Merge branch 'dev'

ChangeLog

@@ -1,3 +1,11 @@
+2015-01-08	Emmanuel Benazera <emmanuel.benazera at inria.fr>
+	* libcmaes v0.9.4
+	- work around clang bug, now working with clang, ref #19
+	- easier build on OSX
+	- added uncertainty handling scheme for noisy objective functions, ref #65
+	- optional support for surrogates at compile time, reducing the overal lib size, ref #90
+	- fixed uninstall of python bindings
+
 2014-11-17	Emmanuel Benazera <emmanuel.benazera at inria.fr>
 	* libcmaes v0.9.3:
 	- full support for surrogates, allowing optimization of costly objective functions, ref #57

configure.ac

@@ -1,6 +1,6 @@
 # configure for libcmaes
 # author
-AC_INIT(libcmaes,0.9.3)
+AC_INIT(libcmaes,0.9.4)
 AC_CONFIG_SRCDIR(Makefile.am)
 AM_INIT_AUTOMAKE([subdir-objects])
 AM_CONFIG_HEADER([libcmaes_config.h])
@@ -13,8 +13,9 @@ AC_CONFIG_FILES(libcmaes.pc)
 AC_PROG_INSTALL
 AC_CANONICAL_HOST
 
-AC_PROG_CC(g++ c++) # force c++ compiler on C files, otherwise BBOB lib doesn't link properly
-AC_PROG_CXX
+AC_PROG_CC(clang++ g++ c++) # force c++ compiler on C files, otherwise BBOB lib doesn't link properly
+AC_PROG_CXX(clang++ g++ c++)
+AM_CONDITIONAL(HAVE_CLANG, test x"$CXX" = xclang++)
 AC_PROG_INSTALL
 AC_PROG_LIBTOOL
 
@@ -137,6 +138,19 @@ AC_HELP_STRING([--enable-onlylib],[Compile the library alone without tests and e
 esac],[onlylib=false])
 AM_CONDITIONAL(HAVE_ONLYLIB, test x$onlylib = xtrue)
 
+# Whether to compile support for surrogates
+AC_ARG_ENABLE(surrog,
+AC_HELP_STRING([--enable-surrog],[Compile the library with support for surrogates (default is YES)]),
+[case "${enableval}" in 
+	yes) surrog=true ;;
+	no) surrog=false ;;
+	*) AC_MSG_ERROR(bad_value ${enableval} for --enable-surrog) ;;
+esac],[surrog=true])
+AM_CONDITIONAL(HAVE_SURROG, test x$surrog = xtrue)
+if test x"$surrog" = x"true"; then
+   AC_DEFINE(HAVE_SURROG, 1, [surrog])
+fi
+
 # Whether debug mode
 AC_ARG_ENABLE(debug,
 AC_HELP_STRING([--enable-debug],[Enable debug mode (default is NO)]),

examples/Makefile.am

@@ -1,4 +1,4 @@
-bin_PROGRAMS=sample_code sample_code_genopheno sample_code_pfunc sample_code_ask_tell sample_code_bounds sample_code_gradient sample_code_lscaling sample_code_lscaling_sigmas lorentzpeakbench sample_code_pffunc sample_code_surrogate1 sample_code_simple_surrogate_rsvm sample_code_surrogate_rsvm test_rsvm
+bin_PROGRAMS=sample_code sample_code_genopheno sample_code_pfunc sample_code_ask_tell sample_code_bounds sample_code_gradient sample_code_lscaling sample_code_lscaling_sigmas sample_code_pffunc
 sample_code_SOURCES=sample-code.cc
 sample_code_genopheno_SOURCES=sample-code-genopheno.cc
 sample_code_pfunc_SOURCES=sample-code-pfunc.cc
@@ -8,17 +8,27 @@ sample_code_gradient_SOURCES=sample-code-gradient.cc
 sample_code_lscaling_SOURCES=sample-code-lscaling.cc
 sample_code_lscaling_sigmas_SOURCES=sample-code-lscaling-sigmas.cc
 sample_code_pffunc_SOURCES=sample-code-pffunc.cc
+
+if HAVE_SURROG
+bin_PROGRAMS += sample_code_surrogate1 test_rsvm
 sample_code_surrogate1_SOURCES=surrogates/sample-code-surrogate1.cc
-sample_code_simple_surrogate_rsvm_SOURCES=surrogates/sample-code-simple-surrogate-rsvm.cc
-sample_code_surrogate_rsvm_SOURCES=surrogates/sample-code-surrogate-rsvm.cc
 test_rsvm_SOURCES=surrogates/test-rsvm.cc
+endif
 
