From c589a2f6afc51d1f314a9956b4c885699fbf71a9 Mon Sep 17 00:00:00 2001
From: Joseph Weston <joseph@weston.cloud>
Date: Thu, 22 Nov 2018 14:21:01 +0100
Subject: [PATCH] refactor adding all loss functions to tests involving a
learner
---
adaptive/tests/test_learners.py | 69 +++++++++++++++++++++------------
1 file changed, 45 insertions(+), 24 deletions(-)
diff --git a/adaptive/tests/test_learners.py b/adaptive/tests/test_learners.py
index e02f2478..b6da0725 100644
--- a/adaptive/tests/test_learners.py
+++ b/adaptive/tests/test_learners.py
@@ -28,6 +28,26 @@ except ModuleNotFoundError:
SKOptLearner = None
+LOSS_FUNCTIONS = {
+ Learner1D: ('loss_per_interval', (
+ adaptive.learner.learner1D.default_loss,
+ adaptive.learner.learner1D.uniform_loss,
+ adaptive.learner.learner1D.curvature_loss_function(),
+ )),
+ Learner2D: ('loss_per_triangle', (
+ adaptive.learner.learner2D.default_loss,
+ adaptive.learner.learner2D.uniform_loss,
+ adaptive.learner.learner2D.minimize_triangle_surface_loss,
+ adaptive.learner.learner2D.resolution_loss_function(),
+ )),
+ LearnerND: ('loss_per_simplex', (
+ adaptive.learner.learnerND.default_loss,
+ adaptive.learner.learnerND.std_loss,
+ adaptive.learner.learnerND.uniform_loss,
+ )),
+}
+
+
def generate_random_parametrization(f):
"""Return a realization of 'f' with parameters bound to random values.
@@ -75,38 +95,26 @@ def maybe_skip(learner):
# All parameters except the first must be annotated with a callable that
# returns a random value for that parameter.
-
-@learn_with(Learner1D, bounds=(-1, 1), loss_per_interval=adaptive.learner.learner1D.default_loss)
-@learn_with(Learner1D, bounds=(-1, 1), loss_per_interval=adaptive.learner.learner1D.uniform_loss)
-@learn_with(Learner1D, bounds=(-1, 1), loss_per_interval=adaptive.learner.learner1D.curvature_loss_function())
+@learn_with(Learner1D, bounds=(-1, 1))
def quadratic(x, m: uniform(0, 10), b: uniform(0, 1)):
return m * x**2 + b
-@learn_with(Learner1D, bounds=(-1, 1), loss_per_interval=adaptive.learner.learner1D.default_loss)
-@learn_with(Learner1D, bounds=(-1, 1), loss_per_interval=adaptive.learner.learner1D.uniform_loss)
-@learn_with(Learner1D, bounds=(-1, 1), loss_per_interval=adaptive.learner.learner1D.curvature_loss_function())
+@learn_with(Learner1D, bounds=(-1, 1))
def linear_with_peak(x, d: uniform(-1, 1)):
a = 0.01
return x + a**2 / (a**2 + (x - d)**2)
-@learn_with(LearnerND, bounds=((-1, 1), (-1, 1)), loss_per_simplex=adaptive.learner.learnerND.default_loss)
-@learn_with(LearnerND, bounds=((-1, 1), (-1, 1)), loss_per_simplex=adaptive.learner.learnerND.std_loss)
-@learn_with(LearnerND, bounds=((-1, 1), (-1, 1)), loss_per_simplex=adaptive.learner.learnerND.uniform_loss)
-@learn_with(Learner2D, bounds=((-1, 1), (-1, 1)), loss_per_triangle=adaptive.learner.learner2D.default_loss)
-@learn_with(Learner2D, bounds=((-1, 1), (-1, 1)), loss_per_triangle=adaptive.learner.learner2D.uniform_loss)
-@learn_with(Learner2D, bounds=((-1, 1), (-1, 1)), loss_per_triangle=adaptive.learner.learner2D.minimize_triangle_surface_loss)
-@learn_with(Learner2D, bounds=((-1, 1), (-1, 1)), loss_per_triangle=adaptive.learner.learner2D.resolution_loss_function())
+@learn_with(LearnerND, bounds=((-1, 1), (-1, 1)))
+@learn_with(Learner2D, bounds=((-1, 1), (-1, 1)))
def ring_of_fire(xy, d: uniform(0.2, 1)):
a = 0.2
x, y = xy
return x + math.exp(-(x**2 + y**2 - d**2)**2 / a**4)
-@learn_with(LearnerND, bounds=((-1, 1), (-1, 1), (-1, 1)), loss_per_simplex=adaptive.learner.learnerND.default_loss)
-@learn_with(LearnerND, bounds=((-1, 1), (-1, 1), (-1, 1)), loss_per_simplex=adaptive.learner.learnerND.std_loss)
-@learn_with(LearnerND, bounds=((-1, 1), (-1, 1), (-1, 1)), loss_per_simplex=adaptive.learner.learnerND.uniform_loss)
+@learn_with(LearnerND, bounds=((-1, 1), (-1, 1), (-1, 1)))
def sphere_of_fire(xyz, d: uniform(0.2, 1)):
a = 0.2
x, y, z = xyz
@@ -120,6 +128,17 @@ def gaussian(n):
# Decorators for tests.
+
+# Create a sequence of learner parameters by adding all
+# possible loss functions to an existing parameter set.
+def add_loss_to_params(learner_type, existing_params):
+ if learner_type not in LOSS_FUNCTIONS:
+ return [existing_params]
+ loss_param, loss_functions = LOSS_FUNCTIONS[learner_type]
+ loss_params = [{loss_param: f} for f in loss_functions]
+ return [dict(**existing_params, **lp) for lp in loss_params]
+
+
def run_with(*learner_types):
pars = []
for l in learner_types:
@@ -127,13 +146,15 @@ def run_with(*learner_types):
if has_marker:
marker, l = l
for f, k in learner_function_combos[l]:
- # Check if learner was marked with our `xfail` decorator
- # XXX: doesn't work when feeding kwargs to xfail.
- if has_marker:
- pars.append(pytest.param(l, f, dict(k),
- marks=[marker]))
- else:
- pars.append((l, f, dict(k)))
+ ks = add_loss_to_params(l, k)
+ for k in ks:
+ # Check if learner was marked with our `xfail` decorator
+ # XXX: doesn't work when feeding kwargs to xfail.
+ if has_marker:
+ pars.append(pytest.param(l, f, dict(k),
+ marks=[marker]))
+ else:
+ pars.append((l, f, dict(k)))
return pytest.mark.parametrize('learner_type, f, learner_kwargs', pars)
--
GitLab