Fixed windows setuptools multiprocessing issues

setup.py test was failing on windows machines because if
__name__==__main__ was not included in the setup.py folder. This has
been fixed. Also updated setup.py to explicitly include all package with
version requirements to help get it working on Travis CI.

bin/M-LOOP

@@ -18,6 +18,7 @@ import sys
 import argparse
 import mloop as ml
 import mloop.launchers as mll
+import multiprocessing as mp
 
 def main(argv):
     
@@ -33,6 +34,7 @@ def main(argv):
     _ = mll.launch_from_file(config_filename)
 
 if __name__=="__main__":
+	mp.freeze_support()
     main(sys.argv[1:])
 
 

examples/gaussian_process_complete_config.txt

@@ -8,7 +8,7 @@ target_cost = 0.1                #cost to beat
 #Gaussian process options
 controller_type = 'gaussian_process'
 num_params = 2                         #number of parameters
-min_boundary = [-10.,-10.]                #minimum boundary
+min_boundary = [-10.,-10.]             #minimum boundary
 max_boundary = [10.,10.]               #maximum boundary
 length_scale = [1.0]                   #initial lengths scales for GP
 cost_has_noise = True                  #whether cost function has noise
@@ -17,10 +17,11 @@ update_hyperparameters = True          #whether noise level and lengths scales a
 trust_region = [5,5]                   #maximum move distance from best params
 default_bad_cost = 10                  #default cost for bad run
 default_bad_uncertainty = 1            #default uncertainty for bad run
-learner_archive_filename = 'a_word'            #filename of gp archive
-learner_archive_file_type = 'mat'              #file type of archive
+learner_archive_filename = 'a_word'    #filename of gp archive
+learner_archive_file_type = 'mat'      #file type of archive
 predict_global_minima_at_end  = True   #find predicted global minima at end 
 predict_local_minima_at_end = True     #find all local minima of landscape at end
+no_delay = True                       #whether to wait for the GP to make predictions or not. Default True (do not wait)  
 
 #Training source options
 training_type = 'random'               #training type can be random or nelder_mead

examples/tutorial_config.txt

@@ -13,7 +13,7 @@ target_cost = 0.01                        #optimization halts when a cost below
 
 #Learner specific options
 first_params = [0.5,0.5]       #first parameters to try
-trust_region = 0.4         #maximum % move distance from best params
+trust_region = 0.4         	   #maximum % move distance from best params
 
 #File format options
 interface_file_type = 'txt'            #file types of *exp_input.mat* and *exp_output.mat*

mloop/controllers.py

@@ -678,10 +678,13 @@ def _shut_down(self):
         '''
         self.log.debug('GP learner end set.')
         self.end_gp_learner.set()
-        self.gp_learner.join(self.gp_learner.learner_wait*3)
+        self.gp_learner.join()
+        #self.gp_learner.join(self.gp_learner.learner_wait*3)
+        '''
         if self.gp_learner.is_alive():
-            self.log.debug('GP Learner did not join in time had to terminate.')
+            self.log.warning('GP Learner did not join in time had to terminate.')
             self.gp_learner.terminate()
+        '''
         self.log.debug('GP learner joined')   
         last_dict = None
         while not self.gp_learner_params_queue.empty():

mloop/learners.py

@@ -296,7 +296,6 @@ def run(self):
         '''
         Puts the next parameters on the queue which are randomly picked from a uniform distribution between the minimum and maximum boundaries when a cost is added to the cost queue.
         '''
-        
         self.log.debug('Starting Random Learner')
         if self.first_params is None:
             next_params = self.min_boundary + nr.rand(self.num_params) * self.diff_boundary

setup.cfg

@@ -2,4 +2,4 @@
 description-file = README.rst
 
 [aliases]
-test=pytest
+test=pytest

setup.py

@@ -3,44 +3,51 @@
 '''
 from __future__ import absolute_import, division, print_function
 
+import multiprocessing as mp
 import mloop as ml
 
 from setuptools import setup, find_packages
-setup(
-    name = 'M-LOOP',
-    version = ml.__version__,
-    packages = find_packages(),
-    scripts = ['./bin/M-LOOP'],
-    
-    setup_requires=['pytest-runner'],
-    install_requires = ['docutils>=0.3'],
-    tests_require=['pytest'],
 
