綱要

boss說增加專案平臺分析方法：

T檢驗（獨立樣本T檢驗）、線性迴歸、二元Logistics迴歸、因子分析、可靠性分析

根本不懂，一臉懵逼狀態，分析部確實有人才，反正我是一臉懵

首先解釋什麼是二元Logistic迴歸分析吧

官方簡介：

連結：https://pythonfordatascience.org/logistic-regression-python/

Logistic regression models are used to analyze the relationship between a dependent variable (DV) and independent variable(s) (IV) when the DV is
 
 dichotomous. The DV is the outcome variable, a.k.a. the predicted variable, and the IV(s) are the variables that are believed to have an influence on the outcome, a.k.a. predictor variables. If the model contains 1 IV, then it is a simple logistic regression model, and if the model contains 2+ IVs, then it is
 
 a multiple logistic regression model.

Assumptions for logistic regression models:

The DV is categorical (binary)
If there are more than 2 categories in terms of types of outcome, a multinomial logistic regression should be used
Independence of observations
Cannot be a repeated measures design, i.e. collecting outcomes at two different time points.
Independent variables are linearly related to the log odds
Absence of multicollinearity
Lack of outliers
 

理解了什麼是二元以後，開始找庫

需要用的包

 這裡需要特別說一下，第一天晚上我就用的logit，但結果不對，然後用機器學習搞，發現結果還不對，用spss比對的值

奇怪，最後沒辦法，只能抱大腿了，因為他們糾結Logit和Logistic的區別，然後有在群裡問了下，有大佬給解惑了

而且也有下面文章給解惑

1. 是 statsmodels 的logit模組

2. 是 sklearn.linear_model 的 LogisticRegression模組

 先說第一種方法

首先借鑑文章連結：https://blog.csdn.net/zj360202/article/details/78688070?utm_source=blogxgwz0

解釋的比較清楚，但是一定要注意一點就是，截距項，我就是在這個地方出的問題，因為我覺得不重要，就沒加

#!/usr/bin/env
# -*- coding:utf-8 -*-

import pandas as pd
import statsmodels.api as sm
import pylab as pl
import numpy as np
from pandas import DataFrame, Series
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from collections import OrderedDict

data = {
    'y': [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1],
    'x': [i for i in range(1, 21)],
}

df = DataFrame(OrderedDict(data))


df["intercept"] = 1.0  # 截距項，很重要的呦，我就錯在這裡了


print(df)
print("==================")
print(len(df))
print(df.columns.values)

print(df[df.columns[1:]])

logit = sm.Logit(df['y'],  df[df.columns[1:]])
#
result = logit.fit()
#
res = result.summary2()

print(res)

這麼寫我覺得更好，因為上面那麼寫執行第二遍的時候總是報錯：

statsmodels.tools.sm_exceptions.PerfectSeparationError: Perfect separation detected, results not available

我改成x， y變數自己是自己的，就莫名其妙的好了

    obj = TwoDimensionalLogisticRegressionModel()
    data_x = obj.SelectVariableSql( UserID, ProjID, QuesID, xVariable, DatabaseName, TableName, CasesCondition)
    data_y = obj.SelectVariableSql( UserID, ProjID, QuesID, yVariable, DatabaseName, TableName, CasesCondition)
    if len(data_x) != len(data_y):
        raise MyCustomError(retcode=4011)
    obj.close()

    df_X = DataFrame(OrderedDict(data_x))
    df_Y = DataFrame(OrderedDict(data_y))

    df_X["intercept"] = 1.0  # 截距項，很重要的呦，我就錯在這裡了
    logit = sm.Logit(df_Y, df_X)
    result = logit.fit()
    res = result.summary()

    data = [j for j in [i for i in str(res).split('\n')][-3].split(' ') if j != ''][1:]

    return data

 第二種方法，機器學習

參考連結：https://zhuanlan.zhihu.com/p/34217858

#!/usr/bin/env python
# -*- coding:utf-8 -*-

from collections import OrderedDict
import pandas as pd



examDict = {
    '學習時間': [i for i in range(1, 20)],
    '通過考試': [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1]
}

examOrderDict = OrderedDict(examDict)
examDF = pd.DataFrame(examOrderDict)
# print(examDF.head())

exam_X = examDF.loc[:, "學習時間"]
exam_Y = examDF.loc[:, "通過考試"]

print(exam_X)
# print(exam_Y)

from sklearn.cross_validation import train_test_split

X_train,X_test,y_train, y_test = train_test_split(exam_X,exam_Y, train_size=0.8)

# print(X_train.values)
print(len(X_train.values))
X_train = X_train.values.reshape(-1, 1)
print(len(X_train))
print(X_train)
X_test = X_test.values.reshape(-1, 1)


from sklearn.linear_model import LogisticRegression

module_1 = LogisticRegression()
module_1.fit(X_train, y_train)

print("coef:", module_1.coef_)

front = module_1.score(X_test,y_test)
print(front)

print("coef:", module_1.coef_)
print("intercept_:", module_1.intercept_)

# 預測
pred1 = module_1.predict_proba(3)
print("預測概率[N/Y]", pred1)

pred2 = module_1.predict(5)
print(pred2)

 但是，機器學習的這個有問題，就是隻抽取了15個值

statsmodels的庫連結

Statsmodels：http://www.statsmodels.org/stable/index.html