nbootstrap to HighscoreSolver

apps/grond

@@ -187,7 +187,6 @@ def command_init(args):
         problem_config = grond.CMTProblemConfig(
             name_template='cmt_%(event_name)s',
             distance_min=2.*km,
-            nbootstrap=100,
             mt_type='deviatoric',
             ranges=dict(
                 time=Range(0, 10.0, relative='add'),

src/optimizers/highscore.py

@@ -325,8 +325,6 @@ class BadProblem(GrondError):
     pass
 
 
-
-
 def solve(problem,
           phases,
           rundir=None,
@@ -427,7 +425,6 @@ def solve(problem,
 
         chains.insert(gbms, iiter)
 
-
         chains_m[0, nlinks] = gm
         chains_m[1:, nlinks] = bms
         chains_i[:, nlinks] = iiter
@@ -539,13 +536,11 @@ class HighScoreOptimizer(Optimizer):
         Optimizer.__init__(self)
         self._kwargs = kwargs
 
-
     def solve(
               problem,
               phases,
               rundir=None,
 
-              chain_length_factor=
               chain_length_factor=8.0,
               standard_deviation_estimator='median_density_single_chain',
 
@@ -635,6 +630,7 @@ class StandardDeviationEstimatorChoice(StringChoice):
 
 
 class HighScoreOptimizerConfig(OptimizerConfig):
+    nbootstrap = Int.T(default=100)
     niter_uniform = Int.T(default=1000)
     niter_transition = Int.T(default=0)
     niter_explorative = Int.T(default=10000)
@@ -650,6 +646,7 @@ class HighScoreOptimizerConfig(OptimizerConfig):
 
     def get_optimizer_kwargs(self):
         return dict(
+            nbootstrap=self.nbootstrap,
             niter_uniform=self.niter_uniform,
             niter_transition=self.niter_transition,
             niter_explorative=self.niter_explorative,

src/problems/cmt.py

@@ -3,7 +3,7 @@ import math
 import logging
 
 from pyrocko import gf, util, moment_tensor as mtm
-from pyrocko.guts import String, Float, Dict, Int, StringChoice
+from pyrocko.guts import String, Float, Dict, StringChoice
 
 from .base import Problem, ProblemConfig
 from ..meta import Forbidden, expand_template, Parameter

src/problems/double_dc.py

@@ -18,7 +18,6 @@ class DoubleDCProblemConfig(ProblemConfig):
 
     ranges = Dict.T(String.T(), gf.Range.T())
     distance_min = Float.T(default=0.0)
-    nbootstrap = Int.T(default=100)
 
     def get_problem(self, event, targets):
         if event.depth is None:
@@ -38,7 +37,6 @@ class DoubleDCProblemConfig(ProblemConfig):
             targets=targets,
             ranges=self.ranges,
             distance_min=self.distance_min,
-            nbootstrap=self.nbootstrap,
             norm_exponent=self.norm_exponent)
 
         return problem

src/problems/rectangular.py

@@ -18,7 +18,6 @@ class RectangularProblemConfig(ProblemConfig):
     ranges = Dict.T(String.T(), gf.Range.T())
     apply_balancing_weights = Bool.T(default=False)
     decimation_factor = Int.T(default=1)
-    nbootstrap = Int.T(default=10)
     distance_min = Float.T(default=0.)
 
     def get_problem(self, event, targets):
@@ -35,7 +34,6 @@ class RectangularProblemConfig(ProblemConfig):
             name=expand_template(self.name_template, event.name),
             apply_balancing_weights=self.apply_balancing_weights,
             base_source=base_source,
-            nbootstrap=self.nbootstrap,
             distance_min=self.distance_min,
             targets=targets,
             ranges=self.ranges,

src/solvers/highscore.py

@@ -58,6 +58,7 @@ def local_std(xs):
 
 def solve(problem,
           rundir=None,
+          nbootstrap=100,
           niter_uniform=1000,
           niter_transition=1000,
           niter_explorative=10000,
@@ -78,8 +79,8 @@ def solve(problem,
     npar = problem.nparameters
 
     nlinks_cap = int(round(chain_length_factor * npar + 1))
-    chains_m = num.zeros((1 + problem.nbootstrap, nlinks_cap), num.float)
-    chains_i = num.zeros((1 + problem.nbootstrap, nlinks_cap), num.int)
+    chains_m = num.zeros((1 + nbootstrap, nlinks_cap), num.float)
+    chains_i = num.zeros((1 + nbootstrap, nlinks_cap), num.int)
     nlinks = 0
     mbx = None
 
@@ -98,7 +99,7 @@ def solve(problem,
     local_sxs = None
     xhist = num.zeros((niter, npar))
     isbad_mask = None
-    accept_sum = num.zeros(1 + problem.nbootstrap, dtype=num.int)
+    accept_sum = num.zeros(1 + nbootstrap, dtype=num.int)
     accept_hist = num.zeros(niter, dtype=num.int)
     pnames = problem.parameter_names
 
@@ -164,7 +165,7 @@ def solve(problem,
                     x = problem.random_uniform(xbounds)
                 else:
                     # ibootstrap_choice = num.random.randint(
-                    #     0, 1 + problem.nbootstrap)
+                    #     0, 1 + nbootstrap)
                     ibootstrap_choice = num.argmin(accept_sum)
 
                     if phase in ('transition', 'explorative'):
@@ -309,7 +310,7 @@ def solve(problem,
             accept = (chains_i[:, nlinks_cap-1] != iiter).astype(num.int)
             nlinks -= 1
         else:
-            accept = num.ones(1 + problem.nbootstrap, dtype=num.int)
+            accept = num.ones(1 + nbootstrap, dtype=num.int)
 
         if rundir:
             problem.dump_problem_data(
@@ -342,7 +343,7 @@ def solve(problem,
             mxs = []
             local_sxs = []
 
-            for i in xrange(1 + problem.nbootstrap):
+            for i in xrange(1 + nbootstrap):
                 xs = xhist[chains_i[i, :nlinks], :]
                 mx = num.mean(xs, axis=0)
                 cov = num.cov(xs.T)
@@ -436,6 +437,7 @@ class StandardDeviationEstimatorChoice(StringChoice):
 
 
 class HighScoreSolverConfig(SolverConfig):
+    nbootstrap = Int.T(default=100)
     niter_uniform = Int.T(default=1000)
     niter_transition = Int.T(default=0)
     niter_explorative = Int.T(default=10000)
@@ -451,6 +453,7 @@ class HighScoreSolverConfig(SolverConfig):
 
     def get_solver_kwargs(self):
         return dict(
+            nbootstrap=self.nbootstrap,
             niter_uniform=self.niter_uniform,
             niter_transition=self.niter_transition,
             niter_explorative=self.niter_explorative,