 if HAVE_GFLAGS
 bin_PROGRAMS += lorentzpeakbench
 lorentzpeakbench_SOURCES=lorentzpeakbench.cc
+if HAVE_SURROG
+bin_PROGRAMS += sample_code_simple_surrogate_rsvm sample_code_surrogate_rsvm
+sample_code_simple_surrogate_rsvm_SOURCES=surrogates/sample-code-simple-surrogate-rsvm.cc
+sample_code_surrogate_rsvm_SOURCES=surrogates/sample-code-surrogate-rsvm.cc
+endif
 endif
 
 AM_CPPFLAGS=-I../src/ -I$(EIGEN3_INC)
-AM_CXXFLAGS=-g -Wall -fopenmp
+AM_CXXFLAGS=-g -Wall
+if !HAVE_CLANG
+AM_CXXFLAGS+=-fopenmp
+endif
 AM_LDFLAGS=-L../src
 LDADD=-lcmaes -lm $(GLOG_LIBS) $(GFLAGS_LIBS)

examples/sample-code-ask-tell.cc

@@ -82,7 +82,7 @@ int main(int argc, char *argv[])
   std::vector<double> x0(dim,10.0);
   double sigma = 0.1;
 
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma);
+  CMAParameters<> cmaparams(x0,sigma);
   //ESOptimizer<CMAStrategy<CovarianceUpdate>,CMAParameters<>> optim(fsphere,cmaparams);
   ESOptimizer<customCMAStrategy,CMAParameters<>> optim(fsphere,cmaparams);
 

examples/sample-code-bounds.cc

@@ -44,7 +44,7 @@ int main(int argc, char *argv[])
     }
   std::vector<double> x0(dim,1.0); // beware that x0 is within bounds.
   GenoPheno<pwqBoundStrategy> gp(lbounds,ubounds,dim); // genotype / phenotype transform associated to bounds.
-  CMAParameters<GenoPheno<pwqBoundStrategy>> cmaparams(dim,&x0.front(),sigma,-1,0,gp); // -1 for automatically decided lambda, 0 is for random seeding of the internal generator.
+  CMAParameters<GenoPheno<pwqBoundStrategy>> cmaparams(x0,sigma,-1,0,gp); // -1 for automatically decided lambda, 0 is for random seeding of the internal generator.
   cmaparams.set_algo(aCMAES);
   CMASolutions cmasols = cmaes<GenoPheno<pwqBoundStrategy>>(fsphere,cmaparams);
   std::cout << "best solution: " << cmasols << std::endl;

examples/sample-code-genopheno.cc

@@ -52,7 +52,7 @@ int main(int argc, char *argv[])
   double sigma = 0.1;
   //int lambda = 100; // offsprings at each generation.
   GenoPheno<> gp(genof,phenof);
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma,-1,0,gp); // -1 for automatically decided lambda.
+  CMAParameters<> cmaparams(x0,sigma,-1,0,gp); // -1 for automatically decided lambda.
   //cmaparams._algo = BIPOP_CMAES;
   CMASolutions cmasols = cmaes<>(fsphere,cmaparams);
   std::cout << "best solution: " << cmasols << std::endl;

examples/sample-code-gradient.cc

@@ -46,7 +46,7 @@ int main(int argc, char *argv[])
   std::vector<double> x0(dim,10.0);
   double sigma = 0.1;
   //int lambda = 100; // offsprings at each generation.
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma);
+  CMAParameters<> cmaparams(x0,sigma);
   cmaparams.set_algo(aCMAES);
   CMASolutions cmasols = cmaes<>(fsphere,cmaparams,
 				 CMAStrategy<CovarianceUpdate>::_defaultPFunc,  // use default progress function

