Improved NetPramsLner by adding exception mechanism to deal with trai…

…ning data that yields un-normalizable potentials

Signed-off-by: rrtucci <tucci@ar-tiste.com>

MyExceptions.py

@@ -5,7 +5,7 @@
 class BadGraphStructure(Exception):
     """
     An exception class raised when a graph's structure is detected to be
-    illegal. Thrown when an alleged DAG is detected to contain cycles.
+    illegal; for instance, when an alleged DAG is detected to contain cycles.
 
     Attributes
     ----------
@@ -37,5 +37,44 @@ def __repr__(self):
         """
         return self.txt
 
+
+class UnNormalizablePot(Exception):
+    """
+
+    An exception class raised when an attempt to normalize a DiscreteCondPot
+    fails because it leads to division by zero.
+
+    Attributes
+    ----------
+    pa_indices : tuple[int]
+
+    """
+
+    def __init__(self, pa_indices):
+        """
+        Constructor
+
+        Parameters
+        ----------
+        pa_indices : tuple[int]
+            the indices of the parent state pa(C)=y such that
+            pot(C=x|pa(C)=y) = 0 for all x.
+
+        Returns
+        -------
+
+        """
+        self.pa_indices = pa_indices
+
+    def __repr__(self):
+        """
+
+        Returns
+        -------
+        tuple[int]
+
+        """
+        return self.pa_indices
+
 if __name__ == "__main__":
     print(5)

learning/AracneLner.py

@@ -43,6 +43,8 @@ class AracneLner(ChowLiuTreeLner):
     states_df : pandas.DataFrame
         a Pandas DataFrame with training data. column = node and row =
         sample. Each row/sample gives the state of the col/node.
+
+
     ord_nodes : list[DirectedNode]
         a list of DirectedNode's named and in the same order as the column
         labels of self.states_df.
@@ -56,6 +58,7 @@ def __init__(self, states_df, vtx_to_states=None):
         Parameters
         ----------
         states_df : pandas.DataFrame
+
         vtx_to_states : dict[str, list[str]]
             A dictionary mapping each node name to a list of its state names.
             This information will be stored in self.bnet. If

learning/ChowLiuTreeLner.py

@@ -61,7 +61,8 @@ def __init__(self, states_df, vtx_to_states=None):
         None
 
         """
-        NetStrucLner.__init__(self, False, states_df, vtx_to_states)
+        NetStrucLner.__init__(self, False,
+                              states_df, vtx_to_states)
         self.learn_net_struc()
 
     def learn_net_struc(self):

learning/HillClimbingLner.py

@@ -32,7 +32,7 @@ class HillClimbingLner(NetStrucLner):
         a BayesNet in which we store what is learned
     states_df : pandas.DataFrame
         a Pandas DataFrame with training data. column = node and row =
-        sample. Each row/sample gives the state of the col/node.
+        sample. Each row/sample gives the state of the col/node
     ord_nodes : list[DirectedNode]
         a list of DirectedNode's named and in the same order as the column
         labels of self.states_df.
@@ -91,11 +91,12 @@ def __init__(self, states_df, score_type, max_num_mtries,
 
         # get vtx_to_states info from self.bnet
         vtx_to_states1 = {nd.name: nd.state_names for nd in self.bnet.nodes}
-        self.scorer = NetStrucScorer(self.states_df,
-                                     self.vtx_to_parents,
-                                     vtx_to_states1,
-                                     score_type,
-                                     ess)
+        self.scorer = NetStrucScorer(
+            self.states_df,
+            self.vtx_to_parents,
+            vtx_to_states1,
+            score_type,
+            ess)
 
         self.nx_graph = nx.DiGraph()