In [1]:
%matplotlib inline
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import IPython
import SHS_data
import util
import paired_data
import learn
reload(learn)
Out[1]:
<module 'learn' from 'learn.pyc'>
This notebook contains experiments in which a fingerprint is learned from a dataset of cover songs. The main idea behind this is explained in our Audio Bigrams paper [1].
Very briefly explained:
conv2d(X, W)
W the 'salient events'. conv2d(X, w) @ X.T
w a window and @ the matrix product.To evaluate the learned fingerprint, we compare to the state-of-the-art '2D Fourier Transform Magniture Coeffients' by Bertin-Mahieux and Ellis [2], and a simpler fingerprinting approach by Kim et al [3].
We use the Second-hand Song Dataset with dublicates removed as proposed by Julien Osmalskyj.
[1] Van Balen, J., Wiering, F., & Veltkamp, R. (2015). Audio Bigrams as a Unifying Model of Pitch-based Song Description.
[2] Bertin-Mahieux, T., & Ellis, D. P. W. (2012). Large-Scale Cover Song Recognition Using The 2d Fourier Transform Magnitude. In Proc. International Society for Music Information Retrieval Conference.
[3] Kim, S., Unal, E., & Narayanan, S. (2008). Music fingerprint extraction for classical music cover song identification. IEEE Conference on Multimedia and Expo.
In [2]:
n_patches, patch_len = 8, 64
In [3]:
# train, test, validation split
ratio = (50,20,30)
clique_dict, _ = SHS_data.read_cliques()
train_cliques, test_cliques_big, _ = util.split_train_test_validation(clique_dict, ratio=ratio)
# preload training data to memory (just about doable)
print('Preloading training data...')
train_uris = util.uris_from_clique_dict(train_cliques)
chroma_dict = SHS_data.preload_chroma(train_uris)
# make a training dataset of cover and non-cover pairs of songs
print('Preparing training dataset...')
X_A, X_B, Y, pair_uris = paired_data.dataset_of_pairs(train_cliques, chroma_dict,
n_patches=n_patches, patch_len=patch_len)
print(' Training set:', X_A.shape, X_B.shape, Y.shape)
Preloading training data...
Preparing training dataset...
Training set: (32436, 512, 12) (32436, 512, 12) (32436,)
In [4]:
# pick a test subset
n_test_cliques = 50 # e.g., 50 ~ small actual datasets
test_cliques = {uri: test_cliques_big[uri] for uri in test_cliques_big.keys()[:n_test_cliques]}
# preload test data to memory (just about doable)
print('Preloading test data...')
test_uris = util.uris_from_clique_dict(test_cliques)
chroma_dict_T = SHS_data.preload_chroma(test_uris)
# make a test dataset of cover and non-cover pairs of songs
print('Preparing test dataset...')
X_A_T, X_B_T, Y_T, test_pair_uris_T = paired_data.dataset_of_pairs(test_cliques, chroma_dict_T,
n_patches=n_patches, patch_len=patch_len)
print(' Test set:', X_A_T.shape, X_B_T.shape, Y_T.shape)
Preloading test data...
Preparing test dataset...
Test set: (340, 512, 12) (340, 512, 12) (340,)
In [27]:
# for repeated runs with different networks
tf.reset_default_graph()
In [28]:
# make network
network = learn.siamese_network(input_shape=(n_patches*patch_len, 12))
network.add_conv_layer(shape=(1,12), n_filters=12, padding='VALID')
network.add_matmul_layer(filter_len=12, n_filters=12)
In [29]:
alpha = 4
m = 10
lr = 3e-4
batch_size = 100
n_iterations = 3200 # 3200 ~ 10 epoques (train set ~ 320 x 100)
# training metrics
loss, pair_loss, non_pair_loss = network.loss(m=m, alpha=alpha)
bhatt, d_pairs, d_non_pairs = network.bhattacharyya()
# optimiser
train_step = network.train_step(loss, learning_rate=lr)
# choose which metrics to log
metrics = [loss, d_pairs, d_non_pairs]
In [30]:
# start Tensorflow session
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
Exception AssertionError: AssertionError() in <bound method InteractiveSession.__del__ of <tensorflow.python.client.session.InteractiveSession object at 0x128ae8190>> ignored
In [31]:
# train and test batches
train_batches = learn.get_batches([X_A, X_B, Y], batch_size=batch_size)
test_batch = [X_A_T, X_B_T, Y_T]
# train
for step in range(n_iterations):
train_batch = next(train_batches)
# report
network.log_errors(sess, train_batch=train_batch,
test_batch=test_batch, metrics=metrics, log_every=10)
# train
train_feed = {network.x_A:train_batch[0], network.x_B:train_batch[1],
network.is_cover:train_batch[2]}
train_step.run(feed_dict=train_feed)
# report final
network.log_errors(sess, train_batch=train_batch,
test_batch=test_batch, metrics=metrics)
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
0 43.5403 5.11521 7.30174 45.6209 6.25334 7.82205
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
10 46.2566 6.48997 8.09015 43.449 6.51223 8.19473
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
20 43.6188 6.93647 8.16343 42.1766 6.66544 8.40268
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
30 37.6923 6.75306 8.96504 41.2071 6.7171 8.46571
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
40 53.201 8.2057 7.94933 40.528 6.71458 8.45561
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
50 39.752 6.95889 8.67382 40.0099 6.75888 8.50724
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
60 41.5869 7.03172 8.21864 39.6046 6.77288 8.52355
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
70 47.7017 7.55597 7.90635 39.2518 6.58555 8.27234
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
80 40.8777 6.8135 7.96211 39.1295 6.4533 8.08538
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
90 34.3202 6.04549 8.01772 38.8348 6.51763 8.16417
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
100 34.7437 5.53694 7.68401 38.5148 6.59577 8.27186
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
110 39.1977 7.50432 8.94033 38.3297 6.7425 8.46763
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
120 25.0708 5.52274 9.27837 38.165 6.75767 8.49297
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
130 36.8008 6.44687 8.29943 37.9857 6.80255 8.56182
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
140 38.2324 6.60984 8.61963 37.8049 6.80838 8.57763
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
150 32.5302 5.90598 8.0799 37.5349 6.72001 8.47248
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
160 41.4071 7.63957 9.16632 37.3954 6.7734 8.53868
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
170 37.8645 6.86932 8.24409 37.2007 6.74556 8.49805
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
180 35.0457 6.87259 8.82783 37.0049 6.69299 8.43433
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
190 41.3194 7.06996 8.04794 36.8587 6.6826 8.42237
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
200 37.7014 6.39026 7.89669 36.6748 6.70132 8.45841
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
