Open In Colab

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

[15]:

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#

[16]:

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#

[17]:

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

[17]:
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#

[18]:

varnum = 3
n_components = 10
max_num_iterations = 100

[19]:

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

[20]:

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']

[31]:

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

[11]:

gmm_util.bivariate_plot(var_index=[1,2], cmap='viridis',title='Bivariate Plot',fname='test')
../_images/examples_GMM_13_0.svg 
[12]:

gmm_util.summary_plot(pad=0.1)
../_images/examples_GMM_14_0.svg 
[13]:

gmm_util.univariate_conditional_plot(conditioning_data=[0, 0,None])
../_images/examples_GMM_15_0.svg 
[32]:

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