Scikit learn GridSearchCV（）：ValueError:输入包含NaN、无穷大或对数据类型（'；float64'；）太大的值

当我尝试在MLP分类器上执行GridsearchCV时，我在标题中得到ValueError。当然，我检查了我的数据集中是否存在任何np.inf或np.nan，但它们不存在：

    print(np.any(np.isnan(X)))

返回False

    print(np.all(np.isfinite(X)))

返回True

我还将我所有的价值观铸造到np.64

X = X.values.astype(np.float64)
Y = Y.values

我的scikit学习版是0.22.2.post1（最新版本）

我试图执行的代码：

from scipy.stats import randint as sp_randint

hiddenlayers = [(sp_randint.rvs(100,600,1),sp_randint.rvs(100,600,1),), (sp_randint.rvs(100,600,1),)]
alpha_range = 10.0 ** np.arange(-2, 1)


param_grid_MLP = [{'solver': ['lbfgs'],
                   'hidden_layer_sizes': hiddenlayers,
                   'activation': ['identity','tanh', 'relu', 'logistic'],
                   'alpha': alpha_range
                  },
                 {'solver': ['sgd'],
                  'hidden_layer_sizes': hiddenlayers,
                   'activation': ['identity','tanh', 'relu', 'logistic'],
                   'alpha': alpha_range,
                  'learning_rate':['constant','invscaling','adaptive']
                  },
                 {'solver': ['adam'],
                  'hidden_layer_sizes': hiddenlayers,
                   'activation': ['identity','tanh', 'relu', 'logistic'],
                   'alpha': alpha_range
                  }]

mlp = MLPClassifier(random_state=0)
cross_validation = StratifiedKFold(5)

# scoring = {'AUC': 'roc_auc', 
#            'Accuracy': make_scorer(accuracy_score),
#            'Recall':make_scorer(recall_score,pos_label='crafted'),
#            'Precision': make_scorer(precision_score,pos_label='crafted')}

scoring = {'AUC': 'roc_auc', 
           'Accuracy': make_scorer(accuracy_score),
            'Recall':make_scorer(recall_score,pos_label='crafted')}

grid_search_MLP = GridSearchCV(estimator=mlp, 
            param_grid=param_grid_MLP, 
            scoring=scoring,cv=cross_validation.split(X_train,y_train),
            refit='Recall',
            n_jobs=-1,
            verbose=True)

grid_search_MLP.fit(X_train,y_train)

print('Best score: {}'.format(grid_search_MLP.best_score_))
print('Best index: {}'.format(grid_search_MLP.best_index_))
print('Best parameters: {}'.format(grid_search_MLP.best_params_))

mlp = grid_search_MLP.best_estimator_
mlp

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/joblib/externals/loky/process_executor.py", line 418, in _process_worker
    r = call_item()
  File "/usr/local/lib/python3.7/dist-packages/joblib/externals/loky/process_executor.py", line 272, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py", line 608, in __call__
    return self.func(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/joblib/parallel.py", line 256, in __call__
    for func, args, kwargs in self.items]
  File "/usr/local/lib/python3.7/dist-packages/joblib/parallel.py", line 256, in <listcomp>
    for func, args, kwargs in self.items]
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 544, in _fit_and_score
    test_scores = _score(estimator, X_test, y_test, scorer)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 591, in _score
    scores = scorer(estimator, X_test, y_test)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py", line 87, in __call__
    *args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py", line 332, in _score
    return self._sign * self._score_func(y, y_pred, **self._kwargs)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_ranking.py", line 369, in roc_auc_score
    y_score = check_array(y_score, ensure_2d=False)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 578, in check_array
    allow_nan=force_all_finite == 'allow-nan')
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 60, in _assert_all_finite
    msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

完整的错误回溯：

from scipy.stats import randint as sp_randint

hiddenlayers = [(sp_randint.rvs(100,600,1),sp_randint.rvs(100,600,1),), (sp_randint.rvs(100,600,1),)]
alpha_range = 10.0 ** np.arange(-2, 1)


param_grid_MLP = [{'solver': ['lbfgs'],
                   'hidden_layer_sizes': hiddenlayers,
                   'activation': ['identity','tanh', 'relu', 'logistic'],
                   'alpha': alpha_range
                  },
                 {'solver': ['sgd'],
                  'hidden_layer_sizes': hiddenlayers,
                   'activation': ['identity','tanh', 'relu', 'logistic'],
                   'alpha': alpha_range,
                  'learning_rate':['constant','invscaling','adaptive']
                  },
                 {'solver': ['adam'],
                  'hidden_layer_sizes': hiddenlayers,
                   'activation': ['identity','tanh', 'relu', 'logistic'],
                   'alpha': alpha_range
                  }]

mlp = MLPClassifier(random_state=0)
cross_validation = StratifiedKFold(5)