210 52.8262 9.24387 9.32736 36.4069 6.60146 8.35251
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
220 31.2434 5.47828 7.76178 36.3627 6.435 8.14253
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
230 34.6236 5.77741 7.61703 36.2222 6.5667 8.32426
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
240 37.9644 6.67956 8.6221 36.23 6.71368 8.51739
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
250 36.3788 6.19884 8.00849 36.2443 6.78856 8.60522
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
260 36.0509 7.13083 8.43101 36.1497 6.77337 8.58696
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
270 42.693 6.79694 7.56245 35.9742 6.58168 8.34412
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
280 39.1164 7.05445 8.6269 35.8999 6.57557 8.33085
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
290 34.7188 6.57786 8.23342 35.8248 6.6154 8.373
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
300 45.6693 7.30934 7.80138 35.7933 6.69672 8.48203
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
310 36.7344 6.38241 7.98314 35.6762 6.67189 8.453
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
320 47.1647 7.8217 8.15565 35.6293 6.71533 8.5019
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
330 32.1421 6.2349 8.67543 35.6847 6.83613 8.64375
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
340 40.5062 7.88595 8.87262 35.8347 6.95498 8.78423
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
350 27.4655 5.39962 8.05326 35.652 6.92257 8.7466
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
360 40.1694 7.53653 8.42328 35.3601 6.82193 8.61399
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
370 30.9223 6.21849 8.22387 35.1881 6.74259 8.51151
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
380 35.426 6.9648 8.44264 35.1282 6.72618 8.49199
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
390 42.7194 7.07416 7.71899 35.0653 6.59323 8.31123
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
400 45.3913 7.98787 8.17174 35.0801 6.56555 8.2657
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
410 35.7805 6.36642 8.08474 35.0548 6.68359 8.40552
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
420 36.1803 6.45312 7.95312 34.9749 6.74789 8.49626
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
430 37.6193 6.92529 8.3276 34.9315 6.81067 8.58265
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
440 38.9319 6.78643 8.01762 34.8261 6.77917 8.53836
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
450 33.7369 6.49344 8.07451 34.7988 6.80336 8.56528
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
460 38.1374 6.76596 7.98402 34.8667 6.86476 8.64168
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
470 43.9835 8.08917 8.72237 34.8406 6.82863 8.59178
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
480 35.6574 6.18726 7.73042 34.7868 6.79352 8.54169
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
490 39.5931 7.17392 8.15222 34.8427 6.88601 8.64281
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
500 43.6549 8.2044 8.50248 34.8213 6.88969 8.64487
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
510 38.2294 6.54495 7.87691 34.7539 6.84074 8.57236
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
520 34.2626 6.66437 8.2236 34.6841 6.83926 8.56749
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
530 48.4841 7.87987 7.71161 34.5543 6.74858 8.45613
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
540 31.3915 5.99427 8.14784 34.5808 6.5528 8.19332
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
550 42.1952 7.38298 7.93763 34.5451 6.5814 8.23495
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
560 34.6156 6.35396 7.79055 34.4682 6.69274 8.38605
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
570 36.2202 6.72333 8.09955 34.4759 6.84953 8.59265
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
580 35.3645 6.90652 8.68061 34.4963 6.92333 8.68718
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
590 33.6665 6.33403 8.27158 34.3767 6.82098 8.54598
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
600 38.0675 7.19841 8.232 34.3496 6.75815 8.44953
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
610 37.9689 7.11345 8.42095 34.3479 6.76468 8.44263
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
620 38.7353 7.10723 8.29858 34.3295 6.84993 8.55949
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
630 31.9445 6.26122 8.02154 34.3242 6.85445 8.5702
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
640 38.1591 6.90958 8.02217 34.2914 6.89063 8.61935
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
650 30.8554 6.243 8.37868 34.3282 6.96761 8.72102
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
660 29.2718 6.12057 8.23585 34.3961 7.04322 8.82167
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
670 38.7479 7.09476 8.28207 34.3985 7.04976 8.82872
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
680 38.2311 7.05672 8.21698 34.2987 7.00502 8.76104
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
690 32.3838 6.28984 8.20055 34.2234 6.9361 8.65831
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
700 30.3151 6.42901 8.43965 34.1779 6.88622 8.59316
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
710 35.6673 7.16528 8.39311 34.1601 6.832 8.51399
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
720 42.3553 7.44352 7.9438 34.2883 6.69107 8.31691
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
730 35.7597 6.45835 8.05563 34.3149 6.70162 8.32737
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
740 35.002 6.79156 8.53756 34.2163 6.8026 8.46584
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
750 53.3678 8.06812 8.09002 34.1564 6.87437 8.56936
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
760 40.1235 7.476 8.1999 34.1187 6.90099 8.61073
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
770 31.3726 6.63396 8.70383 34.0899 6.86858 8.56428
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
780 34.128 6.99001 9.19669 34.1162 6.92973 8.64883
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
790 38.7198 7.55244 8.69319 34.1323 6.90689 8.62806
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
800 33.2942 5.68088 7.75124 34.1227 6.81422 8.50742
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
810 33.143 6.31509 8.0881 34.1577 6.92928 8.65032
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
820 37.0549 7.30857 8.29879 34.1829 6.95896 8.6778
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
830 35.1722 6.41665 7.8033 34.1705 6.9081 8.60834
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
