def test_runkGTM(self):
gtm = ugtm.runkGTM(data=self.data, k=self.k,
doPCA=True, doKernel=True)
self.assertEqual(gtm.converged, True)
self.assertEqual(gtm.matR.shape, (self.n_individuals, self.n_nodes))
self.assertAlmostEqual(sum(gtm.matR[0, :]), 1.0)
self.assertEqual(gtm.matMeans.shape, (self.n_individuals, 2))
self.assertEqual(gtm.matModes.shape, (self.n_individuals, 2))
def test_runWorkflow(self):
train = np.random.randn(20, 10)
test = np.random.randn(20, 10)
labels = np.random.choice(["class1", "class2"], size=20)
activity = np.random.randn(20)
gtm = ugtm.runGTM(train)
gtm.write("tests/output_tests/testout1")
gtm_coordinates = gtm.matMeans
gtm_modes = gtm.matModes
gtm_responsibilities = gtm.matR
gtm.plot_multipanel(output="tests/output_tests/testout2",
labels=labels,
discrete=True,
pointsize=20)
gtm.plot_multipanel(output="tests/output_tests/testout3",
labels=activity,
discrete=False,
pointsize=20)
gtm.plot_multipanel(output="tests/output_tests/testout4",
labels=labels,
discrete=True,
pointsize=20,
do_interpolate=False)
gtm.plot_multipanel(output="tests/output_tests/testout5",
labels=activity,
discrete=False,
pointsize=20,
do_interpolate=False)
gtm.plot(output="tests/output_tests/testout6", pointsize=20)
gtm.plot(output="tests/output_tests/testout7",
labels=labels,
discrete=True,
pointsize=20)
gtm.plot(output="tests/output_tests/testout8",
labels=activity,
discrete=False,
pointsize=20)
gtm.plot_html(output="tests/output_tests/testout9", pointsize=20)
gtm.plot_html(output="tests/output_tests/testout10",
labels=activity,
discrete=False,
pointsize=20)
gtm.plot_html(output="tests/output_tests/testout11",
labels=labels,
discrete=True,
pointsize=20)
gtm.plot_html(output="tests/output_tests/testout12",
labels=activity,
discrete=False,
pointsize=20,
do_interpolate=False,
ids=labels)
gtm.plot_html(output="tests/output_tests/testout13",
labels=labels,
discrete=True,
pointsize=20,
do_interpolate=False)
transformed = ugtm.transform(optimizedModel=gtm,
train=train,
test=test)
transformed.plot_html(output="tests/output_tests/testout14",
pointsize=20)
transformed.plot(output="tests/output_tests/testout15", pointsize=20)
gtm.plot_html_projection(output="tests/output_tests/testout16",
projections=transformed,
labels=labels,
discrete=True,
pointsize=20)
predicted_labels = ugtm.GTC(train=train, test=test, labels=labels)
predicted_model = ugtm.advancedGTC(train=train,
test=test,
labels=labels)
predicted_model = ugtm.advancedGTC(train=train,
test=test,
labels=labels,
doPCA=True,
n_components=-1)
ugtm.printClassPredictions(predicted_model,
"tests/output_tests/testout17")
predicted = ugtm.GTR(train=train, test=test, labels=activity)
predicted = ugtm.GTC(train=train, test=test, labels=labels)
ugtm.crossvalidateGTC(data=train,
labels=labels,
s=1,
regul=1,
n_repetitions=10,
n_folds=5)
ugtm.crossvalidateGTR(data=train, labels=activity, s=1, regul=1)
ugtm.crossvalidatePCAC(data=train, labels=labels, n_neighbors=7)
ugtm.crossvalidateSVCrbf(data=train, labels=labels, C=1, gamma=1)
ugtm.crossvalidateSVCrbf(data=train, labels=labels, C=1)
ugtm.crossvalidateSVR(data=train, labels=activity, C=1, epsilon=1)
ugtm.crossvalidatePCAR(data=train, labels=activity, n_neighbors=7)
gtm = ugtm.runkGTM(train, doKernel=True, kernel="linear")
prior=args.prior,
do_interpolate=args.interpolate)
exit
# if it's for kGTM visualization
elif args.model == 'kGTM':
# kGTM embedding
print("k:%s, m:%s, l:%s, s:%s" % (k, m, l, s))
matK = ugtm.chooseKernel(data, args.kernel)
start = time.time()
kgtm = ugtm.runkGTM(data=matK,
k=k,
m=m,
s=s,
l=l,
niter=niter,
doKernel=False,
maxdim=maxdim,
doPCA=False,
missing=args.missing,
missing_strategy=args.missing_strategy,
random_state=args.random_state)
end = time.time()
elapsed = end - start
print("time taken for kGTM: ", elapsed)
# make pdf
np.savetxt(args.output + "_matmeans.csv", kgtm.matMeans, delimiter=',')
kgtm.plot_multipanel(labels=labels,
output=args.output,
discrete=discrete,
cname=args.cname,
pointsize=args.pointsize,