def sequence_to_counts(genresequence):
'''Converts a sequence of page-level predictions to
a dictionary of counts reflecting the number of pages
assigned to each genre. Also reports the largest genre.
Note that this function cannot return "bio." If
biography is the largest genre it returns "non"fiction.
It counts bio, but ensures that all votes for bio are also votes
for non.
'''
genrecounts = dict()
for page in genresequence:
utils.addtodict(page, 1, genrecounts)
if page == 'bio':
utils.addtodict('non', 1, genrecounts)
# Convert the dictionary of counts into a sorted list, and take the max.
genretuples = utils.sortkeysbyvalue(genrecounts, whethertoreverse = True)
maxgenre = genretuples[0][1]
if maxgenre == 'bio':
maxgenre = 'non'
return genrecounts, maxgenre
def sequence_to_counts(genresequence):
'''Converts a sequence of page-level predictions to
a dictionary of counts reflecting the number of pages
assigned to each genre. Also reports the largest genre.
Note that this function cannot return "bio." If
biography is the largest genre it returns "non"fiction.
It counts bio, but ensures that all votes for bio are also votes
for non.
'''
genrecounts = dict()
for page in genresequence:
utils.addtodict(page, 1, genrecounts)
if page == 'bio':
utils.addtodict('non', 1, genrecounts)
# Convert the dictionary of counts into a sorted list, and take the max.
genretuples = utils.sortkeysbyvalue(genrecounts, whethertoreverse=True)
maxgenre = genretuples[0][1]
if maxgenre == 'bio':
maxgenre = 'non'
return genrecounts, maxgenre
def comparelists(firstmap, secondmap, genremistakes, correctbygenre,
wordcounts):
if len(firstmap) > len(secondmap):
length = len(secondmap)
elif len(firstmap) == len(secondmap):
length = len(firstmap)
else:
print(
"Error, Will Robinson. There are occasions where the consensus version is shorter but no valid reason for it to be longer."
)
divergence = 0.0
for i in range(length):
generalizedfirst = translate(firstmap[i])
generalizedsecond = translate(secondmap[i])
if effectively_equal(generalizedfirst, generalizedsecond):
utils.addtodict(generalizedsecond, wordcounts[i], correctbygenre)
else:
divergence += wordcounts[i]
utils.addtodict((generalizedsecond, generalizedfirst),
wordcounts[i], genremistakes)
return divergence
def sequence_to_counts(genresequence):
'''Converts a sequence of page-level predictions to
a dictionary of counts reflecting the number of pages
assigned to each genre. Also reports the largest genre.'''
genrecounts = dict()
genrecounts['fic'] = 0
genrecounts['poe'] = 0
genrecounts['dra'] = 0
genrecounts['non'] = 0
for page in genresequence:
indexas = page
# For this purpose, we treat biography and indexes as equivalent to nonfiction.
if page == "bio" or page == "index" or page == "back" or page == "trv":
indexas = "non"
utils.addtodict(indexas, 1, genrecounts)
# Convert the dictionary of counts into a sorted list, and take the max.
genretuples = utils.sortkeysbyvalue(genrecounts, whethertoreverse=True)
maxgenre = genretuples[0][1]
return genrecounts, maxgenre
def sequence_to_counts(genresequence):
'''Converts a sequence of page-level predictions to
a dictionary of counts reflecting the number of pages
assigned to each genre. Also reports the largest genre.'''
genrecounts = dict()
genrecounts['fic'] = 0
genrecounts['poe'] = 0
genrecounts['dra'] = 0
genrecounts['non'] = 0
for page in genresequence:
indexas = page
# For this purpose, we treat biography and indexes as equivalent to nonfiction.
if page == "bio" or page == "index" or page == "back" or page == "trv":
indexas = "non"
utils.addtodict(indexas, 1, genrecounts)
# Convert the dictionary of counts into a sorted list, and take the max.
genretuples = utils.sortkeysbyvalue(genrecounts, whethertoreverse = True)
maxgenre = genretuples[0][1]
return genrecounts, maxgenre
def sequence_to_counts(genresequence):
'''Converts a sequence of page-level predictions to
a dictionary of counts reflecting the number of pages
assigned to each genre.