840 33.401 6.71163 8.48202 34.1611 6.88703 8.57433
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
850 35.4135 6.81086 8.13929 34.1338 6.86589 8.5511
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
860 26.3468 5.93669 8.85734 34.1093 6.78009 8.44083
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
870 36.2175 6.88814 8.44378 34.1679 6.68112 8.31444
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
880 36.0372 6.16858 7.72635 34.1966 6.68438 8.32635
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
890 43.1865 7.86061 8.29977 34.1282 6.83581 8.53219
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
900 36.6404 7.39766 8.59048 34.1248 6.93997 8.66995
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
910 32.7244 6.62801 8.66838 34.1061 6.94823 8.68132
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
920 35.8668 6.49993 7.81155 34.0951 6.8477 8.53208
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
930 33.7229 6.98906 8.57994 34.1012 6.84404 8.51801
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
940 28.5102 6.12539 8.38542 34.0795 6.87923 8.55791
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
950 32.6044 6.57435 8.53108 34.0871 6.91675 8.62265
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
960 34.9701 6.59162 8.0036 34.0742 6.91989 8.63248
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
970 37.269 7.16249 8.34836 34.0777 6.98076 8.7198
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
980 32.3021 6.56554 8.53118 34.1128 7.03928 8.80173
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
990 29.0151 6.42073 8.83196 34.1663 7.07819 8.85767
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1000 35.9751 7.20182 8.79416 34.1567 7.06972 8.84475
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1010 33.3246 6.61792 8.34506 34.0886 6.99864 8.73547
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1020 32.4216 6.70169 8.52849 34.0414 6.93325 8.64604
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1030 32.1027 6.89699 8.85881 33.9935 6.89381 8.59853
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1040 38.6011 7.48165 8.3394 33.9909 6.78536 8.44934
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1050 35.7591 6.76316 8.12063 34.0281 6.73024 8.38151
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1060 33.9061 7.12478 8.93631 33.9881 6.79247 8.4604
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1070 30.3191 6.51493 8.40019 33.9252 6.8389 8.53241
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1080 37.1411 6.96635 8.07509 33.8903 6.89373 8.6153
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1090 37.029 6.83421 8.20588 33.8469 6.86528 8.5812
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1100 38.1007 7.48305 8.89387 33.8535 6.88521 8.60551
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1110 31.2495 6.46967 8.53837 33.8687 6.90266 8.64082
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1120 39.1758 7.72712 8.82406 33.8699 6.8467 8.57271
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1130 36.1927 7.08105 8.19923 33.8932 6.87056 8.60082
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1140 56.0056 8.9149 8.33987 33.9821 6.97592 8.72234
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1150 39.3045 7.99949 8.77745 33.9852 6.96336 8.70898
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1160 32.7479 6.97707 8.89389 33.9384 6.90504 8.6329
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1170 35.0143 7.03698 8.49296 33.9189 6.89618 8.62237
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1180 32.1099 6.56211 8.13006 33.8941 6.81304 8.524
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1190 30.3978 6.19793 8.36756 33.9694 6.67837 8.34843
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1200 39.2971 7.19004 8.16867 34.0131 6.65457 8.32673
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1210 36.4626 6.5944 8.05114 33.9527 6.76221 8.47209
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1220 32.4529 6.86724 9.05621 33.9367 6.8978 8.64784
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1230 41.4659 7.6272 9.02029 33.93 6.97198 8.74908
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1240 34.8943 7.13743 8.65015 33.866 6.88395 8.62387
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1250 36.7508 7.40455 8.89773 33.8602 6.85026 8.57187
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1260 38.7119 7.14474 8.11983 33.864 6.84131 8.54897
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1270 34.1685 6.85832 8.6493 33.8845 6.90202 8.64163
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1280 35.9934 7.03951 8.36304 33.8923 6.90028 8.64919
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1290 32.0587 6.25387 8.18333 33.8646 6.93161 8.69471
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1300 27.181 6.35549 8.95704 33.8786 6.99145 8.77594
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1310 31.4261 6.4896 8.46803 33.9366 7.04232 8.84952
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1320 24.0537 5.54935 8.82415 33.9643 7.0473 8.86199
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1330 29.213 6.17249 8.51995 33.8904 6.99213 8.78233
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1340 32.8295 6.35771 8.26171 33.8304 6.93025 8.69262
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1350 32.9287 6.77638 8.36926 33.7776 6.88046 8.63033
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1360 40.1914 7.56007 8.58243 33.7269 6.834 8.56918
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1370 40.6317 7.60181 8.27243 33.7966 6.71973 8.41388
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1380 33.31 6.34096 8.03639 33.7926 6.73618 8.43343
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1390 34.5966 7.27733 9.0709 33.7529 6.83919 8.57161
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1400 40.4333 7.78061 8.88525 33.7383 6.88957 8.64818
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1410 38.0164 6.95342 8.15991 33.6936 6.84984 8.59869
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1420 31.3494 5.93674 7.98008 33.6819 6.81739 8.55481
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1430 54.7227 9.63138 9.23983 33.7143 6.90109 8.67028
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1440 38.873 7.33136 8.10504 33.7137 6.85084 8.61176
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1450 31.625 6.25335 8.00493 33.7085 6.77023 8.51213
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1460 32.842 6.65889 8.51689 33.7856 6.93151 8.70562
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1470 31.3606 6.71252 8.83867 33.8349 6.96455 8.73802