# scoring = {'AUC': 'roc_auc', 
#            'Accuracy': make_scorer(accuracy_score),
#            'Recall':make_scorer(recall_score,pos_label='crafted'),
#            'Precision': make_scorer(precision_score,pos_label='crafted')}

scoring = {'AUC': 'roc_auc', 
           'Accuracy': make_scorer(accuracy_score),
            'Recall':make_scorer(recall_score,pos_label='crafted')}

grid_search_MLP = GridSearchCV(estimator=mlp, 
            param_grid=param_grid_MLP, 
            scoring=scoring,cv=cross_validation.split(X_train,y_train),
            refit='Recall',
            n_jobs=-1,
            verbose=True)

grid_search_MLP.fit(X_train,y_train)

print('Best score: {}'.format(grid_search_MLP.best_score_))
print('Best index: {}'.format(grid_search_MLP.best_index_))
print('Best parameters: {}'.format(grid_search_MLP.best_params_))

mlp = grid_search_MLP.best_estimator_
mlp

Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/joblib/externals/loky/process_executor.py", line 418, in _process_worker
    r = call_item()
  File "/usr/local/lib/python3.7/dist-packages/joblib/externals/loky/process_executor.py", line 272, in __call__
    return self.fn(*self.args, **self.kwargs)
  File "/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py", line 608, in __call__
    return self.func(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/joblib/parallel.py", line 256, in __call__
    for func, args, kwargs in self.items]
  File "/usr/local/lib/python3.7/dist-packages/joblib/parallel.py", line 256, in <listcomp>
    for func, args, kwargs in self.items]
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 544, in _fit_and_score
    test_scores = _score(estimator, X_test, y_test, scorer)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py", line 591, in _score
    scores = scorer(estimator, X_test, y_test)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py", line 87, in __call__
    *args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py", line 332, in _score
    return self._sign * self._score_func(y, y_pred, **self._kwargs)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_ranking.py", line 369, in roc_auc_score
    y_score = check_array(y_score, ensure_2d=False)
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 578, in check_array
    allow_nan=force_all_finite == 'allow-nan')
  File "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py", line 60, in _assert_all_finite
    msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

回溯（最近一次呼叫最后一次）：
文件“/usr/local/lib/python3.7/dist packages/joblib/externals/loky/process\u executor.py”，第418行，in\u process\u worker
r=调用_项（）
文件“/usr/local/lib/python3.7/dist packages/joblib/externals/loky/process_executor.py”，第272行，在调用中__
返回self.fn（*self.args，**self.kwargs）
文件“/usr/local/lib/python3.7/dist packages/joblb/_parallel_backends.py”，第608行，在调用中__
返回self.func（*args，**kwargs）
文件“/usr/local/lib/python3.7/dist-packages/joblib/parallel.py”，第256行，在调用中__
对于self.items中的func、args、kwargs]
文件“/usr/local/lib/python3.7/dist-packages/joblib/parallel.py”，第256行，在
对于self.items中的func、args、kwargs]
文件“/usr/local/lib/python3.7/dist-packages/sklearn/model\u-selection/\u-validation.py”，第544行，在“fit”和“score”中
测试分数=_分数（估计员、X_测试、y_测试、计分员）
文件“/usr/local/lib/python3.7/dist-packages/sklearn/model\u-selection/\u-validation.py”，第591行，in\u-score
分数=记分员（估计员、X_检验、y_检验）
文件“/usr/local/lib/python3.7/dist packages/sklearn/metrics/_scorer.py”，第87行，在调用中__
*args，**kwargs）
文件“/usr/local/lib/python3.7/dist packages/sklearn/metrics/_scorer.py”，第332行，in_score
返回self.\u签名*self.\u分数\u函数（y，y\u pred，**self.\u kwargs）
文件“/usr/local/lib/python3.7/dist packages/sklearn/metrics/_ranking.py”，第369行，roc_auc_分数
y_分数=检查数组（y_分数，确保2d=假）
文件“/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py”，第578行，在check_数组中
allow_nan=force_all_finite==‘allow nan’）
文件“/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py”，第60行，在assert\u all\u finite中
msg\u dtype（如果msg\u dtype不是None else X.dtype）
ValueError:输入包含NaN、无穷大或对数据类型（'float64'）太大的值。

在我看来，数组中的值可能已损坏，或者是非数值。在转换为float之前，尝试检查数组中是否有其他类型。还要尝试在数组中查找最小值和最大值，这可能有助于查找引发错误的值

在我看来，数组中的值可能已损坏，或者是非数值。在转换为float之前，尝试检查数组中是否有其他类型。还要尝试在数组中查找最小值和最大值，这可能有助于查找引发错误的值

试着给verbose一个大数字，或者逐个运行网格的3部分。如果您意识到sgd给出了问题，这里可能解释了这个问题

试着给verbose一个大数字，或者逐个运行网格的3个部分。如果您意识到

sgd

给出了问题，这里可能会解释它

您从哪里获得阵列？csv文件？你是如何建造它的？你能把它打印出来吗？你从哪里得到你的阵列？csv文件？你是如何建造它的？你能把它打印出来吗？