[PYTHON] I implemented a method to calculate the evaluation index (specificity, NPV) that scikit-learn does not have

Introduction

When I tried to calculate the index described in a certain paper with scikit-learn, there was a method to calculate sensitivity and recall independently, but classification_report is output for both specicity and NPV. Although it is done, a method to calculate independently was not prepared, so I implemented it.

Method of calculation

Look here, high. https://en.wikipedia.org/wiki/Sensitivity_and_specificity

The relationship between terms looks like this. No need to memorize.

• PPV = precision
• TPR = sensitivity = recall
• TNR = specificity --NPV = No other name

Source

Calculate using the confusion matrix.

from sklearn import metrics

def calc_metrics_derived_from_confusion_matrix(metrics_name, y_true, y_predict):
    tn, fp, fn, tp = metrics.confusion_matrix(y_true, y_predict).ravel()
    #  PPV, precision
    #  TP / TP + FP
    if metrics_name in ["PPV", "precision"]:
        return tp / (tp + fp)

    #  NPV
    #  TN / TN + FN
    if metrics_name in ["NPV"]:
        return tn / (tn + fn)

    # sensitivity, recall, TPR
    #  TP / TP + FN
    if metrics_name in ["sensitivity", "recall", "TPR"]:
        return tp / (tp + fn)

    # specificity
    #  TN / TN + FP
    if metrics_name in ["specificity"]:
        return tn / (tn + fp)

How to use

Like this.

calc_metrics_derived_from_confusion_matrix("sensitivity",[0,0,1], [1,0,1])
calc_metrics_derived_from_confusion_matrix("PPV",[0,0,1], [1,0,1])
calc_metrics_derived_from_confusion_matrix("specificity",[0,0,1], [1,0,1])
calc_metrics_derived_from_confusion_matrix("NPV",[0,0,1], [1,0,1])