def get_classvector(classpath, volumeIDs):
with open(classpath, encoding = 'utf-8') as f:
filelines = f.readlines()
classdict = dict()
for line in filelines:
line = line.rstrip()
fields = line.split('\t')
volid = utils.clean_pairtree(fields[0])
theclass = fields[1]
if theclass == 'elite':
intclass = 1
elif theclass == 'vulgar':
intclass = 0
else:
intclass = int(theclass)
classdict[volid] = intclass
if len(volumeIDs) < 1:
volumeIDs = [x for x in classdict.keys()]
classvector = np.zeros(len(volumeIDs))
for idx, anid in enumerate(volumeIDs):
if anid in classdict:
classvector[idx] = classdict[anid]
else:
print('Missing from class metadata: ' + anid)
return classvector, volumeIDs
def get_classvector(classpath, volumeIDs):
with open(classpath, encoding='utf-8') as f:
filelines = f.readlines()
classdict = dict()
for line in filelines:
line = line.rstrip()
fields = line.split('\t')
volid = utils.clean_pairtree(fields[0])
theclass = fields[1]
if theclass == 'elite':
intclass = 1
elif theclass == 'vulgar':
intclass = 0
else:
intclass = int(theclass)
classdict[volid] = intclass
if len(volumeIDs) < 1:
volumeIDs = [x for x in classdict.keys()]
classvector = np.zeros(len(volumeIDs))
for idx, anid in enumerate(volumeIDs):
if anid in classdict:
classvector[idx] = classdict[anid]
else:
print('Missing from class metadata: ' + anid)
return classvector, volumeIDs
def get_featureframe(vocabulary, positiveIDs, negativeIDs, sourcedir):
''' Returns a pandas dataframe with feature counts for all the volumes
to be used in this model.
'''
df = dict()
# We initially construct the data frame as a dictionary of Series.
vocabset = set(vocabulary)
allIDs = positiveIDs + negativeIDs
for v in vocabulary:
df[v] = pd.Series(np.zeros(len(allIDs)), index = allIDs)
for docid in allIDs:
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
with open(path, encoding = 'utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
feature = row['feature']
if feature.startswith('#header'):
feature = feature.replace('#header', '')
if feature in vocabset:
df[feature].loc[docid] = row['count']
# Now let's refashion the dictionary as an actual dataframe.
df = pd.DataFrame(df, index = allIDs)
df = df[vocabulary]
# This reorders the columns to be in vocab order
stdscaler = StandardScaler()
scaleddf = pd.DataFrame(stdscaler.fit_transform(df), index = allIDs)
return scaleddf
def add_to_ficgenre(docid, existingfile, tagas):
global outfieldnames, metadata
with open(existingfile, mode = 'a', encoding = 'utf-8') as f:
writer = csv.DictWriter(f, fieldnames = outfieldnames)
o = dict()
j = metadata[docid]
fields = [j['HTid'], str(j['date']), j['author'], j['title'], j['enumcron']]
print(" | ".join(fields))
o['docid'] = utils.clean_pairtree(j['HTid'])
o['recordid'] = j['recordid']
o['oclc'] = j['OCLC']
o['locnum'] = j['LOCnum']
o['author'] = j['author']
o['imprint'] = j['imprint']
o['date'] = j['date']
o['firstpub'] = input('First publication date? ')
o['birthdate'] = input('Author birth year? ')
o['nationality'] = input('Nationality? ')
o['gender'] = input('Gender? ')
o['title'] = j['title']
o['subjects'] = j['subjects']
o['enumcron'] = j['enumcron']
o['genretags'] = tagas
for key, value in o.items():
if o[key] == '<blank>':
o[key] = ''
writer.writerow(o)
print('Done.')
def get_featureframe(vocabulary, positiveIDs, negativeIDs, sourcedir):
''' Returns a pandas dataframe with feature counts for all the volumes
to be used in this model.
'''
df = dict()
# We initially construct the data frame as a dictionary of Series.
vocabset = set(vocabulary)
allIDs = positiveIDs + negativeIDs
for v in vocabulary:
df[v] = pd.Series(np.zeros(len(allIDs)), index=allIDs)
for docid in allIDs:
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
with open(path, encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
feature = row['feature']
if feature.startswith('#header'):
feature = feature.replace('#header', '')
if feature in vocabset:
df[feature].loc[docid] = row['count']
# Now let's refashion the dictionary as an actual dataframe.
df = pd.DataFrame(df, index=allIDs)
df = df[vocabulary]
# This reorders the columns to be in vocab order
stdscaler = StandardScaler()
scaleddf = pd.DataFrame(stdscaler.fit_transform(df), index=allIDs)
return scaleddf
def get_vocabulary_and_counts_4pages(metadata, allIDs, sourcedir, n):
''' Gets the top n words by docfrequency, but also
returns a dictionary of wordcounts so we don't have to read them again from the
file when generating a feature dataframe.
Adjusted to handle page instances.
'''
doc_freq = Counter()
counts = dict()
id2group = dict()
for docid in allIDs:
path = os.path.join(sourcedir,
utils.clean_pairtree(docid) + '.basic.json.bz2')
volume = parser.PagelistFromJson(path, docid)
pagecounts = volume.get_feature_list()
for idx, page in enumerate(pagecounts):
pageid = docid + '||' + str(idx)
id2group[pageid] = docid
counts[pageid] = page
for key, value in page.items():
doc_freq[key] += 1
vocab = [x[0] for x in doc_freq.most_common(n)]
print('Vocabulary constructed.')
return vocab, counts, id2group
def get_vocabulary_and_counts(metadata, positiveIDs, negativeIDs, sourcedir,
n):
''' Gets the top n words by docfrequency in positiveIDs + negativeIDs, but also
returns a dictionary of wordcounts so we don't have to read them again from the
file when generating a feature dataframe.
'''
allIDs = positiveIDs + negativeIDs
doc_freq = Counter()
counts = dict()
for docid in allIDs:
counts[docid] = Counter()
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
with open(path, encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
word = row['feature']
if len(word) < 1:
continue
ct = float(row['count'])
if word.startswith('#header'):
word = word.replace('#header', '')
#
# This debatable choice treats header words as equivalent
# to occurrences in the body text. In practice, this seems
# to slightly improve performance, at least when you're using
# SVMs and relatively low numbers of features (140-300).
# Otherwise header words are in practice just discarded, because
# e.g. #headeract won't be one of the top 250 words.
doc_freq[word] += 1
counts[docid][word] += ct
# # experimental
# if word.startswith('#'):
# squaredfeature = word + 'sqrd'
# counts[docid][word] = ct * ct
vocab = [x[0] for x in doc_freq.most_common(n)]
print('Vocabulary constructed.')
return vocab, counts
def get_vocabulary_and_counts(metadata, positiveIDs, negativeIDs, sourcedir, n):
''' Gets the top n words by docfrequency in positiveIDs + negativeIDs, but also
returns a dictionary of wordcounts so we don't have to read them again from the
file when generating a feature dataframe.
'''
allIDs = positiveIDs + negativeIDs
doc_freq = Counter()
counts = dict()
for docid in allIDs:
counts[docid] = Counter()
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
with open(path, encoding = 'utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
word = row['feature']
if len(word) < 1:
continue
ct = float(row['count'])
if word.startswith('#header'):
word = word.replace('#header', '')
#
# This debatable choice treats header words as equivalent
# to occurrences in the body text. In practice, this seems
# to slightly improve performance, at least when you're using
# SVMs and relatively low numbers of features (140-300).
# Otherwise header words are in practice just discarded, because
# e.g. #headeract won't be one of the top 250 words.
doc_freq[word] += 1
counts[docid][word] += ct
vocab = [x[0] for x in doc_freq.most_common(n)]
print('Vocabulary constructed.')
return vocab, counts
def get_vocabulary(metadata, positiveIDs, negativeIDs, sourcedir, n):
''' Gets the top n words by docfrequency in positiveIDs + negativeIDs.
