def read_train_data(p=yelp_2013_train, validate_ratio=0.2, preprocess=True, int_label=True):
print "reading raw training data..."
f = open(p)
documents = {}
count = 0.0
for line in f.xreadlines():
count += 1
json_object = json.loads(line)
text = json_object['text']
if preprocess:
text = preprocess_review(text)
if len(text) == 0:
continue
star = json_object['stars']
if int_label:
star = int(star)
if star in documents:
documents[star].append(text)
else:
documents[star] = [text, ]
sample_size = validate_ratio * count
train_x = []
train_y = []
validate_x = []
validate_y = []
for star in documents:
d = documents[star]
star_sample_size = int(len(d) / count * sample_size)
train_x.extend(d[star_sample_size:])
validate_x.extend(d[:star_sample_size])
train_y.extend([star] * (len(d) - star_sample_size))
validate_y.extend([star] * star_sample_size)
return train_x, train_y, validate_x, validate_y
def read_data(p):
label = 1 if p[-3:] == 'pos' else 0
f = open(p)
x = []
for line in f:
line = unicode(line, errors='ignore')
x.append(preprocess_review(line, filters=SIMPLE_FILTERS))
y = [label] * len(x)
return x, y
def grab_data(path):
sentences = []
currdir = os.getcwd()
os.chdir(path)
for ff in glob.glob("*.txt"):
with open(ff, 'r') as f:
sentences.append(preprocess_review(f.readline().strip()))
os.chdir(currdir)
return sentences
def read_test_data(p=yelp_2013_test, preprocess=True, int_label=True):
print "reading raw testing data..."
f = open(p)
test_x = []
test_y = []
for line in f.xreadlines():
json_object = json.loads(line)
text = json_object['text']
if preprocess:
text = preprocess_review(text)
star = json_object['stars']
if int_label:
star = int(star)
test_x.append(text)
test_y.append(star)
return test_x, test_y
def read_tweet_raw(cutoff=10):
# train dataset
f = open(train_path)
train_x = []
train_y = []
for line in f:
data = line.split('","')
label = int(data[0][1])
review = data[-1]
if review[-1] == '\n':
review = review[:-1]
try:
all_words = preprocess_review(review)
except UnicodeDecodeError:
continue
if len(all_words) < cutoff: # filter
continue
train_x.append(" ".join(all_words))
train_y.append(label)
f.close()
return train_x, train_y