def incr_task(topic):
T_ChannelTopicTrends.increment(channel=self.channel,
time_slot=time_slot,
topic=topic,
status=status,
intention_ids=[1],
inc_dict={'topic_count': 1})
return True
def test_transaction(self):
from solariat_bottle.db.channel_topic_trends import ChannelTopicTrends as T_ChannelTopicTrends
#
# class T_ChannelTopicTrends(ChannelTopicTrends, Transactional):
# def upsert(self, w=1):
# return Transactional.upsert(self)
time_slot = datetime_to_timeslot(now(), 'month')
topic = 'laptop'
from itertools import cycle
colliding_topics = [
"oldie", "bt subscribers",
"pisces woman", "layman"]
gen_colliding_topics = cycle(colliding_topics)
status = 0
def incr_task(topic):
T_ChannelTopicTrends.increment(channel=self.channel,
time_slot=time_slot,
topic=topic,
status=status,
intention_ids=[1],
inc_dict={'topic_count': 1})
return True
# get_var('_TEST_TRANSACTION_FAILURE') = True
settings.DEBUG = True
settings.USE_CELERY = False
from multiprocessing import Process
proc_num = 100
processes = [Process(target=incr_task, args=(gen_colliding_topics.next(),)) for i in range(proc_num)]
for proc in processes:
proc.start()
for proc in processes:
proc.join()
for topic in colliding_topics:
colliding_topics.index(topic)
doc = T_ChannelTopicTrends(
channel = self.channel,
time_slot = time_slot,
topic = topic,
status = status
)
doc.reload()
self.assertEqual(doc.filter(intention=1, is_leaf=True)[0].topic_count, proc_num / len(colliding_topics))
self.assertTrue(doc.version > 1)
def test_duplicate_handle_diff_channels(self):
channel3 = TwitterChannel.objects.create_by_user(
self.user,
title='TestChannel3',
type='twitter',
intention_types=SA_TYPES)
duplicate_post = self._create_db_post(
channels=[self.channel2, channel3],
content=self.duplicate_content,
url=self.url,
twitter={
"created_at": "Wed, 06 Aug 2014 18:38:47 +0000",
"id": "497089420017676290"
})
self.assertEqual(len(duplicate_post.channels), 3)
time_slot = datetime_to_timeslot(now(), 'day')
ht_stat = ChannelHotTopics.objects.by_time_span(
channel=self.channel2,
from_ts=datetime_to_timeslot(None, 'day'),
)
tt_stat = ChannelTopicTrends(channel=self.channel2,
time_slot=time_slot,
topic=self.topic,
status=0)
self.assertEqual(ht_stat, self.hot_topic_stat)
self.assertEqual(tt_stat, self.topic_trends_stat)
def purge_outdated_trends_stats(coll, channel, level, delta):
initial_timedelta_arg_name = {"hour": "days", "day": "months"}[level]
timedelta_arg_name = {"hour": "hours", "day": "days"}[level]
start_dt = now() - relativedelta(**{initial_timedelta_arg_name: delta})
current_dt = start_dt
time_step = relativedelta(**{timedelta_arg_name: 1})
ts = datetime_to_timeslot(current_dt, level)
zero_counts = 0
total_records_removed = 0
EMPTY_SLOTS_NUMBER = 10
while zero_counts <= EMPTY_SLOTS_NUMBER:
t0 = datetime.now()
channel_ts_val = ChannelTopicTrends.make_channel_ts(channel, ts)
res = coll.objects.coll.remove(coll.objects.get_query(time_slot=ts))
if res['n'] == 0:
zero_counts += 1
current_dt = current_dt - time_step
total_records_removed += res['n']
ts = datetime_to_timeslot(current_dt, level)
LOGGER.info(
"purging Q:: collection: %s; func: %s; timedelta: %s; date: %s; level: %s; records removed: %s",
coll.__name__,
inspect.stack()[0][3],
datetime.now() - t0, current_dt, level, res['n'])
return total_records_removed
def extend_trends(channel):
from solariat_bottle.db.channel_topic_trends import ChannelTopicTrends
from solariat_bottle.utils.id_encoder import (
pack_components, CHANNEL_WIDTH, TIMESLOT_WIDTH, BIGGEST_STATUS_VALUE,
BIGGEST_TOPIC_VALUE, BIGGEST_TIMESOLT_VALUE)
logger.info("------------------------")
if channel.is_migrated:
logger.info("SKIPPING CHANNEL: %s" % channel.title)
return
lower_bound = ChannelTopicTrends.make_id(channel, 0, 0, 0)
upper_bound = ChannelTopicTrends.make_id(channel, BIGGEST_TIMESOLT_VALUE,
BIGGEST_TOPIC_VALUE,
BIGGEST_STATUS_VALUE)
count = ChannelTopicTrends.objects(id__gte=lower_bound,
id__lte=upper_bound).count()
