#Add the precalculated conversation history to hare objects
hares = []
for conv_hist_file in CONVERSATION_HISTORY_FILES:
status_per_conversation = []
for n, line in enumerate(open(conv_hist_file)):
status_per_conversation.append(loads(line)[:CONVERSATION_LENGTH])
if n % 100 == 0:
print(conv_hist_file, n)
for threshold in THRESHOLDS:
h = Hare(name=threshold)
h.status_per_conversation = status_per_conversation
h.cut_off_value = threshold
hares.append(h)
#Load the conversations
conversations = []
current_conversation = Conversation()
for line in open(CONVERSATIONS_FILE):
line = line.strip()
if len(line) == 0:
continue
elif line[0] == '#':
#For this conversation, we want to know the conversation history for every detector
for detector, thresholds in DETECTOR_THRESHOLDS.items():
conversation_history = loads(open_conversation_history_files[detector].readline())
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: