Goal: Test if the cosine similarity (with zero-mean data) or the euclidean distance with a Gaussian kernel (without zero-mean data) provides a better graph that the cosine distance with a Gaussian kernel (without zero-mean data).
Conclusion: cosine_sim
and euclidean
metrics provide a 2% increase in accuracy with respect to cosine_dist
.
Observations:
Ncv
=20 (thanks to decreased variance), 10 minutes per classification (instead of 20).lg
=100, 10 minutes for feature extraction.cosine_dist
failed because it was miss-spelled. Hopefully all the last experiments used this metric. Accuracy was 64.01% in 13c_novoting
and 64.03% in 13d_noise_level
.euclidean
is 7% faster than cosine_sim
while cosine_dist
is the fastest with an increse of 47% to cosine_sim
.Pname = 'dm'
Pvalues = ['cosine_sim', 'cosine_dim', 'euclidean']
# Regenerate the graph or the features at each iteration.
regen_graph = True
regen_features = True
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'] = 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
# 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'] = 0.1
# 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'
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)
#%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, dm = cosine_sim -------------------- Data: (149000, 96), float32 Elapsed time: 1257.85 seconds All self-referenced in the first column: True dist in [0.560503005981, 1.0] w in [0.560503005981, 1.0] Ones on the diagonal: 149000 (over 149000) assert: True W in [0.0, 1.0] Datasets: L_data : (2925296,), float32 L_indices : (2925296,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2925296,), float32 W_indices : (2925296,), int32 W_indptr : (149001,) , int32 W_shape : (2,) , int64 Attributes: K = 11 dm = cosine_sim Csigma = 1 diag = True laplacian = normalized Overall time: 1267.74 seconds -------------------- Features, dm = cosine_sim -------------------- 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_sim Csigma = 1 diag = True laplacian = normalized Datasets: L_data : (2925296,), float32 L_indices : (2925296,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2925296,), float32 W_indices : (2925296,), 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: 982 seconds
Inner loop: 384 iterations g(Z) = ||X-DZ||_2^2 = 7.365278e+05 rdiff: 0.000154546947268 i(Z) = ||Z||_1 = 7.460725e+04 j(Z) = tr(Z^TLZ) = 2.092982e+04
Global objective: 8.320649e+05
Outer loop: 5 iterations Z in [-0.0605589039624, 0.252947568893] Sparsity of Z: 8,314,786 non-zero entries out of 19,072,000 entries, i.e. 43.6%.
D in [-0.138522788882, 0.704355895519] d in [0.999999701977, 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: 990 seconds -------------------- Classification, dm = cosine_sim -------------------- 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: 65.3 % 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: 60.0 % Clips accuracy: 67.5 % 5 genres: blues, classical, country, disco, hiphop Data: (6000, 128), float64 Labels: (6000,), uint8 67 (+/- 1.5) <- [65 65 64 68 68 68 66 64 66 67] 66 (+/- 3.0) <- [60 70 68 67 64 64 71 66 65 66] 66 (+/- 1.7) <- [67 66 62 67 67 66 67 65 65 68] 66 (+/- 1.1) <- [64 67 66 67 64 66 68 65 66 65] 66 (+/- 2.1) <- [63 66 68 66 65 64 66 71 65 66] 67 (+/- 2.1) <- [65 65 65 71 68 66 65 68 63 65] 67 (+/- 1.6) <- [66 65 66 68 68 65 67 63 68 67] 67 (+/- 1.4) <- [67 66 65 68 66 68 64 67 68 64] 66 (+/- 2.1) <- [68 65 66 68 64 61 68 68 67 66] 67 (+/- 1.8) <- [70 69 65 67 68 67 65 65 66 64] 67 (+/- 1.6) <- [66 68 66 67 68 69 66 66 66 62] 67 (+/- 1.9) <- [63 67 67 68 65 68 67 66 62 68] 67 (+/- 2.3) <- [67 68 68 68 66 65 68 61 63 66] 67 (+/- 1.9) <- [63 65 67 67 67 66 65 65 67 71] 67 (+/- 2.7) <- [65 66 63 66 70 62 67 68 71 64] 66 (+/- 1.4) <- [66 65 65 69 64 66 65 67 65 67] 66 (+/- 1.7) <- [67 63 66 66 68 68 64 66 65 64] 66 (+/- 2.4) <- [66 62 67 66 67 71 64 66 64 66] 67 (+/- 1.6) <- [65 66 67 64 65 66 64 69 67 69] 66 (+/- 1.7) <- [65 67 65 67 68 66 67 64 62 67] Accuracy: 66.5 (+/- 1.94) Mean time (20 cv): 21.83 seconds Overall time: 441.52 seconds -------------------- Graph, dm = cosine_dim -------------------- Data: (149000, 96), float32 Elapsed time: 1294.85 seconds All self-referenced in the first column: True
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-6-a8e755cefddd> in <module>() 8 dist = 1 - dist / 4. 9 else: ---> 10 raise ValueError 11 12 print('dist in [{}, {}]'.format(dist.min(), dist.max())) ValueError:
-------------------- Features, dm = cosine_dim -------------------- 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_sim Csigma = 1 diag = True laplacian = normalized Datasets: L_data : (2925296,), float32 L_indices : (2925296,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2925296,), float32 W_indices : (2925296,), 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: 973 seconds
Inner loop: 384 iterations g(Z) = ||X-DZ||_2^2 = 7.365278e+05 rdiff: 0.000154546947268 i(Z) = ||Z||_1 = 7.460725e+04 j(Z) = tr(Z^TLZ) = 2.092982e+04
Global objective: 8.320649e+05
Outer loop: 5 iterations Z in [-0.0605589039624, 0.252947568893] Sparsity of Z: 8,314,786 non-zero entries out of 19,072,000 entries, i.e. 43.6%.
