gbopt/solver/gbopt.py
def choose_next(self, graph):
        """
        Suggests a new point to evaluate.

        Parameters
        ----------
        graph: GraphObject
            The graph defined by nodes and an adjacency matrix.

        Returns
        -------
        np.ndarray(1,D)
            Suggested point
        """
        if self.graph.lids is None:
            lid = int(self.initial_design(graph.X, 1, rng=self.rng))
        else:
            logger.info("Label propagation through the graph...")
            t = time.time()
            self.label_prop_alg.label_propagate(graph)
            logger.info("Time to propagate the labels: %f", (time.time() - t))
            preds = graph.Y.tolist()
            self.predictions.append(preds)

            logger.info("Maximize acquisition function...")
            t = time.time()
            self.acquisition_func.update(graph)
            lid = self.maximize_func.maximize(graph)
            logger.info("Time to maximize the acquisition function: %f", (time.time() - t))

        return lid

        Suggests a new point to evaluate.

        Parameters
        ----------
        graph: GraphObject
            The graph defined by nodes and an adjacency matrix.

        Returns
        -------
        np.ndarray(1,D)
            Suggested point
        
Suggests a new point to evaluate.
Parameters

GraphObject
The graph defined by nodes and an adjacency matrix.

Returns


Suggests a new point to evaluate . Parameters GraphObject The graph defined by nodes and an adjacency matrix . Returns
def choose_next(self, graph):
        if self.graph.lids is None:
            lid = int(self.initial_design(graph.X, 1, rng=self.rng))
        else:
            logger.info("Label propagation through the graph...")
            t = time.time()
            self.label_prop_alg.label_propagate(graph)
            logger.info("Time to propagate the labels: %f", (time.time() - t))
            preds = graph.Y.tolist()
            self.predictions.append(preds)
            logger.info("Maximize acquisition function...")
            t = time.time()
            self.acquisition_func.update(graph)
            lid = self.maximize_func.maximize(graph)
            logger.info("Time to maximize the acquisition function: %f", (time.time() - t))
        return lid
def choose_next ( self , graph ) : if self . graph . lids is None : lid = int ( self . initial_design ( graph . X , 1 , rng = self . rng ) ) else : logger . info ( "Label propagation through the graph..." ) t = time . time ( ) self . label_prop_alg . label_propagate ( graph ) logger . info ( "Time to propagate the labels: %f" , ( time . time ( ) - t ) ) preds = graph . Y . tolist ( ) self . predictions . append ( preds ) logger . info ( "Maximize acquisition function..." ) t = time . time ( ) self . acquisition_func . update ( graph ) lid = self . maximize_func . maximize ( graph ) logger . info ( "Time to maximize the acquisition function: %f" , ( time . time ( ) - t ) ) return lid
Suggests a new point to evaluate.
Suggests a new point to evaluate .
"""
        Suggests a new point to evaluate.

        Parameters
        ----------
        graph: GraphObject
            The graph defined by nodes and an adjacency matrix.

        Returns
        -------
        np.ndarray(1,D)
            Suggested point
        """
self
graph