def main():
gnome_dates = '/Users/nernst/Documents/projects/msr/data/yearweek.csv'
release_dates = '/Users/nernst/Documents/projects/msr/data/dates-releases.csv'
global gnome_dates_dict
global release_map
reader = csv.reader(open(gnome_dates, 'rb'))
gnome_dates_list = [int(x[0]) for x in reader]
gnome_dates_dict = dict.fromkeys(gnome_dates_list, [])
#open the list of Gnome releases
dr = csv.reader(open(release_dates, 'rb'))
release_map = gnome_dates_dict.copy() #shallow copy
releases = [x for x in dr]
for release in releases:
release_map[int(release[1])] = DateReleaseObj(release)
t = Taxonomy()
for signified in t.get_signified(): # e.g. usability, performance, etc
# print signified + ' & ',
print signified #+ ' , ',
for product in t.get_products():
# print '& ' + product,
print product + ',',
gnome_dates_dict = dict.fromkeys(gnome_dates_list, [])
find_window(product, signified)
def main():
gnome_dates = '/Users/nernst/Documents/projects/msr/data/yearweek.csv'
release_dates = '/Users/nernst/Documents/projects/msr/data/dates-releases.csv'
global gnome_dates_dict
global release_map
reader = csv.reader(open(gnome_dates, 'rb'))
gnome_dates_list = [int(x[0]) for x in reader]
gnome_dates_dict = dict.fromkeys(gnome_dates_list,[])
#open the list of Gnome releases
dr = csv.reader(open(release_dates, 'rb'))
release_map = gnome_dates_dict.copy() #shallow copy
releases = [x for x in dr]
for release in releases:
release_map[int(release[1])] = DateReleaseObj(release)
t = Taxonomy()
for signified in t.get_signified(): # e.g. usability, performance, etc
# print signified + ' & ',
print signified #+ ' , ',
for product in t.get_products():
# print '& ' + product,
print product + ',',
gnome_dates_dict = dict.fromkeys(gnome_dates_list,[])
find_window(product, signified)
def main():
t = Taxonomy()
# signifiers = t.get_signifiers('Efficiency') #extended lists
signifiers = t.get_signifiers_wn('Usability') # wordnet lists
signifier_list = ''
for signifier in signifiers:
signifier_list = signifier + ' ' + signifier_list
get_counts(signifier_list)
def main():
t = Taxonomy()
# signifiers = t.get_signifiers('Efficiency') #extended lists
signifiers = t.get_signifiers_wn('Usability') # wordnet lists
signifier_list = ''
for signifier in signifiers:
signifier_list = signifier + ' ' + signifier_list
get_counts(signifier_list)
def main():
from names import Taxonomy
t = Taxonomy()
# signifiers = t.get_signifiers('Efficiency') #extended lists
signifiers = t.get_signifiers_wn("Usability") # wordnet lists
signifier_list = ""
for signifier in signifiers:
signifier_list = signifier + " " + signifier_list
get_counts(signifier_list)
def main():
t = Taxonomy()
product = "Nautilus"
signified = "Reliability"
# s = t.get_signifiers('Usability')
# alls = ''
# for word in s:
# alls += word
# alls += t.find_spelling(word)
# result = query_database('Evolution', [alls])
# for signified in t.get_signified(): # e.g. usability, performance, etc
# for product in t.get_products():
# result = query_database(product, t.get_signifiers(signified), t) #e.g. usability: usability, usable, etc.
# save_file(result, product, signified)
result = query_database(product, t.get_signifiers(signified), t) #e.g. usability: usability, usable, etc.
save_file(result, product, signified)
def main():
t = Taxonomy()
product = "Nautilus"
signified = "Reliability"
# s = t.get_signifiers('Usability')
# alls = ''
# for word in s:
# alls += word
# alls += t.find_spelling(word)
# result = query_database('Evolution', [alls])
# for signified in t.get_signified(): # e.g. usability, performance, etc
# for product in t.get_products():
# result = query_database(product, t.get_signifiers(signified), t) #e.g. usability: usability, usable, etc.
# save_file(result, product, signified)
result = query_database(product, t.get_signifiers(signified),
t) #e.g. usability: usability, usable, etc.
