Put tests for constrained priors into their own file

Author: AlexAndorra

pymc/tests/test_func_utils.py

@@ -0,0 +1,120 @@
+#   Copyright 2020 The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+import numpy as np
+import pytest
+
+import pymc as pm
+
+
+@pytest.mark.parametrize(
+    "distribution, lower, upper, init_guess, fixed_params",
+    [
+        (pm.Gamma, 0.1, 0.4, {"alpha": 1, "beta": 10}, {}),
+        (pm.Normal, 155, 180, {"mu": 170, "sigma": 3}, {}),
+        (pm.StudentT, 0.1, 0.4, {"mu": 10, "sigma": 3}, {"nu": 7}),
+        (pm.StudentT, 0, 1, {"mu": 5, "sigma": 2, "nu": 7}, {}),
+        # (pm.Exponential, 0, 1, {"lam": 1}, {}), PyMC Exponential gradient is failing miserably,
+        # need to figure out why
+        (pm.HalfNormal, 0, 1, {"sigma": 1}, {}),
+        (pm.Binomial, 0, 8, {"p": 0.5}, {"n": 10}),
+        (pm.Poisson, 1, 15, {"mu": 10}, {}),
+        (pm.Poisson, 19, 41, {"mu": 30}, {}),
+    ],
+)
+@pytest.mark.parametrize("mass", [0.5, 0.75, 0.95])
+def test_find_constrained_prior(distribution, lower, upper, init_guess, fixed_params, mass):
+    with pytest.warns(None) as record:
+        opt_params = pm.find_constrained_prior(
+            distribution,
+            lower=lower,
+            upper=upper,
+            mass=mass,
+            init_guess=init_guess,
+            fixed_params=fixed_params,
+        )
+    assert len(record) == 0
+
+    opt_distribution = distribution.dist(**opt_params)
+    mass_in_interval = (
+        pm.math.exp(pm.logcdf(opt_distribution, upper))
+        - pm.math.exp(pm.logcdf(opt_distribution, lower))
+    ).eval()
+    assert np.abs(mass_in_interval - mass) <= 1e-5
+
+
+@pytest.mark.parametrize(
+    "distribution, lower, upper, init_guess, fixed_params",
+    [
+        (pm.Gamma, 0.1, 0.4, {"alpha": 1}, {"beta": 10}),
+        (pm.Exponential, 0.1, 1, {"lam": 1}, {}),
+        (pm.Binomial, 0, 2, {"p": 0.8}, {"n": 10}),
+    ],
+)
+def test_find_constrained_prior_error_too_large(
+    distribution, lower, upper, init_guess, fixed_params
+):
+    with pytest.warns(UserWarning, match="instead of the requested 95%"):
+        pm.find_constrained_prior(
+            distribution,
+            lower=lower,
+            upper=upper,
+            mass=0.95,
+            init_guess=init_guess,
+            fixed_params=fixed_params,
+        )
+
+
+def test_find_constrained_prior_input_errors():
+    # missing param
+    with pytest.raises(TypeError, match="required positional argument"):
+        pm.find_constrained_prior(
+            pm.StudentT,
+            lower=0.1,
+            upper=0.4,
+            mass=0.95,
+            init_guess={"mu": 170, "sigma": 3},
+        )
+
+    # mass too high
+    with pytest.raises(AssertionError, match="has to be between 0.01 and 0.99"):
+        pm.find_constrained_prior(
+            pm.StudentT,
+            lower=0.1,
+            upper=0.4,
+            mass=0.995,
+            init_guess={"mu": 170, "sigma": 3},
+            fixed_params={"nu": 7},
+        )
+
+    # mass too low
+    with pytest.raises(AssertionError, match="has to be between 0.01 and 0.99"):
+        pm.find_constrained_prior(
+            pm.StudentT,
+            lower=0.1,
+            upper=0.4,
+            mass=0.005,
+            init_guess={"mu": 170, "sigma": 3},
+            fixed_params={"nu": 7},
+        )
+
+    # non-scalar params
+    with pytest.raises(NotImplementedError, match="does not work with non-scalar parameters yet"):
+        pm.find_constrained_prior(
+            pm.MvNormal,
+            lower=0,
+            upper=1,
+            mass=0.95,
+            init_guess={"mu": 5, "cov": np.asarray([[1, 0.2], [0.2, 1]])},
+        )

