import pandas as pd
import os
import numpy as np


from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score

# Trying to set the seed
np.random.seed(0)
import random
random.seed(0)

# Reading file into data frame
cwd = os.getcwd()
filename = cwd + '/default of credit card clients.xls'
nanDict = {}
df = pd.read_excel(filename, header=1, skiprows=0, index_col=0, na_values=nanDict)

df.rename(index=str, columns={"default payment next month": "defaultPaymentNextMonth"}, inplace=True)

# Features and targets 
X = df.loc[:, df.columns != 'defaultPaymentNextMonth'].values
y = df.loc[:, df.columns == 'defaultPaymentNextMonth'].values

# Categorical variables to one-hot's
onehotencoder = OneHotEncoder(categories="auto")

X = ColumnTransformer(
    [("", onehotencoder, [3]),],
    remainder="passthrough"
).fit_transform(X)

y.shape

# Train-test split
trainingShare = 0.5 
seed  = 1
XTrain, XTest, yTrain, yTest=train_test_split(X, y, train_size=trainingShare, \
                                              test_size = 1-trainingShare,
                                             random_state=seed)

# Input Scaling
sc = StandardScaler()
XTrain = sc.fit_transform(XTrain)
XTest = sc.transform(XTest)

# One-hot's of the target vector
Y_train_onehot, Y_test_onehot = onehotencoder.fit_transform(yTrain), onehotencoder.fit_transform(yTest)

# Remove instances with zeros only for past bill statements or paid amounts
'''
df = df.drop(df[(df.BILL_AMT1 == 0) &
                (df.BILL_AMT2 == 0) &
                (df.BILL_AMT3 == 0) &
                (df.BILL_AMT4 == 0) &
                (df.BILL_AMT5 == 0) &
                (df.BILL_AMT6 == 0) &
                (df.PAY_AMT1 == 0) &
                (df.PAY_AMT2 == 0) &
                (df.PAY_AMT3 == 0) &
                (df.PAY_AMT4 == 0) &
                (df.PAY_AMT5 == 0) &
                (df.PAY_AMT6 == 0)].index)
'''
df = df.drop(df[(df.BILL_AMT1 == 0) &
                (df.BILL_AMT2 == 0) &
                (df.BILL_AMT3 == 0) &
                (df.BILL_AMT4 == 0) &
                (df.BILL_AMT5 == 0) &
                (df.BILL_AMT6 == 0)].index)

df = df.drop(df[(df.PAY_AMT1 == 0) &
                (df.PAY_AMT2 == 0) &
                (df.PAY_AMT3 == 0) &
                (df.PAY_AMT4 == 0) &
                (df.PAY_AMT5 == 0) &
                (df.PAY_AMT6 == 0)].index)

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV

lambdas=np.logspace(-5,7,13)
parameters = [{'C': 1./lambdas, "solver":["lbfgs"]}]#*len(parameters)}]
scoring = ['accuracy', 'roc_auc']
logReg = LogisticRegression()
gridSearch = GridSearchCV(logReg, parameters, cv=5, scoring=scoring, refit='roc_auc')