def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a", X_train.shape[1])
learner = RLS(X_train, Y_train)
best_regparam = None
best_accuracy = 0.
#exponential grid of possible regparam values
log_regparams = range(-15, 16)
for log_regparam in log_regparams:
regparam = 2.**log_regparam
#RLS is re-trained with the new regparam, this
#is very fast due to computational short-cut
learner.solve(regparam)
#Leave-one-out cross-validation predictions, this is fast due to
#computational short-cut
P_loo = learner.leave_one_out()
acc = accuracy(Y_train, P_loo)
print("regparam 2**%d, loo-accuracy %f" %(log_regparam, acc))
if acc > best_accuracy:
best_accuracy = acc
best_regparam = regparam
learner.solve(best_regparam)
P_test = learner.predict(X_test)
print("best regparam %f with loo-accuracy %f" %(best_regparam, best_accuracy))
print("test set accuracy %f" %accuracy(Y_test, P_test))
def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a", X_train.shape[1])
learner = RLS(X_train, Y_train)
best_regparam = None
best_accuracy = 0.
#exponential grid of possible regparam values
log_regparams = range(-15, 16)
for log_regparam in log_regparams:
regparam = 2.**log_regparam
#RLS is re-trained with the new regparam, this
#is very fast due to computational short-cut
learner.solve(regparam)
#Leave-one-out cross-validation predictions, this is fast due to
#computational short-cut
P_loo = learner.leave_one_out()
acc = accuracy(Y_train, P_loo)
print("regparam 2**%d, loo-accuracy %f" %(log_regparam, acc))
if acc > best_accuracy:
best_accuracy = acc
best_regparam = regparam
learner.solve(best_regparam)
P_test = learner.predict(X_test)
print("best regparam %f with loo-accuracy %f" %(best_regparam, best_accuracy))
print("test set accuracy %f" %accuracy(Y_test, P_test))
def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a")
#select randomly 500 basis vectors
indices = range(X_train.shape[0])
indices = random.sample(indices, 500)
basis_vectors = X_train[indices]
regparams = [2.**i for i in range(-15, 16)]
gammas = regparams
best_regparam = None
best_gamma = None
best_acc = 0.
best_learner = None
for gamma in gammas:
#New RLS is initialized for each kernel parameter
learner = LeaveOneOutRLS(X_train,
Y_train,
basis_vectors=basis_vectors,
kernel="GaussianKernel",
gamma=gamma,
regparams=regparams,
measure=accuracy)
acc = np.max(learner.cv_performances)
if acc > best_acc:
best_acc = acc
best_regparam = learner.regparam
best_gamma = gamma
best_learner = learner
P_test = best_learner.predict(X_test)
print("best parameters gamma %f regparam %f" % (best_gamma, best_regparam))
print("best leave-one-out accuracy %f" % best_acc)
print("test set accuracy %f" % accuracy(Y_test, P_test))
def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a")
# select randomly 100 basis vectors
indices = range(X_train.shape[0])
indices = random.sample(indices, 100)
basis_vectors = X_train[indices]
regparams = [2.0 ** i for i in range(-15, 16)]
gammas = [2 ** i for i in range(-10, 1)]
best_regparam = None
best_gamma = None
best_acc = 0.0
best_learner = None
for gamma in gammas:
# New RLS is initialized for each kernel parameter
learner = LeaveOneOutRLS(
X_train,
Y_train,
basis_vectors=basis_vectors,
kernel="GaussianKernel",
gamma=gamma,
regparams=regparams,
measure=accuracy,
)
acc = np.max(learner.cv_performances)
print("gamma %f, regparam %f, accuracy %f" % (gamma, learner.regparam, acc))
if acc > best_acc:
best_acc = acc
best_regparam = learner.regparam
best_gamma = gamma
best_learner = learner
P_test = best_learner.predict(X_test)
print("best parameters gamma %f regparam %f" % (best_gamma, best_regparam))
print("best leave-one-out accuracy %f" % best_acc)
print("test set accuracy %f" % accuracy(Y_test, P_test))
from rlscore.measure import accuracy
#My class labels, three examples in positive and two in negative
Y = [-1, -1, -1, 1, 1]
#Some predictions
P = [-1, -1, 1, 1, 1]
print("My accuracy %f" % accuracy(Y, P))
#Accuracy accepts real-valued predictions, P2[i]>0 are mapped to +1, rest to -1
P2 = [-2.7, -1.3, 0.2, 1.3, 1]
print("My accuracy with real-valued predictions %f" % accuracy(Y, P2))
Y2 = [2, 1, 3, 4, 1]
#Labels must be in the set {-1,1}, this will not work
accuracy(Y2, P)
from rlscore.measure import accuracy
#My class labels, three examples in positive and two in negative
Y = [-1, -1, -1, 1, 1]
#Some predictions
P = [-1, -1, 1, 1, 1]
print("My accuracy %f" %accuracy(Y,P))
#Accuracy accepts real-valued predictions, P2[i]>0 are mapped to +1, rest to -1
P2 = [-2.7, -1.3, 0.2, 1.3, 1]
print("My accuracy with real-valued predictions %f" %accuracy(Y,P2))
Y2 = [2, 1 , 3, 4, 1]
#Labels must be in the set {-1,1}, this will not work
accuracy(Y2, P)