Example #1
0
def plot_oneexample(btchroma,
                    mask,
                    p1,
                    p2,
                    methods=(),
                    methods_args=None,
                    measures=(),
                    subplot=None,
                    plotrange=None,
                    **kwargs):
    """
    Utility function to plot the result of many methods on one example.
    Methods and arguments are the same as when we test on a whole dataset.
    First two plots are always original then original*mask.
    INPUT
      btchroma     - btchroma
      mask         - binary mask, 0 = hidden
      p1           - first masked column
      p2           - last masekdcolumn + 1
      methods      - list of methods (lintrans,random,....)
      bethods_args - list of dictionary containing arguments
      measures     - list of measures, put in title (we sort according to first)
      subplot      - if None, images in one column
      plotrange    - if we dont want to plot everything set to (pos1,pos2)
      kwargs       - extra arguments passed to plotall
    """
    from plottools import plotall
    # inits and sanity checks
    for meas in measures:
        assert meas in ('eucl', 'kl', 'cos', 'dent', 'lhalf', 'ddiff', 'jdiff',
                        'thresh', 'leven',
                        'condent'), 'unknown measure: ' + meas
    nmethods = len(methods)
    assert nmethods > 0, 'we need at least one method...!'
    if methods_args is None:
        methods_args = [{}] * len(methods)
    if subplot is None:
        subplot = (nmethods + 2, 1)
    assert np.zeros(subplot).size == len(
        methods) + 2, 'wrong subplot, forgot original and masked original?'
    if plotrange is None:
        plotrange = (0, btchroma.shape[1])
    # impute
    errs = []
    recons = []
    ########## ALGORITHM DEPENDENT
    for im, method in enumerate(methods):
        if method == 'random':
            recon = IMPUTATION.random_patch(btchroma, mask, p1, p2,
                                            **methods_args[im])
        elif method == 'randomfromsong':
            recon = IMPUTATION.random_patch_from_song(btchroma, mask, p1, p2,
                                                      **methods_args[im])
        elif method == 'average':
            recon = IMPUTATION.average_patch(btchroma, mask, p1, p2,
                                             **methods_args[im])
        elif method == 'averageall':
            recon = IMPUTATION.average_patch_all(btchroma, mask, p1, p2,
                                                 **methods_args[im])
        elif method == 'codebook':
            recon, used_codes = IMPUTATION.codebook_patch(
                btchroma, mask, p1, p2, **methods_args[im])
        elif method == 'knn_eucl':
            recon, used_cols = IMPUTATION.knn_patch(btchroma,
                                                    mask,
                                                    p1,
                                                    p2,
                                                    measure='eucl',
                                                    **methods_args[im])
        elif method == 'knn_kl':
            recon, used_cols = IMPUTATION.knn_patch(btchroma,
                                                    mask,
                                                    p1,
                                                    p2,
                                                    measure='kl',
                                                    **methods_args[im])
        elif method == 'lintrans':
            recon, proj = IMPUTATION.lintransform_patch(
                btchroma, mask, p1, p2, **methods_args[im])
        elif method == 'kalman':
            import kalman_toolbox as KALMAN
            recon = KALMAN.imputation(btchroma, p1, p2, **methods_args[im])
        elif method == 'hmm':
            recon, recon2, hmm = IMPUTATION_HMM.imputation(
                btchroma * mask, p1, p2, **methods_args[im])
        elif method == 'siplca':
            rank = methods_args[im]['rank']
            res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma * mask).copy(),
                                                      rank,
                                                      mask,
                                                      convergence_thresh=1e-15,
                                                      **methods_args[im])
            W, Z, H, norm, recon, logprob = res
        elif method == 'siplca2':
            rank = methods_args[im]['rank']
            res = IMPUTATION_PLCA.SIPLCA2_mask.analyze(
                (btchroma * mask).copy(),
                rank,
                mask,
                convergence_thresh=1e-15,
                **methods_args[im])
            W, Z, H, norm, recon, logprob = res
        else:
            print 'unknown method:', method
            return
        # compute errors
        recons.append(recon.copy())
        errs.append(recon_error(btchroma, mask, recon, 'all'))
    # all methods computed
    # now we sort the results according to first measure
    main_errs = map(lambda x: x[measures[0]], errs)
    order = np.array(np.argsort(main_errs))
    methods = map(lambda i: methods[i], order)
    errs = map(lambda i: errs[i], order)
    recons = map(lambda i: recons[i], order)
    # plot
    import pylab as P
    P.figure()
    pos1 = plotrange[0]
    pos2 = plotrange[1]
    ims = [btchroma[:, pos1:pos2], (btchroma * mask)[:, pos1:pos2]]
    ims.extend(map(lambda r: r[:, pos1:pos2], recons))
    titles = ['original', 'original masked']
    for imethod, method in enumerate(methods):
        t = method + ':'
        for m in measures:
            t += ' ' + m + '=' + nice_nums(errs[imethod][m])
        titles.append(t)
    plotall(ims, subplot=subplot, title=titles, cmap='gray_r', colorbar=False)
    P.show()
Example #2
0
def plot_oneexample(btchroma,mask,p1,p2,methods=(),methods_args=None,
                    measures=(),subplot=None,plotrange=None,**kwargs):
    """
    Utility function to plot the result of many methods on one example.
