Self Organizing Maps to detect frauds in credit card applications

self_organizing_maps.py

#Self Organizing Maps to detect frauds in credit card applications

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('Credit_Card_Applications.csv')

X = dataset.iloc[:, :-1].values
y = dataset.iloc[:,-1].values

from sklearn.preprocessing import MinMaxScaler

sc = MinMaxScaler(feature_range=(0,1))
X = sc.fit_transform(X)

from minisom import MiniSom

som = MiniSom(x=10, y=10, input_len=15, sigma=1.0, learning_rate=0.5)
som.random_weights_init(X)
som.train_random(data=X, num_iteration=100)

from pylab import bone, pcolor, colorbar, plot, show

bone()
pcolor(som.distance_map().T)
colorbar()

markers = ['o', 's']
colors = ['r', 'g']

for i,x in enumerate(X):
    w = som.winner(x)
    plot(w[0] + 0.5,
         w[1] + 0.5,
         markers[y[i]],
         markeredgecolor = colors[y[i]],
         markerfacecolor = 'None',
         markersize = 10,
         markeredgewidth = 2)

show()


mapping = som.win_map(X)

frauds = np.concatenate( (mapping[(8,1)], mapping[(9,2)]), axis = 0 )
frauds = sc.inverse_transform(frauds)