def get_parents(node_id):
if not node_id:
return []
node = db_session.query(Node).get(node_id)
nodes = db_session.query(Node).filter(Node.id.in_(node.path), Node.active.is_(True)).order_by(Node.path_length).all()
return nodes
def incoherent_nodes():
node_utterance_ids = (db_session.query(
NodeUtteranceStatus.node_utterance_id,
func.count(NodeUtteranceStatus.node_utterance_id),
).filter(
NodeUtteranceStatus.status == "incoherent",
NodeUtteranceStatus.handled.is_(False),
).group_by(NodeUtteranceStatus.node_utterance_id).order_by(
func.count(NodeUtteranceStatus.node_utterance_id).desc()).all())
for node_utterance_id, count in node_utterance_ids:
node_utterance = db_session.query(NodeUtterance).get(node_utterance_id)
history = (db_session.query(Node).filter(
Node.id.in_(node_utterance.node.path)).order_by(
Node.path_length.asc()).all())
print("+----------------------------------+")
print("node utterance id:", node_utterance_id)
print("node id:", node_utterance.node.id)
print("count:", count)
print("\n")
for h in history[:-1]:
print([x.utterance_text for x in h.utterances])
print(node_utterance.utterance.utterance_text)
print("\n")
user_input = input("Inactivate? Y/n/q ")
if user_input.lower() == "q":
exit()
elif user_input == "" or user_input.lower() == "y":
inactivate_node(node_utterance.node.id)
db_session.query(NodeUtteranceStatus).filter(
NodeUtteranceStatus.node_utterance_id == node_utterance_id,
NodeUtteranceStatus.status == "incoherent",
).update({"handled": True})
db_session.commit()
def submit(external_worker_id, task_id):
if task_id not in TASK_IDS:
raise KeyError('Task id not recognized')
worker = db_session.query(Worker) \
.filter_by(external_worker_id=external_worker_id) \
.first()
if not worker:
worker = Worker(external_worker_id=external_worker_id)
db_session.add(worker)
db_session.commit()
training = db_session.query(Training).filter_by(worker=worker).first()
if not training:
training = Training(worker=worker)
db_session.add(training)
db_session.commit()
if task_id == min(set(TASK_IDS) - set(training.tasks)):
training.tasks = training.tasks + [task_id]
db_session.commit()
new_set = set(TASK_IDS) - set(training.tasks)
if not new_set:
return True
else:
return False
def set_parent(node_id, parent_node_id=None, commit=False):
node = db_session.query(Node).get(node_id)
if not node:
raise Exception("Could not find node")
if parent_node_id:
parent_node = db_session.query(Node).get(parent_node_id)
else:
parent_node = None
old_parent_id = None
if node.parent:
old_parent_id = node.parent.id
node.parent = parent_node
node.path = node.recalculate_path()
def update_children(child_node):
db_session.add(child_node)
child_node.path = child_node.recalculate_path()
for child in child_node.children[:]:
logger.debug("found child %s", child.id)
logger.debug("parent has children %s", child_node.children)
update_children(child)
if old_parent_id and node.children:
update_children(node)
if commit:
db_session.commit()
logger.info("committing!")
def prepare_from_db():
nodes = (db_session.query(Node).options(joinedload(
Node.utterances)).filter(
Node.active.is_(True),
or_(
Node.visited_count > 1,
Node.child_count > 0,
Node.is_user.is_(False),
),
).all())
lookup_table = defaultdict(list)
node_utts = defaultdict(list)
node_visit_counts = {}
id_utt = {}
for node in nodes:
lookup_table[node.parent_id].append(node.id)
node_visit_counts[node.id] = node.visited_count
for utterance in node.utterances:
node_utts[node.id].append(utterance.id)
id_utt[utterance.id] = {"text": utterance.utterance_text}
linked_nodes = {
from_node: to_node
for from_node, to_node in db_session.query(
LinkedNodes.linked_from_node_id,
LinkedNodes.linked_to_node_id).all()
}
return lookup_table, node_utts, id_utt, node_visit_counts, linked_nodes
def get_next_training_for_worker(external_worker_id):
worker = db_session.query(Worker) \
.filter_by(external_worker_id=external_worker_id) \
.first()
training = db_session.query(Training).filter_by(worker=worker).first()
if not training or not training.tasks:
return None
remaining_tasks = set(TASK_IDS) - set(training.tasks)
if not remaining_tasks:
return '__DONE__'
lowest_id_for_non_perfomed_task = min(remaining_tasks)
t = list(
filter(lambda x: x['id'] == lowest_id_for_non_perfomed_task,
TRAINING_DATA))[0]
return {
'id':
t['id'],
'history': [
NodeUtterance(utterance=Utterance(utterance_text=x['text']))
for x in t['history']
],
'replies':
t['replies'],
'description':
t['description']
}
def get_node_utterances(node_id):
node_utterances = (db_session.query(NodeUtterance).filter(
NodeUtterance.node_id == node_id).all())
for node_utterance in node_utterances:
utterances = (db_session.query(Utterance).filter(
Utterance.id == node_utterance.utterance_id).all())
for utterance in utterances:
yield utterance.utterance_text
def activate_node(node_id):
node = db_session.query(Node).get(node_id)
node.active = True
nodes = db_session.query(Node).filter(Node._path.descendant_of(
node._path)).all()
for node in nodes:
node.active = True
db_session.commit()
def add_yes_no_nodes():
text_file = open("nodeIDs_final.txt", "r")
node_ids = text_file.read().split("\n")[:-1]
nodes = (db_session.query(Node).filter(Node.is_user == False,
Node.id.in_(map(int,
node_ids))).all())
# Filter out nodes that already have yes/no children
nodes_add_yes = []
nodes_add_no = []
for node in nodes:
add_yes, add_no = True, True
yes_utterances, no_utterances = [], []
children = db_session.query(Node).filter(
Node.parent_id == node.id).all()
for child in children:
for utterance in get_node_utterances(child.id):
if re.match(
r".*\b(ye(s\b|ah\b|a\b|p\b)|of course|I do\b|absolutely|definitely|ok\b|ok(ay|ey)\b|sounds (good|great)|fine|sure|why not|don't mind|let's do that).*",
utterance,
):
add_yes = False
if re.match(
r".*\b(n(o\bo\b||ot\b|ope\b|a\b|ah\b)|sounds bad|I'm good).*",
utterance,
):
add_no = False
if add_yes:
nodes_add_yes.append(node)
if add_no:
nodes_add_no.append(node)
# Add yes/no nodes and write file with new ids for future reference
added_node_ids = ""
for node in nodes_add_yes:
new_node = create_new_node(
"yes",
source="yes_no_population",
parent_id=node,
species="yes",
commit=False,
)
added_node_ids += (str(new_node.id) + "\t" +
list(get_node_utterances(node.id))[0] + "\tyes\n")
for node in nodes_add_no:
new_node = create_new_node("no",
source="yes_no_population",
parent_id=node,
species="no",
commit=False)
added_node_ids += (str(new_node.id) + "\t" +
list(get_node_utterances(node.id))[0] + "\tno\n")
with open("added_node_ids.txt", "w") as the_file:
the_file.write(added_node_ids)
def merge_by_score():
nodes = (db_session.query(
PotentialNodeMerge.left_node_id,
PotentialNodeMerge.right_node_id,
PotentialNodeMerge.score,
Merging,
).outerjoin(
Merging,
((PotentialNodeMerge.left_node_id == Merging.left_node_id)
& (PotentialNodeMerge.right_node_id == Merging.right_node_id)
| (PotentialNodeMerge.left_node_id == Merging.right_node_id)
& (PotentialNodeMerge.right_node_id == Merging.left_node_id)),
).filter(Merging.id.is_(None)).order_by(
PotentialNodeMerge.score.desc()).all())
used_ids = []
merged_right_nodes = []
for left_node_id, right_node_id, score, _ in nodes:
if (f"{left_node_id}-{right_node_id}" not in used_ids
and left_node_id not in merged_right_nodes
and right_node_id not in merged_right_nodes):
used_ids.append(f"{left_node_id}-{right_node_id}")
used_ids.append(f"{right_node_id}-{left_node_id}")
print("+------------------+")
left_node = db_session.query(Node).get(left_node_id)
right_node = db_session.query(Node).get(right_node_id)
if (left_node.active and right_node.active and left_node.utterances
and right_node.utterances):
print(left_node_id,
[x.utterance_text for x in left_node.utterances])
print("---------------- VS ----------------")
print(right_node_id,
[x.utterance_text for x in right_node.utterances])
print("\nscore", score, "\n")
user_input = input("Merge? Y/n/q ")
if user_input.lower() == "q":
exit()
elif user_input.lower() == "n":
merge_nodes(left_node_id, right_node_id, merged=False)
print("nope!")
