在Pandas中使用idxmax()函数时，我一直收到此错误。
Traceback (most recent call last):
File "/Users/username/College/year-4/fyp-credit-card-fraud/code/main.py", line 20, in <module>
best_c_param = classify.print_kfold_scores(X_training_undersampled, y_training_undersampled)
File "/Users/username/College/year-4/fyp-credit-card-fraud/code/Classification.py", line 39, in print_kfold_scores
best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter']
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/series.py", line 1369, in idxmax
i = nanops.nanargmax(_values_from_object(self), skipna=skipna)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/nanops.py", line 74, in _f
raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
TypeError: reduction operation 'argmax' not allowed for this dtype
我使用的Pandas版本是0.22.0
main.py
import ExploratoryDataAnalysis as eda
import Preprocessing as processor
import Classification as classify
import pandas as pd
data_path = '/Users/username/college/year-4/fyp-credit-card-fraud/data/'
if __name__ == '__main__':
df = pd.read_csv(data_path + 'creditcard.csv')
# eda.init(df)
# eda.check_null_values()
# eda.view_data()
# eda.check_target_classes()
df = processor.noramlize(df)
X_training, X_testing, y_training, y_testing, X_training_undersampled, X_testing_undersampled, \
y_training_undersampled, y_testing_undersampled = processor.resample(df)
best_c_param = classify.print_kfold_scores(X_training_undersampled, y_training_undersampled)
Classification.py
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold, cross_val_score
from sklearn.metrics import confusion_matrix, precision_recall_curve, auc, \
roc_auc_score, roc_curve, recall_score, classification_report
import pandas as pd
import numpy as np
def print_kfold_scores(X_training, y_training):
print('\nKFold\n')
fold = KFold(len(y_training), 5, shuffle=False)
c_param_range = [0.01, 0.1, 1, 10, 100]
results = pd.DataFrame(index=range(len(c_param_range), 2), columns=['C_parameter', 'Mean recall score'])
results['C_parameter'] = c_param_range
j = 0
for c_param in c_param_range:
print('-------------------------------------------')
print('C parameter: ', c_param)
print('\n-------------------------------------------')
recall_accs = []
for iteration, indices in enumerate(fold, start=1):
lr = LogisticRegression(C=c_param, penalty='l1')
lr.fit(X_training.iloc[indices[0], :], y_training.iloc[indices[0], :].values.ravel())
y_prediction_undersampled = lr.predict(X_training.iloc[indices[1], :].values)
recall_acc = recall_score(y_training.iloc[indices[1], :].values, y_prediction_undersampled)
recall_accs.append(recall_acc)
print('Iteration ', iteration, ': recall score = ', recall_acc)
results.ix[j, 'Mean recall score'] = np.mean(recall_accs)
j += 1
print('\nMean recall score ', np.mean(recall_accs))
print('\n')
best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter'] # Error occurs on this line
print('*****************************************************************')
print('Best model to choose from cross validation is with C parameter = ', best_c_param)
print('*****************************************************************')
return best_c_param
造成问题的一行是
best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter']
该程序的输出低于
/Library/Frameworks/Python.framework/Versions/3.6/bin/python3.6 /Users/username/College/year-4/fyp-credit-card-fraud/code/main.py
/Users/username/Library/Python/3.6/lib/python/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
Dataset Ratios
Percentage of genuine transactions: 0.5
Percentage of fraudulent transactions 0.5
Total number of transactions in resampled data: 984
Whole Dataset Split
Number of transactions in training dataset: 199364
Number of transactions in testing dataset: 85443
Total number of transactions in dataset: 284807
Undersampled Dataset Split
Number of transactions in training dataset 688
Number of transactions in testing dataset: 296
Total number of transactions in dataset: 984
KFold
-------------------------------------------
C parameter: 0.01
-------------------------------------------
Iteration 1 : recall score = 0.931506849315
