Add bobyqa minimizer

cosmosis/samplers/maxlike/maxlike_sampler.py

@@ -55,16 +55,33 @@ def likefn(p_in):
         if not np.isfinite(start_like):
             raise RuntimeError('invalid starting point for maxlike')
 
-        result = scipy.optimize.minimize(likefn, start_vector, method=self.method, 
-          jac=False, tol=self.tolerance,  #bounds=bounds, 
-          options={'maxiter':self.maxiter, 'disp':True})
+        if self.method.lower() == "bobyqa":
+            # use the specific pybobyqa minimizer
+            import pybobyqa
+
+            # this works with bounds in the form of a tuple of two arrays
+            lower = np.array([b[0] for b in bounds])
+            upper = np.array([b[1] for b in bounds])
+            kw = {
+                "seek_global_minimum": True,
+                "bounds": (lower,upper),
+                "print_progress": logs.is_enabled_for(logs.NOISY),
+                "rhobeg": 0.1,
+                "rhoend": self.tolerance,
+            }
+            result = pybobyqa.solve(likefn, start_vector, **kw)
+            opt_norm = result.x
+        else:
+            # Use scipy mainimizer instead
+            result = scipy.optimize.minimize(likefn, start_vector, method=self.method,
+            jac=False, tol=self.tolerance,
+            options={'maxiter':self.maxiter, 'disp':True})
+
+            opt_norm = result.x
 
-        opt_norm = result.x
         opt = self.pipeline.denormalize_vector(opt_norm)
 
-
         #Some output - first log the parameters to the screen.
-        #It's not really a warning - that's just a level name
         results = self.pipeline.run_results(opt)
         if self.max_posterior:
             logs.overview("Best fit (by posterior):\n%s"%'   '.join(str(x) for x in opt))

cosmosis/test/test_samplers.py

@@ -140,6 +140,9 @@ def test_gridmax():
 def test_maxlike():
     run('maxlike', True, can_postprocess=False)
 
+def test_bobyqa():
+    run('maxlike', True, can_postprocess=False, method='bobyqa')
+
 def test_metropolis():
     run('metropolis', True, samples=20)
     run('metropolis', True, samples=20, covmat_sample_start=True)

requirements.txt

@@ -14,3 +14,4 @@ dulwich
 urllib3
 scikit-learn
 nautilus-sampler >= 1.0.1
+Py-BOBYQA

setup.py

@@ -196,6 +196,7 @@ def run(self):
     "dulwich",
     "scikit-learn",
     "future",
+    "Py-BOBYQA",
 
 ]