#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Tue Oct 15 10:38:56 2019

@author: nailson

"""

import math

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.preprocessing import OneHotEncoder, StandardScaler

from sklearn.compose import ColumnTransformer, make_column_transformer

from sklearn.impute import SimpleImputer

from sklearn.pipeline import Pipeline

from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV

from sklearn.feature_selection import SelectFromModel

from sklearn.model_selection import cross_val_score, GridSearchCV

from sklearn.metrics import mean_squared_error

from sklearn.neighbors import KNeighborsRegressor

#Import data

df = pd.read_csv('cartola_2014.csv')

####################### Partial EDA ###############################

df.head()

# Total de na por colunas

df.isnull().sum()

df.describe()

df.clube_id.unique()

####################### END Partial EDA ###############################

####################### Data Preprocessing EDA ###############################

df = df.drop(columns=['atleta_id', 'partida_id'])

y_train = df.loc[:, 'nota'].values

df = df.drop(columns=['nota'])

preprocess = make_column_transformer(

)

X_train = preprocess.fit_transform(df)

####################### END Data Preprocessing EDA ###############################

####################### Simple Linear Regression ###############################

classifier = LinearRegression()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_train)

math.sqrt(mean_squared_error(y_train, y_pred))

####################### END Simple Linear Regression ###############################

####################### Regressao Ridge w/ cross validation ###############################

lambdas = np.logspace(-5, -1, 50)

ridge = RidgeCV(alphas = lambdas, fit_intercept=True, cv=10)

ridge.fit(X_train, y_train)

ridge.alpha_

ridge.cv_values_

ridge.score(X_train, y_train)

ridge.get_params()

ridge.coef_

print('Melhor lamda: %0.5f' %ridge.alpha_)

important_variables = []

club_plus_position = 0

for i in range(len(ridge.coef_)):

if abs(ridge.coef_[i]) >= 0.1:

if(i < len(df.drop(columns=['clube_id', 'posicao_id']).columns)):

important_variables.append(df.drop(columns=['clube_id', 'posicao_id']).columns[i])

else:

club_plus_position += 1

print('Variáveis mais importantes para o Ridge: ', important_variables)

print('Juntamente com %d variáveis referentes a posição do jogador e id do clube' %(club_plus_position))

y_pred = ridge.predict(X_train)

math.sqrt(mean_squared_error(y_train, y_pred))

####################### END Regressao Ridge w/ cross validation ###############################

####################### Regressao LASSO w/ cross validation ###############################

lasso = LassoCV(cv=10, alphas=lambdas)

sfm = SelectFromModel(lasso, prefit=True)

sfm.fit(X_train, y_train)

n_features = sfm.transform(X_train)

indices = sfm.get_support(indices=True)

lasso.fit(X_train, y_train)

lasso.coef_

y_pred = lasso.predict(X_train[:, indices])

math.sqrt(mean_squared_error(y_train, y_pred))

####################### END Regressao LASSO w/ cross validation ###############################

####################### KNN w/ cross validation ###############################

knn = KNeighborsRegressor()

param_grid = {'n_neighbors': np.arange(1, 16)}

gscv = GridSearchCV(knn, param_grid, cv=10, verbose=10, n_jobs=-1)

gscv.fit(X_train, y_train)

gscv.best_params_

gscv.best_score_

gscv.best_estimator_

y_pred = gscv.predict(X_train)

math.sqrt(mean_squared_error(y_train, y_pred))

####################### END KNN w/ cross validation ###############################