elif user_input == "" or user_input.lower() == "y":
if right_node.child_count > left_node.child_count:
print(right_node.id, "<-", left_node.id)
merge_nodes(right_node.id, left_node.id, merged=True)
merged_right_nodes.append(left_node.id)
else:
print(left_node.id, "<-", right_node.id)
merge_nodes(left_node.id, right_node.id, merged=True)
merged_right_nodes.append(right_node.id)
db_session.commit()
def inactivate_node(node_id):
node = db_session.query(Node).get(node_id)
node.active = False
nodes = db_session.query(Node).filter(Node._path.descendant_of(
node._path)).all()
for node in nodes:
node.active = False
db_session.query(PotentialNodeMerge).filter(
(PotentialNodeMerge.left_node_id == node_id)
| (PotentialNodeMerge.right_node_id == node_id)).delete()
db_session.commit()
def finish_job(ext_job_id, external_worker_id, answer, corrections, extra_questions,
with_audio, used_text_input, assignment_id, hit_id):
job = get_job(ext_job_id)
nodes = [x.node for x in job.node_utterances]
last_node = nodes[-1] if nodes else None
worker = _create_or_get_worker(external_worker_id)
node = create_new_node([answer], parent_id=last_node.id, source='typed')
node_utterance = node.node_utterances[0]
node_utterance.with_audio = with_audio
node_utterance.used_text_input = used_text_input
node_utterance_worker_job = NodeUtteranceWorkerJob(
node_utterance_id=node_utterance.id,
worker_id=worker.id,
job_id=job.id,
assignment_id=assignment_id,
hit_id=hit_id
)
db_session.add(node_utterance_worker_job)
for old_node_utterance_id, corrected_text in corrections.items():
old_node_utterance = db_session.query(NodeUtterance).get(old_node_utterance_id)
add_utterance_to_node(corrected_text, old_node_utterance.node, 'correction')
node_utterance_status = NodeUtteranceStatus(
node_utterance_id=old_node_utterance.id,
status='corrected'
)
db_session.add(node_utterance_status)
for extra_question in extra_questions:
if extra_question['type'] != 'api':
extra_node_utterance = db_session.query(NodeUtterance).get(extra_question['id'])
for status in ['suitable', 'equivalent', 'needs_correction']:
if extra_question[status]:
db_session.add(NodeUtteranceStatus(
node_utterance_id=extra_node_utterance.id,
referenced_node_utterance_id=node_utterance.id,
status=status
))
else:
# extra_node_utterance = add_utterance_to_node(
# extra_question['text'], node, extra_question['id']
# )
pass
# TODO: set positive scoring for worker and node_utterances
db_session.commit()
def get_ratings(start_time, end_time):
if not end_time:
end_time, = db_session.query(Rating.start_time).filter(
Rating.start_time.isnot(None)).order_by(
Rating.start_time.desc()).first()
if not start_time:
start_time = (end_time - datetime.timedelta(days=1)).replace(hour=0,
minute=0)
return db_session.query(Rating) \
.filter(Rating.start_time >= start_time, Rating.start_time <= end_time)\
.order_by(Rating.start_time.desc())\
.all()
def linked_nodes(linked_to_node_id, linked_from_node_id):
linked_to_node = db_session.query(Node).get(linked_to_node_id)
linked_from_node = db_session.query(Node).get(linked_from_node_id)
link = (db_session.query(LinkedNodes).filter(
LinkedNodes.linked_to_node_id == linked_to_node.id,
LinkedNodes.linked_from_node_id == linked_from_node.id,
).first())
if link:
return
if linked_to_node and linked_from_node:
db_session.add(
LinkedNodes(
linked_to_node_id=linked_to_node_id,
linked_from_node_id=linked_from_node_id,
))
db_session.commit()
def classify_root_nodes():
from fantom_util.models.rnc_mlp_model import RootNodeClassifierMLP