examples/sample-code-lscaling.cc

@@ -44,7 +44,7 @@ int main(int argc, char *argv[])
       ubounds[i] = 2.0;
     }
   GenoPheno<pwqBoundStrategy,linScalingStrategy> gp(lbounds,ubounds,dim);
-  CMAParameters<GenoPheno<pwqBoundStrategy,linScalingStrategy>> cmaparams(dim,&x0.front(),sigma,-1,0,gp); // -1 for automatically decided lambda.
+  CMAParameters<GenoPheno<pwqBoundStrategy,linScalingStrategy>> cmaparams(x0,sigma,-1,0,gp); // -1 for automatically decided lambda.
   cmaparams.set_algo(aCMAES);
   CMASolutions cmasols = cmaes<GenoPheno<pwqBoundStrategy,linScalingStrategy>>(fsphere,cmaparams);
   std::cout << "best solution: " << cmasols << std::endl;

examples/sample-code-pffunc.cc

@@ -46,7 +46,7 @@ int main(int argc, char *argv[])
   std::vector<double> x0(dim,10.0);
   double sigma = 0.1;
   //int lambda = 100; // offsprings at each generation.
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma);
+  CMAParameters<> cmaparams(x0,sigma);
   cmaparams.set_fplot("pffunc.dat"); // DON'T MISS: mandatory output file name.
   CMASolutions cmasols = cmaes<>(rosenbrock,cmaparams,CMAStrategy<CovarianceUpdate>::_defaultPFunc,nullptr,cmasols,plotf);
   std::cout << "best solution: " << cmasols << std::endl;

examples/sample-code-pfunc.cc

@@ -47,7 +47,7 @@ int main(int argc, char *argv[])
   std::vector<double> x0(dim,10.0);
   double sigma = 0.1;
   //int lambda = 100; // offsprings at each generation.
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma);
+  CMAParameters<> cmaparams(x0,sigma);
   //cmaparams._algo = BIPOP_CMAES;
   CMASolutions cmasols = cmaes<>(rosenbrock,cmaparams,select_time);
   std::cout << "best solution: " << cmasols << std::endl;

examples/sample-code.cc

@@ -38,7 +38,7 @@ int main(int argc, char *argv[])
   std::vector<double> x0(dim,10.0);
   double sigma = 0.1;
   //int lambda = 100; // offsprings at each generation.
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma);
+  CMAParameters<> cmaparams(x0,sigma);
   //cmaparams._algo = BIPOP_CMAES;
   CMASolutions cmasols = cmaes<>(fsphere,cmaparams);
   std::cout << "best solution: " << cmasols << std::endl;

examples/surrogates/sample-code-simple-surrogate-rsvm.cc

@@ -65,9 +65,10 @@ template<class TCovarianceUpdate=CovarianceUpdate,class TGenoPheno=GenoPheno<NoB
 	  _rsvm._encode = true;
 	  _rsvm.train(x,_rsvm_iter,cov,xmean);
 
-	  this->set_train_error(this->compute_error(cp,
-						    eostrat<TGenoPheno>::get_solutions().csqinv()));
-
+	  dMat cinv = eostrat<TGenoPheno>::get_solutions().csqinv();
+	  //this->set_train_error(this->compute_error(cp,cinv)); // clang doesn't like this call within a lambda...
+	  this->set_train_error(_rsvm.error(x,x,fvalues,cinv,xmean));
+
 	  //debug
 	  //std::cout << "training error=" << _rsvm.error(x,x,fvalues,cov,xmean) << std::endl;
 	  //std::cout << "train error=" << this->get_train_error() << std::endl;

examples/surrogates/sample-code-surrogate1.cc

@@ -53,7 +53,7 @@ int main(int argc, char *argv[])
   std::vector<double> x0(dim,10.0);
   double sigma = 0.1;
 
-  CMAParameters<> cmaparams(dim,&x0.front(),sigma);
+  CMAParameters<> cmaparams(x0,sigma);
   ESOptimizer<SimpleSurrogateStrategy<CMAStrategy>,CMAParameters<>> optim(fsphere,cmaparams);
   optim.set_ftrain(ftrain);
   optim.set_fpredict(fpredict);

python/lcmaes.cc

@@ -20,7 +20,9 @@
  */
 
 #include "cmaes.h"
+#ifdef HAVE_SURROG
 #include "surrogatestrategy.h"
+#endif
 using namespace libcmaes;
 
