def gen_all_results(folder):
"""Look through all the files in the results directory"""
file_list = sorted([f for (r,d,f) in os.walk(folder)][0])
#for r,d,f in os.walk(folder):
for files in file_list:
if files.endswith(".txt"):
results_filename = os.path.join(folder,files)
best_tuple = exp.parse_results( results_filename )
yield files.split('.')[-2], best_tuple
def gen_all_results(folder):
"""Look through all the files in the results directory"""
file_list = sorted([f for (r, d, f) in os.walk(folder)][0])
#for r,d,f in os.walk(folder):
for files in file_list:
if files.endswith(".txt"):
results_filename = os.path.join(folder, files)
best_tuple = exp.parse_results(results_filename)
yield files.split('.')[-2], best_tuple
def make_all_1d_figures(folder, save_folder='../figures/decomposition/', max_level=None, prefix='', rescale=True, data_folder=None):
"""Crawls the results directory, and makes decomposition plots for each file.
prefix is an optional string prepended to the output directory
"""
#### Quick fix to axis scaling
#### TODO - Ultimately this and the shunt below should be removed / made elegant
if rescale:
data_sets = list(exp.gen_all_datasets("../data/1d_data_rescaled/"))
else:
if data_folder is None:
data_sets = list(exp.gen_all_datasets("../data/1d_data/"))
else:
data_sets = list(exp.gen_all_datasets(data_folder))
for r, file in data_sets:
results_file = os.path.join(folder, file + "_result.txt")
# Is the experiment complete
if os.path.isfile(results_file):
# Find best kernel and produce plots
datafile = os.path.join(r,file + ".mat")
X, y, D = gpml.load_mat(datafile)
if rescale:
# Load unscaled data to remove scaling later
unscaled_file = os.path.join('../data/1d_data/', re.sub('-s$', '', file) + '.mat')
data = gpml.load_mat(unscaled_file)
(X_unscaled, y_unscaled) = (data[0], data[1])
(X_mean, X_scale) = (X_unscaled.mean(), X_unscaled.std())
(y_mean, y_scale) = (y_unscaled.mean(), y_unscaled.std())
else:
(X_mean, X_scale, y_mean, y_scale) = (0,1,0,1)
# A shunt to deal with a legacy issue.
if datafile == '../data/1d_data/01-airline-months.mat':
# Scaling should turn months starting at zero into years starting at 1949
print "Special rescaling for airline months data"
X_mean = X_mean + 1949
X_scale = 1.0/12.0
best_kernel = exp.parse_results(os.path.join(folder, file + "_result.txt"), max_level=max_level)
stripped_kernel = fk.strip_masks(best_kernel.k_opt)
if not max_level is None:
fig_folder = os.path.join(save_folder, (prefix + file + '_max_level_%d' % max_level))
else:
fig_folder = os.path.join(save_folder, (prefix + file))
if not os.path.exists(fig_folder):
os.makedirs(fig_folder)
gpml.plot_decomposition(stripped_kernel, X, y, os.path.join(fig_folder, file), best_kernel.noise, X_mean, X_scale, y_mean, y_scale)
else:
print "Cannnot find file %s" % results_file
def make_all_1d_figures(folders, save_folder='../figures/decomposition/', prefix='', rescale=False, data_folder=None, skip_kernel_evaluation=False, unit='year', all_depths=False):
"""Crawls the results directory, and makes decomposition plots for each file.
