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Abrufen von TypeError: Reduktionsoperation 'argmax' für diesen dtype nicht zulässig, wenn versucht wird, idxmax () zu verwenden

Bei Verwendung der Funktion idxmax() in Pandas wird dieser Fehler weiterhin angezeigt.

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

Die Pandas-Version, die ich verwende, ist 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 __== '__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

Die Leitung, die das Problem verursacht, ist dies

best_c_param = results.loc[results['Mean recall score'].idxmax()]['C_parameter']

Die Ausgabe des Programms ist unten

/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
6
cod3min3

Der Typ der Zellenwerte ist standardmäßig nicht numerisch. argmin(), idxmin(), argmax() und andere ähnliche Funktionen benötigen numerische d-Typen.

Die einfachste Lösung besteht darin, pd.to_numeric() zu verwenden, um Ihre Reihen (oder Spalten) in numerische Typen umzuwandeln. Ein Beispiel mit einem Datenrahmen df mit einer Spalte 'a' Wäre:

df['a'] = pd.to_numeric(df['a'])

Eine vollständigere Antwort zum Typ Casting auf pandas findet sich hier .

Ich hoffe, das hilft :)

3
Lucas Azevedo
#best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']

Wir sollten diese Codezeile ersetzen

Das Hauptproblem:

1) Der Typ von "Mean Recall Score" ist Objekt. Sie können "idxmax ()" nicht verwenden, um den Wert zu berechnen 2) Sie sollten "Mean Call Score" von "Objekt" in "Float" ändern. .3) Sie können apply (pd.to_numeric, errors = 'coerce', axis = 0) verwenden, um solche Aktionen auszuführen. 

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
3
Allen

Versuchen Sie es kurz

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

anstatt

best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']
0
Hadij