Receiving KeyError: “[Int64Index([ ... dtype='int64', length=1323)] 都不在 [columns]”

Question

SUMMARY

When feeding test and train data into a ROC curve plot, I receive the following error:

KeyError: "None of [Int64Index([ 0, 1, 2, ... dtype='int64', length=1323)] are in the [columns]"

The error seems to be saying that it doesn't like the format of my data, but it worked when run the first time and I haven't been able to get it to run again.

Am I incorrectly splitting my data or sending incorrectly formatted data into my function?

WHAT I'VE TRIED

• Read through several StackOverflow posts with the same KeyError
• Re-ead through scikit-learn example I followed
• Reviewed previous versions of my code to troubleshoot

I am running this within a CoLab document and it can be viewed here

CODE

I am using standard dataframes to pull in my X and Y sets:

X = df_full.drop(['Attrition'], axis=1)
y = df_full['Attrition'].as_matrix()

The KeyError traces back to the 8th line here:

def roc_plot(X, Y, Model):
    tprs = []
    aucs = []
    mean_fpr = np.linspace(0, 1, 100)
    plt.figure(figsize=(12,8))
    i = 0
    for train, test in kf.split(X, Y):
        probas_ = model.fit(X[train], Y[train]).predict_proba(X[test])
        # Compute ROC curve and area the curve
        fpr, tpr, thresholds = roc_curve(Y[test], probas_[:, 1])
        tprs.append(np.interp(mean_fpr, fpr, tpr))
        tprs[-1][0] = 0.0
        roc_auc = auc(fpr, tpr)
        aucs.append(roc_auc)
        plt.plot(fpr, tpr, lw=1, alpha=0.3,
                 label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))

        i += 1
    plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',
             label='Chance', alpha=.8)

    mean_tpr = np.mean(tprs, axis=0)
    mean_tpr[-1] = 1.0
    mean_auc = auc(mean_fpr, mean_tpr)
    std_auc = np.std(aucs)
    plt.plot(mean_fpr, mean_tpr, color='b',
             label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
             lw=2, alpha=.8)

    std_tpr = np.std(tprs, axis=0)
    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
    plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
                     label=r'$\pm$ 1 std. dev.')

    plt.xlim([-0.05, 1.05])
    plt.ylim([-0.05, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()

It happens when I run the following with the function:

model = XGBClassifier() # Create the Model
roc_plot(X, Y, Model)

EXPECTED RESULT

I should be able to feed the data, X and Y, into my function.

Answer 1

in this piece of code train, test are arrays of indices, while you using it as a columns when selection from DataFrame:

for train, test in kf.split(X, Y):
    probas_ = model.fit(X[train], Y[train]).predict_proba(X[test])

you should use iloc instead:

    probas_ = model.fit(X.iloc[train], Y.iloc[train]).predict_proba(X.iloc[test])