# Genre recognition: experiment¶

Goal: observe the effect of $\lambda_g$.

Conclusion: A value of $\lambda_s$ between 1 and 10 seems reasonable (when $\lambda_g = \lambda_d = 100$).

Observation:

• Too sparse is very slow (17h) and gives poor accuracy.
• Sparsity is proportional to $\lambda_s$.
• Increasing sparsity correlates with decreasing Dirichlet energy, which makes sense as the energy of the sparse codes diminishes. It trades with reconstruction error.

## Hyper-parameters¶

### Parameter under test¶

In [1]:
Pname = 'ls'
Pvalues = [0.1, 1, 10, 100, 1e3]

# Regenerate the graph or the features at each iteration.
regen_graph = False
regen_features = True


### Model parameters¶

In [2]:
p = {}

# Preprocessing.

# Graph.
p['K'] = 10 + 1  # 5 to 10 + 1 for self-reference
p['dm'] = 'cosine'
p['Csigma'] = 1
p['diag'] = True
p['laplacian'] = 'normalized'

# Feature extraction.
p['m'] = 128  # 64, 128, 512
p['ls'] = 1
p['ld'] = 100
p['le'] = None
p['lg'] = 100

# Classification.
p['scale'] = None
p['Nvectors'] = 6
p['svm_type'] = 'C'
p['kernel'] = 'linear'
p['C'] = 1
p['nu'] = 0.5


### Numerical parameters¶

In [3]:
# Dataset (10,100,644 | 5,100,149 | 2,10,644).
p['Ngenres'] = 5
p['Nclips'] = 100
p['Nframes'] = 149

# Graph.
p['tol'] = 1e-5

# Feature extraction.
p['rtol'] = 1e-6  # 1e-3, 1e-5, 1e-7
p['N_inner'] = 500
p['N_outer'] = 50

# Classification.
p['Nfolds'] = 10
p['Ncv'] = 20
p['dataset_classification'] = 'Z'


## Processing¶

In [4]:
import numpy as np
import time

texperiment = time.time()

# Result dictionary.
res = ['accuracy', 'accuracy_std']
res += ['sparsity', 'atoms']
res += ['objective_g', 'objective_h', 'objective_i', 'objective_j']
res += ['time_features', 'iterations_inner', 'iterations_outer']
res = dict.fromkeys(res)
for key in res.keys():
res[key] = []

def separator(name, parameter=False):
if parameter:
name += ', {} = {}'.format(Pname, p[Pname])
dashes = 20 * '-'
print('\n {} {} {} \n'.format(dashes, name, dashes))
# Fair comparison when tuning parameters.
# Randomnesses: dictionary initialization, training and testing sets.
np.random.seed(1)

In [5]:
#%run gtzan.ipynb
#%run audio_preprocessing.ipynb
if not regen_graph:
separator('Graph')
%run audio_graph.ipynb
if not regen_features:
separator('Features')
%run audio_features.ipynb

# Hyper-parameter under test.
for n_exp, p[Pname] in enumerate(Pvalues):

if regen_graph:
separator('Graph', True)
%run audio_graph.ipynb
if regen_features:
separator('Features', True)
%run audio_features.ipynb
separator('Classification', True)
%run audio_classification.ipynb

# Collect results.
for key in res:
res[key].append(globals()[key])

# Baseline, i.e. classification with spectrograms.
p['dataset_classification'] = 'X'
p['scale'] = 'minmax'  # Todo: should be done in pre-processing.
if not regen_graph and not regen_features:
# Classifier parameters are being tested.
for p[Pname] in Pvalues:
separator('Baseline', True)
%run audio_classification.ipynb
else:
separator('Baseline')
%run audio_classification.ipynb

 -------------------- Graph --------------------

Data: (149000, 96), float32
Elapsed time: 153.62 seconds
All self-referenced in the first column: True
dist in [0.0, 0.491051614285]
w in [0.0378095172346, 1.0]
Ones on the diagonal: 149000 (over 149000)
assert: True
W in [0.0, 1.0]
Datasets:
L_data    : (2396026,), float32
L_indices : (2396026,), int32
L_indptr  : (149001,) , int32
L_shape   : (2,)      , int64
W_data    : (2396026,), float32
W_indices : (2396026,), int32
W_indptr  : (149001,) , int32
W_shape   : (2,)      , int64
Attributes:
K = 11
dm = cosine
Csigma = 1
diag = True
laplacian = normalized
Overall time: 162.83 seconds

-------------------- Features, ls = 0.1 --------------------

Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
Xa: (10, 100, 644, 2, 1024) , float32
Xs: (10, 100, 644, 2, 96)   , float32
Full dataset:
size: N=1,288,000 x n=96 -> 123,648,000 floats
dim: 123,648 features per clip
shape: (10, 100, 644, 2, 96)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<type 'numpy.ndarray'>
Data: (149000, 96), float32
Attributes:
K = 11
dm = cosine
Csigma = 1
diag = True
laplacian = normalized
Datasets:
L_data    : (2396026,), float32
L_indices : (2396026,), int32
L_indptr  : (149001,) , int32
L_shape   : (2,)      , int64
W_data    : (2396026,), float32
W_indices : (2396026,), int32
W_indptr  : (149001,) , int32
W_shape   : (2,)      , int64
Size X: 13.6 M --> 54.6 MiB
Size Z: 18.2 M --> 72.8 MiB
Size D: 12.0 k --> 48.0 kiB
Size E: 12.0 k --> 48.0 kiB
Elapsed time: 9701 seconds

