def load_embeddings(self, embedding_file):
embedding_matrix = load_npy(embedding_file)
if embedding_matrix.ndim == 2:
return embedding_matrix
else:
assert embedding_matrix.ndim == 3
# avgEmb (uniform mean)
return embedding_matrix.mean(axis=1)
def __init__(self, embedding_file, vocab_file, oov, hidden1, hidden2, hidden3, win, alpha, cembedding_file,
infer_side_win):
super().__init__(embedding_file, vocab_file, oov, hidden1, hidden2, hidden3, win, alpha)
self.embedding_matrix = self.load_embeddings(embedding_file)
self.cembedding_matrix = load_npy(cembedding_file)
self.infer_side_win = infer_side_win # context size to each side for avgExp
assert self.infer_side_win > 0
assert self.cembedding_matrix.ndim == 2
# use uniform avg for oov padding
self.oov_vec = self.embedding_matrix[self.oov].mean(axis=0)
self.oov_vecs = [self.oov_vec]*(self.win//2)
parser.add_argument("-f", default="W_w", help="Model file (optional), meant for models per epoch.")
parser.add_argument("-data_path", help="Filepath containing the SCWS dataset.", default="data/SCWS/ratings.txt")
parser.add_argument("-win_size", default=3, type=int,
help="Context window size (n words to the left and n to the right).")
parser.add_argument("-n_most_freq", type=int, help="Only consider n most freq. words from vocabulary.")
args = parser.parse_args()
w_index_path = "{}/w_index.json".format(args.input_dir)
# model_path = "{}/sg.pickle".format(args.input_dir)
log.info("Loading model.")
w_index = load_json(w_index_path)
if args.n_most_freq:
w_index = {w: i for w, i in w_index.items() if i < args.n_most_freq + 1}
print(len(w_index))
embs = load_npy("{}/{}.npy".format(args.input_dir, args.f))
c_embs = load_npy("{}/W_c.npy".format(args.input_dir))
try:
if args.f == "W_w":
n = ""
else:
n = eval(args.f[-1])
assert 0 <= n < 9
bias = load_npy("{}/Wb{}.npy".format(args.input_dir, n))
except FileNotFoundError:
bias = None
log.info("Loading dataset.")
d = Dataset()
d.create(args.data_path, w_index)
def load_embeddings(self, embedding_file):
embedding_matrix = load_npy(embedding_file)
assert embedding_matrix.ndim == 3
# avgExp (weighted mean)
return embedding_matrix