for conversation_index in range(NR_OF_CONVERSATIONS):
try:
current_status = h.status_per_conversation[
conversation_index][utterance_index]
except IndexError:
continue
speakers = h.conversations[conversation_index].all_speakers
per_player.append({
speaker: current_status[speaker] >= threshold
if speaker in current_status.keys() else False
for speaker in speakers
})
true, predicted = h.get_true_and_predicted_scores_at_utterance_index(
utterance_index, categorize_predicted_scores=True)
tp.append(true_positives(true, predicted))
fp.append(false_positives(true, predicted))
for b in BETA_VALUES:
fbeta[b].append(fbeta_score(true, predicted, b))
open(folder_name + 'per_player.js',
'w').write(conv_hist_file + '_per_player[' + str(threshold) +
'] = ' + dumps(per_player))
open(folder_name + 'tp.js',
'w').write(conv_hist_file + '_tp[' + str(threshold) + '] = ' +
dumps(tp))
open(folder_name + 'fp.js',
'w').write(conv_hist_file + '_fp[' + str(threshold) + '] = ' +
dumps(fp))
scores_for_this_conversation = []
#We check what scores this means for every threshold
for threshold in thresholds:
if threshold not in all_scores[detector].keys():
all_scores[detector][threshold] = []
h = Hare()
h.add_conversation(conversation)
h.status_per_conversation = [conversation_history]
h.cut_off_value = threshold
for point_in_conversation in INTERESTING_POINTS_IN_CONVERSATION:
true, predicted = h.get_true_and_predicted_scores_at_utterance_index(point_in_conversation,categorize_predicted_scores=True)
score = fbeta_score(true, predicted, 1)
scores_for_this_conversation.append(score)
if convo_index == 0:
headers.append(detector+':'+str(threshold)+':'+str(point_in_conversation))
all_scores[detector][threshold].append(scores_for_this_conversation[-len(INTERESTING_POINTS_IN_CONVERSATION):])
if convo_index == 0 and PRINT_OUTPUT:
print('convo_id',' '.join(headers))
if PRINT_OUTPUT:
print(convo_index,' '.join([str(s) for s in scores_for_this_conversation]))