AISDKolos

# -*- coding: utf-8 -*-
"""
Created on Tue May  7 12:54:00 2019

@author: lancernik
"""
from requests import get
from requests.exceptions import RequestException
from contextlib import closing
from bs4 import BeautifulSoup
import string
import re
from itertools import groupby
import pandas as pd
import time
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import kde
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
import sklearn.preprocessing






def regress2(x_train, x_test, y_train, y_test): #Model sample

    r = LinearRegression()
    r.fit(x_train,y_train)
    y_train_pred = r.predict(x_train)
    y_test_pred = r.predict(x_test)

    mse = mean_squared_error
    mae = mean_absolute_error

    return{
            "r_score": r.score(x_train,y_train), #??????????
            "MSE_u": mse(y_train,y_train_pred),
            "MSE_t": mse(y_test,y_test_pred),
            "MSA_u": mae(y_train,y_train_pred),
            "MSA_t": mae(y_test,y_test_pred)}

def regress3(x,y):  #Model wielomianowy
    x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2, random_state=12345)
    reg3 = sklearn.preprocessing.PolynomialFeatures(degree=2,include_bias = False)
    x2_train = reg3.fit_transform(x_train)
    x2_test = reg3.fit_transform(x_test)
#    print(reg3.powers_.T)
    params.append("zm. wielom")
    res.append(regress2(x2_train, x2_test, y_train, y_test))


    print(pd.DataFrame(res,index=params))


def generator():

    xlist=[]
    ylist=[]
    for i in range(0,100):

        x = np.random.rand(1)*8 -4
        xlist.append(x)
#        print(x)
        y = 0.5*pow(x,4) + 2*pow(x,2)+1
        ylist.append(y)
#        print(out)
    df = pd.DataFrame({
        'x':xlist,
         'y': ylist})
    return df




df = generator()
a = np.array(df['x'].tolist()).reshape(-1,1)
b = np.array(df['y'].tolist()).reshape(-1,1)


params =['zm. liniowe']
x_train, x_test, y_train, y_test = train_test_split(a,b,test_size = 0.2, random_state=12345)
res = [regress2(x_train, x_test, y_train, y_test)]
reg2 = pd.DataFrame(res,index=params)





print(regress3(a,b))