prefix is an optional string prepended to the output directory
"""
if not isinstance(folders, list):
folders = [folders] # Backward compatibility with specifying one folder
#### Quick fix to axis scaling
#### TODO - Ultimately this and the shunt below should be removed / made elegant
if rescale:
data_sets = list(exp.gen_all_datasets("../data/1d_data_rescaled/"))
else:
if data_folder is None:
data_sets = list(exp.gen_all_datasets("../data/1d_data/"))
else:
data_sets = list(exp.gen_all_datasets(data_folder))
for r, file in data_sets:
results_files = []
for folder in folders:
results_file = os.path.join(folder, file + "_result.txt")
if os.path.isfile(results_file):
results_files.append(results_file)
# Is the experiment complete
if len(results_files) > 0:
# Find best kernel and produce plots
datafile = os.path.join(r,file + ".mat")
data = gpml.load_mat(datafile)
X = data[0]
y = data[1]
D = data[2]
assert D == 1
if rescale:
# Load unscaled data to remove scaling later
unscaled_file = os.path.join('../data/1d_data/', re.sub('-s$', '', file) + '.mat')
data = gpml.load_mat(unscaled_file)
(X_unscaled, y_unscaled) = (data[0], data[1])
(X_mean, X_scale) = (X_unscaled.mean(), X_unscaled.std())
(y_mean, y_scale) = (y_unscaled.mean(), y_unscaled.std())
else:
(X_mean, X_scale, y_mean, y_scale) = (0,1,0,1)
if all_depths:
# A quick version for now TODO - write correct code
models = [exp.parse_results(results_files, max_level=depth) for depth in range(10)]
suffices = ['-depth-%d' % (depth+1) for depth in range(len(models))]
else:
models = [exp.parse_results(results_files)]
suffices = ['']
for (model, suffix) in zip(models, suffices):
model = model.simplified().canonical()
kernel_components = model.kernel.break_into_summands()
kernel_components = ff.SumKernel(kernel_components).simplified().canonical().operands
print model.pretty_print()
fig_folder = os.path.join(save_folder, (prefix + file + suffix))
if not os.path.exists(fig_folder):
os.makedirs(fig_folder)
# First ask GPML to order the components
print 'Determining order of components'
(component_order, mae_data) = gpml.order_by_mae(model, kernel_components, X, y, D, os.path.join(fig_folder, file + suffix), skip_kernel_evaluation=skip_kernel_evaluation)
print 'Plotting decomposition and computing basic stats'
component_data = gpml.component_stats(model, kernel_components, X, y, D, os.path.join(fig_folder, file + suffix), component_order, skip_kernel_evaluation=skip_kernel_evaluation)
print 'Computing model checking stats'
checking_stats = gpml.checking_stats(model, kernel_components, X, y, D, os.path.join(fig_folder, file + suffix), component_order, make_plots=True, skip_kernel_evaluation=skip_kernel_evaluation)
# Now the kernels have been evaluated we can translate the revelant ones
evaluation_data = mae_data
evaluation_data.update(component_data)
evaluation_data.update(checking_stats)
evaluation_data['vars'] = evaluation_data['vars'].ravel()
evaluation_data['cum_vars'] = evaluation_data['cum_vars'].ravel()
evaluation_data['cum_resid_vars'] = evaluation_data['cum_resid_vars'].ravel()
evaluation_data['MAEs'] = evaluation_data['MAEs'].ravel()
evaluation_data['MAE_reductions'] = evaluation_data['MAE_reductions'].ravel()
evaluation_data['monotonic'] = evaluation_data['monotonic'].ravel()
evaluation_data['acf_min_p'] = evaluation_data['acf_min_p'].ravel()
evaluation_data['acf_min_loc_p'] = evaluation_data['acf_min_loc_p'].ravel()
evaluation_data['pxx_max_p'] = evaluation_data['pxx_max_p'].ravel()
evaluation_data['pxx_max_loc_p'] = evaluation_data['pxx_max_loc_p'].ravel()
evaluation_data['qq_d_max_p'] = evaluation_data['qq_d_max_p'].ravel()
evaluation_data['qq_d_min_p'] = evaluation_data['qq_d_min_p'].ravel()
i = 1
short_descriptions = []
while os.path.isfile(os.path.join(fig_folder, '%s_%d.fig' % (file + suffix, i))):
# Describe this component
(summary, sentences, extrap_sentences) = translation.translate_additive_component(kernel_components[component_order[i-1]], X, evaluation_data['monotonic'][i-1], evaluation_data['gradients'][i-1], unit)
short_descriptions.append(summary)
paragraph = '.\n'.join(sentences) + '.'
extrap_paragraph = '.\n'.join(extrap_sentences) + '.'
with open(os.path.join(fig_folder, '%s_%d_description.tex' % (file + suffix, i)), 'w') as description_file:
description_file.write(paragraph)
with open(os.path.join(fig_folder, '%s_%d_extrap_description.tex' % (file + suffix, i)), 'w') as description_file:
description_file.write(extrap_paragraph)
with open(os.path.join(fig_folder, '%s_%d_short_description.tex' % (file + suffix, i)), 'w') as description_file:
description_file.write(summary + '.')
i += 1
# Produce the summary LaTeX document
print 'Producing LaTeX document'
latex_summary = translation.produce_summary_document(file + suffix, i-1, evaluation_data, short_descriptions)
with open(os.path.join(save_folder, '%s.tex' % (file + suffix)), 'w') as latex_file:
latex_file.write(latex_summary)
print 'Saving to ' + (os.path.join(save_folder, '%s.tex' % (file + suffix)))
else:
print "Cannnot find results for %s" % file
def make_all_1d_figures(folders,
save_folder='../figures/decomposition/',
prefix='',
rescale=False,
data_folder=None,
skip_kernel_evaluation=False,
unit='year',
all_depths=False):
"""Crawls the results directory, and makes decomposition plots for each file.
