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Merge pull request #233949 from jluttine/bayespy-0.5.26

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pythonPackages.bayespy: 0.5.22 -> 0.5.26
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Weijia Wang 2023-05-25 13:58:39 +03:00 committed by GitHub
commit 3fbeb75dc0
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 2 additions and 145 deletions
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18
pkgs/development/python-modules/bayespy/default.nix
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@ -4,7 +4,7 @@
buildPythonPackage rec {
pname = "bayespy";
version = "0.5.22";
version = "0.5.26";
# Python 2 not supported and not some old Python 3 because MPL doesn't support
# them properly.
@ -12,23 +12,9 @@ buildPythonPackage rec {
src = fetchPypi {
inherit pname version;
sha256 = "ed0057dc22bd392df4b3bba23536117e1b2866e3201b12c5a37428d23421a5ba";
sha256 = "sha256-NOvuqPKioRIqScd2jC7nakonDEovTo9qKp/uTk9z1BE=";
};
patches = [
# Change from scipy to locally defined epsilon
# https://github.com/bayespy/bayespy/pull/126
(fetchpatch {
name = "locally-defined-epsilon.patch";
url = "https://github.com/bayespy/bayespy/commit/9be53bada763e19c2b6086731a6aa542ad33aad0.patch";
hash = "sha256-KYt/0GcaNWR9K9/uS2OXgK7g1Z+Bayx9+IQGU75Mpuo=";
})
# Fix deprecated numpy types
# https://sources.debian.org/src/python-bayespy/0.5.22-5/debian/patches/pr127-Fix-deprecated-numpy-types.patch/
./pr127-Fix-deprecated-numpy-types.patch
];
nativeCheckInputs = [ pytestCheckHook nose glibcLocales ];
propagatedBuildInputs = [ numpy scipy matplotlib h5py ];

129
pkgs/development/python-modules/bayespy/pr127-Fix-deprecated-numpy-types.patch
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@ -1,129 +0,0 @@
Description: Fix deprecated numpy types
From: Antti Mäkinen <antti.makinen@danfoss.com>
Bug: https://github.com/bayespy/bayespy/pull/127
Bug-Debian: https://bugs.debian.org/1027220
--- a/bayespy/inference/vmp/nodes/categorical_markov_chain.py
+++ b/bayespy/inference/vmp/nodes/categorical_markov_chain.py
@@ -171,7 +171,7 @@ class CategoricalMarkovChainDistribution
# Explicit broadcasting
P = P * np.ones(plates)[...,None,None,None]
# Allocate memory
- Z = np.zeros(plates + (self.N,), dtype=np.int)
+ Z = np.zeros(plates + (self.N,), dtype=np.int64)
# Draw initial state
Z[...,0] = random.categorical(p0, size=plates)
# Create [0,1,2,...,len(plate_axis)] indices for each plate axis and
--- a/bayespy/inference/vmp/nodes/concatenate.py
+++ b/bayespy/inference/vmp/nodes/concatenate.py
@@ -70,7 +70,7 @@ class Concatenate(Deterministic):
)
# Compute start indices for each parent on the concatenated plate axis
- self._indices = np.zeros(len(nodes)+1, dtype=np.int)
+ self._indices = np.zeros(len(nodes)+1, dtype=np.int64)
self._indices[1:] = np.cumsum([int(parent.plates[axis])
for parent in self.parents])
self._lengths = [parent.plates[axis] for parent in self.parents]
--- a/bayespy/inference/vmp/nodes/tests/test_binomial.py
+++ b/bayespy/inference/vmp/nodes/tests/test_binomial.py
@@ -43,7 +43,7 @@ class TestBinomial(TestCase):
X = Binomial(10, 0.7*np.ones((4,3)))
self.assertEqual(X.plates,
(4,3))
- n = np.ones((4,3), dtype=np.int)
+ n = np.ones((4,3), dtype=np.int64)
X = Binomial(n, 0.7)
self.assertEqual(X.plates,
(4,3))
--- a/bayespy/inference/vmp/nodes/tests/test_multinomial.py
+++ b/bayespy/inference/vmp/nodes/tests/test_multinomial.py
@@ -43,7 +43,7 @@ class TestMultinomial(TestCase):
X = Multinomial(10, 0.25*np.ones((2,3,4)))
self.assertEqual(X.plates,
(2,3))
- n = 10 * np.ones((3,4), dtype=np.int)
+ n = 10 * np.ones((3,4), dtype=np.int64)
X = Multinomial(n, [0.1, 0.3, 0.6])
self.assertEqual(X.plates,
(3,4))
--- a/bayespy/inference/vmp/nodes/tests/test_take.py
+++ b/bayespy/inference/vmp/nodes/tests/test_take.py
@@ -89,7 +89,7 @@ class TestTake(TestCase):
# Test matrix indices, no shape
X = GaussianARD(1, 1, plates=(3,), shape=(2,))
- Y = Take(X, np.ones((4, 5), dtype=np.int))
+ Y = Take(X, np.ones((4, 5), dtype=np.int64))
self.assertEqual(
Y.plates,
(4, 5),
@@ -113,7 +113,7 @@ class TestTake(TestCase):
# Test vector indices with more plate axes
X = GaussianARD(1, 1, plates=(4, 2), shape=())
- Y = Take(X, np.ones(3, dtype=np.int))
+ Y = Take(X, np.ones(3, dtype=np.int64))
self.assertEqual(
Y.plates,
(4, 3),
@@ -125,7 +125,7 @@ class TestTake(TestCase):
# Test take on other plate axis
X = GaussianARD(1, 1, plates=(4, 2), shape=())
- Y = Take(X, np.ones(3, dtype=np.int), plate_axis=-2)
+ Y = Take(X, np.ones(3, dtype=np.int64), plate_axis=-2)
self.assertEqual(
Y.plates,
(3, 2),
@@ -141,7 +141,7 @@ class TestTake(TestCase):
ValueError,
Take,
X,
- np.ones(3, dtype=np.int),
+ np.ones(3, dtype=np.int64),
plate_axis=0,
)
--- a/bayespy/utils/tests/test_linalg.py
+++ b/bayespy/utils/tests/test_linalg.py
@@ -126,7 +126,7 @@ class TestBandedSolve(misc.TestCase):
# Random sizes of the blocks
#D = np.random.randint(5, 10, size=N)
# Fixed sizes of the blocks
- D = 5*np.ones(N, dtype=np.int)
+ D = 5*np.ones(N, dtype=np.int64)
# Some helpful variables to create the covariances
W = [np.random.randn(D[i], 2*D[i])
--- a/bayespy/utils/misc.py
+++ b/bayespy/utils/misc.py
@@ -355,7 +355,7 @@ class TestCase(unittest.TestCase):
]
)
]
- ).astype(np.int)
+ ).astype(int)
def pack(x):
return [
--- a/bayespy/utils/random.py
+++ b/bayespy/utils/random.py
@@ -284,7 +284,7 @@ def categorical(p, size=None):
for ind in inds:
z[ind] = np.searchsorted(P[ind], x[ind])
- return z.astype(np.int)
+ return z.astype(int)
def multinomial(n, p, size=None):
@@ -313,7 +313,7 @@ def multinomial(n, p, size=None):
for i in misc.nested_iterator(size):
x[i] = np.random.multinomial(n[i], p[i])
- return x.astype(np.int)
+ return x.astype(int)
def gamma(a, b, size=None):