    Methods and arguments are the same as when we test on a whole dataset.
    First two plots are always original then original*mask.
    INPUT
      btchroma     - btchroma
      mask         - binary mask, 0 = hidden
      p1           - first masked column
      p2           - last masekdcolumn + 1
      methods      - list of methods (lintrans,random,....)
      bethods_args - list of dictionary containing arguments
      measures     - list of measures, put in title (we sort according to first)
      subplot      - if None, images in one column
      plotrange    - if we dont want to plot everything set to (pos1,pos2)
      kwargs       - extra arguments passed to plotall
    """
    from plottools import plotall
    # inits and sanity checks
    for meas in measures:
        assert meas in ('eucl','kl','cos','dent','lhalf','ddiff','jdiff',
                        'thresh','leven','condent'),'unknown measure: '+meas
    nmethods = len(methods)
    assert nmethods > 0,'we need at least one method...!'
    if methods_args is None:
        methods_args = [{}]*len(methods)
    if subplot is None:
        subplot = (nmethods+2,1)
    assert np.zeros(subplot).size == len(methods)+2,'wrong subplot, forgot original and masked original?'
    if plotrange is None:
        plotrange = (0,btchroma.shape[1])
    # impute
    errs = []
    recons = []
    ########## ALGORITHM DEPENDENT
    for im,method in enumerate(methods):
        if method == 'random':
            recon = IMPUTATION.random_patch(btchroma,mask,p1,p2,**methods_args[im])
        elif method == 'randomfromsong':
            recon = IMPUTATION.random_patch_from_song(btchroma,mask,p1,p2,**methods_args[im])
        elif method == 'average':
            recon = IMPUTATION.average_patch(btchroma,mask,p1,p2,**methods_args[im])
        elif method == 'averageall':
            recon = IMPUTATION.average_patch_all(btchroma,mask,p1,p2,**methods_args[im])
        elif method == 'codebook':
            recon,used_codes = IMPUTATION.codebook_patch(btchroma,mask,p1,p2,**methods_args[im])
        elif method == 'knn_eucl':
            recon,used_cols = IMPUTATION.knn_patch(btchroma,mask,p1,p2,measure='eucl',**methods_args[im])
        elif method == 'knn_kl':
            recon,used_cols = IMPUTATION.knn_patch(btchroma,mask,p1,p2,measure='kl',**methods_args[im])
        elif method == 'lintrans':
            recon,proj = IMPUTATION.lintransform_patch(btchroma,mask,p1,p2,**methods_args[im])
        elif method == 'kalman':
            import kalman_toolbox as KALMAN
            recon = KALMAN.imputation(btchroma,p1,p2,**methods_args[im])
        elif method == 'hmm':
            recon,recon2,hmm = IMPUTATION_HMM.imputation(btchroma*mask,p1,p2,
                                                         **methods_args[im])
        elif method == 'siplca':
            rank = methods_args[im]['rank']
            res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma*mask).copy(),
                                                      rank,mask,
                                                      convergence_thresh=1e-15,
                                                      **methods_args[im])
            W, Z, H, norm, recon, logprob = res
        elif method == 'siplca2':
            rank = methods_args[im]['rank']
            res = IMPUTATION_PLCA.SIPLCA2_mask.analyze((btchroma*mask).copy(),
                                                       rank,mask,
                                                       convergence_thresh=1e-15,
                                                       **methods_args[im])
            W, Z, H, norm, recon, logprob = res
        else:
            print 'unknown method:',method
            return
        # compute errors
        recons.append( recon.copy() )
        errs.append( recon_error(btchroma,mask,recon,'all') )
    # all methods computed
    # now we sort the results according to first measure
    main_errs = map(lambda x: x[measures[0]], errs)
    order = np.array(np.argsort(main_errs))
    methods = map(lambda i: methods[i], order)
    errs = map(lambda i: errs[i], order)
    recons = map(lambda i: recons[i], order)
    # plot
    import pylab as P
    P.figure()
    pos1 = plotrange[0]
    pos2 = plotrange[1]
    ims = [btchroma[:,pos1:pos2],(btchroma*mask)[:,pos1:pos2]]
    ims.extend( map(lambda r: r[:,pos1:pos2], recons) )
    titles = ['original', 'original masked']
    for imethod,method in enumerate(methods):
        t = method + ':'
        for m in measures:
            t += ' ' + m + '=' + nice_nums(errs[imethod][m])
        titles.append(t)
    plotall(ims,subplot=subplot,title=titles,cmap='gray_r',colorbar=False)
    P.show()
Example #3
0
def test_maskedpatch_on_dataset(dataset,
                                method='random',
                                ncols=2,
                                win=1,
                                rank=4,
                                codebook=None,
                                nstates=1,
                                verbose=1,
                                **kwargs):
    """
    Dataset is either dir, or list of files
    General method to test a method on a whole dataset for one masked column
    Methods are:
      - random
      - randomfromsong
      - average
      - averageall
      - codebook
      - knn_eucl
      - knn_kl
      - knn_eucl_delta
      - lintrans
      - kalman
      - hmm
      - siplca
      - siplca2
    Used arguments vary based on the method. For SIPLCA, we can use **kwargs
    to set priors.
