/Users/mancn/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/numpy/_core/fromnumeric.py:57: FutureWarning:

'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.

Fitting 3 folds for each of 256 candidates, totalling 768 fits
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=100, random_state=42;, score=-4.637 total time=   0.1s
[CV 2/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=100, random_state=42;, score=-10.386 total time=   0.1s
[CV 3/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=100, random_state=42;, score=-2.341 total time=   0.1s
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=200, random_state=42;, score=-4.090 total time=   0.2s
[CV 2/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=200, random_state=42;, score=-8.373 total time=   0.2s
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=300, random_state=42;, score=-3.619 total time=   0.3s
[CV 3/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=200, random_state=42;, score=-2.196 total time=   0.3s
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=10, n_estimators=100, random_state=42;, score=-4.637 total time=   0.1s
[CV 2/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=300, random_state=42;, score=-6.670 total time=   0.3s
[CV 2/3] END gamma=0.001, learning_rate=0.001, max_depth=10, n_estimators=100, random_state=42;, score=-10.386 total time=   0.1s
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=400, random_state=42;, score=-3.210 total time=   0.3s
[CV 3/3] END gamma=0.001, learning_rate=0.001, max_depth=10, n_estimators=100, random_state=42;, score=-2.341 total time=   0.2s
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=10, n_estimators=200, random_state=42;, score=-4.090 total time=   0.2s
[CV 3/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=300, random_state=42;, score=-2.069 total time=   0.4s
[CV 2/3] END gamma=0.001, learning_rate=0.001, max_depth=8, n_estimators=400, random_state=42;, score=-5.331 total time=   0.4s
[CV 2/3] END gamma=0.001, learning_rate=0.001, max_depth=10, n_estimators=200, random_state=42;, score=-8.373 total time=   0.3s
[CV 1/3] END gamma=0.001, learning_rate=0.001, max_depth=10, n_estimators=300, random_state=42;, score=-3.619 total time=   0.2s

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[34], line 16
     10 # 使用 GridSearchCV 进行网格搜索，寻找最佳参数组合
     11 clf = GridSearchCV(estimator=XGBRegressor(),
     12                    param_grid=grid,
     13                    n_jobs=-1,  # 使用所有可用CPU并行计算
     14                    cv=3,
     15                    verbose=3)  # 3折交叉验证
---> 16 clf.fit(X_train, y_train)
     18 # 打印网格搜索找到的最佳参数和在验证集上的最佳得分
     19 print(f'Best params: {clf.best_params_}')

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/sklearn/base.py:1473, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1466     estimator._validate_params()
   1468 with config_context(
   1469     skip_parameter_validation=(
   1470         prefer_skip_nested_validation or global_skip_validation
   1471     )
   1472 ):
-> 1473     return fit_method(estimator, *args, **kwargs)

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1019, in BaseSearchCV.fit(self, X, y, **params)
   1013     results = self._format_results(
   1014         all_candidate_params, n_splits, all_out, all_more_results
   1015     )
   1017     return results
-> 1019 self._run_search(evaluate_candidates)
   1021 # multimetric is determined here because in the case of a callable
   1022 # self.scoring the return type is only known after calling
   1023 first_test_score = all_out[0]["test_scores"]

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1573, in GridSearchCV._run_search(self, evaluate_candidates)
   1571 def _run_search(self, evaluate_candidates):
   1572     """Search all candidates in param_grid"""
-> 1573     evaluate_candidates(ParameterGrid(self.param_grid))

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/sklearn/model_selection/_search.py:965, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    957 if self.verbose > 0:
    958     print(
    959         "Fitting {0} folds for each of {1} candidates,"
    960         " totalling {2} fits".format(
    961             n_splits, n_candidates, n_candidates * n_splits
    962         )
    963     )
--> 965 out = parallel(
    966     delayed(_fit_and_score)(
    967         clone(base_estimator),
    968         X,
    969         y,
    970         train=train,
    971         test=test,
    972         parameters=parameters,
    973         split_progress=(split_idx, n_splits),
    974         candidate_progress=(cand_idx, n_candidates),
    975         **fit_and_score_kwargs,
    976     )
    977     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    978         enumerate(candidate_params),
    979         enumerate(cv.split(X, y, **routed_params.splitter.split)),
    980     )
    981 )
    983 if len(out) < 1:
    984     raise ValueError(
    985         "No fits were performed. "
    986         "Was the CV iterator empty? "
    987         "Were there no candidates?"
    988     )

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/sklearn/utils/parallel.py:74, in Parallel.__call__(self, iterable)
     69 config = get_config()
     70 iterable_with_config = (
     71     (_with_config(delayed_func, config), args, kwargs)
     72     for delayed_func, args, kwargs in iterable
     73 )
---> 74 return super().__call__(iterable_with_config)

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/joblib/parallel.py:2007, in Parallel.__call__(self, iterable)
   2001 # The first item from the output is blank, but it makes the interpreter
   2002 # progress until it enters the Try/Except block of the generator and
   2003 # reaches the first `yield` statement. This starts the asynchronous
   2004 # dispatch of the tasks to the workers.
   2005 next(output)
-> 2007 return output if self.return_generator else list(output)

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/joblib/parallel.py:1650, in Parallel._get_outputs(self, iterator, pre_dispatch)
   1647     yield
   1649     with self._backend.retrieval_context():
-> 1650         yield from self._retrieve()
   1652 except GeneratorExit:
   1653     # The generator has been garbage collected before being fully
   1654     # consumed. This aborts the remaining tasks if possible and warn
   1655     # the user if necessary.
   1656     self._exception = True

File ~/anaconda3/envs/20240918_163852-wdnotebook/lib/python3.10/site-packages/joblib/parallel.py:1762, in Parallel._retrieve(self)
   1757 # If the next job is not ready for retrieval yet, we just wait for
   1758 # async callbacks to progress.
   1759 if ((len(self._jobs) == 0) or
   1760     (self._jobs[0].get_status(
   1761         timeout=self.timeout) == TASK_PENDING)):
-> 1762     time.sleep(0.01)
   1763     continue
   1765 # We need to be careful: the job list can be filling up as
   1766 # we empty it and Python list are not thread-safe by
   1767 # default hence the use of the lock

KeyboardInterrupt: 

Mean Squared Error = 37.49102220049418