return new
if __name__ == "__main__":
import argparse
import json
import csv
import sys
from corpus import iter_corpus, iter_test_corpus, readDataFile
from predictor import PhraseSentimentPredictor
# parser = argparse.ArgumentParser(description=__doc__)
# parser.add_argument("filename")
# config = parser.parse_args()
# config = json.load(open(config.filename))
##
start=time.time()
predictor = PhraseSentimentPredictor()
# print(iter_corpus())
x_train,x_test,y_train,y_test = readDataFile()
print("data reading finished")
# print(x_test)
predictor.fit(x_train,y_train)
print("fitting takes "+str(time.time()-start))
test = x_test
# prediction = predictor.predict(test)
score = predictor.score(test,y_test,'test')
print("test score {}%".format(score * 100))
print('programme finished!')
value = float(value)
except ValueError:
pass
new[key] = value
return new
if __name__ == "__main__":
import argparse
import json
import csv
import sys
from corpus import iter_corpus, iter_test_corpus
from predictor import PhraseSentimentPredictor
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("filename")
config = parser.parse_args()
config = json.load(open(config.filename))
start=time.time()
predictor = PhraseSentimentPredictor(**config)
predictor.fit(list(iter_corpus()))
print "fitting takes "+str(time.time()-start)
test = list(iter_test_corpus())
#prediction = predictor.predict(test)
score = predictor.score(test,'test')
print("test score {}%".format(score * 100))
print 'programme finished!'
import csv, os
from transformations import ExtractText
if not os.path.exists('./data/vocabulary'):
datapoints = list(iter_corpus())
vocabulary = set()
et = ExtractText()
X = et.transform(datapoints)
for datap in X:
for w in datap.split():
vocabulary.add(w.lower())
vocabulary = list(vocabulary)
vocabulary.sort()
with open('./data/vocabulary', 'wb') as f:
wr = csv.writer(f)
for voc in vocabulary:
wr.writerow([voc])
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("filename")
config = parser.parse_args()
config = json.load(open(config.filename))
factory = lambda: PhraseSentimentPredictor(**config)
factory() # Run once to check config is ok
report = PrintPartialCV()
analyse(factory)
print "Analysis finished!"
