Merge branch 'master' into issue-1294-functional-testing-module

Author: fcooper8472

.github/workflows/copyright-test.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - master
   pull_request:
+    if: github.event.pull_request.draft == false
     branches:
       - '**'
 

.github/workflows/coverage-test.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - master
   pull_request:
+    if: github.event.pull_request.draft == false
     branches:
       - '**'
 

.github/workflows/docs-test.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - master
   pull_request:
+    if: github.event.pull_request.draft == false
     branches:
       - '**'
 

.github/workflows/style-test.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - master
   pull_request:
+    if: github.event.pull_request.draft == false
     branches:
       - '**'
 

.github/workflows/unit-test-os-coverage.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - master
   pull_request:
+    if: github.event.pull_request.draft == false
     branches:
       - '**'
 

.github/workflows/unit-test-python-coverage.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - master
   pull_request:
+    if: github.event.pull_request.draft == false
     branches:
       - '**'
 

pints/_mcmc/_differential_evolution.py

@@ -129,15 +129,25 @@ def current_log_pdfs(self):
         """ See :meth:`MultiChainMCMC.current_log_pdfs()`. """
         return self._current_log_pdfs
 
-    def set_gaussian_error(self, gaussian_error):
+    def gamma(self):
         """
-        If ``True`` sets the error process to be a gaussian error,
-        ``N(0, b*)``; if ``False``, it uses a uniform error ``U(-b*, b*)``;
-        where ``b* = b`` if absolute scaling used and ``b* = mu * b`` if
-        relative scaling is used instead.
+        Returns the coefficient ``gamma`` used in updating the position of each
+        chain.
         """
-        gaussian_error = bool(gaussian_error)
-        self._gaussian_error = gaussian_error
+        return self._gamma
+
+    def gamma_switch_rate(self):
+        """
+        Returns the number of steps between iterations where gamma is set to 1
+        (then reset immediately afterwards).
+        """
+        return self._gamma_switch_rate
+
+    def gaussian_error(self):
+        """
+        Returns whether a Gaussian versus uniform error process is used.
+        """
+        return self._gaussian_error
 
     def _initialise(self):
         """
@@ -167,10 +177,52 @@ def _initialise(self):
         # Update sampler state
         self._running = True
 
+    def n_hyper_parameters(self):
+        """ See :meth:`TunableMethod.n_hyper_parameters()`. """
+        return 5
+
+    def name(self):
+        """ See :meth:`pints.MCMCSampler.name()`. """
+        return 'Differential Evolution MCMC'
+
+    def _r_draw(self, i, num_chains):
+        """
+        Chooses two chain indexes uniformly at random such that they are
+        not the same nor do they equal `i`.
+        """
+        indexes = list(range(num_chains))
+        indexes.pop(i)
+        r1, r2 = np.random.choice(indexes, 2, replace=False)
+        return r1, r2
+
+    def relative_scaling(self):
+        """
+        Returns whether an error process whose standard deviation scales
+        relatively is used (False indicates absolute scale).
+        """
+        return self._relative_scaling
+
+    def scale_coefficient(self):
+        """
+        Sets the scale coefficient ``b`` of the error process used in updating
+        the position of each chain.
+        """
+        return self._b
+
+    def set_gamma(self, gamma):
+        """
+        Sets the coefficient ``gamma`` used in updating the position of each
+        chain.
+        """
+        gamma = float(gamma)
+        if gamma < 0:
+            raise ValueError('Gamma must be non-negative.')
+        self._gamma = gamma
+
     def set_gamma_switch_rate(self, gamma_switch_rate):
         """
         Sets the number of steps between iterations where gamma is set to 1
-        (then reset immediately afterwards)
+        (then reset immediately afterwards).
         """
         if gamma_switch_rate < 1:
             raise ValueError('The interval number of steps between ' +
@@ -180,6 +232,35 @@ def set_gamma_switch_rate(self, gamma_switch_rate):
                              ' gamma=1 iterations must be an integer.')
         self._gamma_switch_rate = gamma_switch_rate
 
