def test_read_vec(self):
module=VECLoader(get_dataset('warlogs'),
id='test_read_vec')
self.assertTrue(module.df() is None)
module.run(0)
s = module.trace_stats(max_runs=1)
df = module.df()
self.assertFalse(df is None)
l = len(df)
self.assertEqual(l, len(df[df[module.UPDATE_COLUMN]==module.last_update()]))
cnt = 1
while not module.is_zombie():
module.run(cnt)
cnt += 1
s = module.trace_stats(max_runs=1)
df = module.df()
ln = len(df)
print "Run time: %gs, loaded %d rows" % (s['duration'].irow(-1), ln)
self.assertEqual(ln-l, len(df[df[module.UPDATE_COLUMN]==module.last_update()]))
l = ln
s = module.trace_stats(max_runs=1)
print "Done. Run time: %gs, loaded %d rows" % (s['duration'].irow(-1), len(module.df()))
df2 = module.df().groupby([Module.UPDATE_COLUMN])
self.assertEqual(cnt, len(df2))
def NOtest_vec_distances(self):
s=Scheduler()
vec=VECLoader(get_dataset('warlogs'),scheduler=s)
dis=PairwiseDistances(metric='cosine',scheduler=s)
dis.input.df = vec.output.df
dis.input.array = vec.output.array
cnt = Every(proc=print_len,constant_time=True,scheduler=s)
cnt.input.df = dis.output.dist
global times
times = 0
s.start()
df = vec.df()
computed = dis.dist()
self.assertEquals(computed.shape[0], len(df))
truth = pairwise_distances(vec.toarray(), metric=dis._metric)
self.assertTrue(np.allclose(truth, computed))
