def predict_undirected_graph(self, data):
        """Build a skeleton using a pairwise independence criterion.

        Args:
            data (pandas.DataFrame): Raw data table

        Returns:
            networkx.Graph: Undirected graph representing the skeleton.
        """
        graph = Graph()

        for idx_i, i in enumerate(data.columns):
            for idx_j, j in enumerate(data.columns[idx_i+1:]):
                score = self.predict(data[i].values, data[j].values)
                if abs(score) > 0.001:
                    graph.add_edge(i, j, weight=score)

        return graph
def predict_undirected_graph(self, data):
        """Build a skeleton using a pairwise independence criterion.

        Args:
            data (pandas.DataFrame): Raw data table

        Returns:
            networkx.Graph: Undirected graph representing the skeleton.
        """
        graph = Graph()

        for idx_i, i in enumerate(data.columns):
            for idx_j, j in enumerate(data.columns[idx_i+1:]):
                score = self.predict(data[i].values, data[j].values)
                if abs(score) > 0.001:
                    graph.add_edge(i, j, weight=score)

        return graph
def predict_undirected_graph ( self , data ) : graph = Graph ( ) for idx_i , i in enumerate ( data . columns ) : for idx_j , j in enumerate ( data . columns [ idx_i + 1 : ] ) : score = self . predict ( data [ i ] . values , data [ j ] . values ) if abs ( score ) > 0.001 : graph . add_edge ( i , j , weight = score ) return graph
Build a skeleton using a pairwise independence criterion.

        Args:
            data (pandas.DataFrame): Raw data table

        Returns:
            networkx.Graph: Undirected graph representing the skeleton.
Build a skeleton using a pairwise independence criterion .