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Normalised stats #396

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merged 6 commits into from
Sep 27, 2024
Merged

Normalised stats #396

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7754bab
allow passing a `NestedSamples.stats` instance as a normalisation ref…
lukashergt Sep 19, 2024
fa31ee3
add tests for the normalisation kwarg `norm` for `NestedSamples.stats`
lukashergt Sep 19, 2024
44334bd
bump version to 2.9.0
lukashergt Sep 19, 2024
55a3928
improve docstring for `norm` kwarg, saying what columns will addition…
lukashergt Sep 26, 2024
60030df
Merge branch 'master' into normalised_stats
lukashergt Sep 27, 2024
fa919c1
Merge branch 'master' into normalised_stats
lukashergt Sep 27, 2024
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2 changes: 1 addition & 1 deletion README.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
anesthetic: nested sampling post-processing
===========================================
:Authors: Will Handley and Lukas Hergt
:Version: 2.8.14
:Version: 2.9.0
:Homepage: https://github.com/handley-lab/anesthetic
:Documentation: http://anesthetic.readthedocs.io/

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2 changes: 1 addition & 1 deletion anesthetic/_version.py
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@@ -1 +1 @@
__version__ = '2.8.14'
__version__ = '2.9.0'
37 changes: 32 additions & 5 deletions anesthetic/samples.py
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Expand Up @@ -761,7 +761,7 @@ def ns_output(self, *args, **kwargs):
" as well as average loglikelihoods: help(samples.logL_P)"
)

def stats(self, nsamples=None, beta=None):
def stats(self, nsamples=None, beta=None, norm=None):
r"""Compute Nested Sampling statistics.

Using nested sampling we can compute:
Expand Down Expand Up @@ -822,20 +822,27 @@ def stats(self, nsamples=None, beta=None):
beta : float, array-like, optional
inverse temperature(s) beta=1/kT. Default self.beta

norm : Series, :class:`Samples`, optional
:meth:`NestedSamples.stats` output used for normalisation.
Can be either a Series of mean values or Samples produced with
matching `nsamples` and `beta`. In addition to the columns
['logZ', 'D_KL', 'logL_P', 'd_G'], this adds the normalised
versions ['Delta_logZ', 'Delta_D_KL', 'Delta_logL_P', 'Delta_d_G'].

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Returns
-------
if beta is scalar and nsamples is None:
Series, index ['logZ', 'd_G', 'DK_L', 'logL_P']
Series, index ['logZ', 'd_G', 'D_KL', 'logL_P']
elif beta is scalar and nsamples is int:
:class:`Samples`, index range(nsamples),
columns ['logZ', 'd_G', 'DK_L', 'logL_P']
columns ['logZ', 'd_G', 'D_KL', 'logL_P']
elif beta is array-like and nsamples is None:
:class:`Samples`, index beta,
columns ['logZ', 'd_G', 'DK_L', 'logL_P']
columns ['logZ', 'd_G', 'D_KL', 'logL_P']
elif beta is array-like and nsamples is int:
:class:`Samples`, index :class:`pandas.MultiIndex` the product of
beta and range(nsamples)
columns ['logZ', 'd_G', 'DK_L', 'logL_P']
columns ['logZ', 'd_G', 'D_KL', 'logL_P']
"""
logw = self.logw(nsamples, beta)
if nsamples is None and beta is None:
Expand All @@ -861,6 +868,26 @@ def stats(self, nsamples=None, beta=None):
samples.set_label('d_G', r'$d_\mathrm{G}$')

samples.label = self.label

if norm is not None:
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samples['Delta_logZ'] = samples['logZ'] - norm['logZ']
samples.set_label('Delta_logZ',
r"$\Delta\ln\mathcal{Z}$")

samples['Delta_D_KL'] = samples['D_KL'] - norm['D_KL']
samples.set_label('Delta_D_KL',
r"$\Delta\mathcal{D}_\mathrm{KL}$")

samples['Delta_logL_P'] = samples['logL_P'] - norm['logL_P']
samples.set_label(
'Delta_logL_P',
r"$\Delta\langle\ln\mathcal{L}\rangle_\mathcal{P}$"
)

samples['Delta_d_G'] = samples['d_G'] - norm['d_G']
samples.set_label('Delta_d_G',
r"$\Delta d_\mathrm{G}$")

return samples

def logX(self, nsamples=None):
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54 changes: 54 additions & 0 deletions tests/test_samples.py
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Expand Up @@ -946,39 +946,93 @@ def test_stats():
beta = [0., 0.5, 1.]

