def nice_nums(val):
return evaluation.nice_nums(val)
def errs_to_str(errs):
s = 'eucl. = ' + nice_nums(errs['eucl'])
s += ', delta diff. = ' + nice_nums(errs['ddiff'])
s += ', D-ENT = ' + nice_nums(errs['dent'])
return s
# load btchroma, create mask
btchroma = sio.loadmat('/home/thierry/Columbia/covers80/coversongs/covers32kENmats/john_lennon+Double_Fantasy+05-I_m_Losing_You.mp3.mat')['btchroma']
p1=185;p2=p1+15;mask=np.ones(btchroma.shape);mask[:,p1:p2]=0.
# method: lintrans
recon_lintrans,tmp = IMPUTATION.lintransform_patch(btchroma,mask,p1,p2,win=1)
err_lintrans = evaluation.recon_error(btchroma,mask,recon_lintrans)
# method: random
recon_rand = IMPUTATION.random_patch(btchroma,mask,p1,p2)
err_rand = evaluation.recon_error(btchroma,mask,recon_rand)
# method: average
recon_avg = IMPUTATION.average_patch(btchroma,mask,p1,p2,win=1)
err_avg = evaluation.recon_error(btchroma,mask,recon_avg)
# method: NN
recon_nn,tmp = IMPUTATION.knn_patch(btchroma,mask,p1,p2,win=8,measure='eucl')
err_nn = evaluation.recon_error(btchroma,mask,recon_nn)
# method: SIPLCA
recon_siplca = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma*mask).copy(),
50,mask,win=40,
convergence_thresh=1e-15)[4]
err_siplca = evaluation.recon_error(btchroma,mask,recon_siplca)
# load btchroma, create mask
btchroma = sio.loadmat(
'/home/thierry/Columbia/covers80/coversongs/covers32kENmats/john_lennon+Double_Fantasy+05-I_m_Losing_You.mp3.mat'
)['btchroma']
p1 = 185
p2 = p1 + 15
mask = np.ones(btchroma.shape)
mask[:, p1:p2] = 0.
# method: lintrans
recon_lintrans, tmp = IMPUTATION.lintransform_patch(btchroma,
mask,
p1,
p2,
win=1)
err_lintrans = evaluation.recon_error(btchroma, mask, recon_lintrans)
# method: random
recon_rand = IMPUTATION.random_patch(btchroma, mask, p1, p2)
err_rand = evaluation.recon_error(btchroma, mask, recon_rand)
# method: average
recon_avg = IMPUTATION.average_patch(btchroma, mask, p1, p2, win=1)
err_avg = evaluation.recon_error(btchroma, mask, recon_avg)
# method: NN
recon_nn, tmp = IMPUTATION.knn_patch(btchroma,
mask,
p1,
p2,
win=8,
measure='eucl')
err_nn = evaluation.recon_error(btchroma, mask, recon_nn)
# method: SIPLCA
# go
res = learn_kalman_missing_patch(btchroma*mask,p1,p2,A,C,Q,R,initx,initV,
niter,diagQ=1,diagR=1)
recon = res[0]
recon[:,:p1] = btchroma[:,:p1]
recon[:,p2:] = btchroma[:,p2:]
assert not np.isnan(recon).any(),'recon has NaN'
assert not np.isinf(recon).any(),'recon has inf'
recon[np.where(recon>1.)] = 1.
recon[np.where(recon<0.)] = 0.
# analysis
print 'recon shape:',recon.shape
div_eucl = evaluation.recon_error(btchroma,mask,recon,measure='eucl')
div_kl = evaluation.recon_error(btchroma,mask,recon,measure='kl')
print 'eucl div:',div_eucl
print 'kl div:',div_kl
# comparison
import imputation as IMPUTATION
print 'average, win=3'
recon = IMPUTATION.average_patch(btchroma,mask,p1,p2,win=3)
div_eucl = evaluation.recon_error(btchroma,mask,recon,measure='eucl')
div_kl = evaluation.recon_error(btchroma,mask,recon,measure='kl')
print 'eucl div:',div_eucl
print 'kl div:',div_kl
print 'kkn eucl:'
recon,used_cols = IMPUTATION.knn_patch(btchroma,mask,p1,p2,win=7,measure='eucl')
div_eucl = evaluation.recon_error(btchroma,mask,recon,measure='eucl')
def compare_all(btchroma, mask, masked_cols, codebook=None):
"""
Compare all the algorithms we have so far.
A lot of parameters hard-coded, but...
It will get improved.
