def __init__(self, corrections=None, spoken=None, target=None): self.train_path, self.dev_path, self.test_path = filepaths() self.trainset, self.devset, self.testset = returnDatasets() self.languages_known = Counter() self.languages_learning = Counter() self.languages = languages() self.ch = CorrectionHelper(corrections, spoken, target)
def __init__(self):
self.train, self.dev, self.test = returnDatasets()
self.SPEAKING = 0
self.STUDYING = 1
self.ENTRY = 2
self.INCORRECT = 3
self.CORRECTIONS = 4
def __init__(self): self.train, self.dev, self.test = returnDatasets() self.SPEAKING = 0 self.STUDYING = 1 self.ENTRY = 2 self.INCORRECT = 3 self.CORRECTIONS = 4
def __init__(self, corrections=None, spoken=None, target=None):
self.train_path, self.dev_path, self.test_path = filepaths()
self.trainset, self.devset, self.testset = returnDatasets()
self.languages_known = Counter()
self.languages_learning = Counter()
self.languages = languages()
self.ch = CorrectionHelper(corrections, spoken, target)
def fit(self, x, y=None):
return self
def transform(self, pairs):
features = np.recarray(shape=(len(pairs), ),
dtype=[('spoken', object),
('edit_dist', object)])
for i, pair in enumerate(pairs):
spoken, count = pair
features['spoken'][i] = spoken
features['edit_dist'][i] = count
return features
if __name__ == '__main__':
train, dev, test = returnDatasets()
''' Set up the classes. '''
#gfe = GiveawayFeatureExtraction()
#sfe = SocialFeatureExtraction()
sse = SyntacticStructExtraction()
ce = CorrectionExtraction()
sp = SetProcessing()
datalist = sp.convertDataToList(train)
#dev = sp.convertDataToList(dev)
#test = sp.convertDataToList(test)
#merged = sp.mergeLists(train, dev, test)
#english, french, spanish, japanese, korean, mandarin = sp.returnSplitDatasets(train, 5, False)
'''Return the individual sets by native language.'''
'''Takes approx. 1 second.'''
print("Collecting test sets...")
langs.append(data[sp.SPEAKING])
print(langs)
vect = CountVectorizer()
X_train_counts = vect.fit_transform(entries)
tfidf = TfidfTransformer(use_idf=True).fit(X_train_counts)
X_train_tfidf = tfidf.transform(X_train_counts)
X_train_tfidf = X_train_tfidf.toarray()
tree = SGDClassifier()
tree.fit(X_train_tfidf, langs)
result = tree.predict(X_train_tfidf)
print(np.mean(result == langs))
print(metrics.classification_report(langs, result, target_names=langs))
def findMeanWordVector(words, model, num_feats):
# Finds the average of all the word vectors.
featureVec = np.zeros((num_feats,),dtype="float32")
num_words = 0
index2word_set = set(model.index2word)
for word in words:
if word in index2word_set:
nwords = nwords + 1.
featureVec = np.add(featureVec,model[word])
featureVec = np.divide(featureVec,num_words)
return featureVec
if __name__ == '__main__':
train, dev, test = returnDatasets()
bagOfWords(dev[:2])
