ret = []
for op in ops:
ret.append(list(map(lambda x: tr[x], op)))
return ret
if True:
train_data = get_nice_data(get_data('reviews.json'))
train_data = list(map(lambda x: np.array(x), train_data))
scores = []
for train_idx, test_idx in KFold(len(train_data[0]), n_folds=7, \
shuffle=True):
X_train = train_data[0][train_idx]
Y_train = train_data[1][train_idx]
X_test, Y_test = Solution._remove_differencies((train_data[0][test_idx],\
train_data[1][test_idx]), True)
sol = Solution(True)
sol.train((X_train, Y_train))
# sometimes it says "AttributeError: '_ConstantPredictor'
# object has no attribute 'predict_proba'". It happens when some
# opinion is presented in all training data. I think it's data problem,
# not classificator's.
answer = sol.getClasses(X_test)
transformer = encode_ops(answer + Y_test)
answer = transform(answer, transformer)
Y_test = transform(Y_test, transformer)
f_m = f1_score(Y_test, answer, labels=range(len(transformer)), average='micro')