save_file(result, product, signified)
from names import Taxonomy
import subprocess
t = Taxonomy()
for sign in t.get_signified():
wordlist = t.get_signifiers(sign)
for word in wordlist:
p = subprocess.Popen(["perl", "typo.pl", word],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE)
(out, err) = p.communicate()
print out
# print word
def main():
t = Taxonomy()
#result = query_database('Nautilus', t.get_signifiers('Portability'))
result = [{
200136L: 156L
}, {
200138L: 2L
}, {
200139L: 1L
}, {
200140L: 1L
}, {
200142L: 3L
}, {
200144L: 2L
}, {
200145L: 1L
}, {
200149L: 2L
}, {
200151L: 1L
}, {
200203L: 2L
}, {
200204L: 1L
}, {
200205L: 1L
}, {
200206L: 2L
}, {
200207L: 2L
}, {
200209L: 2L
}, {
200210L: 1L
}, {
200211L: 1L
}, {
200216L: 1L
}, {
200217L: 1L
}, {
200218L: 1L
}, {
200219L: 1L
}, {
200221L: 1L
}, {
200222L: 3L
}, {
200223L: 1L
}, {
200224L: 12L
}, {
200225L: 1L
}, {
200226L: 2L
}, {
200227L: 2L
}, {
200228L: 2L
}, {
200230L: 2L
}, {
200231L: 3L
}, {
200232L: 1L
}, {
200233L: 1L
}, {
200235L: 1L
}, {
200236L: 2L
}, {
200237L: 1L
}, {
200239L: 2L
}, {
200240L: 2L
}, {
200241L: 2L
}, {
200242L: 2L
}, {
200243L: 2L
}, {
200246L: 1L
}, {
200247L: 1L
}, {
200248L: 3L
}, {
200250L: 1L
}, {
200251L: 3L
}, {
200252L: 1L
}, {
200301L: 1L
}, {
200302L: 1L
}, {
200304L: 2L
}, {
200306L: 1L
}, {
200307L: 2L
}, {
200312L: 1L
}, {
200313L: 1L
}, {
200315L: 1L
}, {
200316L: 1L
}, {
200319L: 1L
}, {
200322L: 4L
}, {
200323L: 1L
}, {
200326L: 1L
}, {
200327L: 1L
}, {
200329L: 1L
}, {
200330L: 1L
}, {
200332L: 2L
}, {
200334L: 1L
}, {
200337L: 3L
}, {
200341L: 1L
}, {
200342L: 1L
}, {
200345L: 2L
}, {
200348L: 1L
}, {
200351L: 1L
}, {
200402L: 1L
}, {
200404L: 11L
}, {
200406L: 2L
}, {
200407L: 1L
}, {
200408L: 1L
}, {
200410L: 2L
}, {
200412L: 2L
}, {
200414L: 1L
}, {
200417L: 2L
}, {
200419L: 1L
}, {
200420L: 2L
}, {
200421L: 2L
}, {
200422L: 1L
}, {
200423L: 1L
}, {
200424L: 1L
}, {
200426L: 5L
}, {
200427L: 3L
}, {
200428L: 1L
}, {
200429L: 1L
}, {
200434L: 2L
}, {
200435L: 1L
}, {
200436L: 1L
}, {
200438L: 1L
}, {
200441L: 1L
}, {
200442L: 1L
}, {
200501L: 3L
}, {
200502L: 2L
}, {
200504L: 1L
}, {
200506L: 1L
}, {
200507L: 2L
}, {
200508L: 2L
}, {
200511L: 1L
}, {
200512L: 3L
}, {
200519L: 2L
}, {
200520L: 4L
}, {
200522L: 1L
}, {
200523L: 1L
}, {
200525L: 1L
}, {
200528L: 1L
}, {
200529L: 1L
}, {
200531L: 2L
}, {
200534L: 5L
}, {
200537L: 1L
}, {
200538L: 2L
}, {
200540L: 1L
}, {
200543L: 3L
}, {
200548L: 1L
}, {
200550L: 1L
}, {
200601L: 2L
}, {
200602L: 3L
}, {
200603L: 3L
}, {
200605L: 2L
}, {
200607L: 3L
}, {
200608L: 3L
}, {
200609L: 1L
}, {
200611L: 4L
}, {
200612L: 2L
