cdt/causality/graph/GIES.py
def create_graph_from_data(self, data):
        """Run the GIES algorithm.

        Args:
            data (pandas.DataFrame): DataFrame containing the data

        Returns:
            networkx.DiGraph: Solution given by the GIES algorithm.
        """
        # Building setup w/ arguments.
        self.arguments['{SCORE}'] = self.scores[self.score]
        self.arguments['{VERBOSE}'] = str(self.verbose).upper()

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

        return nx.relabel_nodes(nx.DiGraph(results),
                                {idx: i for idx, i in enumerate(data.columns)})
def create_graph_from_data(self, data):
        """Run the GIES algorithm.

        Args:
            data (pandas.DataFrame): DataFrame containing the data

        Returns:
            networkx.DiGraph: Solution given by the GIES algorithm.
        """
        # Building setup w/ arguments.
        self.arguments['{SCORE}'] = self.scores[self.score]
        self.arguments['{VERBOSE}'] = str(self.verbose).upper()

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

        return nx.relabel_nodes(nx.DiGraph(results),
                                {idx: i for idx, i in enumerate(data.columns)})
def create_graph_from_data ( self , data ) : # Building setup w/ arguments. self . arguments [ '{SCORE}' ] = self . scores [ self . score ] self . arguments [ '{VERBOSE}' ] = str ( self . verbose ) . upper ( ) results = self . _run_gies ( data , verbose = self . verbose ) return nx . relabel_nodes ( nx . DiGraph ( results ) , { idx : i for idx , i in enumerate ( data . columns ) } )
Run the GIES algorithm.

        Args:
            data (pandas.DataFrame): DataFrame containing the data

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