Source code for PyMca5.PyMcaMath.mva.NNMAModule

__author__ = "Uwe Schmitt uschmitt@mineway.de, wrapped by V.A. Sole - ESRF"
__license__ = "BSD"
__doc__ = """
This module is a simple wrapper to the py_nnma module of Uwe Schmitt (uschmitt@mineway.de)
in order to integrate it into PyMca. What follows is the documentation of py_nnma

py_nnma:  python modules for nonnegative matrix approximation (NNMA)

(c) 2009 Uwe Schmitt, uschmitt@mineway.de

NNMA minimizes  dist(Y, A X)

       where:  Y >= 0,  m x n
               A >= 0,  m x k
               X >= 0,  n x k

               k < min(m,n)

     dist(A,B) can be || A - B ||_fro
                   or   KL(A,B)


This moudule provides the following functions:

    NMF, NMFKL, SNMF, RRI, ALS, GDCLS, GDCLS_L1, FNMAI, FNMAI_SPARSE,
    NNSC and FastHALS

The common parameters when calling such a function are:

    input:

            Y           --   the matrix for decomposition, maybe dense
                             from numpy or sparse from scipy.sparse
                             package

            k           --   number of componnets to estimate

            Astart
            Xstart      --   matrices to start iterations. Maybe None
                             for using random start matrices.

            eps         --   termination swell value

            maxcount    --   max number of iterations to be performed

            verbose     --   if False: produce no output durint interations
                             if integer: give all 'verbose' itetations some
                             output about current state of iterations

    output:

            A, X        --   result matrices of algorithm

            obj         --   value of objective function of last iteration

            count       --   number of iterations done

            converged   --   flag: indicates if iterations stoped within
                             max number of iterations

The following extra parameters exist depending on algorithm:

    RRI      :  damping parameter 'psi' (default: 1e-12)

    SNMF     :  sparsity parameter 'sparse_par' (default: 0)

    ALS      :  regularization parameter 'regul' for stabilizing iterations
                (default value 0). needed if objective value jitters.

    GCDLS    :  'regul' for l2-smoothness of X (default 0)

    GDCLS_L1 :  'regul' for l1-smoothness of X (default 0)

    FNMAI    :  'stabil' for stabilizing algorithm (default value 1e-12)
                'alpha'  for stepsize  (default value 0.1)
                'tau'    for number of inner iterations (default value 2)

    FNMAI_SPARSE : as FNMAI plus
                'regul'  for l1-smoothness of X (default 0)

    NNSC     :  'alpha'       for stepsize of gradient update of A
                'sparse_par'  for sparsity

This module is based on:

    - Daniel D. Lee and H. Sebastian Seung:

          "Algorithms for non-negative matrix factorization",
          in Advances in Neural Information Processing 13
          (Proc. NIPS*2000) MIT Press, 2001.

          "Learning the parts of objects by non-negative matrix
           factorization",
          Nature, vol. 401, no. 6755, pp. 788-791, 1999.

    - A. Cichocki and A-H. Phan:

          "Fast local algorithms for large scale Nonnegative Matrix and
           Tensor Factorizations",
          IEICE Transaction on Fundamentals,
          in print March 2009.

    - P. O. Hoyer

          "Non-negative Matrix Factorization with sparseness
           constraints",
          Journal of Machine Learning Research, vol. 5, pp. 1457-1469,
          2004.


    - Dongmin Kim, Suvrit Sra,Inderjit S. Dhillon:

           "Fast Newton-type Methods for the Least Squares Nonnegative Matrix
           Approximation Problem"
           SIAM Data Mining (SDM), Apr. 2007


    - Ngoc-Diep Ho:

        dissertation from
        http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-06052008-235205/


#############################################################################

Copyright (c) 2009 Uwe Schmitt, uschmitt@mineway.de

All rights reserved.

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    * copyright notice, this list of conditions and the following
    * disclaimer in the documentation and/or other materials provided
    * with the distribution.  Neither the name of the <ORGANIZATION>
    * nor the names of its contributors may be used to endorse or
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    * prior written permission.

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"""
import numpy
try:
    import mdp
    if mdp.__version__ >= '2.6':
        MDP = True
    else:
        MDP = False
except:
    MDP = False