D in [-0.138522788882, 0.704355895519] d in [0.999999701977, 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: 982 seconds -------------------- Classification, dm = cosine_dim -------------------- 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: 65.3 % 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: 60.0 % Clips accuracy: 67.5 % 5 genres: blues, classical, country, disco, hiphop Data: (6000, 128), float64 Labels: (6000,), uint8 67 (+/- 1.5) <- [65 65 64 68 68 68 66 64 66 67] 66 (+/- 3.0) <- [60 70 68 67 64 64 71 66 65 66] 66 (+/- 1.7) <- [67 66 62 67 67 66 67 65 65 68] 66 (+/- 1.1) <- [64 67 66 67 64 66 68 65 66 65] 66 (+/- 2.1) <- [63 66 68 66 65 64 66 71 65 66] 67 (+/- 2.1) <- [65 65 65 71 68 66 65 68 63 65] 67 (+/- 1.6) <- [66 65 66 68 68 65 67 63 68 67] 67 (+/- 1.4) <- [67 66 65 68 66 68 64 67 68 64] 66 (+/- 2.1) <- [68 65 66 68 64 61 68 68 67 66] 67 (+/- 1.8) <- [70 69 65 67 68 67 65 65 66 64] 67 (+/- 1.6) <- [66 68 66 67 68 69 66 66 66 62] 67 (+/- 1.9) <- [63 67 67 68 65 68 67 66 62 68] 67 (+/- 2.3) <- [67 68 68 68 66 65 68 61 63 66] 67 (+/- 1.9) <- [63 65 67 67 67 66 65 65 67 71] 67 (+/- 2.7) <- [65 66 63 66 70 62 67 68 71 64] 66 (+/- 1.4) <- [66 65 65 69 64 66 65 67 65 67] 66 (+/- 1.7) <- [67 63 66 66 68 68 64 66 65 64] 66 (+/- 2.4) <- [66 62 67 66 67 71 64 66 64 66] 67 (+/- 1.6) <- [65 66 67 64 65 66 64 69 67 69] 66 (+/- 1.7) <- [65 67 65 67 68 66 67 64 62 67] Accuracy: 66.5 (+/- 1.94) Mean time (20 cv): 21.77 seconds Overall time: 440.49 seconds -------------------- Graph, dm = euclidean -------------------- Data: (149000, 96), float32 Elapsed time: 1183.53 seconds All self-referenced in the first column: True dist in [0.0, 2.08629345894] w in [0.198544532061, 1.0] Ones on the diagonal: 149000 (over 149000) assert: True W in [0.0, 1.0] Datasets: L_data : (2919394,), float32 L_indices : (2919394,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2919394,), float32 W_indices : (2919394,), int32 W_indptr : (149001,) , int32 W_shape : (2,) , int64 Attributes: K = 11 dm = euclidean Csigma = 1 diag = True laplacian = normalized Overall time: 1193.83 seconds -------------------- Features, dm = euclidean -------------------- 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 : (2919394,), float32 L_indices : (2919394,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2919394,), float32 W_indices : (2919394,), 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: 905 seconds
Inner loop: 358 iterations g(Z) = ||X-DZ||_2^2 = 7.395564e+05 rdiff: 0.000189513942163 i(Z) = ||Z||_1 = 7.337558e+04 j(Z) = tr(Z^TLZ) = 2.211262e+04
Global objective: 8.350446e+05
Outer loop: 5 iterations Z in [-0.108043551445, 0.236873194575] Sparsity of Z: 8,158,022 non-zero entries out of 19,072,000 entries, i.e. 42.8%.
