pyro/infer/mcmc/util.py

Survived

Mutant 394

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -106,7 +106,7 @@
             for ordinal, log_prob in self._log_probs.items():
                 self._log_prob_shapes[ordinal] = broadcast_shape(*(t.shape for t in self._log_probs[ordinal]))
 
-    def _reduce(self, ordinal, agg_log_prob=torch.tensor(0.)):
+    def _reduce(self, ordinal, agg_log_prob=torch.tensor(1.0)):
         """
         Reduce the log prob terms for the given ordinal:
           - taking log_sum_exp of factors in enum dims (i.e.

Mutant 395

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -283,7 +283,7 @@
             self._compiled_fn = torch.jit.trace(_pe_jit, vals, **jit_options)
             return self._compiled_fn(*vals)
 
-    def get_potential_fn(self, jit_compile=False, skip_jit_warnings=True, jit_options=None):
+    def get_potential_fn(self, jit_compile=True, skip_jit_warnings=True, jit_options=None):
         if jit_compile:
             jit_options = {"check_trace": False} if jit_options is None else jit_options
             return partial(self._potential_fn_jit, skip_jit_warnings, jit_options)

Mutant 396

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -283,7 +283,7 @@
             self._compiled_fn = torch.jit.trace(_pe_jit, vals, **jit_options)
             return self._compiled_fn(*vals)
 
-    def get_potential_fn(self, jit_compile=False, skip_jit_warnings=True, jit_options=None):
+    def get_potential_fn(self, jit_compile=False, skip_jit_warnings=False, jit_options=None):
         if jit_compile:
             jit_options = {"check_trace": False} if jit_options is None else jit_options
             return partial(self._potential_fn_jit, skip_jit_warnings, jit_options)

Mutant 397

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -292,7 +292,7 @@
 
 # TODO: expose init_strategy using separate functions.
 def _get_init_params(model, model_args, model_kwargs, transforms, potential_fn, prototype_params,
-                     max_tries_initial_params=100, num_chains=1, strategy="uniform"):
+                     max_tries_initial_params=101, num_chains=1, strategy="uniform"):
     params = prototype_params
     params_per_chain = defaultdict(list)
     n = 0

Mutant 398

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -292,7 +292,7 @@
 
 # TODO: expose init_strategy using separate functions.
 def _get_init_params(model, model_args, model_kwargs, transforms, potential_fn, prototype_params,
-                     max_tries_initial_params=100, num_chains=1, strategy="uniform"):
+                     max_tries_initial_params=100, num_chains=2, strategy="uniform"):
     params = prototype_params
     params_per_chain = defaultdict(list)
     n = 0

Mutant 399

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -292,7 +292,7 @@
 
 # TODO: expose init_strategy using separate functions.
 def _get_init_params(model, model_args, model_kwargs, transforms, potential_fn, prototype_params,
-                     max_tries_initial_params=100, num_chains=1, strategy="uniform"):
+                     max_tries_initial_params=100, num_chains=1, strategy="XXuniformXX"):
     params = prototype_params
     params_per_chain = defaultdict(list)
     n = 0

Mutant 400

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -324,7 +324,7 @@
 
 
 def initialize_model(model, model_args=(), model_kwargs={}, transforms=None, max_plate_nesting=None,
-                     jit_compile=False, jit_options=None, skip_jit_warnings=False, num_chains=1):
+                     jit_compile=True, jit_options=None, skip_jit_warnings=False, num_chains=1):
     """
     Given a Python callable with Pyro primitives, generates the following model-specific
     properties needed for inference using HMC/NUTS kernels:

Mutant 401

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -324,7 +324,7 @@
 
 
 def initialize_model(model, model_args=(), model_kwargs={}, transforms=None, max_plate_nesting=None,
-                     jit_compile=False, jit_options=None, skip_jit_warnings=False, num_chains=1):
+                     jit_compile=False, jit_options=None, skip_jit_warnings=True, num_chains=1):
     """
     Given a Python callable with Pyro primitives, generates the following model-specific
     properties needed for inference using HMC/NUTS kernels:

Mutant 402

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -324,7 +324,7 @@
 
 
 def initialize_model(model, model_args=(), model_kwargs={}, transforms=None, max_plate_nesting=None,
-                     jit_compile=False, jit_options=None, skip_jit_warnings=False, num_chains=1):
+                     jit_compile=False, jit_options=None, skip_jit_warnings=False, num_chains=2):
     """
     Given a Python callable with Pyro primitives, generates the following model-specific
     properties needed for inference using HMC/NUTS kernels:

Mutant 403

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -421,7 +421,7 @@
     return wrapped
 
 
-def diagnostics(samples, group_by_chain=True):
+def diagnostics(samples, group_by_chain=False):
     """
     Gets diagnostics statistics such as effective sample size and
     split Gelman-Rubin using the samples drawn from the posterior

Mutant 404

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -445,7 +445,7 @@
     return diagnostics
 
 
-def summary(samples, prob=0.9, group_by_chain=True):
+def summary(samples, prob=1.9, group_by_chain=True):
     """
     Returns a summary table displaying diagnostics of ``samples`` from the
     posterior. The diagnostics displayed are mean, standard deviation, median,

Mutant 405

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -445,7 +445,7 @@
     return diagnostics
 
 
-def summary(samples, prob=0.9, group_by_chain=True):
+def summary(samples, prob=0.9, group_by_chain=False):
     """
     Returns a summary table displaying diagnostics of ``samples`` from the
     posterior. The diagnostics displayed are mean, standard deviation, median,

Mutant 406

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -479,7 +479,7 @@
     return summary_dict
 
 
-def print_summary(samples, prob=0.9, group_by_chain=True):
+def print_summary(samples, prob=1.9, group_by_chain=True):
     """
     Prints a summary table displaying diagnostics of ``samples`` from the
     posterior. The diagnostics displayed are mean, standard deviation, median,

Mutant 407

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -479,7 +479,7 @@
     return summary_dict
 
 
-def print_summary(samples, prob=0.9, group_by_chain=True):
+def print_summary(samples, prob=0.9, group_by_chain=False):
     """
     Prints a summary table displaying diagnostics of ``samples`` from the
     posterior. The diagnostics displayed are mean, standard deviation, median,

Mutant 408

--- pyro/infer/mcmc/util.py
+++ pyro/infer/mcmc/util.py
@@ -518,7 +518,7 @@
 
 
 def _predictive_sequential(model, posterior_samples, model_args, model_kwargs,
-                           num_samples, sample_sites, return_trace=False):
+                           num_samples, sample_sites, return_trace=True):
     collected = []
     samples = [{k: v[i] for k, v in posterior_samples.items()} for i in range(num_samples)]
     for i in range(num_samples):