def make_page_classifier(self):
"""set up classifier to evaluate whether a page (Response object) is a papers source"""
classifier = BinaryNaiveBayes(prior_yes=0.6)
classifier.likelihood(
"contains at least 2 links to '.pdf' or '.doc'",
lambda r: len(re.findall(r'href=[^>]+\.(?:pdf|docx?)\b', r.text, re.IGNORECASE)) > 1,
p_ifyes=0.99, p_ifno=0.2)
classifier.likelihood(
"contains 'syllabus'",
lambda r: 'syllabus' in r.textlower,
p_ifyes=0.1, p_ifno=0.2)
classifier.likelihood(
"contains conference keywords",
lambda r: r.textlower.count('schedule') + r.textlower.count('break') + r.textlower.count('dinner') > 2,
p_ifyes=0.01, p_ifno=0.2)
classifier.likelihood(
"author name in url",
lambda r: r.authorname.split()[-1].lower() in r.url.lower(),
p_ifyes=0.6, p_ifno=0.1)
return classifier
#libpath = os.path.join(curpath, os.path.pardir)
#sys.path.insert(0, libpath)
from opp.subjectivebayes import BinaryNaiveBayes
from opp.debug import debug, debuglevel
"""
classifier to evaluate whether a pdf/word document is a paper (or
book etc.), as opposed to a handout, a cv, lecture slides etc.
"""
classifier = BinaryNaiveBayes(prior_yes=0.6)
def bad_url(doc):
pat = re.compile(r'\bcours|\blecture|\btalk|handout|teaching')
return pat.search(doc.url.lower())
classifier.likelihood('bad url', bad_url, p_ifyes=0.05, p_ifno=0.2)
def bad_anchortext(doc):
pat = re.compile(r'^site\s*map$|^home|page\b|\bslides\b|handout')
return pat.search(doc.link.anchortext.lower())
classifier.likelihood('bad anchortext', bad_anchortext, p_ifyes=0.005, p_ifno=0.3)
def good_linkcontext(doc):
pat = re.compile(r'penultimate|draft|forthcoming')
return pat.search(doc.link.context.lower())
classifier.likelihood('good link context', good_linkcontext, p_ifyes=0.2, p_ifno=0.05)
def course_words(doc):
# note: 'course' is also common in 'of course', 'in the course of',
# 'essay' is common in discussions of Locke
pat = re.compile(r'seminar|schedule|readings|textbook|students|handout|\bweek|hours/', re.I)
def in_beginning(regex):
reg = re.compile(regex, re.I)
def check(doc):
if not doc.content:
return Ellipsis
beginning = doc.content[:5000]
return reg.search(beginning)
return check
# =========================================================================
bookfilter = BinaryNaiveBayes(prior_yes=0.2)
bookfilter.likelihood('numwords', length,
p_ifyes=nbinom(7, 0.0001), p_ifno=nbinom(1, 0.0001))
# TODO: add more features? "Acknowledgements" section? Occurrences of
# "this book" TOC? Index? ...
# =========================================================================
chapterfilter = BinaryNaiveBayes(prior_yes=0.2)
chapterfilter.likelihood('numwords', length,
p_ifyes=nbinom(2, 0.0002), p_ifno=nbinom(3, 0.0002))
chapterfilter.likelihood('"chapter" occurs in link context', in_context('chapter'),
p_ifyes=0.7, p_ifno=0.05)
# TODO: add features?
def test_basic():
nb = BinaryNaiveBayes(prior_yes=0.5)
nb.likelihood('', lambda x: True, p_ifyes=0.3, p_ifno=0.1)
# 0.3 * 0.5 / 0.3 * 0.5 + 0.1 * 0.5 = 0.75
assert 0.749 < nb.test(0) < 0.751
def test_medical():
nb = BinaryNaiveBayes(prior_yes=0.0001)
nb.likelihood('', lambda x: True, p_ifyes=0.99, p_ifno=0.01)
assert 0.0097 < nb.test(0) < 0.0099