D in [-0.135033249855, 0.678504288197] d in [0.999999701977, 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: 915 seconds -------------------- Classification, dm = euclidean -------------------- 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: 65.3 % 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: 59.3 % Clips accuracy: 66.5 % 5 genres: blues, classical, country, disco, hiphop Data: (6000, 128), float64 Labels: (6000,), uint8 67 (+/- 1.6) <- [65 69 63 68 68 68 66 66 68 67] 67 (+/- 2.7) <- [62 70 69 67 63 67 69 64 64 68] 67 (+/- 2.1) <- [67 66 62 68 69 66 68 66 64 69] 67 (+/- 1.6) <- [66 67 65 68 64 67 70 65 66 66] 67 (+/- 2.3) <- [63 65 65 66 64 66 68 72 66 67] 67 (+/- 2.4) <- [66 65 66 71 68 65 65 71 64 64] 67 (+/- 1.8) <- [67 63 67 68 67 67 66 64 66 69] 67 (+/- 1.2) <- [66 65 67 66 65 66 66 68 69 65] 67 (+/- 1.8) <- [68 65 66 67 65 63 68 67 66 69] 67 (+/- 1.2) <- [69 67 66 67 66 67 66 65 67 65] 67 (+/- 1.9) <- [68 66 67 64 66 70 66 65 67 64] 67 (+/- 1.9) <- [64 68 68 68 65 68 65 67 64 68] 67 (+/- 2.2) <- [68 69 68 67 67 65 66 61 64 68] 67 (+/- 2.5) <- [65 64 66 69 66 66 65 63 67 72] 67 (+/- 3.1) <- [65 68 63 65 70 60 68 69 70 64] 66 (+/- 2.1) <- [68 65 63 70 66 67 64 68 65 64] 66 (+/- 1.9) <- [66 62 66 66 70 67 64 67 66 65] 66 (+/- 2.2) <- [65 62 67 67 67 69 65 67 62 67] 67 (+/- 1.7) <- [64 66 66 64 67 66 65 67 68 70] 67 (+/- 2.0) <- [66 67 65 68 68 67 67 64 62 69] Accuracy: 66.8 (+/- 2.08) Mean time (20 cv): 21.58 seconds Overall time: 436.91 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: 59.3 % 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: 54.8 % Clips accuracy: 62.5 % 5 genres: blues, classical, country, disco, hiphop Data: (6000, 96), float64 Labels: (6000,), uint8 58 (+/- 1.5) <- [57 59 55 59 59 59 57 55 59 59] 59 (+/- 2.7) <- [52 61 62 58 59 58 63 58 57 58] 59 (+/- 2.4) <- [60 57 53 61 59 58 59 58 56 62] 59 (+/- 1.8) <- [57 60 56 61 56 59 60 60 59 56] 59 (+/- 2.2) <- [55 57 56 58 57 59 60 62 58 61] 59 (+/- 1.7) <- [56 59 57 61 60 56 59 59 58 55] 59 (+/- 1.0) <- [59 57 58 61 58 58 59 57 59 59] 59 (+/- 2.1) <- [57 57 57 57 57 58 59 61 63 55] 59 (+/- 2.3) <- [59 56 57 57 55 55 59 61 59 62] 59 (+/- 1.9) <- [59 58 60 62 56 62 58 59 59 56] 59 (+/- 2.5) <- [57 61 58 60 55 64 56 56 58 55] 59 (+/- 1.5) <- [60 59 61 59 56 60 59 59 57 56] 59 (+/- 2.8) <- [59 62 57 58 52 62 60 55 59 58] 59 (+/- 2.0) <- [59 55 58 58 56 59 57 57 62 60] 58 (+/- 2.6) <- [57 60 54 59 60 54 58 57 62 58] 59 (+/- 2.1) <- [56 61 55 61 56 61 57 59 57 58] 59 (+/- 1.8) <- [58 57 59 61 60 61 55 59 60 56] 59 (+/- 1.4) <- [59 58 60 58 58 57 58 57 55 60] 58 (+/- 1.3) <- [57 57 56 57 58 59 59 60 59 59] 59 (+/- 2.4) <- [58 60 57 61 62 61 56 54 58 59] Accuracy: 58.7 (+/- 2.07) Mean time (20 cv): 19.52 seconds Overall time: 395.24 seconds
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))
dm = ['cosine_sim', 'cosine_dim', 'euclidean'] res['accuracy_std'] = [1.9390674663066498, 1.9390674663066498, 2.0771909450559005] res['objective_j'] = [20929.82177734375, 20929.82177734375, 22112.623596191406] res['objective_i'] = [74607.25, 74607.25, 73375.578125] res['objective_h'] = [0, 0, 0] res['objective_g'] = [736527.8125, 736527.8125, 739556.40625] res['baseline'] = [58.740000000000023, 58.740000000000023, 58.740000000000023] res['time_features'] = [981.9027650356293, 973.1077239513397, 905.0378549098969] res['baseline_std'] = 2.07362002305 res['sparsity'] = [43.596822567114096, 43.596822567114096, 42.77486367449664] res['iterations_inner'] = [384, 384, 358] res['iterations_outer'] = [5, 5, 5] res['accuracy'] = [66.520833333333357, 66.520833333333357, 66.76666666666668]
g(Z) = ||X-DZ||_2^2, h(Z) = ||Z-EX||_2^2, i(Z) = ||Z||_1, j(Z) = tr(Z^TLZ)
Dictionary atoms for dm = cosine_sim Dictionary atoms for dm = cosine_dim Dictionary atoms for dm = euclidean Experiment time: 8369 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, "