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