예제 #1
0
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
예제 #9
0
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
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        200348L: 4L
    }, {
        200349L: 5L
    }, {
        200350L: 3L
    }, {
        200351L: 4L
    }, {
        200401L: 3L
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        200402L: 4L
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        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
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        200443L: 1L
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        200449L: 3L
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        200450L: 2L
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        200501L: 3L
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        200502L: 2L
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        200503L: 1L
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        200504L: 3L
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        200505L: 1L
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        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
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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()
예제 #11
0
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'   
예제 #12
0
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'