+    def set_gaussian_error(self, gaussian_error):
+        """
+        If ``True`` sets the error process to be a gaussian error,
+        ``N(0, b*)``; if ``False``, it uses a uniform error ``U(-b*, b*)``;
+        where ``b* = b`` if absolute scaling used and ``b* = mu * b`` if
+        relative scaling is used instead.
+        """
+        gaussian_error = bool(gaussian_error)
+        self._gaussian_error = gaussian_error
+
+    def set_hyper_parameters(self, x):
+        """
+        The hyper-parameter vector is ``[gamma, gaussian_scale_coefficient,
+        gamma_switch_rate, gaussian_error, relative_scaling]``.
+
+        See :meth:`TunableMethod.set_hyper_parameters()`.
+        """
+        self.set_gamma(x[0])
+        self.set_scale_coefficient(x[1])
+        try:
+            int_x2 = int(x[2])
+        except (ValueError, TypeError):
+            raise ValueError('The interval number of steps between ' +
+                             'gamma=1 iterations must be convertable ' +
+                             'to an integer.')
+        self.set_gamma_switch_rate(int_x2)
+        self.set_gaussian_error(x[3])
+        self.set_relative_scaling(x[4])
+
     def set_relative_scaling(self, relative_scaling):
         """
         Sets whether to use an error process whose standard deviation scales
@@ -193,9 +274,15 @@ def set_relative_scaling(self, relative_scaling):
         else:
             self._b_star = np.repeat(self._b, self._n_parameters)
 
-    def name(self):
-        """ See :meth:`pints.MCMCSampler.name()`. """
-        return 'Differential Evolution MCMC'
+    def set_scale_coefficient(self, b):
+        """
+        Sets the scale coefficient ``b`` of the error process used in updating
+        the position of each chain.
+        """
+        b = float(b)
+        if b < 0:
+            raise ValueError('Scale coefficient must be non-negative.')
+        self._b = b
 
     def tell(self, proposed_log_pdfs):
         """ See :meth:`pints.MultiChainMCMC.tell()`. """
@@ -246,56 +333,3 @@ def tell(self, proposed_log_pdfs):
         # Return samples to add to chains
         self._current.setflags(write=False)
         return self._current
-
-    def set_scale_coefficient(self, b):
-        """
-        Sets the scale coefficient ``b`` of the error process used in updating
-        the position of each chain.
-        """
-        b = float(b)
-        if b < 0:
-            raise ValueError('Scale coefficient must be non-negative.')
-        self._b = b
-
-    def set_gamma(self, gamma):
-        """
-        Sets the coefficient ``gamma`` used in updating the position of each
-        chain.
-        """
-        gamma = float(gamma)
-        if gamma < 0:
-            raise ValueError('Gamma must be non-negative.')
-        self._gamma = gamma
-
-    def n_hyper_parameters(self):
-        """ See :meth:`TunableMethod.n_hyper_parameters()`. """
-        return 5
-
-    def set_hyper_parameters(self, x):
-        """
-        The hyper-parameter vector is ``[gamma, gaussian_scale_coefficient,
-        gamma_switch_rate, gaussian_error, relative_scaling]``.
-
-        See :meth:`TunableMethod.set_hyper_parameters()`.
-        """
-        self.set_gamma(x[0])
-        self.set_scale_coefficient(x[1])
-        try:
-            int_x2 = int(x[2])
-        except (ValueError, TypeError):
-            raise ValueError('The interval number of steps between ' +
-                             'gamma=1 iterations must be convertable ' +
-                             'to an integer.')
-        self.set_gamma_switch_rate(int_x2)
-        self.set_gaussian_error(x[3])
-        self.set_relative_scaling(x[4])
-
-    def _r_draw(self, i, num_chains):
-        """
-        Chooses two chain indexes uniformly at random such that they are
-        not the same nor do they equal `i`.
-        """
-        indexes = list(range(num_chains))
-        indexes.pop(i)
-        r1, r2 = np.random.choice(indexes, 2, replace=False)
-        return r1, r2

pints/tests/test_mcmc_controller.py

@@ -231,6 +231,43 @@ def f(x):
         self.assertEqual(chains.shape[2], n_parameters)
         self.assertIs(chains, mcmc.chains())
 
