Genre recognition: experiment

Goal: assess the usefulness of the auto-encoder with and without the graph when faced with noiseless data. The euclidean distance metric is used to construct the graph.

Conclusion: In a noiseless setting, our structuring auto-encoder improves the accuracy by 7.3% while a sparse auto-encoder imporves the performance by 6.2% compared to the baseline (raw spectrograms).

Observations:

  • Comparison with results who should be similar.
    • Baseline is the same as 13e_dm, 13c_novoting and 13b_noise_lg (voting).
    • Graph-less is the same as 13c_novoting and 13b_noise_lg (voting).
    • Graph-based is 1% less than 13e_dm. The only difference I see is a different kNN from FLANN (see the numbers in dist or w).

Hyper-parameters

Parameter under test

In [1]:
Pname = 'lg'
Pvalues = [None, 100]

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

Model parameters

In [2]:
p = {}

# Preprocessing.

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

# Feature extraction.
p['m'] = 128  # 64, 128, 512
p['ls'] = 1
p['ld'] = 10
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
p['majority_voting'] = False

Data parameters

In [3]:
# HDF5 data stores.
p['folder'] = 'data'
p['filename_gtzan'] = 'gtzan.hdf5'
p['filename_audio'] = 'audio.hdf5'
p['filename_graph'] = 'graph.hdf5'
p['filename_features'] = 'features.hdf5'

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

# Added white noise.
p['noise_std'] = 0

Numerical parameters

In [4]:
# Graph.
p['tol'] = 1e-5

# Feature extraction.
p['rtol'] = 1e-5  # 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 [5]:
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 [6]:
#%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 p[Pname] in Pvalues:

    if regen_graph:
        separator('Graph', True)
        %run audio_graph.ipynb
    if regen_features:
        separator('Features', True)
        p['filename_features'] = 'features_{}_{}.hdf5'.format(Pname, p[Pname])
        %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
res['baseline'] = len(Pvalues) * [accuracy]
res['baseline_std'] = accuracy_std
 -------------------- Graph -------------------- 

Data: (149000, 96), float32
Elapsed time: 188.94 seconds
All self-referenced in the first column: True
dist in [0.0, 1.47963690758]
w in [0.00555037613958, 1.0]
Ones on the diagonal: 149000 (over 149000)
assert: True
W in [0.0, 1.0]
Datasets:
  L_data    : (2397786,), float32
  L_indices : (2397786,), int32
  L_indptr  : (149001,) , int32
  L_shape   : (2,)      , int64
  W_data    : (2397786,), float32
  W_indices : (2397786,), int32
  W_indptr  : (149001,) , int32
  W_shape   : (2,)      , int64
Attributes:
  K = 11
  dm = euclidean
  Csigma = 1
  diag = True
  laplacian = normalized
Overall time: 198.11 seconds

 -------------------- Features, lg = None -------------------- 

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 = euclidean
  Csigma = 1
  diag = True
  laplacian = normalized
Datasets:
  L_data    : (2397786,), float32
  L_indices : (2397786,), int32
  L_indptr  : (149001,) , int32
  L_shape   : (2,)      , int64
  W_data    : (2397786,), float32
  W_indices : (2397786,), 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: 1143 seconds
Inner loop: 898 iterations
g(Z) = ||X-DZ||_2^2 = 4.231717e+04
rdiff: 0.00242506942781
i(Z) = ||Z||_1 = 6.887042e+04
Global objective: 1.111876e+05
Outer loop: 14 iterations

Z in [-1.15960109234, 1.81123566628]
Sparsity of Z: 905,112 non-zero entries out of 19,072,000 entries, i.e. 4.7%.
D in [-0.760848701, 0.938695788383]
d in [0.999999701977, 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: 1150 seconds

