def train_rls():
#Trains RLS with automatically selected regularization parameter
X_train, Y_train, X_test, Y_test = load_housing()
regparams = [2.**i for i in range(-15, 16)]
learner = LeaveOneOutRLS(X_train, Y_train, regparams = regparams, measure=cindex)
loo_errors = learner.cv_performances
P_test = learner.predict(X_test)
print("leave-one-out cindex " +str(loo_errors))
print("chosen regparam %f" %learner.regparam)
print("test cindex %f" %cindex(Y_test, P_test))
def train_rls():
#Trains RLS with automatically selected regularization parameter
X_train, Y_train, X_test, Y_test = load_housing()
regparams = [2.**i for i in range(-15, 16)]
learner = LeaveOneOutRLS(X_train, Y_train, regparams = regparams)
loo_errors = learner.cv_performances
P_test = learner.predict(X_test)
print("leave-one-out errors " +str(loo_errors))
print("chosen regparam %f" %learner.regparam)
print("test error %f" %sqerror(Y_test, P_test))
def train_rls():
X_train, Y_train, X_test, Y_test = load_wine()
#Map labels from set {1,2,3} to one-vs-all encoding
Y_train = to_one_vs_all(Y_train, False)
Y_test = to_one_vs_all(Y_test, False)
regparams = [2.**i for i in range(-15, 16)]
learner = LeaveOneOutRLS(X_train, Y_train, regparams=regparams, measure=ova_accuracy)
P_test = learner.predict(X_test)
#ova_accuracy computes one-vs-all classification accuracy directly between transformed
#class label matrix, and a matrix of predictions, where each column corresponds to a class
print("test set accuracy %f" %ova_accuracy(Y_test, P_test))
def train_rls():
X_train, Y_train, X_test, Y_test = load_wine()
#Map labels from set {1,2,3} to one-vs-all encoding
Y_train = to_one_vs_all(Y_train)
Y_test = to_one_vs_all(Y_test)
regparams = [2.**i for i in range(-15, 16)]
learner = LeaveOneOutRLS(X_train, Y_train, regparams=regparams, measure=ova_accuracy)
P_test = learner.predict(X_test)
#ova_accuracy computes one-vs-all classification accuracy directly between transformed
#class label matrix, and a matrix of predictions, where each column corresponds to a class
print("test set accuracy %f" %ova_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():
#Selects both the gamma parameter for Gaussian kernel, and regparam with loocv
X_train, Y_train, X_test, Y_test = load_housing()
regparams = [2.**i for i in range(-15, 16)]
gammas = regparams
best_regparam = None
best_gamma = None
best_error = float("inf")
best_learner = None
for gamma in gammas:
#New RLS is initialized for each kernel parameter
learner = LeaveOneOutRLS(X_train,
Y_train,
kernel="GaussianKernel",
gamma=gamma,
regparams=regparams)
e = np.min(learner.cv_performances)
if e < best_error:
best_error = e
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 error %f" % best_error)
print("test error %f" % sqerror(Y_test, P_test))
def fit(self,
X_src,
y_src,
X_tgt_known,
y_tgt_known,
X_tgt_unknown,
y_tgt_unknown,
verbose=False):
# Map labels from set {1,2,3} to one-vs-all encoding
if np.count_nonzero(y_src) >= len(y_src):
zerolabels = False
else:
zerolabels = True
y_src = to_one_vs_all(y_src, zerolabels)
regparams = [2.**i for i in range(-15, 16)]
if len(np.unique(y_src)) > 2:
self.measure = ova_accuracy
else:
self.measure = accuracy
self.learner = LeaveOneOutRLS(X_src,
y_src,
regparams=regparams,
measure=self.measure)
p_tgt = self.learner.predict(X_tgt_known)
# ova_accuracy computes one-vs-all classification accuracy directly between transformed
# class label matrix, and a matrix of predictions, where each column corresponds to a class
self.learner = RLS(X_src, y_src)
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
self.learner.solve(regparam)
# Leave-one-out cross-validation predictions, this is fast due to
# computational short-cut
P_loo = self.learner.leave_one_out()
acc = self.measure(y_src, P_loo)
if verbose == True:
print("LooRLS regparam 2**%d, loo-accuracy %f" %
(log_regparam, acc))
if acc > best_accuracy:
best_accuracy = acc
best_regparam = regparam
self.learner.solve(best_regparam)
if verbose == True:
print("LooRLS best regparam %f with loo-accuracy %f" %
(best_regparam, best_accuracy))
