2, self.input1_inds, self.input2_inds, None, None,
weights)
G = LinearOperator((pko.shape[1], pko.shape[1]),
matvec=mv,
dtype=np.float64)
v_init = pko.rmatvec(v_init)
'''if 'warm_start' in kwargs:
x0 = np.array(kwargs['warm_start']).reshape(kronfcount, order = 'F')
else:
x0 = None'''
minres(G, v_init, maxiter=maxiter, callback=cgcb, tol=1e-20
) #[0].reshape((pko_T.shape[0], pko.shape[1]), order='F')
self.predictor = LinearPairwisePredictor(self.W, self.input1_inds,
self.input2_inds, weights)
if not self.callbackfun is None:
self.callbackfun.finished(self)
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(
)
#K_train1, K_train2, Y_train, K_test1, K_test2, Y_test, X_train1, X_train2, X_test1, X_test2 = self.generate_xortask(trainpos1 = 1, trainneg1 = 1, trainpos2 = 1, trainneg2 = 1, testpos1 = 1, testneg1 = 1, testpos2 = 1, testneg2 = 1)
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]
rowstimescols = train_rows * train_columns
allindices = np.arange(rowstimescols)
all_label_row_inds, all_label_col_inds = np.unravel_index(
allindices, (train_rows, train_columns), order='F')
#incinds = np.random.permutation(allindices)
#incinds = np.random.choice(allindices, 50, replace = False)
incinds = np.random.choice(allindices, 40, replace=False)
label_row_inds, label_col_inds = all_label_row_inds[
incinds], all_label_col_inds[incinds]
Y_train_known_outputs = Y_train.reshape(rowstimescols,
order='F')[incinds]
alltestindices = np.arange(test_rows * test_columns)
all_test_label_row_inds, all_test_label_col_inds = np.unravel_index(
alltestindices, (test_rows, test_columns), order='F')
#Train an ordinary RLS regressor for reference
params = {}
params["X"] = np.kron(K_train2, K_train1)[np.ix_(incinds, incinds)]
params["kernel"] = "PrecomputedKernel"
params["Y"] = Y_train_known_outputs
params["regparam"] = regparam
ordrls_learner = RLS(**params)
ordrls_model = ordrls_learner.predictor
K_Kron_test = np.kron(K_test2, K_test1)[:, incinds]
ordrls_testpred = ordrls_model.predict(K_Kron_test)
ordrls_testpred = ordrls_testpred.reshape((test_rows, test_columns),
order='F')
#Train linear Kronecker RLS
class TestCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
tp = LinearPairwisePredictor(learner.W).predict(
X_test1, X_test2)
print(
str(self.round) + ' ' +
str(np.mean(np.abs(tp -
ordrls_testpred.ravel(order='F')))))
def finished(self, learner):
print('finished')
params = {}
params["regparam"] = regparam
params["X1"] = X_train1
params["X2"] = X_train2
params["Y"] = Y_train_known_outputs
params["label_row_inds"] = label_row_inds
params["label_col_inds"] = label_col_inds
tcb = TestCallback()
params['callback'] = tcb
linear_kron_learner = CGKronRLS(**params)
linear_kron_testpred = linear_kron_learner.predict(
X_test1, X_test2).reshape((test_rows, test_columns), order='F')
linear_kron_testpred_alt = linear_kron_learner.predict(
X_test1, X_test2, [0, 0, 1], [0, 1, 0])
#Train kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["K1"] = K_train1
params["K2"] = K_train2
params["Y"] = Y_train_known_outputs
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 = KernelPairwisePredictor(learner.A, learner.input1_inds,
learner.input2_inds).predict(
K_test1, K_test2)
print(
str(self.round) + ' ' +
str(np.mean(np.abs(tp -
ordrls_testpred.ravel(order='F')))))
def finished(self, learner):
print('finished')
tcb = KernelCallback()
params['callback'] = tcb
kernel_kron_learner = CGKronRLS(**params)
kernel_kron_testpred = kernel_kron_learner.predict(
K_test1, K_test2).reshape((test_rows, test_columns), order='F')
kernel_kron_testpred_alt = kernel_kron_learner.predict(
K_test1, K_test2, [0, 0, 1], [0, 1, 0])
