# %reset

import os
import pandas as pd
import numpy as np

from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from math import sqrt

path = os.getcwd()
os.chdir('/Users/marin/TEACHING/2324/M2-GLM-HAX912X/TP')

data = pd.read_csv("creditcard.csv")
data.shape

count_classes = pd.value_counts(data['Class'], sort = True).sort_index()
count_classes

X = data.loc[:, data.columns != 'Class']
y = data['Class']
scaler = preprocessing.StandardScaler().fit(X)
X = scaler.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 1974)
print("")
print("Number transactions train dataset: ", len(X_train))
print("Number transactions test dataset: ", len(X_test))
print("Total number of transactions: ", len(X_train)+len(X_test))


number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)
normal_indices = data[data.Class == 0].index
random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)
random_normal_indices = np.array(random_normal_indices)
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])
under_sample_data = data.iloc[under_sample_indices,:]
X_undersample = under_sample_data.loc[:, under_sample_data.columns != 'Class']
y_undersample = under_sample_data['Class']

X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample                                                                                ,y_undersample,test_size = 0.3,random_state = 1981)

print("")
print("Number transactions train dataset: ",len(X_train_undersample))
print("Number transactions test dataset: ",len(X_test_undersample))
print("Total number of transactions: ", len(X_train_undersample)+len(X_test_undersample))


forest = RandomForestClassifier(n_estimators=1000, max_features=int(sqrt(30)),oob_score=True,n_jobs=-1)
forest = forest.fit(X_train_undersample,y_train_undersample) 
y_chap = forest.predict(X_test_undersample)
table = pd.crosstab(y_test_undersample,y_chap)
table