pymc/tests/test_util.py

@@ -142,104 +142,3 @@ def fn(a=UNSET):
     help(fn)
     captured = capsys.readouterr()
     assert "a=UNSET" in captured.out
-
-
-@pytest.mark.parametrize(
-    "distribution, lower, upper, init_guess, fixed_params",
-    [
-        (pm.Gamma, 0.1, 0.4, {"alpha": 1, "beta": 10}, {}),
-        (pm.Normal, 155, 180, {"mu": 170, "sigma": 3}, {}),
-        (pm.StudentT, 0.1, 0.4, {"mu": 10, "sigma": 3}, {"nu": 7}),
-        (pm.StudentT, 0, 1, {"mu": 5, "sigma": 2, "nu": 7}, {}),
-        # (pm.Exponential, 0, 1, {"lam": 1}, {}), PyMC Exponential gradient is failing miserably, need to figure out why
-        (pm.HalfNormal, 0, 1, {"sigma": 1}, {}),
-        (pm.Binomial, 0, 8, {"p": 0.5}, {"n": 10}),
-        (pm.Poisson, 1, 15, {"mu": 10}, {}),
-        (pm.Poisson, 19, 41, {"mu": 30}, {}),
-    ],
-)
-@pytest.mark.parametrize("mass", [0.5, 0.75, 0.95])
-def test_find_constrained_prior(distribution, lower, upper, init_guess, fixed_params, mass):
-    with pytest.warns(None) as record:
-        opt_params = pm.find_constrained_prior(
-            distribution,
-            lower=lower,
-            upper=upper,
-            mass=mass,
-            init_guess=init_guess,
-            fixed_params=fixed_params,
-        )
-    assert len(record) == 0
-
-    opt_distribution = distribution.dist(**opt_params)
-    mass_in_interval = (
-        pm.math.exp(pm.logcdf(opt_distribution, upper))
-        - pm.math.exp(pm.logcdf(opt_distribution, lower))
-    ).eval()
-    assert np.abs(mass_in_interval - mass) <= 1e-5
-
-
-@pytest.mark.parametrize(
-    "distribution, lower, upper, init_guess, fixed_params",
-    [
-        (pm.Gamma, 0.1, 0.4, {"alpha": 1}, {"beta": 10}),
-        (pm.Exponential, 0.1, 1, {"lam": 1}, {}),
-        (pm.Binomial, 0, 2, {"p": 0.8}, {"n": 10}),
-    ],
-)
-def test_find_constrained_prior_error_too_large(
-    distribution, lower, upper, init_guess, fixed_params
-):
-    with pytest.warns(UserWarning, match="instead of the requested 95%"):
-        pm.find_constrained_prior(
-            distribution,
-            lower=lower,
-            upper=upper,
-            mass=0.95,
-            init_guess=init_guess,
-            fixed_params=fixed_params,
-        )
-
-
-def test_find_constrained_prior_input_errors():
-    # missing param
-    with pytest.raises(TypeError, match="required positional argument"):
-        pm.find_constrained_prior(
-            pm.StudentT,
-            lower=0.1,
-            upper=0.4,
-            mass=0.95,
-            init_guess={"mu": 170, "sigma": 3},
-        )
-
-    # mass too high
-    with pytest.raises(AssertionError, match="has to be between 0.01 and 0.99"):
-        pm.find_constrained_prior(
-            pm.StudentT,
-            lower=0.1,
-            upper=0.4,
-            mass=0.995,
-            init_guess={"mu": 170, "sigma": 3},
-            fixed_params={"nu": 7},
-        )
-
-    # mass too low
-    with pytest.raises(AssertionError, match="has to be between 0.01 and 0.99"):
-        pm.find_constrained_prior(
-            pm.StudentT,
-            lower=0.1,
-            upper=0.4,
-            mass=0.005,
-            init_guess={"mu": 170, "sigma": 3},
-            fixed_params={"nu": 7},
-        )
-
-    # non-scalar params
-    with pytest.raises(NotImplementedError, match="does not work with non-scalar parameters yet"):
-        pm.find_constrained_prior(
-            pm.MvNormal,
-            lower=0,
-            upper=1,
-            mass=0.95,
-            init_guess={"mu": 5, "cov": np.asarray([[1, 0.2], [0.2, 1]])},
-        )