}, {
200613L: 1L
}, {
200614L: 1L
}, {
200618L: 1L
}, {
200620L: 2L
}, {
200621L: 1L
}, {
200627L: 1L
}, {
200628L: 1L
}, {
200630L: 2L
}, {
200637L: 1L
}, {
200640L: 2L
}, {
200642L: 1L
}, {
200643L: 1L
}, {
200644L: 1L
}, {
200646L: 2L
}, {
200647L: 1L
}, {
200648L: 1L
}, {
200650L: 1L
}, {
200651L: 1L
}, {
200653L: 1L
}, {
200701L: 1L
}, {
200702L: 2L
}, {
200704L: 1L
}, {
200706L: 1L
}, {
200707L: 2L
}, {
200711L: 2L
}, {
200716L: 1L
}, {
200718L: 1L
}, {
200722L: 1L
}, {
200724L: 1L
}, {
200725L: 1L
}, {
200726L: 1L
}, {
200728L: 1L
}, {
200729L: 1L
}, {
200730L: 2L
}, {
200731L: 1L
}, {
200738L: 1L
}, {
200739L: 2L
}, {
200740L: 2L
}, {
200742L: 1L
}, {
200744L: 3L
}, {
200745L: 1L
}, {
200747L: 1L
}, {
200750L: 1L
}, {
200801L: 1L
}, {
200802L: 2L
}, {
200803L: 1L
}, {
200804L: 2L
}, {
200805L: 1L
}, {
200806L: 3L
}, {
200807L: 1L
}, {
200808L: 4L
}, {
200811L: 1L
}, {
200813L: 2L
}, {
200815L: 2L
}, {
200817L: 3L
}, {
200818L: 4L
}, {
200819L: 1L
}, {
200820L: 1L
}, {
200822L: 2L
}, {
200823L: 1L
}, {
200824L: 2L
}, {
200825L: 4L
}, {
200829L: 2L
}, {
200831L: 1L
}, {
200832L: 3L
}, {
200833L: 2L
}, {
200834L: 2L
}, {
200835L: 5L
}, {
200836L: 2L
}, {
197905L: 1L
}, {
199949L: 1L
}, {
200002L: 1L
}, {
200015L: 2L
}, {
200016L: 4L
}, {
200018L: 3L
}, {
200019L: 5L
}, {
200021L: 1L
}, {
200022L: 2L
}, {
200023L: 1L
}, {
200025L: 2L
}, {
200029L: 1L
}, {
200030L: 5L
}, {
200031L: 1L
}, {
200033L: 1L
}, {
200034L: 1L
}, {
200035L: 3L
}, {
200036L: 2L
}, {
200037L: 2L
}, {
200038L: 3L
}, {
200040L: 5L
}, {
200041L: 3L
}, {
200042L: 6L
}, {
200043L: 3L
}, {
200045L: 2L
}, {
200046L: 2L
}, {
200047L: 2L
}, {
200048L: 2L
}, {
200049L: 2L
}, {
200050L: 5L
}, {
200051L: 2L
}, {
200052L: 1L
}, {
200053L: 8L
}, {
200101L: 2L
}, {
200102L: 2L
}, {
200103L: 4L
}, {
200104L: 3L
}, {
200105L: 1L
}, {
200106L: 2L
}, {
200107L: 6L
}, {
200108L: 10L
}, {
200109L: 6L
}, {
200111L: 3L
}, {
200112L: 2L
}, {
200113L: 8L
}, {
200114L: 2L
}, {
200116L: 4L
}, {
200118L: 4L
}, {
200119L: 1L
}, {
200121L: 1L
}, {
200122L: 4L
}, {
200123L: 10L
}, {
200126L: 6L
}, {
200130L: 3L
}, {
200131L: 6L
}, {
200132L: 18L
}, {
200133L: 16L
}, {
200134L: 3L
}, {
200135L: 7L
}, {
200138L: 2L
}, {
200139L: 2L
}, {
200143L: 5L
}, {
200144L: 2L
}, {
200145L: 2L
}, {
200146L: 1L
}, {
200147L: 4L
}, {
200148L: 5L
}, {
200149L: 4L
}, {
200150L: 1L
}, {
200152L: 2L
}, {
200201L: 2L
}, {
200203L: 1L
}, {
200204L: 1L
}, {
200205L: 9L
}, {
200206L: 1L
}, {
200207L: 2L
}, {
200208L: 2L
}, {
200209L: 2L
}, {
200210L: 4L
}, {
200211L: 7L
}, {
200213L: 8L
}, {
200214L: 2L
}, {
200215L: 3L
}, {
200216L: 2L
}, {
200217L: 10L