logger.info("CHANNEL START: %s (%s)" % (channel.title, count))
from solariat.db.fields import BytesField
l = BytesField().to_mongo
limit = 100
offset = 0
while offset <= count:
logger.info("--> channel: %s offset %s of %s" %
(channel.title, offset, count))
query = ChannelTopicTrends.objects(id__gte=lower_bound,
id__lte=upper_bound)
query = query.skip(offset).limit(100)
for trend in query:
channel_num, topic_hash, status, time_slot = trend.unpacked
channel_ts = pack_components(
(channel_num, CHANNEL_WIDTH),
(time_slot, TIMESLOT_WIDTH),
)
ChannelTopicTrends.objects.coll.update(
{"_id": l(trend.id)}, {"$set": {
"ct": l(channel_ts)
}},
upsert=False)
offset += limit
channel.is_migrated = True
channel.save()
logger.info("CHANNEL END: %s (%s)" % (channel.title, count))
def print_db_records():
# print "Topics:"
# for row in ChannelHotTopics.objects():
# print "{0: ^14s} | {1: ^4s}".format(row.topic, decode_timeslot(row.time_slot))
# print
print "Trends:"
for row in ChannelTopicTrends.objects():
print u"{0: ^14s} | {1: ^4s}".format(row.topic,
decode_timeslot(row.time_slot))
print
print
def test_number_of_leafs(self):
""" Note: leaf means it is a stat record for a specific topic (max tri-gram),
not a smaller part of the topic
"""
content = "I need a mac laptop"
self._create_db_post(content)
leaf_stats = [
s for s in ChannelTopicTrends.objects() if s.filter(is_leaf=True)
]
self.assertEqual(
len(leaf_stats),
2) # ("mac laptop") x (hour + day) #NO __ALL__, it is not a leaf
def test_number_of_stats_intention_id(self):
content = "I need a mac laptop"
#topics: "mac laptop"
#terms: "mac laptop", "laptop"
post = self._create_db_post(content)
intention_title = post.speech_acts[0]['intention_type']
intention_id = get_sa_type_id(intention_title)
stats = [
s for s in ChannelTopicTrends.objects()
if s.filter(intention=int(intention_id))
]
self.assertEqual(len(stats), (2 + 1) * 2)
needs_count = sum(
s.filter(intention=int(intention_id), is_leaf=False)[0].topic_count
for s in ChannelTopicTrends.objects() if s.topic != ALL_TOPICS)
self.assertEqual(needs_count, 2 * 2)
stats = [
s for s in ChannelTopicTrends.objects() if s.filter(intention=15)
]
self.assertEqual(len(stats), 0)
def test_number_of_nodes(self):
""" Note: node means it is a stat record for a smaller part of a bigger topic,
not a topic itself
"""
content = "I need a mac laptop"
self._create_db_post(content)
node_stats = [
True for s in ChannelTopicTrends.objects()
if s.filter(is_leaf=False)
]
self.assertEqual(
len(node_stats),
(2 + 1) * 2) # ("mac laptop", "laptop", "__ALL__") x (hour + day )
def trends_mark_to_remove(time_slot, channel_or_tag, counter):
channel_ts_val = ChannelTopicTrends.make_channel_ts(
channel_or_tag, time_slot)
# import ipdb; ipdb.set_trace()
t0 = datetime.now()
res = ChannelTopicTrends.objects.coll.update(
{FT("channel_ts"): to_binary(channel_ts_val)},
{'$set': {
FT('gc_counter'): counter
}},
multi=True)
LOGGER.info(
"purging Q:: channel: %s; collection: ChannelTopicTrends; func: %s; timedelta: %s"
% (channel_or_tag.title, inspect.stack()[0][3], datetime.now() - t0))
return res
def _store_existing_data(self):
# Keep track of what was in database when this was called
self.ctt = {}
self.ctt_bk = {}
self.cht = {}
self.ct = {}
self.ctt_count = ChannelTopicTrends.objects.count()
self.cht_count = ChannelHotTopics.objects.count()
self.ct_count = ChannelTrends.objects.count()
for ctt in ChannelTopicTrends.objects():
self.ctt_bk[ctt.data['_id']] = ctt.data
self.ctt[ctt.data['_id']] = self._process_es(ctt)
for cht in ChannelHotTopics.objects():
self.cht[cht.data['_id']] = self._process_es(cht)
for ct in ChannelTrends.objects():
self.ct[ct.data['_id']] = self._process_es(ct)
def test_outdated_trends4(self):
"""
all existing hour stats should be kept
"""
date_now = now()
date_old = now() - relativedelta(
days=get_var('TOPIC_TRENDS_HOUR_STATS_KEEP_DAYS') - 1, hours=23)
self._make_laptops_and_icecream(_created=date_old)
