em_bbgp.py

import numpy as np
import pandas as pd
from sklearn.mixture import GaussianMixture
import matplotlib.pyplot as plt
import seaborn as sns

simData = pd.read_csv('./summary.additive_select.csv', ',')

CLUSTER=11
BIAS = 'BBGP'
OUTPUT = './em_1d_bbgp_spherical/'

ks = simData['KS']
bbgp = simData['BBGP']
gcmh = simData['GCMH']
name = simData['2L_POS']

feature = pd.concat([bbgp], axis=1)
# K-MEANS Algorithm
logPATH = OUTPUT + 'em_' + BIAS + '_' + str(CLUSTER) + '.csv'
logW = open(logPATH, 'w')

for i in range(2, CLUSTER):
    model = GaussianMixture(n_components=i, covariance_type='spherical', max_iter=300)
    simData['label'] = model.fit_predict(feature)

    bic = model.bic(feature)
    aic = model.aic(feature)
    logW.write(str(aic) + ',\t' + str(bic) + '\n')

    fig = plt.figure()
    sns.set_style('white')
    plt.scatter(y=feature, x=simData['label'], c=simData['label'])
    plt.xticks(range(0,i))
    plt.savefig(OUTPUT + 'em_' + str(i) + '.png')
    plt.close()

    simData.to_csv(OUTPUT + 'em_' + str(i) + '.csv')

logW.close()