def visualize(self, out_filename="out/training_bs.html", out_filename_pickle="out/training_bs.pickle"):
# Do bootstrap for the confusion table.
n_bs = 1
widgets = [progressbar.Percentage(),
' ', progressbar.Bar(),
' ', progressbar.ETA(),
' ', progressbar.AdaptiveETA()]
bs_progress = progressbar.ProgressBar(widgets=widgets).start()
cts = []
for bs_iter in bs_progress(range(n_bs)):
n_dialogs = len(self.training_dialogs)
dataset = self.training_dialogs
tracker = Tracker(self.model, inv=False)
tracker.simulate(dataset)
cts.append(tracker.out_data['confusion_tables'])
ct = bootstrap.from_all_confusion_tables(cts)
context = {}
context['tracker'] = tracker.out_data
context['bootstrap_ct'] = ct
context['mean_score'] = np.mean([ctt.mean_score for ctt in ct.values()])
context['model'] = self.model
context['training_metrics'] = self.training_metrics
context['training_data'] = self.training_dialogs
env = Environment(loader=FileSystemLoader('tpl'))
env.globals.update(zip=zip)
tpl = env.get_template('training.html')
with open(out_filename, "w") as f_out:
f_out.write(tpl.render(**context))
with open(out_filename_pickle, "w") as f_out:
info = {
'mean_score': float(context['mean_score']),
'losses': [float(x) for x in context['training_metrics']['losses']],
#'simulation': context['tracker']['simulation']
}
f_out.write(json.dumps(info))
