When using the idxmax() function in Pandas, I keep receiving this error.
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/", line 1369, in idxmax i = nanops.nanargmax(_values_from_object(self), skipna=skipna) File "/Library/Frameworks/", line 74, in _f raise TypeError(msg.format(name=f.__name__.replace('nan', ''))) TypeError: reduction operation 'argmax' not allowed for this dtype The Pandas version I am using is 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 The line that is causing the problem is this
best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter']
The output of the program is below
/Library/Frameworks/ /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/", line 1369, in idxmax i = nanops.nanargmax(_values_from_object(self), skipna=skipna) File "/Library/Frameworks/", 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 4 Answers
The type of the cell values are, by default, non-numeric. argmin(), idxmin(), argmax() and other similar functions need the dtypes to be numeric.
The easiest solution is to use pd.to_numeric() in order to convert your series (or columns) to numeric types. An example with a data frame df with a column 'a' would be:
df['a'] = pd.to_numeric(df['a']) A more complete answer on type casting on pandas can be found here.
#best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter'] We should replace this line of code
The main problem:
1) the type of "mean recall score" is object, you can't use "idxmax()" to calculate the value 2) you should change "mean recall score" from "object " to "float" 3) you can use apply(pd.to_numeric, errors = 'coerce', axis = 0) to do such things.
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 In short, try this
best_c = results_table.loc[results_table['Mean recall score'].astype(float).idxmax()]['C_parameter'] instead of
best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter'] If NaN are present (and we can sort of see this by the stack trace) then when you think you are working with a data frame of numerics, you could well have mixed types, and in particular, a string among numerics. Let me give you 3 code examples, the first 2 work, the last doesn't and is likely your case.
This represents all numeric data, it will work with 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, 0.4] the_df = pd.DataFrame(the_dict) This represents a numeric nan, it will work 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) This could be the exact problem reported by the OP, but if it turns out we have mixed types in any fashion, we will get the error the OP reported.
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) 
