costbenefit_analysis.py

import numpy as np
from sklearn.metrics import roc_curve, auc, confusion_matrix
import matplotlib.pyplot as plt

def standard_confusion_matrix(y_true, y_pred):
    [[tn, fp], [fn, tp]] = confusion_matrix(y_true, y_pred)
    return np.array([[tp, fp], [fn, tn]])

def profit_curve(cost_benefit, predicted_probs, labels):
    n_obs = float(len(labels))
    thresholds = np.arange(0,1,0.01)
    profits = []
    for threshold in thresholds:
        y_predict = predicted_probs >= threshold
        confusion_matrix = standard_confusion_matrix(labels, y_predict)
        threshold_profit = np.sum(confusion_matrix * cost_benefit) / n_obs
        profits.append([threshold_profit,threshold])
    return profits

def plot_model_profits(profits, save_path=None, n_days='30 days'):
    threshold = []
    profit = []
    for p in profits:
        threshold.append(p[1])
        profit.append(p[0])
    plt.figure(figsize=(4,3))
    plt.plot(threshold, profit)
    plt.ylim(0,8.5)
    plt.title("{} Profit Curve".format(n_days))
    plt.xlabel("TPR-FPR Threshold")
    plt.ylabel("Profit ($/user)")
    if save_path:
        plt.savefig(save_path)
    else:
        plt.show()