Run PyDREAM with python 3.8

.travis.yml

@@ -1,6 +1,6 @@
 language: python
 env:
-- PYVER=2.7
+- PYVER=3.8
 - PYVER=3.6
 - PYVER=3.7
 before_install:
@@ -10,10 +10,8 @@ before_install:
 - bash miniconda.sh -b -p $HOME/miniconda
 - export PATH="/home/travis/miniconda/bin:$PATH"
 - conda update --yes conda
-- conda install --yes -c conda-forge -c alubbock python="$PYVER" numpy scipy nose
+- conda install --yes -c conda-forge -c alubbock python="$PYVER" numpy scipy nose cython
   pysb
-- pip install python-dateutil
-- pip install -r requirements.txt
 install:
 - pip install .
 script:
@@ -27,4 +25,4 @@ deploy:
   on:
     branch: master
     tags: true
-    condition: $PYVER = 2.7
+    condition: $PYVER = 3.6

pydream/Dream.py

@@ -5,8 +5,8 @@
 from . import Dream_shared_vars
 from datetime import datetime
 import traceback
-import multiprocess as mp
-import multiprocess.pool as mp_pool
+import multiprocessing as mp
+from multiprocessing import pool
 import time
 
 class Dream():
@@ -55,11 +55,18 @@ class Dream():
         A model name to be used as a prefix when saving history and crossover value files.
     hardboundaries : bool
         Whether to relect point back into bounds of hard prior (i.e., if using a uniform prior, reflect points outside of boundaries back in, so you don't waste time looking at points with logpdf = -inf).
+    mp_context : a multiprocessing context or None. It will be used to launch the workers
     """
-
-    def __init__(self, model, variables=None, nseedchains=None, nCR=3, adapt_crossover=True, adapt_gamma=False, crossover_burnin=None, DEpairs=1, lamb=.05, zeta=1e-12, history_thin=10, snooker=.10, p_gamma_unity=.20, gamma_levels=1, start_random=True, save_history=True, history_file=False, crossover_file=False, gamma_file=False, multitry=False, parallel=False, verbose=False, model_name=False, hardboundaries=True, **kwargs):
 
-        #Set model and variable attributes (if no variables passed, set to all parameters)
+    def __init__(self, model, variables=None, nseedchains=None, nCR=3, adapt_crossover=True, adapt_gamma=False,
+                 crossover_burnin=None, DEpairs=1, lamb=.05, zeta=1e-12, history_thin=10, snooker=.10,
+                 p_gamma_unity=.20, gamma_levels=1, start_random=True, save_history=True, history_file=False,
+                 crossover_file=False, gamma_file=False, multitry=False, parallel=False, verbose=False,
+                 model_name=False, hardboundaries=True, mp_context=None, **kwargs):
+
+        # Set Dream multiprocessing context
+        self.mp_context = mp_context
+        # Set model and variable attributes (if no variables passed, set to all parameters)
         self.model = model
         self.model_name = model_name
         if variables is None:
@@ -846,11 +853,9 @@ def mt_evaluate_logps(self, parallel, multitry, proposed_pts, pfunc, ref=False):
 
         #If using multi-try and running in parallel farm out proposed points to process pool.
         if parallel:
-            p = mp.Pool(multitry)
-            args = list(zip([self]*multitry, np.squeeze(proposed_pts)))
-            logps = p.map(call_logp, args)
-            p.close()
-            p.join()
+            args = list(zip([self] * multitry, np.squeeze(proposed_pts)))
+            with pool.Pool(multitry, context=self.mp_context) as p:
+                logps = p.map(call_logp, args)
             log_priors = [val[0] for val in logps]
             log_likes = [val[1] for val in logps]
 
@@ -990,22 +995,81 @@ def metrop_select(mr, q, q0):
         # Reject proposed value
         return q0
 
-
-class NoDaemonProcess(mp.Process):
-    def __init__(self, group=None, target=None, name=None, args=(), kwargs={}):
-        mp.Process.__init__(self, group, target, name, args, kwargs)
 
