Merge pull request #55 from eth-cscs/release-0.5.5

Release 0.5.5

Makefile

@@ -30,6 +30,7 @@ unittest:
 unittest_mpi:
 	echo "Running MPI backend unit tests.."
 	mpirun -np 2 python3 -m unittest discover -s tests -v -p "backend_tests_mpi.py" || (echo "Error in MPI unit tests."; exit 1)
+	mpirun -np 3 python3 -m unittest discover -s tests -v -p "backend_tests_mpi_model_mpi.py" || (echo "Error in MPI unit tests."; exit 1)
 
 exampletest: $(MAKEDIRS)
 	echo "Testing standard examples.."

README.md

@@ -26,9 +26,10 @@ scientists by providing
 # Documentation
 For more information, check out the
 
-* [Documentation](http://abcpy.readthedocs.io/en/v0.5.4) 
-* [Examples](https://github.com/eth-cscs/abcpy/tree/v0.5.4/examples) directory and
-* [Reference](http://abcpy.readthedocs.io/en/v0.5.4/abcpy.html)
+* [Documentation](http://abcpy.readthedocs.io/en/v0.5.5) 
+* [Examples](https://github.com/eth-cscs/abcpy/tree/v0.5.5/examples) directory and
+* [Reference](http://abcpy.readthedocs.io/en/v0.5.5/abcpy.html)
+
 
 Further, we provide a
 [collection of models](https://github.com/eth-cscs/abcpy-models) for which ABCpy
@@ -54,31 +55,31 @@ finally CSCS (Swiss National Super Computing Center) for their generous support.
 
 There is a paper in the proceedings of the 2017 PASC conference. In case you use
 ABCpy for your publication, we would appreciate a citation. You can use
-[this](https://github.com/eth-cscs/abcpy/blob/v0.5.4/doc/literature/DuttaS-ABCpy-PASC-2017.bib)
+[this](https://github.com/eth-cscs/abcpy/blob/v0.5.5/doc/literature/DuttaS-ABCpy-PASC-2017.bib)
+
 BibTex reference.
 
 
 ## Other Refernces
 
 Publications in which ABCpy was applied:
 
-* R. Dutta, M. Schoengens, A. Ummadisingu, J. P.  Onnela, A. Mira, "ABCpy: A
-  High-Performance Computing Perspective to Approximate Bayesian Computation",
-  2017, arXiv:1711.04694
-
 * R. Dutta, J. P.  Onnela, A. Mira, "Bayesian Inference of Spreading Processes
-  on Networks", 2017, arXiv:1709.08862
+  on Networks", 2018, Proc. R. Soc. A, 474(2215), 20180129.
+
+* R. Dutta, Z. Faidon Brotzakis and A. Mira, "Bayesian Calibration of
+  Force-fields from Experimental Data: TIP4P Water", 2018, Journal of Chemical Physics 149, 154110.
 
 * R. Dutta, B. Chopard, J. Lätt, F. Dubois, K. Zouaoui Boudjeltia and A. Mira,
   "Parameter Estimation of Platelets Deposition: Approximate Bayesian
-  Computation with High Performance Computing", 2017, arXiv:1710.01054
+  Computation with High Performance Computing", 2018, Frontiers in physiology, 9.
 
 * A. Ebert, R. Dutta, P. Wu, K. Mengersen and A. Mira, "Likelihood-Free
   Parameter Estimation for Dynamic Queueing Networks", 2018, arXiv:1804.02526
 
-* R. Dutta, Z. Faidon Brotzakis and A. Mira, "Bayesian Calibration of
-  Force-fields from Experimental Data: TIP4P Water", 2018, arXiv:1804.02742
-
+* R. Dutta, M. Schoengens, A. Ummadisingu, N. Widerman, J. P.  Onnela, A. Mira, "ABCpy: A
+  High-Performance Computing Perspective to Approximate Bayesian Computation",
+  2017, arXiv:1711.04694
 
 ## License
 ABCpy is published under the BSD 3-clause license, see [here](LICENSE).

VERSION

@@ -1 +1 @@
-0.5.4
+0.5.5

abcpy/approx_lhd.py

@@ -69,6 +69,8 @@ def __init__(self, statistics_calc):
     def likelihood(self, y_obs, y_sim):
         # print("DEBUG: SynLiklihood.likelihood().")
         if not isinstance(y_obs, list):
+            # print("type(y_obs) : ", type(y_obs), " , type(y_sim) : ", type(y_sim))
+            # print("y_obs : ", y_obs)
             raise TypeError('Observed data is not of allowed types')
 
         if not isinstance(y_sim, list):
@@ -81,14 +83,11 @@ def likelihood(self, y_obs, y_sim):
 
         # Extract summary statistics from the simulated data
         stat_sim = self.statistics_calc.statistics(y_sim)
-
+        
         # Compute the mean, robust precision matrix and determinant of precision matrix
-        # print("DEBUG: meansim computation.")
         mean_sim = np.mean(stat_sim,0)
-        # print("DEBUG: robust_precision_sim computation.")
         lw_cov_, _ = ledoit_wolf(stat_sim)
         robust_precision_sim = np.linalg.inv(lw_cov_)
-        # print("DEBUG: robust_precision_sim_det computation..")
         robust_precision_sim_det = np.linalg.det(robust_precision_sim)
         # print("DEBUG: combining.")
         tmp1 = robust_precision_sim * np.array(self.stat_obs.reshape(-1,1) - mean_sim.reshape(-1,1)).T

abcpy/backends/__init__.py

@@ -2,13 +2,25 @@
 
 
 def BackendMPI(*args,**kwargs):
+    # import and setup module mpimanager
+    import abcpy.backends.mpimanager
+    master_node_ranks = [0]
+    process_per_model = 1
+    if 'master_node_ranks' in kwargs:
+        master_node_ranks = kwargs['master_node_ranks']
+    if 'process_per_model' in kwargs:
+        process_per_model = kwargs['process_per_model']
+    abcpy.backends.mpimanager.create_mpi_manager(master_node_ranks, process_per_model)
+
+    # import BackendMPI and return and instance
     from abcpy.backends.mpi import BackendMPI
     return BackendMPI(*args,**kwargs)
 
+
 def BackendMPITestHelper(*args,**kwargs):
     from abcpy.backends.mpi import BackendMPITestHelper 
     return BackendMPITestHelper(*args,**kwargs)
 
 def BackendSpark(*args,**kwargs):
     from  abcpy.backends.spark import BackendSpark
-    return BackendSpark(*args,**kwargs)
+    return BackendSpark(*args,**kwargs)

abcpy/backends/base.py

@@ -226,3 +226,12 @@ def __init__(self, object):
     def value(self):
         return self.object
 
+
+class NestedParallelizationController():
+    @abstractmethod
+    def nested_execution(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def run_nested(self, func, *args, **kwargs):
+        raise NotImplementedError