def getSentiment():
dataSet = StanfordSentiment()
tokens = dataSet.tokens()
# Load the word vectors we trained earlier
_, wordVectors, _ = load_saved_params()
dimVectors = wordVectors.shape[1]
# Load the test sentences
sentences = dataSet.getTestSentences()
print sentences
nSentences = len(sentences)
sentenceFeatures = np.zeros((nSentences, dimVectors))
sentenceLabels = np.zeros((nSentences, ), dtype=np.int32)
for i in xrange(nSentences):
words, sentenceLabels[i] = sentences[i]
#print sentences[i]
#print words
#print sentenceLabels[i]
sentenceFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
#train weights
random.seed(3141)
np.random.seed(59265)
weights = np.random.randn(dimVectors, nSentences)
regularization = 0.00001
weights = sgd(lambda weights: softmax_wrapper(
sentenceFeatures, sentenceLabels, weights, regularization),
weights,
3.0,
10,
PRINT_EVERY=10)
#pred = np.sum((weights * sentenceFeatures.T).T, axis = 1)
#pred = softmax(pred)
#testAccuracy = accuracy(sentenceLabels, pred)
prob = softmax(sentenceFeatures.dot(weights))
#prob = normalizeRows(prob)
_, _, pred = softmaxRegression(sentenceFeatures, sentenceLabels, weights)
pred = 1 / (1 + np.exp(-pred))
#for polarity in pred:
#print categorify(polarity)
#_, _, pred = softmaxRegression(sentenceFeatures, sentenceLabels, weights)
for label in pred:
print categorify(label)
#pred = categorify(pred)
print prob
print pred
tokens = dataset.tokens()
nWords = len(tokens)
# Load the word vectors we trained earlier
_, wordVectors0, _ = load_saved_params()
wordVectors = (wordVectors0[:nWords, :] + wordVectors0[nWords:, :])
dimVectors = wordVectors.shape[1]
# Load the train set
trainset = dataset.getTrainSentences()
nTrain = len(trainset)
trainFeatures = np.zeros((nTrain, dimVectors))
trainLabels = np.zeros((nTrain, ), dtype=np.int32)
for i in xrange(nTrain):
words, trainLabels[i] = trainset[i]
trainFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
# Prepare dev set features
devset = dataset.getDevSentences()
nDev = len(devset)
devFeatures = np.zeros((nDev, dimVectors))
devLabels = np.zeros((nDev, ), dtype=np.int32)
for i in xrange(nDev):
words, devLabels[i] = devset[i]
devFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
# Try our regularization parameters
results = []
for regularization in REGULARIZATION:
random.seed(3141)
np.random.seed(59265)
tokens = dataset.tokens()
nWords = len(tokens)
# Load the word vectors we trained earlier
_, wordVectors0, _ = load_saved_params()
wordVectors = (wordVectors0[:nWords,:] + wordVectors0[nWords:,:])
dimVectors = wordVectors.shape[1]
# Load the train set
trainset = dataset.getTrainSentences()
nTrain = len(trainset)
trainFeatures = np.zeros((nTrain, dimVectors))
trainLabels = np.zeros((nTrain,), dtype=np.int32)
for i in xrange(nTrain):
words, trainLabels[i] = trainset[i]
trainFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
# Prepare dev set features
devset = dataset.getDevSentences()
nDev = len(devset)
devFeatures = np.zeros((nDev, dimVectors))
devLabels = np.zeros((nDev,), dtype=np.int32)
for i in xrange(nDev):
words, devLabels[i] = devset[i]
devFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
# Try our regularization parameters
results = []
for regularization in REGULARIZATION:
random.seed(3141)
np.random.seed(59265)
