Goal: Check the influence of the distance metric (euclidean or cosine).
Conclusion: The cosine metric seems more appropriate.
Observations:
Pname = 'dm'
Pvalues = ['cosine', '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
# 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
# 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'] = 40
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 -------------------- Data: (149000, 96), float32 Elapsed time: 159.32 seconds All self-referenced in the first column: True dist in [0.0, 0.550418317318] w in [0.0235210377723, 1.0] Ones on the diagonal: 149000 (over 149000) assert: True W in [0.0, 1.0] Datasets: L_data : (2375982,), float32 L_indices : (2375982,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2375982,), float32 W_indices : (2375982,), int32 W_indptr : (149001,) , int32 W_shape : (2,) , int64 Attributes: K = 11 dm = cosine Csigma = 1 diag = True laplacian = normalized Overall time: 168.58 seconds -------------------- Features, dm = cosine -------------------- 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 : (2375982,), float32 L_indices : (2375982,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2375982,), float32 W_indices : (2375982,), 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: 2421 seconds
Inner loop: 1143 iterations g(Z) = ||X-DZ||_2^2 = 7.682912e+04 rdiff: 5.1351784894e-05 i(Z) = ||Z||_1 = 5.650498e+04 j(Z) = tr(Z^TLZ) = 1.030553e+04
Global objective: 1.436396e+05
Outer loop: 7 iterations Z in [-0.109829813242, 0.799122154713] Sparsity of Z: 3,959,579 non-zero entries out of 19,072,000 entries, i.e. 20.8%.
D in [-0.0257097557187, 0.876985788345] d in [0.999999582767, 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: 2429 seconds -------------------- Classification, dm = cosine -------------------- 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: 76.8 % 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: 64.6 % Clips accuracy: 73.0 % 5 genres: blues, classical, country, disco, hiphop Data: (500, 1536), float64 Labels: (500,), uint8 73 (+/- 6.3) <- [72 80 64 72 74 86 68 66 78 74] 73 (+/- 7.3) <- [70 74 86 64 82 78 68 68 62 74] 74 (+/- 5.6) <- [80 82 78 74 74 76 60 74 72 74] 73 (+/- 6.7) <- [57 80 72 70 64 76 80 74 78 74] 73 (+/- 5.1) <- [68 78 76 82 70 74 70 64 76 76] 72 (+/- 4.2) <- [68 78 74 74 74 78 70 70 74 64] 73 (+/- 7.3) <- [70 82 56 80 70 76 68 78 78 76] 74 (+/- 5.9) <- [70 66 72 74 86 66 80 76 78 74] 72 (+/- 7.2) <- [84 64 64 76 78 84 68 68 68 70] 74 (+/- 6.2) <- [76 72 86 74 78 74 72 70 60 74] 73 (+/- 6.1) <- [82 72 70 74 82 76 74 68 60 72] 72 (+/- 4.9) <- [74 66 74 84 68 70 68 74 74 70] 73 (+/- 8.8) <- [80 64 57 76 76 78 70 84 60 82] 74 (+/- 3.7) <- [76 72 80 70 74 72 68 72 78 78] 73 (+/- 7.3) <- [68 64 84 62 72 70 82 82 74 70] 75 (+/- 4.8) <- [72 84 82 78 70 74 74 74 72 68] 73 (+/- 2.2) <- [72 74 70 74 72 74 74 70 72 78] 75 (+/- 5.1) <- [78 76 80 64 78 72 76 68 78 80] 73 (+/- 6.6) <- [66 76 80 72 80 72 76 60 82 68] 73 (+/- 5.8) <- [66 78 74 76 74 68 76 60 80 74] 73 (+/- 6.6) <- [72 80 76 80 74 68 60 74 80 64] 73 (+/- 4.7) <- [72 70 80 78 80 68 70 66 70 72] 73 (+/- 8.5) <- [64 66 76 74 78 78 76 86 54 74] 73 (+/- 8.9) <- [68 74 72 76 84 64 88 68 56 76] 73 (+/- 5.9) <- [66 70 84 72 68 74 72 68 74 84] 73 (+/- 3.9) <- [76 72 74 70 80 72 74 72 64 72] 74 (+/- 3.8) <- [78 70 70 76 72 72 68 80 78 74] 73 (+/- 7.7) <- [72 82 70 76 66 68 60 68 86 80] 73 (+/- 5.8) <- [72 66 68 80 74 68 70 80 82 66] 72 (+/- 3.9) <- [74 74 68 68 