total_trends = ChannelTopicTrends.objects().count()
hour_trends = total_trends / 2
day_trends = total_trends / 2
stats = purge_stats(self.channel)
self.assertEqual(day_trends, 6)
self.assertEqual(hour_trends, 6)
self.assertEqual(stats['discard_junk_stats']['trends_day_count'], 0)
self.assertEqual(stats['discard_junk_stats']['trends_hour_count'], 0)
def _compare_existing_data(self):
# Compare what is currently in database with what we have stored
for ctt in ChannelTopicTrends.objects():
for data in ctt.data['es']:
keys = tuple(sorted(data.keys()))
values = tuple(sorted(data.values()))
self.assertTrue((keys, values) in self.ctt[ctt.data['_id']])
for cht in ChannelHotTopics.objects():
for data in cht.data['es']:
keys = tuple(sorted(data.keys()))
values = tuple(sorted(data.values()))
self.assertTrue((keys, values) in self.cht[cht.data['_id']])
for ct in ChannelTrends.objects():
for data in ct.data['es']:
keys = tuple(sorted(data.keys()))
values = tuple(sorted(data.values()))
self.assertTrue((keys, values) in self.ct[ct.data['_id']])
def test_outdated_trends2(self):
"""
all existing stats should be kept, cause it's not too old
"""
date_now = now()
date_old = now() - relativedelta(
months=get_var('TOPIC_TRENDS_DAY_STATS_KEEP_MONTHS'))
self._make_laptops_and_icecream(_created=date_now)
total_trends = ChannelTopicTrends.objects().count()
hour_trends = total_trends / 2
day_trends = total_trends / 2
stats = purge_stats(self.channel)
self.assertEqual(day_trends, 6)
self.assertEqual(hour_trends, 6)
self.assertEqual(stats['discard_junk_stats']['trends_day_count'], 0)
self.assertEqual(stats['discard_junk_stats']['trends_hour_count'], 0)
def trends_mark_to_keep(time_slot, channel_or_tag, topics):
channel_ts_val = ChannelTopicTrends.make_channel_ts(
channel_or_tag, time_slot)
t0 = datetime.now()
res = ChannelTopicTrends.objects.coll.update(
{
FT("channel_ts"): to_binary(channel_ts_val),
FT('topic'): {
"$in": topics + ["__ALL__"]
}
}, {'$set': {
FT('gc_counter'): MARKED_TO_KEEP
}},
multi=True)
LOGGER.info(
"purging Q:: channel: %s; collection: ChanneTopicTrends; func: %s; timedelta: %s"
% (channel_or_tag.title, inspect.stack()[0][3], datetime.now() - t0))
return res
def test_outdated_trends3(self):
"""
all existing hour stats should be removed, cause it's too old
"""
date_now = now()
date_old = now() - relativedelta(
days=get_var('TOPIC_TRENDS_HOUR_STATS_KEEP_DAYS'), hours=1)
LOGGER.info(
"11111111, %s, %s, %s" %
(date_now, date_old, get_var('TOPIC_TRENDS_HOUR_STATS_KEEP_DAYS')))
self._make_laptops_and_icecream(_created=date_old)
total_trends = ChannelTopicTrends.objects().count()
hour_trends = total_trends / 2
day_trends = total_trends / 2
stats = purge_stats(self.channel)
self.assertEqual(day_trends, 6)
self.assertEqual(hour_trends, 6)
self.assertEqual(stats['discard_junk_stats']['trends_day_count'], 0)
self.assertEqual(stats['discard_junk_stats']['trends_hour_count'], 6)
def setUp(self):
super(TestDuplicatePostProcessing, self).setUp()
self.created = now()
self.url = '%s/posts/%s' % (get_var('HOST_DOMAIN'), str(ObjectId()))
self.content = "I'm so much want to buy a new laptop"
self.duplicate_content = "I'm so much want to find a laptop"
self.channel2 = TwitterChannel.objects.create_by_user(
self.user,
title='TestChannel2',
type='twitter',
intention_types=SA_TYPES)
self.post = self._create_db_post(
channels=[self.channel, self.channel2],
content=self.content,
url=self.url,
twitter={
"created_at": "Wed, 06 Aug 2014 18:38:47 +0000",
"id": "497089420017676290"
})
time_slot = datetime_to_timeslot(now(), 'day')
self.topic = "laptop"
self.hot_topic_stat = ChannelHotTopics.objects.by_time_span(
channel=self.channel2,
from_ts=datetime_to_timeslot(None, 'day'),
)
self.topic_trends_stat = ChannelTopicTrends(channel=self.channel2,
time_slot=time_slot,
topic=self.topic,
status=0)
def _update_monthly_cht_values(channel, from_date_end, to_date_end, topics):
""" Do upsert on monthly values based on the daily values.
