}
all_result_files = set(listdir(RESULT_LOCATION))
todo = set(all_detectors.keys()) - all_result_files
if len(todo) == 0:
print('There are result files for all detectors. Stopping')
break
print('Detectors without result files:', ', '.join(todo))
detector = list(todo)[0]
print('Doing', detector)
if len(conversations) == 0:
print('Importing conversations')
conversations = import_conversations(CONVERSATIONS_FILE)
print('Loading pretrained model')
if detector in ['m04']:
exp_hare = Hare(name='dict_based')
exp_hare.brain = DictBasedBrain()
exp_hare.brain.bad_words = [
'f**k', 'fck', 'fuk', 'shit', 'stfu', 'wtf', 'suck', 'noob',
'newb', 'n00b', 'f*g', 'loser'
]
else:
exp_hare = load_pretrained(PRETRAINED_MODELS_LOCATION +
all_detectors[detector])
result_file = open(RESULT_LOCATION + detector, 'w')
from sys import argv
from hare import Hare
from hare.brain import DictBasedBrain
from hare.conversation import import_conversations
THRESHOLDS = [0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5, 0.75]
CONVERSATIONS_FILE = '../datasets/LoL/heldout_conversations_anon.txt'
NR_OF_CONVERSATIONS = 1000
print('Importing conversations')
conversations = import_conversations(CONVERSATIONS_FILE)[:NR_OF_CONVERSATIONS]
print('Loading pretrained model')
exp_hare = Hare(name='dict_based')
exp_hare.brain = DictBasedBrain()
exp_hare.brain.bad_words = [
'f**k', 'fck', 'fuk', 'shit', 'stfu', 'wtf', 'suck', 'noob', 'newb',
'n00b', 'f*g', 'loser'
]
result_file = open('moba_dic', 'w')
for n, conversation in enumerate(conversations):
print('Processing conv', n)
exp_hare.add_conversation(conversation)
exp_hare.save_history_for_conversation(result_file, n)
'moba_bigru_embeddings_100': [
0.001, 0.0025, 0.005, 0.0075, 0.01, 0.025, 0.05, 0.075, 0.1, 0.25, 0.5,
0.75, 1
]
}
BETA_VALUES = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
CONVERSATIONS_FILE = HARE_ROOT + '/datasets/LoL/heldout_conversations_anon.txt'
CONVERSATION_LENGTH = 200
NR_OF_CONVERSATIONS = 10
OUTPUT_FOLDER = ESSAY_ROOT + 'precalculated_data/'
#From all heldout conversations, load the first 10
conversations = import_conversations(
CONVERSATIONS_FILE, cutoff_point=CONVERSATION_LENGTH)[:NR_OF_CONVERSATIONS]
#Cut away utterances by some speakers
#Save the true target data
open(OUTPUT_FOLDER + 'target.js', 'w').write(
dumps(
[conversation.speakers_with_labels for conversation in conversations]))
#Go through all detectors, with all thresholds
hares = []
for conv_hist_file, thresholds in CONVERSATION_HISTORY_FILES_WITH_THRESHOLDS.items(
):
#For each conversation, read the status at every point during the conversation