def train(color, trainExamples, syzygy):
return submission.learnPredictor(trainExamples, trainExamples,
extractWordFeatures, color, syzygy)
import sys
maxN = 10
test_error = []
trainExamples = readExamples('polarity.train')
devExamples = readExamples('polarity.dev')
for n in range(maxN):
sys.stdout.write(
'\r=== Learning Character Features #{} Predictor ==='.format(n + 1))
sys.stdout.flush()
featureExtractor = extractCharacterFeatures(n)
weights = learnPredictor(trainExamples,
devExamples,
featureExtractor,
numIters=20,
eta=0.01)
devError = evaluatePredictor(
devExamples, lambda (x):
(1 if dotProduct(featureExtractor(x), weights) >= 0 else -1))
test_error.append(devError)
print 'Test Error from #1 to #{}:'.format(maxN)
for e in test_error:
print '{:.3f}'.format(e),
print
print '=== Learning Reference Word Feature Predictor ==='
featureExtractor = extractWordFeatures
weights = learnPredictor(trainExamples,
def featureExtractor(x):
# x = "took Mauritius into"
phi = defaultdict(float)
#phi[x] = 1
tokens = x.split()
left, entity, right = tokens[0], tokens[1:-1], tokens[-1]
phi['entity is ' + ' '.join(entity)] = 1
phi['left is ' + left] = 1
phi['right is ' + right] = 1
for word in entity:
phi['entity contains ' + word] = 1
phi['entity contains prefix ' + word[:4]] = 1
phi['entity contains suffix ' + word[-4:]] = 1
return phi
# Learn a predictor
weights = submission.learnPredictor(trainExamples, devExamples,
featureExtractor, 30, 0.05)
util.outputWeights(weights, 'weights')
util.outputErrorAnalysis(devExamples, featureExtractor, weights,
'error-analysis')
# Test!!!
testExamples = util.readExamples('names.test')
predictor = lambda x: 1 if util.dotProduct(featureExtractor(x), weights
) > 0 else -1
print 'test error =', util.evaluatePredictor(testExamples, predictor)
# configuration
DATABASE = '/tmp/tweetAnalyzer.db'
DEBUG = True
SECRET_KEY = 'development key'
USERNAME = '******'
PASSWORD = '******'
# create our little application :)
app = Flask(__name__)
app.config.from_object(__name__)
# test the classifier on a large dataset
trainExamples = readExamples('sentiment/polarity.train')
featureExtractor = submission.extractCharacterFeatures(3)
weights = submission.learnPredictor(trainExamples, None, featureExtractor)
print "Training complete"
@app.route('/', methods=['GET', 'POST'])
@app.route('/?hash_tag=<hash_tag>')
def show_entries(hash_tag=""):
hash_tag = ""
result = []
prediction = ('','')
value = 0
if len(request.args) != 0:
print "Evaluating" + request.args.get('hash_tag')
hash_tag = request.args.get('hash_tag')
def train(color, trainExamples, syzygy):
return submission.learnPredictor(trainExamples, trainExamples, extractWordFeatures,color, syzygy)
