def build_question_and_duration(split="tiny"):
data = data_readers.read_corpus(split)
questions = []
question_durations_sec = []
response_times_sec = []
session_ids = []
sessions = data_util.get_sessions(data)
for session in progressbar.progressbar(sessions):
for question, response in session.iter_question_and_response():
questions.append(question.row.text)
question_durations_sec.append(question.duration)
response_times_sec.append((response.row.created_at - question.row.created_at).seconds)
session_ids.append(session.id)
dataset = pd.DataFrame.from_dict({"session_id": session_ids, "question": questions, "question_duration_sec": question_durations_sec, "response_time_sec": response_times_sec})
return dataset
def build_question_and_sentiment(split="tiny"):
data = data_readers.read_corpus(split)
pool = Pool(12)
sessions = data_util.get_sessions(data)
combined_results = defaultdict(list)
for session_result in progressbar.progressbar(pool.imap(process_session, sessions), max_value=len(sessions)):
for k, v in session_result.items():
combined_results[k].extend(v)
session_ids = combined_results["session_ids"]
questions = combined_results["questions"]
sentiments = combined_results["sentiments"]
response_times_sec = combined_results["response_times_sec"]
dataset = pd.DataFrame.from_dict({"session_id": session_ids, "question": questions, "question_sentiment": sentiments, "response_time_sec": response_times_sec})
return dataset
def build_question_only(split="tiny", concatenator=None):
data = data_readers.read_corpus(split)
questions = []
response_times_sec = []
session_ids = []
sessions = data_util.get_sessions(data)
progress = progressbar.ProgressBar(max_value=len(sessions)).start()
for i, session in enumerate(sessions):
for question, response in session.iter_question_and_response(concatenator=concatenator):
questions.append(question.row.text)
response_times_sec.append((response.row.created_at - question.row.created_at).seconds)
session_ids.append(session.id)
progress.update(i)
dataset = pd.DataFrame.from_dict({"session_id": session_ids, "question": questions, "response_time_sec": response_times_sec})
progress.finish()
return dataset
def build_label_counts(split="tiny"):
data = data_readers.read_corpus(split)
label_counts = []
response_times_sec = []
session_ids = []
sessions = data_util.get_sessions(data)
for session in progressbar.progressbar(sessions):
counts = defaultdict(int)
for question, response in session.iter_question_and_response():
response_time_sec = (response.row.created_at - question.row.created_at).seconds
response_times_sec.append(response_time_sec)
label_counts.append(tuple(counts[label] for label in Config.LABELS))
session_ids.append(session.id)
counts[get_response_time_label(response_time_sec)] += 1
dataset = pd.DataFrame.from_dict({"session_id": session_ids, "response_time_sec": response_times_sec, "label_counts": label_counts})
return dataset
def build_question_with_context_window(split="tiny", window_size=0):
data = data_readers.read_corpus(split)
sessions = data_util.get_sessions(data)
questions = []
response_times_sec = []
session_ids = []
turn_texts = defaultdict(list)
turn_speakers = defaultdict(list)
turn_times = defaultdict(list)
for session in progressbar.progressbar(sessions):
for question, response in session.iter_question_and_response():
questions.append(question.row.text)
response_times_sec.append((response.row.created_at - question.row.created_at).seconds)
session_ids.append(session.id)
times = defaultdict(lambda: 0)
texts = defaultdict(lambda: [])
speakers = defaultdict(lambda: Config.EMPTY_TAG)
prev = question.row.created_at
for i, turn in enumerate(session.iter_turns(start_row=question.index, num_turns=window_size+1, direction=-1)):
texts[i] = turn.text
speakers[i] = turn.sent_from
times[i] = int((prev - turn.created_at).seconds)
prev = turn.created_at
for i in range(1, window_size+1):
turn_texts["turn_text-%d" % i].append(texts[i])
turn_speakers["turn_speaker-%d" % i].append(speakers[i])
turn_times["turn_time-%d" % i].append(times[i])
columns = {"session_id": session_ids, "question": questions, "response_time_sec": response_times_sec}
columns.update(turn_texts)
columns.update(turn_speakers)
columns.update(turn_times)
dataset = pd.DataFrame.from_dict(columns)
return dataset
def split_data(data, tiny_f=0.01, train_f=0.7, dev_f=0.15, test_f=0.15):
session_ids = data.session_id.unique()
assert abs(train_f + dev_f + test_f - 1.0) * len(session_ids) < 1
assert tiny_f < train_f
log_info("Splitting %d session_ids" % len(session_ids))
log_info("Extracting Sessions")
sessions = data_util.get_sessions(data)
print("\tExtracted %d sessions" % len(sessions))
session_id_to_num_questions = defaultdict(int)
num_questions_to_session_ids = defaultdict(list)
for session in sessions:
num_questions = len(tuple(session.iter_question_and_response()))
session_id_to_num_questions[session.id] = num_questions
num_questions_to_session_ids[num_questions].append(session.id)
groups = get_stratified_session_ids(num_questions_to_session_ids, min([train_f, dev_f, test_f]))
session_id_splits = defaultdict(list)
for group in groups:
np.random.seed(seed=Config.SEED)
np.random.shuffle(group)
train_split, dev_split, test_split = np.split(group, [int(np.round(train_f * len(group))), int(np.round((train_f + dev_f) * len(group)))])
session_id_splits["train"].extend(train_split)
session_id_splits["dev"].extend(dev_split)
session_id_splits["test"].extend(test_split)
# tiny is a subset of train
session_id_splits["tiny"] = session_id_splits["train"][:int(np.round(tiny_f * len(session_ids)))]
for split, ids in session_id_splits.items():
num_q = sum([session_id_to_num_questions[id] for id in ids])
print("\t%s: %d sessions, %d questions" % (split, len(ids), num_q))
return data[data.session_id.isin(session_id_splits["tiny"])],\
data[data.session_id.isin(session_id_splits["train"])],\
data[data.session_id.isin(session_id_splits["dev"])],\
data[data.session_id.isin(session_id_splits["test"])]
