def bagOfWords(dataset):
sp = SetProcessing()
datalist = sp.convertDataToList(dataset)
japanese, korean, mandarin = sp.organizeEasternLanguages(datalist)
datalist = datalist[870:970]
pairs = sp.buildSpeakingLearningPairs(datalist)
print(pairs)
entries = []
langs = []
korean = korean[:10]
japanese = japanese[:10]
for s in korean:
datalist.append(s)
for fr in japanese:
datalist.append(fr)
for data in datalist:
entries.append(data[sp.ENTRY])
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 w2vRetrieve(datalist, language_tag=None):
if language_tag is None:
modelpath = MODEL_PATH
else:
modelpath = 'wordvecs/dim%s_window%s_%s' % (VEC_DIM, CONTEXT, language_tag)
sp = SetProcessing()
t0 = time()
sentences = []
counter = 0
for value in datalist:
if counter % 100 is 0:
print("currently looked at %s sets" % counter)
if isWestern(value[sp.STUDYING]):
sentences.append(w2vWestern(value[sp.ENTRY]))
counter += 1
else:
sentences.append(w2vEastern(value[sp.ENTRY]))
counter += 1
wordvec = Word2Vec(sentences, size=VEC_DIM, window=CONTEXT, alpha=ALPHA, workers=WORKERS, min_count=FREQ_SKIP)
wordvec.init_sims(replace=True)
wordvec.save(modelpath)
print("Saved word2vec model in %s seconds!" % (time()-t0))
return wordvec
if __name__ == '__main__':
s = {
'fold': 3, # 5 folds 0,1,2,3,4
'lr': 0.0627142536696559,
'verbose': 1,
'decay': False, # decay on the learning rate if improvement stops
'win': 7, # number of words in the context window
'bs': 9, # number of backprop through time steps
'nhidden': 100, # number of hidden units
'seed': 345,
'emb_dimension': 100, # dimension of word embedding
'nepochs': 50,
'vocab_size': 1000
}
sp = SetProcessing()
train = sp.convertDataToList(sp.train)
dev = sp.convertDataToList(sp.dev)
test = sp.convertDataToList(sp.test)
train_x, train_y = sp.returnEntriesWithSpoken(train)
dev_x, dev_y = sp.returnEntriesWithSpoken(dev)
test_x, test_y = sp.returnEntriesWithSpoken(test)
all_train_x = train_x.append([i for i in dev_x])
all_train_y = train_y.append([j for j in dev_y])
np.random.seed(s['seed'])
random.seed(s['seed'])
'''
nh :: dimension of the hidden layer
scores = cross_validation.cross_val_score(clf, train_x, train_y, scoring='f1_weighted')
if __name__ == '__main__':
s = {'fold':3, # 5 folds 0,1,2,3,4
'lr':0.0627142536696559,
'verbose':1,
'decay':False, # decay on the learning rate if improvement stops
'win':7, # number of words in the context window
'bs':9, # number of backprop through time steps
'nhidden':100, # number of hidden units
'seed':345,
'emb_dimension':100, # dimension of word embedding
'nepochs':50,
'vocab_size':1000}
sp = SetProcessing()
train = sp.convertDataToList(sp.train)
dev = sp.convertDataToList(sp.dev)
test = sp.convertDataToList(sp.test)
train_x, train_y = sp.returnEntriesWithSpoken(train)
dev_x, dev_y = sp.returnEntriesWithSpoken(dev)
test_x, test_y = sp.returnEntriesWithSpoken(test)
all_train_x = train_x.append([i for i in dev_x])
all_train_y = train_y.append([j for j in dev_y])
np.random.seed(s['seed'])
random.seed(s['seed'])
'''
('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...")
western_native, eastern_native = sp.organizeDataByRegion(train)
english_native, french_native, spanish_native = sp.organizeWesternLanguages(
western_native)
japanese_native, korean_native, mandarin_native = sp.organizeEasternLanguages(
eastern_native)
'''Return the individual sets by language being studied.'''
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import SGDClassifier, SGDRegressor
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.metrics import metrics
from sklearn.grid_search import GridSearchCV
import numpy as np
import pickle
import itertools
import random
sp = SetProcessing()
'''TODO'''
n_samples = 2000
n_features = 500
n_languages = len(languages())
def runSGDPipeline(entries, langs):
t0 = time()
sgd_pipeline = Pipeline([('vect',
CountVectorizer(ngram_range=(1, 1),
max_features=n_features)),
('tfidf', TfidfTransformer(use_idf=True)),
('clf',
SGDClassifier(loss='squared_hinge',
penalty='l2',
