From 3a8d202dc03e81ab18b7f0a95335fe111fd13a86 Mon Sep 17 00:00:00 2001
From: Philipp Arras <parras@mpa-garching.mpg.de>
Date: Tue, 19 May 2020 14:50:27 +0200
Subject: [PATCH] Add some testing
---
nifty6/minimization/iteration_controllers.py | 5 ++++-
nifty6/plot.py | 2 +-
test/test_kl.py | 15 ++++++++++++---
3 files changed, 17 insertions(+), 5 deletions(-)
diff --git a/nifty6/minimization/iteration_controllers.py b/nifty6/minimization/iteration_controllers.py
index 85c081906..dfa8618f2 100644
--- a/nifty6/minimization/iteration_controllers.py
+++ b/nifty6/minimization/iteration_controllers.py
@@ -108,7 +108,7 @@ class EnergyHistory(object):
self._lst = []
@property
- def timestamps(self):
+ def time_stamps(self):
return [x for x, _ in self._lst]
@property
@@ -128,6 +128,9 @@ class EnergyHistory(object):
self._lst += other._lst
return self
+ def __len__(self):
+ return len(self._lst)
+
def append_history(func):
@functools.wraps(func)
diff --git a/nifty6/plot.py b/nifty6/plot.py
index 92c5e2a82..8ba30f87c 100644
--- a/nifty6/plot.py
+++ b/nifty6/plot.py
@@ -288,7 +288,7 @@ def _plot_history(f, ax, **kwargs):
plt.yscale(kwargs.pop("yscale", "linear"))
mi, ma = np.inf, -np.inf
for i, fld in enumerate(f):
- xcoord = date2num([dt.fromtimestamp(ts) for ts in fld.timestamps])
+ xcoord = date2num([dt.fromtimestamp(ts) for ts in fld.time_stamps])
ycoord = fld.energy_values
ax.scatter(xcoord, ycoord, label=label[i], alpha=alpha[i],
color=color[i], s=size[i])
diff --git a/test/test_kl.py b/test/test_kl.py
index eadbcccfe..fa43322c1 100644
--- a/test/test_kl.py
+++ b/test/test_kl.py
@@ -39,7 +39,7 @@ def test_kl(constants, point_estimates, mirror_samples, mf):
import numpy as np
lh = ift.GaussianEnergy(domain=op.target, sampling_dtype=np.float64) @ op
ic = ift.GradientNormController(iteration_limit=5)
- ic.activate_and_reset_logging()
+ ic.enable_logging()
h = ift.StandardHamiltonian(lh, ic_samp=ic)
mean0 = ift.from_random('normal', h.domain)
@@ -51,7 +51,14 @@ def test_kl(constants, point_estimates, mirror_samples, mf):
point_estimates=point_estimates,
mirror_samples=mirror_samples,
napprox=0)
- ic.pop_history()
+ assert_(len(ic.history) > 0)
+ assert_(len(ic.history) == len(ic.history.time_stamps))
+ assert_(len(ic.history) == len(ic.history.energy_values))
+ ic.history.reset()
+ assert_(len(ic.history) == 0)
+ assert_(len(ic.history) == len(ic.history.time_stamps))
+ assert_(len(ic.history) == len(ic.history.energy_values))
+
locsamp = kl._local_samples
klpure = ift.MetricGaussianKL(mean0,
h,
@@ -87,8 +94,10 @@ def test_kl(constants, point_estimates, mirror_samples, mf):
# Test constants (after some minimization)
cg = ift.GradientNormController(iteration_limit=5)
- minimizer = ift.NewtonCG(cg)
+ minimizer = ift.NewtonCG(cg, activate_logging=True)
kl, _ = minimizer(kl)
+ if len(constants) != 2:
+ assert_(len(minimizer.inversion_history) > 0)
diff = (mean0 - kl.position).to_dict()
for kk in constants:
assert_allclose(diff[kk].val, 0*diff[kk].val)
--
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