"""
from solariat.utils.timeslot import datetime_to_timeslot
from solariat_bottle.utils.id_encoder import get_topic_hash
from solariat_nlp.utils.topics import get_subtopics
from solariat_bottle.db.speech_act import SpeechActMap
from solariat_bottle.db.channel_hot_topics import ChannelHotTopics
from solariat_bottle.db.channel_topic_trends import ChannelTopicTrends
from solariat_bottle.db.channel_stats_base import CountDict, batch_insert
start_time = datetime.now()
statuses = SpeechActMap.STATUS_NAME_MAP.keys()
insertable_values = {}
if not topics:
logger.warning("No topics found for channel %s." % (channel.title, ))
return
month_intervals = _generate_day_level_ranges(from_date_end, to_date_end)
for topic in topics:
for from_date, to_date in month_intervals:
or_query = []
# $match query
for topic, status in product([topic], statuses):
from_id = ChannelTopicTrends.make_id(
channel, datetime_to_timeslot(from_date, 'day'), topic,
status)
to_id = ChannelTopicTrends.make_id(
channel, datetime_to_timeslot(to_date, 'day'), topic,
status)
or_query.append({"_id": {"$gte": from_id, "$lte": to_id}})
if len(or_query) == 1:
match_query = or_query[0]
else:
match_query = {"$or": or_query}
pipeline = [{
"$match": match_query
}, {
"$unwind": '$es'
}, {
'$group': {
'_id': {
'grp_at': '$es.at',
'grp_if': '$es.if',
'grp_in': '$es.in',
'grp_le': '$es.le',
'grp_tc': '$tc',
'grp_ss': '$ss'
},
'count': {
'$sum': '$es.tt'
}
}
}]
month_level_counts = {}
agreggation_result = ChannelHotTopics.objects.coll.aggregate(
pipeline)
if agreggation_result['ok']:
for aggregated_count in agreggation_result['result']:
month_id = ChannelHotTopics.make_id(
channel=channel,
time_slot=datetime_to_timeslot(from_date, 'month'),
topic=aggregated_count['_id']['grp_tc'],
status=aggregated_count['_id']['grp_ss'])
if month_id in month_level_counts:
month_doc = month_level_counts[month_id]
else:
hashed_parents = map(
get_topic_hash,
get_subtopics(aggregated_count['_id']['grp_tc']))
month_doc = ChannelHotTopics(
channel=channel,
hashed_parents=hashed_parents,
time_slot=datetime_to_timeslot(from_date, 'month'),
topic=aggregated_count['_id']['grp_tc'],
status=aggregated_count['_id']['grp_ss'])
month_doc.version = 0
month_doc.embedded_dict = {}
month_level_counts[month_id] = month_doc
es_key = (aggregated_count['_id']['grp_at'],
aggregated_count['_id']['grp_if'],
aggregated_count['_id']['grp_in'],
aggregated_count['_id']['grp_le'])
