如何使用Python和sklearn编写多元对数回归?

问题描述

我写了一个用于多元多项式回归的代码，我使用了sklearn的多项式特征和变换函数.是否可以进行多元对数回归?sklearn是否像对多项式特征一样具有某种对数转换?如何在python中编写多元对数回归?

I wrote a code for multivariate polynomial regression, I used polynomial features and transformation function from sklearn. Is it possible to make multivariate logarithmic regression?Does sklearn have some kind of logarithmic transformation, like it has for polynomial features?How can I write multivariate logarithmic regression in python?

这是我的多元多项式特征代码:

This is my code for multivariate polynomial features:

import numpy as np
import pandas as pd
import math
import xlrd
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures


#Reading data from excel

data = pd.read_excel("DataSet.xls").round(2)
data_size = data.shape[0]
#print("Number of data:",data_size,"\n",data.head())

def polynomial_prediction_of_future_strength(input_data, cement, blast_fur_slug,fly_ash,
                                              water, superpl, coarse_aggr, fine_aggr, days):

    variables = prediction_accuracy(input_data)[2]
    results = prediction_accuracy(input_data)[3]
    n = results.shape[0]
    results = results.values.reshape(n,1) #reshaping the values so that variables and results have the same shape

    #transforming the data into polynomial function
    Poly_Regression = PolynomialFeatures(degree=2)
    poly_variables = Poly_Regression.fit_transform(variables)

    #accuracy of prediction(splitting the dataset on train and test)
    poly_var_train, poly_var_test, res_train, res_test = train_test_split(poly_variables, results, test_size = 0.3, random_state = 4)

    input_values = [cement, blast_fur_slug, fly_ash, water, superpl, coarse_aggr, fine_aggr, days]
    input_values = Poly_Regression.transform([input_values]) #transforming the data for prediction in polynomial function

    regression = linear_model.LinearRegression() #making the linear model
    model = regression.fit(poly_var_train, res_train) #fitting polynomial data to the model

    predicted_strength = regression.predict(input_values) #strength prediction
    predicted_strength = round(predicted_strength[0,0], 2)

    score = model.score(poly_var_test, res_test) #accuracy prediction
    score = round(score*100, 2)

    accuracy_info = "Accuracy of concrete class prediction: " + str(score) + " %\n"
    prediction_info = "Prediction of future concrete class after "+ str(days)+" days: "+ str(predicted_strength)

    info = "\n" + accuracy_info + prediction_info

    return info

#print(polynomial_prediction_of_future_strength(data, 214.9 , 53.8, 121.9, 155.6, 9.6, 1014.3, 780.6, 7))

推荐答案

如果您想使用功能的对数，一种选择是Box-Cox变换和OLS，您可以使用PowerTransformer在sklearn中应用它. https://scikit-learn. org/stable/modules/generation/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer

If you want to fit with the logarithms of your features, one option is the Box-Cox Transform then OLS, which you can apply in sklearn using the PowerTransformer. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html#sklearn.preprocessing.PowerTransformer

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