Inner loop: 5341 iterations
g(Z) = ||X-DZ||_2^2 = 4.705818e+04
rdiff: 0.00038527825897
i(Z) = ||Z||_1 = 3.500366e+04
j(Z) = tr(Z^TLZ) = 1.133580e+05

Global objective: 1.954199e+05

Outer loop: 50 iterations

Z in [-0.336860924959, 0.360780477524]
Sparsity of Z: 18,052,225 non-zero entries out of 19,072,000 entries, i.e. 94.7%.

D in [-0.33844050765, 0.390165150166]
d in [0.999999761581, 1.00000011921]
Constraints on D: True

Datasets:
D : (128, 96)             , float32
X : (5, 100, 149, 2, 96)  , float32
Z : (5, 100, 149, 2, 128) , float32
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Overall time: 9708 seconds

-------------------- Classification, ls = 0.1 --------------------

Software versions:
numpy: 1.8.2
sklearn: 0.14.1
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
D : (128, 96)               , float32
X : (5, 100, 149, 2, 96)    , float32
Z : (5, 100, 149, 2, 128)   , float32
Full dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<type 'numpy.ndarray'>
Flattened frames:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 298, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Feature vectors:
size: N=6,000 x n=128 -> 768,000 floats
dim: 1,536 features per clip
shape: (5, 100, 6, 2, 128)

5 genres: blues, classical, country, disco, hiphop
Training data: (3600, 128), float64
Testing data: (2400, 128), float64
Training labels: (3600,), uint8
Testing labels: (2400,), uint8
Accuracy: 68.5 %
5 genres: blues, classical, country, disco, hiphop
Training data: (300, 1536), float64
Testing data: (200, 1536), float64
Training labels: (300,), uint8
Testing labels: (200,), uint8
Feature vectors accuracy: 55.5 %
Clips accuracy: 61.5 %
5 genres: blues, classical, country, disco, hiphop
Data: (500, 1536), float64
Labels: (500,), uint8
69 (+/- 8.3) <- [62 76 72 60 70 82 62 54 76 72]
65 (+/- 4.7) <- [62 68 57 57 64 70 72 68 64 70]
68 (+/- 2.5) <- [64 72 72 70 68 66 66 68 70 68]
70 (+/- 4.0) <- [64 74 66 74 68 66 74 72 76 70]
68 (+/- 5.9) <- [62 68 64 74 68 78 74 57 70 64]
67 (+/- 7.1) <- [62 82 62 57 74 64 66 62 74 64]
69 (+/- 5.8) <- [56 70 64 72 68 74 68 74 78 70]
68 (+/- 4.7) <- [74 60 70 70 74 62 62 66 68 70]
67 (+/- 4.9) <- [76 60 66 68 66 70 62 66 60 72]
66 (+/- 4.7) <- [70 60 70 62 74 62 72 64 62 66]
67 (+/- 6.1) <- [66 62 68 76 78 68 70 60 57 64]
67 (+/- 7.0) <- [68 54 66 80 66 64 64 68 76 60]
69 (+/- 7.4) <- [84 62 76 76 66 68 60 66 60 68]
70 (+/- 6.9) <- [66 72 84 66 72 76 64 68 74 57]
67 (+/- 6.2) <- [70 56 74 57 66 74 62 72 66 72]
68 (+/- 7.1) <- [60 82 68 60 64 68 74 62 78 68]
68 (+/- 3.6) <- [66 70 66 62 66 76 68 70 66 70]
69 (+/- 4.7) <- [68 68 72 64 72 76 72 64 62 76]
68 (+/- 6.6) <- [60 72 68 64 78 62 76 57 74 66]
66 (+/- 5.4) <- [57 60 62 68 76 70 60 68 70 68]
Accuracy: 67.8 (+/- 6.01)
Mean time (20 cv): 35.68 seconds
Overall time: 719.18 seconds

-------------------- Features, ls = 1 --------------------

The memory_profiler extension is already loaded. To reload it, use:
The line_profiler extension is already loaded. To reload it, use:
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
Xa: (10, 100, 644, 2, 1024) , float32
Xs: (10, 100, 644, 2, 96)   , float32
Full dataset:
size: N=1,288,000 x n=96 -> 123,648,000 floats
dim: 123,648 features per clip
shape: (10, 100, 644, 2, 96)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<type 'numpy.ndarray'>
Data: (149000, 96), float32
Attributes:
K = 11
dm = cosine
Csigma = 1
diag = True
laplacian = normalized
Datasets:
L_data    : (2396026,), float32
L_indices : (2396026,), int32
L_indptr  : (149001,) , int32
L_shape   : (2,)      , int64
W_data    : (2396026,), float32
W_indices : (2396026,), int32
W_indptr  : (149001,) , int32
W_shape   : (2,)      , int64
Size X: 13.6 M --> 54.6 MiB
Size Z: 18.2 M --> 72.8 MiB
Size D: 12.0 k --> 48.0 kiB
Size E: 12.0 k --> 48.0 kiB
Elapsed time: 12253 seconds

