我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用hyperopt.space_eval()。
def start_optimization(self, max_evals): logger.info("Started optimization for task %s", self.task) space = self.space.hyperopt() best = fmin(self.score, space, algo=tpe.suggest, max_evals=max_evals) self.best = space_eval(space, best) return self.best
def eval_hyperopt_space(space, vals): """ Evaluate a set of parameter values within the hyperopt space. Optionally unpacks the values, if they are wrapped in lists. :param space: dict the hyperopt space dictionary :param vals: dict the values from a hyperopt trial :return: evaluated space """ unpacked_vals = unpack_hyperopt_vals(vals) return space_eval(space, unpacked_vals)
def run(self): start = time.time() trials = Trials() best = fmin(self._obj, self.model_param_space._build_space(), tpe.suggest, self.max_evals, trials) best_params = space_eval(self.model_param_space._build_space(), best) best_params = self.model_param_space._convert_int_param(best_params) trial_rmses = np.asarray(trials.losses(), dtype=float) best_ind = np.argmin(trial_rmses) best_rmse_mean = trial_rmses[best_ind] best_rmse_std = trials.trial_attachments(trials.trials[best_ind])["std"] self.logger.info("-"*50) self.logger.info("Best RMSE") self.logger.info(" Mean: %.6f"%best_rmse_mean) self.logger.info(" std: %.6f"%best_rmse_std) self.logger.info("Best param") self.task._print_param_dict(best_params) end = time.time() _sec = end - start _min = int(_sec/60.) self.logger.info("Time") if _min > 0: self.logger.info(" %d mins"%_min) else: self.logger.info(" %d secs"%_sec) self.logger.info("-"*50) #------------------------ Main -------------------------
def xgb2(train2, y, test2, v, z): cname = sys._getframe().f_code.co_name N_splits = 9 N_seeds = 4 from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval dtrain = xgb.DMatrix(train2, y) def step_xgb(params): cv = xgb.cv(params=params, dtrain=dtrain, num_boost_round=10000, early_stopping_rounds=100, nfold=10, seed=params['seed']) score = cv.ix[len(cv)-1, 0] print(cname, score, len(cv), params) return dict(loss=score, status=STATUS_OK) space_xgb = dict( max_depth = hp.choice('max_depth', range(2, 8)), subsample = hp.quniform('subsample', 0.6, 1, 0.05), colsample_bytree = hp.quniform('colsample_bytree', 0.6, 1, 0.05), learning_rate = hp.quniform('learning_rate', 0.005, 0.03, 0.005), min_child_weight = hp.quniform('min_child_weight', 1, 6, 1), gamma = hp.quniform('gamma', 0.5, 10, 0.05), objective = 'binary:logistic', eval_metric = 'logloss', seed = 1, silent = 1 ) trs = load_state(cname + '_trials') if trs == None: tr = Trials() else: tr, _ = trs if len(tr.trials) > 0: print('reusing %d trials, best was:'%(len(tr.trials)), space_eval(space_xgb, tr.argmin)) for n in range(5): best = fmin(step_xgb, space_xgb, algo=tpe.suggest, max_evals=len(tr.trials) + 1, trials = tr) save_state(cname + '_trials', (tr, space_xgb)) xgb_params = space_eval(space_xgb, best) print(xgb_params) xgb_common(train2, y, test2, v, z, N_seeds, N_splits, cname, xgb_params)
def xgb2(train2, y, test2, v, z): cname = sys._getframe().f_code.co_name N_splits = 9 N_seeds = 4 from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval dtrain = xgb.DMatrix(train2, y) def step_xgb(params): cv = xgb.cv(params=params, dtrain=dtrain, num_boost_round=10000, early_stopping_rounds=100, nfold=10, seed=params['seed']) score = cv.ix[len(cv)-1, 0] print(cname, score, len(cv), params) return dict(loss=score, status=STATUS_OK) space_xgb = dict( max_depth = hp.choice('max_depth', range(2, 8)), subsample = hp.quniform('subsample', 0.6, 1, 