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1480 31.5728 6.08035 8.36825 33.7796 6.89014 8.64703
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1490 33.6217 6.9793 8.59088 33.7506 6.86062 8.61119
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1500 37.0351 7.22274 8.42918 33.7359 6.81136 8.5581
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1510 37.0648 7.83505 9.65056 33.7751 6.71522 8.43622
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1520 37.2239 6.56562 8.02321 33.8702 6.62508 8.32316
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1530 30.661 5.48209 7.70208 33.8555 6.65325 8.3676
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1540 29.3265 6.15493 8.38461 33.7765 6.82922 8.59892
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1550 29.6952 6.09139 8.40399 33.7525 6.92062 8.71221
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1560 39.4029 7.44419 8.64244 33.708 6.91376 8.70222
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1570 32.4833 7.04628 9.15551 33.6999 6.81566 8.5597
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1580 33.2639 6.46292 8.43392 33.7102 6.80814 8.54457
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1590 41.9434 7.40033 7.93677 33.7252 6.87233 8.63051
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1600 34.069 6.91117 8.54427 33.7667 6.89243 8.67149
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1610 34.8842 7.0494 8.34573 33.745 6.89555 8.68356
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1620 35.88 7.06653 8.7262 33.7455 6.94939 8.75594
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1630 37.5796 7.14999 8.59704 33.7829 6.99488 8.82079
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1640 34.7304 7.07697 8.57783 33.8317 7.02212 8.86765
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1650 38.949 7.67518 8.72907 33.8325 7.01627 8.85859
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1660 31.983 6.99454 8.83013 33.7347 6.93703 8.74318
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1670 30.3901 6.35448 8.61376 33.6731 6.87417 8.65877
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1680 38.2459 7.6628 8.83631 33.6257 6.86102 8.64726
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1690 43.8054 7.9722 8.12203 33.6476 6.74483 8.47846
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1700 29.9021 6.12553 8.37323 33.7003 6.70568 8.42439
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1710 31.1781 6.62417 8.47918 33.671 6.78703 8.52637
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1720 34.9695 6.38537 7.96302 33.6379 6.81398 8.57271
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1730 36.2132 6.9211 8.38906 33.6248 6.84091 8.61484
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1740 37.3857 7.26034 8.54898 33.6039 6.79437 8.55041
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1750 32.8849 6.88984 8.70927 33.6044 6.84565 8.6191
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1760 38.5669 7.50796 8.62642 33.6201 6.84088 8.62339
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1770 37.8636 6.61536 7.77578 33.6276 6.75395 8.51208
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1780 32.4461 6.4933 8.11031 33.6359 6.84092 8.61924
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1790 31.0141 6.59001 8.57286 33.7164 6.94204 8.73037
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1800 35.638 6.70556 8.16909 33.7092 6.89755 8.67206
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1810 37.0556 7.15579 8.56646 33.6853 6.83395 8.59118
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1820 39.6005 8.11184 9.49712 33.6821 6.83127 8.5937
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1830 39.9962 7.3435 8.21658 33.7125 6.74303 8.48718
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1840 34.3519 7.08461 8.73561 33.8079 6.64186 8.3543
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1850 29.1798 5.56802 7.80151 33.85 6.61625 8.32922
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1860 36.3462 7.20291 8.28331 33.72 6.72984 8.48321
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1870 38.006 7.32936 8.29435 33.6529 6.85304 8.64054
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1880 33.168 6.98912 8.77069 33.636 6.9241 8.73213
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1890 33.1079 6.19797 8.13516 33.6529 6.83325 8.59376
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1900 34.2317 6.2855 8.05744 33.6788 6.81752 8.56666
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1910 36.0811 6.56317 7.82806 33.6942 6.82306 8.56516
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1920 35.9319 7.20199 8.57408 33.7373 6.86194 8.63178
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1930 36.3441 6.40205 7.76193 33.744 6.86365 8.64559
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1940 28.4428 6.38035 8.67351 33.7212 6.90339 8.70284
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1950 26.6382 5.94821 8.50783 33.7205 6.9529 8.76907
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1960 30.211 6.33177 8.40975 33.7444 6.98103 8.81614
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1970 29.6717 6.65581 9.15595 33.7733 6.99219 8.84215
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1980 34.9931 7.00131 8.51661 33.722 6.95932 8.79193
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
1990 36.5749 7.47415 9.04464 33.6675 6.92893 8.74539
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2000 33.3544 6.97706 8.49984 33.5913 6.87588 8.68253
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2010 42.2321 8.13229 8.76843 33.5359 6.82147 8.60486
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2020 38.2397 7.47949 8.50417 33.6104 6.74108 8.48808
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2030 31.0192 6.3203 8.44109 33.6198 6.75738 8.50111
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2040 35.4496 6.91778 8.73346 33.6081 6.78241 8.53513
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2050 40.1613 7.71319 8.45566 33.5859 6.80191 8.5687
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2060 27.0829 5.89952 8.88099 33.567 6.77471 8.53011
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2070 30.9376 5.94887 8.17314 33.5586 6.77315 8.52712
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2080 32.7774 6.75044 8.30347 33.569 6.83413 8.61459
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2090 40.2881 7.909 8.44208 33.5814 6.80394 8.58157
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2100 35.8956 6.41085 7.69688 33.5777 6.75334 8.519
