Merge pull request #265 from DavAug/issue-264-minor-bugs

Issue 264 minor bugs

chi/_log_pdfs.py

@@ -759,6 +759,8 @@ def sample_initial_parameters(self, n_samples=1, seed=None):
         # (Pooled and heterogen. dimensions do not count as bottom parameters)
         dims = []
         current_dim = 0
+        if isinstance(population_model, chi.ReducedPopulationModel):
+            population_model = population_model.get_population_model()
         try:
             pop_models = population_model.get_population_models()
         except AttributeError:

chi/_mechanistic_models.py

@@ -1062,7 +1062,7 @@ def parameters(self):
         return copy.copy(names)
 
     def set_dosing_regimen(
-            self, dose, start, duration=0.01, period=None, num=None):
+            self, dose, start=0, duration=0.01, period=None, num=None):
         """
         Sets the dosing regimen with which the compound is administered.
 

chi/_population_models.py

@@ -2966,7 +2966,8 @@ def n_hierarchical_parameters(self, n_ids):
         # If parameters have been fixed, updated number of population
         # parameters
         if self._fixed_params_mask is not None:
-            n_pop = int(np.sum(self._fixed_params_mask))
+            n_fixed = int(np.sum(self._fixed_params_mask))
+            n_pop = self._n_parameters - n_fixed
 
         return (n_indiv, n_pop)
 

chi/_predictive_models.py

@@ -89,7 +89,7 @@ def sample(
         raise NotImplementedError
 
     def set_dosing_regimen(
-            self, dose, start, duration=0.01, period=None, num=None):
+            self, dose, start=0, duration=0.01, period=None, num=None):
         """
         Sets the dosing regimen of the administered compound.
 
@@ -755,7 +755,7 @@ def sample(
         return samples
 
     def set_dosing_regimen(
-            self, dose, start, duration=0.01, period=None, num=None):
+            self, dose, start=0, duration=0.01, period=None, num=None):
         """
         Sets the dosing regimen with which the compound is administered.
 
@@ -1073,7 +1073,7 @@ def sample(
         return measurements
 
     def set_dosing_regimen(
-            self, dose, start, duration=0.01, period=None, num=None):
+            self, dose, start=0, duration=0.01, period=None, num=None):
         """
         Sets the dosing regimen with which the compound is administered.
 
@@ -1389,7 +1389,7 @@ def sample(
         return samples
 
     def set_dosing_regimen(
-            self, dose, start, duration=0.01, period=None, num=None):
+            self, dose, start=0, duration=0.01, period=None, num=None):
         """
         Sets the dosing regimen with which the compound is administered.
 

chi/tests/test_log_pdfs.py

@@ -1501,6 +1501,59 @@ def test_sample_initial_parameters(self):
             n_samples=n_samples)
         self.assertEqual(samples.shape, (10, 3))
 
+        # Test fixed population parameters
+        # Fix population parameters
+        population_model = chi.ComposedPopulationModel([
+            chi.CovariatePopulationModel(
+                chi.GaussianModel(), chi.LinearCovariateModel()),
+            chi.PooledModel(n_dim=2)])
+        population_model = chi.ReducedPopulationModel(population_model)
+        population_model.fix_parameters({'Std. Dim. 1': 1})
+
+        # Create hierarchical log-likelihood
+        log_likelihood = chi.HierarchicalLogLikelihood(
+            log_likelihoods, population_model, covariates=covariates)
+
+        # Define log-prior
+        log_prior = pints.ComposedLogPrior(
+            pints.LogNormalLogPrior(1, 1),
+            pints.LogNormalLogPrior(1, 1),
+            pints.LogNormalLogPrior(1, 1),
+            pints.LogNormalLogPrior(1, 1),
+            pints.LogNormalLogPrior(1, 1))
+
+        # Create log-posterior
+        log_posterior = chi.HierarchicalLogPosterior(
+            log_likelihood, log_prior)
+
+        n_samples = 10
+        samples = log_posterior.sample_initial_parameters(
+            n_samples=n_samples)
+        self.assertEqual(samples.shape, (10, 7))
+
+        # Fix all parameters but one
+        population_model.fix_parameters({
+            'Mean Dim. 1': 1,
+            'Mean Dim. 1 Cov. 1': 1,
+            'Std. Dim. 1 Cov. 1': 1,
+            'Pooled Dim. 1': 1})
+
+        # Create hierarchical log-likelihood
+        log_likelihood = chi.HierarchicalLogLikelihood(
+            log_likelihoods, population_model, covariates=covariates)
+
+        # Define log-prior
+        log_prior = pints.LogNormalLogPrior(1, 1)
+
+        # Create log-posterior
+        log_posterior = chi.HierarchicalLogPosterior(
+            log_likelihood, log_prior)
+
+        n_samples = 10
+        samples = log_posterior.sample_initial_parameters(
+            n_samples=n_samples)
+        self.assertEqual(samples.shape, (10, 3))
+
 
 class TestLogLikelihood(unittest.TestCase):
     """

setup.py

@@ -9,7 +9,7 @@
 setup(
     # Module name
     name='chi-drm',
-    version='0.2.0',
+    version='0.2.1',
     description='Package to model dose response dynamics',
     long_description=readme,
     long_description_content_type="text/markdown",