if __name__ == "__main__": import argparse import json import csv import sys from samr.corpus import iter_corpus, iter_test_corpus from samr.predictor import PhraseSentimentPredictor parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("filename") config = parser.parse_args() config = json.load(open(config.filename)) t1=int(round(time.time() *1000)) predictor = PhraseSentimentPredictor(**config) predictor.fit(list(iter_corpus())) t2=int(round(time.time() *1000)) test = list(iter_test_corpus()) prediction = predictor.predict2(test) t3=int(round(time.time() *1000)) print t2-t1, t3-t2 writer = csv.writer(sys.stdout) writer.writerow(("PhraseId", "Sentiment")) for datapoint, sentiment in zip(test, prediction): writer.writerow((datapoint.phraseid, sentiment))
if __name__ == "__main__": import argparse import json import csv import sys from samr.corpus import iter_corpus, iter_test_corpus from samr.predictor import PhraseSentimentPredictor parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("filename") config = parser.parse_args() config = json.load(open(config.filename)) t1 = int(round(time.time() * 1000)) predictor = PhraseSentimentPredictor(**config) predictor.fit(list(iter_corpus())) t2 = int(round(time.time() * 1000)) test = list(iter_test_corpus()) prediction = predictor.predict2(test) t3 = int(round(time.time() * 1000)) print t2 - t1, t3 - t2 writer = csv.writer(sys.stdout) writer.writerow(("PhraseId", "Sentiment")) for datapoint, sentiment in zip(test, prediction): writer.writerow((datapoint.phraseid, sentiment))