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2110 36.2524 7.32888 8.76616 33.632 6.88474 8.66646
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2120 37.9977 7.38408 8.52772 33.6833 6.91474 8.69326
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2130 34.9821 6.89098 8.50812 33.6706 6.85865 8.62528
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2140 32.5159 6.31817 8.25483 33.6554 6.83374 8.59773
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2150 38.214 6.64846 8.04292 33.666 6.77865 8.54075
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2160 30.2028 6.24266 8.41041 33.7071 6.6943 8.43498
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2170 39.4688 7.1967 7.90824 33.7516 6.62246 8.35037
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2180 35.6266 6.93709 8.34997 33.7223 6.63809 8.37904
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2190 39.4659 7.36593 8.23649 33.6053 6.78587 8.57131
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2200 34.9227 6.74645 8.29555 33.562 6.88621 8.69641
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2210 41.9647 8.09618 9.10945 33.5346 6.87206 8.6697
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2220 29.5655 6.15514 8.4867 33.5728 6.8067 8.56903
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2230 38.4021 6.76226 8.03534 33.5901 6.78604 8.53807
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2240 34.2024 7.14758 8.6379 33.6251 6.83068 8.5992
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2250 30.5691 6.73444 8.79514 33.6743 6.85405 8.64039
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2260 30.0031 6.4241 8.49177 33.6472 6.87502 8.67593
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2270 32.368 6.99029 8.92381 33.6523 6.92775 8.74561
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2280 30.0711 6.16796 8.46692 33.674 6.95837 8.79468
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2290 43.7473 7.34428 7.77529 33.6968 6.97332 8.83253
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2300 36.9423 7.59138 9.37934 33.6993 6.97597 8.83501
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2310 40.187 7.96975 8.78384 33.6271 6.93221 8.76844
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2320 34.4945 7.07228 8.55705 33.5507 6.8703 8.68643
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2330 35.0818 7.07348 8.35306 33.4937 6.83513 8.64555
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2340 37.0864 7.27612 8.28827 33.5239 6.74466 8.51255
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2350 31.8484 6.5135 8.43533 33.5593 6.71811 8.47383
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2360 36.7855 6.95111 8.41953 33.5561 6.77337 8.53667
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2370 30.434 6.14985 8.19993 33.5379 6.76831 8.53524
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2380 35.3326 7.089 8.4211 33.5185 6.7652 8.53269
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2390 29.3465 6.34878 8.76276 33.5041 6.73385 8.48848
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2400 33.4879 6.88616 8.62524 33.4965 6.80439 8.58291
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2410 29.7325 6.04466 8.19736 33.5106 6.79989 8.59115
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2420 28.9911 6.07004 8.59687 33.5088 6.73517 8.51121
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2430 34.3545 7.1851 8.73451 33.5306 6.83518 8.62501
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2440 34.0302 6.69113 8.41707 33.5986 6.91074 8.70085
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2450 35.6532 7.50233 9.09378 33.6014 6.88363 8.66956
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2460 38.6834 7.48097 8.5332 33.5754 6.83518 8.61532
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2470 30.9127 6.74616 9.23295 33.5744 6.79971 8.57959
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2480 52.1498 8.9436 8.41685 33.6068 6.72403 8.4909
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2490 29.4145 6.49049 8.57138 33.662 6.61445 8.35309
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2500 41.028 7.64705 8.63686 33.7093 6.57457 8.31236
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2510 33.3531 6.65922 8.67756 33.5608 6.71557 8.49712
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2520 33.914 6.10012 7.67795 33.5033 6.82959 8.63741
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2530 37.9089 7.40426 8.35262 33.4676 6.88219 8.70177
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2540 37.5141 6.6187 7.7352 33.4754 6.80216 8.58116
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2550 29.5016 6.85595 9.56591 33.4903 6.78506 8.55391
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2560 37.9762 6.87381 7.85687 33.5177 6.79492 8.55917
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2570 36.7791 6.91866 7.96428 33.582 6.84007 8.63018
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2580 37.2223 7.18969 8.3332 33.5848 6.85056 8.65273
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2590 37.8625 7.37283 8.59364 33.5734 6.89786 8.72015
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2600 32.2061 6.57541 8.67927 33.5715 6.93348 8.77015
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2610 35.1567 6.96721 8.39081 33.6001 6.96116 8.82388
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2620 25.3519 5.73145 8.71873 33.6102 6.9579 8.83221
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2630 30.39 6.10911 8.13727 33.5396 6.90931 8.75883
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2640 33.7564 6.79418 8.53406 33.4881 6.87133 8.70159
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2650 32.6678 6.94454 9.00355 33.4347 6.83144 8.65761
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2660 33.3746 6.95041 8.58976 33.4188 6.76854 8.56639
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2670 50.1821 8.63994 8.11408 33.4871 6.71048 8.48516
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2680 34.4756 6.78371 8.38488 33.4867 6.7546 8.53539
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2690 35.619 6.71675 8.02968 33.4853 6.76036 8.54088
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2700 36.608 7.08445 8.47241 33.462 6.77041 8.55801
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2710 34.8888 6.73326 8.5158 33.4379 6.73093 8.50132
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2720 33.8214 6.46193 8.08747 33.4176 6.74708 8.52393
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2730 38.1245 7.33127 8.59081 33.4263 6.81311 8.6215
