def test_entropy(self):
qs = QS.Entropy()
scores = qs.score(*self.args)
self.assertEqual(scores.shape, self.unlabeled.y.shape)
self.assertNotIn(np.NaN, scores)
choice = qs.choose(scores)
self.assertEqual(choice, 2)
def test_entropy(self):
qs_kwargs = {'model_change': False}
qs = QS.Entropy(**qs_kwargs)
al = ActiveLearningModel(self.clf, qs, **self.kwargs)
scores, choices = al.run(self.train_X, self.test_X, self.train_y,
self.test_y)
self.assertEqual(scores.shape, (self.ndraws, ))
def test_distdiv(self):
qs1 = QS.Entropy()
qs2 = QS.MinMax()
lam = 0.5 # Gives equal weight to qs1 and qs2, like beta=1 above.
qs = QS.DistDivSampler(qs1=qs1,
qs2=qs2,
lam=lam,
choice_metric=np.argmax)
scores = qs.score(*self.args)
self.assertEqual(scores.shape, self.unlabeled.y.shape)
self.assertNotIn(np.NaN, scores)
choice = qs.choose(scores)
self.assertEqual(choice, 3)
def test_combined(self):
qs1 = QS.Entropy()
qs2 = QS.MinMax()
beta = 1
qs = QS.CombinedSampler(qs1=qs1,
qs2=qs2,
beta=beta,
choice_metric=np.argmax)
scores = qs.score(*self.args)
self.assertEqual(scores.shape, self.unlabeled.y.shape)
self.assertNotIn(np.NaN, scores)
choice = qs.choose(scores)
self.assertEqual(choice, 3)
def test_distdiv_dyn(self):
ent = QS.Entropy()
mm = QS.MinMax()
qs = QS.DistDivSampler(qs1=ent, qs2=mm, lam="dynamic")
self.run_test(qs)
def test_distdiv_even(self):
ent = QS.Entropy()
mm = QS.MinMax()
qs = QS.DistDivSampler(qs1=ent, qs2=mm, lam=0.5)
self.run_test(qs)
def test_combined_dyn(self):
ent = QS.Entropy()
mm = QS.MinMax()
qs = QS.CombinedSampler(qs1=ent, qs2=mm, beta="dynamic")
self.run_test(qs)
def test_entropy(self):
qs = QS.Entropy()
self.run_test(qs)