def generate_tex_template(best_dict,
name_list,
save="",
template_string=template_string,
experiment_name="Name",
num_evals="\\#eval"):
tex = StringIO()
result_values = OrderedDict([(name[0], dict()) for name in name_list])
means = [np.mean(best_dict[name]) for name in result_values]
stds = [np.std(best_dict[name]) for name in result_values]
mins = [np.min(best_dict[name]) for name in result_values]
maxs = [np.max(best_dict[name]) for name in result_values]
for name in result_values:
values = result_values[name]
values["mean"] = np.mean(best_dict[name])
values["mean_best"] = True if \
wrapping_util.float_eq(values["mean"], min(means)) else False
values["std"] = np.std(best_dict[name])
values["std_best"] = True if \
wrapping_util.float_eq(values["std"], min(stds)) else False
values["min"] = np.min(best_dict[name])
values["min_best"] = True if\
wrapping_util.float_eq(values["min"], min(mins)) else False
values["max"] = np.max(best_dict[name])
values["max_best"] = True if\
wrapping_util.float_eq(values["max"], min(maxs)) else False
if jinja2:
template = Template(template_string)
tex.write(
template.render(result_values=result_values,
experiment=experiment_name,
evals=num_evals))
else:
tex.write("Name & #evals")
for name in result_values:
values = result_values[name]
tex.write(" & ")
tex.write(values["mean"])
tex.write("$\\pm$")
tex.write(values["std"])
tex.write(" & ")
tex.write(values["min"])
tex.write("\\\\")
tex.seek(0)
table = tex.getvalue()
if save != "":
with open(save, "w") as fh:
fh.write(table)
else:
return table
def generate_tex_template(best_dict, name_list, save="",
template_string=template_string, experiment_name="Name",
num_evals="\\#eval"):
tex = StringIO()
result_values = OrderedDict([(name[0], dict()) for name in name_list])
means = [np.mean(best_dict[name]) for name in result_values]
stds = [np.std(best_dict[name]) for name in result_values]
mins = [np.min(best_dict[name]) for name in result_values]
maxs = [np.max(best_dict[name]) for name in result_values]
for name in result_values:
values = result_values[name]
values["mean"] = np.mean(best_dict[name])
values["mean_best"] = True if \
wrapping_util.float_eq(values["mean"], min(means)) else False
values["std"] = np.std(best_dict[name])
values["std_best"] = True if \
wrapping_util.float_eq(values["std"], min(stds)) else False
values["min"] = np.min(best_dict[name])
values["min_best"] = True if\
wrapping_util.float_eq(values["min"], min(mins)) else False
values["max"] = np.max(best_dict[name])
values["max_best"] = True if\
wrapping_util.float_eq(values["max"], min(maxs)) else False
if jinja2:
template = Template(template_string)
tex.write(template.render(result_values=result_values,
experiment=experiment_name, evals=num_evals))
else:
tex.write("Name & #evals")
for name in result_values:
values = result_values[name]
tex.write(" & ")
tex.write(values["mean"])
tex.write("$\\pm$")
tex.write(values["std"])
tex.write(" & ")
tex.write(values["min"])
tex.write("\\\\")
tex.seek(0)
table = tex.getvalue()
if save != "":
with open(save, "w") as fh:
fh.write(table)
else:
return table
def _sanity_check(self):
total_wallclock_time = 0
finite_instance_results = 0
for trial in self.trials:
self._trial_sanity_check(trial)
# Backwards compability with numpy 1.6
wallclock_time = np.nansum(trial['instance_durations'])
total_wallclock_time += wallclock_time if np.isfinite(wallclock_time) else 0
assert (wrapping_util.float_eq(total_wallclock_time,
self.total_wallclock_time)), \
(total_wallclock_time, self.total_wallclock_time)
def _sanity_check(self):
total_wallclock_time = 0
for trial in self.trials:
self._trial_sanity_check(trial)
# Backwards compability with numpy 1.6
with warnings.catch_warnings():
warnings.simplefilter("ignore")
wallclock_time = np.nansum(trial['instance_durations'])
test_wallclock_time = np.nansum(trial['test_instance_durations'])
total_wallclock_time += wallclock_time if \
np.isfinite(wallclock_time) else 0
total_wallclock_time += test_wallclock_time if \
np.isfinite(test_wallclock_time) else 0
if not wrapping_util.float_eq(total_wallclock_time,
self.total_wallclock_time):
raise ValueError("Found an error in the time measurement. The "
"values %f and %f should be equal, but aren't" %
(total_wallclock_time, self.total_wallclock_time))
def _sanity_check(self):
total_wallclock_time = 0
for trial in self.trials:
self._trial_sanity_check(trial)
# Backwards compability with numpy 1.6
with warnings.catch_warnings():
warnings.simplefilter("ignore")
wallclock_time = np.nansum(trial['instance_durations'])
test_wallclock_time = np.nansum(
trial['test_instance_durations'])
total_wallclock_time += wallclock_time if \
np.isfinite(wallclock_time) else 0
total_wallclock_time += test_wallclock_time if \
np.isfinite(test_wallclock_time) else 0
if not wrapping_util.float_eq(total_wallclock_time,
self.total_wallclock_time):
raise ValueError("Found an error in the time measurement. The "
"values %f and %f should be equal, but aren't" %
(total_wallclock_time, self.total_wallclock_time))
