cdt/causality/graph/GIES.py
def orient_undirected_graph(self, data, graph):
        """Run GIES on an undirected graph.

        Args:
            data (pandas.DataFrame): DataFrame containing the data
            graph (networkx.Graph): Skeleton of the graph to orient

        Returns:
            networkx.DiGraph: Solution given by the GIES algorithm.

        """
        # Building setup w/ arguments.
        self.arguments['{VERBOSE}'] = str(self.verbose).upper()
        self.arguments['{SCORE}'] = self.scores[self.score]

        fe = DataFrame(nx.adj_matrix(graph, weight=None).todense())
        fg = DataFrame(1 - fe.values)

        results = self._run_gies(data, fixedGaps=fg, verbose=self.verbose)

        return nx.relabel_nodes(nx.DiGraph(results),
                                {idx: i for idx, i in enumerate(data.columns)})
def orient_undirected_graph(self, data, graph):
        """Run GIES on an undirected graph.

        Args:
            data (pandas.DataFrame): DataFrame containing the data
            graph (networkx.Graph): Skeleton of the graph to orient

        Returns:
            networkx.DiGraph: Solution given by the GIES algorithm.

        """
        # Building setup w/ arguments.
        self.arguments['{VERBOSE}'] = str(self.verbose).upper()
        self.arguments['{SCORE}'] = self.scores[self.score]

        fe = DataFrame(nx.adj_matrix(graph, weight=None).todense())
        fg = DataFrame(1 - fe.values)

        results = self._run_gies(data, fixedGaps=fg, verbose=self.verbose)

        return nx.relabel_nodes(nx.DiGraph(results),
                                {idx: i for idx, i in enumerate(data.columns)})
def orient_undirected_graph ( self , data , graph ) : # Building setup w/ arguments. self . arguments [ '{VERBOSE}' ] = str ( self . verbose ) . upper ( ) self . arguments [ '{SCORE}' ] = self . scores [ self . score ] fe = DataFrame ( nx . adj_matrix ( graph , weight = None ) . todense ( ) ) fg = DataFrame ( 1 - fe . values ) results = self . _run_gies ( data , fixedGaps = fg , verbose = self . verbose ) return nx . relabel_nodes ( nx . DiGraph ( results ) , { idx : i for idx , i in enumerate ( data . columns ) } )
Run GIES on an undirected graph.

        Args:
            data (pandas.DataFrame): DataFrame containing the data
            graph (networkx.Graph): Skeleton of the graph to orient

        Returns:
            networkx.DiGraph: Solution given by the GIES algorithm.
Run GIES on an undirected graph .