prefix is an optional string prepended to the output directory
"""
if not isinstance(folders, list):
folders = [folders
] # Backward compatibility with specifying one folder
#### Quick fix to axis scaling
#### TODO - Ultimately this and the shunt below should be removed / made elegant
if rescale:
data_sets = list(exp.gen_all_datasets("../data/1d_data_rescaled/"))
else:
if data_folder is None:
data_sets = list(exp.gen_all_datasets("../data/1d_data/"))
else:
data_sets = list(exp.gen_all_datasets(data_folder))
for r, file in data_sets:
results_files = []
for folder in folders:
results_file = os.path.join(folder, file + "_result.txt")
if os.path.isfile(results_file):
results_files.append(results_file)
# Is the experiment complete
if len(results_files) > 0:
# Find best kernel and produce plots
datafile = os.path.join(r, file + ".mat")
data = gpml.my_load_mat(datafile)
X = data[0]
y = data[1]
M = y.shape[1]
D = data[2]
iiii = 1
y = y[:, iiii]
assert D == 1
if rescale:
# Load unscaled data to remove scaling later
unscaled_file = os.path.join('../data/1d_data/',
re.sub('-s$', '', file) + '.mat')
data = gpml.load_mat(unscaled_file)
(X_unscaled, y_unscaled) = (data[0], data[1])
(X_mean, X_scale) = (X_unscaled.mean(), X_unscaled.std())
(y_mean, y_scale) = (y_unscaled.mean(), y_unscaled.std())
else:
(X_mean, X_scale, y_mean, y_scale) = (0, 1, 0, 1)
if all_depths:
# A quick version for now TODO - write correct code
models = [
exp.parse_results(results_files, max_level=depth)[0]
for depth in range(10)
]
suffices = [
'-depth-%d' % (depth + 1) for depth in range(len(models))
]
else:
models = [exp.parse_results(results_files)[0]]
try:
suffices = ['-' + str(data[5][iiii]).replace(" ", "")]
#suffices = ['-'+str(data[5][iiii][0][0]).replace(" ","")] uncomment this if you test given dataset(house, stock and so on)
except:
suffices = ['-' + str(iiii)]
#suffices = ['-'+str(iiii)]
best_depth = exp.parse_results(results_files)[1]
params_filename = '/home/heechan/gpss-research-srkl' + results_files[
0][2:] + 'lvl_' + str(best_depth) + '_0.mat1.mat'
scale_params = scipy.io.loadmat(params_filename)['scale']
scl1 = scale_params[0][iiii][0][0]
scl2 = scale_params[0][iiii][1][0]
for (model, suffix) in zip(models, suffices):
model = model.simplified().canonical()
model.kernel = model.kernel * ff.ConstKernel(
sf=scl2) + ff.ConstKernel(sf=scl1)
kernel_components = model.kernel.break_into_summands()
kernel_components = ff.SumKernel(
kernel_components).simplified().canonical().operands
print model.pretty_print()
fig_folder = os.path.join(save_folder,
(prefix + file + suffix))
if not os.path.exists(fig_folder):
os.makedirs(fig_folder)
# First ask GPML to order the components
print 'Determining order of components'
(component_order, mae_data) = gpml.order_by_mae(
model,
kernel_components,
X,
y,
D,
os.path.join(fig_folder, file + suffix),
skip_kernel_evaluation=skip_kernel_evaluation)
print 'Plotting decomposition and computing basic stats'
component_data = gpml.component_stats(
model,
kernel_components,
X,
y,
D,
os.path.join(fig_folder, file + suffix),
component_order,
skip_kernel_evaluation=skip_kernel_evaluation)
print 'Computing model checking stats'
checking_stats = gpml.checking_stats(
model,
kernel_components,
X,
y,
D,
os.path.join(fig_folder, file + suffix),
component_order,
make_plots=True,
skip_kernel_evaluation=skip_kernel_evaluation)
# Now the kernels have been evaluated we can translate the revelant ones
evaluation_data = mae_data
evaluation_data.update(component_data)
evaluation_data.update(checking_stats)
evaluation_data['vars'] = evaluation_data['vars'].ravel()
evaluation_data['cum_vars'] = evaluation_data[
'cum_vars'].ravel()
evaluation_data['cum_resid_vars'] = evaluation_data[
'cum_resid_vars'].ravel()
evaluation_data['MAEs'] = evaluation_data['MAEs'].ravel()
evaluation_data['MAE_reductions'] = evaluation_data[
'MAE_reductions'].ravel()
evaluation_data['monotonic'] = evaluation_data[
'monotonic'].ravel()
evaluation_data['acf_min_p'] = evaluation_data[
'acf_min_p'].ravel()
evaluation_data['acf_min_loc_p'] = evaluation_data[
'acf_min_loc_p'].ravel()
evaluation_data['pxx_max_p'] = evaluation_data[
'pxx_max_p'].ravel()
evaluation_data['pxx_max_loc_p'] = evaluation_data[
'pxx_max_loc_p'].ravel()
evaluation_data['qq_d_max_p'] = evaluation_data[
'qq_d_max_p'].ravel()
evaluation_data['qq_d_min_p'] = evaluation_data[
'qq_d_min_p'].ravel()
i = 1
short_descriptions = []
while os.path.isfile(
os.path.join(fig_folder,
'%s_%d.fig' % (file + suffix, i))):
# Describe this component
(summary, sentences, extrap_sentences
) = translation.translate_additive_component(
kernel_components[component_order[i - 1]], X,
evaluation_data['monotonic'][i - 1],
evaluation_data['gradients'][i - 1], unit)
short_descriptions.append(summary)
paragraph = '.\n'.join(sentences) + '.'
