Testing filtering

sigopt · Jan 30, 2018 · 10b22ae · 10b22ae
1 parent 9102f65
commit 10b22ae
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Showing 7 changed files with 23 additions and 5 deletions.
diff --git a/examples/bo_branin.cpp b/examples/bo_branin.cpp
@@ -38,6 +38,12 @@ int main(int nargs, char *args[])
     par.random_seed = 0;
     par.verbose_level = 1;
     par.noise = 1e-10;
+    par.filtering_startup = 10;
+    par.filtering_interval = 2;
+    par.up_margin = 5;
+    par.low_margin = 1e-20;
+    par.kernel.name = "kSum(kNoise,kMaternARD5)";
+
     //bayesopt::utils::ParamLoader::save("bo_branin.txt", par);
   }
 

diff --git a/include/gauss_distribution.hpp b/include/gauss_distribution.hpp
@@ -58,7 +58,7 @@ namespace bayesopt
     double quantile(double p) 
     {
       double x = boost::math::quantile(d_,p);
-      return (x - mean_) / std_;
+      return x * std_ + mean_;
     };
 
 

diff --git a/include/student_t_distribution.hpp b/include/student_t_distribution.hpp
@@ -29,6 +29,7 @@
 // for student t distribution
 #include <boost/math/distributions/students_t.hpp> 
 #include "prob_distribution.hpp"
+#include "log.hpp"
 
 namespace bayesopt
 {
@@ -69,7 +70,7 @@ namespace bayesopt
     double quantile(double p) 
     {
       double x = boost::math::quantile(d_,p);
-      return (x - mean_) / std_;
+      return x * std_ + mean_;
     };
 
     /** 

diff --git a/python/demo_quad.py b/python/demo_quad.py
@@ -50,6 +50,10 @@ def evaluateSample(self,Xin):
 params['n_iterations'] = 50
 params['n_iter_relearn'] = 5
 params['n_init_samples'] = 2
+params['filtering_startup'] = 10
+params['filtering_interval'] = 2
+params['up_margin'] = 5
+params['low_margin'] = 1e-10
 
 print("Callback implementation")
 

diff --git a/src/posterior_empirical.cpp b/src/posterior_empirical.cpp
@@ -50,10 +50,10 @@ namespace bayesopt
     else
       {
 	kOptimizer->setAlgorithm(COMBINED);
-	kOptimizer->setMaxEvals(20*nhp);
+	kOptimizer->setMaxEvals(100*nhp);
       }
     //Limits in log space
-    kOptimizer->setLimits(svectord(nhp,-6.0),svectord(nhp,1.0));
+    kOptimizer->setLimits(svectord(nhp,-6.0),svectord(nhp,6.0));
   } 
 
   EmpiricalBayes::~EmpiricalBayes()

diff --git a/src/robust_filtering.cpp b/src/robust_filtering.cpp
@@ -30,6 +30,7 @@ namespace bayesopt
     up_margin = (100.0 - parameters.up_margin) / 100.0;
     low_margin = parameters.low_margin / 100.0;
     par2.surr_name = "sStudentTProcessNIG";
+    par2.noise = 1e-3;
     mRobustModel.reset(PosteriorModel::create(dim,par2,eng));
   }
 
@@ -55,12 +56,18 @@ namespace bayesopt
           {
             mFilteredData->mX.push_back(XX[i]);
             utils::append(mFilteredData->mY, YY[i]);
+            FILE_LOG(logINFO) << "Keeped value:" << YY[i] << " with thresholds:" << f_low << ", " << f_up << " and mean:" << pd->getMean();
+          }
+        else
+          {
+            FILE_LOG(logINFO) << "REMOVED value:" << YY[i] << " with thresholds:" << f_low << ", " << f_up;
           }
       }
     if (mFilteredData->getNSamples() <= n_points * 0.5)
       {
         mFilteredData->mX = XX;
         mFilteredData->mY = YY;
+        FILE_LOG(logINFO) << "TOO MANY POINTS REMOVED.";
       }
 
     return mFilteredData.get();

diff --git a/src/student_t_distribution.cpp b/src/student_t_distribution.cpp
@@ -33,7 +33,7 @@ namespace bayesopt
   double StudentTDistribution::negativeProbabilityOfImprovement(double min,
 								double epsilon)
   {  
-    return -cdf(d_,(min - mean_ + epsilon)/std_);
+    return -boost::math::cdf(d_,(min - mean_ + epsilon)/std_);
   }  // negativeProbabilityOfImprovement