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2740 41.5325 8.12752 8.97337 33.4316 6.77803 8.58031
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2750 33.3673 6.24628 7.93099 33.4225 6.75348 8.54605
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2760 34.4898 7.22083 9.04758 33.5009 6.89681 8.70904
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2770 43.9961 8.14167 8.52988 33.5277 6.90693 8.7171
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2780 31.756 6.2247 8.48434 33.4904 6.82053 8.61094
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2790 32.1326 6.83941 8.68861 33.4826 6.78761 8.57711
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2800 42.1534 7.51799 8.00094 33.4987 6.72521 8.51055
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2810 32.5269 6.5364 8.23403 33.5565 6.63578 8.39374
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2820 34.6161 6.78274 8.52618 33.6156 6.57068 8.31983
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2830 36.3156 6.68271 7.92236 33.5518 6.6231 8.39044
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2840 32.8818 6.61501 8.2705 33.4528 6.7733 8.58068
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2850 34.3701 6.76283 8.38704 33.4137 6.86455 8.69313
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2860 27.954 5.70166 8.5213 33.3817 6.81784 8.62163
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2870 31.8443 6.95499 8.4802 33.414 6.78338 8.56617
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2880 33.9277 6.79365 8.49715 33.4428 6.7752 8.54854
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2890 36.4027 6.78913 8.40834 33.4884 6.82182 8.61234
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2900 33.3286 6.99973 8.59824 33.5256 6.84014 8.64451
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2910 36.3923 7.21456 8.63042 33.5016 6.86514 8.68354
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2920 26.2691 5.91867 8.68341 33.5046 6.9069 8.7398
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2930 34.7535 6.6529 7.95745 33.5284 6.92987 8.78178
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2940 37.114 6.88656 8.08281 33.5461 6.94535 8.82676
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2950 29.4851 6.82189 9.18459 33.521 6.93512 8.81223
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2960 35.0428 7.01226 8.6075 33.4554 6.89536 8.74985
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2970 27.2787 6.38478 9.18814 33.3909 6.83406 8.67089
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2980 30.2172 6.48661 8.71847 33.355 6.8046 8.63491
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
2990 41.4874 7.73534 8.42874 33.3999 6.72232 8.51742
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3000 37.4132 7.34945 8.4391 33.4243 6.71056 8.49601
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3010 32.0406 6.80794 8.84238 33.4276 6.7555 8.54561
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3020 32.9977 6.89584 8.41426 33.4116 6.75595 8.5484
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3030 36.3105 7.31893 8.6239 33.3958 6.74314 8.52779
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3040 28.8677 6.37877 8.4617 33.3766 6.71466 8.48883
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3050 28.7933 6.22567 8.82355 33.3703 6.7978 8.60041
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3060 29.2274 6.45005 8.8938 33.3781 6.77909 8.59095
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3070 27.8928 5.19329 7.79018 33.3696 6.70884 8.50312
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3080 33.4264 6.65784 8.50039 33.4013 6.84595 8.66053
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3090 32.1336 7.11876 9.01658 33.4612 6.9146 8.73181
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3100 39.1469 6.88132 8.25148 33.4224 6.85798 8.66628
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3110 34.3302 6.36508 8.35128 33.3906 6.79239 8.5918
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3120 31.1982 6.16945 8.21163 33.3925 6.74799 8.54914
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3130 30.721 6.65844 8.75108 33.4464 6.67358 8.45524
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3140 32.4117 6.91693 8.96086 33.5261 6.57255 8.32732
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3150 34.0715 5.67127 7.57098 33.5753 6.54318 8.29888
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3160 33.6351 6.8174 8.32194 33.4083 6.71279 8.51775
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3170 33.3606 7.20612 8.84145 33.3617 6.81837 8.64439
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3180 31.2317 6.79606 8.574 33.3329 6.84472 8.67246
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3190 32.6545 6.63326 8.27141 33.3595 6.78418 8.5793
TR.loss TR.d_pairs TR.d_non_pairs TE.loss TE.d_pairs TE.d_non_pairs
3200 33.0516 7.24116 9.36907 33.3831 6.77395 8.56043
In [32]:
plt.figure(figsize=(10,4))
plt.plot(network.train_log['TR.loss']);
plt.plot(network.train_log['TE.loss'], color='k');
plt.title('train (b) and test (k) loss function');
In [33]:
def plot_distances(feed):
y_A, y_B = sess.run([network.subnet_A[-1], network.subnet_B[-1]],
feed_dict=feed)
is_cover = feed[network.is_cover]
pair_dists = np.sqrt(np.sum((y_A - y_B)**2, axis=1))[np.where(is_cover==1)]
non_pair_dists = np.sqrt(np.sum((y_A - y_B)**2, axis=1))[np.where(is_cover==0)]
bins = np.arange(0,20,0.5)
plt.figure(figsize=(16,4))
plt.subplot(121)
plt.hist(non_pair_dists, bins=bins, alpha=0.5);
plt.hist(pair_dists, bins=bins, color='r', alpha=0.5);
plt.subplot(143)
plt.boxplot([non_pair_dists, pair_dists]);
# train distances
train_feed = {network.x_A:train_batch[0], network.x_B:train_batch[1], network.is_cover: train_batch[2]}
plot_distances(train_feed)
# test distances
test_feed = {network.x_A: X_A_T, network.x_B: X_B_T, network.is_cover: Y_T}
plot_distances(test_feed)
In [34]:
import main
import fingerprints as fp
In [35]:
def fingerprint(chroma, n_patches=8, patch_len=64):
n_frames, n_bins = chroma.shape
if not n_frames == n_patches * patch_len:
chroma = paired_data.patchwork(chroma, n_patches=n_patches,
patch_len=patch_len)
fps = []
for i in range(12):
chroma_trans = np.roll(chroma, -i, axis=1)
chroma_tensor = chroma_trans.reshape((1, n_patches*patch_len, 12))
network_out = network.subnet_A[-1]
fp = network_out.eval(feed_dict={network.x_A : chroma_tensor})
fps.append(fp.flatten())
return fps
Kim, S., Unal, E., & Narayanan, S. (2008). Music fingerprint extraction for classical music cover song identification. IEEE Conference on Multimedia and Expo.