    RETURN
      - all errors, as a list of dict
    """
    # get all matfiles
    if type(dataset).__name__ == 'str':
        matfiles = get_all_matfiles(dataset)
    else:
        matfiles = dataset
    # init
    total_cnt = 0
    all_errs = []
    # some specific inits
    if codebook != None and not type(codebook) == type([]):
        codebook = [p.reshape(12, codebook.shape[1] / 12) for p in codebook]
        print 'codebook in ndarray format transformed to list'
    # iterate
    for matfile in matfiles:
        btchroma = sio.loadmat(matfile)['btchroma']
        if len(btchroma.shape) < 2:
            continue
        if btchroma.shape[1] < MINLENGTH or np.isnan(btchroma).any():
            continue
        mask, p1, p2 = MASKING.random_patch_mask(btchroma, ncols=ncols, win=25)
        ########## ALGORITHM DEPENDENT
        if method == 'random':
            recon = IMPUTATION.random_patch(btchroma, mask, p1, p2)
        elif method == 'randomfromsong':
            recon = IMPUTATION.random_patch_from_song(btchroma, mask, p1, p2)
        elif method == 'average':
            recon = IMPUTATION.average_patch(btchroma, mask, p1, p2, win=win)
        elif method == 'averageall':
            recon = IMPUTATION.average_patch_all(btchroma, mask, p1, p2)
        elif method == 'codebook':
            recon, used_codes = IMPUTATION.codebook_patch(
                btchroma, mask, p1, p2, codebook)
        elif method == 'knn_eucl':
            recon, used_cols = IMPUTATION.knn_patch(btchroma,
                                                    mask,
                                                    p1,
                                                    p2,
                                                    win=win,
                                                    measure='eucl')
        elif method == 'knn_kl':
            recon, used_cols = IMPUTATION.knn_patch(btchroma,
                                                    mask,
                                                    p1,
                                                    p2,
                                                    win=win,
                                                    measure='kl')
        elif method == 'knn_eucl_delta':
            recon, used_cols = IMPUTATION.knn_patch_delta(btchroma,
                                                          mask,
                                                          p1,
                                                          p2,
                                                          win=win,
                                                          measure='eucl')
        elif method == 'lintrans':
            recon, proj = IMPUTATION.lintransform_patch(btchroma,
                                                        mask,
                                                        p1,
                                                        p2,
                                                        win=win)
        elif method == 'kalman':
            import kalman_toolbox as KALMAN
            recon = KALMAN.imputation(btchroma,
                                      p1,
                                      p2,
                                      dimstates=nstates,
                                      **kwargs)
        elif method == 'hmm':
            recon, recon2, hmm = IMPUTATION_HMM.imputation(btchroma * mask,
                                                           p1,
                                                           p2,
                                                           nstates=nstates,
                                                           **kwargs)
        elif method == 'siplca':
            res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma * mask).copy(),
                                                      rank,
                                                      mask,
                                                      win=win,
                                                      convergence_thresh=1e-15,
                                                      **kwargs)
            W, Z, H, norm, recon, logprob = res
        elif method == 'siplca2':
            res = IMPUTATION_PLCA.SIPLCA2_mask.analyze(
                (btchroma * mask).copy(),
                rank,
                mask,
                win=win,
                convergence_thresh=1e-15,
                **kwargs)
            W, Z, H, norm, recon, logprob = res
        else:
            print 'unknown method:', method
            return
        ########## ALGORITHM DEPENDENT END
        # measure recon
        errs = recon_error(btchroma, mask, recon, measure='all')
        all_errs.append(errs)
        total_cnt += 1
    # done
    if verbose > 0:
        print 'number of songs tested:', total_cnt
        for meas in np.sort(all_errs[0].keys()):
            errs = map(lambda d: d[meas], all_errs)
            print 'average', meas, '=', np.mean(errs), '(', np.std(errs), ')'
    return all_errs
Example #4
0
def test_maskedpatch_on_dataset(dataset,method='random',ncols=2,win=1,rank=4,codebook=None,nstates=1,verbose=1,**kwargs):
    """
    Dataset is either dir, or list of files
    General method to test a method on a whole dataset for one masked column
    Methods are:
      - random
      - randomfromsong
      - average
      - averageall
      - codebook
      - knn_eucl
      - knn_kl
      - knn_eucl_delta
      - lintrans
      - kalman
      - hmm
      - siplca
      - siplca2
    Used arguments vary based on the method. For SIPLCA, we can use **kwargs
    to set priors.
