Multivariate polynomial regression with Python

Question

Recently I started to learn sklearn, numpy and pandas and I made a function for multivariate linear regression. Im wondering, is it possible to make multivariate polynomial regression?

This is my code for multivariate polynomial regression, it shows this error:

in check_consistent_length " samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [8, 3]

Do you know whats the problem?

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

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

    variables = prediction_accuracy(input_data)[4]
    results = prediction_accuracy(input_data)[5]

    var_train, var_test, res_train, res_test = train_test_split(variables, results, test_size = 0.3, random_state = 4)

    Poly_Regression = PolynomialFeatures(degree=2)
    poly_var_train = Poly_Regression.fit_transform(var_train)
    poly_var_test = Poly_Regression.fit_transform(var_test)

    input_values = [cement, blast_fur_slug, fly_ash, water, superpl, coarse_aggr, fine_aggr, days]

    regression = linear_model.LinearRegression()
    model = regression.fit(poly_var_train, res_train)

    predicted_strenght = regression.predict([input_values])
    predicted_strenght = round(predicted_strenght[0], 2)

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


    print(prediction, score)

a = polynomial_prediction_of_future_strenght(data_less_than_28days, 260.9, 100.5, 78.3, 200.6, 8.6, 864.5, 761.5, 28)

Answer 1

You can transform your features to polynomial using this sklearn module and then use these features in your linear regression model.

from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model

poly = PolynomialFeatures(degree=2)
poly_variables = poly.fit_transform(variables)

poly_var_train, poly_var_test, res_train, res_test = train_test_split(poly_variables, results, test_size = 0.3, random_state = 4)

regression = linear_model.LinearRegression()

model = regression.fit(poly_var_train, res_train)
score = model.score(poly_var_test, res_test)

Also, in your code you are training your model on the entire dataset and then you split it into train and test. This means that your model has already seen your test data while training. You need to split first, then train your model only on training data and then test the score on the test set. I have included these changes as well. :)