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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: TextMiner (
# Copyright 2018 @ AINLP
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import numpy as np
def get_word_index(path='./data/aclimdb_word_index.json'):
with open(path) as f:
return json.load(f)
def load_data(path='./data/aclimdb.npz', num_words=None, skip_top=0,
seed=113, start_char=1, oov_char=2, index_from=3):
"""A simplify version of the origin load_data function
with np.load(path) as f:
x_train, labels_train = f['x_train'], f['y_train']
x_test, labels_test = f['x_test'], f['y_test']
indices = np.arange(len(x_train))
x_train = x_train[indices]
labels_train = labels_train[indices]
indices = np.arange(len(x_test))
x_test = x_test[indices]
labels_test = labels_test[indices]
xs = np.concatenate([x_train, x_test])
labels = np.concatenate([labels_train, labels_test])
if start_char is not None:
xs = [[start_char] + [w + index_from for w in x] for x in xs]
elif index_from:
xs = [[w + index_from for w in x] for x in xs]
if not num_words:
num_words = max([max(x) for x in xs])
# 0 (padding), 1 (start), 2(OOV)
if oov_char is not None:
xs = [[w if (skip_top <= w < num_words) else oov_char for w in x]
for x in xs]
xs = [[w for w in x if skip_top <= w < num_words]
for x in xs]
idx = len(x_train)
x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])
return (x_train, y_train), (x_test, y_test)