#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 16 21:32:56 2018

@author: hericsonaraujo
"""

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, make_scorer
from sklearn.model_selection import train_test_split

# Import basic libraries
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# import visualization libraries
import seaborn as sns
import matplotlib.pyplot as plt
#from ggplot import *

df = pd.read_csv('UCI_Credit_Card.csv')
df.sample(5)

df = df.rename(columns={'default.payment.next.month': 'def_pay',
                        'PAY_0': 'PAY_1'})
df.head()

df.info()

fil = (df.EDUCATION == 5) | (df.EDUCATION == 6) | (df.EDUCATION == 0)
df.loc[fil, 'EDUCATION'] = 4
df.EDUCATION.value_counts()

df.loc[df.MARRIAGE == 0, 'MARRIAGE'] = 3
df.MARRIAGE.value_counts()

print (float(df.def_pay.sum())/float(len(df.def_pay)))


fil = (df.PAY_1 == -2) | (df.PAY_1 == -1) | (df.PAY_1 == 0)
df.loc[fil, 'PAY_1'] = 0
fil = (df.PAY_2 == -2) | (df.PAY_2 == -1) | (df.PAY_2 == 0)
df.loc[fil, 'PAY_2'] = 0
fil = (df.PAY_3 == -2) | (df.PAY_3 == -1) | (df.PAY_3 == 0)
df.loc[fil, 'PAY_3'] = 0
fil = (df.PAY_4 == -2) | (df.PAY_4 == -1) | (df.PAY_4 == 0)
df.loc[fil, 'PAY_4'] = 0
fil = (df.PAY_5 == -2) | (df.PAY_5 == -1) | (df.PAY_5 == 0)
df.loc[fil, 'PAY_5'] = 0
fil = (df.PAY_6 == -2) | (df.PAY_6 == -1) | (df.PAY_6 == 0)
df.loc[fil, 'PAY_6'] = 0
late = df[['PAY_1','PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']]
#draw_histograms(late, late.columns, 2, 3, 10)

y = df['def_pay'].copy()
y.sample(5)

features = ['LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1', 'PAY_2',
       'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',
       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',
       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']
X = df[features].copy()
X.columns

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

print(df.def_pay.describe())
print("---------------------------")
print(y_train.describe())
print("---------------------------")
print(y_test.describe())

#create the classifier
classifier = DecisionTreeClassifier(max_depth=10, random_state=14)
# training the classifier
classifier.fit(X_train, y_train)
# do our predictions on the test
predictions = classifier.predict(X_test)
# see how good we did on the test
print(accuracy_score(y_true = y_test, y_pred = predictions))

classifier = DecisionTreeClassifier(max_depth=100, random_state=14)
classifier.fit(X_train, y_train)
predictions = classifier.predict(X_test)
print(accuracy_score(y_true = y_test, y_pred = predictions))