Merge pull request #952 from pymc-devs/verbosity_flag

Verbosity flag (working)

Author: Chris Fonnesbeck

pymc3/examples/discrete_find_MAP.py

@@ -30,7 +30,7 @@
 # to specify if we would like to use the discrete variables.
 
 with model:
-    print(mc.find_MAP(vars=model.vars, disp=True))
+    print(mc.find_MAP(vars=model.vars))
 
 # We set the `disp` variable to display a warning that we are using a
 # non-gradient minimization technique, as discrete variables do not give much

pymc3/model.py

@@ -158,16 +158,25 @@ def logpt(self):
         return t.sum(self.logp_elemwiset)
 
 class Model(Context, Factor):
-    """Encapsulates the variables and likelihood factors of a model."""
+    """Encapsulates the variables and likelihood factors of a model.
+    
+    Parameters
+    ----------
+        verbose : int
+            Model verbosity setting, determining how much feedback various operations
+            provide. Normal verbosity is verbose=1 (default), silence is verbose=0, high
+            is any value greater than 1.
+    """
 
-    def __init__(self):
+    def __init__(self, verbose=1):
         self.named_vars = {}
         self.free_RVs = []
         self.observed_RVs = []
         self.deterministics = []
         self.potentials = []
         self.missing_values = []
         self.model = self
+        self.verbose = verbose
 
     @property
     @memoize
@@ -227,11 +236,12 @@ def Var(self, name, dist, data=None):
                 self.free_RVs.append(var)
             else:
                 var = TransformedRV(name=name, distribution=dist, model=self, transform=dist.transform)
-                print('Applied {transform}-transform to {name}'
-                      ' and added transformed {orig_name} to model.'.format(
-                          transform=dist.transform.name,
-                          name=name,
-                          orig_name='{}_{}'.format(name, dist.transform.name)))
+                if self.verbose:
+                    print('Applied {transform}-transform to {name}'
+                          ' and added transformed {orig_name} to model.'.format(
+                              transform=dist.transform.name,
+                              name=name,
+                              orig_name='{}_{}'.format(name, dist.transform.name)))
                 self.deterministics.append(var)
                 return var
         elif isinstance(data, dict):

pymc3/sampling.py

@@ -61,7 +61,8 @@ def assign_step_methods(model, step=None,
 
             selected = max(competences.keys(), key=(lambda k: competences[k]))
 
-            print('Assigned {0} to {1}'.format(selected.__name__, var))
+            if model.verbose:
+                print('Assigned {0} to {1}'.format(selected.__name__, var))
             selected_steps[selected].append(var)
 
     # Instantiate all selected step methods

pymc3/tuning/starting.py

@@ -15,7 +15,7 @@
 
 
 def find_MAP(start=None, vars=None, fmin=None, return_raw=False,
-             disp=False, model=None, *args, **kwargs):
+             model=None, *args, **kwargs):
     """
     Sets state to the local maximum a posteriori point given a model.
     Current default of fmin_Hessian does not deal well with optimizing close
@@ -32,9 +32,6 @@ def find_MAP(start=None, vars=None, fmin=None, return_raw=False,
         `scipy.optimize.fmin_powell` which will perform better).
     return_raw : Bool
         Whether to return extra value returned by fmin (Defaults to `False`)
-    disp : Bool
-        Display helpful warnings, and verbose output of `fmin` (Defaults to
-        `False`)
     model : Model (optional if in `with` context)
     *args, **kwargs
         Extra args passed to fmin
@@ -48,7 +45,9 @@ def find_MAP(start=None, vars=None, fmin=None, return_raw=False,
     vars = inputvars(vars)
 
     disc_vars = list(typefilter(vars, discrete_types))
-
+    
+    disp = model.verbose > 1
+    
     if disc_vars and disp:
         print("Warning: vars contains discrete variables. MAP " +
               "estimates may not be accurate for the default " +