def run_methods(train_points, train_targets, test_points, test_targets,
model_parameters, optimizer_options, file_name, ind_num, title, show=False):
method = 'means'
print('Finding means...')
means = KMeans(n_clusters=ind_num, n_init=1, max_iter=20)
means.fit(train_points.T)
inputs = means.cluster_centers_.T
print('...found')
for optimizer, color, opts in zip(['L-BFGS-B', 'Projected Newton'], ['-kx', '-mx'],
optimizer_options):
print('Optimizer', optimizer)
model_covariance_obj = SquaredExponential(np.copy(model_parameters))
new_gp = GPR(model_covariance_obj, method=method, optimizer=optimizer)
res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opts, inputs=inputs)
name = optimizer
metric = lambda w: new_gp.get_prediction_quality(w, train_points, train_targets, test_points, test_targets)
x_lst, y_lst = res.plot_performance(metric, 'i', freq=1)
plt.plot(x_lst, y_lst, color, label=name)
plt.xlabel('Epoch')
plt.ylabel('$R^2$-score on test data')
plt.legend()
plt.title(title)
plt.savefig('../Plots/vi_variations/'+file_name + '.pgf')
if show:
plt.show()
def run_methods(train_points, train_targets, test_points, test_targets,
model_parameters, optimizer_options, file_name, ind_num, title, show=False):
print('Finding means...')
means = KMeans(n_clusters=ind_num, n_init=1, max_iter=40)
means.fit(train_points.T)
inputs = means.cluster_centers_.T
print('...found')
method = 'svi'
parametrization = 'cholesky'
optimizer = 'L-BFGS-B'
color = '-go'
opts = optimizer_options[0]
print('svi-L-BFGS-B')
model_covariance_obj = SquaredExponential(np.copy(model_parameters))
new_gp = GPR(model_covariance_obj, method=method, parametrization=parametrization)
res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opts, inputs=inputs)
name = 'svi-L-BFGS-B'
metric = lambda w: new_gp.get_prediction_quality(w, test_points, test_targets)
x_lst, y_lst = res.plot_performance(metric, 'i', freq=1)
plt.plot(x_lst, y_lst, color, label=name)
print('vi-means')
method = 'means'
optimizer = 'L-BFGS-B'
opt_options = optimizer_options[1]
model_covariance_obj = SquaredExponential(np.copy(model_parameters))
new_gp = GPR(model_covariance_obj, method=method, optimizer=optimizer)
res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opt_options, inputs=inputs)
name = 'vi-means'
metric = lambda w: new_gp.get_prediction_quality(w, train_points, train_targets, test_points, test_targets)
x_lst, y_lst = res.plot_performance(metric, 'i', freq=1)
plt.plot(x_lst, y_lst, '-kx', label=name)
print(x_lst[-1])
plt.xlabel('Epoch')
plt.ylabel('$R^2$-score on test data')
plt.legend()
plt.title(title)
plt.savefig('../Plots/vi_vs_svi/'+file_name + '.pgf')
if show:
plt.show()
def run_methods(train_points, train_targets, test_points, test_targets,
model_parameters, optimizer_options, file_name, ind_num, title, show=False):
print('Finding means...')
means = KMeans(n_clusters=ind_num, n_init=1, max_iter=40)
means.fit(train_points.T)
inputs = means.cluster_centers_.T
print('...found')
# method = 'svi'
# parametrization = 'natural'
# # optimizer = 'L-BFGS-B'
# color = '-yo'
# opts = optimizer_options[0]
# print('svi')
# model_covariance_obj = SquaredExponential(np.copy(model_parameters))
# new_gp = GPR(model_covariance_obj, method=method, parametrization=parametrization)
# res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opts, inputs=inputs)
# name = 'svi-natural'
# metric = lambda w: new_gp.get_prediction_quality(w, test_points, test_targets)
# x_lst, y_lst = res.plot_performance(metric, 'i', freq=1)
# plt.plot(x_lst, y_lst, color, label=name)
print('vi-means')
method = 'means'
opt_options = optimizer_options[1]
model_covariance_obj = SquaredExponential(np.copy(model_parameters))
new_gp = GPR(model_covariance_obj, method=method)
res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opt_options, inputs=inputs)
name = 'vi-means'
metric = lambda w: new_gp.get_prediction_quality(w, train_points, train_targets, test_points, test_targets)
x_lst, y_lst = res.plot_performance(metric, 'i', freq=1)
plt.plot(x_lst, y_lst, '-kx', label=name)
print(x_lst[-1])
plt.ylabel('$R^2$-score on test data')
plt.legend()
plt.title(title)
# plt.savefig('../Plots/vi_vs_svi/'+file_name + '.pgf')
if show:
plt.show()
def run_methods(train_points, train_targets, test_points, test_targets,
model_parameters, optimizer_options, file_name, ind_num, title, show=False):
method = 'svi'
parametrization = 'cholesky'
means = KMeans(n_clusters=ind_num, n_init=3, max_iter=100, random_state=241)
means.fit(train_points.T)
inputs = means.cluster_centers_.T
# for optimizer, color, opts in zip(['SAG', 'FG', 'L-BFGS-B'], ['-ro', '-bo', '-go'],
# optimizer_options[:-1]):
# print('Optimizer', optimizer)
# model_covariance_obj = SquaredExponential(np.copy(model_parameters))
# new_gp = GPR(model_covariance_obj, method=method, parametrization=parametrization, optimizer=optimizer)
# res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opts, inputs=inputs)
# name = 'svi-' + optimizer
# metric = lambda w: new_gp.get_prediction_quality(w, test_points, test_targets)
# x_lst, y_lst = res.plot_performance(metric, 'i', freq=5)
# plt.plot(x_lst, y_lst, color, label=name)
parametrization = 'natural'
print('Natural parametrization')
opt_options = optimizer_options[-1]
model_covariance_obj = SquaredExponential(np.copy(model_parameters))
new_gp = GPR(model_covariance_obj, method=method, parametrization=parametrization)
res = new_gp.fit(train_points, train_targets, num_inputs=ind_num, optimizer_options=opt_options, inputs=inputs)
name = 'svi-natural'
metric = lambda w: new_gp.get_prediction_quality(w, test_points, test_targets)
x_lst, y_lst = res.plot_performance(metric, 'i', freq=5)
print(y_lst)
plt.plot(x_lst, y_lst, '-yo', label=name)
plt.xlabel('Epoch')
plt.ylabel('$R^2$-score on test data')
plt.legend()
plt.title(title)
# plt.savefig('../Plots/svi_variations/'+file_name + '.pgf')
if show:
plt.show()