CrossValidation.py

#Cross Validation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import RepeatedKFold
from sklearn.model_selection import cross_val_score
import seaborn as sns
from sklearn.linear_model import LinearRegression, Lasso,ElasticNet, Ridge, MultiTaskLasso, LassoLars, OrthogonalMatchingPursuit
from sklearn.model_selection import train_test_split
from sklearn import metrics
from collections import defaultdict

class predit:
    def bestFitLine(self):

        datadict={"size":[1300,1491,1526,1533,1680,1680,1869,1890,1920,1936,1950,1953,2016,2117,3072,3182,3196,3842,2268,2280,2628,2645,3000],
                  "price":[124000,75500,86000,97000,85400,100000,106000,113000,122500,84500,151000,83000,106000,168500,178740,192500,215000,275000,173000, 179400,175500,172500,173733]}
        df=pd.DataFrame.from_dict(datadict)

        x_train, x_test, y_train, y_test = train_test_split(df["size"], df["price"], test_size= 0.2, random_state=0)    
        x_train= x_train.values.reshape(-1, 1)
        y_train= y_train.values.reshape(-1, 1)
        x_test = x_test.values.reshape(-1, 1)
        models = []
        models.append(('LR', LinearRegression()))
        models.append(('LASSO', Lasso()))  
        models.append(('EN', ElasticNet()))
        models.append(('Ridge', Ridge()))
        models.append(('MultiTaskLasso', MultiTaskLasso()))
        models.append(('LarsLasso', LassoLars()))
        models.append(('OMP', OrthogonalMatchingPursuit()))

        results = []
        names = []
        scoremap={}
        for name, model in models:
            kfold = RepeatedKFold(n_splits=4, n_repeats=5)
            cv_results = cross_val_score(model, x_train, y_train, cv=kfold)
            results.append(cv_results)
            names.append(name)
            msg = "%s: %f (%f)" % (name,  cv_results.mean(), cv_results.std())
            print(msg)
            scoremap[name]=cv_results
            print('{}:{}'.format(name,cv_results))

        return(scoremap) 

Object= predit()
scoremap= Object.bestFitLine()
size=[1300,1491,1526,1533,1680,1680,1869,1890,1920,1936,1950,1953,2016,2117,3072,3182,3196,3842,2268,2280,2628,2645,3000]
print(scoremap)
plt.figure(figsize=(20, 10))
scoremap = pd.DataFrame(scoremap)
sns.boxplot(data=scoremap)