test_cliques_big:
results: {'mean r5': 0.073941374228151044, 'mean ap': 0.069848998736677395, 'mean p1': 0.097366977509599564}
In [36]:
results = main.run_leave_one_out_experiment(test_cliques_big,
fp_function=fp.cov,
print_every=50)
print('results:', results)
Computing fingerprints...
Fingerprinting track 50/3646...
Fingerprinting track 100/3646...
Fingerprinting track 150/3646...
Fingerprinting track 200/3646...
Fingerprinting track 250/3646...
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Running queries...
Running queries for clique 50/1166
Running queries for clique 100/1166
Running queries for clique 150/1166
Running queries for clique 200/1166
Running queries for clique 250/1166
Running queries for clique 300/1166
Running queries for clique 350/1166
Running queries for clique 400/1166
Running queries for clique 450/1166
Running queries for clique 500/1166
Running queries for clique 550/1166
Running queries for clique 600/1166
Running queries for clique 650/1166
Running queries for clique 700/1166
Running queries for clique 750/1166
Running queries for clique 800/1166
Running queries for clique 850/1166
Running queries for clique 900/1166
Running queries for clique 950/1166
Running queries for clique 1000/1166
Running queries for clique 1050/1166
Running queries for clique 1100/1166
Running queries for clique 1150/1166
results: {'mean r5': 0.073941374228151044, 'mean ap': 0.069848998736677395, 'mean p1': 0.097366977509599564}
Bertin-Mahieux, T., & Ellis, D. P. W. (2012). Large-Scale Cover Song Recognition Using The 2d Fourier Transform Magnitude. In Proc. International Society for Music Information Retrieval Conference.
The PCA step at the end of the algorithm is not implemented at the moment. Without PCA, a patch length of 64 was found to be better than the original 75, and mean pooling across patches worked better than median-pooling, so these parameters were used instead. The difference between PCA and no PCA performance on a 12K test set was 0.08912 vs. 0.09475 mean average precision, or about 6%.
test_cliques_big:
results: {'mean r5': 0.11885786185784572, 'mean ap': 0.11072061679198177, 'mean p1': 0.14070213933077344}
In [37]:
results = main.run_leave_one_out_experiment(test_cliques_big,
fp_function=fp.fourier,
print_every=50)
print('results:', results)
Computing fingerprints...
Fingerprinting track 50/3646...
Fingerprinting track 100/3646...
Fingerprinting track 150/3646...
Fingerprinting track 200/3646...
Fingerprinting track 250/3646...
Fingerprinting track 300/3646...
Fingerprinting track 350/3646...
Fingerprinting track 400/3646...
Fingerprinting track 450/3646...
Fingerprinting track 500/3646...
Fingerprinting track 550/3646...
Fingerprinting track 600/3646...
Fingerprinting track 650/3646...
Fingerprinting track 700/3646...
Fingerprinting track 750/3646...
Fingerprinting track 800/3646...
Fingerprinting track 850/3646...
Fingerprinting track 900/3646...
Fingerprinting track 950/3646...
Fingerprinting track 1000/3646...
Fingerprinting track 1050/3646...
Fingerprinting track 1100/3646...
Fingerprinting track 1150/3646...
Fingerprinting track 1200/3646...
Fingerprinting track 1250/3646...
Fingerprinting track 1300/3646...
Fingerprinting track 1350/3646...
Fingerprinting track 1400/3646...
Fingerprinting track 1450/3646...
Fingerprinting track 1500/3646...
Fingerprinting track 1550/3646...
Fingerprinting track 1600/3646...
Fingerprinting track 1650/3646...
Fingerprinting track 1700/3646...
Fingerprinting track 1750/3646...
Fingerprinting track 1800/3646...
Fingerprinting track 1850/3646...
Fingerprinting track 1900/3646...
Fingerprinting track 1950/3646...
Fingerprinting track 2000/3646...
Fingerprinting track 2050/3646...
Fingerprinting track 2100/3646...
Fingerprinting track 2150/3646...
Fingerprinting track 2200/3646...
Fingerprinting track 2250/3646...
Fingerprinting track 2300/3646...
Fingerprinting track 2350/3646...
Fingerprinting track 2400/3646...
Fingerprinting track 2450/3646...
Fingerprinting track 2500/3646...
Fingerprinting track 2550/3646...
Fingerprinting track 2600/3646...
Fingerprinting track 2650/3646...
Fingerprinting track 2700/3646...
Fingerprinting track 2750/3646...
Fingerprinting track 2800/3646...
Fingerprinting track 2850/3646...
Fingerprinting track 2900/3646...
Fingerprinting track 2950/3646...
Fingerprinting track 3000/3646...
