GMM Visualization

The following notebook is comprised of 4 primary steps:

1. Initialize required packages, directories and parameters
2. Load Multivariate data
3. Create the GMM
4. Visualizate GMM

import sys, os
import pygeostat as gs
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline

Loading CCG Executable

outdir = 'Output'
gs.mkdir(outdir)
#path to GSLIB executables
exe_dir="../pygeostat/executable/"

gs.PlotStyle['font.size'] = 12
gs.Parameters['data.tmin'] = -998

Loading data

dfl = gs.ExampleData('point2d_mv')
dfl.head()

	X	Y	Var1	Var2	Var3
0	28949.708226	96548.685172	0.78641	2.96716	-1.47010
1	24610.158555	96251.877101	0.54300	2.13614	0.74573
2	27719.964681	90687.703337	-0.39320	0.48682	0.23470
3	26910.320345	92501.863566	0.44938	1.27002	-0.31840
4	21725.601294	92981.211253	1.38558	1.77734	2.40020

Multivariate Case

Using GMM

varnum = 3
n_components = 10
max_num_iterations = 100

gmm = gs.Program(program='gmm_fit')

parstr = """    Parameters for GMM_EM
               *********************

START OF PARAMETERS:
{file}                - file with data
{varnum} 3 4 5           - Number of variables and columns
-998 1e21             - trimming limits
{output}               - output file
{n_components}          - number of components
0.0001                   - regularization constant (treat instability)
{max_num_iterations}  - maximum number of iterations for EM algorithm
14641                 - seed number
0                     - fit only homotopic data (1=yes; 0=no)

=================================================================
This program fit a Gaussian mixture to the data based on the EM (Expected maximum liklihood)
algorithm.
"""
gmm.run(parstr=parstr.format(file=dfl.flname,
                             varnum=varnum, 
                             n_components=n_components, 
                             max_num_iterations=max_num_iterations, 
                             output=os.path.join(outdir, 'gmm_fit.out')), 
        liveoutput=False)

Calling:  ['gmm_fit', 'temp']

gmm_util = gs.GmmUtility(gmm_file=os.path.join(outdir, 'gmm_fit.out'), 
                      data=dfl.data, variable_names=['Var1', 'Var2','Var3'])

gmm_util.bivariate_plot(var_index=[1,2], cmap='viridis',title='Bivariate Plot',fname='test')

gmm_util.summary_plot(pad=0.1)

gmm_util.univariate_conditional_plot(conditioning_data=[0, 0,None])

# Clean up
try:
    gs.rmfile('test.png')
    gs.rmfile('temp')
    gs.rmdir(outdir)
except:
    pass