-    # make 'daemon' attribute always return False
-    def _get_daemon(self):
+# Pythons 2.7, 3.4-3.7.0, and 3.7.1 have three different implementations of
+# pool.Pool().Process(), and the type of the result varies based on the default
+# multiprocessing context, so we need to dynamically patch the daemon property
+class NonDaemonMixin(object):
+    @property
+    def daemon(self):
         return False
-    def _set_daemon(self, value):
+
+    @daemon.setter
+    def daemon(self, val):
+        pass
+
+
+try:
+    from multiprocessing import context
+
+
+    # Exists on all platforms
+    class NonDaemonSpawnProcess(NonDaemonMixin, context.SpawnProcess):
+        pass
+
+
+    class NonDaemonSpawnContext(context.SpawnContext):
+        Process = NonDaemonSpawnProcess
+
+
+    _nondaemon_context_mapper = {
+        'spawn': NonDaemonSpawnContext()
+    }
+
+    # POSIX only
+    try:
+        class NonDaemonForkProcess(NonDaemonMixin, context.ForkProcess):
+            pass
+
+
+        class NonDaemonForkContext(context.ForkContext):
+            Process = NonDaemonForkProcess
+
+
+        _nondaemon_context_mapper['fork'] = NonDaemonForkContext()
+    except AttributeError:
+        pass
+    # POSIX only
+    try:
+        class NonDaemonForkServerProcess(NonDaemonMixin, context.ForkServerProcess):
+            pass
+
+
+        class NonDaemonForkServerContext(context.ForkServerContext):
+            Process = NonDaemonForkServerProcess
+
+
+        _nondaemon_context_mapper['forkserver'] = NonDaemonForkServerContext()
+    except AttributeError:
+        pass
+
+
+    class DreamPool(pool.Pool):
+        def __init__(self, processes=None, initializer=None, initargs=(),
+                     maxtasksperchild=None, context=None):
+            if context is None:
+                context = mp.get_context()
+            context = _nondaemon_context_mapper[context._name]
+            super(DreamPool, self).__init__(processes=processes,
+                                            initializer=initializer,
+                                            initargs=initargs,
+                                            maxtasksperchild=maxtasksperchild,
+                                            context=context)
+
+except ImportError:
+    class NonDaemonProcess(NonDaemonMixin, mp.Process):
         pass
-    daemon = property(_get_daemon, _set_daemon)
 
-#A subclass of multiprocessing.pool.Pool that allows processes to launch child processes (this is necessary for Dream to use multi-try)
-#Taken from http://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic
-class DreamPool(mp_pool.Pool):
-    def __init__(self, processes=None, initializer=None, initargs=None, maxtasksperchild=None):
-        mp_pool.Pool.__init__(self, processes, initializer, initargs, maxtasksperchild)
-    Process = NoDaemonProcess
 
+    class DreamPool(pool.Pool):
+        Process = NonDaemonProcess

pydream/core.py

@@ -1,13 +1,14 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-import multiprocess as mp
+import multiprocessing as mp
 from . import Dream_shared_vars
 from .Dream import Dream, DreamPool
 from .model import Model
 import traceback
 
-def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None, restart=False, verbose=True, nverbose=10, tempering=False, **kwargs):
+
+def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None, restart=False, verbose=True, nverbose=10, tempering=False, mp_context=None, **kwargs):
     """Run DREAM given a set of parameters with priors and a likelihood function.
 
     Parameters
@@ -28,6 +29,7 @@ def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None,
         Whether to print verbose output (including acceptance or rejection of moves and the current acceptance rate).  Default: True
     tempering: Boolean, optional
         Whether to use parallel tempering for the DREAM chains.  Warning: this feature is untested.  Use at your own risk! Default: False
+    mp_context: multiprocessing context or None. It will be used to launch the workers
     kwargs:
         Other arguments that will be passed to the Dream class on initialization.  For more information, see Dream class.
 
@@ -51,11 +53,15 @@ def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None,
     model = Model(likelihood=likelihood, sampled_parameters=parameters)
 
     if restart:
-        step_instance = Dream(model=model, variables=parameters, history_file=kwargs['model_name']+'_DREAM_chain_history.npy', crossover_file=kwargs['model_name']+'_DREAM_chain_adapted_crossoverprob.npy', gamma_file=kwargs['model_name']+'_DREAM_chain_adapted_gammalevelprob.npy', verbose=verbose, **kwargs)
+        step_instance = Dream(model=model, variables=parameters,
+                              history_file=kwargs['model_name'] + '_DREAM_chain_history.npy',
+                              crossover_file=kwargs['model_name'] + '_DREAM_chain_adapted_crossoverprob.npy',
+                              gamma_file=kwargs['model_name'] + '_DREAM_chain_adapted_gammalevelprob.npy',
+                              verbose=verbose, mp_context=mp_context, **kwargs)
     else:
-        step_instance = Dream(model=model, variables=parameters, verbose=verbose, **kwargs)
+        step_instance = Dream(model=model, variables=parameters, verbose=verbose, mp_context=mp_context, **kwargs)
 
-    pool = _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=start)
+    pool = _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=start, mp_context=mp_context)
 
     if tempering:        
 
