[EXPR-2]cleanup

SirIsaac/fittingProblem.py

@@ -12,7 +12,6 @@
 
 # 9.9.2015 directory needed for direct calling using MPI?
 import os
-from scipy.special import logsumexp
 
 SIRISAACDIR = os.path.abspath(os.path.dirname(__file__))
 
@@ -174,7 +173,6 @@ def fitAll(self,usePreviousParams=True,fitPerfectModel=False,resume=True,
                                   to the number of fittingModels in
                                   self.fittingModelNames.
         """
-        print("verboseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee", self.verbose)
         oldFitParameters = []
         oldCost = scipy.inf
 
@@ -187,7 +185,6 @@ def fitAll(self,usePreviousParams=True,fitPerfectModel=False,resume=True,
 
         for name in self.fittingModelNames:
           fittingModel = self.fittingModelDict[name]
-          print("fittingModel",fittingModel)
           # 4.18.2012
           if self.costDict.has_key(name) and resume:
             # We've already fit this one.
@@ -202,7 +199,6 @@ def fitAll(self,usePreviousParams=True,fitPerfectModel=False,resume=True,
 
             if usePreviousParams:
                 fittingModel.initializeParameters(oldFitParameters)
-            print("oldfitpramsssssssssss",oldFitParameters)
             # 4.17.2012
             if self.smallerBestParamsDict.has_key(name):
                 smallerBestParams = self.smallerBestParamsDict[name]
@@ -214,13 +210,10 @@ def fitAll(self,usePreviousParams=True,fitPerfectModel=False,resume=True,
             # 9.20.2012 XXX Should we never include priors for cost?
             if fittingDataDerivs is not None: includePriors = False
             else: includePriors = True
-            print("include priors",includePriors)
             newFitParameters =                                                      \
               fittingModel.fitToData(self.fittingData,self.indepParamsList,         \
                                      otherStartingPoint=smallerBestParams,          \
                                      fittingDataDerivs=fittingDataDerivs,**kwargs)
-            print("fffffffffffffffffffffffffffffffffffff")
-            print(newFitParameters)
 
             if not hasattr(self,'fittingDataDerivs'):
                 self.fittingDataDerivs = None
@@ -235,9 +228,6 @@ def fitAll(self,usePreviousParams=True,fitPerfectModel=False,resume=True,
                 # (Note: This assumes that the default values for the new
                 # parameters make the new (more complex) model behave the
                 # same as the old one before they are changed.)
-                print("new_osssssssssssssssss")
-                print(newCost)
-                print(oldCost)
                 if newCost > oldCost:
                     fittingModel.initializeParameters(oldFitParameters)
                     newFitParameters =                                              \
@@ -275,11 +265,7 @@ def fitAll(self,usePreviousParams=True,fitPerfectModel=False,resume=True,
             fittingModel.initializeParameters(newFitParameters)
             oldCost = newCost
             oldFitParameters = newFitParameters
-            print("old cose llllllllllllllllllllllllllllllllll",oldCost)
-            print("old fit parameters",oldFitParameters)
             self._UpdateDicts(name,includePriors=includePriors)
-          print("diiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiccccccccccccccccccccccccccccccccccccccctttttttttttttttt")
-          print(self.logLikelihoodDict)
           # 5.6.2013 update old files if needed
           if not hasattr(self,'logLikelihoodDict'):
               self._UpdateDicts(name)
@@ -1817,7 +1803,7 @@ def localFitToData_parallel(self,numprocs,fittingData,dataModel,
 
         # call mpi
         stdoutFile = open(prefix+"stdout.txt",'w')
-        subprocess.call([ "mpirun", "-np",str(numprocs),"--allow-run-as-root","python",
+        subprocess.call([ "mpirun", "-np",str(numprocs),"python",
                           os.path.join(SIRISAACDIR, "localFitParallel.py"),
                           inputDictFilename],
                         stderr=stdoutFile,stdout=stdoutFile,env=os.environ)