We display:
1) original beat chromagram
2) masked beat chromagram
3) imputation by random
4) imputation by averaging nearby columns
5) imputation by knn on the rest of the song
6) imputation by linear prediction
7) imputation by NMF
8) imputation by codebook, if provided
To init mask and masked_cols, something like:
mask,masked_cols = masking.random_col_mask(btchroma,ncols=1,win=30)
"""
# for displaying purposes, display window size 50
pos1 = masked_cols[0] - 5
pos2 = masked_cols[0] + 5
allimages = []
titles = []
xlabels = []
# original beat chroma and masked one
im1 = btchroma[:, pos1:pos2].copy()
im2 = (btchroma * mask)[:, pos1:pos2].copy()
allimages.append(im1)
titles.append('original beat chroma')
xlabels.append('')
allimages.append(im2)
titles.append('masked beat chroma')
xlabels.append('')
# 3) random
recon = IMPUTATION.random_col(btchroma, mask, masked_cols)
im3_err = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im3 = recon[:, pos1:pos2].copy()
allimages.append(im3)
titles.append('random')
xlabels.append('err=' + str(im3_err))
# 4) average nearby columns
recon = IMPUTATION.average_col(btchroma, mask, masked_cols, win=3)
im4_err = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im4 = recon[:, pos1:pos2].copy()
allimages.append(im4)
titles.append('average 2 nearby cols')
xlabels.append('err=' + str(im4_err))
# 5) knn
recon, used_cols = IMPUTATION.eucldist_cols(btchroma,
mask,
masked_cols,
win=7)
im5_err = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im5 = recon[:, pos1:pos2].copy()
allimages.append(im5)
titles.append('knn for the whole song')
xlabels.append('err=' + str(im5_err))
# 6) linear prediction
recon, proj = IMPUTATION.lintransform_cols(btchroma,
mask,
masked_cols,
win=1)
im6_err = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im6 = recon[:, pos1:pos2].copy()
allimages.append(im6)
titles.append('linear prediction (1 col)')
xlabels.append('err=' + str(im6_err))
# 7) SIPLCA
res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma * mask).copy(),
4,
mask,
win=5)
W, Z, H, norm, recon, logprob = res
im7_err = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im7 = recon[:, pos1:pos2].copy()
allimages.append(im7)
titles.append('SIPLCA, rank=4, win=5')
xlabels.append('err=' + str(im7_err))
# 7) SIPLCA 2
res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma * mask).copy(),
25,
mask,
win=10)
W, Z, H, norm, recon, logprob = res
im7_err_bis = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im7_bis = recon[:, pos1:pos2].copy()
allimages.append(im7_bis)
titles.append('SIPLCA, rank=25, win=10')
xlabels.append('err=' + str(im7_err_bis))
# 8) codebook
if codebook != None:
cb = [p.reshape(12, p.size / 12) for p in codebook]
recon, used_codes = IMPUTATION.codebook_cols(btchroma, mask,
masked_cols, cb)
im8_err = EVAL.recon_error(btchroma, mask, recon, measure='eucl')
im8 = recon[:, pos1:pos2].copy()
allimages.append(im8)
titles.append('codebook, ' + str(codebook.shape[0]) +
' codes of length ' + str(codebook.shape[1] / 12))
xlabels.append('err=' + str(im8_err))
# ALL IMAGES CREATED
fig = plt.figure()
fig.subplots_adjust(hspace=0.4)
blackbarsfun = lambda: plt.gca().axvline(
linewidth=2, color='0.', x=4.5) and plt.gca().axvline(
linewidth=2, color='0.', x=5.5)
# plotall
plotall(allimages,
subplot=(3, 3),
cmap='gray_r',
title=titles,
xlabel=xlabels,
axvlines=blackbarsfun,
colorbar=False)
def compare_all(btchroma,mask,masked_cols,codebook=None):
"""
Compare all the algorithms we have so far.
A lot of parameters hard-coded, but...
It will get improved.
We display:
1) original beat chromagram
2) masked beat chromagram
3) imputation by random
4) imputation by averaging nearby columns
5) imputation by knn on the rest of the song
6) imputation by linear prediction
7) imputation by NMF
8) imputation by codebook, if provided
To init mask and masked_cols, something like:
mask,masked_cols = masking.random_col_mask(btchroma,ncols=1,win=30)
"""
# for displaying purposes, display window size 50
pos1 = masked_cols[0] - 5
pos2 = masked_cols[0] + 5
allimages = []
titles = []
xlabels = []
# original beat chroma and masked one
im1 = btchroma[:,pos1:pos2].copy()
im2 = (btchroma * mask)[:,pos1:pos2].copy()
allimages.append(im1)
titles.append('original beat chroma')
xlabels.append('')
allimages.append(im2)
titles.append('masked beat chroma')
xlabels.append('')
# 3) random
recon = IMPUTATION.random_col(btchroma,mask,masked_cols)
