def test_progress_called(self):
mock_progress = Mock(side_effect=lambda it: it)
calculate_sliding_score(
self.metric, self.labels_true, self.labels_pred, progress=mock_progress
)
mock_progress.assert_called()
def test_default_is_to_align_at_end(self):
window_loc1, score1 = calculate_sliding_score(
*self.args, **self.kwargs, length_mismatch_policy="align_end"
)
window_loc2, score2 = calculate_sliding_score(*self.args, **self.kwargs)
np.testing.assert_equal(window_loc1, window_loc2)
np.testing.assert_equal(score1, score2)
def test_align_at_start(self):
window_loc1, score1 = calculate_sliding_score(
*self.args, **self.kwargs, length_mismatch_policy="align_start"
)
n = min(self.n_samples_true, self.n_samples_pred)
window_loc2, score2 = calculate_sliding_score(
self.args[0], self.labels_true[:n], self.labels_pred, **self.kwargs
)
np.testing.assert_equal(window_loc1, window_loc2)
np.testing.assert_equal(score1, score2)
def test_align_at_end(self):
window_loc1, score1 = calculate_sliding_score(
*self.args, **self.kwargs, length_mismatch_policy="align_end"
)
n = min(self.n_samples_true, self.n_samples_pred)
shift = self.n_samples_true - n
window_loc2, score2 = calculate_sliding_score(
self.args[0], self.labels_true[-n:], self.labels_pred, **self.kwargs
)
np.testing.assert_equal(window_loc1, window_loc2 + shift)
np.testing.assert_equal(score1, score2)
def test_returns_empty_if_window_size_larger_than_labels_length(self):
window_loc, score = calculate_sliding_score(
lambda x, y: 1, [1, 2, 3], [0, 1, 2], window_size=5
)
self.assertEqual(len(window_loc), 0)
self.assertEqual(len(score), 0)
def test_step_does_not_go_below_one(self):
window_loc, _ = calculate_sliding_score(
self.metric,
self.labels_true,
self.labels_pred,
window_size=self.window_size,
overlap_fraction=1,
)
self.assertGreater(len(window_loc), 0)
np.testing.assert_equal(np.diff(window_loc), 1)
def test_custom_step_overrides_fraction(self):
step = 2
window_loc, _ = calculate_sliding_score(
self.metric,
self.labels_true,
self.labels_pred,
window_size=self.window_size,
step=step,
)
self.assertGreater(len(window_loc), 0)
np.testing.assert_equal(np.diff(window_loc), step)
def setUp(self):
self.n_samples = 200
self.window_size = 13
self.overlap_fraction = 0.3
self.rng = np.random.default_rng(0)
self.metric_output = self.rng.normal(size=self.n_samples)
self.metric = Mock(side_effect=self.metric_output)
self.labels_true = self.rng.normal(size=self.n_samples)
self.labels_pred = self.rng.normal(size=self.n_samples)
self.window_loc, self.score = calculate_sliding_score(
self.metric,
self.labels_true,
self.labels_pred,
window_size=self.window_size,
overlap_fraction=self.overlap_fraction,
)
monitor=["r", "nsm.weights_", "xcorr.coef_"],
)
t1 = time.time()
print(f"Median accuracy score xcorr: {two_ar3.result_xcorr[0]:.2}. "
f"(Took {t1 - t0:.2f} seconds.)")
# %%
two_ar3.rolling_score_biowta = []
two_ar3.rolling_score_xcorr = []
for i in tqdm(range(two_ar3.n_signals)):
crt_dataset = two_ar3.dataset[i]
crt_r_biowta = two_ar3.result_biowta[1].history[i].r
crt_inferred_biowta = np.argmax(crt_r_biowta, axis=1)
crt_loc_biowta, crt_sliding_score_biowta = calculate_sliding_score(
two_ar3.metric, crt_dataset.usage_seq, crt_inferred_biowta)
two_ar3.rolling_score_biowta.append(
(crt_loc_biowta, crt_sliding_score_biowta))
crt_r_xcorr = two_ar3.result_xcorr[1].history[i].r
crt_inferred_xcorr = np.argmax(crt_r_xcorr, axis=1)
crt_loc_xcorr, crt_sliding_score_xcorr = calculate_sliding_score(
two_ar3.metric, crt_dataset.usage_seq, crt_inferred_xcorr)
two_ar3.rolling_score_xcorr.append(
(crt_loc_xcorr, crt_sliding_score_xcorr))
# %% [markdown]
# ### Plots for BioWTA results
# %%
def test_raises_value_error_if_policy_is_raise(self):
with self.assertRaises(ValueError):
calculate_sliding_score(
*self.args, **self.kwargs, length_mismatch_policy="raise",
)
def test_returns_empty_for_empty_inputs(self):
window_loc, score = calculate_sliding_score(lambda x, y: 1, [], [])
self.assertEqual(len(window_loc), 0)
self.assertEqual(len(score), 0)