def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a", X_train.shape[1])
lpo_aucs = []
test_aucs = []
for i in range(1000):
X_small = X_train[i * 30:i * 30 + 30]
Y_small = Y_train[i * 30:i * 30 + 30]
pairs_start = []
pairs_end = []
for i in range(len(Y_small)):
for j in range(len(Y_small)):
if Y_small[i] == 1. and Y_small[j] == -1.:
pairs_start.append(i)
pairs_end.append(j)
learner = RLS(X_small, Y_small)
pairs_start = np.array(pairs_start)
pairs_end = np.array(pairs_end)
P_start, P_end = learner.leave_pair_out(pairs_start, pairs_end)
lpo_a = np.mean(P_start > P_end + 0.5 * (P_start == P_end))
P_test = learner.predict(X_test)
test_a = auc(Y_test, P_test)
lpo_aucs.append(lpo_a)
test_aucs.append(test_a)
print("mean lpo over auc over 1000 repetitions: %f" % np.mean(lpo_aucs))
print("mean test auc over 1000 repetitions %f" % np.mean(test_aucs))
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 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))
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.**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.
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))
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_auc = 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 = auc(Y_train, P_loo)
print("regparam 2**%d, loo-auc %f" %(log_regparam, acc))
if acc > best_auc:
best_auc = acc
best_regparam = regparam
learner.solve(best_regparam)
P_test = learner.predict(X_test)
print("best regparam %f with loo-auc %f" %(best_regparam, best_auc))
print("test set auc %f" %auc(Y_test, P_test))
def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
#subsample, leave-pair-out on whole data would take
#a lot of time
X_train = X_train[:1000]
Y_train = Y_train[:1000]
X_test, Y_test, foo = read_svmlight("a1a", X_train.shape[1])
regparams = [2.**-5, 1., 2.**5]
learner = LeavePairOutRLS(X_train, Y_train, regparams=regparams)
print("best regparam %f" % learner.regparam)
print("lpo auc " + str(learner.cv_performances))
P_test = learner.predict(X_test)
print("test auc %f" % auc(Y_test, P_test))
def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
# subsample, leave-pair-out on whole data would take
# a lot of time
X_train = X_train[:1000]
Y_train = Y_train[:1000]
X_test, Y_test, foo = read_svmlight("a1a", X_train.shape[1])
regparams = [2.0 ** -5, 1.0, 2.0 ** 5]
learner = LeavePairOutRLS(X_train, Y_train, regparams=regparams)
print("best regparam %f" % learner.regparam)
print("lpo auc " + str(learner.cv_performances))
P_test = learner.predict(X_test)
print("test auc %f" % auc(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])
loo_aucs = []
test_aucs = []
for i in range(1000):
X_small = X_train[i * 30:i * 30 + 30]
Y_small = Y_train[i * 30:i * 30 + 30]
learner = RLS(X_small, Y_small)
P_loo = learner.leave_one_out()
loo_a = auc(Y_small, P_loo)
P_test = learner.predict(X_test)
test_a = auc(Y_test, P_test)
loo_aucs.append(loo_a)
test_aucs.append(test_a)
print("mean loo auc over 1000 repetitions %f" % np.mean(loo_aucs))
print("mean test auc over 1000 repetitions %f" % np.mean(test_aucs))
def train_rls():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a", X_train.shape[1])
loo_aucs = []
test_aucs = []
for i in range(1000):
X_small = X_train[i*30: i*30 + 30]
Y_small = Y_train[i*30: i*30 + 30]
learner = RLS(X_small, Y_small)
P_loo = learner.leave_one_out()
loo_a = auc(Y_small, P_loo)
P_test = learner.predict(X_test)
test_a = auc(Y_test, P_test)
loo_aucs.append(loo_a)
test_aucs.append(test_a)
print("mean loo auc over 1000 repetitions %f" %np.mean(loo_aucs))
print("mean test auc over 1000 repetitions %f" %np.mean(test_aucs))
def print_stats():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a")
print("Adult data set characteristics")
print("Training set: %d instances, %d features" % X_train.shape)
print("Test set: %d instances, %d features" % X_test.shape)
def print_stats():
X_train, Y_train, foo = read_svmlight("a1a.t")
X_test, Y_test, foo = read_svmlight("a1a")
print("Adult data set characteristics")
print("Training set: %d instances, %d features" %X_train.shape)
print("Test set: %d instances, %d features" %X_test.shape)
