plot_functions.py

# Plot Functions 

import matplotlib.pyplot as plt
import numpy as np
from sklearn import metrics

def plot_confusion_matrix(cm, classes, title, cmap):
    "function for plotting confusion matrix"
    plt.clf()
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    classnames = classes
    plt.title(title)
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    tick_marks = np.arange(len(classnames))
    plt.xticks(tick_marks, classnames, rotation=45)
    plt.yticks(tick_marks, classnames)
    s = [['TN','FP'], ['FN', 'TP']]
 
    for i in range(2):
        for j in range(2):
            plt.text(j,i, str(s[i][j])+" = "+str(cm[i][j]))
    plt.show()


def plot_roc_auc(arg1, arg2, arg3):
    "a function to plot roc_auc"
    fig, ax = plt.subplots(figsize=(8, 6))
    for i, v in arg1: 
        y_score = v.predict_proba(arg2)[:, 1]
        fpr, tpr, _ = metrics.roc_curve(arg3, y_score) 
        roc_auc = metrics.auc(fpr, tpr)
        plt.plot(fpr, tpr,lw=2, label= i + ' (area = %0.2f)' % roc_auc) 
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('ROC Curve')
    plt.legend(loc="lower right")
    plt.show()