def test_hs_report_entry_label(self):
ds = Dataset(self.X, self.y)
qs = HS(ds, self.classes, random_state=1126)
y_report = []
for i in range(len(self.y)):
y_report.append(qs.report_entry_label(i))
assert_array_equal(y_report, self.y)
def test_hs_active_selecting(self):
ds = Dataset(self.X, self.y[:10] + [None] * (len(self.y) - 10))
qs = HS(ds, self.classes, active_selecting=True, random_state=1126)
qseq = run_qs(ds, qs, self.y, len(self.y)-10)
assert_array_equal(
np.concatenate([qseq[:10], qseq[-10:]]),
np.array([39, 126, 66, 135, 37, 33, 118, 132, 142, 144,
89, 117, 48, 67, 75, 14, 79, 62, 105, 19])
)
def test_hs_random_selecting(self):
ds = Dataset(self.X, self.y[:10] + [None] * (len(self.y) - 10))
qs = HS(ds, self.classes, active_selecting=False, random_state=1126)
qseq = run_qs(ds, qs, self.y, len(self.y)-10)
assert_array_equal(
np.concatenate([qseq[:10], qseq[-10:]]),
np.array([39, 126, 66, 135, 37, 33, 118, 132, 142, 144,
71, 28, 63, 41, 140, 34, 20, 110, 136, 36])
)
def test_hs_active_selecting(self):
ds = Dataset(self.X, self.y[:10] + [None] * (len(self.y) - 10))
qs = HS(ds, self.classes, active_selecting=True, random_state=1126)
qseq = run_qs(ds, qs, self.y, len(self.y) - 10)
assert_array_equal(
np.concatenate([qseq[:10], qseq[-10:]]),
np.array([
48, 143, 13, 64, 101, 108, 51, 87, 36, 28, 43, 118, 47, 25, 81,
82, 95, 40, 67, 120
]))
def test_hs_random_selecting(self):
ds = Dataset(self.X, self.y[:10] + [None] * (len(self.y) - 10))
qs = HS(ds, self.classes, active_selecting=False, random_state=1126)
qseq = run_qs(ds, qs, self.y, len(self.y) - 10)
assert_array_equal(
np.concatenate([qseq[:10], qseq[-10:]]),
np.array([
48, 143, 13, 142, 88, 130, 29, 87, 36, 28, 58, 137, 49, 105,
76, 71, 63, 47, 64, 55
]))
def test_hs_subsampling(self):
ds = Dataset(self.X, self.y[:10] + [None] * (len(self.y) - 10))
sub_qs = UncertaintySampling(ds,
model=SVM(gamma='auto', decision_function_shape='ovr'))
qs = HS(ds, self.classes, subsample_qs=sub_qs, random_state=1126)
qseq = run_qs(ds, qs, self.y, len(self.y)-10)
assert_array_equal(
np.concatenate([qseq[:10], qseq[-10:]]),
np.array([120, 50, 33, 28, 78, 133, 52, 124, 102, 109,
81, 108, 10, 89, 126, 114, 92, 48, 25, 13])
)
def test_hs_report_all_label(self):
ds = Dataset(self.X, self.y)
qs = HS(ds, self.classes, random_state=1126)
y_report = qs.report_all_label()
assert_array_equal(y_report, self.y)