def plot_gef_load_Z01_smooth_2d_mean():
X, y, D = fear_load_mat('../data/gef_load_full_Xy.mat', 1)
kernel = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
(fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064)) + fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718)))
kernel_1 = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064))
kernel_2 = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718))
min_T = -3.0
max_T = 1.0
N_T = 10
temps = np.repeat(np.linspace(min_T, max_T, N_T), 499)
input = np.tile(X[0:499, :], (N_T, 1))
input[:, 9] = temps
posterior_mean = gpml.posterior_mean(kernel,
kernel_2,
X[0:499, :],
y[0:499],
input,
iters=300)
X_plt = X[0:499, 0]
Y_plt = np.linspace(min_T, max_T, N_T)
Z_plt = np.reshape(posterior_mean, (N_T, 499), 'A')
data = {'X': X_plt, 'Y': Y_plt, 'Z': Z_plt, 'post_mean': posterior_mean}
scipy.io.savemat('temp_data.mat', data)
def plot_gef_load_Z01_split_mean_temp():
X, y, D = fear_load_mat('../data/gef_load_full_Xy.mat', 1)
kernel = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
(fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064)) + fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718)))
kernel_1 = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064))
posterior_mean_1 = gpml.posterior_mean(kernel,
kernel_1,
X[0:499, :],
y[0:499],
iters=10)
kernel_2 = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718))
posterior_mean_2 = gpml.posterior_mean(kernel,
kernel_2,
X[0:499, :],
y[0:499],
iters=10)
plt.figure()
host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.85)
par1 = host.twinx()
# host.set_xlim(0, 2)
# host.set_ylim(0, 2)
host.set_xlabel("Temperature (T09)")
# par1.set_ylabel("Periodic component")
plt.title('Posterior mean function')
host.set_ylabel("Load posterior mean")
p2, = host.plot(X[0:499, 9], y[0:499], 'o', alpha=0.5)
p1, = host.plot(X[0:499, 9], posterior_mean_2, 'o')
# par1.set_ylim(0, 4)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
# par1.axis["right"].label.set_color(p2.get_color())
plt.draw()
plt.show()
def fear_experiment(data_file, results_filename, y_dim=1, subset=None, max_depth=2, k=2, verbose=True, sleep_time=60, n_sleep_timeout=20, re_submit_wait=60, \
description=''):
'''Recursively search for the best kernel'''
X, y, D = fear_load_mat(data_file, y_dim)
# Subset if necessary
if not subset is None:
X = X[subset, :]
y = y[subset]
##### This should be abstracted
seed_kernels = [fk.MaskKernel(D, i, fk.SqExpKernel(0., 0.)) for i in range(D)] + \
[fk.MaskKernel(D, i, fk.SqExpPeriodicKernel(0., 0., 0.)) for i in range(D)] + \
[fk.MaskKernel(D, i, fk.RQKernel(0., 0., 0.)) for i in range(D)]
nll_key = 1
laplace_key = 2
BIC_key = 3
active_key = BIC_key
results = []
results_sequence = []
for r in range(max_depth):
if r == 0:
new_results = fear_run_experiments(seed_kernels, X, y, verbose=verbose, \
sleep_time=sleep_time, n_sleep_timeout=n_sleep_timeout, re_submit_wait=re_submit_wait)
else:
new_results = fear_run_experiments(fear_expand_kernels(D, seed_kernels, verbose=verbose), X, y, verbose=verbose, \
sleep_time=sleep_time, n_sleep_timeout=n_sleep_timeout, re_submit_wait=re_submit_wait)
results = results + new_results
print
results = sorted(results, key=lambda p: p[active_key], reverse=True)
for kernel, nll, laplace, BIC in results:
print nll, laplace, BIC, kernel.pretty_print()
seed_kernels = [
r[0] for r in sorted(new_results, key=lambda p: p[active_key])[0:k]
]
results_sequence.append(results)
# Write results to a file
results = sorted(results, key=lambda p: p[active_key], reverse=True)
with open(results_filename, 'w') as outfile:
outfile.write(
'Experiment results for\n datafile = %s\n y_dim = %d\n subset = %s\n max_depth = %f\n k = %f\n Description = %s\n\n'
% (data_file, y_dim, subset, max_depth, k, description))
for (i, results) in enumerate(results_sequence):
outfile.write('\n%%%%%%%%%% Level %d %%%%%%%%%%\n\n' % i)
for kernel, nll, laplace, BIC in results:
outfile.write('nll=%f, laplace=%f, BIC=%f, kernel=%s\n' %
(nll, laplace, BIC, kernel.__repr__()))
def plot_gef_load_Z01():
# This kernel was chosen from a run of gef_load datapoints.
# kernel = eval(ProductKernel([ covMask(ndim=12, active_dimension=0, base_kernel=RQKernel(lengthscale=0.268353, output_variance=-0.104149, alpha=-2.105742)), covMask(ndim=12, active_dimension=9, base_kernel=SqExpKernel(lengthscale=1.160242, output_variance=0.004344)), SumKernel([ covMask(ndim=12, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=-0.823413, period=0.000198, output_variance=-0.917064)), covMask(ndim=12, active_dimension=0, base_kernel=RQKernel(lengthscale=-0.459219, output_variance=-0.077250, alpha=-2.212718)) ]) ]))
X, y, D = fear_load_mat('../data/gef_load_full_Xy.mat', 1)
kernel = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
(fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064)) + fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718)))
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X[0:499, :])
pylab.figure()
pylab.plot(X[0:499, 0], y[0:499])
pylab.title('GEFCom2012 Z01 and T09 - first 500 data points')
pylab.xlabel('Time')
pylab.ylabel('Load')
def full_gef_load_experiment(zone=1, max_depth=5, verbose=True):
'''Round 2'''
# seed_kernels = [fk.MaskKernel(2, 0, fk.SqExpKernel(0, 0)),
# fk.MaskKernel(2, 1, fk.SqExpKernel(0, 0))]
seed_kernels = [fk.MaskKernel(12, i, fk.SqExpKernel(0., 0.)) for i in range(12)] + \
[fk.MaskKernel(12, i, fk.SqExpPeriodicKernel(0., 0., 0.)) for i in range(12)] + \
[fk.MaskKernel(12, i, fk.RQKernel(0., 0., 0.)) for i in range(12)]
X, y = load_full_gef_load()
# subsample data.
X = X[0:299, :]
y = y[0:299, zone - 1]
# max_depth = 5
k = 2 # Expand k best
nll_key = 1
BIC_key = 2
active_key = BIC_key
results = []
for i in range(max_depth):
if i:
expand = True
else:
expand = False
new_results = structure_search.try_expanded_kernels(
X,
y,
D=12,
seed_kernels=seed_kernels,
expand=expand,
verbose=verbose)
results = results + new_results
print
results = sorted(results, key=lambda p: p[active_key], reverse=True)
for kernel, nll, BIC in results:
print nll, BIC, kernel.pretty_print()
seed_kernels = [
r[0] for r in sorted(new_results, key=lambda p: p[active_key])[0:k]
]