im3_err = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im3 = recon[:,pos1:pos2].copy()
allimages.append(im3)
titles.append('random')
xlabels.append('err='+str(im3_err))
# 4) average nearby columns
recon = IMPUTATION.average_col(btchroma,mask,masked_cols,win=3)
im4_err = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im4 = recon[:,pos1:pos2].copy()
allimages.append(im4)
titles.append('average 2 nearby cols')
xlabels.append('err='+str(im4_err))
# 5) knn
recon,used_cols = IMPUTATION.eucldist_cols(btchroma,mask,masked_cols,win=7)
im5_err = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im5 = recon[:,pos1:pos2].copy()
allimages.append(im5)
titles.append('knn for the whole song')
xlabels.append('err='+str(im5_err))
# 6) linear prediction
recon,proj = IMPUTATION.lintransform_cols(btchroma,mask,masked_cols,win=1)
im6_err = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im6 = recon[:,pos1:pos2].copy()
allimages.append(im6)
titles.append('linear prediction (1 col)')
xlabels.append('err='+str(im6_err))
# 7) SIPLCA
res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma*mask).copy(),
4,mask,win=5)
W, Z, H, norm, recon, logprob = res
im7_err = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im7 = recon[:,pos1:pos2].copy()
allimages.append(im7)
titles.append('SIPLCA, rank=4, win=5')
xlabels.append('err='+str(im7_err))
# 7) SIPLCA 2
res = IMPUTATION_PLCA.SIPLCA_mask.analyze((btchroma*mask).copy(),
25,mask,win=10)
W, Z, H, norm, recon, logprob = res
im7_err_bis = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im7_bis = recon[:,pos1:pos2].copy()
allimages.append(im7_bis)
titles.append('SIPLCA, rank=25, win=10')
xlabels.append('err='+str(im7_err_bis))
# 8) codebook
if codebook != None:
cb = [p.reshape(12,p.size/12) for p in codebook]
recon,used_codes = IMPUTATION.codebook_cols(btchroma,mask,masked_cols,cb)
im8_err = EVAL.recon_error(btchroma,mask,recon,measure='eucl')
im8 = recon[:,pos1:pos2].copy()
allimages.append(im8)
titles.append('codebook, '+str(codebook.shape[0])+' codes of length '+str(codebook.shape[1]/12))
xlabels.append('err='+str(im8_err))
# ALL IMAGES CREATED
fig = plt.figure()
fig.subplots_adjust(hspace=0.4)
blackbarsfun = lambda: plt.gca().axvline(linewidth=2,color='0.',x=4.5) and plt.gca().axvline(linewidth=2,color='0.',x=5.5)
# plotall
plotall(allimages,subplot=(3,3),cmap='gray_r',title=titles,xlabel=xlabels,axvlines=blackbarsfun,colorbar=False)
originals = []
recons = []
texts = []
def nice_nums(val):
return evaluation.nice_nums(val)
# EASY RECONSTRUCTION
bt = sio.loadmat('/home/thierry/Columbia/covers80/coversongs/covers32kENmats/beatles+Revolver+14-Tomorrow_Never_Knows.mp3.mat')['btchroma']
mask,p1,p2 = masking.random_patch_mask(bt,ncols=15,win=25)
recon,tmp = IMPUTATION.lintransform_patch(bt,mask,p1,p2,win=1)
errs = evaluation.recon_error(bt,mask,recon)
s = 'method: linear transform\n'
s += 'eucl = ' + nice_nums(errs['eucl']) + '\n'
s += 'd1/2 = ' + nice_nums(errs['lhalf']) + '\n'
s += 'cond. ent. = ' + nice_nums(errs['condent']) + '\n'
s += 'delta diff. = ' + nice_nums(errs['ddiff']) + '\n'
s += 'D-ENT = ' + nice_nums(errs['dent']) + '\n'
s += 'Jensen diff. = ' + nice_nums(errs['jdiff']) + '\n'
originals.append( bt[:,p1:p2].copy() )
recons.append( recon[:,p1:p2].copy() )
texts.append(s)
# SMOOTH RECONSTRUCTION
bt = sio.loadmat('/home/thierry/Columbia/covers80/coversongs/covers32kENmats/metallica+Live_Shit_Binge_And_Purge_Disc_3_+09-Stone_Cold_Crazy.mp3.mat')['btchroma']
mask,p1,p2 = masking.random_patch_mask(bt,ncols=15,win=25)
initx,
initV,
niter,
diagQ=1,
diagR=1)
recon = res[0]
recon[:, :p1] = btchroma[:, :p1]
recon[:, p2:] = btchroma[:, p2:]
assert not np.isnan(recon).any(), 'recon has NaN'
assert not np.isinf(recon).any(), 'recon has inf'
recon[np.where(recon > 1.)] = 1.
recon[np.where(recon < 0.)] = 0.
# analysis
print 'recon shape:', recon.shape
div_eucl = evaluation.recon_error(btchroma, mask, recon, measure='eucl')
div_kl = evaluation.recon_error(btchroma, mask, recon, measure='kl')
print 'eucl div:', div_eucl
print 'kl div:', div_kl
# comparison
import imputation as IMPUTATION
print 'average, win=3'
recon = IMPUTATION.average_patch(btchroma, mask, p1, p2, win=3)
div_eucl = evaluation.recon_error(btchroma, mask, recon, measure='eucl')
div_kl = evaluation.recon_error(btchroma, mask, recon, measure='kl')
print 'eucl div:', div_eucl
print 'kl div:', div_kl
print 'kkn eucl:'
recon, used_cols = IMPUTATION.knn_patch(btchroma,
mask,