0.05), colsample_bytree = hp.quniform('colsample_bytree', 0.6, 1, 0.05), learning_rate = hp.quniform('learning_rate', 0.005, 0.03, 0.005), min_child_weight = hp.quniform('min_child_weight', 1, 6, 1), gamma = hp.quniform('gamma', 0.5, 10, 0.05), objective = 'binary:logistic', eval_metric = 'logloss', seed = 1, silent = 1 ) trs = load_state(cname + '_trials') if trs == None: tr = Trials() else: tr, _ = trs if len(tr.trials) > 0: print('reusing %d trials, best was:'%(len(tr.trials)), space_eval(space_xgb, tr.argmin)) for n in range(15): best = fmin(step_xgb, space_xgb, algo=tpe.suggest, max_evals=len(tr.trials) + 1, trials = tr) save_state(cname + '_trials', (tr, space_xgb)) xgb_params = space_eval(space_xgb, best) print(xgb_params) xgb_common(train2, y, test2, v, z, N_seeds, N_splits, cname, xgb_params)
def xgb2(train2, y, test2, v, z): cname = sys._getframe().f_code.co_name N_splits = 9 N_seeds = 4 from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval dtrain = xgb.DMatrix(train2, y) def step_xgb(params): cv = xgb.cv(params=params, dtrain=dtrain, num_boost_round=10000, early_stopping_rounds=100, nfold=10, seed=params['seed']) score = cv.ix[len(cv)-1, 0] print(cname, score, len(cv), params) return dict(loss=score, status=STATUS_OK) space_xgb = dict( max_depth = hp.choice('max_depth', range(2, 8)), subsample = hp.quniform('subsample', 0.6, 1, 0.05), colsample_bytree = hp.quniform('colsample_bytree', 0.6, 1, 0.05), learning_rate = hp.quniform('learning_rate', 0.005, 0.03, 0.005), min_child_weight = hp.quniform('min_child_weight', 1, 6, 1), gamma = hp.quniform('gamma', 0.5, 10, 0.05), objective = 'binary:logistic', eval_metric = 'logloss', seed = 1, silent = 1 ) trs = load_state(cname + '_trials') if trs == None: tr = Trials() else: tr, _ = trs if len(tr.trials) > 0: print('reusing %d trials, best was:'%(len(tr.trials)), space_eval(space_xgb, tr.argmin)) for n in range(25): best = fmin(step_xgb, space_xgb, algo=tpe.suggest, max_evals=len(tr.trials) + 1, trials = tr) save_state(cname + '_trials', (tr, space_xgb)) xgb_params = space_eval(space_xgb, best) print(xgb_params) xgb_common(train2, y, test2, v, z, N_seeds, N_splits, cname, xgb_params)
def xgb3(train2, y, test2, v, z): cname = sys._getframe().f_code.co_name N_splits = 9 N_seeds = 4 from hyperopt import fmin, tpe, hp, STATUS_OK, Trials, space_eval dtrain = xgb.DMatrix(train2, y) def step_xgb(params): cv = xgb.cv(params=params, dtrain=dtrain, num_boost_round=10000, early_stopping_rounds=100, nfold=10, seed=params['seed']) score = cv.ix[len(cv)-1, 0] print(cname, score, len(cv), params) return dict(loss=score, status=STATUS_OK) space_xgb = dict( max_depth = hp.choice('max_depth', range(2, 8)), subsample = hp.quniform('subsample', 0.6, 1, 0.05), colsample_bytree = hp.quniform('colsample_bytree', 0.6, 1, 0.05), learning_rate = hp.quniform('learning_rate', 0.005, 0.03, 0.005), min_child_weight = hp.quniform('min_child_weight', 1, 6, 1), gamma = hp.quniform('gamma', 0, 10, 0.05), alpha = hp.quniform('alpha', 0.0, 1, 0.0001), objective = 'binary:logistic', eval_metric = 'logloss', seed = 1, silent = 1 ) trs = load_state(cname + '_trials') if trs == None: tr = Trials() else: tr, _ = trs if len(tr.trials) > 0: print('reusing %d trials, best was:'%(len(tr.trials)), space_eval(space_xgb, tr.argmin)) for n in range(25): best = fmin(step_xgb, space_xgb, algo=tpe.suggest, max_evals=len(tr.trials) + 1, trials = tr) save_state(cname + '_trials', (tr, space_xgb)) xgb_params = space_eval(space_xgb, best) print(xgb_params) xgb_common(train2, y, test2, v, z, N_seeds, N_splits, cname, xgb_params)