'''
allIDs = positiveIDs + negativeIDs
doc_freq = Counter()
for docid in allIDs:
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
with open(path, encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
word = row['feature']
if word.startswith('#header'):
word = word.replace('#header', '')
doc_freq[word] += 1
vocab = [x[0] for x in doc_freq.most_common(n)]
print('Vocabulary constructed.')
return vocab
def get_vocabulary(metadata, positiveIDs, negativeIDs, sourcedir, n):
''' Gets the top n words by docfrequency in positiveIDs + negativeIDs.
'''
allIDs = positiveIDs + negativeIDs
doc_freq = Counter()
for docid in allIDs:
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
with open(path, encoding = 'utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
word = row['feature']
if word.startswith('#header'):
word = word.replace('#header', '')
doc_freq[word] += 1
vocab = [x[0] for x in doc_freq.most_common(n)]
print('Vocabulary constructed.')
return vocab
nonusa += 1
if gender == "f":
female += 1
elif gender == "m":
male += 1
bydate = dict()
fictionmetadata = dict()
datesbydocid = dict()
with open("/Users/tunder/work/genre/metadata/ficmeta.csv", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
docid = utils.clean_pairtree(row["htid"])
fictionmetadata[docid] = row
date = utils.date_row(row)
datesbydocid[docid] = date
if docid in selected:
continue
if date in bydate:
bydate[date].append(docid)
else:
bydate[date] = [docid]
controlset = set()
controlmeta = dict()
usedfromselected = list()
outmeta = outmeta.assign(author = outmeta.author.map(lower_and_trim))
outmeta.to_csv('mallet80metadata4experiment.tsv', sep = '\t', index_label = 'docid')
# make paths to these volumes
import SonicScrewdriver as utils
import os
missing = set()
idmapper = dict()
for anid in allselected:
path, postfix = utils.pairtreepath(anid, '/Volumes/TARDIS/work/ef/fic/')
totalpath = path + postfix + '/' + utils.clean_pairtree(anid) + '.json.bz2'
if not os.path.isfile(totalpath):
if '$' in anid:
newid = anid.replace('uc1.b', 'uc1.$b')
else:
newid = anid.replace('uc1.$b', 'uc1.b')
path, postfix = utils.pairtreepath(newid, '/Volumes/TARDIS/work/ef/fic/')
totalpath = path + postfix + '/' + utils.clean_pairtree(newid) + '.json.bz2'
if os.path.isfile(totalpath):
idmapper[anid] = totalpath
else:
missing.add(anid)
else:
idmapper[anid] = totalpath
for key, value in self.totalcounts.items():
writer.writerow([key, value / self.totaltokens])
writer.writerow(['#sentencelength', self.sentencelength])
writer.writerow(['#typetoken', self.typetoken])
writer.writerow(['#linelength', self.linelength])
def get_volume_features(self):
'''
Just like write_volume_features, except we return them
as a dictionary.
'''
if self.totaltokens < 1:
return Counter(), 0
else:
return self.totalcounts, self.totaltokens
if __name__ == "__main__":
meta = pd.read_csv('/Users/tunder/Dropbox/python/train20/bzipmeta.csv', dtype = 'object', index_col = 'docid')
for index, row in meta.iterrows():
inpath = row['filepath']
vol = VolumeFromJson(inpath, index)
outpath = '/Volumes/TARDIS/work/train20/' + utils.clean_pairtree(index) + '.csv'
vol.write_volume_features(outpath, override = True)
def get_meta():
meta = dict()
meta['aubirth'] = input('Authors year of birth? ')
meta['augender'] = input('Authors gender? ')
meta['national origin'] = input('Authors nationality? ')
meta['firstpub'] = input('Date of first publication? ')
return meta
reviews = '/Users/tunder/Dropbox/ted/reception/reviewed/lists/ReviewedTitles1820-1839.csv'
with open(reviews, encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
htid = utils.clean_pairtree(row['HTid'])
pubdate = int(row['date'][0:4])
firstpub = int(row['firstpub'])
yrrev = int(row['yrrev'])
if pubdate > yrrev + 5:
date = yrrev
print(str(pubdate) + " => " + str(yrrev))
else:
date = pubdate
jgenre = row['Jgenre']
if jgenre == 'poe':
selecteddates[htid] = date
selected.append(htid)
pathdict = dict()
with open('../noveltmmeta/get_EF/ids2pathlist.tsv', encoding='utf-8') as f:
for line in f:
fields = line.strip().split('\t')
pathdict[fields[0]] = fields[1]
meta = pd.read_csv('topicsample.tsv', sep='\t')
outrows = []
missing = 0
themissing = []
for d in meta.docid:
cleand = utils.clean_pairtree(d)
dollarless = d.replace('$', '')
if d in pathdict:
outrows.append((d, pathdict[d]))
elif cleand in pathdict:
outrows.append((cleand, pathdict[cleand]))
elif dollarless in pathdict:
outrows.append((dollarless, pathdict[dollarless]))
else:
missing += 1
themissing.append(d)
with open('pathlist.tsv', mode='w', encoding='utf-8') as f:
f.write('docid\tpath\n')
for docid, path in outrows:
import csv
import SonicScrewdriver as utils
import random
import os.path
selecteddates = dict()
selected = list()
reviews = '/Users/tunder/Dropbox/ted/reception/reviewed/lists/ReviewedTitles1880-1899_200.csv'
with open(reviews, encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
htid = utils.clean_pairtree(row['HTid'])
pubdate = int(row['date'])
firstpub = int(row['firstpub'])
yrrev = int(row['yrrev'])
if pubdate > yrrev + 5:
date = yrrev
print(str(pubdate) + " => " + str(yrrev))
else:
date = pubdate
jgenre = row['Jgenre']
if jgenre == 'fic':
selecteddates[htid] = date
selected.append(htid)
def main(sourcedirs, metapath, modeldir, outpath, pairtree=False):
'''
This function can be called from outside the module; it accepts
path information and then iterates through all the files it
finds in the metadata at "metapath."
If the pairtree flag is True, we assume sourcedir is the root
of a pairtree structure. Otherwise we assume it's a flat list.
'''
# We're going to store all the models, by name, in a dictionary:
models = []
modelpaths = glob.glob(modeldir + '*.p')
assert len(modelpaths) == 1
model = loadamodel(modelpaths[0])
# Now get metadata.
metadata = get_metadata(metapath)
notfound = dict()
c = 0
path = ''
for docid in metadata.index:
print(c)
c += 1
if pairtree:
found = False
for sourcedir in sourcedirs:
path = get_pairtree(sourcedir, docid)
if os.path.isfile(path):
found = True
chosenpath = path
if not found:
print(path)
print('file not found')
error = 'file not found'
wordcount = 0
else:
pagecounts, pageids, error = get_counts_4pages(
chosenpath, docid)
else:
path = os.path.join(sourcedir,
utils.clean_pairtree(docid) + '.csv')
pagecounts, pageids, error = pagecounts4file(path)
if error == 'success':
volumejson = predict_volume(model, pageids, pagecounts, docid)
volumestring = json.dumps(volumejson)
with open(outpath, mode='a', encoding='utf-8') as f:
f.write(volumestring + '\n')
print(docid)