Iteration 2 : recall score = 0.917808219178
Iteration 3 : recall score = 1.0
Iteration 4 : recall score = 0.959459459459
Iteration 5 : recall score = 0.954545454545
Mean recall score 0.9526639965
-------------------------------------------
C parameter: 0.1
-------------------------------------------
Iteration 1 : recall score = 0.849315068493
Iteration 2 : recall score = 0.86301369863
Iteration 3 : recall score = 0.915254237288
Iteration 4 : recall score = 0.945945945946
Iteration 5 : recall score = 0.909090909091
Mean recall score 0.89652397189
-------------------------------------------
C parameter: 1
-------------------------------------------
Iteration 1 : recall score = 0.86301369863
Iteration 2 : recall score = 0.86301369863
Iteration 3 : recall score = 0.983050847458
Iteration 4 : recall score = 0.945945945946
Iteration 5 : recall score = 0.924242424242
Mean recall score 0.915853322981
-------------------------------------------
C parameter: 10
-------------------------------------------
Iteration 1 : recall score = 0.849315068493
Iteration 2 : recall score = 0.876712328767
Iteration 3 : recall score = 0.983050847458
Iteration 4 : recall score = 0.945945945946
Iteration 5 : recall score = 0.939393939394
Mean recall score 0.918883626012
-------------------------------------------
C parameter: 100
-------------------------------------------
Iteration 1 : recall score = 0.86301369863
Iteration 2 : recall score = 0.876712328767
Iteration 3 : recall score = 0.983050847458
Iteration 4 : recall score = 0.945945945946
Iteration 5 : recall score = 0.924242424242
Mean recall score 0.918593049009
Traceback (most recent call last):
File "/Users/username/College/year-4/fyp-credit-card-fraud/code/main.py", line 20, in <module>
best_c_param = classify.print_kfold_scores(X_training_undersampled, y_training_undersampled)
File "/Users/username/College/year-4/fyp-credit-card-fraud/code/Classification.py", line 39, in print_kfold_scores
best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter']
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/series.py", line 1369, in idxmax
i = nanops.nanargmax(_values_from_object(self), skipna=skipna)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/nanops.py", line 74, in _f
raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
TypeError: reduction operation 'argmax' not allowed for this dtype
Process finished with exit code 1
答案 0 :(得分：4)
#best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']
1）＆＃34;平均召回得分的类型＆＃34;是对象，你不能使用＆＃34; idxmax（）＆＃34;计算价值 2）你应该改变＆＃34;意思是召回得分＆＃34;来自＆＃34;对象＆＃34;到＆＃34;漂浮＆＃34; 3）你可以使用apply（pd.to_numeric，errors =＆＃39; coerce＆＃39;，axis = 0）来做这些事情。
best_c = results_table
best_c.dtypes.eq(object) # you can see the type of best_c
new = best_c.columns[best_c.dtypes.eq(object)] #get the object column of the best_c
best_c[new] = best_c[new].apply(pd.to_numeric, errors = 'coerce', axis=0) # change the type of object
best_c
best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter'] #calculate the mean values
答案 1 :(得分：1)
默认情况下，单元格值的类型为非数字。 argmin()，idxmin()，argmax()和其他类似函数需要dtypes为数字。
最简单的解决方案是使用pd.to_numeric()来将系列（或列）转换为数字类型。带有df列的数据帧'a'的示例为：
df['a'] = pd.to_numeric(df['a'])
有关here的熊猫类型转换的更完整答案。
希望有帮助：）
答案 2 :(得分：0)
简而言之，试试这个
best_c = results_table.loc[results_table['Mean recall score'].astype(float).idxmax()]['C_parameter']
代替
best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']
答案 3 :(得分：0)
如果存在NaN（并且我们可以通过堆栈跟踪看到这种情况），那么当您认为您正在使用数字数据框时，很可能混合类型，尤其是数字之间的字符串。让我给您提供3个代码示例，前2个有效，最后一个无效，很可能是您的情况。
这代表所有数值数据，它将与idxmax一起使用
the_dict = {}
the_dict['a'] = [0.1, 0.2, 0.5]
the_dict['b'] = [0.3, 0.4, 0.6]
the_dict['c'] = [0.25, 0.3, 0.9]
the_dict['d'] = [0.2, 0.1, 0.4]
the_df = pd.DataFrame(the_dict)
这代表一个数字nan，它将运行idxmax
the_dict = {}
the_dict['a'] = [0.1, 0.2, 0.5]
the_dict['b'] = [0.3, 0.4, 0.6]
the_dict['c'] = [0.25, 0.3, 0.9]
the_dict['d'] = [0.2, 0.1, np.NaN]
the_df = pd.DataFrame(the_dict)
这可能是OP报告的确切问题，但是如果事实证明我们以任何方式混合了类型，我们将得到OP报告的错误。
the_dict = {}
the_dict['a'] = [0.1, 0.2, 0.5]
the_dict['b'] = [0.3, 0.4, 0.6]
the_dict['c'] = [0.25, 0.3, 0.9]
the_dict['d'] = [0.2, 0.1, 'NaN']
the_df = pd.DataFrame(the_dict)