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))
def test_csv_distances(self):
s=Scheduler()
vec=CSVLoader(get_dataset('smallfile'),index_col=False,header=None,scheduler=s)
dis=PairwiseDistances(metric='euclidean',scheduler=s)
dis.input.df = vec.output.df
cnt = Every(proc=print_len,constant_time=True,scheduler=s)
cnt.input.df = dis.output.dist
global times
times = 0
s.start(ten_times)
df = vec.df()
computed = dis.dist()
#self.assertEquals(computed.shape[0], len(df))
del df[CSVLoader.UPDATE_COLUMN]
offset=0
size=offset+5000
truth = pairwise_distances(df.iloc[offset:size], metric=dis._metric)
dist = computed[offset:size,offset:size]
self.assertTrue(np.allclose(truth, dist,atol=1e-7)) # reduce tolerance
def test_MDS_csv(self):
s = self.scheduler()
vec = CSVLoader(get_dataset('smallfile'),
index_col=False,
header=None,
scheduler=s)
dis = PairwiseDistances(metric='euclidean', scheduler=s)
dis.input.df = vec.output.df
cnt = Every(proc=self.terse, constant_time=True, scheduler=s)
cnt.input.df = dis.output.dist
global times
times = 0
s.start(ten_times)