 -------------------- Classification, lg = None -------------------- 

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: 74.8 %
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: 61.4 %
Clips accuracy: 71.0 %
5 genres: blues, classical, country, disco, hiphop
Data: (6000, 128), float64
Labels: (6000,), uint8
  76 (+/- 1.3) <- [75 77 75 77 77 76 76 73 77 76]
  76 (+/- 1.2) <- [74 77 75 76 74 76 77 73 75 75]
  76 (+/- 1.6) <- [77 74 77 78 75 75 72 74 76 76]
  75 (+/- 1.4) <- [76 73 75 75 72 73 77 76 76 75]
  76 (+/- 1.8) <- [74 73 76 77 75 74 78 78 77 75]
  76 (+/- 1.6) <- [77 77 73 74 78 75 77 78 76 76]
  76 (+/- 1.9) <- [78 74 75 76 76 73 75 73 77 79]
  76 (+/- 1.3) <- [76 75 73 76 77 75 76 75 78 74]
  76 (+/- 1.8) <- [77 72 76 76 73 74 78 77 76 77]
  76 (+/- 1.6) <- [77 73 76 75 74 77 78 74 76 73]
  76 (+/- 2.2) <- [74 78 78 75 76 78 74 76 71 74]
  76 (+/- 2.0) <- [74 78 75 77 72 75 74 78 75 78]
  76 (+/- 1.8) <- [77 76 73 76 76 77 79 75 73 74]
  76 (+/- 2.0) <- [76 75 74 78 76 78 76 73 75 80]
  76 (+/- 2.4) <- [74 75 75 74 79 72 76 78 80 75]
  76 (+/- 1.7) <- [74 78 74 78 74 74 75 77 76 76]
  76 (+/- 1.7) <- [76 75 77 77 77 74 76 77 72 74]
  76 (+/- 2.4) <- [75 71 76 73 79 79 74 77 76 76]
  77 (+/- 1.5) <- [75 78 77 73 77 76 77 76 76 78]
  76 (+/- 0.9) <- [77 75 76 75 77 76 76 75 74 76]
Accuracy: 76.0 (+/- 1.77)
Mean time (20 cv): 30.97 seconds
Overall time: 624.45 seconds

 -------------------- Features, lg = 100 -------------------- 

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 = euclidean
  Csigma = 1
  diag = True
  laplacian = normalized
Datasets:
  L_data    : (2397786,), float32
  L_indices : (2397786,), int32
  L_indptr  : (149001,) , int32
  L_shape   : (2,)      , int64
  W_data    : (2397786,), float32
  W_indices : (2397786,), 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: 2573 seconds
Inner loop: 1243 iterations
g(Z) = ||X-DZ||_2^2 = 6.872115e+04
rdiff: 0.00151142965386
i(Z) = ||Z||_1 = 5.812207e+04
j(Z) = tr(Z^TLZ) = 9.091110e+03
Global objective: 1.359343e+05
Outer loop: 6 iterations

Z in [-0.233149766922, 1.01959300041]
Sparsity of Z: 3,418,431 non-zero entries out of 19,072,000 entries, i.e. 17.9%.
D in [-0.137461602688, 0.915598213673]
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: 2581 seconds

