class InputTransformer:
def __init__(self):
self.encoder = Encoder()
def transform(self, X_train, y_train, augment):
X_train = list(X_train)
y_train = list(y_train)
print('before augmenting', len(X_train))
if augment is not None:
X_train, y_train = augment(X_train, y_train)
print('after augmetning', len(X_train), len(y_train))
def char_func(char):
# word = WordNetLemmatizer().lemmatize(word)
return self.encoder.transform(char) + 1
X_train = [
preprocess_chars(ingredients, char_func) for ingredients in X_train
]
lengths = numpy.array(list(len(x) for x in X_train))
print(lengths.min(), lengths.mean(), lengths.max(), lengths.std())
X_train = sequence.pad_sequences(X_train, maxlen=600)
print("ingredients")
print(X_train[:3])
label_transform = LabelBinarizer()
y_train = label_transform.fit_transform(y_train)
return X_train, y_train
def test_encoder(self):
encoder = Encoder()
self.assertEqual(encoder.transform("a"), 0)
self.assertEqual(encoder.transform("b"), 1)
self.assertEqual(encoder.transform("a"), 0)