Note that this version of the function is slightly different
from the version in MetadataCascades, in allowing a wider range
of genres and not initializing anything to zero.'''
genrecounts = dict()
for page in genresequence:
utils.addtodict(page, 1, genrecounts)
return genrecounts
def sequence_to_counts(genresequence):
'''Converts a sequence of page-level predictions to
a dictionary of counts reflecting the number of pages
assigned to each genre.
Note that this version of the function is slightly different
from the version in MetadataCascades, in allowing a wider range
of genres and not initializing anything to zero.'''
genrecounts = dict()
for page in genresequence:
utils.addtodict(page, 1, genrecounts)
return genrecounts
def compare_two_lists(truelist, predicted, wordsperpage, whethertocountwords):
global genretranslations
assert (len(truelist) == len(predicted))
errorsbygenre = dict()
correctbygenre = dict()
accurate = 0
inaccurate = 0
totaltruegenre = dict()
for index, truegenre in enumerate(truelist):
if truegenre in genretranslations:
truegenre = genretranslations[truegenre]
if whethertocountwords:
increment = wordsperpage[index]
else:
increment = 1
utils.addtodict(truegenre, increment, totaltruegenre)
predictedgenre = predicted[index]
if genresareequal(truegenre, predictedgenre):
utils.addtodict(truegenre, increment, correctbygenre)
accurate += increment
else:
utils.addtodict((truegenre, predictedgenre), increment,
errorsbygenre)
inaccurate += increment
return totaltruegenre, correctbygenre, errorsbygenre, accurate, inaccurate
def compare_two_lists(truelist, predicted, wordsperpage, whethertocountwords):
global genretranslations
assert(len(truelist) == len(predicted))
errorsbygenre = dict()
correctbygenre = dict()
accurate = 0
inaccurate = 0
totaltruegenre = dict()
for index, truegenre in enumerate(truelist):
if truegenre in genretranslations:
truegenre = genretranslations[truegenre]
if whethertocountwords:
increment = wordsperpage[index]
else:
increment = 1
utils.addtodict(truegenre, increment, totaltruegenre)
predictedgenre = predicted[index]
if genresareequal(truegenre, predictedgenre):
utils.addtodict(truegenre, increment, correctbygenre)
accurate += increment
else:
utils.addtodict((truegenre, predictedgenre), increment, errorsbygenre)
inaccurate += increment
return totaltruegenre, correctbygenre, errorsbygenre, accurate, inaccurate
def comparelists(firstmap, secondmap, genremistakes, correctbygenre, wordcounts):
if len(firstmap) > len(secondmap):
length = len(secondmap)
elif len(firstmap) == len(secondmap):
length = len(firstmap)
else:
print("Error, Will Robinson. There are occasions where the consensus version is shorter but no valid reason for it to be longer.")
divergence = 0.0
for i in range(length):
generalizedfirst = translate(firstmap[i])
generalizedsecond = translate(secondmap[i])
if effectively_equal(generalizedfirst, generalizedsecond):
utils.addtodict(generalizedsecond, wordcounts[i], correctbygenre)
else:
divergence += wordcounts[i]
utils.addtodict((generalizedsecond, generalizedfirst), wordcounts[i], genremistakes)
return divergence
def resolve_voting(votes, tiebreaker):
electorate = len(votes)
results = dict()
for vote in votes:
# if vote == "bio":
# vote = "non"
utils.addtodict(vote, 1, results)
candidate = utils.sortkeysbyvalue(results, whethertoreverse=True)
dissent = (electorate - candidate[0][0]) / electorate
if len(candidate) < 2:
# There is only one candidate.
return candidate[0][1], dissent, candidate[0][1]
elif candidate[0][0] > candidate[1][0]:
# We have a majority.
return candidate[0][1], dissent, candidate[1][1]
else:
# We have a tie.
if tiebreaker == candidate[0][1]:
print("Tiebreaker " + tiebreaker)
return candidate[0][1], dissent, candidate[1][1]
elif tiebreaker == candidate[1][1]:
print("Tiebreaker " + tiebreaker)
return candidate[1][1], dissent, candidate[0][1]
else:
print("Tie in spite of " + tiebreaker)
win = random.choice([candidate[0][1], candidate[1][1]])
if win == candidate[0][1]:
runnerup = candidate[1][1]
else:
runnerup = candidate[0][1]
return win, dissent, runnerup
def resolve_voting(votes, tiebreaker):
electorate = len(votes)
results = dict()
for vote in votes:
# if vote == "bio":
# vote = "non"
utils.addtodict(vote, 1, results)
candidate = utils.sortkeysbyvalue(results, whethertoreverse = True)
dissent = (electorate - candidate[0][0]) / electorate
if len(candidate) < 2:
# There is only one candidate.
return candidate[0][1], dissent, candidate[0][1]
elif candidate[0][0] > candidate[1][0]:
# We have a majority.
return candidate[0][1], dissent, candidate[1][1]
else:
# We have a tie.
if tiebreaker == candidate[0][1]:
print("Tiebreaker " + tiebreaker)
return candidate[0][1], dissent, candidate[1][1]
elif tiebreaker == candidate[1][1]:
print("Tiebreaker " + tiebreaker)
return candidate[1][1], dissent, candidate[0][1]
else:
print("Tie in spite of " + tiebreaker)
win = random.choice([candidate[0][1], candidate[1][1]])
if win == candidate[0][1]:
runnerup = candidate[1][1]
else:
runnerup = candidate[0][1]
return win, dissent, runnerup
def censor(htid, genresequence):
htid = utils.pairtreelabel(htid)
# convert the htid into a dirty pairtree label for metadata matching
# Create a dictionary with entries for all possible conditions, initially set negative.
symptoms = [
"weakconfirmation", "weakdenial", "strongconfirmation", "strongdenial",
"modelagrees", "modeldisagrees"
]
reported = dict()
for symptom in symptoms:
reported[symptom] = 0
couldbefiction = True
# Now we need to assess the largest genre in this volume.
genrecounts = dict()
genrecounts['fic'] = 0
genrecounts['poe'] = 0
genrecounts['dra'] = 0
genrecounts['non'] = 0
for page in genresequence:
indexas = page
# For this purpose, we treat biography and indexes as equivalent to nonfiction.
if page == "bio" or page == "index" or page == "back":
indexas = "non"
utils.addtodict(indexas, 1, genrecounts)
# Convert the dictionary of counts into a sorted list, and take the max.
genretuples = utils.sortkeysbyvalue(genrecounts, whethertoreverse=True)
maxgenre = genretuples[0][1]
if htid not in rowindices and htid not in modelindices:
return genresequence, reported
if htid in rowindices:
genrestring = metadata["genres"][htid]
genreinfo = genrestring.split(";")