 -------------------- Classification, lg = 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: 75.6 %
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: 63.5 %
Clips accuracy: 69.0 %
5 genres: blues, classical, country, disco, hiphop
Data: (6000, 128), float64
Labels: (6000,), uint8
  77 (+/- 1.7) <- [76 77 75 80 76 79 74 77 76 78]
  77 (+/- 1.7) <- [72 77 78 77 76 77 79 78 76 76]
  77 (+/- 1.2) <- [77 74 77 77 78 78 78 76 76 77]
  77 (+/- 1.8) <- [75 74 78 76 77 75 81 76 78 76]
  77 (+/- 2.2) <- [75 72 79 75 78 77 79 77 78 80]
  77 (+/- 1.5) <- [75 76 75 76 79 78 78 79 76 75]
  77 (+/- 1.5) <- [78 74 76 77 78 75 77 76 75 80]
  77 (+/- 1.5) <- [80 78 76 76 78 77 76 75 78 75]
  77 (+/- 1.1) <- [76 77 77 76 75 75 78 78 76 79]
  77 (+/- 1.2) <- [78 77 77 78 78 77 76 76 77 74]
  77 (+/- 2.5) <- [76 76 81 75 76 81 74 76 74 75]
  77 (+/- 1.7) <- [75 79 78 77 73 78 75 77 78 77]
  77 (+/- 1.7) <- [75 80 77 75 78 76 79 75 75 77]
  77 (+/- 2.2) <- [77 75 74 78 75 77 80 73 77 80]
  76 (+/- 2.4) <- [73 80 76 74 79 73 76 77 79 73]
  77 (+/- 1.2) <- [78 77 76 77 77 77 75 80 76 76]
  77 (+/- 1.4) <- [76 75 76 77 78 76 78 77 77 74]
  77 (+/- 1.8) <- [77 73 78 75 77 80 76 78 78 77]
  77 (+/- 1.0) <- [75 76 77 75 76 77 77 78 78 77]
  77 (+/- 1.5) <- [77 78 75 77 80 76 77 75 76 77]
Accuracy: 77.1 (+/- 1.70)
Mean time (20 cv): 19.31 seconds
Overall time: 390.47 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: (6000, 96), float64
Labels: (6000,), uint8
  70 (+/- 1.9) <- [70 71 66 71 68 71 67 68 69 72]
  69 (+/- 1.9) <- [66 70 69 71 67 68 72 68 70 70]
  70 (+/- 1.5) <- [69 70 68 72 69 71 68 68 69 72]
  70 (+/- 1.6) <- [68 70 69 69 67 72 72 69 70 67]
  70 (+/- 2.2) <- [67 68 67 68 69 66 73 71 71 72]
  70 (+/- 2.9) <- [67 70 62 70 72 69 73 71 71 70]
  70 (+/- 2.1) <- [71 69 71 72 69 68 67 66 70 73]
  70 (+/- 2.3) <- [69 72 66 71 69 68 70 68 73 66]
  70 (+/- 1.9) <- [71 67 68 69 67 69 70 73 68 73]
  70 (+/- 1.7) <- [73 68 71 69 69 71 70 68 70 67]
  70 (+/- 1.8) <- [68 71 72 67 70 72 67 70 69 67]
  70 (+/- 1.8) <- [68 69 70 73 70 70 68 69 66 70]
  70 (+/- 1.6) <- [67 71 66 70 69 71 71 69 67 70]
  70 (+/- 1.5) <- [71 69 70 70 69 67 71 69 67 72]
  70 (+/- 1.8) <- [70 70 67 67 69 66 70 71 72 69]
  70 (+/- 1.9) <- [68 72 68 72 69 71 69 73 68 68]
  70 (+/- 1.5) <- [66 69 69 69 71 69 69 69 72 71]
  70 (+/- 1.2) <- [69 67 70 68 69 70 69 71 69 70]
  70 (+/- 2.3) <- [65 71 68 67 70 72 72 67 69 71]
  70 (+/- 2.7) <- [69 67 66 71 73 72 70 66 68 74]
Accuracy: 69.8 (+/- 1.96)
Mean time (20 cv): 15.09 seconds
Overall time: 305.83 seconds

Results

In [7]:
print('{} = {}'.format(Pname, Pvalues))
for key, value in res.items():
    if key is not 'atoms':
        print('res[\'{}\'] = {}'.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
    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
    params['markersize'] = 10
    plt.xlim(-margin, len(Pvalues)-1+margin)
    if 'ylim' in kwargs:
        plt.ylim(kwargs['ylim'])
    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()

def div(l):
    div = Pvalues if Pname is l else [p[l]]
    return np.array([1 if v is None else v for v in div])

# Classification results.
res['chance'] = len(Pvalues) * [100./p['Ngenres']]
res['chance_std'] = 0
err=['accuracy_std', 'baseline_std', 'chance_std']
plot('accuracy', 'baseline', 'chance', err=err, ylim=[0,100])

# Features extraction results.
if regen_features:
    plot('objective_g', 'objective_i', 'objective_j', log=True)
    # Unweighted objectives.
    print('g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)')
    res['objective_g_un'] = res['objective_g'] / div('ld')
    res['objective_i_un'] = res['objective_i'] / div('ls')
    res['objective_j_un'] = res['objective_j'] / div('lg')
    plot('objective_g_un', 'objective_i_un', 'objective_j_un', log=True)
    plot('sparsity', ylim=[0,100])
    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))
lg = [None, 100]
res['accuracy_std'] = [1.7664541931979625, 1.696892584828056]
res['objective_j'] = [0, 9091.1102294921875]
res['objective_i'] = [68870.421875, 58122.0703125]
res['objective_h'] = [0, 0]
res['objective_g'] = [42317.16796875, 68721.15234375]
res['baseline'] = [69.785833333333315, 69.785833333333315]
res['time_features'] = [1142.8163068294525, 2572.735995054245]
res['baseline_std'] = 1.95713633631
res['sparsity'] = [4.745763422818792, 17.923820260067114]
res['iterations_inner'] = [898, 1243]
res['iterations_outer'] = [14, 6]
res['accuracy'] = [76.034166666666607, 77.083333333333357]
/usr/lib/python2.7/dist-packages/numpy/ma/core.py:3847: UserWarning: Warning: converting a masked element to nan.
  warnings.warn("Warning: converting a masked element to nan.")
g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)
Dictionary atoms for lg = None
Dictionary atoms for lg = 100
Experiment time: 5257 seconds
/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, "