def test_text_sentiment_greater_than_zero(self):
from bixin import predict
a = predict(text)
assert isinstance(a, float) == True
assert a > 0
def use_predict_directly(self):
from bixin import predict
assert predict.classifier.initialized == True
a = predict(text)
order = int(log2(size) / 10) if size else 0
# format file size
# (.4g results in rounded numbers for exact matches and max 3 decimals,
# should never resort to exponent values)
return '{:.4g} {}'.format(size / (1 << (order * 10)), _suffixes[order])
if __name__ == "__main__":
# tokenizer.initialize()
predict.classifier.initialize(include_tc=True)
t1 = time()
# bare_dict()
# classifier = Classifier(pos_emotion,pos_envalute,neg_emotion,neg_envalute,degrees,negations,places)
if len(sys.argv) > 1:
flag = predict(sys.argv[1], include_tc=True, debug=True)
print(flag)
else:
from os import walk
DIR = os.path.join(os.path.dirname(__file__), "..", "test_data")
N = os.path.abspath(DIR)
files = []
for (dirpath, dirnames, filenames) in walk(N):
files.extend([
os.path.join(dirpath, x) for x in filenames
if x.endswith(".txt")
])
count = 0
right = 0.0