-    package_data = {
-        # If any package contains *.txt or *.rst files, include them:
-        '': ['*.txt','*.md'],
-    },
-    author = 'Michael R Hush',
-    author_email = 'MichaelRHush@gmail.com',
-    description = 'M-LOOP: Machine-learning online optimization package. A python package of automated optimization tools - enhanced with machine-learning - for quantum scientific experiments, computer controlled systems or other optimization tasks.',
-    license = 'MIT',
-    keywords = 'automated machine learning optimization optimisation science experiment quantum',
-    url = 'https://github.com/michaelhush/M-LOOP/', 
-    download_url = 'https://github.com/michaelhush/M-LOOP/tarball/v2.0.2',
+def main():
+    setup(
+        name = 'M-LOOP',
+        version = ml.__version__,
+        packages = find_packages(),
+        scripts = ['./bin/M-LOOP'],
+        
+        setup_requires=['pytest-runner'],
+        install_requires = ['docutils>=0.3','numpy>=1.11','scipy>=0.17','matplotlib>=1.5','pytest>=2.9'],
+        tests_require=['pytest','setuptools>=26'],
+        
+        package_data = {
+            # If any package contains *.txt or *.rst files, include them:
+            '': ['*.txt','*.md'],
+        },
+        author = 'Michael R Hush',
+        author_email = 'MichaelRHush@gmail.com',
+        description = 'M-LOOP: Machine-learning online optimization package. A python package of automated optimization tools - enhanced with machine-learning - for quantum scientific experiments, computer controlled systems or other optimization tasks.',
+        license = 'MIT',
+        keywords = 'automated machine learning optimization optimisation science experiment quantum',
+        url = 'https://github.com/michaelhush/M-LOOP/', 
+        download_url = 'https://github.com/michaelhush/M-LOOP/tarball/v2.0.2',
+    
+        classifiers = ['Development Status :: 2 - Pre-Alpha',
+                      'Intended Audience :: Science/Research',
+                      'Intended Audience :: Manufacturing',
+                      'License :: OSI Approved :: MIT License',
+                      'Natural Language :: English',
+                      'Operating System :: MacOS :: MacOS X',
+                      'Operating System :: POSIX :: Linux',
+                      'Operating System :: Microsoft :: Windows',
+    				  'Programming Language :: Python :: 2.7',
+    				  'Programming Language :: Python :: 3.4',
+    				  'Programming Language :: Python :: 3.5',
+                      'Programming Language :: Python :: Implementation :: CPython',
+                      'Topic :: Scientific/Engineering',
+                      'Topic :: Scientific/Engineering :: Artificial Intelligence',
+                      'Topic :: Scientific/Engineering :: Physics']
+    )
 
-    classifiers = ['Development Status :: 2 - Pre-Alpha',
-                  'Intended Audience :: Science/Research',
-                  'Intended Audience :: Manufacturing',
-                  'License :: OSI Approved :: MIT License',
-                  'Natural Language :: English',
-                  'Operating System :: MacOS :: MacOS X',
-                  'Operating System :: POSIX :: Linux',
-                  'Operating System :: Microsoft :: Windows',
-				  'Programming Language :: Python :: 2.7',
-				  'Programming Language :: Python :: 3.4',
-				  'Programming Language :: Python :: 3.5',
-                  'Programming Language :: Python :: Implementation :: CPython',
-                  'Topic :: Scientific/Engineering',
-                  'Topic :: Scientific/Engineering :: Artificial Intelligence',
-                  'Topic :: Scientific/Engineering :: Physics']
-)
+if __name__=='__main__':
+    mp.freeze_support()
+    main()  

tests/test_examples.py

@@ -10,13 +10,14 @@
 import mloop.utilities as mlu
 import logging
 import numpy as np
+import multiprocessing
 
 class TestExamples(unittest.TestCase):
     
     @classmethod
     def setUpClass(cls):
         os.chdir(mlu.mloop_path + '/../tests')
-        cls.override_dict = {'file_log_level':logging.DEBUG,'console_log_level':logging.WARNING,'visualizations':False}
+        cls.override_dict = {'file_log_level':logging.DEBUG,'console_log_level':logging.DEBUG,'visualizations':False}
         cls.fake_experiment = mlt.FakeExperiment()
         cls.fake_experiment.start()
     
@@ -56,7 +57,7 @@ def test_nelder_mead_simple_config(self):
         controller = mll.launch_from_file(mlu.mloop_path+'/../examples/nelder_mead_simple_config.txt',
                                           **self.override_dict)
         self.asserts_for_cost_and_params(controller)
-        
+    
     def test_nelder_mead_complete_config(self):
         controller = mll.launch_from_file(mlu.mloop_path+'/../examples/nelder_mead_complete_config.txt',
                                           **self.override_dict)
@@ -66,7 +67,7 @@ def test_gaussian_process_simple_config(self):
         controller = mll.launch_from_file(mlu.mloop_path+'/../examples/gaussian_process_simple_config.txt',
                                           **self.override_dict)
         self.asserts_for_cost_and_params(controller)
-        
+     
     def test_gaussian_process_complete_config(self):
         controller = mll.launch_from_file(mlu.mloop_path+'/../examples/gaussian_process_complete_config.txt',
                                           **self.override_dict)
@@ -80,6 +81,8 @@ def test_tutorial_config(self):
     def asserts_for_cost_and_params(self,controller):
         self.assertTrue(controller.best_cost<=controller.target_cost)
         self.assertTrue(np.sum(np.square(controller.best_params))<=controller.target_cost)
-        
+    
+    
 if __name__ == "__main__":
+    mp.freeze_support()
     unittest.main()