from sklearn.model_selection import KFold, cross_val_score, GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn import metrics
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
x = df.drop(['Customer_Lifetime_Value'],axis=1)
y = df['Customer_Lifetime_Value']
x_train,x_test,y_train,y_test=train_test_split(x\
                ,y,test_size=0.30,random_state=42)

class Model_Selector():
    
    

    def __init__(self):
        print("Model Selector object created")
        
    """
    This method helps to select
    the best machine learning 
    model to compute the relationship
    betweem i/p and d/p variable
    
    """    
        
        
    def Regression_Model_Selector(self,df):
        seed = 42
        models = []
        models.append(("LR", LinearRegression()))
        models.append(("RF", RandomForestRegressor()))
        models.append(("KNN", KNeighborsRegressor()))
        models.append(("CART", DecisionTreeRegressor()))
        models.append(("XGB", XGBRegressor()))
        result = []
        names = []
        scoring = 'r2'
        seed = 42
        
        

        for name, model in models:
            kfold = KFold(n_splits = 5, random_state =seed)
            cv_results = cross_val_score(model, x_train,\
                    y_train, cv = kfold, scoring = scoring)
            result.append(cv_results)
            names.append(name)
            msg = (name, cv_results.mean(), cv_results.std())
            print(msg)
            
            
            
        fig = plt.figure(figsize = (8,4))
        fig.suptitle('Algorithm Comparison')
        ax = fig.add_subplot(1,1,1)
        plt.boxplot(result)
        ax.set_xticklabels(names)
        plt.show()