Introduced test for _get_scaling_

Co-authored-by: Icyshaman <[email protected]>

Author: 2 people

pymc/tests/test_logprob.py

@@ -33,7 +33,13 @@
 from pymc.aesaraf import floatX, walk_model
 from pymc.distributions.continuous import HalfFlat, Normal, TruncatedNormal, Uniform
 from pymc.distributions.discrete import Bernoulli
-from pymc.distributions.logprob import ignore_logprob, joint_logpt, logcdf, logp
+from pymc.distributions.logprob import (
+    _get_scaling,
+    ignore_logprob,
+    joint_logpt,
+    logcdf,
+    logp,
+)
 from pymc.model import Model, Potential
 from pymc.tests.helpers import select_by_precision
 
@@ -43,6 +49,44 @@ def assert_no_rvs(var):
     return var
 
 
+def test_get_scaling():
+
+    assert _get_scaling(None, (2, 3), 2).eval() == 1
+    # ndim >=1 & ndim<1
+    assert _get_scaling(45, (2, 3), 1).eval() == 22.5
+    assert _get_scaling(45, (2, 3), 0).eval() == 45
+
+    # list or tuple tests
+    # total_size contains other than Ellipsis, None and Int
+    with pytest.raises(TypeError):
+        _get_scaling([2, 4, 5, 9, 11.5], (2, 3), 2)
+    # check with Ellipsis
+    with pytest.raises(ValueError):
+        _get_scaling([1, 2, 5, Ellipsis, Ellipsis], (2, 3), 2)
+    with pytest.raises(ValueError):
+        _get_scaling([1, 2, 5, Ellipsis], (2, 3), 2)
+
+    assert _get_scaling([Ellipsis], (2, 3), 2).eval() == 1
+
+    assert _get_scaling([4, 5, 9, Ellipsis, 32, 12], (2, 3, 2), 5).eval() == 960
+    assert _get_scaling([4, 5, 9, Ellipsis], (2, 3, 2), 5).eval() == 15
+    # total_size with no Ellipsis (end = [ ])
+    with pytest.raises(ValueError):
+        _get_scaling([1, 2, 5], (2, 3), 2)
+
+    assert _get_scaling([], (2, 3), 2).eval() == 1
+    assert _get_scaling((), (2, 3), 2).eval() == 1
+    # total_size invalid type
+    with pytest.raises(TypeError):
+        _get_scaling({1, 2, 5}, (2, 3), 2)
+
+    # test with rvar from model graph
+    with Model() as m2:
+        rv_var = Uniform("a", 0.0, 1.0)
+    total_size = []
+    assert _get_scaling(total_size, shape=rv_var.shape, ndim=rv_var.ndim).eval() == 1.0
+
+
 def test_joint_logpt_basic():
     """Make sure we can compute a log-likelihood for a hierarchical model with transforms."""