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Generative Topographic Mapping (GTM) for python, GTM classification and GTM regression

Project description


sklearn integration

ugtm v2.0 provides sklearn-compatible GTM transformer (eGTM), GTM classifier (eGTC) and GTM regressor (eGTR):

from ugtm import eGTM, eGTC, eGTR
import numpy as np

# Dummy train and test
X_train = np.random.randn(100, 50)
X_test = np.random.randn(50, 50)
y_train = np.random.choice([1, 2, 3], size=100)

# GTM transformer
transformed = eGTM().fit(X_train).transform(X_test)

# Predict new labels using GTM classifier (GTC)
predicted_labels = eGTC().fit(X_train, y_train).predict(X_test)

# Predict new continuous outcomes using GTM regressor (GTR)
predicted_labels = eGTR().fit(X_train, y_train).predict(X_test)

The following sections will show functions no defined within the sklearn framework.

Basic functions

ugtm provides an implementation of GTM (Generative Topographic Mapping), kGTM (kernel Generative Topographic Mapping), GTM classification models (kNN, Bayes) and GTM regression models. ugtm also implements cross-validation options which can be used to compare GTM classification models to SVM classification models, and GTM regression models to SVM regression models. Typical usage:

#!/usr/bin/env python

import ugtm
import numpy as np

#generate sample data and labels: replace this with your own data

#build GTM map

#plot GTM map (html)

For installation instructions, cf.

Construct and plot GTM maps (or kGTM maps)

A gtm object can be created by running the function runGTM on a dataset. Parameters for runGTM are: k = sqrt(number of nodes), m = sqrt(number of rbf centres), s = RBF width factor, regul = regularization coefficient. The number of iteration for the expectation-maximization algorithm is set to 200 by default. This is an example with random data:

import ugtm

#import numpy to generate random data
import numpy as np

#generate random data (independent variables x),
#discrete labels (dependent variable y),
#and continuous labels (dependent variable y),
#to experiment with categorical or continuous outcomes

train = np.random.randn(20,10)
test = np.random.randn(20,10)

#create a gtm object and write model
gtm = ugtm.runGTM(train)

#run verbose
gtm = ugtm.runGTM(train, verbose=True)

#to run a kernel GTM model instead, run following:
gtm = ugtm.runkGTM(train, doKernel=True, kernel="linear")

#access coordinates (means or modes), and responsibilities of gtm object
gtm_coordinates = gtm.matMeans
gtm_modes = gtm.matModes
gtm_responsibilities = gtm.matR

Plot html maps

Call the plot_html() function on the gtm object:

#run model on train
gtm = ugtm.runGTM(train)

# ex. plot gtm object with landscape, html: labels are continuous

# ex. plot gtm object with landscape, html: labels are discrete

# ex. plot gtm object with landscape, html: labels are continuous
# no interpolation between nodes
gtm.plot_html(output="testout12",labels=activity,discrete=False,pointsize=20, \

# ex. plot gtm object with landscape, html: labels are discrete,
# no interpolation between nodes
gtm.plot_html(output="testout13",labels=labels,discrete=True,pointsize=20, \

Plot pdf maps

Call the plot() function on the gtm object:

#run model on train
gtm = ugtm.runGTM(train)

# ex. plot gtm object, pdf: no labels

# ex. plot gtm object with landscape, pdf: labels are discrete

# ex. plot gtm object with landscape, pdf: labels are continuous

Plot multipanel views

Call the plot_multipanel() function on the gtm object. This plots a general model view, showing means, modes, landscape with or without points. The plot_multipanel function only works if you have defined labels:

#run model on train
gtm = ugtm.runGTM(train)

# ex. with discrete labels and inter-node interpolation

# ex. with continuous labels and inter-node interpolation

# ex. with discrete labels and no inter-node interpolation
gtm.plot_multipanel(output="testout4",labels=labels,discrete=True,pointsize=20, \

# ex. with continuous labels and no inter-node interpolation
gtm.plot_multipanel(output="testout5",labels=activity,discrete=False,pointsize=20, \

Project new data onto existing GTM map

New data can be projected on the GTM map by using the transform() function, which takes as input the gtm model, a training and test set. The train set is then only used to perform data preprocessing on the test set based on the train (for example: apply the same PCA transformation to the train and test sets before running the algorithm):

#run model on train
gtm = ugtm.runGTM(train,doPCA=True)

#test new data (test)

#plot transformed test (html)

#plot transformed test (pdf)

#plot transformed data on existing classification model,
#using training set labels
                         labels=labels, \

7. Output predictions for a test set: GTM regression (GTR) and classification (GTC)

The GTR() function implements the GTM regression model (cf. references) and GTC() function a GTM classification model (cf. references):

#continuous labels (prediction by GTM regression model)

#discrete labels (prediction by GTM classification model)

8. Advanced GTM predictions with per-class probabilities

Per-class probabilities for a test set can be given by the advancedGTC() function (you can set the m, k, regul, s parameters just as with runGTM):

#get whole output model and label predictions for test set

#write whole predicted model with per-class probabilities

9. Crossvalidation experiments

Different crossvalidation experiments were implemented to compare GTC and GTR models to classical machine learning methods:

#crossvalidation experiment: GTM classification model implemented in ugtm,
#here: set hyperparameters s=1 and regul=1 (set to -1 to optimize)

#crossvalidation experiment: GTM regression model

#you can also run the following functions to compare
#with other classification/regression algorithms:

#crossvalidation experiment, k-nearest neighbours classification
#on 2D PCA map with 7 neighbors (set to -1 to optimize number of neighbours)

#crossvalidation experiment, SVC rbf classification model (sklearn implementation):

#crossvalidation experiment, linear SVC classification model (sklearn implementation):

#crossvalidation experiment, linear SVC regression model (sklearn implementation):

#crossvalidation experiment, k-nearest neighbours regression on 2D PCA map with 7 neighbors:

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