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<p>在Pandas中使用<code>idxmax()</code>函数时,我一直收到这个错误。</p>
<pre><code>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
</code></pre>
<p>我使用的Pandas版本是<code>0.22.0</code></p>
<p>主.py</p>
<pre><code>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)
</code></pre>
<p>分类.py</p>
<pre><code>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.<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>(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
</code></pre>
<p>导致问题的原因是</p>
<p><code>best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter']</code></p>
<p>程序的输出如下</p>
<pre><code>/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)
<a href="https://www.cnpython.com/pypi/dataset" class="inner-link">dataset</a> 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
</code></pre>