class MeanEmbeddingVectorizer(object):

	def __init__(self, word_model):
		self.word_model = word_model
		self.vector_size = word_model.wv.vector_size

	def fit(self):  # comply with scikit-learn transformer requirement
		return self

	def transform(self, docs):  # comply with scikit-learn transformer requirement
		doc_word_vector = self.word_average_list(docs)
		return doc_word_vector

	def word_average(self, sent):
		"""
		Compute average word vector for a single doc/sentence.


		:param sent: list of sentence tokens
		:return:
			mean: float of averaging word vectors
		"""
		mean = []
		for word in sent:
			if word in self.word_model.wv.vocab:
				mean.append(self.word_model.wv.get_vector(word))

		if not mean:  # empty words
			# If a text is empty, return a vector of zeros.
			logging.warning("cannot compute average owing to no vector for {}".format(sent))
			return np.zeros(self.vector_size)
		else:
			mean = np.array(mean).mean(axis=0)
			return mean


	def word_average_list(self, docs):
		"""
		Compute average word vector for multiple docs, where docs had been tokenized.

		:param docs: list of sentence in list of separated tokens
		:return:
			array of average word vector in shape (len(docs),)
		"""
		return np.vstack([self.word_average(sent) for sent in docs])