analysis.py 文件源码

def get_calibration_metrics(model, data):
    scores = (data['X'] * data['Y']).dot(model)

    #distinct scores

    #compute calibration error at each score

    full_metrics = {
        'scores': float('nan'),
        'count': float('nan'),
        'predicted_risk': float('nan'),
        'empirical_risk': float('nan')
    }

    cal_error = np.sqrt(np.sum(a*(a-b)^2)) ( - full_metrics['empirical_risk'])

    summary_metrics = {
        'mean_calibration_error': float('nan')
    }

    #counts
    #metrics
    #mean calibration error across all scores

    pass