Split up test_ode.py

.github/workflows/tests.yml

@@ -80,7 +80,8 @@ jobs:
             pymc/tests/gp/test_util.py
             pymc/tests/test_model.py
             pymc/tests/test_model_graph.py
-            pymc/tests/test_ode.py
+            pymc/tests/ode/test_ode.py
+            pymc/tests/ode/test_utils.py
             pymc/tests/test_profile.py
             pymc/tests/test_quadpotential.py
 
@@ -151,7 +152,7 @@ jobs:
         test-subset:
           - pymc/tests/test_variational_inference.py pymc/tests/test_initial_point.py
           - pymc/tests/test_pickling.py pymc/tests/test_profile.py pymc/tests/test_step.py
-          - pymc/tests/gp/test_cov.py pymc/tests/gp/test_gp.py pymc/tests/gp/test_mean.py pymc/tests/gp/test_util.py pymc/tests/test_ode.py pymc/tests/test_smc.py pymc/tests/test_parallel_sampling.py
+          - pymc/tests/gp/test_cov.py pymc/tests/gp/test_gp.py pymc/tests/gp/test_mean.py pymc/tests/gp/test_util.py pymc/tests/ode/test_ode.py pymc/tests/ode/test_utils.py pymc/tests/test_smc.py pymc/tests/test_parallel_sampling.py
           - pymc/tests/test_sampling.py pymc/tests/test_posteriors.py
 
       fail-fast: false
@@ -364,7 +365,7 @@ jobs:
         floatx: [float32]
         python-version: ["3.10"]
         test-subset:
-        - pymc/tests/test_sampling.py pymc/tests/test_ode.py
+        - pymc/tests/test_sampling.py pymc/tests/ode/test_ode.py pymc/tests/ode/test_utils.py
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:

pymc/tests/test_ode.py → pymc/tests/ode/test_ode.py

@@ -18,54 +18,14 @@
 import numpy as np
 import pytest
 
-from scipy.integrate import odeint
 from scipy.stats import norm
 
 import pymc as pm
 
 from pymc.ode import DifferentialEquation
-from pymc.ode.utils import augment_system
 from pymc.tests.helpers import fast_unstable_sampling_mode
 
 
-def test_gradients():
-    """Tests the computation of the sensitivities from the Aesara computation graph"""
-
-    # ODE system for which to compute gradients
-    def ode_func(y, t, p):
-        return np.exp(-t) - p[0] * y[0]
-
-    # Computation of graidients with Aesara
-    augmented_ode_func = augment_system(ode_func, 1, 1 + 1)
-
-    # This is the new system, ODE + Sensitivities, which will be integrated
-    def augmented_system(Y, t, p):
-        dydt, ddt_dydp = augmented_ode_func(Y[:1], t, p, Y[1:])
-        derivatives = np.concatenate([dydt, ddt_dydp])
-        return derivatives
-
-    # Create real sensitivities
-    y0 = 0.0
-    t = np.arange(0, 12, 0.25).reshape(-1, 1)
-    a = 0.472
-    p = np.array([y0, a])
-
-    # Derivatives of the analytic solution with respect to y0 and alpha
-    # Treat y0 like a parameter and solve analytically.  Then differentiate.
-    # I used CAS to get these derivatives
-    y0_sensitivity = np.exp(-a * t)
-    a_sensitivity = (
-        -(np.exp(t * (a - 1)) - 1 + (a - 1) * (y0 * a - y0 - 1) * t) * np.exp(-a * t) / (a - 1) ** 2
-    )
-
-    sensitivity = np.c_[y0_sensitivity, a_sensitivity]
-
-    integrated_solutions = odeint(func=augmented_system, y0=[y0, 1, 0], t=t.ravel(), args=(p,))
-    simulated_sensitivity = integrated_solutions[:, 1:]
-
-    np.testing.assert_allclose(sensitivity, simulated_sensitivity, rtol=1e-5)
-
-
 def test_simulate():
     """Tests the integration in DifferentialEquation"""
 

pymc/tests/ode/test_utils.py

@@ -0,0 +1,56 @@
+#   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 scipy.integrate as ode
+
+from pymc.ode.utils import augment_system
+
+
+def test_gradients():
+    """Tests the computation of the sensitivities from the Aesara computation graph"""
+
+    # ODE system for which to compute gradients
+    def ode_func(y, t, p):
+        return np.exp(-t) - p[0] * y[0]
+
+    # Computation of graidients with Aesara
+    augmented_ode_func = augment_system(ode_func, 1, 1 + 1)
+
+    # This is the new system, ODE + Sensitivities, which will be integrated
+    def augmented_system(Y, t, p):
+        dydt, ddt_dydp = augmented_ode_func(Y[:1], t, p, Y[1:])
+        derivatives = np.concatenate([dydt, ddt_dydp])
+        return derivatives
+
+    # Create real sensitivities
+    y0 = 0.0
+    t = np.arange(0, 12, 0.25).reshape(-1, 1)
+    a = 0.472
+    p = np.array([y0, a])
+
+    # Derivatives of the analytic solution with respect to y0 and alpha
+    # Treat y0 like a parameter and solve analytically.  Then differentiate.
+    # I used CAS to get these derivatives
+    y0_sensitivity = np.exp(-a * t)
+    a_sensitivity = (
+        -(np.exp(t * (a - 1)) - 1 + (a - 1) * (y0 * a - y0 - 1) * t) * np.exp(-a * t) / (a - 1) ** 2
+    )
+
+    sensitivity = np.c_[y0_sensitivity, a_sensitivity]
+
+    integrated_solutions = ode.odeint(func=augmented_system, y0=[y0, 1, 0], t=t.ravel(), args=(p,))
+    simulated_sensitivity = integrated_solutions[:, 1:]
+
+    np.testing.assert_allclose(sensitivity, simulated_sensitivity, rtol=1e-5)