def plot(cls, V, W, Z, H, curriter=-1):
WZH = cls.reconstruct(W, Z, H)
plottools.plotall([V, WZH], subplot=(3, 1), align='xy', cmap=plt.cm.hot)
plottools.plotall(
9 * [None] + [W, Z, H],
subplot=(4, 3),
clf=False,
align='',
cmap=plt.cm.hot,
colorbar=False)
plt.draw()
def plot(cls, V, W, Z, H, curriter=-1):
WZH = cls.reconstruct(W, Z, H)
plottools.plotall([V, WZH],
subplot=(3, 1),
align='xy',
cmap=plt.cm.hot)
plottools.plotall(9 * [None] + [W, Z, H],
subplot=(4, 3),
clf=False,
align='',
cmap=plt.cm.hot,
colorbar=False)
plt.draw()
def plot(self, V, W, Z, H, curriter=-1):
rank = len(Z)
nrows = rank + 2
WZH = self.reconstruct(W, Z, H, circular=self.circular)
plottools.plotall([V, WZH] + [self.reconstruct(W[:,z,:], Z[z], H[z,:],
circular=self.circular)
for z in xrange(len(Z))],
title=['V (Iteration %d)' % curriter,
'Reconstruction'] +
['Basis %d reconstruction' % x
for x in xrange(len(Z))],
colorbar=False, grid=False, cmap=plt.cm.hot,
subplot=(nrows, 2), order='c', align='xy')
plottools.plotall([None] + [Z], subplot=(nrows, 2), clf=False,
plotfun=lambda x: plt.bar(np.arange(len(x)) - 0.4, x),
xticks=[[], range(rank)], grid=False,
colorbar=False, title='Z')
plots = [None] * (3*nrows + 2)
titles = plots + ['W%d' % x for x in range(rank)]
wxticks = [[]] * (3*nrows + rank + 1) + [range(0, W.shape[2], 10)]
plots.extend(W.transpose((1, 0, 2)))
plottools.plotall(plots, subplot=(nrows, 6), clf=False, order='c',
align='xy', cmap=plt.cm.hot, colorbar=False,
ylabel=r'$\parallel$', grid=False,
title=titles, yticks=[[]], xticks=wxticks)
plots = [None] * (2*nrows + 2)
titles=plots + ['H%d' % x for x in range(rank)]
if np.squeeze(H).ndim < 4:
plotH = np.squeeze(H)
else:
plotH = H.sum(2)
if rank == 1:
plotH = [plotH]
plots.extend(plotH)
plottools.plotall(plots, subplot=(nrows, 3), order='c', align='xy',
grid=False, clf=False, title=titles, yticks=[[]],
colorbar=False, cmap=plt.cm.hot, ylabel=r'$*$',
xticks=[[]]*(3*nrows-1) + [range(0, V.shape[1], 100)])
plt.draw()
if __name__ == '__main__':
btchroma145 = sio.loadmat(
'/home/thierry/Columbia/covers80/coversongs/covers32k/Caroline_No/beach_boys+Pet_Sounds+13-Caroline_No.mp3.mat'
)['btchroma']
mask, masked_cols = masking.random_col_mask(btchroma145, ncols=1, win=30)
recon, lt = imputation.lintransform_cols(btchroma145,
mask,
masked_cols,
win=1)
pos1 = masked_cols[0] - 7
pos2 = masked_cols[0] + 7
im1 = btchroma145[:, pos1:pos2].copy()
im2 = (btchroma145 * mask)[:, pos1:pos2].copy()
im3 = recon[:, pos1:pos2].copy()
# plot all this
fig = P.figure()
fig.subplots_adjust(hspace=0.4)
blackbarsfun = lambda: P.gca().axvline(linewidth=2, color='0.', x=6.5
) and P.gca().axvline(
linewidth=2, color='0.', x=7.5)
plotall([im1, im2, im3],
subplot=(3, 1),
cmap='gray_r',
title=['original', 'original masked', 'reconstruction'],
axvlines=blackbarsfun,
colorbar=False,
xticks=[()] * 3)
P.show()
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 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()
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()
import numpy as np
import scipy.io as sio
from plottools import plotall
import pylab as P
sys.path.append('../PythonSrc')
import evaluation
import masking
import imputation
if __name__ == '__main__':
btchroma145 = sio.loadmat('/home/thierry/Columbia/covers80/coversongs/covers32k/Caroline_No/beach_boys+Pet_Sounds+13-Caroline_No.mp3.mat')['btchroma']
mask,masked_cols = masking.random_col_mask(btchroma145,ncols=1,win=30)
recon,lt = imputation.lintransform_cols(btchroma145,mask,masked_cols,win=1)
pos1 = masked_cols[0] - 7
pos2 = masked_cols[0] + 7
im1 = btchroma145[:,pos1:pos2].copy()
im2 = (btchroma145 * mask)[:,pos1:pos2].copy()
im3 = recon[:,pos1:pos2].copy()
# plot all this
fig = P.figure()
fig.subplots_adjust(hspace=0.4)
blackbarsfun = lambda: P.gca().axvline(linewidth=2,color='0.',x=6.5) and P.gca().axvline(linewidth=2,color='0.',x=7.5)
plotall([im1,im2,im3],subplot=(3,1),cmap='gray_r',
title=['original','original masked','reconstruction'],
axvlines=blackbarsfun,colorbar=False,xticks=[()]*3)
P.show()
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)