print('Predictions: Linear CgKronRLS, Kernel CgKronRLS, ordinary RLS')
print('[0, 0]: ' + str(linear_kron_testpred[0, 0]) + ' ' +
str(kernel_kron_testpred[0, 0]) + ' ' +
str(ordrls_testpred[0, 0])
) #, linear_kron_testpred_alt[0], kernel_kron_testpred_alt[0]
print('[0, 1]: ' + str(linear_kron_testpred[0, 1]) + ' ' +
str(kernel_kron_testpred[0, 1]) + ' ' +
str(ordrls_testpred[0, 1])
) #, linear_kron_testpred_alt[1], kernel_kron_testpred_alt[1]
print('[1, 0]: ' + str(linear_kron_testpred[1, 0]) + ' ' +
str(kernel_kron_testpred[1, 0]) + ' ' +
str(ordrls_testpred[1, 0])
) #, linear_kron_testpred_alt[2], kernel_kron_testpred_alt[2]
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,
decimal=5)
np.testing.assert_almost_equal(kernel_kron_testpred,
ordrls_testpred,
decimal=4)
#Train multiple kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["K1"] = [K_train1, K_train1]
params["K2"] = [K_train2, K_train2]
params["weights"] = [1. / 3, 2. / 3]
params["Y"] = Y_train_known_outputs
params["label_row_inds"] = [label_row_inds, label_row_inds]
params["label_col_inds"] = [label_col_inds, label_col_inds]
class KernelCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
tp = KernelPairwisePredictor(
learner.A, learner.input1_inds, learner.input2_inds,
params["weights"]).predict([K_test1, K_test1],
[K_test2, K_test2])
print(
str(self.round) + ' ' +
str(np.mean(np.abs(tp -
ordrls_testpred.ravel(order='F')))))
def finished(self, learner):
print('finished')
tcb = KernelCallback()
params['callback'] = tcb
mkl_kernel_kron_learner = CGKronRLS(**params)
mkl_kernel_kron_testpred = mkl_kernel_kron_learner.predict(
[K_test1, K_test1], [K_test2, K_test2]).reshape(
(test_rows, test_columns), order='F')
#kernel_kron_testpred_alt = kernel_kron_learner.predict(K_test1, K_test2, [0, 0, 1], [0, 1, 0])
'''
#Train linear multiple kernel Kronecker RLS
params = {}
params["regparam"] = regparam
params["X1"] = [X_train1, X_train1]
params["X2"] = [X_train2, X_train2]
params["weights"] = [1. / 3, 2. / 3]
params["Y"] = Y_train_known_outputs
params["label_row_inds"] = [label_row_inds, label_row_inds]
params["label_col_inds"] = [label_col_inds, label_col_inds]
mkl_linear_kron_learner = CGKronRLS(**params)
mkl_linear_kron_testpred = mkl_linear_kron_learner.predict([X_test1, X_test1], [X_test2, X_test2]).reshape((test_rows, test_columns), order = 'F')
#kernel_kron_testpred_alt = kernel_kron_learner.predict(K_test1, K_test2, [0, 0, 1], [0, 1, 0])
print('Predictions: Linear CgKronRLS, MKL Kernel CgKronRLS, MKL linear CgKronRLS, ordinary RLS')
print('[0, 0]: ' + str(linear_kron_testpred[0, 0]) + ' ' + str(mkl_kernel_kron_testpred[0, 0]) + ' ' + str(mkl_linear_kron_testpred[0, 0]) + ' ' + str(ordrls_testpred[0, 0]))#, linear_kron_testpred_alt[0], kernel_kron_testpred_alt[0]
print('[0, 1]: ' + str(linear_kron_testpred[0, 1]) + ' ' + str(mkl_kernel_kron_testpred[0, 1]) + ' ' + str(mkl_linear_kron_testpred[0, 1]) + ' ' + str(ordrls_testpred[0, 1]))#, linear_kron_testpred_alt[1], kernel_kron_testpred_alt[1]
print('[1, 0]: ' + str(linear_kron_testpred[1, 0]) + ' ' + str(mkl_kernel_kron_testpred[1, 0]) + ' ' + str(mkl_linear_kron_testpred[1, 0]) + ' ' + str(ordrls_testpred[1, 0]))#, linear_kron_testpred_alt[2], kernel_kron_testpred_alt[2]
print('Meanabsdiff: MKL kernel KronRLS - ordinary RLS')
print(str(np.mean(np.abs(mkl_kernel_kron_testpred - ordrls_testpred))))
np.testing.assert_almost_equal(mkl_kernel_kron_testpred, ordrls_testpred, decimal=3)
'''
#Train polynomial kernel Kronecker RLS
params = {}
params["regparam"] = regparam
#params["K1"] = [K_train1, K_train1, K_train2]
#params["K2"] = [K_train1, K_train2, K_train2]
#params["weights"] = [1., 2., 1.]