# It's a semicolon-delimited list of items.
for info in genreinfo:
if info == "Biography" or info == "Autobiography":
couldbefiction = False
if info == "biog?" and maxgenre == "non":
reported["weakconfirmation"] = 1
if info == "biog?" and maxgenre != "non":
reported["weakdenial"] = 1
if info == "Not fiction" and maxgenre == "non":
reported["weakconfirmation"] = 1
if info == "Not fiction" and maxgenre == "fic":
reported["weakdenial"] = 1
if (info == "Fiction" or info == "Novel") and maxgenre == "fic":
reported["strongconfirmation"] = 1
if (info == "Fiction" or info == "Novel") and maxgenre != "fic":
reported["strongdenial"] = 1
if info == "Biography" and maxgenre == "non":
reported["strongconfirmation"] = 1
if info == "Biography" and maxgenre != "non":
reported["strongdenial"] = 1
if info == "Autobiography" and maxgenre == "non":
reported["strongconfirmation"] = 1
if info == "Autobiography" and maxgenre != "non":
reported["strongdenial"] = 1
if (info == "Poetry" or info == "Poems") and maxgenre == "poe":
reported["strongconfirmation"] = 1
if (info == "Poetry" or info == "Poems") and maxgenre != "poe":
reported["strongdenial"] = 1
if (info == "Drama" or info == "Tragedies"
or info == "Comedies") and maxgenre == "dra":
reported["strongconfirmation"] = 1
if (info == "Drama" or info == "Tragedies"
or info == "Comedies") and maxgenre != "dra":
reported["strongdenial"] = 1
if (info == "Catalog" or info == "Dictionary"
or info == "Bibliographies") and maxgenre == "non":
reported["strongconfirmation"] = 1
couldbefiction = False
if (info == "Catalog" or info == "Dictionary"
or info == "Bibliographies") and maxgenre != "non":
reported["strongdenial"] = 1
else:
print("Skipped.")
if htid in modelindices:
modelpredictions = dict()
for genre, genrecolumn in modeldata.items():
if not genre in options:
# this column is not a genre!
continue
modelpredictions[genre] = float(genrecolumn[htid])
predictionlist = utils.sortkeysbyvalue(modelpredictions,
whethertoreverse=True)
modelprediction = predictionlist[0][1]
modelconfidence = predictionlist[0][0]
nextclosest = predictionlist[1][0]
# Take the top prediction.
# For purposes of this routine, treat biography as nonfiction:
if modelprediction == "bio":
modelprediction = "non"
if maxgenre == modelprediction:
reported["modelagrees"] = 1 ## modelconfidence - nextclosest
reported["modeldisagrees"] = 0
if maxgenre != modelprediction:
## divergence = modelconfidence - modelpredictions[maxgenre]
reported["modeldisagrees"] = 1
reported["modelagrees"] = 0
## print(maxgenre + " ≠ " + modelprediction)
else:
reported["modelagrees"] = 0
reported["modeldisagrees"] = 0
modelprediction = "unknown"
if not couldbefiction:
numberofpages = len(genresequence)
for i in range(numberofpages):
if genresequence[i] == "fic":
genresequence[i] = "non"
return genresequence, reported
with open(metafile, encoding='utf-8') as f:
reader = csv.reader(f)
for row in reader:
# skip header
if row[0] == "idcode":
continue
code = row[0]
if code in badids:
continue
date = int(row[1])
dateset.add(date)
wordcount = int(row[3])
id2date[code] = date
utils.addtodict(date, wordcount, date2wordcount)
id2wordcount[code] = wordcount
def pricesymbol(keyword):
if keyword == '$':
return True
elif keyword == '£':
return True
elif keyword == '¢':
return True
elif keyword == '£':
return True
else:
return False
def censor(htid, genresequence):
htid = utils.pairtreelabel(htid)
# convert the htid into a dirty pairtree label for metadata matching
# Create a dictionary with entries for all possible conditions, initially set negative.
symptoms = ["weakconfirmation", "weakdenial", "strongconfirmation", "strongdenial", "modelagrees", "modeldisagrees"]
reported = dict()
for symptom in symptoms:
reported[symptom] = 0
couldbefiction = True
# Now we need to assess the largest genre in this volume.
genrecounts = dict()
genrecounts['fic'] = 0
genrecounts['poe'] = 0
genrecounts['dra'] = 0
genrecounts['non'] = 0
for page in genresequence:
indexas = page
# For this purpose, we treat biography and indexes as equivalent to nonfiction.
if page == "bio" or page == "index" or page == "back":
indexas = "non"
utils.addtodict(indexas, 1, genrecounts)