}, {
200218L: 2L
}, {
200219L: 3L
}, {
200220L: 5L
}, {
200221L: 10L
}, {
200222L: 4L
}, {
200223L: 3L
}, {
200224L: 14L
}, {
200225L: 9L
}, {
200226L: 5L
}, {
200227L: 5L
}, {
200228L: 7L
}, {
200230L: 6L
}, {
200231L: 7L
}, {
200232L: 1L
}, {
200233L: 3L
}, {
200234L: 5L
}, {
200235L: 6L
}, {
200236L: 2L
}, {
200237L: 1L
}, {
200239L: 2L
}, {
200240L: 2L
}, {
200241L: 4L
}, {
200242L: 4L
}, {
200243L: 4L
}, {
200244L: 2L
}, {
200245L: 3L
}, {
200246L: 2L
}, {
200247L: 5L
}, {
200248L: 5L
}, {
200250L: 1L
}, {
200251L: 3L
}, {
200252L: 1L
}, {
200301L: 2L
}, {
200302L: 1L
}, {
200303L: 3L
}, {
200304L: 2L
}, {
200305L: 3L
}, {
200306L: 8L
}, {
200307L: 4L
}, {
200308L: 5L
}, {
200309L: 1L
}, {
200310L: 1L
}, {
200311L: 3L
}, {
200312L: 2L
}, {
200313L: 3L
}, {
200315L: 1L
}, {
200316L: 1L
}, {
200318L: 1L
}, {
200319L: 7L
}, {
200320L: 5L
}, {
200321L: 1L
}, {
200322L: 4L
}, {
200323L: 1L
}, {
200324L: 3L
}, {
200325L: 2L
}, {
200326L: 12L
}, {
200327L: 5L
}, {
200328L: 4L
}, {
200329L: 2L
}, {
200330L: 2L
}, {
200331L: 5L
}, {
200332L: 4L
}, {
200333L: 1L
}, {
200334L: 1L
}, {
200335L: 2L
}, {
200337L: 8L
}, {
200338L: 4L
}, {
200341L: 5L
}, {
200342L: 5L
}, {
200345L: 5L
}, {
200347L: 1L
}, {
200348L: 4L
}, {
200349L: 5L
}, {
200350L: 3L
}, {
200351L: 4L
}, {
200401L: 3L
}, {
200402L: 4L
}, {
200403L: 4L
}, {
200404L: 13L
}, {
200405L: 7L
}, {
200406L: 2L
}, {
200407L: 3L
}, {
200408L: 1L
}, {
200409L: 2L
}, {
200410L: 5L
}, {
200412L: 2L
}, {
200413L: 2L
}, {
200414L: 3L
}, {
200416L: 1L
}, {
200417L: 3L
}, {
200418L: 1L
}, {
200419L: 4L
}, {
200420L: 5L
}, {
200421L: 3L
}, {
200422L: 2L
}, {
200423L: 1L
}, {
200424L: 1L
}, {
200425L: 2L
}, {
200426L: 5L
}, {
200427L: 3L
}, {
200428L: 2L
}, {
200429L: 1L
}, {
200430L: 1L
}, {
200431L: 5L
}, {
200434L: 2L
}, {
200435L: 3L
}, {
200436L: 1L
}, {
200437L: 1L
}, {
200438L: 3L
}, {
200440L: 2L
}, {
200441L: 1L
}, {
200442L: 4L
}, {
200443L: 1L
}, {
200449L: 3L
}, {
200450L: 2L
}, {
200501L: 3L
}, {
200502L: 2L
}, {
200503L: 1L
}, {
200504L: 3L
}, {
200505L: 1L
}, {
200506L: 1L
}, {
200507L: 5L
}, {
200508L: 5L
}, {
200511L: 1L
}, {
200512L: 2L
}, {
200514L: 4L
}, {
200515L: 1L
}, {
200519L: 4L
}, {
200520L: 4L
}, {
200521L: 2L
}, {
200522L: 1L
}, {
200523L: 1L
}, {
200524L: 5L
}, {
200525L: 2L
}, {
200527L: 1L
}, {
200528L: 3L
}, {
200529L: 4L
}, {
200531L: 4L
}, {
200532L: 2L
}, {
200533L: 2L
}, {
200534L: 5L
}, {
200535L: 2L
}, {
200537L: 1L
}, {
200538L: 2L
}, {
200539L: 1L
}, {
200540L: 1L
}, {
200543L: 3L
}, {
200546L: 2L
}, {
200548L: 1L
}, {
200549L: 1L
}, {
200550L: 2L
}, {
200601L: 2L
}, {
200602L: 3L
}, {
200603L: 3L
}, {