 #include <boost/python.hpp>
@@ -138,6 +140,7 @@ GenoPheno<TBoundStrategy,TScalingStrategy> make_genopheno(const boost::python::l
   return GenoPheno<TBoundStrategy,TScalingStrategy>(&vlbounds.front(),&vubounds.front(),dim);
 }
 
+#ifdef HAVE_SURROG
 /* wrapper to surrogate cmaes high level function. */
 int csurrfunc_f(const boost::python::list &candidates, boost::python::object &pyArray)
 {
@@ -199,6 +202,7 @@ template <class TGenoPheno=GenoPheno<NoBoundStrategy>>
     optim.optimize();
     return optim.get_solutions();
   }
+#endif
 
 BOOST_PYTHON_MODULE(lcmaes)
 {
@@ -401,6 +405,7 @@ BOOST_PYTHON_MODULE(lcmaes)
   def("pcmaes_pwqb_ls",pcmaes<GenoPheno<pwqBoundStrategy,linScalingStrategy>>,args("fitfunc","parameters"));
 
 
+#ifdef HAVE_SURROG
   /*- surrogates -*/
   def_function<int(const boost::python::list&,boost::python::object&)>("csurrfunc_pbf","training function for python");
   scope().attr("csurrfunc_bf") = csurrfunc_bf;
@@ -411,5 +416,6 @@ BOOST_PYTHON_MODULE(lcmaes)
   def("surrpcmaes_pwqb",surrpcmaes<GenoPheno<pwqBoundStrategy>>,args("fitfunc","trainfunc","predictfunc","parameters","exploit","l"));
   def("surrpcmaes_ls",surrpcmaes<GenoPheno<NoBoundStrategy,linScalingStrategy>>,args("fitfunc","trainfunc","predictfunc","parameters","exploit","l"));
   def("surrpcmaes_pwqb_ls",surrpcmaes<GenoPheno<pwqBoundStrategy,linScalingStrategy>>,args("fitfunc","trainfunc","predictfunc","parameters","exploit","l"));
+#endif
 
 } // end boost

python/ptest.py

@@ -2,10 +2,10 @@
 
 # input parameters for a 10-D problem
 x = [10]*10
-olambda = 10 # lambda is a reserved keyword in python, using olambda instead.
+lambda_ = 10 # lambda is a reserved keyword in python, using lambda_ instead.
 seed = 0 # 0 for seed auto-generated within the lib.
 sigma = 0.1
-p = lcmaes.make_simple_parameters(x,sigma,olambda,seed)
+p = lcmaes.make_simple_parameters(x,sigma,lambda_,seed)
 p.set_str_algo("acmaes")
 
 # objective function.
@@ -24,8 +24,8 @@ def nfitfunc(x,n):
 # collect and inspect results
 bcand = cmasols.best_candidate()
 bx = lcmaes.get_candidate_x(bcand)
-print "best x=",bx
-print "distribution mean=",lcmaes.get_solution_xmean(cmasols)
+print("best x=",bx)
+print("distribution mean=",lcmaes.get_solution_xmean(cmasols))
 cov = lcmaes.get_solution_cov(cmasols) # numpy array
-print "cov=",cov
-print "elapsed time=",cmasols.elapsed_time(),"ms"
+print("cov=",cov)
+print("elapsed time=",cmasols.elapsed_time(),"ms")

python/ptest_bounds.py

@@ -2,13 +2,13 @@
 
 # input parameters for a 10-D problem
 x = [3]*10
-olambda = 10 # lambda is a reserved keyword in python, using olambda instead.
+lambda_ = 10 # lambda is a reserved keyword in python, using lambda_ instead.
 seed = 0 # 0 for seed auto-generated within the lib.
 sigma = 0.1
 lbounds = [-4]*10
 ubounds = [4]*10
 gp = lcmaes.make_genopheno_pwqb(lbounds,ubounds,10)
-p = lcmaes.make_parameters_pwqb(x,sigma,olambda,seed,gp)
+p = lcmaes.make_parameters_pwqb(x,sigma,lambda_,seed,gp)
 