extrap_paragraph = '.\n'.join(extrap_sentences) + '.'
with open(
os.path.join(
fig_folder,
'%s_%d_description.tex' % (file + suffix, i)),
'w') as description_file:
description_file.write(paragraph)
with open(
os.path.join(
fig_folder, '%s_%d_extrap_description.tex' %
(file + suffix, i)), 'w') as description_file:
description_file.write(extrap_paragraph)
with open(
os.path.join(
fig_folder, '%s_%d_short_description.tex' %
(file + suffix, i)), 'w') as description_file:
description_file.write(summary + '.')
i += 1
# Produce the summary LaTeX document
print 'Producing LaTeX document'
latex_summary = translation.produce_summary_document(
file + suffix, i - 1, evaluation_data, short_descriptions)
with open(
os.path.join(save_folder, '%s.tex' % (file + suffix)),
'w') as latex_file:
latex_file.write(latex_summary)
print 'Saving to ' + (os.path.join(save_folder, '%s.tex' %
(file + suffix)))
else:
print "Cannnot find results for %s" % file
def make_all_1d_figures(folder,
save_folder='../figures/decomposition/',
max_level=None,
prefix='',
rescale=True,
data_folder=None):
"""Crawls the results directory, and makes decomposition plots for each file.
prefix is an optional string prepended to the output directory
"""
#### Quick fix to axis scaling
#### TODO - Ultimately this and the shunt below should be removed / made elegant
if rescale:
data_sets = list(exp.gen_all_datasets("../data/1d_data_rescaled/"))
else:
if data_folder is None:
data_sets = list(exp.gen_all_datasets("../data/1d_data/"))
else:
data_sets = list(exp.gen_all_datasets(data_folder))
for r, file in data_sets:
results_file = os.path.join(folder, file + "_result.txt")
# Is the experiment complete
if os.path.isfile(results_file):
# Find best kernel and produce plots
datafile = os.path.join(r, file + ".mat")
X, y, D = gpml.load_mat(datafile)
if rescale:
# Load unscaled data to remove scaling later
unscaled_file = os.path.join('../data/1d_data/',
re.sub('-s$', '', file) + '.mat')
data = gpml.load_mat(unscaled_file)
(X_unscaled, y_unscaled) = (data[0], data[1])
(X_mean, X_scale) = (X_unscaled.mean(), X_unscaled.std())
(y_mean, y_scale) = (y_unscaled.mean(), y_unscaled.std())
else:
(X_mean, X_scale, y_mean, y_scale) = (0, 1, 0, 1)
# A shunt to deal with a legacy issue.
if datafile == '../data/1d_data/01-airline-months.mat':
# Scaling should turn months starting at zero into years starting at 1949
print "Special rescaling for airline months data"
X_mean = X_mean + 1949
X_scale = 1.0 / 12.0
best_kernel = exp.parse_results(os.path.join(
folder, file + "_result.txt"),
max_level=max_level)
stripped_kernel = fk.strip_masks(best_kernel.k_opt)
if not max_level is None:
fig_folder = os.path.join(
save_folder, (prefix + file + '_max_level_%d' % max_level))
else:
fig_folder = os.path.join(save_folder, (prefix + file))
if not os.path.exists(fig_folder):
os.makedirs(fig_folder)
gpml.plot_decomposition(stripped_kernel, X, y,
os.path.join(fig_folder,
file), best_kernel.noise,
X_mean, X_scale, y_mean, y_scale)
else:
print "Cannnot find file %s" % results_file
