add adaptive/tests/test_average_learner1d.py

Author: basnijholt

adaptive/tests/test_average_learner1d.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pandas as pd
+from pandas.testing import assert_series_equal
+
+from adaptive import AverageLearner1D
+from adaptive.tests.test_learners import (
+    generate_random_parametrization,
+    noisy_peak,
+    simple_run,
+)
+
+
+def almost_equal_dicts(a, b):
+    assert_series_equal(pd.Series(sorted(a.items())), pd.Series(sorted(b.items())))
+
+
+def test_copy_from():
+    f = generate_random_parametrization(noisy_peak)
+    learner = AverageLearner1D(f, bounds=[-2, 2])
+    control = AverageLearner1D(f, bounds=[-2, 2])
+    learner._recompute_losses_factor = 1
+    control._recompute_losses_factor = 1
+    simple_run(learner, 100)
+    control.copy_from(learner)
+
+    almost_equal_dicts(learner.data, control.data)
+    almost_equal_dicts(learner.error, control.error)
+    almost_equal_dicts(learner.rescaled_error, control.rescaled_error)
+    almost_equal_dicts(learner.neighbors, control.neighbors)
+    almost_equal_dicts(learner.neighbors_combined, control.neighbors_combined)
+    assert learner.npoints == control.npoints
+    assert learner.nsamples == control.nsamples
+    assert len(learner._data_samples) == len(control._data_samples)
+    assert learner._data_samples.keys() == control._data_samples.keys()
+    for k, v1 in learner._data_samples.items():
+        v2 = control._data_samples[k]
+        assert len(v1) == len(v2)
+        np.testing.assert_almost_equal(np.sort(v1), np.sort(v2))
+
+    almost_equal_dicts(learner.losses, control.losses)
+    np.testing.assert_almost_equal(learner.loss(), control.loss())
+
+    # Try if the control is runnable
+    simple_run(control, 200)