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))