nodes = (db_session.query(Node).outerjoin(
RootNode, RootNode.node_id == Node.id).filter(
Node.active.is_(True), Node.parent_id.is_(None),
RootNode.id.is_(None)).order_by(Node.visited_count.desc()).all())
rnc_mlp = RootNodeClassifierMLP()
for node in nodes:
print("+----------------------+")
utterances = [x.utterance_text for x in node.utterances]
score_results = rnc_mlp.predict_list(utterances)
for utterance, score in zip(utterances, score_results):
print(f"{utterance}: {score[0]}")
print("\navg:", (sum(score_results) / len(score_results))[0], "\n")
user_input = input("Root node? Y/n/q ")
if user_input.lower() == "q":
exit()
elif user_input.lower() == "n":
node.active = False
for utterance in utterances:
db_session.add(
RootNode(node_id=node.id,
utterance=utterance,
is_root_node=False))
elif user_input == "" or user_input.lower() == "y":
for utterance in utterances:
db_session.add(
RootNode(node_id=node.id,
utterance=utterance,
is_root_node=True))
db_session.commit()
def nodes():
nodes = db_session.query(Node).filter(Node.parent_id.is_(None),
Node.active.is_(True))
count = 0
allNode = []
returnNode = {}
for node in nodes:
allNode.append(node)
shuffle(allNode)
for node in allNode:
utt = []
tree_nodes = []
stack = [node]
while stack:
cur_node = stack[0]
stack = stack[1:]
tree_nodes.append(cur_node)
for child in cur_node.children:
stack.append(child)
for el in tree_nodes:
for utterances in el.utterances:
utt.append(utterances.utterance_text)
returnNode[count] = utt
count = count + 1
if count == 30:
break
return returnNode
def named_entity_merge(node_id):
nodes = (db_session.query(Node).options(joinedload(
Node.utterances), joinedload(
Node.node_utterances)).filter(Node.parent_id == node_id).all())
to_merge = []
categories = ["movies", "musicians", "bands"]
for node in nodes:
done = False
if not node.children:
for utterance in node.utterances:
utterance_text = utterance.utterance_text
# print(utterance_text)
for category in categories:
for item in nem[category]:
if f" {item.lower()} " in f" {utterance_text} ":
print(f"found {item} in {utterance_text}")
to_merge.append(node)
done = True
if done:
break
if done:
break
if done:
break
return to_merge
def check_for_worker_eligibilitiy_for_qualification():
workers = db_session.query(Worker).filter(Worker.has_more_than_20_qualifaction.is_(False), Worker.source == 'mturk').all()
for worker in workers:
if worker.job_counts > 20:
mturk.qualify_worker_for_has_more_than_20_qualification(worker.external_worker_id)
worker.has_more_than_20_qualifaction = True
db_session.commit()
def populate(conversation_id=None, automate=False):
root_node_utterances = _get_utterance_lookup_table(None)
global file_name
file_name = (
FANTOM_WORKDIR + "/fantom_util/fantom_util/graph_tools/possible_root_nodes.txt"
)
os.system("aws s3 cp s3://SOME_AWS_BUCKET_URL/possible_root_nodes.txt " + file_name)
if not conversation_id:
conversation_ids = (
db_session.query(Conversation.conversation_id)
.filter(Conversation.conversation_id != None)
.distinct(Conversation.conversation_id)
.all()
)
if automate:
for conversation_id in tqdm(conversation_ids):
process_conversation(conversation_id, root_node_utterances, automate)
else:
stop = False
while not stop:
conversation_id = random.choice(conversation_ids)[0]
process_conversation(conversation_id, root_node_utterances, automate)
response = input("Continue? Y/n\n")
if response.lower() == "n":
stop = True
else:
process_conversation(conversation_id, root_node_utterances, False)
os.system(
"aws s3 cp " + file_name + " s3://SOME_AWS_BUCKET_URL/possible_root_nodes.txt"