params["pko"] = pairwise_kernel_operator.PairwiseKernelOperator(
[K_train1, K_train1, K_train2], [K_train1, K_train2, K_train2],
[label_row_inds, label_row_inds, label_col_inds],
[label_row_inds, label_col_inds, label_col_inds],
[label_row_inds, label_row_inds, label_col_inds],
[label_row_inds, label_col_inds, label_col_inds], [1., 2., 1.])
params["Y"] = Y_train_known_outputs
#params["label_row_inds"] = [label_row_inds, label_row_inds, label_col_inds]
#params["label_col_inds"] = [label_row_inds, label_col_inds, label_col_inds]
class KernelCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
#tp = KernelPairwisePredictor(learner.A, learner.input1_inds, learner.input2_inds, params["weights"]).predict([K_test1, K_test1], [K_test2, K_test2])
#print(str(self.round) + ' ' + str(np.mean(np.abs(tp - ordrls_testpred.ravel(order = 'F')))))
def finished(self, learner):
print('finished')
tcb = KernelCallback()
params['callback'] = tcb
poly_kernel_kron_learner = CGKronRLS(**params)
pko = pairwise_kernel_operator.PairwiseKernelOperator(
[K_test1, K_test1, K_test2], [K_test1, K_test2, K_test2], [
all_test_label_row_inds, all_test_label_row_inds,
all_test_label_col_inds
], [
all_test_label_row_inds, all_test_label_col_inds,
all_test_label_col_inds
], [label_row_inds, label_row_inds, label_col_inds],
[label_row_inds, label_col_inds, label_col_inds], [1., 2., 1.])
#poly_kernel_kron_testpred = poly_kernel_kron_learner.predict(pko = pko)
poly_kernel_kron_testpred = poly_kernel_kron_learner.predict(
[K_test1, K_test1, K_test2], [K_test1, K_test2, K_test2], [
all_test_label_row_inds, all_test_label_row_inds,
all_test_label_col_inds
], [
all_test_label_row_inds, all_test_label_col_inds,
all_test_label_col_inds
])
#print(poly_kernel_kron_testpred, 'Polynomial kernel via CGKronRLS')
#Train an ordinary RLS regressor with polynomial kernel for reference
params = {}
params["X"] = np.hstack([
np.kron(np.ones((X_train2.shape[0], 1)), X_train1),
np.kron(X_train2, np.ones((X_train1.shape[0], 1)))
])[incinds]
#params["X"] = np.hstack([np.kron(X_train1, np.ones((X_train2.shape[0], 1))), np.kron(np.ones((X_train1.shape[0], 1)), X_train2)])[incinds]
params["kernel"] = "PolynomialKernel"
params["Y"] = Y_train_known_outputs
params["regparam"] = regparam
ordrls_poly_kernel_learner = RLS(**params)
X_dir_test = np.hstack([
np.kron(np.ones((X_test2.shape[0], 1)), X_test1),
np.kron(X_test2, np.ones((X_test1.shape[0], 1)))
])
#X_dir_test = np.hstack([np.kron(X_test1, np.ones((X_test2.shape[0], 1))), np.kron(np.ones((X_test1.shape[0], 1)), X_test2)])
ordrls_poly_kernel_testpred = ordrls_poly_kernel_learner.predict(
X_dir_test)
#print(ordrls_poly_kernel_testpred, 'Ord. poly RLS')
print(
'Meanabsdiff: Polynomial kernel KronRLS - Ordinary polynomial kernel RLS'
)
print(
str(
np.mean(
np.abs(poly_kernel_kron_testpred -
ordrls_poly_kernel_testpred))))
'''
#Train polynomial kernel Kronecker RLS
params = {}
params["regparam"] = regparam
#params["X1"] = [X_train1, X_train1, X_train2]
#params["X2"] = [X_train1, X_train2, X_train2]
params["K1"] = [K_train1, K_train1, K_train2]
params["K2"] = [K_train1, K_train2, K_train2]
params["weights"] = [1., 2., 1.]
