def build_demo_data(kvl):
label_store = LabelStore(kvl)
topic = 'where_are_aid_workers_housed_near_Monrovia'
subtopics = ['Tanji_Fish_Curing_Site',
'Camp_Ramrod',
'Town_of_Wamba']
subtopic_to_documents = {
0: [(random_sid(), '2100-%d|%s' % (len(subtopics[0]), subtopics[0]), 3),
(random_sid(), '15-93|we_drove_out_to_the_other_side_' +
'of_the_river_delta_to_a_small_fish_smoking_camp', 2)
],
1: [(random_sid(), '3120-%d|%s' % (len(subtopics[1]), subtopics[1]), 2),
(random_sid(), '200-217|Ramrod_(Facility)', 3)
],
2: [(random_sid(), '3120-%d|%s' % (len(subtopics[2]), subtopics[2]), 3),
(random_sid(), '53-63|Wamba_Town', 2),
(random_sid(), '44-50|Woomba', 1)
]
}
for idx, subtopic in enumerate(subtopics):
for stream_id, subtopic_id2, rating in subtopic_to_documents[idx]:
print stream_id
label = Label(topic, stream_id, 'John', CorefValue.Positive,
subtopic_id1=subtopic,
subtopic_id2=subtopic_id2,
rating=rating)
label_store.put(label)
def build_demo_data(kvl):
label_store = LabelStore(kvl)
topic = 'where_are_aid_workers_housed_near_Monrovia'
subtopics = ['Tanji_Fish_Curing_Site', 'Camp_Ramrod', 'Town_of_Wamba']
subtopic_to_documents = {
0:
[(random_sid(), '2100-%d|%s' % (len(subtopics[0]), subtopics[0]), 3),
(random_sid(), '15-93|we_drove_out_to_the_other_side_' +
'of_the_river_delta_to_a_small_fish_smoking_camp', 2)],
1:
[(random_sid(), '3120-%d|%s' % (len(subtopics[1]), subtopics[1]), 2),
(random_sid(), '200-217|Ramrod_(Facility)', 3)],
2:
[(random_sid(), '3120-%d|%s' % (len(subtopics[2]), subtopics[2]), 3),
(random_sid(), '53-63|Wamba_Town', 2),
(random_sid(), '44-50|Woomba', 1)]
}
for idx, subtopic in enumerate(subtopics):
for stream_id, subtopic_id2, rating in subtopic_to_documents[idx]:
print stream_id
label = Label(topic,
stream_id,
'John',
CorefValue.Positive,
subtopic_id1=subtopic,
subtopic_id2=subtopic_id2,
rating=rating)
label_store.put(label)
def build_test_data(kvl):
topics = ['topic1', 'topic2', 'topic3']
subtopics = ['subtopic1', 'subtopic2', 'subtopic3']
relevances = [[1, 2, 3]]*3
offset = '13-235'
label_store = LabelStore(kvl)
for t_idx, topic in enumerate(topics):
for s_idx, subtopic in enumerate(subtopics):
label = Label(topic, 'doc'+str(t_idx)+str(s_idx),
'me', CorefValue.Positive,
subtopic_id1=subtopic,
subtopic_id2=offset+'|'+'some text',
relevance=relevances[t_idx][s_idx])
label_store.put(label)
def build_test_data(kvl):
topics = ['topic1', 'topic2', 'topic3']
subtopics = ['subtopic1', 'subtopic2', 'subtopic3']
relevances = [[1, 2, 3]] * 3
offset = '13-235'
label_store = LabelStore(kvl)
for t_idx, topic in enumerate(topics):
for s_idx, subtopic in enumerate(subtopics):
label = Label(topic,
'doc' + str(t_idx) + str(s_idx),
'me',
CorefValue.Positive,
subtopic_id1=subtopic,
subtopic_id2=offset + '|' + 'some text',
relevance=relevances[t_idx][s_idx])
label_store.put(label)
def build_test_data(kvl):
topics = ['topic1', 'topic2', 'topic3']
subtopics = ['subtopic1', 'subtopic2', 'subtopic3']
offset = '13,235'
ratings = [[1, 2, 3]]*3
label_store = LabelStore(kvl)
for t_idx, topic in enumerate(topics):
for s_idx, subtopic in enumerate(subtopics):
meta = dict(topic_name=topic, topic_id=str(t_idx),
passage_text='howdy',
subtopic_name='bye bye',
)
label = Label(str(t_idx), 'doc'+str(t_idx)+str(s_idx),
'me', CorefValue.Positive,
subtopic_id1=subtopic,
subtopic_id2=offset,
rating=ratings[t_idx][s_idx],
meta=meta,
)
label_store.put(label)