Inner loop: 6914 iterations
g(Z) = ||X-DZ||_2^2 = 8.763252e+04
rdiff: 0.0028535187222
i(Z) = ||Z||_1 = 1.608560e+05
j(Z) = tr(Z^TLZ) = 1.016077e+05

Global objective: 3.500962e+05

Outer loop: 50 iterations

Z in [-0.93200981617, 0.979693233967]
Sparsity of Z: 7,669,812 non-zero entries out of 19,072,000 entries, i.e. 40.2%.

D in [-0.967727899551, 0.986068367958]
d in [0.999999642372, 1.00000035763]
Constraints on D: True

Datasets:
D : (128, 96)             , float32
X : (5, 100, 149, 2, 96)  , float32
Z : (5, 100, 149, 2, 128) , float32
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Overall time: 12262 seconds

-------------------- Classification, ls = 1 --------------------

Software versions:
numpy: 1.8.2
sklearn: 0.14.1
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
D : (128, 96)               , float32
X : (5, 100, 149, 2, 96)    , float32
Z : (5, 100, 149, 2, 128)   , float32
Full dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<type 'numpy.ndarray'>
Flattened frames:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 298, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Feature vectors:
size: N=6,000 x n=128 -> 768,000 floats
dim: 1,536 features per clip
shape: (5, 100, 6, 2, 128)

5 genres: blues, classical, country, disco, hiphop
Training data: (3600, 128), float64
Testing data: (2400, 128), float64
Training labels: (3600,), uint8
Testing labels: (2400,), uint8
Accuracy: 76.7 %
5 genres: blues, classical, country, disco, hiphop
Training data: (300, 1536), float64
Testing data: (200, 1536), float64
Training labels: (300,), uint8
Testing labels: (200,), uint8
Feature vectors accuracy: 65.0 %
Clips accuracy: 77.0 %
5 genres: blues, classical, country, disco, hiphop
Data: (500, 1536), float64
Labels: (500,), uint8
73 (+/- 5.2) <- [64 74 76 76 68 82 70 66 76 74]
73 (+/- 8.6) <- [68 72 74 50 82 80 74 80 74 76]
73 (+/- 6.4) <- [70 78 82 66 78 64 64 78 78 70]
72 (+/- 6.3) <- [68 78 74 64 76 76 72 72 82 60]
72 (+/- 5.6) <- [66 70 80 70 76 76 74 62 80 70]
73 (+/- 4.6) <- [76 80 74 78 74 76 64 70 68 72]
72 (+/- 6.2) <- [60 80 66 76 82 72 68 74 70 74]
74 (+/- 4.5) <- [72 72 80 76 82 74 74 70 66 70]
73 (+/- 6.8) <- [76 74 68 72 88 64 70 80 70 66]
73 (+/- 5.8) <- [78 62 82 72 70 76 74 72 64 76]
72 (+/- 5.7) <- [72 78 70 80 78 68 78 62 66 72]
72 (+/- 5.0) <- [66 66 74 80 76 70 68 74 80 70]
75 (+/- 5.3) <- [76 74 72 80 80 72 66 80 66 80]
74 (+/- 5.0) <- [70 76 76 76 86 70 70 76 72 68]
73 (+/- 7.5) <- [78 68 82 56 82 72 74 74 66 76]
74 (+/- 5.7) <- [72 82 76 76 74 74 80 72 78 60]
73 (+/- 4.8) <- [70 82 72 72 72 80 74 72 64 76]
73 (+/- 5.1) <- [66 78 82 68 70 74 80 70 70 74]
74 (+/- 4.6) <- [68 74 74 78 80 76 76 68 78 66]
73 (+/- 6.8) <- [78 80 68 82 74 80 62 64 72 68]
Accuracy: 73.1 (+/- 5.91)
Mean time (20 cv): 34.25 seconds
Overall time: 690.40 seconds

-------------------- Features, ls = 10 --------------------

The memory_profiler extension is already loaded. To reload it, use:
The line_profiler extension is already loaded. To reload it, use:
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
Xa: (10, 100, 644, 2, 1024) , float32
Xs: (10, 100, 644, 2, 96)   , float32
Full dataset:
size: N=1,288,000 x n=96 -> 123,648,000 floats
dim: 123,648 features per clip
shape: (10, 100, 644, 2, 96)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<type 'numpy.ndarray'>
Data: (149000, 96), float32
Attributes:
K = 11
dm = cosine
Csigma = 1
diag = True
laplacian = normalized
Datasets:
L_data    : (2396026,), float32
L_indices : (2396026,), int32
L_indptr  : (149001,) , int32
L_shape   : (2,)      , int64
W_data    : (2396026,), float32
W_indices : (2396026,), int32
W_indptr  : (149001,) , int32
W_shape   : (2,)      , int64
Size X: 13.6 M --> 54.6 MiB
Size Z: 18.2 M --> 72.8 MiB
Size D: 12.0 k --> 48.0 kiB
Size E: 12.0 k --> 48.0 kiB
Elapsed time: 2786 seconds

Inner loop: 1487 iterations
g(Z) = ||X-DZ||_2^2 = 4.794027e+05
rdiff: 0.00355280129654
i(Z) = ||Z||_1 = 6.617271e+05
j(Z) = tr(Z^TLZ) = 6.198570e+04

Global objective: 1.203116e+06

Outer loop: 15 iterations

Z in [-0.486856818199, 1.19251167774]
Sparsity of Z: 1,504,216 non-zero entries out of 19,072,000 entries, i.e. 7.9%.

D in [-0.72549778223, 0.919754981995]
d in [0.999999642372, 1.00000023842]
Constraints on D: True

Datasets:
D : (128, 96)             , float32
X : (5, 100, 149, 2, 96)  , float32
Z : (5, 100, 149, 2, 128) , float32
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Overall time: 2796 seconds

-------------------- Classification, ls = 10 --------------------

Software versions:
numpy: 1.8.2
sklearn: 0.14.1
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
D : (128, 96)               , float32
X : (5, 100, 149, 2, 96)    , float32
Z : (5, 100, 149, 2, 128)   , float32
Full dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<type 'numpy.ndarray'>
Flattened frames:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 298, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Feature vectors:
size: N=6,000 x n=128 -> 768,000 floats
dim: 1,536 features per clip
shape: (5, 100, 6, 2, 128)

5 genres: blues, classical, country, disco, hiphop
Training data: (3600, 128), float64
Testing data: (2400, 128), float64
Training labels: (3600,), uint8
Testing labels: (2400,), uint8
Accuracy: 74.5 %
5 genres: blues, classical, country, disco, hiphop
Training data: (300, 1536), float64
Testing data: (200, 1536), float64
Training labels: (300,), uint8
Testing labels: (200,), uint8
Feature vectors accuracy: 61.0 %
Clips accuracy: 68.0 %
5 genres: blues, classical, country, disco, hiphop
Data: (500, 1536), float64
Labels: (500,), uint8
71 (+/- 5.0) <- [78 74 68 66 62 72 74 64 74 74]
71 (+/- 8.6) <- [64 78 80 54 80 80 76 68 62 72]
72 (+/- 4.0) <- [74 74 78 74 68 68 64 76 74 72]
71 (+/- 6.1) <- [57 74 72 72 70 70 80 70 80 66]
73 (+/- 4.4) <- [62 76 68 78 74 74 76 74 72 72]
73 (+/- 6.1) <- [62 82 78 66 76 74 74 70 78 66]
70 (+/- 6.1) <- [68 80 62 72 66 66 64 70 78 78]
71 (+/- 5.1) <- [66 66 66 72 80 70 74 80 68 70]
71 (+/- 5.7) <- [78 64 66 78 72 76 64 68 66 78]
69 (+/- 5.7) <- [82 66 74 70 70 66 62 62 72 70]
67 (+/- 9.4) <- [72 66 64 78 82 76 62 48 62 64]
72 (+/- 6.9) <- [72 64 80 80 72 72 57 78 76 66]
69 (+/- 5.7) <- [68 60 74 78 74 66 62 70 62 72]
71 (+/- 4.3) <- [72 70 78 72 78 68 66 72 64 72]
70 (+/- 7.0) <- [74 57 78 57 78 74 74 70 66 72]
71 (+/- 5.1) <- [70 78 74 78 68 72 76 66 62 68]
69 (+/- 5.5) <- [66 68 68 74 68 78 76 62 60 70]
71 (+/- 5.4) <- [70 68 80 64 74 72 74 62 76 66]
70 (+/- 5.5) <- [66 74 72 76 68 66 74 64 80 62]
69 (+/- 9.0) <- [64 78 62 84 72 78 66 64 72 52]
Accuracy: 70.6 (+/- 6.33)
Mean time (20 cv): 24.71 seconds
Overall time: 498.90 seconds

-------------------- Features, ls = 100 --------------------

The memory_profiler extension is already loaded. To reload it, use:
The line_profiler extension is already loaded. To reload it, use:
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
Xa: (10, 100, 644, 2, 1024) , float32
Xs: (10, 100, 644, 2, 96)   , float32
Full dataset:
size: N=1,288,000 x n=96 -> 123,648,000 floats
dim: 123,648 features per clip
shape: (10, 100, 644, 2, 96)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<type 'numpy.ndarray'>
Data: (149000, 96), float32
Attributes:
K = 11
dm = cosine
Csigma = 1
diag = True
laplacian = normalized
Datasets:
L_data    : (2396026,), float32
L_indices : (2396026,), int32
L_indptr  : (149001,) , int32
L_shape   : (2,)      , int64
W_data    : (2396026,), float32
W_indices : (2396026,), int32
W_indptr  : (149001,) , int32
W_shape   : (2,)      , int64
Size X: 13.6 M --> 54.6 MiB
Size Z: 18.2 M --> 72.8 MiB
Size D: 12.0 k --> 48.0 kiB
Size E: 12.0 k --> 48.0 kiB
Elapsed time: 784 seconds

Inner loop: 404 iterations
g(Z) = ||X-DZ||_2^2 = 2.531211e+06
rdiff: 0.00806285653041
i(Z) = ||Z||_1 = 1.136244e+05
j(Z) = tr(Z^TLZ) = 2.192487e+03

Global objective: 2.647028e+06

Outer loop: 11 iterations

Z in [0.0, 0.657184481621]
Sparsity of Z: 18,422 non-zero entries out of 19,072,000 entries, i.e. 0.1%.

D in [-0.00530179822817, 0.607872366905]
d in [0.999999582767, 1.00000023842]
Constraints on D: True

Datasets:
D : (128, 96)             , float32
X : (5, 100, 149, 2, 96)  , float32
Z : (5, 100, 149, 2, 128) , float32
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Overall time: 795 seconds

-------------------- Classification, ls = 100 --------------------

Software versions:
numpy: 1.8.2
sklearn: 0.14.1
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
D : (128, 96)               , float32
X : (5, 100, 149, 2, 96)    , float32
Z : (5, 100, 149, 2, 128)   , float32
Full dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<type 'numpy.ndarray'>
Flattened frames:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 298, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Feature vectors:
size: N=6,000 x n=128 -> 768,000 floats
dim: 1,536 features per clip
shape: (5, 100, 6, 2, 128)

5 genres: blues, classical, country, disco, hiphop
Training data: (3600, 128), float64
Testing data: (2400, 128), float64
Training labels: (3600,), uint8
Testing labels: (2400,), uint8
Accuracy: 25.2 %
5 genres: blues, classical, country, disco, hiphop
Training data: (300, 1536), float64
Testing data: (200, 1536), float64
Training labels: (300,), uint8
Testing labels: (200,), uint8
Feature vectors accuracy: 24.1 %
Clips accuracy: 24.0 %
5 genres: blues, classical, country, disco, hiphop
Data: (500, 1536), float64
Labels: (500,), uint8
18 (+/- 5.1) <- [22 26 16 10 14 22 16 12 24 20]
18 (+/- 4.4) <- [12 14 24 22 18 14 22 24 16 14]
20 (+/- 3.0) <- [22 18 16 18 18 24 20 18 26 22]
18 (+/- 4.8) <- [18 14 24 18 24 12 16 12 20 26]
18 (+/- 3.9) <- [12 20 20 12 22 18 22 18 24 16]
19 (+/- 4.2) <- [16 18 18 24 20 12 14 26 16 22]
19 (+/- 4.1) <- [14 20 22 16 26 20 18 24 12 18]
20 (+/- 5.0) <- [22 26 20 16 26 20 14 24 18 10]
19 (+/- 2.9) <- [16 14 22 16 20 18 20 20 24 20]
20 (+/- 3.3) <- [22 26 24 22 18 18 16 18 22 16]
18 (+/- 3.8) <- [24 14 24 16 18 14 20 22 14 16]
18 (+/- 3.8) <- [24 18 22 18 14 20 20 14 12 14]
19 (+/- 2.5) <- [22 20 20 14 20 22 18 18 16 16]
19 (+/- 4.4) <- [20 22 14 22 28 18 22 14 14 16]
19 (+/- 4.4) <- [24 22 24 18 22 14 18 12 14 24]
19 (+/- 3.9) <- [18 26 20 12 20 14 18 18 20 24]
18 (+/- 3.9) <- [16 24 18 16 18 24 12 16 14 22]
20 (+/- 3.9) <- [28 24 24 18 20 16 16 16 18 18]
19 (+/- 5.3) <- [14 30 14 14 18 12 22 22 22 22]
18 (+/- 5.4) <- [20 26  6 22 22 20 18 12 18 20]
Accuracy: 18.8 (+/- 4.23)
Mean time (20 cv): 41.79 seconds
Overall time: 843.73 seconds

-------------------- Features, ls = 1000.0 --------------------

The memory_profiler extension is already loaded. To reload it, use:
The line_profiler extension is already loaded. To reload it, use:
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
Xa: (10, 100, 644, 2, 1024) , float32
Xs: (10, 100, 644, 2, 96)   , float32
Full dataset:
size: N=1,288,000 x n=96 -> 123,648,000 floats
dim: 123,648 features per clip
shape: (10, 100, 644, 2, 96)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<type 'numpy.ndarray'>
Data: (149000, 96), float32
Attributes:
/home/ubuntu/.virtualenvs/dlaudio/lib/python2.7/site-packages/IPython/kernel/__main__.py:86: RuntimeWarning: divide by zero encountered in double_scalars
/home/ubuntu/dlaudio/pyunlocbox/solvers.py:451: RuntimeWarning: invalid value encountered in multiply
x = self.z - self.step * self.f2.grad(self.z)

  K = 11
dm = cosine
Csigma = 1
diag = True
laplacian = normalized
Datasets:
L_data    : (2396026,), float32
L_indices : (2396026,), int32
L_indptr  : (149001,) , int32
L_shape   : (2,)      , int64
W_data    : (2396026,), float32
W_indices : (2396026,), int32
W_indptr  : (149001,) , int32
W_shape   : (2,)      , int64
Size X: 13.6 M --> 54.6 MiB
Size Z: 18.2 M --> 72.8 MiB
Size D: 12.0 k --> 48.0 kiB
Size E: 12.0 k --> 48.0 kiB
Elapsed time: 61361 seconds

/usr/lib/pymodules/python2.7/matplotlib/scale.py:90: RuntimeWarning: invalid value encountered in less_equal
mask = a <= 0.0
/usr/lib/python2.7/dist-packages/numpy/ma/core.py:790: RuntimeWarning: invalid value encountered in less_equal
return umath.less_equal(x, self.critical_value)

Inner loop: 49601 iterations

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-d4babd0f7a1f> in <module>()
----> 1 ret = ae.plot_objective()
2 iterations_inner, iterations_outer = ret[:2]
3 objective_g, objective_h, objective_i, objective_j = ret[2:]

<ipython-input-5-4835eaf15bda> in plot_objective(self)
254         if self.ld is not None:
255             name = 'g(Z) = ||X-DZ||_2^2'
--> 256             plt.semilogy(self.objective_g, '.-', label=name)
257             print(name + ' = {:e}'.format(self.objective_g[-1]))
258             rdiff(self.objective_g[-1], self.g_d.eval(self.D))

/usr/lib/pymodules/python2.7/matplotlib/pyplot.pyc in semilogy(*args, **kwargs)
3121         ax.hold(hold)
3122     try:
-> 3123         ret = ax.semilogy(*args, **kwargs)
3124         draw_if_interactive()
3125     finally:

/usr/lib/pymodules/python2.7/matplotlib/axes.pyc in semilogy(self, *args, **kwargs)
4375         b = self._hold
4376         self._hold = True  # we've already processed the hold
-> 4377         l = self.plot(*args, **kwargs)
4378         self._hold = b  # restore the hold
4379

/usr/lib/pymodules/python2.7/matplotlib/axes.pyc in plot(self, *args, **kwargs)
4139             lines.append(line)
4140
-> 4141         self.autoscale_view(scalex=scalex, scaley=scaley)
4142         return lines
4143

/usr/lib/pymodules/python2.7/matplotlib/axes.pyc in autoscale_view(self, tight, scalex, scaley)
1961                 y1 += delta
1962             if not _tight:
-> 1963                 y0, y1 = ylocator.view_limits(y0, y1)
1964             self.set_ybound(y0, y1)
1965

/usr/lib/pymodules/python2.7/matplotlib/ticker.pyc in view_limits(self, vmin, vmax)
1483         if minpos <= 0 or not np.isfinite(minpos):
1484             raise ValueError(
-> 1485                 "Data has no positive values, and therefore can not be "
1486                 "log-scaled.")
1487

ValueError: Data has no positive values, and therefore can not be log-scaled.
 -------------------- Classification, ls = 1000.0 --------------------

Software versions:
numpy: 1.8.2
sklearn: 0.14.1
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
D : (128, 96)               , float32
X : (5, 100, 149, 2, 96)    , float32
Z : (5, 100, 149, 2, 128)   , float32
Full dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 149, 2, 128)
<type 'numpy.ndarray'>
Flattened frames:
size: N=149,000 x n=128 -> 19,072,000 floats
dim: 38,144 features per clip
shape: (5, 100, 298, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Truncated and grouped:
size: N=135,000 x n=128 -> 17,280,000 floats
dim: 34,560 features per clip
shape: (5, 100, 6, 45, 128)
Feature vectors:
size: N=6,000 x n=128 -> 768,000 floats
dim: 1,536 features per clip
shape: (5, 100, 6, 2, 128)