 # objective function.
 def nfitfunc(x,n):
@@ -26,8 +26,8 @@ def nfitfunc(x,n):
 # collect and inspect results
 bcand = cmasols.best_candidate()
 bx = lcmaes.get_candidate_x(bcand)
-print "best x=",bx
-print "distribution mean=",lcmaes.get_solution_xmean(cmasols)
+print("best x=",bx)
+print("distribution mean=",lcmaes.get_solution_xmean(cmasols))
 cov = lcmaes.get_solution_cov(cmasols) # numpy array
-print "cov=",cov
-print "elapsed time=",cmasols.elapsed_time(),"ms"
+print("cov=",cov)
+print("elapsed time=",cmasols.elapsed_time(),"ms")

python/surrptest.py

@@ -7,10 +7,10 @@
 x0 = 2
 dim = 10
 x = [x0]*dim
-olambda = 10 # lambda is a reserved keyword in python, using olambda instead.
+lambda_ = 10 # lambda is a reserved keyword in python, using lambda_ instead.
 seed = 0 # 0 for seed auto-generated within the lib.
 sigma = 0.1
-p = lcmaes.make_simple_parameters(x,sigma,olambda,seed)
+p = lcmaes.make_simple_parameters(x,sigma,lambda_,seed)
 p.set_str_algo("cmaes")
 #p.set_max_iter(20)
 p.set_ftarget(1e-3)
@@ -101,8 +101,8 @@ def nsurrfunc(c,m):
 # collect and inspect results
 bcand = cmasols.best_candidate()
 bx = lcmaes.get_candidate_x(bcand)
-print "best x=",bx," / fevals=",cmasols.fevals()
-print "distribution mean=",lcmaes.get_solution_xmean(cmasols)
+print("best x=",bx," / fevals=",cmasols.fevals())
+print("distribution mean=",lcmaes.get_solution_xmean(cmasols))
 cov = lcmaes.get_solution_cov(cmasols) # numpy array
 #print "cov=",cov
-print "elapsed time=",cmasols.elapsed_time(),"ms"
+print("elapsed time=",cmasols.elapsed_time(),"ms")

src/Makefile.am

@@ -4,12 +4,20 @@ libcmaesdatadir = $(datadir)
 libcmaesincludedir = $(includedir)/libcmaes
 
 libcmaes_LTLIBRARIES=libcmaes.la
-libcmaes_la_SOURCES=libcmaes_config.h cmaes.h eo_matrix.h cmastrategy.cc esoptimizer.h esostrategy.h esostrategy.cc cmasolutions.h cmasolutions.cc parameters.h cmaparameters.h cmaparameters.cc cmastopcriteria.h cmastopcriteria.cc ipopcmastrategy.h ipopcmastrategy.cc bipopcmastrategy.h bipopcmastrategy.cc covarianceupdate.h covarianceupdate.cc acovarianceupdate.h acovarianceupdate.cc vdcmaupdate.h vdcmaupdate.cc pwq_bound_strategy.h pwq_bound_strategy.cc eigenmvn.h candidate.h genopheno.h noboundstrategy.h scaling.h llogging.h surrogatestrategy.cc surrogatestrategy.h surrogates/rankingsvm.hpp surrogates/surr_rsvm_strategy.hpp pli.h errstats.cc errstats.h contour.h
+libcmaes_la_SOURCES=libcmaes_config.h cmaes.h eo_matrix.h cmastrategy.cc esoptimizer.h esostrategy.h esostrategy.cc cmasolutions.h cmasolutions.cc parameters.h cmaparameters.h cmaparameters.cc cmastopcriteria.h cmastopcriteria.cc ipopcmastrategy.h ipopcmastrategy.cc bipopcmastrategy.h bipopcmastrategy.cc covarianceupdate.h covarianceupdate.cc acovarianceupdate.h acovarianceupdate.cc vdcmaupdate.h vdcmaupdate.cc pwq_bound_strategy.h pwq_bound_strategy.cc eigenmvn.h candidate.h genopheno.h noboundstrategy.h scaling.h llogging.h pli.h errstats.cc errstats.h contour.h
 
