Python-Regression

本文归纳整理了线性回归（LinearRegression）、岭回归（Ridge）、Lasso回归（Lasso）、弹性网（ElasticNet）、K近邻、SVM等10多种机器学习模块sklearn中回归算法调用及实现过程，有需要的可以参考一下:

1.加载所需模块

import scipy
import mglearn
import numpy as np
import pandas as pd
import seaborn as sns 
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
#Method:1-4
from sklearn import linear_model
from sklearn.linear_model import LinearRegression,Ridge,Lasso,ElasticNet
#Method:5-10
from sklearn import neighbors,svm,tree,ensemble
#Method:11-12
from sklearn.ensemble import BaggingRegressor
from sklearn.tree import ExtraTreeRegressor

2.各种回归调用方式

##2.Choose  Regression Method
model = []
#2.1 LinearRegression
model_LinearRegression = linear_model.LinearRegression()
model.append(model_LinearRegression)
#2.2 RidgeRegression
model_Ridge = Ridge()
model.append(model_Ridge)
#2.3 Lasso
model_Lasso=Lasso()
model.append(model_Lasso)
#2.4 ElasticNet
model_ElasticNET = linear_model.ElasticNet(alpha=0.1,l1_ratio=0.5)
model.append(model_ElasticNET)
#2.5 KNN Regression
model_KNeighborsRegressor = neighbors.KNeighborsRegressor()
model.append(model_KNeighborsRegressor)
#2.6 SVM Regression
model_SVR = svm.SVR()
model.append(model_SVR)
#2.7 DecisionTreeRegression
model_DecisionTreeRegressor = tree.DecisionTreeRegressor()
model.append(model_DecisionTreeRegressor)
#2.8 RandomForestRegression
model_RandomForestRegressor = ensemble.RandomForestRegressor(n_estimators=20)
model.append(model_RandomForestRegressor)
#2.9 Adaboost Regression
model_AdaBoostRegressor = ensemble.AdaBoostRegressor(n_estimators=50)
model.append(model_AdaBoostRegressor)
#2.10 GBRT Regression
model_GradientBoostingRegressor = ensemble.GradientBoostingRegressor(n_estimators=100)
model.append(model_GradientBoostingRegressor)
#2.11 Bagging Regression
model_BaggingRegressor = BaggingRegressor()
model.append(model_BaggingRegressor)
#2.12 ExtraTree Regression
model_ExtraTreeRegressor = ExtraTreeRegressor()
model.append(model_ExtraTreeRegressor)

3.加载数据

##3.Load data
path = '/home/Desktop/qqq.xlsx'
file = pd.read_excel(path,sheet_name='Sheet1')
data = file.copy()
#Data split
#X_data = data.iloc[:,1:]
#归一化处理
def regulait(df_X):
    df_scaler = preprocessing.MinMaxScaler()
    X_scaler = df_scaler.fit_transform(df_X)
    X_scaler = pd.DataFrame(X_scaler,columns=df_X.columns)
    return X_scaler
X_data = regulait(data.iloc[:,1:])
y_data = data.iloc[:,0]
X_train,X_test,y_train,y_test = train_test_split(X_data,y_data,test_size=0.2,random_state=0)

4.模型拟合及可视化

##4.Regression
def try_different_method(model):
    model.fit(X_train,y_train)
    score = model.score(X_test, y_test)
    result = model.predict(X_test)
    plt.figure(figsize=(10,8),dpi=200)
    plt.plot(np.arange(len(result)), y_test,'go-',label='true value')
    plt.plot(np.arange(len(result)),result,'ro-',label='predict value')
    plt.title("Model Score of  {}  is: {:.2f}".format(method,score))
    #Get axis
    ax=plt.gca()
    ax.spines['bottom'].set_linewidth(1)
    ax.spines['left'].set_linewidth(1)
    ax.spines['right'].set_linewidth(1)
    ax.spines['top'].set_linewidth(1)
    plt.grid(True)
    plt.legend()
    plt.show()

5.结果展示

##5.Method call
for method in model:
    try_different_method(method)

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