(self, job)
function to register finished runs

            Every time a run has finished, this function should be called
            to register it with the result logger. If overwritten, make
            sure to call this method from the base class to ensure proper
            logging.


            Parameters:
            -----------
            job_id: dict
                a dictionary containing all the info about the run
            job_result: dict
                contains all the results of the job, i.e. it's a dict with
                the keys 'loss' and 'info'
function to register finished runs
function to register finished runs
def new_result(self, job):
        """
            function to register finished runs

            Every time a run has finished, this function should be called
            to register it with the result logger. If overwritten, make
            sure to call this method from the base class to ensure proper
            logging.


            Parameters:
            -----------
            job_id: dict
                a dictionary containing all the info about the run
            job_result: dict
                contains all the results of the job, i.e. it's a dict with
                the keys 'loss' and 'info'

        """
        super().new_result(job)

        conf = ConfigSpace.Configuration(self.config_space, job.kwargs['config']).get_array()

        epochs = len(job.result["info"]["learning_curve"])
        budget = int(job.kwargs["budget"])

        t_idx = np.linspace(budget / epochs, budget, epochs) / self.max_budget
        x_new = np.repeat(conf[None, :], t_idx.shape[0], axis=0)

        x_new = np.concatenate((x_new, t_idx[:, None]), axis=1)

        # Smooth learning curve
        lc = smoothing(job.result["info"]["learning_curve"])

        # Flip learning curves since LC-Net wants increasing curves
        lc_new = [1 - y for y in lc]

        if self.train is None:
            self.train = x_new
            self.train_targets = lc_new
        else:
            self.train = np.append(self.train, x_new, axis=0)
            self.train_targets = np.append(self.train_targets, lc_new, axis=0)

        if self.counter >= self.n_points:

            self.lock.acquire()
            y_min = np.min(self.train_targets)
            y_max = np.max(self.train_targets)

            train_targets = (self.train_targets - y_min) / (y_max - y_min)

            self.model.train(self.train, train_targets)
            self.is_trained = True
            self.counter = 0
            self.lock.release()

        else:
            self.counter += epochs
def new_result ( self , job ) : super ( ) . new_result ( job ) conf = ConfigSpace . Configuration ( self . config_space , job . kwargs [ 'config' ] ) . get_array ( ) epochs = len ( job . result [ "info" ] [ "learning_curve" ] ) budget = int ( job . kwargs [ "budget" ] ) t_idx = np . linspace ( budget / epochs , budget , epochs ) / self . max_budget x_new = np . repeat ( conf [ None , : ] , t_idx . shape [ 0 ] , axis = 0 ) x_new = np . concatenate ( ( x_new , t_idx [ : , None ] ) , axis = 1 ) # Smooth learning curve lc = smoothing ( job . result [ "info" ] [ "learning_curve" ] ) # Flip learning curves since LC-Net wants increasing curves lc_new = [ 1 - y for y in lc ] if self . train is None : self . train = x_new self . train_targets = lc_new else : self . train = np . append ( self . train , x_new , axis = 0 ) self . train_targets = np . append ( self . train_targets , lc_new , axis = 0 ) if self . counter >= self . n_points : self . lock . acquire ( ) y_min = np . min ( self . train_targets ) y_max = np . max ( self . train_targets ) train_targets = ( self . train_targets - y_min ) / ( y_max - y_min ) self . model . train ( self . train , train_targets ) self . is_trained = True self . counter = 0 self . lock . release ( ) else : self . counter += epochs