    RETURN
      - all errors, as a list of dict
    """
    # get all matfiles
    if type(dataset).__name__ == 'str':
        matfiles = get_all_matfiles(dataset)
    else:
        matfiles = dataset
    # init
    total_cnt = 0
    all_errs = []
    # some specific inits
    if codebook != None and not type(codebook) == type([]):
        codebook = [p.reshape(12,codebook.shape[1]/12) for p in codebook]
        print 'codebook in ndarray format transformed to list'
    # iterate
    for matfile in matfiles:
        btchroma = sio.loadmat(matfile)['btchroma']
        if len(btchroma.shape) < 2:
            continue
        if btchroma.shape[1] < MINLENGTH or np.isnan(btchroma).any():
            continue
        mask,p1,p2 = MASKING.random_patch_mask(btchroma,ncols=ncols,win=25)
        ########## ALGORITHM DEPENDENT
        if method == 'random':
            recon = IMPUTATION.random_patch(btchroma,mask,p1,p2)
        elif method == 'randomfromsong':
            recon = IMPUTATION.random_patch_from_song(btchroma,mask,p1,p2)
        elif method == 'average':
            recon = IMPUTATION.average_patch(btchroma,mask,p1,p2,win=win)
        elif method == 'averageall':
            recon = IMPUTATION.average_patch_all(btchroma,mask,p1,p2)
        elif method == 'codebook':
            recon,used_codes = IMPUTATION.codebook_patch(btchroma,mask,p1,p2,codebook)
        elif method == 'knn_eucl':
            recon,used_cols = IMPUTATION.knn_patch(btchroma,mask,p1,p2,win=win,measure='eucl')
        elif method == 'knn_kl':
            recon,used_cols = IMPUTATION.knn_patch(btchroma,mask,p1,p2,win=win,measure='kl')
        elif method == 'knn_eucl_delta':
            recon,used_cols = IMPUTATION.knn_patch_delta(btchroma,mask,p1,p2,win=win,measure='eucl')
        elif method == 'lintrans':
            recon,proj = IMPUTATION.lintransform_patch(btchroma,mask,p1,p2,win=win)
        elif method == 'kalman':
            import kalman_toolbox as KALMAN
            recon = KALMAN.imputation(btchroma,p1,p2,
                                      dimstates=nstates,**kwargs)
        elif method == 'hmm':
            recon,recon2,hmm = IMPUTATION_HMM.imputation(btchroma*mask,p1,p2,
                                                         nstates=nstates,
                                                         **kwargs)
        elif method == 'siplca':
            res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma*mask).copy(),
                                                      rank,mask,win=win,
                                                      convergence_thresh=1e-15,
                                                      **kwargs)
            W, Z, H, norm, recon, logprob = res
        elif method == 'siplca2':
            res = IMPUTATION_PLCA.SIPLCA2_mask.analyze((btchroma*mask).copy(),
                                                       rank,mask,win=win,
                                                       convergence_thresh=1e-15,
                                                       **kwargs)
            W, Z, H, norm, recon, logprob = res
        else:
            print 'unknown method:',method
            return
        ########## ALGORITHM DEPENDENT END
        # measure recon
        errs = recon_error(btchroma,mask,recon,measure='all')
        all_errs.append( errs )
        total_cnt += 1
    # done
    if verbose > 0:
        print 'number of songs tested:',total_cnt
        for meas in np.sort(all_errs[0].keys()):
            errs = map(lambda d: d[meas], all_errs)
            print 'average',meas,'=',np.mean(errs),'(',np.std(errs),')'
    return all_errs