机器学习，超参优化时报错
hp_space_reg = {
'clf_type': hp.choice('clf_type', [
{
'type': "LinearRegression",
'clf': {
"normalize" : hp.choice("lr.normalize", [True, False])
}
},
{
'type': "SVR",
'clf': {
'C': hp.loguniform('svm.C', -4.0 * np.log(10.0), 4.0 * np.log(10.0)),
'kernel': 'rbf',
'gamma': hp.choice('svm.gamma', ['auto', 'scale'])
}
},
{
'type': "XGBRegressor",
'clf': {
"n_estimators": hp.choice("n_estimators_xgb", range(10, 100)),
"max_depth": hp.choice("max_depth_xgb", range(3, 10)),
"learning_rate" : hp.loguniform('learning_rate_xgb', -1.0 * np.log(3.0), 1.0 * np.log(3.0)),
"gamma": hp.loguniform('gamma_xgb', -1.0 * np.log(10.0), 1.0 * np.log(10.0)),
"subsample" :hp.choice("subsample_xgb",[1,0.9,0.8,0.7,0.6]),
"n_jobs":hp.choice("n_jobs_xgb",[-1])
}
},
{
'type': "DecisionTreeRegressor",
'clf': {
"max_depth": hp.choice("max_depth_dtr", range(3, 10)),
"criterion": hp.choice("criterion_dtr",["mse", "friedman_mse", "mae"]),
}
},
{
'type': "RandomForestRegressor",
'clf': {
"n_estimators": hp.choice("n_estimators_rf", range(10, 100)),
"max_depth": hp.choice("max_depth_rf", range(3, 10)),
"min_samples_split": hp.choice("min_samples_split_rf", range(2, 3)),
"min_samples_leaf": hp.choice(" min_samples_leaf_rf", range(1, 3)),
"min_weight_fraction_leaf": hp.choice("min_weight_fraction_leaf_rf", [0,0.1,0.2,0.3]),
"max_features":hp.choice("max_features_rf",["auto", "sqrt", "log2"]),
"n_jobs":hp.choice("n_jobs_rf",[-1])
}
},
])
}
random_state=0
best_reg = opt(X_train_wrapper_reg, y_train_reg, space=hp_space_reg, max_evals=100, scoring="r2", random_state=random_state)
score = best_reg.score(X_test_wrapper_reg, y_test_reg)
print("test accuracy:", score)
print("best model", best_reg)
AttributeError Traceback (most recent call last)
<ipython-input-39-c27a503ee0cf> in <module>
54 }
55 random_state=0
---> 56 best_reg = opt(X_train_wrapper_reg, y_train_reg, space=hp_space_reg, max_evals=100, scoring="r2", random_state=random_state)
57 score = best_reg.score(X_test_wrapper_reg, y_test_reg)
58 print("test accuracy:", score)
~\syy\OSCs20210518\hyper_opt.py in opt(X, y, space, algo, max_evals, scoring, random_state)
61
62 trials_clf2 = Trials()
---> 63 best_clf2 = fmin(partial(f_to_min2, X=X, y=y, scoring=scoring, random_state=random_state),
64 space=space, algo=algo, max_evals=max_evals,
65 trials=trials_clf2, rstate=np.random.RandomState(random_state))
~\anaconda3\lib\site-packages\hyperopt\fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
538
539 if allow_trials_fmin and hasattr(trials, "fmin"):
--> 540 return trials.fmin(
541 fn,
542 space,
~\anaconda3\lib\site-packages\hyperopt\base.py in fmin(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)
669 from .fmin import fmin
670
--> 671 return fmin(
672 fn,
673 space,
~\anaconda3\lib\site-packages\hyperopt\fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
584
585 # next line is where the fmin is actually executed
--> 586 rval.exhaust()
587
588 if return_argmin:
~\anaconda3\lib\site-packages\hyperopt\fmin.py in exhaust(self)
362 def exhaust(self):
363 n_done = len(self.trials)
--> 364 self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
365 self.trials.refresh()
366 return self
~\anaconda3\lib\site-packages\hyperopt\fmin.py in run(self, N, block_until_done)
277 # processes orchestration
278 new_trials = algo(
--> 279 new_ids, self.domain, trials, self.rstate.integers(2 ** 31 - 1)
280 )
281 assert len(new_ids) >= len(new_trials)
AttributeError: 'numpy.random.mtrand.RandomState' object has no attribute 'integers'
本人代码初学者，不太清楚该从何入手解决。
希望可以正常运行输出结果，麻烦大家了！谢谢！
这个是你调用方法或属性不存在，可能是模块的版本更新去掉某些方法属性。可以降低版本试试
版本不匹配，检查一下hyperopt的numpy版本要求是多少，对应一下就可以了