5 genres: blues, classical, country, disco, hiphop
Training data: (3600, 128), float64
Testing data: (2400, 128), float64
Training labels: (3600,), uint8
Testing labels: (2400,), uint8
Accuracy: 25.2 %
5 genres: blues, classical, country, disco, hiphop
Training data: (300, 1536), float64
Testing data: (200, 1536), float64
Training labels: (300,), uint8
Testing labels: (200,), uint8
Feature vectors accuracy: 24.1 %
Clips accuracy: 24.0 %
5 genres: blues, classical, country, disco, hiphop
Data: (500, 1536), float64
Labels: (500,), uint8
18 (+/- 5.1) <- [22 26 16 10 14 22 16 12 24 20]
18 (+/- 4.4) <- [12 14 24 22 18 14 22 24 16 14]
20 (+/- 3.0) <- [22 18 16 18 18 24 20 18 26 22]
18 (+/- 4.8) <- [18 14 24 18 24 12 16 12 20 26]
18 (+/- 3.9) <- [12 20 20 12 22 18 22 18 24 16]
19 (+/- 4.2) <- [16 18 18 24 20 12 14 26 16 22]
19 (+/- 4.1) <- [14 20 22 16 26 20 18 24 12 18]
20 (+/- 5.0) <- [22 26 20 16 26 20 14 24 18 10]
19 (+/- 2.9) <- [16 14 22 16 20 18 20 20 24 20]
20 (+/- 3.3) <- [22 26 24 22 18 18 16 18 22 16]
18 (+/- 3.8) <- [24 14 24 16 18 14 20 22 14 16]
18 (+/- 3.8) <- [24 18 22 18 14 20 20 14 12 14]
19 (+/- 2.5) <- [22 20 20 14 20 22 18 18 16 16]
19 (+/- 4.4) <- [20 22 14 22 28 18 22 14 14 16]
19 (+/- 4.4) <- [24 22 24 18 22 14 18 12 14 24]
19 (+/- 3.9) <- [18 26 20 12 20 14 18 18 20 24]
18 (+/- 3.9) <- [16 24 18 16 18 24 12 16 14 22]
20 (+/- 3.9) <- [28 24 24 18 20 16 16 16 18 18]
19 (+/- 5.3) <- [14 30 14 14 18 12 22 22 22 22]
18 (+/- 5.4) <- [20 26  6 22 22 20 18 12 18 20]
Accuracy: 18.8 (+/- 4.23)
Mean time (20 cv): 41.86 seconds
Overall time: 845.31 seconds

-------------------- Baseline --------------------

Software versions:
numpy: 1.8.2
sklearn: 0.14.1
Attributes:
sr = 22050
labels = ['blues' 'classical' 'country' 'disco' 'hiphop' 'jazz' 'metal' 'pop'
'reggae' 'rock']
Datasets:
D : (128, 96)               , float32
X : (5, 100, 149, 2, 96)    , float32
Z : (5, 100, 149, 2, 128)   , float32
Full dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<class 'h5py._hl.dataset.Dataset'>
Reduced dataset:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 149, 2, 96)
<type 'numpy.ndarray'>
Flattened frames:
size: N=149,000 x n=96 -> 14,304,000 floats
dim: 28,608 features per clip
shape: (5, 100, 298, 96)
Truncated and grouped:
size: N=135,000 x n=96 -> 12,960,000 floats
dim: 25,920 features per clip
shape: (5, 100, 6, 45, 96)
Truncated and grouped:
size: N=135,000 x n=96 -> 12,960,000 floats
dim: 25,920 features per clip
shape: (5, 100, 6, 45, 96)
Feature vectors:
size: N=6,000 x n=96 -> 576,000 floats
dim: 1,152 features per clip
shape: (5, 100, 6, 2, 96)

5 genres: blues, classical, country, disco, hiphop
Training data: (3600, 96), float64
Testing data: (2400, 96), float64
Training labels: (3600,), uint8
Testing labels: (2400,), uint8
Accuracy: 68.8 %
5 genres: blues, classical, country, disco, hiphop
Training data: (300, 1152), float64
Testing data: (200, 1152), float64
Training labels: (300,), uint8
Testing labels: (200,), uint8
Feature vectors accuracy: 62.1 %
Clips accuracy: 68.5 %
5 genres: blues, classical, country, disco, hiphop
Data: (500, 1152), float64
Labels: (500,), uint8
67 (+/- 8.0) <- [72 74 60 48 62 74 72 66 66 74]
66 (+/- 6.5) <- [70 60 62 52 64 70 72 68 66 76]
68 (+/- 6.7) <- [74 68 84 62 68 70 62 62 70 62]
68 (+/- 5.5) <- [60 72 62 66 74 74 72 64 76 64]
67 (+/- 5.1) <- [57 74 66 70 68 64 72 62 74 64]
68 (+/- 6.1) <- [68 78 74 64 72 70 68 57 57 66]
66 (+/- 3.7) <- [62 70 60 68 68 66 60 68 70 68]
67 (+/- 5.9) <- [68 60 57 68 80 64 64 68 64 72]
65 (+/- 6.0) <- [68 54 68 66 76 62 68 70 62 57]
66 (+/- 6.3) <- [72 62 78 64 74 64 62 57 60 70]
65 (+/- 6.6) <- [72 64 54 66 74 64 72 56 57 66]
67 (+/- 6.1) <- [66 56 60 76 66 70 66 70 76 62]
68 (+/- 7.3) <- [78 66 57 76 70 68 57 76 57 70]
68 (+/- 5.1) <- [68 64 74 66 74 72 66 70 56 68]
68 (+/- 5.1) <- [76 57 76 68 66 68 64 66 66 70]
68 (+/- 7.4) <- [66 82 72 76 60 56 66 62 72 70]
68 (+/- 7.5) <- [70 62 60 64 60 82 80 68 64 72]
67 (+/- 4.6) <- [74 66 76 66 66 62 70 64 62 64]
68 (+/- 7.2) <- [56 70 76 70 72 60 72 70 76 56]
67 (+/- 5.7) <- [70 78 62 74 64 70 68 57 68 62]
Accuracy: 67.1 (+/- 6.30)
Mean time (20 cv): 15.11 seconds
Overall time: 306.66 seconds


