零零散散写了一些，主要是占个坑：
AUC作为一个常用的评价指标，无论是作为最后模型效果评价还是前期的特征选择，都发挥着不可替代的作用，下面我们详细介绍下这个指标。
1.定义
2.实现
# coding=utf-8
# auc值的大小可以理解为: 随机抽一个正样本和一个负样本，正样本预测值比负样本大的概率
# 根据这个定义，我们可以自己实现计算auc
from sklearn.metrics import roc_curve, auc, roc_auc_score
import random
import time
import sys
import codecs
import numpy as np
def timeit(func):
"""
装饰器，计算函数执行时间
"""
def wrapper(*args, **kwargs):
time_start = time.time()
result = func(*args, **kwargs)
time_end = time.time()
exec_time = time_end - time_start
print("{function} exec time: {time}s".format(function=func.__name__, time=exec_time))
return result
return wrapper
def gen_label_pred(n_sample):
"""
随机生成n个样本的标签和预测值
"""
labels = [random.randint(0, 1) for _ in range(n_sample)]
preds = [random.random() for _ in range(n_sample)]
return labels, preds
def load_label_pred(label_file):
with codecs.open(label_file, "r", "utf-8") as f:
labels = np.array([float(l.strip().split(" ")[0]) for l in f.readlines()])
with codecs.open(label_file, "r", "utf-8") as f:
preds = np.array([float(l.strip().split(" ")[1]) for l in f.readlines()])
return labels, preds
@timeit
def sklearn_auc_api(labels, preds):
"""
直接调用sklearn包中的结果
"""
auc = roc_auc_score(labels, preds)
return auc
#print("auc:"+str(auc))
@timeit
def naive_auc(labels, preds):
"""
最简单粗暴的方法
先排序，然后统计有多少正负样本对满足：正样本预测值>负样本预测值, 再除以总的正负样本对个数
复杂度 O(NlogN), N为样本数
"""
n_pos = sum(labels)
n_neg = len(labels) - n_pos
total_pair = n_pos * n_neg
labels_preds = zip(labels, preds)
labels_preds = sorted(labels_preds, key=lambda x: x[1])
accumulated_neg = 0
satisfied_pair = 0
for i in range(len(labels_preds)):
if labels_preds[i][0] == 1:
satisfied_pair += accumulated_neg
else:
accumulated_neg += 1
return satisfied_pair / float(total_pair)
@timeit
def approximate_auc(labels, preds, n_bins=100):
"""
近似方法，将预测值分桶(n_bins)，对正负样本分别构建直方图，再统计满足条件的正负样本对
复杂度 O(N)
这种方法有什么缺点？怎么分桶？
"""
n_pos = sum(labels)
n_neg = len(labels) - n_pos
total_pair = n_pos * n_neg
pos_histogram = [0 for _ in range(n_bins)]
neg_histogram = [0 for _ in range(n_bins)]
bin_width = 1.0 / n_bins
for i in range(len(labels)):
nth_bin = int(preds[i] / bin_width)
if labels[i] == 1:
pos_histogram[nth_bin] += 1
else:
neg_histogram[nth_bin] += 1
accumulated_neg = 0
satisfied_pair = 0
for i in range(n_bins):
satisfied_pair += (pos_histogram[i] * accumulated_neg + pos_histogram[i] * neg_histogram[i] * 0.5)
accumulated_neg += neg_histogram[i]
return satisfied_pair / float(total_pair)
if __name__ == "__main__":
#labels, preds = gen_label_pred(10000000)
labels, preds = load_label_pred(sys.argv[1])
naive_auc_rst = naive_auc(labels, preds)
#approximate_auc_rst = approximate_auc(labels, preds)
approximate_auc_rst = 0
sklearn_rst = sklearn_auc_api(labels, preds)
print("naive auc result:{},approximate auc result:{},sklearn auc result:{}".format(naive_auc_rst, approximate_auc_rst, sklearn_rst))
"""
naive_auc exec time: 31.7306630611s
approximate_auc exec time: 2.32403683662s
naive auc result:0.500267265728,approximate auc result:0.50026516844
"""
3.应用