from . import py_nnma
DEBUG = 0

function_list = ['FNMAI', 'ALS', 'FastHALS', 'GDCLS']
function_dict = {"NNSC": py_nnma.NNSC,
                 "FNMAI_SPARSE": py_nnma.FNMAI_SPARSE,
                 "FNMAI": py_nnma.FNMAI,
                 "GDCLS_L1": py_nnma.GDCLS_L1,
                 "GDCLS": py_nnma.GDCLS,
                 "ALS": py_nnma.ALS,
                 "NMFKL": py_nnma.NMFKL,
                 "NMF": py_nnma.NMF,
                 "RRI": py_nnma.RRI,
                 "FastHALS": py_nnma.FastHALS,
                 "SNMF": py_nnma.SNMF,
                 }
[docs]def nnma(stack, ncomponents, binning=None, function=None, eps=5e-5, verbose=DEBUG, maxcount=1000, kmeans=False): if kmeans and (not MDP): raise ValueError("K Means not supported") #I take the defaults for the other parameters param = dict(alpha=.1, tau=2, regul=1e-2, sparse_par=1e-1, psi=1e-3) if function is None: function = 'FNMAI' nnma_function = function_dict[function] if binning is None: binning = 1 if hasattr(stack, "info") and hasattr(stack, "data"): data = stack.data[:] else: data = stack[:] oldShape = data.shape if len(data.shape) == 3: r, c, N = data.shape if isinstance(data, numpy.ndarray): data.shape = r*c, N else: r, N = data.shape c = 1 if isinstance(data, numpy.ndarray): if binning > 1: data=numpy.reshape(data,[data.shape[0], data.shape[1]/binning, binning]) data=numpy.sum(data , axis=-1) N=N/binning else: oldData = data N = int(N/binning) try: data = numpy.zeros((r, c, N), oldData.dtype) except MemoryError: try: data = numpy.zeros((r, c, N), numpy.float32) except MemoryError: text = "NNMAModule only works properly on numpy arrays.\n" text += "Memory Error: Higher binning may help." raise TypeError(text) if binning == 1: if len(oldShape) == 3: for i in range(r): data[i,:,:] = oldData[i,:,:] data.shape = r * c, N else: data.shape = r * c, N for i in range(r*c): data[i,:] = oldData[i,:] else: if len(oldShape) == 3: for i in range(r): tmpData = oldData[i,:,:] tmpData.shape = c, N, binning data[i,:,:] = numpy.sum(tmpData, axis=-1) data.shape = r * c, N else: data.shape = r * c, N for i in range(r*c): tmpData = oldData[i,:] tmpData.shape = N, binning data[i,:] = numpy.sum(tmpData, axis=-1) #mindata = data.min() #numpy.add(data, -mindata+1, data) #I do not know the meaning of these paramenters #py_nnma.scale(newdata) param = dict(alpha=.1, tau=2, regul=1e-2, sparse_par=1e-1, psi=1e-3) #Start tolerance #1E+3 is conservative/fast #1E-3 is probably slow Astart = None Xstart = None #for i in range(start_ncomponents, ncomponents): converged = False while not converged: A, X, obj, count, converged = nnma_function(data, ncomponents, Astart, Xstart, eps=eps, maxcount=maxcount, verbose=verbose, **param) if not converged: print("WARNING: Possible problems converging") #if binning > 1: # numpy.add(data, mindata-1, data) #data.shape = oldShape images = A.T if 0: images.shape = ncomponents, r, c return images, numpy.ones((ncomponents), numpy.float32),X #order and scale images according to Gerd Wellenreuthers' recipe #normalize all maps to be in the range [0, 1] for i in range(ncomponents): norm_factor = numpy.max(images[i, :]) if norm_factor > 0: images[i, :] *= 1.0/norm_factor X[i, :] *= norm_factor #sort NNMA-spectra and maps total_nnma_intensity = [] for i in range(ncomponents): total_nnma_intensity += [[numpy.sum(images[i,:])*\ numpy.sum(X[i,:]), i]] sorted_idx = [item[1] for item in sorted(total_nnma_intensity)] sorted_idx.reverse() #original data intensity original_intensity = numpy.sum(data) #final values if kmeans: n_more = 1 else: n_more = 0 new_images = numpy.zeros((ncomponents + n_more, r*c), numpy.float32) new_vectors = numpy.zeros((X.shape[0]+n_more, X.shape[1]), numpy.float32) values = numpy.zeros((ncomponents+n_more,), numpy.float32) for i in range(ncomponents): idx = sorted_idx[i] if 1: new_images[i, :] = images[idx, :] else: #imaging the projected sum gives same results Atmp = images[idx, :] Atmp.shape = -r*c, 1 Xtmp = X[idx,:] Xtmp.shape = 1, -1 new_images[i, :] = numpy.sum(numpy.dot(Atmp, Xtmp), axis=1) new_vectors[i,:] = X[idx,:] values[i] = 100.*total_nnma_intensity[idx][0]/original_intensity new_images.shape = ncomponents + n_more, r, c if kmeans: classifier = mdp.nodes.KMeansClassifier(ncomponents) for i in range(ncomponents): classifier.train(new_vectors[i:i+1]) k = 0 for i in range(r): for j in range(c): spectrum = data[k:k+1,:] new_images[-1, i,j] = classifier.label(spectrum)[0] k += 1 return new_images, values, new_vectors
if __name__ == "__main__": from PyMca.PyMcaIO import EDFStack from PyMca.PyMcaIO import EdfFile import os import sys import time inputfile = "D:\DATA\COTTE\ch09\ch09__mca_0005_0000_0000.edf" if len(sys.argv) > 1: inputfile = sys.argv[1] print(inputfile) elif os.path.exists(inputfile): print("Using a default test case") else: print("Usage:") print("python NNMAModule.py indexed_edf_stack") sys.exit(0) stack = EDFStack.EDFStack(inputfile) r0, c0, n0 = stack.data.shape ncomponents = 10 outfile = os.path.basename(inputfile)+"ICA.edf" e0 = time.time() images, eigenvalues, eigenvectors = nnma(stack.data, ncomponents, binning=1) print("elapsed = %f" % (time.time() - e0)) if os.path.exists(outfile): os.remove(outfile) f = EdfFile.EdfFile(outfile) for i in range(ncomponents): f.WriteImage({}, images[i,:]) sys.exit(0)