200605L: 2L
}, {
200606L: 4L
}, {
200607L: 3L
}, {
200608L: 3L
}, {
200609L: 4L
}, {
200611L: 8L
}, {
200612L: 3L
}, {
200613L: 4L
}, {
200614L: 4L
}, {
200618L: 1L
}, {
200619L: 1L
}, {
200620L: 4L
}, {
200621L: 1L
}, {
200627L: 1L
}, {
200628L: 3L
}, {
200630L: 3L
}, {
200634L: 4L
}, {
200636L: 3L
}, {
200637L: 3L
}, {
200638L: 1L
}, {
200639L: 1L
}, {
200640L: 3L
}, {
200642L: 6L
}, {
200643L: 1L
}, {
200644L: 2L
}, {
200646L: 3L
}, {
200647L: 5L
}, {
200648L: 1L
}, {
200650L: 2L
}, {
200651L: 2L
}, {
200653L: 1L
}, {
200701L: 4L
}, {
200702L: 5L
}, {
200704L: 1L
}, {
200706L: 1L
}, {
200707L: 2L
}, {
200709L: 3L
}, {
200711L: 3L
}, {
200714L: 1L
}, {
200715L: 1L
}, {
200716L: 7L
}, {
200718L: 1L
}, {
200719L: 1L
}, {
200722L: 1L
}, {
200723L: 1L
}, {
200724L: 2L
}, {
200725L: 1L
}, {
200726L: 1L
}, {
200728L: 1L
}, {
200729L: 2L
}, {
200730L: 2L
}, {
200731L: 4L
}, {
200739L: 2L
}, {
200740L: 2L
}, {
200742L: 1L
}, {
200744L: 3L
}, {
200745L: 1L
}, {
200747L: 1L
}, {
200748L: 1L
}, {
200750L: 1L
}, {
200801L: 3L
}, {
200802L: 2L
}, {
200803L: 1L
}, {
200804L: 5L
}, {
200805L: 3L
}, {
200806L: 1L
}, {
200807L: 7L
}, {
200808L: 7L
}, {
200809L: 4L
}, {
200810L: 1L
}, {
200811L: 3L
}, {
200813L: 2L
}, {
200815L: 1L
}, {
200817L: 1L
}, {
200818L: 3L
}, {
200820L: 1L
}, {
200822L: 1L
}, {
200823L: 1L
}, {
200824L: 6L
}, {
200825L: 2L
}, {
200829L: 1L
}, {
200832L: 1L
}, {
200833L: 2L
}, {
200835L: 5L
}, {
200836L: 1L
}]
total = [{
189953L: 33L
}, {
200107L: 6L
}, {
200108L: 4L
}, {
200109L: 1L
}, {
200110L: 3L
}, {
200111L: 1L
}, {
200112L: 1L
}, {
200113L: 2L
}, {
200114L: 1L
}, {
200115L: 1L
}, {
200116L: 11L
}, {
200117L: 8L
}, {
200118L: 8L
}, {
200119L: 5L
}, {
200120L: 24L
}, {
200121L: 8L
}, {
200122L: 3L
}, {
200123L: 6L
}, {
200124L: 5L
}, {
200125L: 1L
}, {
200126L: 1L
}, {
200127L: 2L
}, {
200128L: 2L
}, {
200129L: 2L
}, {
200130L: 3L
}, {
200131L: 5L
}, {
200132L: 1L
}, {
200133L: 10L
}, {
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normalize(result, total)
plt.title(#"Frequency of signifier occurrence over time\n" + 'Product: ' + project + ", signifier: " + signifier
# +
'r^2={0:.2%}'.format(r2), verticalalignment='bottom')
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()
return r2
def export():
F = plt.gcf()
F.savefig('/Users/nernst/Documents/papers/current-papers/refsq/figures/abs/' + project + '-'+ signifier + '-line.pdf')
if __name__ == '__main__':
import pickle
from names import Taxonomy
t = Taxonomy()
products = ['Evolution', 'Nautilus', 'Deskbar', 'Metacity', 'Ekiga', 'Totem', 'Evince', 'Empathy']
#products = ['Totem']
