Question

我正在尝试从python脚本导入一个函数，该脚本使用我的活动Jupyter笔记本中存在的变量。当我直接将功能复制到笔记本中并运行它们时，它们可以工作，但是当我导入它们时，它们无法识别我的变量。

这是我导入的脚本scripting_bayes.py：

import xgboost as xgb
import numpy as np
from hyperopt import fmin, tpe, hp, STATUS_OK, Trials



def optimize(X_train, y_train, X_val, y_val):
    def hyperopt_train_test(params, X_train, y_train, X_val, y_val):
        xg = xgb.XGBClassifier(**params, 
                               nthread=10, 
                               objective='binary:logistic')

        xg.fit(X_train_os, y_train_os)
        y_pred = xg.predict(X_val)
        return recall_score(y_val, y_pred)



    def f(params):
        recall = hyperopt_train_test(params, X_train, y_train, X_val, y_val)
        return {'loss': -recall, 'status': STATUS_OK}




    trials = Trials()

    space = {
        'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
        'gamma':  hp.uniform('gamma', 0.5, 5),
        'subsample': hp.uniform('subsample', 0.05, 0.2),
        'colsample_bytree': hp.uniform('colsample_bytree', 0.1, 0.6),
        'max_depth':  hp.choice('max_depth', np.arange(3, 6, dtype=int))
    }

    best_params = fmin(f, space, algo=tpe.suggest, max_evals=100, trials=trials)
    return best_params

然后我在Jupter笔记本中将X_train_os，Y_train_os，X_val和y_val作为活动的numpy数组。当我运行导入并调用该函数时，出现一个错误，它无法识别我对X_train_os的第一个变量引用。

from modeling_bayes import optimize
best_parameters = optimize(X_train_os, y_train_os, X_val, y_val)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-9-aafa5107a6e9> in <module>()
----> 1 best_parameters = optimize(X_train_os, y_train_os, X_val, y_val)

D:\main-project\sandbox\pipeline\V2\modeling_bayes.py in optimize(X_train, y_train, X_val, y_val)
     48     }
     49 
---> 50     best_params = fmin(f, space, algo=tpe.suggest, max_evals=100, trials=trials)
     51     return best_params

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len)
    365             verbose=verbose,
    366             catch_eval_exceptions=catch_eval_exceptions,
--> 367             return_argmin=return_argmin,
    368         )
    369 

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\base.py in fmin(self, fn, space, algo, max_evals, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin)
    633             pass_expr_memo_ctrl=pass_expr_memo_ctrl,
    634             catch_eval_exceptions=catch_eval_exceptions,
--> 635             return_argmin=return_argmin)
    636 
    637 

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\fmin.py in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len)
    383                     max_queue_len=max_queue_len)
    384     rval.catch_eval_exceptions = catch_eval_exceptions
--> 385     rval.exhaust()
    386     if return_argmin:
    387         return trials.argmin

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\fmin.py in exhaust(self)
    242     def exhaust(self):
    243         n_done = len(self.trials)
--> 244         self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
    245         self.trials.refresh()
    246         return self

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\fmin.py in run(self, N, block_until_done)
    216             else:
    217                 # -- loop over trials and do the jobs directly
--> 218                 self.serial_evaluate()
    219 
    220             if stopped:

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\fmin.py in serial_evaluate(self, N)
    135                 ctrl = base.Ctrl(self.trials, current_trial=trial)
    136                 try:
--> 137                     result = self.domain.evaluate(spec, ctrl)
    138                 except Exception as e:
    139                     logger.info('job exception: %s' % str(e))

C:\ProgramData\Anaconda3\lib\site-packages\hyperopt\base.py in evaluate(self, config, ctrl, attach_attachments)
    838                 memo=memo,
    839                 print_node_on_error=self.rec_eval_print_node_on_error)
--> 840             rval = self.fn(pyll_rval)
    841 
    842         if isinstance(rval, (float, int, np.number)):

D:\main-project\sandbox\pipeline\V2\modeling_bayes.py in f(params)
     18 
     19     def f(params):
---> 20         recall = hyperopt_train_test(params, X_train, y_train, X_val, y_val)
     21         return {'loss': -recall, 'status': STATUS_OK}
     22 

D:\main-project\sandbox\pipeline\V2\modeling_bayes.py in hyperopt_train_test(params, X_train, y_train, X_val, y_val)
     11                                objective='binary:logistic')
     12 
---> 13         xg.fit(X_train_os, y_train_os)
     14         y_pred = xg.predict(X_val)
     15         return recall_score(y_val, y_pred)

NameError: name 'X_train_os' is not defined

Answer 1

内部hyperopt_train_test接受参数X_train, y_train，而内部则使用X_train_os, y_train_os。这是错字吗？

导入功能以在活动的jupyter笔记本中使用变量

1 个答案: