minor refactorings; removed some unreachable code

Double-checked exception cases and added some doctests in
diagnostics/sample_chains.py

pysgmcmc/diagnostics/sample_chains.py

@@ -47,6 +47,14 @@ def __init__(self, chain_id, samples, varnames=None):
         >>> trace.n_vars, trace.varnames == names, len(trace.varnames) == trace.n_vars
         (2, True, True)
 
+        If `varnames` is `None`, anonymous names resulting from enumerating all
+        target parameters are used:
+
+        >>> dummy_samples = [[0., 0.], [0.2, -0.2], [0.3, -0.5], [0.1, 0.]]
+        >>> trace = PYSGMCMCTrace(chain_id=0, samples=dummy_samples, varnames=None)
+        >>> trace.varnames
+        ['0', '1']
+
         """
         self.chain = chain_id
 
@@ -156,16 +164,16 @@ def from_sampler(cls, chain_id, sampler, n_samples, varnames=None):
             sample for sample, _ in islice(sampler, n_samples)
         ]
 
-        # try to read variable names from sampler parameters
+        # ensure all sampler target parameters have a name
+        # => tensorflow names variables automatically, so this assumption
+        # is fair
+        assert all(hasattr(param, "name") for param in sampler.params)
+
+        # read variable names from sampler parameters
         if varnames is None:
-            try:
-                varnames = [
-                    param.name for param in sampler.params
-                ]
-            except AttributeError:
-                # could not read sampler parameters, passing `None`
-                # which will use enumerated names for the parameters
-                varnames = None
+            varnames = [
+                param.name for param in sampler.params
+            ]
         return PYSGMCMCTrace(chain_id, samples, varnames)
 
     def __len__(self):
@@ -206,6 +214,7 @@ def get_values(self, varname, burn=0, thin=1):
         This method makes each variable in a trace accessible by its name:
 
         >>> import tensorflow as tf
+        >>> graph = tf.Graph()
         >>> params = [tf.Variable(0., name="x"), tf.Variable(0., name="y")]
         >>> params[0].name, params[1].name
         ('x_1:0', 'y_1:0')
@@ -219,6 +228,17 @@ def get_values(self, varname, burn=0, thin=1):
         >>> trace.get_values(varname="x_1:0"), trace.get_values(varname="y_1:0")
         (array([ 0. ,  0.2,  0.3,  0.1]), array([ 0. , -0.2, -0.5,  0. ]))
 
+        If a queried name does not correspond to any parameter in the trace,
+        a `ValueError` is raised:
+
+        >>> names = [variable.name for variable in params]
+        >>> dummy_samples = [[0., 0.], [0.2, -0.2], [0.3, -0.5], [0.1, 0.]]
+        >>> trace = PYSGMCMCTrace(chain_id=0, samples=dummy_samples, varnames=names)
+        >>> trace.get_values(varname="FANTASYVARNAME")
+        Traceback (most recent call last):
+          ...
+        ValueError: Queried `PYSGMCMCTrace` for values of parameter with name 'FANTASYVARNAME' but the trace does not contain any parameter of that name. Known variable names were: '['x_1:0', 'y_1:0']'
+
         """
 
         if varname not in self.varnames:

pysgmcmc/samplers/base_classes.py

@@ -273,19 +273,12 @@ def __next__(self, feed_dict=None):
 
         """
         assert (feed_dict is None or hasattr(feed_dict, "update"))
+        # Ensure self.Theta_t and self.Cost are defined
+        assert hasattr(self, "Theta_t") or not hasattr(self, "Cost")
 
         if feed_dict is None:
             feed_dict = dict()
 
-        if not hasattr(self, "Theta_t") or not hasattr(self, "Cost"):
-            # Ensure self.Theta_t and self.Cost are defined
-            raise ValueError(
-                "MCMCSampler subclass attempted to compute the next sample "
-                "with corresponding costs, but one of the "
-                "two necessary sampler member variables 'Theta_t' and 'Cost' "
-                "were not found in the samplers instance dictionary."
-            )
-
         feed_dict.update(self._next_batch())
         feed_dict.update(self._next_stepsize())
         params, cost = self.session.run(

pysgmcmc/tensor_utils.py

@@ -80,30 +80,27 @@ def vectorize(tensor):
     >>> v = vectorize([1.0])
     Traceback (most recent call last):
      ...
-    AssertionError: Unsupported input to tensor_utils.vectorize: [1.0] is not a tensorflow.Tensor subclass
+    ValueError: Unsupported input to tensor_utils.vectorize: [1.0] is not a tensorflow.Tensor subclass
 
     """
 
-    error_msg = ("Unsupported input to tensor_utils.vectorize: "
-                 "{value} is not a tensorflow.Tensor subclass".format(value=tensor))
+    def vectorized_shape(tensor):
+        # Compute vectorized shape
+        n_elements = np.prod(np.asarray(tensor.shape, dtype=np.int))
+        return (n_elements, 1)
 
-    assert isinstance(tensor, (tf.Variable, tf.Tensor,)), error_msg
-
-    # Compute vectorized shape
-    n_elements = np.prod(np.asarray(tensor.shape, dtype=np.int))
-    vectorized_shape = (n_elements, 1)
-
-    if type(tensor) == tf.Variable:
+    if isinstance(tensor, tf.Variable):
         return tf.Variable(
-            tf.reshape(tensor.initialized_value(), shape=vectorized_shape)
+            tf.reshape(tensor.initialized_value(), shape=vectorized_shape(tensor))
         )
 
     elif isinstance(tensor, tf.Tensor):
-        return tf.reshape(tensor, shape=vectorized_shape)
+        return tf.reshape(tensor, shape=vectorized_shape(tensor))
 
     else:
         raise ValueError(
-            error_msg
+            "Unsupported input to tensor_utils.vectorize: "
+            "{value} is not a tensorflow.Tensor subclass".format(value=tensor)
         )
 
 
@@ -607,28 +604,3 @@ def uninitialized_params(params, session):
     )
 
     return [param for param, flag in zip(params, init_flag) if not flag]
-
-
-def all_uninitialized_variables(session, scope=None):
-    """
-    Return all uninitialized `tensorflow.Variable` objects in the
-    current default graph. Uses `session` to determine if a variable
-    was initialized.
-
-    Parameters
-    ----------
-    session : tf.Session
-        Session used to determine which variables are uninitialized.
-
-    Returns
-    ----------
-    params_uninitialized: list of tensorflow.Variable objects
-        All `tensorflow.Variable` objects in the current default graph
-        that were not yet initialized.
-
-    """
-
-    return uninitialized_params(
-        tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=scope),
-        session=session
-    )