keywords = ['Efficiency', 'Portability', 'Maintainability', 'Reliability', 'Functionality', 'Usability']
data_dict = {}
save_file = open('/Users/nernst/Documents/papers/current-papers/refsq/abs-latex-refsq.csv', 'w')
save_file.write('File-Keyword, r2, slope, intercept, n\n')
# for product in products:
# for key in keywords:
product = 'Nautilus'
key = 'Portability'
filename = product + '-' + key + '.pcl'
print filename
f = open('/Users/nernst/Documents/papers/current-papers/refsq/data/pickles/ext/'+ filename)
df = pickle.load(f)
f.close()
def get_counts(keyword):
""" store in the database"""
store_cursor = connect_corpus("data_objects")
t = Taxonomy()
prod = 'Nautilus'
total = 50
query_string = """ select event from data_objects.refsq_data
order by rand() limit %(total)d""" % {"prod":prod, "total":total} # msr_type = "Mail" where product = "%(prod)s"
# = """select event from data_objects.refsq_data where match(event)
# against (\'%(key)s\' in boolean mode)
# order by rand() limit %(total)d""" % {"key":keyword, "total":total}
try:
# print query_string
store_cursor.execute(query_string)
yes = 0.0
no = 0.0
i = 0
wn_fn_count = 0
ext_fn_count = 0
for result in store_cursor.fetchall():
event = result.values()[0]
#result = str(store_cursor.fetchall().values()[0]) #{'count(*)': 6L} dict
print event
i = i + 1
answer = raw_input("\n******\n"+ str(i) + "/" + str(total) + " Quality: [u/r/m/e/p/f/n]: ")
#wait for user input
if answer != 'n':
yes = yes + 1.0
#check against our algorithm
if answer == 'u':
signifiers_ext = t.get_signifiers('Usability')
signifiers_wn = t.get_signifiers_wn('Usability')
#print signifiers
elif answer == 'r':
signifiers_ext = t.get_signifiers('Reliability')
signifiers_wn = t.get_signifiers_wn('Reliability')
elif answer == 'm':
signifiers_ext = t.get_signifiers('Maintainability')
signifiers_wn = t.get_signifiers_wn('Maintainability')
elif answer == 'e':
signifiers_ext = t.get_signifiers('Efficiency')
signifiers_wn = t.get_signifiers_wn('Efficiency')
elif answer == 'p':
signifiers_ext = t.get_signifiers('Portability')
signifiers_wn = t.get_signifiers_wn('Portability')
elif answer == 'f':
signifiers_ext = t.get_signifiers('Functionality')
signifiers_wn = t.get_signifiers_wn('Functionality')
found_wn, found_ext = check_list(event, signifiers_wn, signifiers_ext)
if not found_wn:
# false negative
wn_fn_count += 1
print wn_fn_count
if not found_ext:
ext_fn_count += 1
print ext_fn_count
else:
no = no + 1.0 # TODO could extend this to see whether the standard query matches it even so
wn_rate = wn_fn_count/float(yes)
ext_rate = ext_fn_count/float(yes) #of the ones we marked, how many were not found?