else:
notfound[docid] = error
print(docid, error)
with open('fictionpagesnotfound.txt', mode='a', encoding='utf-8') as f:
for vol, reason in notfound.items():
f.write(vol + '\t' + reason + '\n')
index_col='docid',
sep='\t')
ctr = 0
for index, row in meta.iterrows():
ctr += 1
if ctr % 100 == 1:
print(ctr)
inpath = rootpath + row['path']
if not os.path.isfile(inpath):
missing += 1
print('missing')
else:
vol = VolumeFromJson(inpath, index)
outpath = outfolder + utils.clean_pairtree(index) + '.txt'
metarow = vol.write_volume(outpath,
folder=outfolder,
override=True,
translator=translator,
use_headers=False,
skip_front=.1,
skip_back=0.05)
all_outrows.append(metarow)
print(missing)
columns = [
'docid', 'htid', 'totaltokens', 'skipped_pages', 'trimmed_pages',
'path'
]
print('Done\a')
elif args[1] == 'wholevolume':
missing = 0
path_to_meta = args[2]
meta = pd.read_csv(path_to_meta,
dtype='object',
index_col='docid',
sep='\t')
for index, row in meta.iterrows():
inpath = rootpath + row['path']
try:
vol = VolumeFromJson(inpath, index)
outpath = '../data/' + utils.clean_pairtree(index) + '.tsv'
vol.write_volume_features([outpath],
folder='../data/',
override=True,
translator=translator,
use_headers=False,
skip_front=0,
skip_back=0)
except:
missing += 1
print(missing)
elif args[1] == 'divided':
path_to_meta = args[2]
def main(sourcedir, metapath, modeldir, outpath, pairtree = False):
'''
This function can be called from outside the module; it accepts
path information and then iterates through all the files it
finds in the metadata at "metapath."
If the pairtree flag is True, we assume sourcedir is the root
of a pairtree structure. Otherwise we assume it's a flat list.
'''
global allnames, top1000words
alternatesource = '/projects/ichass/usesofscale/post23/englishmonographs1980-2016/'
# We're going to store all the models, by name, in a dictionary:
models = dict()
modelpaths = glob.glob(modeldir + '*.p')
for apath in modelpaths:
name = apath.replace(modeldir, '')
name = name.replace('.p', '')
models[name] = loadamodel(apath)
# Now get metadata.
metadata = get_metadata(metapath)
nonficprobs = []
juvieprobs = []
wordcounts = []
c = 0
for docid in metadata.index:
print(c)
c += 1
if pairtree:
path1 = get_pairtree(sourcedir, docid)
path2 = get_pairtree(alternatesource, docid)
if os.path.isfile(path1):
chosenpath = path1
elif os.path.isfile(path2):
chosenpath = path2
else:
print(path1)
print(path2)
print('file not found')
error = 'file not found'
wordcount = 0
counts, error, wordcount = counts4json(chosenpath, docid)
else:
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
counts, error, wordcount = counts4file(path)
if error == 'success':
nonficprob, juvenileprob = volume_classification(models, counts)
else:
nonficprob = 0.5
juvenileprob = 0.5
nonficprobs.append(nonficprob)
juvieprobs.append(juvenileprob)
wordcounts.append(wordcount)
metadata.loc[ : , 'nonficprob'] = pd.Series(nonficprobs, index = metadata.index)
metadata.loc[ : , 'juvenileprob'] = pd.Series(juvieprobs, index = metadata.index)
metadata.loc[ : , 'wordcount'] = pd.Series(wordcounts, index = metadata.index)
metadata.to_csv(outpath)
metasource = pd.read_csv(args[1], sep='\t')
missing = 0
docstoprocess = metasource.docid
for idx, docid in enumerate(docstoprocess):
if idx % 100 == 1:
print(idx)
if docid in translations:
docid = translations[docid]
path, postfix = utils.pairtreepath(docid, '')
inpath = rootpath + path + postfix + '/' + utils.clean_pairtree(
docid) + '.json.bz2'
if os.path.isfile(inpath):
pass
elif 'uc1.b' in docid:
newdoc = docid.replace('uc1.b', 'uc1.$b')
path, postfix = utils.pairtreepath(newdoc, '')
inpath = rootpath + path + postfix + '/' + utils.clean_pairtree(
newdoc) + '.json.bz2'
if os.path.isfile(inpath):
translations[docid] = newdoc
else:
missing += 1
print(missing, inpath, 'not found.')