68 74 80 68 72 76] 75 (+/- 7.1) <- [76 64 76 68 72 82 80 88 66 74] 72 (+/- 5.2) <- [66 74 68 84 72 68 78 68 70 74] 72 (+/- 4.6) <- [70 70 82 64 72 74 76 72 70 68] 73 (+/- 8.8) <- [88 80 82 64 78 62 62 78 66 72] 74 (+/- 7.5) <- [90 72 78 64 74 70 80 70 78 64] 74 (+/- 5.5) <- [68 76 66 74 74 74 76 68 86 78] 73 (+/- 4.9) <- [82 72 70 70 74 68 72 68 76 82] 74 (+/- 5.7) <- [72 84 72 78 66 68 82 70 70 76] 72 (+/- 5.4) <- [74 76 76 78 80 68 64 66 66 74] 74 (+/- 3.2) <- [74 72 66 78 76 76 74 76 72 74] Accuracy: 73.2 (+/- 6.13) Mean time (40 cv): 18.45 seconds Overall time: 742.22 seconds -------------------- Graph, dm = euclidean -------------------- Data: (149000, 96), float32 Elapsed time: 215.49 seconds All self-referenced in the first column: True dist in [0.0, 1.54432785511] w in [0.00310685997829, 1.0] Ones on the diagonal: 149000 (over 149000) assert: True W in [0.0, 1.0] Datasets: L_data : (2358832,), float32 L_indices : (2358832,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2358832,), float32 W_indices : (2358832,), int32 W_indptr : (149001,) , int32 W_shape : (2,) , int64 Attributes: K = 11 dm = euclidean Csigma = 1 diag = True laplacian = normalized Overall time: 225.32 seconds -------------------- Features, dm = euclidean -------------------- The memory_profiler extension is already loaded. To reload it, use: %reload_ext memory_profiler The line_profiler extension is already loaded. To reload it, use: %reload_ext line_profiler 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 : (2358832,), float32 L_indices : (2358832,), int32 L_indptr : (149001,) , int32 L_shape : (2,) , int64 W_data : (2358832,), float32 W_indices : (2358832,), 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: 2869 seconds
Inner loop: 1357 iterations g(Z) = ||X-DZ||_2^2 = 6.993616e+04 rdiff: 4.51025160641e-05 i(Z) = ||Z||_1 = 5.727794e+04 j(Z) = tr(Z^TLZ) = 8.521706e+03
Global objective: 1.357358e+05
Outer loop: 7 iterations Z in [-0.193726345897, 1.06732833385] Sparsity of Z: 3,632,050 non-zero entries out of 19,072,000 entries, i.e. 19.0%.
D in [-0.0318740010262, 0.893661141396] d in [0.999999582767, 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: 2877 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: 77.0 % 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: 62.7 % Clips accuracy: 70.0 % 5 genres: blues, classical, country, disco, hiphop Data: (500, 1536), float64 Labels: (500,), uint8 71 (+/- 6.1) <- [72 80 68 68 70 84 70 62 72 66] 71 (+/- 8.0) <- [68 78 78 56 82 78 70 62 66 76] 72 (+/- 5.4) <- [74 76 82 68 72 72 60 70 74 70] 70 (+/- 5.8) <- [57 76 64 72 68 68 76 72 78 72] 71 (+/- 2.7) <- [70 70 76 74 66 74 72 70 70 70] 72 (+/- 4.6) <- [74 72 76 74 70 68 80 74 70 62] 68 (+/- 5.9) <- [64 76 56 76 66 72 64 70 72 68] 70 (+/- 7.8) <- [70 68 62 70 86 57 78 64 76 70] 70 (+/- 6.8) <- [76 62 74 64 80 80 64 64 72 64] 69 (+/- 7.0) <- [80 68 78 74 68 70 68 64 57 57] 70 (+/- 7.8) <- [78 64 68 72 78 80 68 68 52 70] 71 (+/- 7.3) <- [68 64 76 82 68 74 56 72 80 70] 70 (+/- 7.5) <- [78 66 56 72 72 68 62 82 64 76] 71 (+/- 3.9) <- [70 76 74 72 64 66 70 74 68 76] 70 (+/- 6.9) <- [68 60 80 60 72 68 78 78 74 66] 74 (+/- 6.7) <- [62 84 76 78 64 76 74 68 80 76] 70 (+/- 4.2) <- [68 68 66 74 64 76 74 64 74 70] 72 (+/- 6.2) <- [68 72 82 62 72 76 66 76 78 64] 69 (+/- 5.5) <- [64 76 72 72 68 64 72 60 78 66] 70 (+/- 5.8) <- [72 76 60 74 78 68 72 62 72 64] 71 (+/- 8.5) <- [74 74 78 74 72 64 50 80 78 66] 70 (+/- 4.4) <- [68 70 74 78 68 74 62 68 74 68] 70 (+/- 2.9) <- [68 70 72 68 72 72 66 74 66 74] 69 (+/- 6.4) <- [64 76 70 72 76 64 80 62 60 68] 70 (+/- 5.2) <- [74 70 74 68 64 64 66 64 74 80] 72 (+/- 5.1) <- [66 68 68 66 80 70 78 74 68 78] 71 (+/- 5.2) <- [74 64 72 74 64 80 64 76 72 72] 70 (+/- 6.4) <- [78 74 74 72 60 64 62 68 80 72] 70 (+/- 6.8) <- [72 66 64 76 66 57 64 80 76 76] 72 (+/- 4.5) <- [68 74 74 72 66 64 78 70 74 78] 72 (+/- 7.4) <- [76 68 74 57 74 84 76 74 60 72] 70 (+/- 4.7) <- [64 72 70 74 78 66 72 66 62 72] 70 (+/- 3.7) <- [72 74 72 62 68 68 76 70 72 70] 72 (+/- 9.0) <- [90 76 80 60 68 66 62 80 66 68] 71 (+/- 9.3) <- [88 68 74 57 72 66 82 66 78 57] 70 (+/- 5.6) <- [64 66 66 68 82 72 72 64 76 66] 69 (+/- 8.1) <- [82 68 54 66 80 66 72 60 74 72] 71 (+/- 7.1) <- [64 80 72 80 68 60 78 64 68 78] 70 (+/- 4.5) <- [66 66 68 78 76 70 62 68 70 72] 71 (+/- 5.6) <- [72 72 64 80 76 62 64 76 72 70] Accuracy: 70.5 (+/- 6.34) Mean time (40 cv): 18.70 seconds Overall time: 752.01 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] 66 (+/- 7.4) <- [62 62 60 78 72 68 56 74 72 56] 67 (+/- 4.7) <- [66 68 66 57 64 68 72 66 76 62] 65 (+/- 5.5) <- [68 60 60 68 72 56 72 68 70 60] 67 (+/- 8.2) <- [62 64 57 74 72 56 82 66 60 76] 66 (+/- 4.0) <- [66 66 68 62 62 60 68 64 70 74] 68 (+/- 8.0) <- [66 56 66 76 66 76 74 68 52 76] 69 (+/- 5.3) <- [76 70 60 70 62 74 66 76 66 66] 67 (+/- 7.5) <- [68 64 78 68 57 60 57 66 80 74] 67 (+/- 4.7) <- [62 62 64 74 66 66 64 76 70 70] 66 (+/- 6.2) <- [52 66 62 70 70 66 72 66 64 76] 65 (+/- 4.6) <- [70 56 68 62 64 70 66 64 57 68] 68 (+/- 5.5) <- [62 72 64 78 64 64 68 66 62 76] 66 (+/- 6.9) <- [66 68 74 57 76 66 76 57 62 57] 68 (+/- 9.7) <- [90 70 72 56 56 62 62 70 64 76] 68 (+/- 6.5) <- [84 66 74 64 64 66 64 70 66 60] 68 (+/- 7.0) <- [74 68 52 76 66 57 72 70 72 68] 67 (+/- 5.6) <- [66 70 57 74 74 66 72 60 62 72] 69 (+/- 6.6) <- [60 78 72 74 70 68 78 62 60 64] 67 (+/- 6.1) <- [74 68 62 74 66 80 62 62 64 62] 67 (+/- 6.7) <- [64 72 56 76 68 66 64 80 62 64] Accuracy: 67.1 (+/- 6.43) Mean time (40 cv): 14.97 seconds Overall time: 602.81 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 / (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()
# 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)
# 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)')
div = np.array(Pvalues if Pname is 'ld' else p['ld'])
res['objective_g_un'] = res['objective_g'] / div
div = np.array(Pvalues if Pname is 'ls' else p['ls'])
res['objective_i_un'] = res['objective_i'] / div
div = np.array(Pvalues if Pname is 'lg' else p['lg'])
res['objective_j_un'] = res['objective_j'] / div
plot('objective_g_un', 'objective_i_un', 'objective_j_un', 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))
dm = ['cosine', 'euclidean'] res['accuracy_std'] = [6.1280319026584698, 6.3406446833109902] res['objective_j'] = [10305.532073974609, 8521.7063903808594] res['objective_i'] = [56504.98046875, 57277.94140625] res['objective_h'] = [0, 0] res['objective_g'] = [76829.12109375, 69936.162109375] res['baseline'] = [67.059999999999974, 67.059999999999974] res['time_features'] = [2421.2433190345764, 2868.63295006752] res['baseline_std'] = 6.42778344377 res['sparsity'] = [20.761215394295302, 19.043886325503355] res['iterations_inner'] = [1143, 1357] res['iterations_outer'] = [7, 7] res['accuracy'] = [73.165000000000035, 70.535000000000082]
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 Dictionary atoms for dm = euclidean Experiment time: 7804 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, "