# Convert the dictionary of counts into a sorted list, and take the max.
genretuples = utils.sortkeysbyvalue(genrecounts, whethertoreverse = True)
maxgenre = genretuples[0][1]
if htid not in rowindices and htid not in modelindices:
return genresequence, reported
if htid in rowindices:
genrestring = metadata["genres"][htid]
genreinfo = genrestring.split(";")
# It's a semicolon-delimited list of items.
for info in genreinfo:
if info == "Biography" or info == "Autobiography":
couldbefiction = False
if info == "biog?" and maxgenre == "non":
reported["weakconfirmation"] = 1
if info == "biog?" and maxgenre != "non":
reported["weakdenial"] = 1
if info == "Not fiction" and maxgenre == "non":
reported["weakconfirmation"] = 1
if info == "Not fiction" and maxgenre == "fic":
reported["weakdenial"] = 1
if (info == "Fiction" or info == "Novel") and maxgenre == "fic":
reported["strongconfirmation"] = 1
if (info == "Fiction" or info == "Novel") and maxgenre != "fic":
reported["strongdenial"] = 1
if info == "Biography" and maxgenre == "non":
reported["strongconfirmation"] = 1
if info == "Biography" and maxgenre != "non":
reported["strongdenial"] = 1
if info == "Autobiography" and maxgenre == "non":
reported["strongconfirmation"] = 1
if info == "Autobiography" and maxgenre != "non":
reported["strongdenial"] = 1
if (info == "Poetry" or info == "Poems") and maxgenre == "poe":
reported["strongconfirmation"] = 1
if (info == "Poetry" or info == "Poems") and maxgenre != "poe":
reported["strongdenial"] = 1
if (info == "Drama" or info == "Tragedies" or info == "Comedies") and maxgenre == "dra":
reported["strongconfirmation"] = 1
if (info == "Drama" or info == "Tragedies" or info == "Comedies") and maxgenre != "dra":
reported["strongdenial"] = 1
if (info == "Catalog" or info == "Dictionary" or info=="Bibliographies") and maxgenre == "non":
reported["strongconfirmation"] = 1
couldbefiction = False
if (info == "Catalog" or info == "Dictionary" or info=="Bibliographies") and maxgenre != "non":
reported["strongdenial"] = 1
else:
print("Skipped.")
if htid in modelindices:
modelpredictions = dict()
for genre, genrecolumn in modeldata.items():
if not genre in options:
# this column is not a genre!
continue
modelpredictions[genre] = float(genrecolumn[htid])
predictionlist = utils.sortkeysbyvalue(modelpredictions, whethertoreverse = True)
modelprediction = predictionlist[0][1]
modelconfidence = predictionlist[0][0]
nextclosest = predictionlist[1][0]
# Take the top prediction.
# For purposes of this routine, treat biography as nonfiction:
if modelprediction == "bio":
modelprediction = "non"
if maxgenre == modelprediction:
reported["modelagrees"] = 1 ## modelconfidence - nextclosest
reported["modeldisagrees"] = 0
if maxgenre != modelprediction:
## divergence = modelconfidence - modelpredictions[maxgenre]
reported["modeldisagrees"] = 1
reported["modelagrees"] = 0
## print(maxgenre + " ≠ " + modelprediction)
else:
reported["modelagrees"] = 0
reported["modeldisagrees"] = 0
modelprediction = "unknown"
if not couldbefiction:
numberofpages = len(genresequence)
for i in range(numberofpages):
if genresequence[i] == "fic":
genresequence[i] = "non"
return genresequence, reported
with open(metafile, encoding = 'utf-8') as f:
reader = csv.reader(f)
for row in reader:
# skip header
if row[0] == "idcode":
continue
code = row[0]
if code in badids:
continue
date = int(row[1])
dateset.add(date)
wordcount = int(row[3])
id2date[code] = date
utils.addtodict(date, wordcount, date2wordcount)
id2wordcount[code] = wordcount
def pricesymbol(keyword):
if keyword == '$':
return True
elif keyword == '£':
return True
elif keyword == '¢':
return True
elif keyword == '£':
return True
else:
return False
def priceinsnippet(snippet):
filepath = os.path.join(sourcedir, filename)
with open(filepath, encoding='utf-8') as f:
filelines = f.readlines()
pagelist = [filelines]
