def correlate_note_search_with_num_notes_created(users):
rsize = r.c()
rsearch = r.c()
for u in users:
zoo = wuw.reduceRepeatLogsValues2(u.activitylog_set.filter(action='search').values())
rsize = r.c(rsize, u.note_owner.count())
rsearch = r.c(rsearch, len(zoo) )
return rsize,rsearch,r('cor.test')(notes,searches)
def correlate_note_search_with_num_notes_created(users):
rsize = r.c()
rsearch = r.c()
for u in users:
zoo = wuw.reduceRepeatLogsValues2(
u.activitylog_set.filter(action='search').values())
rsize = r.c(rsize, u.note_owner.count())
rsearch = r.c(rsearch, len(zoo))
return rsize, rsearch, r('cor.test')(notes, searches)
def searchterms(users):
totalfreq = nltk.FreqDist()
total = 0
for u in users:
zoo = wuw.reduceRepeatLogsValues2(u.activitylog_set.filter(action='search').values())
terms = [ getQuery(z) for z in zoo if getQuery(z) is not None ]
total = total + len(terms)
totalfreq = totalfreq + nltk.FreqDist(terms)
return totalfreq,[ z['when'] for z in zoo ],total
def times_search_used(users):
times = {}
for u in users:
if u.activitylog_set.filter(action='search').count() == 0: continue
times[u.id] = len([
z for z in wuw.reduceRepeatLogsValues2(
u.activitylog_set.filter(action='search').values())
if getQuery(z) is not None
])
return times
def searchterms(users):
totalfreq = nltk.FreqDist()
total = 0
for u in users:
zoo = wuw.reduceRepeatLogsValues2(
u.activitylog_set.filter(action='search').values())
terms = [getQuery(z) for z in zoo if getQuery(z) is not None]
total = total + len(terms)
totalfreq = totalfreq + nltk.FreqDist(terms)
return totalfreq, [z['when'] for z in zoo], total
def correlate_note_search_with_mean_alive(users):
rsize = r.c()
rsearch = r.c()
for u in users:
print u
zoo = wuw.reduceRepeatLogsValues2(list(u.activitylog_set.filter(action='search').values()))
if len(zoo) == 0:
print "warning zero"
continue
#if u.activitylog_set.count() == 0: continue
try:
rsize = r.c(rsize, wuw.user_mean_alive(u.id).values()[0])
rsearch = r.c(rsearch, len(zoo) )
except:
print sys.exc_info()
return rsize,rsearch,r('cor.test')(rsize,rsearch)
def get_search_hits(users):
us = {}
for u in users:
usearches = []
zoo = [z for z in wuw.reduceRepeatLogsValues2(u.activitylog_set.filter(action='search').values()) if getQuery(z) is not None]
for z in zoo:
s = z.get("search",'')
if s is None or len(s.strip()) == 0: continue
try:
usearches.append(json.loads(s)["hits"])
except:
import sys
print sys.exc_info()
if len(usearches) > 0:
us[u.id] = usearches
return us
def correlate_note_search_with_mean_alive(users):
rsize = r.c()
rsearch = r.c()
for u in users:
print u
zoo = wuw.reduceRepeatLogsValues2(
list(u.activitylog_set.filter(action='search').values()))
if len(zoo) == 0:
print "warning zero"
continue
#if u.activitylog_set.count() == 0: continue
try:
rsize = r.c(rsize, wuw.user_mean_alive(u.id).values()[0])
rsearch = r.c(rsearch, len(zoo))
except:
print sys.exc_info()
return rsize, rsearch, r('cor.test')(rsize, rsearch)
def get_search_hits(users):
us = {}
for u in users:
usearches = []
zoo = [
z for z in wuw.reduceRepeatLogsValues2(
u.activitylog_set.filter(action='search').values())
if getQuery(z) is not None
]
for z in zoo:
s = z.get("search", '')
if s is None or len(s.strip()) == 0: continue
try:
usearches.append(json.loads(s)["hits"])
except:
import sys
print sys.exc_info()
if len(usearches) > 0:
us[u.id] = usearches
return us
def times_search_used(users):
times = {}
for u in users:
if u.activitylog_set.filter(action='search').count() == 0: continue
times[u.id] = len([z for z in wuw.reduceRepeatLogsValues2(u.activitylog_set.filter(action='search').values()) if getQuery(z) is not None])
return times
