From 03e68f4ae28146f851afc0d15cb5d2dae23fb7eb Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Fri, 23 Nov 2018 12:01:56 +0100
Subject: [PATCH] add '_recompute_losses_factor' such that we can set it in the
test
and have fewer xfailing tests
---
adaptive/learner/learner1D.py | 7 ++++++-
adaptive/tests/test_learners.py | 20 +++++++++-----------
2 files changed, 15 insertions(+), 12 deletions(-)
diff --git a/adaptive/learner/learner1D.py b/adaptive/learner/learner1D.py
index f1559954..ebac1b2d 100644
--- a/adaptive/learner/learner1D.py
+++ b/adaptive/learner/learner1D.py
@@ -228,6 +228,11 @@ class Learner1D(BaseLearner):
self.losses = {}
self.losses_combined = {}
+ # When the scale changes by a factor 2, the losses are
+ # recomputed. This is tunable such that we can test
+ # the learners behavior in the tests.
+ self._recompute_losses_factor = 2
+
self.data = {}
self.pending_points = set()
@@ -447,7 +452,7 @@ class Learner1D(BaseLearner):
self._update_losses(x, real=True)
# If the scale has increased enough, recompute all losses.
- if self._scale[1] > 2 * self._oldscale[1]:
+ if self._scale[1] > self._recompute_losses_factor * self._oldscale[1]:
for interval in self.losses:
self._update_interpolated_loss_in_interval(*interval)
diff --git a/adaptive/tests/test_learners.py b/adaptive/tests/test_learners.py
index 3cbe43ce..6f99e3da 100644
--- a/adaptive/tests/test_learners.py
+++ b/adaptive/tests/test_learners.py
@@ -229,22 +229,19 @@ def test_learner_accepts_lists(learner_type, bounds):
simple(learner, goal=lambda l: l.npoints > 10)
-@run_with(xfail(Learner1D), Learner2D, LearnerND)
+@run_with(Learner1D, Learner2D, LearnerND)
def test_adding_existing_data_is_idempotent(learner_type, f, learner_kwargs):
"""Adding already existing data is an idempotent operation.
Either it is idempotent, or it is an error.
This is the only sane behaviour.
-
- This test will fail for the Learner1D because the losses are normalized by
- _scale which is updated after every point. After one iteration of adding
- points, the _scale could be different from what it was when calculating
- the losses of the intervals. Readding the points a second time means
- that the losses are now all normalized by the correct _scale.
"""
f = generate_random_parametrization(f)
learner = learner_type(f, **learner_kwargs)
control = learner_type(f, **learner_kwargs)
+ if learner_type is Learner1D:
+ learner._recompute_losses_factor = 1
+ control._recompute_losses_factor = 1
N = random.randint(10, 30)
control.ask(N)
@@ -298,14 +295,11 @@ def test_adding_non_chosen_data(learner_type, f, learner_kwargs):
assert set(pls) == set(cpls)
-@run_with(xfail(Learner1D), xfail(Learner2D), xfail(LearnerND), AverageLearner)
+@run_with(Learner1D, xfail(Learner2D), xfail(LearnerND), AverageLearner)
def test_point_adding_order_is_irrelevant(learner_type, f, learner_kwargs):
"""The order of calls to 'tell' between calls to 'ask'
is arbitrary.
- This test will fail for the Learner1D for the same reason as described in
- the doc-string in `test_adding_existing_data_is_idempotent`.
-
This test will fail for the Learner2D because
`interpolate.interpnd.estimate_gradients_2d_global` will give different
outputs based on the order of the triangles and values in
@@ -315,6 +309,10 @@ def test_point_adding_order_is_irrelevant(learner_type, f, learner_kwargs):
learner = learner_type(f, **learner_kwargs)
control = learner_type(f, **learner_kwargs)
+ if learner_type is Learner1D:
+ learner._recompute_losses_factor = 1
+ control._recompute_losses_factor = 1
+
N = random.randint(10, 30)
control.ask(N)
xs, _ = learner.ask(N)
--
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