如何用 Python 和 sklearn 编写多元对数回归? [英] How to write multivariate logarithmic regression with Python and sklearn?

问题描述

我写了一个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 是否有某种对数变换，就像多项式特征一样?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,"
",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) + " %
"
    prediction_info = "Prediction of future concrete class after "+ str(days)+" days: "+ str(predicted_strength) 

    info = "
" + 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

如果您想拟合特征的对数，一种选择是 Box-Cox 变换，然后是 OLS，您可以使用 PowerTransformer 在 sklearn 中应用它.https://scikit-learn.org/stable/modules/generated/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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