(self, job, update_model=True)
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: hpbandster.distributed.dispatcher.Job object
				contains all the info about the run
function to register finished runs
function to register finished runs
def new_result(self, job, update_model=True):
		"""
			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: hpbandster.distributed.dispatcher.Job object
				contains all the info about the run
		"""

		super().new_result(job)

		if job.result is None:
			# One could skip crashed results, but we decided 
			# assign a +inf loss and count them as bad configurations
			loss = np.inf
		else:
			loss = job.result["loss"]

		budget = job.kwargs["budget"]

		if budget not in self.configs.keys():
			self.configs[budget] = []
			self.losses[budget] = []
	
			
		if len(self.configs.keys()) == 1:
			min_num_points = 6
		else:
			min_num_points = self.min_points_in_model


		# skip model building if we already have a bigger model
		if max(list(self.kde_models.keys()) + [-np.inf]) > budget:
			return

		# We want to get a numerical representation of the configuration in the original space

		conf = ConfigSpace.Configuration(self.configspace, job.kwargs["config"]).get_array().tolist()
		
			
		#import pdb; pdb.set_trace()
		
		
		if conf in self.configs[budget]:
			i = self.configs[budget].index(conf)
			self.losses[budget][i].append(loss)
			print('-'*50)
			print('ran config %s with loss %f again'%(conf, loss))
		else:
			self.configs[budget].append(conf)
			self.losses[budget].append([loss])

		
		# skip model building:
		#		a) if not enough points are available
		
		tmp = np.array([np.mean(r) for r in self.losses[budget]])
		if np.sum(np.isfinite(tmp)) < min_num_points:
			self.logger.debug("Only %i successful run(s) for budget %f available, need more than %s -> can't build model!"%(np.sum(np.isfinite(tmp)), budget, min_num_points))
			return

		#		b) during warnm starting when we feed previous results in and only update once
		if not update_model:
			return



		if budget not in self.kde_models.keys():
			self.kde_models[budget] = {
				'good': MultivariateKDE(self.configspace, min_bandwidth=self.min_bandwidth, fully_dimensional=self.fully_dimensional),
				'bad' : MultivariateKDE(self.configspace, min_bandwidth=self.min_bandwidth, fully_dimensional=self.fully_dimensional)
			}	


		#import pdb; pdb.set_trace()
		num_configs = len(self.losses[budget])
		
		train_configs = np.array(self.configs[budget][-num_configs:])
		train_losses =  np.array(list(map(np.mean, self.losses[budget][-num_configs:])))

		n_good= max(3,(num_configs * self.top_n_percent) // 100)
		n_bad = num_configs-n_good

		# Refit KDE for the current budget
		idx = np.argsort(train_losses)

		train_data_good = self.impute_conditional_data(train_configs[idx[:n_good]])
		train_data_bad  = self.impute_conditional_data(train_configs[idx[n_good:n_good+n_bad+1]])


		self.kde_models[budget]['bad'].fit(train_data_bad, bw_estimator=self.bw_estimator)
		self.kde_models[budget]['good'].fit(train_data_good, bw_estimator=self.bw_estimator)
		
		
		if self.bw_estimator in ['mlcv'] and n_good < 3:
			self.kde_models[budget]['good'].bandwidths[:] = self.kde_models[budget]['bad'].bandwidths

		# update probs for the categorical parameters for later sampling
		self.logger.debug('done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n\n\n\n\n'%(budget, n_good, n_bad, np.min(train_losses)))
def new_result ( self , job , update_model = True ) : super ( ) . new_result ( job ) if job . result is None : # One could skip crashed results, but we decided  # assign a +inf loss and count them as bad configurations loss = np . inf else : loss = job . result [ "loss" ] budget = job . kwargs [ "budget" ] if budget not in self . configs . keys ( ) : self . configs [ budget ] = [ ] self . losses [ budget ] = [ ] if len ( self . configs . keys ( ) ) == 1 : min_num_points = 6 else : min_num_points = self . min_points_in_model # skip model building if we already have a bigger model if max ( list ( self . kde_models . keys ( ) ) + [ - np . inf ] ) > budget : return # We want to get a numerical representation of the configuration in the original space conf = ConfigSpace . Configuration ( self . configspace , job . kwargs [ "config" ] ) . get_array ( ) . tolist ( ) #import pdb; pdb.set_trace() if conf in self . configs [ budget ] : i = self . configs [ budget ] . index ( conf ) self . losses [ budget ] [ i ] . append ( loss ) print ( '-' * 50 ) print ( 'ran config %s with loss %f again' % ( conf , loss ) ) else : self . configs [ budget ] . append ( conf ) self . losses [ budget ] . append ( [ loss ] ) # skip model building: #		a) if not enough points are available tmp = np . array ( [ np . mean ( r ) for r in self . losses [ budget ] ] ) if np . sum ( np . isfinite ( tmp ) ) < min_num_points : self . logger . debug ( "Only %i successful run(s) for budget %f available, need more than %s -> can't build model!" % ( np . sum ( np . isfinite ( tmp ) ) , budget , min_num_points ) ) return #		b) during warnm starting when we feed previous results in and only update once if not update_model : return if budget not in self . kde_models . keys ( ) : self . kde_models [ budget ] = { 'good' : MultivariateKDE ( self . configspace , min_bandwidth = self . min_bandwidth , fully_dimensional = self . fully_dimensional ) , 'bad' : MultivariateKDE ( self . configspace , min_bandwidth = self . min_bandwidth , fully_dimensional = self . fully_dimensional ) } #import pdb; pdb.set_trace() num_configs = len ( self . losses [ budget ] ) train_configs = np . array ( self . configs [ budget ] [ - num_configs : ] ) train_losses = np . array ( list ( map ( np . mean , self . losses [ budget ] [ - num_configs : ] ) ) ) n_good = max ( 3 , ( num_configs * self . top_n_percent ) // 100 ) n_bad = num_configs - n_good # Refit KDE for the current budget idx = np . argsort ( train_losses ) train_data_good = self . impute_conditional_data ( train_configs [ idx [ : n_good ] ] ) train_data_bad = self . impute_conditional_data ( train_configs [ idx [ n_good : n_good + n_bad + 1 ] ] ) self . kde_models [ budget ] [ 'bad' ] . fit ( train_data_bad , bw_estimator = self . bw_estimator ) self . kde_models [ budget ] [ 'good' ] . fit ( train_data_good , bw_estimator = self . bw_estimator ) if self . bw_estimator in [ 'mlcv' ] and n_good < 3 : self . kde_models [ budget ] [ 'good' ] . bandwidths [ : ] = self . kde_models [ budget ] [ 'bad' ] . bandwidths # update probs for the categorical parameters for later sampling self . logger . debug ( 'done building a new model for budget %f based on %i/%i split\nBest loss for this budget:%f\n\n\n\n\n' % ( budget , n_good , n_bad , np . min ( train_losses ) ) )