else:
missing += 1
fieldnames = reader.fieldnames
for row in reader:
inferred = forceint(row['inferreddate'])
firstpub = forceint(row['firstpub'])
if inferred < firstpub:
print(row['author'])
print(row['docid'])
print('inferred: ' + str(inferred))
print('firstpub: ' + str(firstpub))
date = int(input('Date of first publication: '))
row['firstpub'] = str(date)
if row['docid'] in existing:
print('existing ' + row['docid'])
existing.add(row['docid'])
row['docid'] = utils.clean_pairtree(row['docid'])
allrows.append(row)
authornames.add(row['author'])
authornames = list(authornames)
synonyms = dict()
for name in authornames:
for anothername in authornames:
if name == anothername:
continue
if name in synonyms:
if synonyms[name] == anothername:
continue
def get_meta():
meta = dict()
meta['aubirth'] = input('Authors year of birth? ')
meta['augender'] = input ('Authors gender? ')
meta['national origin'] = input('Authors nationality? ')
meta['firstpub'] = input('Date of first publication? ')
return meta
reviews = '/Users/tunder/Dropbox/ted/reception/reviewed/lists/ReviewedTitles1860-1879_200.csv'
with open(reviews, encoding = 'utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
htid = utils.clean_pairtree(row['HTid'])
pubdate = int(row['date'][0:4])
firstpub = int(row['firstpub'])
yrrev = int(row['yrrev'])
if pubdate > yrrev + 5:
date = yrrev
print(str(pubdate) + " => " + str(yrrev))
else:
date = pubdate
jgenre = row['Jgenre']
if jgenre == 'fic':
selecteddates[htid] = date
selected.append(htid)
'/Volumes/TARDIS/work/fullmeta/ic_monographs.tsv'
]
genrecats = [
'suspense', 'adventure', 'western', 'mystery', 'detective',
'science fiction', 'fantasy', 'horror', 'gothic', 'romance', 'pulp'
]
doublets = []
for afile in tocheck:
with open(afile, encoding='utf-8') as f:
reader = csv.DictReader(f, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
docid = row['docid']
alternative = utils.clean_pairtree(docid)
if docid in docs2categorize:
dothis = True
d = docid
elif alternative in docs2categorize:
dothis = True
d = alternative
else:
dothis = False
if dothis:
g = row['genres'].lower() + " " + row['subjects'].lower()
genre = 'none'
for cat in genrecats:
if cat in g:
genre = 'genre'
fieldnames = reader.fieldnames
for row in reader:
inferred = forceint(row['inferreddate'])
firstpub = forceint(row['firstpub'])
if inferred < firstpub:
print(row['author'])
print(row['docid'])
print('inferred: ' + str(inferred))
print('firstpub: ' + str(firstpub))
date = int(input('Date of first publication: '))
row['firstpub'] = str(date)
if row['docid'] in existing:
print('existing ' + row['docid'])
existing.add(row['docid'])
row['docid'] = utils.clean_pairtree(row['docid'])
allrows.append(row)
authornames.add(row['author'])
authornames = list(authornames)
synonyms = dict()
for name in authornames:
for anothername in authornames:
if name == anothername:
continue
if name in synonyms:
if synonyms[name] == anothername:
continue
# getidstoadd
import SonicScrewdriver as utils
import os
with open('/Users/tunder/Dropbox/GenreProject/python/granger/correctedmeta.tsv', encoding = 'utf-8') as f:
filelines = f.readlines()
ids2get = [x.split('\t')[0] for x in filelines]
fileswehave = os.listdir('/Users/tunder/Dropbox/GenreProject/python/granger/elite/')
idswehave = set([x.replace('.poe.tsv','') for x in fileswehave if x.endswith('.poe.tsv')])
with open('/Users/tunder/Dropbox/GenreProject/python/granger/ids2get.tsv', mode = 'w', encoding = 'utf-8') as f:
for anid in ids2get:
if anid not in idswehave and utils.clean_pairtree(anid) not in idswehave:
f.write(utils.dirty_pairtree(anid) + '\n')
# getidstoadd
import SonicScrewdriver as utils
import os
with open(
'/Users/tunder/Dropbox/GenreProject/python/granger/correctedmeta.tsv',
encoding='utf-8') as f:
filelines = f.readlines()
ids2get = [x.split('\t')[0] for x in filelines]
fileswehave = os.listdir(
'/Users/tunder/Dropbox/GenreProject/python/granger/elite/')
idswehave = set(
[x.replace('.poe.tsv', '') for x in fileswehave if x.endswith('.poe.tsv')])
with open('/Users/tunder/Dropbox/GenreProject/python/granger/ids2get.tsv',
mode='w',
encoding='utf-8') as f:
for anid in ids2get:
if anid not in idswehave and utils.clean_pairtree(
anid) not in idswehave:
f.write(utils.dirty_pairtree(anid) + '\n')
# a newer metadata set.
import csv
import SonicScrewdriver as utils
import random
selecteddates = dict()
selected = list()
reviews = '/Users/tunder/Dropbox/ted/reception/reviewed/lists/ReviewedTitles1840-1859_200.csv'
with open(reviews, encoding = 'utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
htid = utils.clean_pairtree(row['HTid'])
pubdate = int(row['date'])
firstpub = int(row['firstpub'])
yrrev = int(row['yrrev'])
if pubdate > yrrev + 5:
date = yrrev
print(str(pubdate) + " => " + str(yrrev))
else:
date = pubdate
jgenre = row['Jgenre']
if jgenre == 'poe':
selecteddates[htid] = date
selected.append(htid)
def main(sourcedir, metapath, modeldir, outpath, pairtree=False):
'''
This function can be called from outside the module; it accepts
path information and then iterates through all the files it
finds in the metadata at "metapath."
If the pairtree flag is True, we assume sourcedir is the root
of a pairtree structure. Otherwise we assume it's a flat list.
'''
global allnames, top1000words
# We're going to store all the models, by name, in a dictionary:
models = dict()
for name in allnames:
models[name] = loadamodel(modeldir + name)
# Now get metadata.
metadata = get_metadata(metapath)
predictedgenres = []
predictedprobs = []
explanations = []
wordcounts = []
englishpcts = []
c = 0
for docid in metadata.index:
print(c)
c += 1
if pairtree:
path = get_pairtree(sourcedir, docid)
counts, error, wordcount = counts4json(path, docid)
else:
path = os.path.join(sourcedir,
utils.clean_pairtree(docid) + '.csv')
counts, error, wordcount = counts4file(path)
if error == 'success':
genredict = make_genredict(metadata, docid)
englishpct = get_english_percent(counts, top1000words)
genre, probability, explanation = volume_classification(
models, counts, genredict)
else:
englishpct = 0
genre = 'NA'
probability = 0
explanation = error
predictedgenres.append(genre)
predictedprobs.append(probability)
explanations.append(explanation)
wordcounts.append(wordcount)
englishpcts.append(englishpct)
metadata.loc[:, 'predictedgenre'] = pd.Series(predictedgenres,
index=metadata.index)
metadata.loc[:, 'probability'] = pd.Series(predictedprobs,
index=metadata.index)
metadata.loc[:, 'wordcount'] = pd.Series(wordcounts, index=metadata.index)
metadata.loc[:, 'englishpct'] = pd.Series(englishpcts,
index=metadata.index)
metadata.loc[:, 'explanation'] = pd.Series(explanations,
index=metadata.index)
metadata.to_csv(outpath)
def main(sourcedir, metapath, modeldir, outpath, pairtree = False):
'''
This function can be called from outside the module; it accepts
path information and then iterates through all the files it
finds in the metadata at "metapath."