-libcmaesinclude_HEADERS = cmaes.h eo_matrix.h cmastrategy.h esoptimizer.h esostrategy.h cmasolutions.h parameters.h cmaparameters.h cmastopcriteria.h ipopcmastrategy.h bipopcmastrategy.h covarianceupdate.h acovarianceupdate.h vdcmaupdate.h pwq_bound_strategy.h eigenmvn.h candidate.h genopheno.h noboundstrategy.h scaling.h llogging.h surrogatestrategy.h errstats.h pli.h contour.h
+libcmaesinclude_HEADERS = cmaes.h eo_matrix.h cmastrategy.h esoptimizer.h esostrategy.h cmasolutions.h parameters.h cmaparameters.h cmastopcriteria.h ipopcmastrategy.h bipopcmastrategy.h covarianceupdate.h acovarianceupdate.h vdcmaupdate.h pwq_bound_strategy.h eigenmvn.h candidate.h genopheno.h noboundstrategy.h scaling.h llogging.h errstats.h pli.h contour.h
+
+if HAVE_SURROG
+libcmaes_la_SOURCES += surrogatestrategy.cc surrogatestrategy.h surrogates/rankingsvm.hpp surrogates/surr_rsvm_strategy.hpp
+libcmaesinclude_HEADERS += surrogatestrategy.h
+endif
 
 AM_CPPFLAGS=-I$(EIGEN3_INC)
-AM_CXXFLAGS=-Wall -Wextra -g -fopenmp
+AM_CXXFLAGS=-Wall -Wextra -g
+if !HAVE_CLANG
+AM_CXXFLAGS += -fopenmp
+endif
 
 uninstall-hook:
 	if test -d ${libcmaesincludedir}; then rm -rf ${libcmaesincludedir}; fi

src/candidate.h

@@ -121,6 +121,23 @@ namespace libcmaes
    dVec _x; /**< function parameter vector. */
   };
 
+  class RankedCandidate : public Candidate
+  {
+  public:
+    RankedCandidate(const double &fvalue_mut,
+		    Candidate &c,
+		    const int &idx)
+      :Candidate(c.get_fvalue(),dVec()),_idx(idx),_fvalue_mut(fvalue_mut)
+    {}
+    ~RankedCandidate() {}
+
+    int _idx = -1;
+    double _fvalue_mut;
+    int _r1 = 0;
+    int _r2 = 0;
+    double _delta = 0.0;
+  };
+
 }
 
 #endif

src/cmaparameters.cc

@@ -109,6 +109,10 @@ namespace libcmaes
 
     // active cma.
     _deltamaxsigma = std::numeric_limits<double>::max();
+
+    // uncertainty handling.
+    this->_rlambda = std::max(0.1,2.0/Parameters<TGenoPheno>::_lambda);
+    this->_alphathuh = 1 + 2.0/(Parameters<TGenoPheno>::_dim+10.0);
   }
 
   template <class TGenoPheno>

src/cmasolutions.h

@@ -49,8 +49,10 @@ namespace libcmaes
     friend class CovarianceUpdate;
     friend class ACovarianceUpdate;
     template <class U> friend class errstats;
+#ifdef HAVE_SURROG
     template <template <class X,class Y> class U, class V, class W> friend class SimpleSurrogateStrategy;
     template <template <class X,class Y> class U, class V, class W> friend class ACMSurrogateStrategy;
+#endif
     friend class VDCMAUpdate;
 
   public:
@@ -429,6 +431,10 @@ namespace libcmaes
     Candidate _initial_candidate;
 
     dVec _v; /**< complementary vector for use in vdcma. */
+
+    std::vector<RankedCandidate> _candidates_uh; /**< temporary set of candidates used by uncertainty handling scheme. */
+    int _lambda_reev; /**< number of reevaluated solutions at current step. */
+    double _suh; /**< uncertainty level computed by uncertainty handling procedure. */
   };
 
   std::ostream& operator<<(std::ostream &out,const CMASolutions &cmas);