Source code for PyMca5.tests.PCAToolsTest

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# The PyMca X-Ray Fluorescence Toolkit
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__author__ = "V. Armando Sole - ESRF Data Analysis"
__contact__ = "sole@esrf.fr"
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
import unittest
import numpy
import numpy.linalg
try:
    import mdp
    MDP = True
except:
    # MDP can give very weird errors
    MDP = False

[docs]class testPCATools(unittest.TestCase):
[docs] def testPCAToolsImport(self): from PyMca5.PyMcaMath.mva import PCATools
[docs] def testPCAToolsCovariance(self): from PyMca5.PyMcaMath.mva.PCATools import getCovarianceMatrix x = numpy.array([[0.0, 2.0, 3.0], [3.0, 0.0, -1.0], [4.0, -4.0, 4.0], [4.0, 4.0, 4.0]]) nSpectra = x.shape[0] # test just multiplication tmpArray = numpy.dot(x.T, x) for force in [True, False]: pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x, force=force, center=False) self.assertTrue(numpy.allclose(tmpArray, pymcaCov * (nData - 1))) # calculate covariance using numpy numpyCov = numpy.cov(x.T) numpyAvg = x.sum(axis=0).reshape(-1, 1) / nSpectra tmpArray = x.T - numpyAvg numpyCov2 = numpy.dot(tmpArray, tmpArray.T) / nSpectra numpyAvg = numpyAvg.reshape(1, -1) # calculate covariance using PCATools and 2D stack # directly and dynamically loading data for force in [False, True]: pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x, force=force, center=True) self.assertTrue(numpy.allclose(numpyCov, pymcaCov)) self.assertTrue(numpy.allclose(numpyAvg, pymcaAvg)) self.assertTrue(nData == nSpectra) # calculate covariance using PCATools and 3D stack # directly and dynamically loading data x.shape = 2, 2, -1 for force in [False, True]: pymcaCov, pymcaAvg, nData = getCovarianceMatrix(x, force=force, center=True) self.assertTrue(numpy.allclose(numpyCov, pymcaCov)) self.assertTrue(numpy.allclose(numpyAvg, pymcaAvg)) self.assertTrue(nData == nSpectra)
[docs] def testPCAToolsPCA(self): from PyMca5.PyMcaMath.mva.PCATools import numpyPCA x = numpy.array([[0.0, 2.0, 3.0], [3.0, 0.0, -1.0], [4.0, -4.0, 4.0], [4.0, 4.0, 4.0]]) # that corresponds to 4 spectra of 3 channels nSpectra = x.shape[0] # calculate eigenvalues and eigenvectors with numpy tmpArray = numpy.dot(x.T, x)/(nSpectra - 1) numpyEigenvalues, numpyEigenvectors = numpy.linalg.eigh(tmpArray) # sort from higher to lower idx = list(range(numpyEigenvalues.shape[0]-1, -1 , -1)) numpyEigenvalues = numpy.take(numpyEigenvalues, idx) numpyEigenvectors = numpyEigenvectors[:, ::-1].T # now use PyMca # centering has to be false to obtain the same results for force in [True, False]: images, eigenvalues, eigenvectors = numpyPCA(x, ncomponents=x.shape[1], force=force, center=False, scale=False) self.assertTrue(numpy.allclose(eigenvalues, numpyEigenvalues)) self.assertTrue(numpy.allclose(eigenvectors, numpyEigenvectors)) # test with a different shape x.shape = 2, 2, -1 for force in [True, False]: images, eigenvalues, eigenvectors = numpyPCA(x, ncomponents=3, force=force, center=False, scale=False) self.assertTrue(numpy.allclose(eigenvalues, numpyEigenvalues)) self.assertTrue(numpy.allclose(eigenvectors, numpyEigenvectors))
if MDP: def testPCAToolsMDP(self): from PyMca5.PyMcaMath.mva.PCATools import getCovarianceMatrix, numpyPCA x = numpy.array([[0.0, 2.0, 3.0], [3.0, 0.0, -1.0], [4.0, -4.0, 4.0], [4.0, 4.0, 4.0]]) # use mdp pcaNode = mdp.nodes.PCANode() pcaNode.train(x) pcaNode.stop_training() pcaEigenvectors = pcaNode.v.T # and compare with PyMca for force in [True, False]: images, eigenvalues, eigenvectors = numpyPCA(x, ncomponents=x.shape[1], force=force, center=True, scale=False) # the eigenvalues must be the same self.assertTrue(numpy.allclose(eigenvalues, pcaNode.d)) # the eigenvectors can be multiplied by -1 for i in range(x.shape[1]): if (eigenvectors[i,0] >= 0 and pcaEigenvectors[i,0] >=0) or\ (eigenvectors[i,0] <= 0 and pcaEigenvectors[i,0] <=0): # both same sign self.assertTrue(numpy.allclose(eigenvectors[i], pcaEigenvectors[i])) else: self.assertTrue(numpy.allclose(-eigenvectors[i], pcaEigenvectors[i]))
[docs]def getSuite(auto=True): testSuite = unittest.TestSuite() if auto: testSuite.addTest(\ unittest.TestLoader().loadTestsFromTestCase(testPCATools)) else: # use a predefined order testSuite.addTest(testPCATools("testPCAToolsImport")) testSuite.addTest(testPCATools("testPCAToolsCovariance")) testSuite.addTest(testPCATools("testPCAToolsPCA")) if MDP: testSuite.addTest(testPCATools("testPCAToolsMDP")) return testSuite
[docs]def test(auto=False): unittest.TextTestRunner(verbosity=2).run(getSuite(auto=auto))
if __name__ == '__main__': test()