If the pairtree flag is True, we assume sourcedir is the root
of a pairtree structure. Otherwise we assume it's a flat list.
'''
global allnames, top1000words
# We're going to store all the models, by name, in a dictionary:
models = dict()
for name in allnames:
models[name] = loadamodel(modeldir + name)
# Now get metadata.
metadata = get_metadata(metapath)
predictedgenres = []
predictedprobs = []
explanations = []
wordcounts = []
englishpcts = []
c = 0
for docid in metadata.index:
print(c)
c += 1
if pairtree:
path = get_pairtree(sourcedir, docid)
counts, error, wordcount = counts4json(path, docid)
else:
path = os.path.join(sourcedir, utils.clean_pairtree(docid) + '.csv')
counts, error, wordcount = counts4file(path)
if error == 'success':
genredict = make_genredict(metadata, docid)
englishpct = get_english_percent(counts, top1000words)
genre, probability, explanation = volume_classification(models, counts, genredict)
else:
englishpct = 0
genre = 'NA'
probability = 0
explanation = error
predictedgenres.append(genre)
predictedprobs.append(probability)
explanations.append(explanation)
wordcounts.append(wordcount)
englishpcts.append(englishpct)
metadata.loc[ : , 'predictedgenre'] = pd.Series(predictedgenres, index = metadata.index)
metadata.loc[ : , 'probability'] = pd.Series(predictedprobs, index = metadata.index)
metadata.loc[ : , 'wordcount'] = pd.Series(wordcounts, index = metadata.index)
metadata.loc[ : , 'englishpct'] = pd.Series(englishpcts, index = metadata.index)
metadata.loc[ : , 'explanation'] = pd.Series(explanations, index = metadata.index)
metadata.to_csv(outpath)
aliases[row['alias']] = row['ourname']
# find matches
key2docid = dict()
volbackup = dict()
with open('../metadata/filtered_fiction_plus_18c.tsv', encoding='utf-8') as f:
reader = csv.DictReader(f, delimiter='\t')
for row in reader:
try:
intval = int(row['docid'])
docid = row['docid']
except:
docid = utils.clean_pairtree(row['docid'])
possibleauth = row['author']
possibletitle = normalize_title(row['title'])
found = False
for key, value in genremeta.items():
author, title, normauth = key
if match(normauth, title, possibleauth, possibletitle):
key2docid[key] = docid
volbackup[key] = utils.clean_pairtree(row['volid'])
found = True
print('Found: ', possibleauth, author, possibletitle)
break
print('Found a total of ', len(key2docid))
list_of_dataframes = []
idset = set()
list_of_files = args[1:]
root = '../rawdata/'
list_of_paths = [root + x for x in list_of_files]
for p in list_of_paths:
df = pd.read_csv(p, index_col='docid')
list_of_dataframes.append(df)
idset = idset | set(df.index)
ids = []
for anid in idset:
ids.append(utils.clean_pairtree(str(anid)))
allpaths = set()
with open('/Volumes/TARDIS/work/ef/htrc-ef-all-files.txt',
encoding='utf-8') as f:
for line in f:
line = line.strip()
allpaths.add(line)
missing = set()
found = set()
mapping = dict()
path2id = dict()
#things we already have:
nonusa += 1
if gender == 'f':
female += 1
elif gender == 'm':
male += 1
bydate = dict()
fictionmetadata = dict()
datesbydocid = dict()
with open('/Users/tunder/work/genre/metadata/ficmeta.csv',
encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
docid = utils.clean_pairtree(row['htid'])
fictionmetadata[docid] = row
date = utils.date_row(row)
datesbydocid[docid] = date
if docid in selected:
continue
if date in bydate:
bydate[date].append(docid)
else:
bydate[date] = [docid]
controlset = set()
controlmeta = dict()
usedfromselected = list()