+    def test_hyperparameters_constant(self):
+        # Test that sampler hyperparameter remain same before and after run
+
+        # single chain method
+        n_chains = 1
+        x0 = np.array(self.real_parameters) * 1.1
+        xs = [x0]
+        mcmc = pints.MCMCController(
+            self.log_posterior, n_chains, xs, method=pints.HamiltonianMCMC)
+        step_size = 0.77
+        for sampler in mcmc.samplers():
+            sampler.set_leapfrog_step_size(step_size)
+        mcmc.set_max_iterations(5)
+        mcmc.set_log_to_screen(False)
+        mcmc.run()
+        for sampler in mcmc.samplers():
+            self.assertEqual(sampler.leapfrog_step_size()[0], step_size)
+
+        # test multiple chain method
+        # Set up problem for 10 chains
+        x0 = np.array(self.real_parameters)
+        xs = []
+        for i in range(10):
+            f = 0.9 + 0.2 * np.random.rand()
+            xs.append(x0 * f)
+        n_chains = len(xs)
+
+        meth = pints.DifferentialEvolutionMCMC
+        mcmc = pints.MCMCController(
+            self.log_posterior, n_chains, xs, method=meth)
+        switch_rate = 4
+        mcmc.samplers()[0].set_gamma_switch_rate(switch_rate)
+        mcmc.set_max_iterations(5)
+        mcmc.set_log_to_screen(False)
+        mcmc.run()
+        self.assertEqual(mcmc.samplers()[0].gamma_switch_rate(), switch_rate)
+
     def test_multi(self):
         # Test with a multi-chain method
 

pints/tests/test_mcmc_differential_evolution.py

@@ -149,31 +149,31 @@ def test_set_hyper_parameters(self):
         self.assertEqual(mcmc.n_hyper_parameters(), 5)
 
         mcmc.set_hyper_parameters([0.5, 0.6, 20, 0, 0])
-        self.assertEqual(mcmc._gamma, 0.5)
-        self.assertEqual(mcmc._b, 0.6)
-        self.assertEqual(mcmc._gamma_switch_rate, 20)
-        self.assertTrue(not mcmc._gaussian_error)
-        self.assertTrue(not mcmc._relative_scaling)
+        self.assertEqual(mcmc.gamma(), 0.5)
+        self.assertEqual(mcmc.scale_coefficient(), 0.6)
+        self.assertEqual(mcmc.gamma_switch_rate(), 20)
+        self.assertTrue(not mcmc.gaussian_error())
+        self.assertTrue(not mcmc.relative_scaling())
 
         mcmc.set_gamma(0.5)
-        self.assertEqual(mcmc._gamma, 0.5)
+        self.assertEqual(mcmc.gamma(), 0.5)
         self.assertRaisesRegex(ValueError,
                                'non-negative', mcmc.set_gamma, -1)
 
         mcmc.set_scale_coefficient(1)
-        self.assertTrue(not mcmc._relative_scaling)
+        self.assertTrue(not mcmc.relative_scaling())
         self.assertRaisesRegex(ValueError,
                                'non-negative', mcmc.set_scale_coefficient, -1)
 
         mcmc.set_gamma_switch_rate(11)
-        self.assertEqual(mcmc._gamma_switch_rate, 11)
+        self.assertEqual(mcmc.gamma_switch_rate(), 11)
         self.assertRaisesRegex(
             ValueError, 'integer', mcmc.set_gamma_switch_rate, 11.5)
         self.assertRaisesRegex(
             ValueError, 'exceed 1', mcmc.set_gamma_switch_rate, 0)
 
         mcmc.set_gaussian_error(False)
-        self.assertTrue(not mcmc._gaussian_error)
+        self.assertTrue(not mcmc.gaussian_error())
 
         mcmc.set_relative_scaling(0)
         self.assertTrue(np.array_equal(mcmc._b_star,
@@ -184,7 +184,7 @@ def test_set_hyper_parameters(self):
 
         # test implicit conversion to int
         mcmc.set_hyper_parameters([0.5, 0.6, 20.2, 0, 0])
-        self.assertEqual(mcmc._gamma_switch_rate, 20)
+        self.assertEqual(mcmc.gamma_switch_rate(), 20)
         self.assertRaisesRegex(
             ValueError, 'convertable to an integer',
             mcmc.set_hyper_parameters, (0.5, 0.6, 'sdf', 0, 0))