#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')


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from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score

class ModelSelectionHelper:

    def __init__(self, models, params):
        print('init model selection helper notebook')
        self.models = models
        self.params = params
        self.keys = models.keys()
        self.grid_searches = {}
        
    def fit(self, X, y, scoring=None, n_jobs=3, refit=True, cv=5, verbose=1, return_train_score=True):
        for key in self.keys:
            print('---------------------------------------------------------------------------')
            print(key)
            
            model = self.models[key]
            params = self.params[key]
            
            grid_search = GridSearchCV(model, params, cv=cv, n_jobs=n_jobs,
                verbose=verbose, scoring=scoring, refit=refit,
                return_train_score=return_train_score)
            
            grid_search.fit(X, y)
            
            self.grid_searches[key] = grid_search
            
            #best_params_ and best_score_ are only available if refit=True
            if (refit==True): 
                print('best_params_: ', grid_search.best_params_)
                print('best_score_: ', grid_search.best_score_)
                
    def get_model_best_estimator(self, key):
        return self.grid_searches[key].best_estimator_


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