)
def set_incoherent(ext_job_id, external_worker_id, incoherent_node_utterance_id, with_audio, assignment_id, hit_id):
job = get_job(ext_job_id)
worker = _create_or_get_worker(external_worker_id)
incoherent_node_utterance = db_session.query(NodeUtterance).get(incoherent_node_utterance_id)
incoherent_node_utterance_worker_job = IncoherentNodeUtteranceWorkerJob(
node_utterance_id=incoherent_node_utterance.id,
worker_id=worker.id,
job_id=job.id,
assignment_id=assignment_id,
hit_id=hit_id
)
db_session.add(incoherent_node_utterance_worker_job)
node_utterance_status = NodeUtteranceStatus(
with_audio=with_audio,
node_utterance_id=incoherent_node_utterance.id,
status='incoherent'
)
db_session.add(node_utterance_status)
# TODO: set negative scoring for worker and node_utterances
db_session.commit()
def main(synonym_path):
synonym_objects = get_synonym_objects(synonym_path)
root_nodes = (db_session.query(Node).options(
joinedload(Node.children),
joinedload(Node.utterances)).filter(Node.parent_id.is_(None)).all())
merge_synonyms(root_nodes, synonym_objects)
db_session.commit()
def get_graph():
nodes = db_session.query(Node).filter(Node.active.is_(True)).all()
max_visits = 0
for node in nodes:
max_visits = max(max_visits, node.visited_count)
output = ['digraph {']
# For each node
for node in nodes:
text = '<br/>'.join(['- {} (nu: {}, u: {})'.format(x.utterance.utterance_text.replace("'", "\\'").replace('"', '\\"'), x.id, x.utterance.id) for x in node.node_utterances])
# set the color to grey if it is a user utterance otherwise black
color = '#000000' if node.is_user else '#00ff00'
thickness = max(1, 4 * node.visited_count/max_visits)
# print the node with it's properties
output.append(f'{node.id}[label=<<b>{node.id}</b> ({node.visited_count})<br/>{text}>,color="{color}",penwidth={thickness}];')
# if the root node is not this nodes parent
if node.parent_id:
# print it with a link to it's parent
output.append(f'{node.parent_id} -> {node.id};')
output.append('}')
return ' '.join(output)
def remove_profane_utterances():
utterances = db_session.query(Utterance).all()
for utterance in tqdm(utterances):
text = normalize_text(utterance.utterance_text)
if text and re.search(EXCLUDED_UTTERANCES, text) and utterance.node_utterances:
node_ids = [x.node.id for x in utterance.node_utterances if x.node.is_user and x.node.active]
if node_ids:
print(text, node_ids)
def read_utterances_from_new_db():
root_nodes = (db_session.query(Node).options(
joinedload(Node.children),
joinedload(Node.utterances)).filter(Node.parent_id.is_(None)).all())
utterances = []
for root_node in root_nodes:
utterances += count_utterances(root_node)
return utterances, len(utterances)
def _create_or_get_worker(external_worker_id, source=None):
worker = db_session.query(Worker) \
.filter_by(external_worker_id=external_worker_id) \
.first()
if not worker:
worker = Worker(external_worker_id=external_worker_id, source=source)
db_session.add(worker)
db_session.flush()
return worker
def create_jobs(job_type, amount=1):
if job_type not in ['user', 'system', SPECIES_TAG]:
raise Exception('work type: "{}" does not exist. Use either system_task or user_task')
job_filter = [Node.active_child_count == 0, Node.visited_count > 1]
if job_type == SPECIES_TAG:
job_filter = [Node.species == SPECIES_TAG, Node.active_child_count < 3]
nodes = db_session.query(Node) \
.filter(
Node.score > 0,
Node.is_user == (job_type != 'user'),
Node.active.is_(True),
*job_filter
)\
.order_by(Node.score.desc()) \
.all()
created_jobs = []
