from word2veckeras.doc2veckeras import SentenceClassifier, Doc2VecClassifier
from word2veckeras.treebank import TreeBank
treebank = TreeBank('./trees')
Xtest, Ytest = treebank.sents_labels('test',
only_root=False,
pos_neg_label=False)
X, Y = treebank.sents_labels('train', only_root=False, pos_neg_label=False)
# X,Y =treebank.sents_labels('dev',only_root=False,pos_neg_label=False)
# n_sample=300
# X=X[:n_sample]
# Y=Y[:n_sample]
clf1 = SentenceClassifier(doc2vec=gensim.models.doc2vec.Doc2Vec())
clf1.fit(X, Y)
print clf1.score(Xtest, Ytest)
from sklearn.grid_search import GridSearchCV, ParameterSampler, ParameterGrid
clf2 = Doc2VecClassifier()
tuned_parameters = [{
'dm': [1],
'dm_concat': [0, 1],
'size': [200, 300, 400],
'window': [4, 8],
'min_count': [0, 9],
'sample': [0, 1e-5],
'iter': [1]
}]