SirIsaac/localFitParallel.py

@@ -63,7 +63,6 @@
 
 ### Master Process ###
 if MPI_myID == MASTER_PROCESS:
-    print("kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk")
     from simplePickle import save
 
     num_processors = comm.Get_size()
@@ -72,8 +71,6 @@
 
     # list of startParams indices to pass to the workers
     work_array = range(len(startParamsList))
-    print("work arrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr")
-    print(work_array)
     work_size = len(work_array)
 
     # Dispatch jobs to worker processes
@@ -121,8 +118,6 @@
         comm.send(DIE_INDEX, dest=proc)
 
     # Write data to file
-    print("in localfittttttttttttttttt")
-    # print(allOutputsDict)
     save(allOutputsDict,outputFilename)
 
 else:

SirIsaac/mpi_basic.py

@@ -8,7 +8,6 @@
 # Run like so:
 #   mpirun -np 3 python mpi_test.py test_input_filename
 #
-# RUN apt-get update & apt-get install python2-dev build-essential libssl-dev libffi-dev libxml2-dev libxslt1-dev zlib1g-dev
 
 import sys
 from simplePickle import load,save

test/test_fitting_problem.py

@@ -6,18 +6,15 @@
 #
 # Tests for fittingProblem.py
 #
-'''
+
 import unittest
+
 from SirIsaac.fittingProblem import *
 import SloppyCell.ReactionNetworks as scrn
 
 def mock_net():
     """
-    from SirIsaac import fittingProblem
-import SirIsaac
-
-
-Create a simple mock SloppyCell Network object
+    Create a simple mock SloppyCell Network object
     """
     return scrn.Network('mocknet')
 
@@ -51,52 +48,4 @@ def test_fitting_problem_init(self):
                                indepParamsList=indepParamsList)
 
         self.assertEqual(None, ftest.getBestModel())
-'''
-import scipy, pylab
-from SirIsaac import fittingProblem
-
-
-def test_x(self):
-    data = scipy.loadtxt('simpleExample_data.txt')
-    indepParamsList = [ [ expt[0] ] for expt in data ]
-    indepParamsList[:3]
-    sirIsaacData = []
-    for expt in data:
-        sirIsaacData.append( { 'x': { expt[1]: ( expt[2], expt[3] ) } } )
-    sirIsaacData[:3]
-    outputNames = ['x']
-    indepParamNames = ['x_init']
-    complexityStepsize = 2 # increase complexity with steps of size 2
-    complexityMax = 25 # don't try models with complexity > 25
-    complexityList = range(0,complexityMax,complexityStepsize) 
-
-    # ensGen controls the generation of the initial ensemble of 
-    # parameter starting points.
-    totalSteps = 1e3
-    keepSteps = 10
-    seeds = (1,1) # use a fixed random seed
-    ensTemperature = 100.
-    ensGen = fittingProblem.EnsembleGenerator( totalSteps, keepSteps,
-        temperature=ensTemperature, seeds=seeds )
-
-    # Parameters that control when local fitting stops.
-    avegtol = 1e-2
-    maxiter = 100
-
-    # priorSigma controls the width of priors on all parameters
-    priorSigma = 3.
-
-    # If you have pypar installed, you can run on multiple processors
-    numprocs = 6
-
-    # We'll only use a subset of our data to make the example run faster
-    N = 20
-
-    p = fittingProblem.PowerLawFittingProblem( complexityList, 
-        sirIsaacData[:N], indepParamsList=indepParamsList[:N], 
-        outputNames=outputNames, indepParamNames=indepParamNames, 
-        ensGen=ensGen, avegtol=avegtol, maxiter=maxiter,
-        priorSigma=priorSigma, numprocs=numprocs, verbose=True )
-    p.fitAll()
-    #
-    fittingProblem.save(p,'simpleExample_savedFittingProblem.data')
+{"mode":"full","isActive":false}