def main(param_file=None):
# setup
p, base_path, output_dir = tools.setup(param_file)
result_path = path.join(base_path, p['result_path'])
logger = tools.get_logger('gensim', path.join(output_dir, "run.log"))
logger.info("running %s" % ' '.join(sys.argv))
logger.info('load the articles..')
article_path = path.join(result_path, p['article_label'])
wiki = pickle.load(open(path.join(article_path, 'articles.pickle')))
logger.info('load dictionary and models')
dictionary = Dictionary.load(path.join(result_path,
p['model_label'],
'dic.dict'))
model_path = path.join(result_path, p['model_label'])
lsi = LsiModel.load(path.join(model_path, 'lsi.model'))
pre = pickle.load(open(path.join(model_path, 'pre.model')))
if int(p['num_topics']) > lsi.num_topics:
logger.error('model to small')
lsi.num_topics = int(p['num_topics'])
data = {}
for topic, entries in wiki.iteritems():
logger.info('working on: %s' % topic)
data[topic] = {}
data[topic]['keys'] = []
vecs = []
data[topic]['ratings'] = []
for key, val in entries.iteritems():
data[topic]['keys'].append(key)
vecs.append(lsi[pre[dictionary.doc2bow(val['text'])]])
data[topic]['ratings'].append(val['rating'])
data[topic]['vecs'] = np.squeeze(np.array(vecs)[:, :, 1:2]).T
U, d, _ = np.linalg.svd(data[topic]['vecs'], full_matrices=False)
data[topic]['U'] = U
data[topic]['d'] = d
f = open(os.path.join(output_dir, "data.pickle"), 'wb')
pickle.dump(data, f)
def main(param_file=None):
# setup
p, base_path, output_dir = tools.setup(param_file)
result_path = path.join(base_path, p['result_path'])
logger = tools.get_logger('gensim', path.join(output_dir, "run.log"))
logger.info("running %s" % ' '.join(sys.argv))
logger.info('loading models and dictionary')
dictionary = Dictionary.load(path.join(result_path,
p['model_label'],
'dic.dict'))
model_path = path.join(result_path, p['model_label'])
lsi = LsiModel.load(path.join(model_path, 'lsi.model'))
pre = pickle.load(open(path.join(model_path, 'pre.model')))
lsi.num_topics = p['num_topics']
logger.info('load wikipedia articles')
article_path = path.join(result_path, p['article_label'])
wiki = pickle.load(open(path.join(article_path, 'articles.pickle')))
times = np.zeros((1, len(wiki)))
count = 0
for query_key, query in wiki.iteritems():
logger.info("working on: %s" % query_key)
n = len(query)
human = [val['rating'] for val in query.itervalues()]
t0 = time.time()
corpus = [lsi[pre[dictionary.doc2bow(val['text'])]]
for val in query.itervalues()]
sim_res = MatrixSimilarity(corpus)[corpus]
sim_res.save(path.join(output_dir, 'sim_' + query_key))
avg = np.mean(sim_res, axis=0)
idx = np.argsort(avg)
times[count] = time.time() - t0
# compute correlation with human rating
res = np.zeros((n, 1))
for i in range(n):
human_r = [human[j] for j in idx[i:]]
res[i, 0] = np.mean(human_r)
# plot correlation
fig = plt.figure()
ax = fig.add_subplot(3, 1, 1)
ax.plot(res)
ax = fig.add_subplot(3, 1, 2)
ratings = [val['rating'] for val in query.itervalues()]
ax.scatter(avg[idx], [ratings[i] for i in idx])
# plot similarity distribution
ax = fig.add_subplot(3, 1, 3)
ax.bar(range(n), avg[idx])
# Set the x tick labels to the group_labels defined above and rotate
ax.set_xticks(range(n))
k = [key + ' ' + str(query[key]['rating']) for key in query.keys()]
ax.set_xticklabels([k[i] for i in idx])
fig.autofmt_xdate()
plt.savefig(path.join(output_dir, query_key + '.' + p['format']))
plt.close()
logger.info('average similarity calculation time: %f' % np.mean(times))