changed defaults

This commit resolves #87. The default options for a host of parameters has
been changed to enable more stable behavior for the average user. These are:
- `nlive` has been changed to `500` everywhere to enable better default
performance in moderate dimensions. This includes in `nlive_init` and
`nlive_batch` in `dynamicsampler`.
- `update_interval` has been increased to `0.8`.
- The `dlogz` formula for the static `sampler` has been modified to
`1e-3 * (nlive - 1) + 0.01` so that it is a better compromise between
evidence and posterior estimation. This gives `~0.5` when `nlive=500`,
similar to the previous `nestle` value, which I think is more appropriate.

I might revisit the sampling options in the near future to further accomodate
users who want to apply it right away to arbitrary-dimensional problems.

dynesty/__init__.py

@@ -10,4 +10,4 @@
 from . import utils
 
 
-__version__ = "0.9.1d"
+__version__ = "0.9.1e"

dynesty/dynamicsampler.py

@@ -585,7 +585,7 @@ def results(self):
 
         return Results(results)
 
-    def sample_initial(self, nlive=100, update_interval=None,
+    def sample_initial(self, nlive=500, update_interval=None,
                        first_update=None, maxiter=None, maxcall=None,
                        logl_max=np.inf, dlogz=0.01, live_points=None):
         """
@@ -598,7 +598,7 @@ def sample_initial(self, nlive=100, update_interval=None,
         ----------
         nlive : int, optional
             The number of live points to use for the baseline nested
-            sampling run. Default is `100`.
+            sampling run. Default is `500`.
 
         update_interval : int or float, optional
             If an integer is passed, only update the bounding distribution
@@ -873,7 +873,7 @@ def sample_initial(self, nlive=100, update_interval=None,
         self.saved_batch_nlive.append(self.nlive_init)  # initial nlive
         self.saved_batch_bounds.append((-np.inf, np.inf))  # initial bounds
 
-    def sample_batch(self, nlive_new=100, update_interval=None,
+    def sample_batch(self, nlive_new=500, update_interval=None,
                      logl_bounds=None, maxiter=None, maxcall=None,
                      save_bounds=True):
         """
@@ -885,7 +885,7 @@ def sample_batch(self, nlive_new=100, update_interval=None,
         Parameters
         ----------
         nlive_new : int
-            Number of new live points to be added. Default is `100`.
+            Number of new live points to be added. Default is `500`.
 
         update_interval : int or float, optional
             If an integer is passed, only update the bounding distribution
@@ -1370,9 +1370,9 @@ def combine_runs(self):
         self.saved_batch_nlive.append(max(new_n))
         self.saved_batch_bounds.append((llmin, llmax))
 
-    def run_nested(self, nlive_init=100, maxiter_init=None,
+    def run_nested(self, nlive_init=500, maxiter_init=None,
                    maxcall_init=None, dlogz_init=0.01, logl_max_init=np.inf,
-                   nlive_batch=100, wt_function=None, wt_kwargs=None,
+                   nlive_batch=500, wt_function=None, wt_kwargs=None,
                    maxiter_batch=None, maxcall_batch=None,
                    maxiter=None, maxcall=None, maxbatch=None,
                    stop_function=None, stop_kwargs=None, use_stop=True,
@@ -1388,7 +1388,7 @@ def run_nested(self, nlive_init=100, maxiter_init=None,
         ----------
         nlive_init : int, optional
             The number of live points used during the initial ("baseline")
-            nested sampling run. Default is `100`.
+            nested sampling run. Default is `500`.
 
         maxiter_init : int, optional
             Maximum number of iterations for the initial baseline nested
@@ -1417,7 +1417,7 @@ def run_nested(self, nlive_init=100, maxiter_init=None,
 
         nlive_batch : int, optional
             The number of live points used when adding additional samples
-            from a nested sampling run within each batch. Default is `100`.
+            from a nested sampling run within each batch. Default is `500`.
 
         wt_function : func, optional
             A cost function that takes a :class:`Results` instance
@@ -1594,7 +1594,7 @@ def run_nested(self, nlive_init=100, maxiter_init=None,
         if print_progress:
             sys.stderr.write("\n")
 
-    def add_batch(self, nlive=100, wt_function=None, wt_kwargs=None,
+    def add_batch(self, nlive=500, wt_function=None, wt_kwargs=None,
                   maxiter=None, maxcall=None, save_bounds=True,
                   print_progress=True, print_func=None, stop_val=None):
         """
@@ -1606,7 +1606,7 @@ def add_batch(self, nlive=100, wt_function=None, wt_kwargs=None,
         ----------
         nlive : int, optional
             The number of live points used when adding additional samples
-            in the batch. Default is `100`.
+            in the batch. Default is `500`.
 
         wt_function : func, optional
             A cost function that takes a `Results` instance

dynesty/dynesty.py

@@ -37,9 +37,9 @@
 SQRTEPS = math.sqrt(float(np.finfo(np.float64).eps))
 
 
-def NestedSampler(loglikelihood, prior_transform, ndim, nlive=250,
+def NestedSampler(loglikelihood, prior_transform, ndim, nlive=500,
                   bound='multi', sample='unif',
-                  update_interval=0.6, first_update=None,
+                  update_interval=0.8, first_update=None,
                   npdim=None, rstate=None, queue_size=None, pool=None,
                   use_pool=None, live_points=None,
                   logl_args=None, logl_kwargs=None,
@@ -314,8 +314,8 @@ def prior_transform(u):
 
 def DynamicNestedSampler(loglikelihood, prior_transform, ndim,
                          bound='multi', sample='unif',
-                         update_interval=0.6, first_update=None,
-                         npdim=None, rstate=None, queue_size=1, pool=None,
+                         update_interval=0.8, first_update=None,
+                         npdim=None, rstate=None, queue_size=None, pool=None,
                          use_pool=None, logl_args=None, logl_kwargs=None,
                          ptform_args=None, ptform_kwargs=None,
                          enlarge=None, bootstrap=None,

dynesty/sampler.py

@@ -785,7 +785,7 @@ def run_nested(self, maxiter=None, maxcall=None, dlogz=None,
             `ln(z + z_est) - ln(z) < dlogz`, where `z` is the current
             evidence from all saved samples and `z_est` is the estimated
             contribution from the remaining volume. If `add_live` is `True`,
-            the default is `0.005 * (nlive + 1)`. Otherwise, the
+            the default is `1e-3 * (nlive - 1) + 0.01`. Otherwise, the
             default is `0.01`.
 
         logl_max : float, optional
@@ -817,7 +817,7 @@ def run_nested(self, maxiter=None, maxcall=None, dlogz=None,
         # Define our stopping criteria.
         if dlogz is None:
             if add_live:
-                dlogz = 0.005 * (self.nlive + 1.)
+                dlogz = 1e-3 * (self.nlive - 1.) + 0.01
             else:
                 dlogz = 0.01