@@ -72,7 +78,8 @@ def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None,
         returned_vals = pool.map(_sample_dream, args)
         sampled_params = [val[0] for val in returned_vals]
         log_ps = [val[1] for val in returned_vals]   
-
+    pool.close()
+    pool.join()
     return sampled_params, log_ps
 
 def _sample_dream(args):
@@ -236,7 +243,7 @@ def _sample_dream_pt_chain(args):
 
     return q1, logprior1, loglike1, dream_instance
 
-def _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=None):
+def _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=None, mp_context=None):
 
     min_njobs = (2*len(step_instance.DEpairs))+1
     if nchains < min_njobs:
@@ -262,23 +269,28 @@ def _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=None):
         raise Exception('The size of the seeded starting history is insufficient.  Increase nseedchains>=%s.' %str(min_nseedchains))
 
     current_position_dim = nchains*step_instance.total_var_dimension
-    history_arr = mp.Array('d', [0]*int(arr_dim))
+    # Get context to define arrays
+    if mp_context is None:
+        ctx = mp.get_context(mp_context)
+    else:
+        ctx = mp_context
+    history_arr = ctx.Array('d', [0] * int(arr_dim))
     if step_instance.history_file != False:
         history_arr[0:len_old_history] = old_history.flatten()
     nCR = step_instance.nCR
     ngamma = step_instance.ngamma
     crossover_setting = step_instance.CR_probabilities
-    crossover_probabilities = mp.Array('d', crossover_setting)  
-    ncrossover_updates = mp.Array('d', [0]*nCR)
-    delta_m = mp.Array('d', [0]*nCR)
+    crossover_probabilities = ctx.Array('d', crossover_setting)
+    ncrossover_updates = ctx.Array('d', [0] * nCR)
+    delta_m = ctx.Array('d', [0] * nCR)
     gamma_level_setting = step_instance.gamma_probabilities
-    gamma_probabilities = mp.Array('d', gamma_level_setting)
-    ngamma_updates = mp.Array('d', [0]*ngamma)
-    delta_m_gamma = mp.Array('d', [0]*ngamma)
-    current_position_arr = mp.Array('d', [0]*current_position_dim)
-    shared_nchains = mp.Value('i', nchains)
-    n = mp.Value('i', 0)
-    tf = mp.Value('c', b'F')
+    gamma_probabilities = ctx.Array('d', gamma_level_setting)
+    ngamma_updates = ctx.Array('d', [0] * ngamma)
+    delta_m_gamma = ctx.Array('d', [0] * ngamma)
+    current_position_arr = ctx.Array('d', [0] * current_position_dim)
+    shared_nchains = ctx.Value('i', nchains)
+    n = ctx.Value('i', 0)
+    tf = ctx.Value('c', b'F')
 
     if step_instance.crossover_burnin == None:
         step_instance.crossover_burnin = int(np.floor(niterations/10))
@@ -288,9 +300,12 @@ def _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=None):
             print('Warning: start position provided but random_start set to True.  Overrode random_start value and starting walk at provided start position.')
             step_instance.start_random = False
 
-    p = DreamPool(nchains, initializer=_mp_dream_init, initargs=(history_arr, current_position_arr, shared_nchains, crossover_probabilities, ncrossover_updates, delta_m, gamma_probabilities, ngamma_updates, delta_m_gamma, n, tf, ))
-    #p = mp.pool.ThreadPool(nchains, initializer=_mp_dream_init, initargs=(history_arr, current_position_arr, shared_nchains, crossover_probabilities, ncrossover_updates, delta_m, gamma_probabilities, ngamma_updates, delta_m_gamma, n, tf, ))
-    #p = mp.Pool(nchains, initializer=_mp_dream_init, initargs=(history_arr, current_position_arr, shared_nchains, crossover_probabilities, ncrossover_updates, delta_m, gamma_probabilities, ngamma_updates, delta_m_gamma, n, tf, ))
+    p = DreamPool(nchains, context=ctx, initializer=_mp_dream_init,
+                  initargs=(history_arr, current_position_arr, shared_nchains,
+                            crossover_probabilities, ncrossover_updates, delta_m,
+                            gamma_probabilities, ngamma_updates, delta_m_gamma, n, tf,))
+    # p = mp.pool.ThreadPool(nchains, initializer=_mp_dream_init, initargs=(history_arr, current_position_arr, shared_nchains, crossover_probabilities, ncrossover_updates, delta_m, gamma_probabilities, ngamma_updates, delta_m_gamma, n, tf, ))
+    # p = mp.Pool(nchains, initializer=_mp_dream_init, initargs=(history_arr, current_position_arr, shared_nchains, crossover_probabilities, ncrossover_updates, delta_m, gamma_probabilities, ngamma_updates, delta_m_gamma, n, tf, ))
 
     return p
 

pydream/parameters.py

@@ -32,8 +32,6 @@ def random(self, reseed=False):
         else:
             random_seed = None
 
-        random = self.dist.rvs(random_state=random_seed)
-
         return self.dist.rvs(random_state=random_seed)
 
     def prior(self, q0):

pydream/tests/test_dream.py

@@ -8,7 +8,7 @@
 import unittest
 from os import remove
 
-import multiprocess as mp
+import multiprocessing as mp
 import numpy as np
 import pydream.Dream_shared_vars
 from pydream.Dream import Dream

setup.py

@@ -13,7 +13,7 @@ def readme():
       author='Erin Shockley',
       author_email='[email protected]',
       packages=['pydream'],
-      install_requires=['numpy', 'scipy', 'multiprocess'],
+      install_requires=['numpy', 'scipy'],
       zip_safe=False,
       test_suite='nose.collector',
       tests_require=['nose'],