# Default increment for all existign stats to 0, we will add to this later.
month_doc.embedded_dict[es_key] = CountDict(
{'topic_count': aggregated_count['count']})
for key in month_level_counts:
insertable_values[key] = month_level_counts[key]
else:
logger.warning("Pipeline failed. Returned %s." %
agreggation_result)
if insertable_values:
ChannelHotTopics.objects.coll.remove(
{'_id': {
'$in': insertable_values.keys()
}})
batch_insert(insertable_values.values())
logger.info("Integrating monthly level topics took: " +
str(datetime.now() - start_time))
def test_multi_post(self):
contents = [
'Any recommendations for a basketball scholarship? I need a basketball scholarship.',
'Any recommendations for a basketball scholarship? I need a basketball scholarship.',
'I love my display!',
'My display is just not working out for me :-(',
'Any recommendations for a display?', 'I like my display'
]
for content in contents:
post = self._create_db_post(content, channel=self.channel)
from solariat_bottle.db.speech_act import SpeechActMap
stats_by_topic_intention = {}
#Calculating stats iterating through SAM
from solariat_bottle.db.post.base import Post
for post in Post.objects(channels__in=[self.channel.id]):
for sa in post.speech_acts:
topics = sa['intention_topics']
int_id = sa['intention_type_id']
topics.append('__ALL__')
for topic in topics:
if topic in stats_by_topic_intention:
if str(int_id) in stats_by_topic_intention[topic]:
stats_by_topic_intention[topic][str(int_id)] += 1
else:
stats_by_topic_intention[topic][str(int_id)] = 1
else:
stats_by_topic_intention[topic] = {str(int_id): 1}
expected_stats_from_sam = {
u'basketball scholarship': {
'1': 2,
'2': 2
},
u'display': {
'1': 1,
'3': 1,
'4': 2
},
'__ALL__': {
'1': 3,
'3': 1,
'2': 2,
'4': 2
}
}
self.assertDictEqual(stats_by_topic_intention, expected_stats_from_sam)
time_slot = datetime_to_timeslot(
Post.objects(
channels__in=[self.channel.id]).limit(1)[0].created_at, 'hour')
status = SpeechActMap.ACTIONABLE
#Now verify SAM stats correspond to ChannelTopicTrends stats
for topic, sa_stats in stats_by_topic_intention.iteritems():
if topic == '__ALL__':
continue
stat = ChannelTopicTrends(channel=self.channel,
time_slot=time_slot,
topic=topic,
status=status)
stat.reload()
ctt_by_int = {}
filtered = stat.filter(is_leaf=True, intention__ne=0)
for s in filtered:
ctt_by_int[str(s.intention)] = s.topic_count
self.assertDictEqual(ctt_by_int, sa_stats)
def test_stat_update(self):
time_slot = datetime_to_timeslot(now(), 'hour')
topic = 'laptop'
agent_id = 12345
stat = ChannelTopicTrends(channel=self.channel,
time_slot=time_slot,
topic=topic,
status=0)
stat.compute_increments(is_leaf=True,
intention_ids=JUNK,
agent=None,
inc_dict={'topic_count': 1},
n=1)
stat.compute_increments(is_leaf=False,
intention_ids=HELP,
agent=None,
inc_dict={'topic_count': 1},
n=1)
stat.upsert()
stat = ChannelTopicTrends.objects.get(id=stat.id)
stat.compute_increments(is_leaf=True,
intention_ids=JUNK,
agent=agent_id,
inc_dict={'topic_count': 2},
n=1)
stat.compute_increments(is_leaf=False,
intention_ids=HELP,
agent=None,
lang_id=EN,
inc_dict={'topic_count': 2},
n=1)
stat.upsert()
stat.reload()
expected_stats = [
(ALL_AGENTS, Term, ALL_INTENTIONS_INT, LALL, 1 + 2), # +2 for EN
(ALL_AGENTS, Term, HELP, LALL, 1 + 2),
(ALL_AGENTS, Term, ALL_INTENTIONS_INT, EN, 2),
(ALL_AGENTS, Term, HELP, EN, 2),
(ALL_AGENTS, Topic, ALL_INTENTIONS_INT, LALL,
1 + 2), # +2 from specific agent
(ALL_AGENTS, Topic, JUNK, LALL, 1 + 2),
(agent_id, Topic, ALL_INTENTIONS_INT, LALL, 2),
(agent_id, Topic, JUNK, LALL, 2)
]
self.assert_stats(stat, expected_stats)
self.assertFalse(stat.filter(agent=0, is_leaf=True,
intention=10)) # no such combination
