def create_trials(losses, statuses, vals, scope_keys): trials = Trials() tids = trials.new_trial_ids(len(losses)) specs = [None for x in range(len(tids))] results = [] miscs = [] for i in range(len(tids)): idxs_content = [[i] for key in scope_keys] idxs_vals_content = [] for key in scope_keys: idxs_vals_content.append([vals[key][i]]) results.append(dict(loss=losses[i], status=statuses[i])) miscs.append( dict(tid=tids[i], cmd=None, idxs=dict(zip(scope_keys, idxs_content)), vals=dict(zip(scope_keys, idxs_vals_content)))) trials.insert_trial_docs( trials.new_trial_docs( tids, specs, results, miscs, )) trials.refresh() return trials
def create_trials(self, complete, losses): if len(complete) > 0: trials = Trials() hist = self.create_history(complete) index = 0 for c in complete: if c >= len(losses): error( "Index {} is larger than the size of losses {}".format( c, len(losses))) loss = losses[c] rval_specs = [None] new_id = index rval_results = [] rval_results.append(create_ok_result(loss, c)) rval_miscs = [] rval_miscs.append(self.create_misc(index, hist)) hyperopt_trial = trials.new_trial_docs([new_id], rval_specs, rval_results, rval_miscs)[0] index += 1 if self.response_shaping is True: # transform log applied loss for enhancing optimization performance #debug("before scaling: {}".format(loss)) if self.shaping_func == "log_err": loss = apply_log_err(loss) elif self.shaping_func == "hybrid_log": loss = apply_hybrid_log(loss) else: debug("Invalid shaping function: {}".format( self.shaping_func)) hyperopt_trial['result'] = { 'loss': float(loss), 'status': STATUS_OK } hyperopt_trial['state'] = JOB_STATE_DONE #debug("History appended: {}-{}".format(c, loss)) trials.insert_trial_doc(hyperopt_trial) trials.refresh() return trials else: return Trials()
def create_trials(self, completed, losses): if len(completed) > 0: trials = Trials() hist = self.create_history(completed) #index = 0 #for c in completed: for index in range(len(completed)): c = completed[index] loss = losses[index] rval_specs = [None] new_id = index rval_results = [ ] rval_results.append(create_ok_result(loss, c)) rval_miscs = [ ] rval_miscs.append(self.create_misc(index, hist)) hopt_trial = trials.new_trial_docs([new_id], rval_specs, rval_results, rval_miscs)[0] if self.response_shaping is True: # transform log applied loss for enhancing optimization performance #debug("before scaling: {}".format(loss)) if self.shaping_func == "log_err": loss = apply_log_err(loss) elif self.shaping_func == "hybrid_log": loss = apply_hybrid_log(loss) else: debug("Invalid shaping function: {}".format(self.shaping_func)) if loss != None: hopt_trial['result'] = {'loss': float(loss), 'status': STATUS_OK} hopt_trial['state'] = JOB_STATE_DONE #debug("History appended: {}-{}".format(c, loss)) trials.insert_trial_doc(hopt_trial) trials.refresh() return trials else: return Trials()