@@ -47,7 +47,6 @@ def disaster_model():
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return disaster_model , years
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- @pytest .mark .filterwarnings ("error" )
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def test_marginalized_bernoulli_logp ():
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"""Test logp of IR TestFiniteMarginalDiscreteRV directly"""
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mu = pt .vector ("mu" )
@@ -77,7 +76,6 @@ def test_marginalized_bernoulli_logp():
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)
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- @pytest .mark .filterwarnings ("error" )
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def test_marginalized_basic ():
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data = [2 ] * 5
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@@ -122,7 +120,6 @@ def test_marginalized_basic():
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)
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- @pytest .mark .filterwarnings ("error" )
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def test_multiple_independent_marginalized_rvs ():
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with MarginalModel () as m :
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sigma = pm .HalfNormal ("sigma" )
@@ -149,7 +146,6 @@ def test_multiple_independent_marginalized_rvs():
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np .testing .assert_array_almost_equal (y_logp , y_ref_logp )
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- @pytest .mark .filterwarnings ("error" )
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def test_multiple_dependent_marginalized_rvs ():
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"""Test that marginalization works when there is more than one dependent RV"""
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with MarginalModel () as m :
@@ -188,7 +184,6 @@ def test_rv_dependent_multiple_marginalized_rvs():
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np .testing .assert_allclose (np .exp (logp ({"z" : 2 })), 0.1 * 0.3 )
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- @pytest .mark .filterwarnings ("error" )
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def test_nested_marginalized_rvs ():
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"""Test that marginalization works when there are nested marginalized RVs"""
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@@ -226,7 +221,6 @@ def test_nested_marginalized_rvs():
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)
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- @pytest .mark .filterwarnings ("error" )
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def test_marginalized_change_point_model (disaster_model ):
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m , years = disaster_model
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@@ -245,7 +239,6 @@ def test_marginalized_change_point_model(disaster_model):
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@pytest .mark .slow
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- @pytest .mark .filterwarnings ("error" )
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def test_marginalized_change_point_model_sampling (disaster_model ):
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m , _ = disaster_model
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@@ -428,7 +421,6 @@ def true_sub_idx_logp(y):
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np .testing .assert_almost_equal (logsumexp (post .lp_sub_idx , axis = - 1 ), 0 )
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- @pytest .mark .filterwarnings ("error" )
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def test_not_supported_marginalized ():
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"""Marginalized graphs with non-Elemwise Operations are not supported as they
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would violate the batching logp assumption"""
@@ -477,7 +469,6 @@ def test_not_supported_marginalized():
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m .marginalize (x )
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- @pytest .mark .filterwarnings ("error" )
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def test_marginalized_deterministic_and_potential ():
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rng = np .random .default_rng (299 )
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@@ -503,7 +494,6 @@ def test_marginalized_deterministic_and_potential():
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assert pot_value .eval ({y_value : 2 , z_value : 5 }) == 8
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- @pytest .mark .filterwarnings ("error" )
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def test_not_supported_marginalized_deterministic_and_potential ():
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with MarginalModel () as m :
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x = pm .Bernoulli ("x" , p = 0.7 )
@@ -526,7 +516,6 @@ def test_not_supported_marginalized_deterministic_and_potential():
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m .marginalize ([x ])
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- @pytest .mark .filterwarnings ("error" )
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@pytest .mark .parametrize (
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"transform, expected_warning" ,
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(
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