params["Y"] = Y_train_known_outputs
params["label_row_inds"] = [label_row_inds, label_row_inds, label_col_inds]
params["label_col_inds"] = [label_row_inds, label_col_inds, label_col_inds]
class KernelCallback():
def __init__(self):
self.round = 0
def callback(self, learner):
self.round = self.round + 1
#tp = KernelPairwisePredictor(learner.A, learner.input1_inds, learner.input2_inds, params["weights"]).predict([K_test1, K_test1], [K_test2, K_test2])
#print(str(self.round) + ' ' + str(np.mean(np.abs(tp - ordrls_testpred.ravel(order = 'F')))))
def finished(self, learner):
print('finished')
tcb = KernelCallback()
params['callback'] = tcb
poly_kernel_linear_kron_learner = CGKronRLS(**params)
#poly_kernel_linear_kron_testpred = poly_kernel_linear_kron_learner.predict([X_test1, X_test1, X_test2], [X_test1, X_test2, X_test2], [all_test_label_row_inds, all_test_label_row_inds, all_test_label_col_inds], [all_test_label_row_inds, all_test_label_col_inds, all_test_label_col_inds])
poly_kernel_linear_kron_testpred = poly_kernel_linear_kron_learner.predict([K_test1, K_test1, K_test2], [K_test1, K_test2, K_test2], [all_test_label_row_inds, all_test_label_row_inds, all_test_label_col_inds], [all_test_label_row_inds, all_test_label_col_inds, all_test_label_col_inds])
#print(poly_kernel_kron_testpred, 'Polynomial kernel via CGKronRLS (linear)')
print('Meanabsdiff: Polynomial kernel KronRLS (linear) - Ordinary polynomial kernel RLS')
print(str(np.mean(np.abs(poly_kernel_linear_kron_testpred - ordrls_poly_kernel_testpred))))
'''
'''
def __init__(self, **kwargs):
self.Y = kwargs["Y"]
#self.Y = array_tools.as_2d_array(Y)
self.trained = False
if "regparam" in kwargs:
self.regparam = kwargs["regparam"]
else:
self.regparam = 0.
regparam = self.regparam
if CALLBACK_FUNCTION in kwargs:
self.callbackfun = kwargs[CALLBACK_FUNCTION]
else:
self.callbackfun = None
if "compute_risk" in kwargs:
self.compute_risk = kwargs["compute_risk"]
else:
self.compute_risk = False
if 'K1' in kwargs or 'pko' in kwargs:
if 'pko' in kwargs:
pko = kwargs['pko']
else:
self.input1_inds = np.array(kwargs["label_row_inds"],
dtype=np.int32)
self.input2_inds = np.array(kwargs["label_col_inds"],
dtype=np.int32)
K1 = kwargs['K1']
K2 = kwargs['K2']
if 'weights' in kwargs: weights = kwargs['weights']
else: weights = None
pko = pairwise_kernel_operator.PairwiseKernelOperator(
K1, K2, self.input1_inds, self.input2_inds,
self.input1_inds, self.input2_inds, weights)
self.pko = pko
if 'maxiter' in kwargs: maxiter = int(kwargs['maxiter'])
else: maxiter = None
Y = np.array(self.Y).ravel(order='F')
self.bestloss = float("inf")
def mv(v):
return pko.matvec(v) + regparam * v
def mvr(v):
raise Exception('This function should not be called!')