# The wordcounter module expects a list of pages, each of which is a list of lines.
# Ebooks have no pages -- at least as I currently receive them -- so we treat it
# all as one giant page.
tokenstream = wordcounter.makestream(pagelist)
wordcounts, wordsfused, triplets, alphanum_tokens = wordcounter.count_tokens(
tokenstream,
targetwords=targetwords,
targetphrases=targetphrases,
verbose=verbose)
thisyear = summedbydate[date]
for word, count in wordcounts.items():
utils.addtodict("moneywords", count, thisyear)
utils.addtodict("totalwords", alphanum_tokens, thisyear)
outfile = "/Users/tunder/Dropbox/Documents/Conferences (UIUC)/Rutgers talk/1950data.tsv"
with open(outfile, mode='w', encoding='utf-8') as f:
f.write('year\tmoneywords\ttotalwords\n')
for date in datelist:
outline = str(date) + '\t' + str(
summedbydate[date]["moneywords"]) + '\t' + str(
summedbydate[date]["totalwords"]) + '\n'
f.write(outline)
allLCs = dict()
bio = dict()
fic = dict()
poe = dict()
dra = dict()
ctr = 0
for rowidx in rowindices:
loc = metadata["LOCnum"][rowidx]
probablybiography, probablydrama, probablyfiction, probablypoetry, maybefiction = choose_cascade(rowidx)
LC = letterpart(loc)
utils.addtodict(LC, 1, allLCs)
if probablybiography:
utils.addtodict(LC, 1, bio)
if probablydrama:
utils.addtodict(LC, 1, dra)
if probablyfiction:
utils.addtodict(LC, 1, fic)
if probablypoetry:
utils.addtodict(LC, 1, poe)
if maybefiction:
utils.addtodict(LC, 0.1, fic)
ctr += 1
if ctr % 1000 == 1:
print(ctr)
continue
filepath = os.path.join(sourcedir, filename)
with open(filepath, encoding = 'utf-8') as f:
filelines = f.readlines()
pagelist = [filelines]
# The wordcounter module expects a list of pages, each of which is a list of lines.
# Ebooks have no pages -- at least as I currently receive them -- so we treat it
# all as one giant page.
tokenstream = wordcounter.makestream(pagelist)
wordcounts, wordsfused, triplets, alphanum_tokens = wordcounter.count_tokens(tokenstream, targetwords=targetwords, targetphrases=targetphrases, verbose = verbose)
thisyear = summedbydate[date]
for word, count in wordcounts.items():
utils.addtodict("moneywords", count, thisyear)
utils.addtodict("totalwords", alphanum_tokens, thisyear)
outfile = "/Users/tunder/Dropbox/Documents/Conferences (UIUC)/Rutgers talk/1950data.tsv"
with open(outfile, mode='w', encoding='utf-8') as f:
f.write('year\tmoneywords\ttotalwords\n')
for date in datelist:
outline = str(date) + '\t' + str(summedbydate[date]["moneywords"]) + '\t' + str(summedbydate[date]["totalwords"]) + '\n'
f.write(outline)
allLCs = dict()
bio = dict()
fic = dict()
poe = dict()
dra = dict()
ctr = 0
for rowidx in rowindices:
loc = metadata["LOCnum"][rowidx]
probablybiography, probablydrama, probablyfiction, probablypoetry, maybefiction = choose_cascade(
rowidx)
LC = letterpart(loc)
utils.addtodict(LC, 1, allLCs)
if probablybiography:
utils.addtodict(LC, 1, bio)
if probablydrama:
utils.addtodict(LC, 1, dra)
if probablyfiction:
utils.addtodict(LC, 1, fic)
if probablypoetry:
utils.addtodict(LC, 1, poe)
if maybefiction:
utils.addtodict(LC, 0.1, fic)
ctr += 1
if ctr % 1000 == 1:
print(ctr)
