# -*- coding: utf-8 -*-
"""
Created on Wed Mar 1 10:53:48 2017
@author: LIDEHUA424
"""
import pandas as pd
import statsmodels.api as sm
import pylab as pl
import numpy as np
# 加载数据
# 备用地址: http://cdn.powerxing.com/files/lr-binary.csv
df = pd.read_csv("http://www.ats.ucla.edu/stat/data/binary.csv")
# 浏览数据集
print (df.head())
# admit gre gpa rank
# 0 0 380 3.61 3
# 1 1 660 3.67 3
# 2 1 800 4.00 1
# 3 1 640 3.19 4
# 4 0 520 2.93 4
# 重命名'rank'列，因为dataframe中有个方法名也为'rank'
df.columns = ["admit", "gre", "gpa", "prestige"]
print (df.columns)
# array([admit, gre, gpa, prestige], dtype=object)
# summarize the data
print (df.describe())
# admit gre gpa prestige
# count 400.000000 400.000000 400.000000 400.00000
# mean 0.317500 587.700000 3.389900 2.48500
# std 0.466087 115.516536 0.380567 0.94446
# min 0.000000 220.000000 2.260000 1.00000
# 25% 0.000000 520.000000 3.130000 2.00000
# 50% 0.000000 580.000000 3.395000 2.00000
# 75% 1.000000 660.000000 3.670000 3.00000
# max 1.000000 800.000000 4.000000 4.00000
# 查看每一列的标准差
print (df.std())
# admit 0.466087
# gre 115.516536
# gpa 0.380567
# prestige 0.944460
# 频率表，表示prestige与admin的值相应的数量关系
print (pd.crosstab(df['admit'], df['prestige'], rownames=['admit']))
# prestige 1 2 3 4
# admit
# 0 28 97 93 55
# 1 33 54 28 12
# plot all of the columns
df.hist()
pl.show()
# 将prestige设为虚拟变量
dummy_ranks = pd.get_dummies(df['prestige'], prefix='prestige')
print (dummy_ranks.head())
# prestige_1 prestige_2 prestige_3 prestige_4
# 0 0 0 1 0
# 1 0 0 1 0
# 2 1 0 0 0
# 3 0 0 0 1
# 4 0 0 0 1
# 为逻辑回归创建所需的data frame
# 除admit、gre、gpa外，加入了上面常见的虚拟变量（注意，引入的虚拟变量列数应为虚拟变量总列数减1，减去的1列作为基准）
cols_to_keep = ['admit', 'gre', 'gpa']
test1 = dummy_ranks.ix[:, 'prestige_2':]
test2 = df[cols_to_keep]
data = df[cols_to_keep].join(dummy_ranks.ix[:, 'prestige_2':])
print (data.head())
# admit gre gpa prestige_2 prestige_3 prestige_4
# 0 0 380 3.61 0 1 0
# 1 1 660 3.67 0 1 0
# 2 1 800 4.00 0 0 0
# 3 1 640 3.19 0 0 1
# 4 0 520 2.93 0 0 1
# 需要自行添加逻辑回归所需的intercept变量
data['intercept'] = 1.0
# 指定作为训练变量的列，不含目标列`admit`
train_cols = data.columns[1:]
# Index([gre, gpa, prestige_2, prestige_3, prestige_4], dtype=object)
logit = sm.Logit(data['admit'], data[train_cols])
# 拟合模型
result = logit.fit()
# 查看数据的要点
print (result.summary())
# 查看每个系数的置信区间
print (result.conf_int())
# 构建预测集
# 与训练集相似，一般也是通过 pd.read_csv() 读入
# 在这边为方便，我们将训练集拷贝一份作为预测集（不包括 admin 列）
import copy
combos = copy.deepcopy(data)
# 数据中的列要跟预测时用到的列一致
predict_cols = combos.columns[1:]
# 预测集也要添加intercept变量
combos['intercept'] = 1.0
# 进行预测，并将预测评分存入 predict 列中
combos['predict'] = result.predict(combos[predict_cols])
# 预测完成后，predict 的值是介于 [0, 1] 间的概率值
# 我们可以根据需要，提取预测结果
# 例如，假定 predict > 0.5，则表示会被录取
# 在这边我们检验一下上述选取结果的精确度
total = 0
hit = 0
for value in combos.values:
# 预测分数 predict, 是数据中的最后一列
predict = value[-1]
# 实际录取结果
admit = int(value[0])
# 假定预测概率大于0.5则表示预测被录取
if predict > 0.5:
total += 1
# 表示预测命中
if admit == 1:
hit += 1
# 输出结果
print ('Total: %d, Hit: %d, Precision: %.2f' % (total, hit, 100.0*hit/total))
# Total: 49, Hit: 30, Precision: 61.22