def validateResponse(self, response, queryTerms):
from bs4 import BeautifulSoup
if response.status_code == 200:
soup = BeautifulSoup(response.text)
from irlib.preprocessor import Preprocessor
from irlib.matrix import Matrix
from irlib.metrics import Metrics
prep = Preprocessor()
mx = Matrix()
metric = Metrics()
terms = prep.ngram_tokenizer(text=soup.get_text())
mx.add_doc(doc_id=response.url,
doc_terms=terms,
frequency=True,
do_padding=True)
'''for doc in mx.docs:
distance = metric.euclid_vectors(doc['terms'], q_vector)
print distance
'''
cnt = Counter()
for word in terms:
cnt[word] += 1
tableTerms = PrettyTable(["Term", "Frequency"])
for word in sorted(cnt, key=cnt.get, reverse=True):
if word.encode('ascii').lower() in queryTerms.encode(
'ascii').lower().split():
tableTerms.add_row([word, cnt[word]])
print tableTerms
else:
print "[-] Response for %s is %s " % (response.url,
response.status_code)
def __validateResponse(self, response, queryTerms):
from bs4 import BeautifulSoup
if response.status_code == 200:
soup = BeautifulSoup(response.text)
from irlib.preprocessor import Preprocessor
from irlib.matrix import Matrix
from irlib.metrics import Metrics
prep = Preprocessor()
mx = Matrix()
metric = Metrics()
terms = prep.ngram_tokenizer(text=soup.get_text())
mx.add_doc(doc_id=response.url,
doc_terms=terms,
frequency=True,
do_padding=True)
cnt = Counter()
for word in terms:
cnt[word] += 1
table = Texttable()
table.set_cols_align(["l", "l"])
table.set_cols_valign(["m", "m"])
table.set_cols_width([40, 55])
rows = [["Term", "Frequency"]]
for word in sorted(cnt, key=cnt.get, reverse=True):
if word.lower() in queryTerms.lower().split():
rows.append([word, cnt[word]])
table.add_rows(rows)
print table.draw() + "\n"
else:
print "[-] Response for %s is %s " % (response.url,
response.status_code)
def test_stemmer_lower(self):
p = Preprocessor(lower=True, stem=True)
stemmed = p.stemmer('Running')
if my_nltk:
self.assertEqual(stemmed, 'run')
else:
self.assertTrue(False, 'NLTK is not installed')
def main():
# Load configuration from file
config = Configuration(config_file='/home/huma/Downloads/irlib-0.1.1/irlib/classify.conf')
try:
config.load_configuration()
config_data = config.get_configuration()
except:
print("Error loading configuration file.")
print("Classifier aborting.")
raise
# config.display_configuration()
print(config)
# sys.exit()
myfolds = config.get_folds()
correctness = 0
# Preporcessor: tokenizer, stemmer, etc.
prep_lower = config_data['lower']
prep_stem = config_data['stem']
prep_pos = config_data['pos']
prep_ngram = config_data['ngram']
prep = Preprocessor(pattern='\W+', lower=prep_lower, stem=prep_stem, pos=prep_pos, ngram=prep_ngram)
for myfold in myfolds:
ev = Evaluation(config=config, fold=myfold)
if config_data['classifier'] == 'rocchio':
ml = Rocchio(verbose=VERBOSE, fold=myfold, config=config, ev=ev)
elif config_data['classifier'] == 'knn':
ml = KNN(verbose=VERBOSE, fold=myfold, config=config, ev=ev)
else:
ml = NaiveBayes(verbose=VERBOSE, fold=myfold, config=config, ev=ev)
training(config, myfold, ml, prep)
ml.do_padding()
ml.calculate_training_data()
# r.display_idx()
ml.diagnose()
testing(config, myfold, ml, ev, prep)
k = config_data['k']
results = ev.calculate(review_spam=True, k=k)
print('Accuracy for fold %d: %s' % (myfold, results))
correctness += results
print("Average accuracy for all folds:", correctness / len(myfolds))
def readfiles(fold_path='all-folds/fold1/'):
prep = Preprocessor()
mx = Matrix()
files = os.listdir(fold_path)
for filename in files:
fd = open('%s/%s' % (fold_path, filename), 'r')
file_data = fd.read()
terms = prep.ngram_tokenizer(text=file_data)
mx.add_doc(doc_id=filename,
doc_terms=terms,
frequency=True,
do_padding=True)
print 'Number of read documents:', len(mx.docs)
print 'Number of read terms', len(mx.terms)
#print mx.terms[0:5], mx.terms[-5:-1]
print mx.terms
print mx.docs
# Load the three modules:
from irlib.preprocessor import Preprocessor
from irlib.matrix import Matrix
from irlib.metrics import Metrics
import difflib
# Create instances for their classes:
prep = Preprocessor()
mx = Matrix()
metric = Metrics()
q_vector = []
def generateMatrix():
fd = open('./content_transfer_data/roto-sent-data.train-ONLYTAGS.src', 'r')
count = 1
for line in fd.readlines():
terms = line.split(' ')
terms = [x.strip() for x in terms]
mx.add_doc(doc_id=str(count),
doc_terms=terms,
frequency=True,
do_padding=True)
count += 1
if count % 1000 == 0:
print count
mx.dump('IRmatrix.train.src.csv', delimiter='\t', header=True)
def __init__(self):
self._mx = Matrix()
self._prep = Preprocessor(pattern='\W+', lower=True, stem=True)
def test_term2ch(self):
p = Preprocessor()
charlist = p.term2ch('help')
self.assertEqual(charlist, ['h', 'e', 'l', 'p'])
def test_3gram_tokenizer(self):
p = Preprocessor(lower=False, stem=False, ngram=3)
returned_tokens = p.ngram_tokenizer('how do you do?')
expected_tokens = ['how do you', 'do you do']
self.assertEqual(returned_tokens, expected_tokens)
def test_tokenizer_lower(self):
p = Preprocessor(lower=True, stem=False)
tokens = p.tokenizer('This is IRLib')
self.assertEqual(tokens, ['this', 'is', 'irlib'])