vals = ['logZ', 'D_KL', 'logL_P', 'd_G']
delta_vals = ['Delta_logZ', 'Delta_D_KL', 'Delta_logL_P', 'Delta_d_G']

labels = [r'$\ln\mathcal{Z}$',
r'$\mathcal{D}_\mathrm{KL}$',
r'$\langle\ln\mathcal{L}\rangle_\mathcal{P}$',
r'$d_\mathrm{G}$']
delta_labels = [r'$\Delta\ln\mathcal{Z}$',
r'$\Delta\mathcal{D}_\mathrm{KL}$',
r'$\Delta\langle\ln\mathcal{L}\rangle_\mathcal{P}$',
r'$\Delta d_\mathrm{G}$']

stats = pc.stats()
assert isinstance(stats, WeightedLabelledSeries)
assert_array_equal(stats.drop_labels().index, vals)
assert_array_equal(stats.get_labels(), labels)

stats = pc.stats(norm=pc.stats())
assert isinstance(stats, WeightedLabelledSeries)
assert_array_equal(stats.drop_labels().index, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)

stats = pc.stats(nsamples=nsamples)
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals)
assert_array_equal(stats.get_labels(), labels)
assert stats.index.name == 'samples'
assert_array_equal(stats.index, range(nsamples))

stats = pc.stats(nsamples=nsamples, norm=pc.stats())
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)
assert stats.index.name == 'samples'
assert_array_equal(stats.index, range(nsamples))

stats = pc.stats(nsamples=nsamples, norm=pc.stats(nsamples=nsamples))
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)
assert stats.index.name == 'samples'
assert_array_equal(stats.index, range(nsamples))

stats = pc.stats(beta=beta)
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals)
assert_array_equal(stats.get_labels(), labels)
assert stats.index.name == 'beta'
assert_array_equal(stats.index, beta)

stats = pc.stats(beta=beta, norm=pc.stats())
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)
assert stats.index.name == 'beta'
assert_array_equal(stats.index, beta)

stats = pc.stats(beta=beta, norm=pc.stats(beta=beta))
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)
assert stats.index.name == 'beta'
assert_array_equal(stats.index, beta)

stats = pc.stats(nsamples=nsamples, beta=beta)
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals)
assert_array_equal(stats.get_labels(), labels)
assert stats.index.names == ['beta', 'samples']
assert stats.index.levshape == (len(beta), nsamples)

stats = pc.stats(nsamples=nsamples, beta=beta, norm=pc.stats())
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)
assert stats.index.names == ['beta', 'samples']
assert stats.index.levshape == (len(beta), nsamples)

stats = pc.stats(nsamples=nsamples, beta=beta,
norm=pc.stats(nsamples=nsamples, beta=beta))
assert isinstance(stats, WeightedLabelledDataFrame)
assert_array_equal(stats.drop_labels().columns, vals + delta_vals)
assert_array_equal(stats.get_labels(), labels + delta_labels)
assert stats.index.names == ['beta', 'samples']
assert stats.index.levshape == (len(beta), nsamples)

for beta in [1., 0., 0.5]:
np.random.seed(42)
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Why do you need a seed here?

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I managed to get failing tests in what follows, because of changes to the seed from the tests I added above... Nothing dramatic, just bad chance in combination with the tests checking against 3*std or similar. I could have fiddled around with tolerances, but just trying a new seed there was enough, so I did not dig deeper...

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@AdamOrmondroyd AdamOrmondroyd Sep 27, 2024 •
edited
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ok, I'll reapprove when you've done the version number ofc this is a minor bump

pc.beta = beta
n = 1000
PC = pc.stats(n, beta)
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