def _init(self):
if os.path.exists(self.trace_path):
os.remove(self.trace_path)
trace = Trace(self.trace_path, trace_level=2)
trace.write( 1, 'main', {
'hp_space': self.optimizer.hp_space,
'__dict__': self.__dict__,
'ds_name':self.evaluator.ds_name,
} )
self.trace = trace
self.pool = make_pool(self.Pool)
self._last_print = 0.
self.last_eval_size = 0
self.result_dict = {}
self.hp_id_seq = []
self.hp_id_pending = {}
self.lock = RLock()
self.y_dict = self.evaluator.get_y_dict()
self.y_val_m = self.y_dict['val']
self.trace.write(1,'y',self.y_dict) # trace this info for further analysis
self.m = len(self.y_val_m)
if self.salad_size == 1:
self.bootstrap_matrix_km = np.ones( (1,self.m) )/ self.m
else:
rng = np.random.RandomState(self.seed) # provides the opportunity to have the same bootstrap matrix across different experiments.
self.bootstrap_matrix_km = base.build_bootstrap_matrix(self.salad_size,self.m,self.essr, rng)
def _analyze_greedy(self,hp_id_N, ensemble_size=10):
if not hasattr(self, 'greedy_w_km'):
self.greedy_w_km = base.build_bootstrap_matrix(100, len(self.y_val_m), self.essr)
loss_mN = self._get_loss_matrix(hp_id_N)
ensemble_d, prob_d = greedy_ensemble(loss_mN, self.greedy_w_km, ensemble_size)
hp_id_d = hp_id_N[ensemble_d]
return self._eval_salad( hp_id_d, prob_d )
def _analyze_greedy(self, hp_id_N, ensemble_size=10):
if not hasattr(self, 'greedy_w_km'):
self.greedy_w_km = base.build_bootstrap_matrix(
100, len(self.y_val_m), self.essr)
loss_mN = self._get_loss_matrix(hp_id_N)
ensemble_d, prob_d = greedy_ensemble(loss_mN, self.greedy_w_km,
ensemble_size)
hp_id_d = hp_id_N[ensemble_d]
return self._eval_salad(hp_id_d, prob_d)
def _init(self):
if os.path.exists(self.trace_path):
os.remove(self.trace_path)
trace = Trace(self.trace_path, trace_level=2)
trace.write(
1, 'main', {
'hp_space': self.optimizer.hp_space,
'__dict__': self.__dict__,
'ds_name': self.evaluator.ds_name,
})
self.trace = trace
self.pool = make_pool(self.Pool)
self._last_print = 0.
self.last_eval_size = 0
self.result_dict = {}
self.hp_id_seq = []
self.hp_id_pending = {}
self.lock = RLock()
self.y_dict = self.evaluator.get_y_dict()
self.y_val_m = self.y_dict['val']
self.trace.write(1, 'y',
self.y_dict) # trace this info for further analysis
self.m = len(self.y_val_m)
if self.salad_size == 1:
self.bootstrap_matrix_km = np.ones((1, self.m)) / self.m
else:
rng = np.random.RandomState(
self.seed
) # provides the opportunity to have the same bootstrap matrix across different experiments.
self.bootstrap_matrix_km = base.build_bootstrap_matrix(
self.salad_size, self.m, self.essr, rng)