for node in nodes:
history_ids = node.path[-MAX_DIALOGUE_HISTORY:]
history = db_session\
.query(Node)\
.filter(Node.id.in_(history_ids), Node.active.is_(True))\
.options(joinedload(Node.utterances), joinedload(Node.node_utterances))\
.order_by(Node.path_length.asc())\
.all()
history_length = len(history)
if len(history_ids) != history_length:
logger.warning(f'history_ids != history, {history_ids} != {history}')
continue
job_node_utterances = []
for index, history_node in enumerate(history):
pool_of_node_utterances = []
for node_utterance in history_node.node_utterances:
if _check_node_utterance_eligibility(node_utterance, index == history_length - 1, job_type):
pool_of_node_utterances.append(node_utterance)
if pool_of_node_utterances:
job_node_utterances.append(random.choice(pool_of_node_utterances))
if len(history_ids) == len(job_node_utterances):
job = Job(job_type=job_type, persona_sample=get_persona_sample())
db_session.add(job)
db_session.flush()
for i, node_utterance in enumerate(job_node_utterances):
db_session.add(JobNodeUtterance(job_id=job.id, node_utterance_id=node_utterance.id, position=i))
created_jobs.append(job)
if len(created_jobs) == amount:
break
db_session.commit()
print(f'created {len(created_jobs)} jobs')
return created_jobs
def find_duplicate_utterances():
duplicate_utterances = db_session.query(Utterance.utterance_text).group_by(Utterance.utterance_text).having(func.count(Utterance.utterance_text) > 1).all()
for utterance_text, in duplicate_utterances:
utterances = db_session.query(Utterance).filter(Utterance.utterance_text == utterance_text).all()
print(utterance_text)
print([(x.id, len(x.node_utterances)) for x in utterances])
nu_count = 0
candidate_utterance = utterances[0]
for utterance in utterances:
if utterance.node_utterances:
candidate_utterance = utterance
nu_count += 1
if nu_count == 0 or nu_count == 1:
print('deleting..')
for utterance in utterances:
if utterance.id != candidate_utterance.id:
print('removing..', utterance.id)
db_session.delete(utterance)
print('-------------------')
db_session.commit()
def get_synonym_objects(synonym_path):
synonym_objects = []
with open(synonym_path, "r") as f:
for synonym in f.readlines():
utterance = (db_session.query(Utterance).filter_by(
utterance_text=synonym.strip()).first())
if not utterance:
utterance = Utterance(utterance_text=synonym.strip())
db_session.add(utterance)
db_session.flush()
synonym_objects.append(utterance)
return synonym_objects
def add_utterance_to_node(utterance_text, node, source):
if not isinstance(utterance_text, Utterance):
utterance = (db_session.query(Utterance).filter_by(
utterance_text=utterance_text).first())
if not utterance:
utterance = Utterance(utterance_text=utterance_text)
db_session.add(utterance)
db_session.flush()
else:
utterance = utterance_text
node.utterances.append(utterance)
db_session.flush()
node_utterance = (db_session.query(NodeUtterance).filter_by(
node_id=node.id).filter_by(utterance_id=utterance.id).first())
node_utterance.source = source
db_session.flush()
return node_utterance
def read_utterances_from_new_db():
root_nodes = (db_session.query(Node).options(
joinedload(Node.children),
joinedload(Node.utterances)).filter(Node.parent_id.is_(None)).all())
global features
global fe
features = gen_feature_dict(sp.TOPIC, sp.SENTIMENT)
fe = feature_extractor.FeatureExtractor(features)
utterance_data = {}
for root_node in root_nodes:
utterance_data.update(count_utterances(root_node, True, False, None))
return utterance_data, len(utterance_data)