## Results¶

In [6]:
print('{}: {}'.format(Pname, Pvalues))
for key, value in res.items():
if key is not 'atoms':
print('{}: {}'.format(key, value))

def plot(*args, **kwargs):
plt.figure(figsize=(8,5))
x = range(len(Pvalues))
log = 'log' in kwargs and kwargs['log'] is True
pltfunc = plt.semilogy if log else plt.plot
for var in args:
pltfunc(x, res[var], '.-', label=var)
plt.xlim(0, len(Pvalues)-1)
plt.title('{} vs {}'.format(', '.join(args), Pname))
plt.xlabel(Pname)
plt.ylabel(' ,'.join(args))
plt.xticks(x, Pvalues)
plt.grid(True); plt.legend(loc='best'); plt.show()

# Classification results.
plot('accuracy')

# Features extraction results.
if regen_features:
plot('objective_g', 'objective_i', 'objective_j', log=True)
plot('sparsity')
plot('time_features')
plot('iterations_inner')
plot('iterations_outer')

for i, fig in enumerate(res['atoms']):
print('Dictionary atoms for {} = {}'.format(Pname, Pvalues[i]))
fig.show()

print('Experiment time: {:.0f} seconds'.format(time.time() - texperiment))

ls: [0.1, 1, 10, 100, 1000.0]
accuracy_std: [6.0086520951041953, 5.9056244377711673, 6.3308767165377695, 4.2293734760600197, 4.2293734760600197]
objective_j: [113358.04443359375, 101607.73315429688, 61985.699462890625, 2192.4869537353516, 2192.4869537353516]
objective_i: [35003.662499999999, 160855.953125, 661727.109375, 113624.37744140625, 113624.37744140625]
objective_h: [0, 0, 0, 0, 0]
objective_g: [47058.1787109375, 87632.51953125, 479402.734375, 2531210.7421875, 2531210.7421875]
time_features: [9700.575558900833, 12253.139140844345, 2786.0791459083557, 783.5279347896576, 61360.89183306694]
sparsity: [94.65302537751678, 40.215037751677855, 7.887038590604027, 0.09659186241610739, 0.09659186241610739]
iterations_inner: [5341, 6914, 1487, 404, 404]
iterations_outer: [50, 50, 15, 11, 11]
accuracy: [67.810000000000045, 73.060000000000016, 70.599999999999994, 18.819999999999979, 18.819999999999979]

Dictionary atoms for ls = 0.1
Dictionary atoms for ls = 1
Dictionary atoms for ls = 10
Dictionary atoms for ls = 100
Dictionary atoms for ls = 1000.0

/usr/lib/pymodules/python2.7/matplotlib/figure.py:371: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
"matplotlib is currently using a non-GUI backend, "

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-a013a76f3200> in <module>()
31     for i, fig in enumerate(res['atoms']):
32         print('Dictionary atoms for {} = {}'.format(Pname, Pvalues[i]))
---> 33         fig.show()
34
35 print('Experiment time: {:.0f} seconds'.format(time.time() - texperiment))

AttributeError: 'function' object has no attribute 'show'

### Unweighted objectives¶

In [3]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def plot(*args, **kwargs):
plt.figure(figsize=(8,5))
x = range(len(Pvalues))
log = 'log' in kwargs and kwargs['log'] is True
pltfunc = plt.semilogy if log else plt.plot
params = {}
params['linestyle'] = '-'
params['marker'] = '.'
params['markersize'] = 10
for i, var in enumerate(args):
if 'err' in kwargs:
pltfunc = plt.errorbar
params['yerr'] = res[kwargs['err'][i]]
params['capsize'] = 5
pltfunc(x, res[var], label=var, **params)
for i,j in zip(x, res[var]):
plt.annotate('{:.2f}'.format(j), xy=(i,j), xytext=(5,5), textcoords='offset points')
margin = 0.25 / (len(Pvalues)-1)
params['markersize'] = 10
plt.xlim(-margin, len(Pvalues)-1+margin)
plt.title('{} vs {}'.format(', '.join(args), Pname))
plt.xlabel(Pname)
plt.ylabel(' ,'.join(args))
plt.xticks(x, Pvalues)
plt.grid(True); plt.legend(loc='best'); plt.show()

Pname = 'ls'
Pvalues = np.array([0.1,1,10,100,1e3])
res = {}
res['objective_j'] = [113358.04443359375, 101607.73315429688, 61985.699462890625, 2192.4869537353516, 2192.4869537353516]
res['objective_i'] = [35003.662499999999, 160855.953125, 661727.109375, 113624.37744140625, 113624.37744140625]
res['obj