print ext_fn_count
print "Number of matches was %s" % (yes)
print "False negative rate was wn: %s and ext: %s" % (str(wn_rate),str(ext_rate))
except (ValueError):
print 'Error in query syntax'
from names import Taxonomy
import subprocess
t = Taxonomy()
for sign in t.get_signified():
wordlist = t.get_signifiers(sign)
for word in wordlist:
p = subprocess.Popen(
["perl", "typo.pl", word], stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE
)
(out, err) = p.communicate()
print out
# print word
def get_counts(keyword):
""" store in the database"""
store_cursor = connect_corpus("data_objects")
t = Taxonomy()
prod = 'Nautilus'
total = 50
query_string = """ select event from data_objects.refsq_data
order by rand() limit %(total)d""" % {
"prod": prod,
"total": total
} # msr_type = "Mail" where product = "%(prod)s"
# = """select event from data_objects.refsq_data where match(event)
# against (\'%(key)s\' in boolean mode)
# order by rand() limit %(total)d""" % {"key":keyword, "total":total}
try:
# print query_string
store_cursor.execute(query_string)
yes = 0.0
no = 0.0
i = 0
wn_fn_count = 0
ext_fn_count = 0
for result in store_cursor.fetchall():
event = result.values()[0]
#result = str(store_cursor.fetchall().values()[0]) #{'count(*)': 6L} dict
print event
i = i + 1
answer = raw_input("\n******\n" + str(i) + "/" + str(total) +
" Quality: [u/r/m/e/p/f/n]: ")
#wait for user input
if answer != 'n':
yes = yes + 1.0
#check against our algorithm
if answer == 'u':
signifiers_ext = t.get_signifiers('Usability')
signifiers_wn = t.get_signifiers_wn('Usability')
#print signifiers
elif answer == 'r':
signifiers_ext = t.get_signifiers('Reliability')
signifiers_wn = t.get_signifiers_wn('Reliability')
elif answer == 'm':
signifiers_ext = t.get_signifiers('Maintainability')
signifiers_wn = t.get_signifiers_wn('Maintainability')
elif answer == 'e':
signifiers_ext = t.get_signifiers('Efficiency')
signifiers_wn = t.get_signifiers_wn('Efficiency')
elif answer == 'p':
signifiers_ext = t.get_signifiers('Portability')
signifiers_wn = t.get_signifiers_wn('Portability')
elif answer == 'f':
signifiers_ext = t.get_signifiers('Functionality')
signifiers_wn = t.get_signifiers_wn('Functionality')
found_wn, found_ext = check_list(event, signifiers_wn,
signifiers_ext)
if not found_wn:
# false negative
wn_fn_count += 1
print wn_fn_count
if not found_ext:
ext_fn_count += 1
print ext_fn_count
else:
no = no + 1.0 # TODO could extend this to see whether the standard query matches it even so
wn_rate = wn_fn_count / float(yes)
ext_rate = ext_fn_count / float(
yes) #of the ones we marked, how many were not found?
print ext_fn_count
print "Number of matches was %s" % (yes)
print "False negative rate was wn: %s and ext: %s" % (str(wn_rate),
str(ext_rate))
except (ValueError):
print 'Error in query syntax'