TypeError: reduction operation 'argmax' not allowed for this dtype


I don't actually know what is wrong with my code. Could anyone help?

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 def printing_Kfold_scores(x_train_data,y_train_data): fold = KFold(len(y_train_data),5,shuffle=False) # Different C parameters c_param_range = [0.01,0.1,1,10,100] results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score']) results_table['C_parameter'] = c_param_range # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1] j = 0 for c_param in c_param_range: print('-------------------------------------------') print('C parameter: ', c_param) print('-------------------------------------------') print('') recall_accs = [] for iteration, indices in enumerate(fold,start=1): # Call the logistic regression model with a certain C parameter lr = LogisticRegression(C = c_param, penalty = 'l1') # Use the training data to fit the model. In this case, we use the portion of the fold to train the model # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1] lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel()) # Predict values using the test indices in the training data y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values) # Calculate the recall score and append it to a list for recall scores representing the current c_parameter recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample) recall_accs.append(recall_acc) print('Iteration ', iteration,': recall score = ', recall_acc) # The mean value of those recall scores is the metric we want to save and get hold of. results_table.ix[j,'Mean recall score'] = np.mean(recall_accs) j += 1 print('') print('Mean recall score ', np.mean(recall_accs)) print('') best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter'] # Finally, we can check which C parameter is the best amongst the chosen. print('*********************************************************************************') print('Best model to choose from cross validation is with C parameter = ', best_c) print('*********************************************************************************') return best_c best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)


I know where the problem is: the dtype of results_table['Mean recall score'] is object! the idxmax not allowed for the "object"

you should change it to float, here is my solution:

results_table['Mean recall score']=results_table['Mean recall'].astype('float64') best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']

And this will work!


the last answer is right but might should change

results_table['Mean recall score']=results_table['Mean recall'].astype('float64')


results_table['Mean recall score']=results_table['Mean recall score'].astype('float64')


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