辅助编程coding的两种工具：Github Copilot、Cursor

目录

Cursor

简介

Cursor is an editor made for programming with AI. It’s early days, but right now Cursor can help you with a few things…

下载地址：

https://www.cursor.so/

使用技巧：

https://twitter.com/amanrsanger

CHAT:

example 1：

注意：

对于上面最后一张图的中的代码，如果直接在IDE里面运行是不会报错的，但是有一句代码

vif["VIF"] = [variance_inflation_factor(df.values, i) for i in range(df.shape[1]-1)]

是不符合多重共线性分析或者VIF的数学原理的。因为VIF是对自变量间线性关系的分析，如果直接调用OLS；如果把OLS里面的目标函数换成非线性方程，就是表达的非线性关系。而上面的代码是把df.values都传入了variance_inflation_factor函数，包括了自变量和因变量，因此是不符合多重共线性分析原理的。
所以应改成：

import pandas as pd

data = {'x1': [1, 2, 3, 4, 5],
        'x2': [2, 4, 6, 8, 10],
        'x3': [3, 6, 9, 12, 15],
        'y': [2, 4, 6, 8, 10]}

df = pd.DataFrame(data)

from statsmodels.stats.outliers_influence import variance_inflation_factor

# Get the VIF for each feature
vif = pd.DataFrame()
vif["feature"] = df.columns[:-1]
# vif["VIF"] = [variance_inflation_factor(df.values, i) for i in range(df.shape[1]-1)]
vif["VIF"] = [variance_inflation_factor(df.values[:, :-1], i) for i in range(df.shape[1]-1)]

# Print the results
print(vif)

原理解释：

def variance_inflation_factor(exog, exog_idx):
    """
    Variance inflation factor, VIF, for one exogenous variable

    The variance inflation factor is a measure for the increase of the
    variance of the parameter estimates if an additional variable, given by
    exog_idx is added to the linear regression. It is a measure for
    multicollinearity of the design matrix, exog.

    One recommendation is that if VIF is greater than 5, then the explanatory
    variable given by exog_idx is highly collinear with the other explanatory
    variables, and the parameter estimates will have large standard errors
    because of this.

    Parameters
    ----------
    exog : {ndarray, DataFrame}
        design matrix with all explanatory variables, as for example used in
        regression
    exog_idx : int
        index of the exogenous variable in the columns of exog

    Returns
    -------
    float
        variance inflation factor

    Notes
    -----
    This function does not save the auxiliary regression.

    See Also
    --------
    xxx : class for regression diagnostics  TODO: does not exist yet

    References
    ----------
    https://en.wikipedia.org/wiki/Variance_inflation_factor
    """
    k_vars = exog.shape[1]
    exog = np.asarray(exog)
    x_i = exog[:, exog_idx]
    mask = np.arange(k_vars) != exog_idx
    x_noti = exog[:, mask]
    r_squared_i = OLS(x_i, x_noti).fit().rsquared
    vif = 1. / (1. - r_squared_i)
    return vif

example 2：

GPT-4太大写不了，给出的是调GPT-2的示例代码。

Github Copilot

官网

https://github.com/features/copilot

简介

目前有个人版、商业版和教育版三种，教育版里面学生账户比教师账户的免费功能更多，只需要一个学校邮箱和一卡通就可以认证学生账户或者教师账户，还是很方便。

以插件方式安装

pycharm

自动写代码

以下代码除了第一行注释都是copilot自动补全的

example 1：写一个mysql取数据的类

# 写一个mysql取数据的类
import pymysql
class Mysql:
    def __init__(self,host,user,password,database,port=3306):
        self.host = host
        self.user = user
        self.password = password
        self.database = database
        self.port = port
        self.conn = pymysql.connect(host=self.host,user=self.user,password=self.password,database=self.database,port=self.port)
        self.cursor = self.conn.cursor()
    def fetchone(self,sql):
        self.cursor.execute(sql)
        return self.cursor.fetchone()
    def fetchall(self,sql):
        self.cursor.execute(sql)
        return self.cursor.fetchall()
    def close(self):
        self.cursor.close()
        self.conn.close()
    def commit(self):
        self.conn.commit()
    def rollback(self):
        self.conn.rollback()
    def execute(self,sql):
        self.cursor.execute(sql)
        self.commit()
    def executemany(self,sql,values):
        self.cursor.executemany(sql,values)
        self.commit()

example 2：写一个多重共线性检测的类

from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
class Multicollinearity:
    def __init__(self,df):
        self.df = df
    def get_vif(self):
        vif = pd.DataFrame()
        vif["VIF Factor"] = [self.get_vif_score(i) for i in range(self.df.shape[1])]
        vif["features"] = self.df.columns
        return vif
    def get_vif_score(self,i):
        y = self.df.iloc[:,i]
        x = self.df.drop(self.df.columns[i],axis=1)
        lr = LinearRegression()
        lr.fit(x,y)
        r2 = r2_score(y,lr.predict(x))
        return 1/(1-r2)

总结

用工具自动写代码的时候，最好要用实际例子或实际数据检查一下，就算没有报错，对于数值计算最好也要debug跑一遍看它生成的代码是否符合你的描述或者数学原理。具体原因见上文的注意。