def cgcb(v):
if self.compute_risk:
P = sampled_kronecker_products.sampled_vec_trick(
v, K2, K1, self.input2_inds, self.input1_inds,
self.input2_inds, self.input1_inds)
z = (Y - P)
Ka = sampled_kronecker_products.sampled_vec_trick(
v, K2, K1, self.input2_inds, self.input1_inds,
self.input2_inds, self.input1_inds)
loss = (np.dot(z, z) + regparam * np.dot(v, Ka))
print("loss", 0.5 * loss)
if loss < self.bestloss:
self.A = v.copy()
self.bestloss = loss
else:
self.A = v
if not self.callbackfun is None:
self.predictor = KernelPairwisePredictor(
self.A, self.pko.col_inds_K1, self.pko.col_inds_K2,
self.pko.weights)
self.callbackfun.callback(self)
G = LinearOperator((self.Y.shape[0], self.Y.shape[0]),
matvec=mv,
rmatvec=mvr,
dtype=np.float64)
self.A = minres(G,
self.Y,
maxiter=maxiter,
callback=cgcb,
tol=1e-20)[0]
self.predictor = KernelPairwisePredictor(self.A,
self.pko.col_inds_K1,
self.pko.col_inds_K2,
self.pko.weights)
else: #Primal case. Does not work with the operator interface yet.
self.input1_inds = np.array(kwargs["label_row_inds"],
dtype=np.int32)
self.input2_inds = np.array(kwargs["label_col_inds"],
dtype=np.int32)
X1 = kwargs['X1']
X2 = kwargs['X2']
self.X1, self.X2 = X1, X2
if 'maxiter' in kwargs: maxiter = int(kwargs['maxiter'])
else: maxiter = None
if isinstance(X1, (list, tuple)):
raise NotImplementedError(
"Got list or tuple as X1 but multiple kernel learning has not been implemented for the primal case yet."
)
if 'weights' in kwargs: weights = kwargs['weights']
else: weights = np.ones((len(X1)))
x1tsize, x1fsize = X1[0].shape #m, d
x2tsize, x2fsize = X2[0].shape #q, r
else:
weights = None
x1tsize, x1fsize = X1.shape #m, d
x2tsize, x2fsize = X2.shape #q, r
kronfcount = x1fsize * x2fsize
Y = np.array(self.Y).ravel(order='F')
self.bestloss = float("inf")
def mv(v):
v_after = sampled_kronecker_products.sampled_vec_trick(
v, X2, X1, self.input2_inds, self.input1_inds)
v_after = sampled_kronecker_products.sampled_vec_trick(
v_after, X2.T, X1.T, None, None, self.input2_inds,
self.input1_inds) + regparam * v
return v_after
def mvr(v):
raise Exception('This function should not be called!')
def cgcb(v):
if self.compute_risk:
P = sampled_kronecker_products.sampled_vec_trick(
v, X2, X1, self.input2_inds, self.input1_inds)
z = (Y - P)
loss = (np.dot(z, z) + regparam * np.dot(v, v))
if loss < self.bestloss:
self.W = v.copy().reshape((x1fsize, x2fsize),
order='F')
self.bestloss = loss
else:
self.W = v.reshape((x1fsize, x2fsize), order='F')
if not self.callbackfun is None:
self.predictor = LinearPairwisePredictor(self.W)
self.callbackfun.callback(self)
G = LinearOperator((kronfcount, kronfcount),
matvec=mv,
rmatvec=mvr,
dtype=np.float64)
v_init = np.array(self.Y).reshape(self.Y.shape[0])
v_init = sampled_kronecker_products.sampled_vec_trick(
v_init, X2.T, X1.T, None, None, self.input2_inds,
self.input1_inds)
v_init = np.array(v_init).reshape(kronfcount)
if 'warm_start' in kwargs:
x0 = np.array(kwargs['warm_start']).reshape(kronfcount,
order='F')
else:
x0 = None
minres(G, v_init, x0=x0, maxiter=maxiter, callback=cgcb,
tol=1e-20)[0].reshape((x1fsize, x2fsize), order='F')
self.predictor = LinearPairwisePredictor(self.W, self.input1_inds,
self.input2_inds, weights)
if not self.callbackfun is None:
self.callbackfun.finished(self)