Fingerprinting track 3050/3646...
Fingerprinting track 3100/3646...
Fingerprinting track 3150/3646...
Fingerprinting track 3200/3646...
Fingerprinting track 3250/3646...
Fingerprinting track 3300/3646...
Fingerprinting track 3350/3646...
Fingerprinting track 3400/3646...
Fingerprinting track 3450/3646...
Fingerprinting track 3500/3646...
Fingerprinting track 3550/3646...
Fingerprinting track 3600/3646...
Running queries...
Running queries for clique 50/1166
Running queries for clique 100/1166
Running queries for clique 150/1166
Running queries for clique 200/1166
Running queries for clique 250/1166
Running queries for clique 300/1166
Running queries for clique 350/1166
Running queries for clique 400/1166
Running queries for clique 450/1166
Running queries for clique 500/1166
Running queries for clique 550/1166
Running queries for clique 600/1166
Running queries for clique 650/1166
Running queries for clique 700/1166
Running queries for clique 750/1166
Running queries for clique 800/1166
Running queries for clique 850/1166
Running queries for clique 900/1166
Running queries for clique 950/1166
Running queries for clique 1000/1166
Running queries for clique 1050/1166
Running queries for clique 1100/1166
Running queries for clique 1150/1166
results: {'mean r5': 0.11885786185784572, 'mean ap': 0.11072061679198177, 'mean p1': 0.14070213933077344}
We now see that with the right configuration, we are able to make the fingerprinter do a little bit better than the 2d-DFT-based fingerprints:
test_cliques_big with W_1 = 12x12 and W_2 = 12x12.
fp_results: {'mean r5': 0.13775251746235256, 'mean ap': 0.12335753431869072, 'mean p1': 0.15551289083927591}
In [38]:
results = main.run_leave_one_out_experiment(test_cliques_big,
fp_function=fingerprint,
print_every=50)
print('fp_results:', results)
Computing fingerprints...
Fingerprinting track 50/3646...
Fingerprinting track 100/3646...
Fingerprinting track 150/3646...
Fingerprinting track 200/3646...
Fingerprinting track 250/3646...
Fingerprinting track 300/3646...
Fingerprinting track 350/3646...
Fingerprinting track 400/3646...
Fingerprinting track 450/3646...
Fingerprinting track 500/3646...
Fingerprinting track 550/3646...
Fingerprinting track 600/3646...
Fingerprinting track 650/3646...
Fingerprinting track 700/3646...
Fingerprinting track 750/3646...
Fingerprinting track 800/3646...
Fingerprinting track 850/3646...
Fingerprinting track 900/3646...
Fingerprinting track 950/3646...
Fingerprinting track 1000/3646...
Fingerprinting track 1050/3646...
Fingerprinting track 1100/3646...
Fingerprinting track 1150/3646...
Fingerprinting track 1200/3646...
Fingerprinting track 1250/3646...
Fingerprinting track 1300/3646...
Fingerprinting track 1350/3646...
Fingerprinting track 1400/3646...
Fingerprinting track 1450/3646...
Fingerprinting track 1500/3646...
Fingerprinting track 1550/3646...
Fingerprinting track 1600/3646...
Fingerprinting track 1650/3646...
Fingerprinting track 1700/3646...
Fingerprinting track 1750/3646...
Fingerprinting track 1800/3646...
Fingerprinting track 1850/3646...
Fingerprinting track 1900/3646...
Fingerprinting track 1950/3646...
Fingerprinting track 2000/3646...
Fingerprinting track 2050/3646...
Fingerprinting track 2100/3646...
Fingerprinting track 2150/3646...
Fingerprinting track 2200/3646...
Fingerprinting track 2250/3646...
Fingerprinting track 2300/3646...
Fingerprinting track 2350/3646...
Fingerprinting track 2400/3646...
Fingerprinting track 2450/3646...
Fingerprinting track 2500/3646...
Fingerprinting track 2550/3646...
Fingerprinting track 2600/3646...
Fingerprinting track 2650/3646...
Fingerprinting track 2700/3646...
Fingerprinting track 2750/3646...
Fingerprinting track 2800/3646...
Fingerprinting track 2850/3646...
Fingerprinting track 2900/3646...
Fingerprinting track 2950/3646...
Fingerprinting track 3000/3646...
Fingerprinting track 3050/3646...
Fingerprinting track 3100/3646...
Fingerprinting track 3150/3646...
Fingerprinting track 3200/3646...
Fingerprinting track 3250/3646...
Fingerprinting track 3300/3646...
Fingerprinting track 3350/3646...
Fingerprinting track 3400/3646...
Fingerprinting track 3450/3646...
Fingerprinting track 3500/3646...
Fingerprinting track 3550/3646...
Fingerprinting track 3600/3646...
Running queries...
Running queries for clique 50/1166
Running queries for clique 100/1166
Running queries for clique 150/1166
Running queries for clique 200/1166
Running queries for clique 250/1166
Running queries for clique 300/1166
Running queries for clique 350/1166
Running queries for clique 400/1166
Running queries for clique 450/1166
Running queries for clique 500/1166
Running queries for clique 550/1166
Running queries for clique 600/1166
Running queries for clique 650/1166
Running queries for clique 700/1166
Running queries for clique 750/1166
Running queries for clique 800/1166
Running queries for clique 850/1166
Running queries for clique 900/1166
Running queries for clique 950/1166
Running queries for clique 1000/1166
Running queries for clique 1050/1166
Running queries for clique 1100/1166
Running queries for clique 1150/1166
fp_results: {'mean r5': 0.13775251746235256, 'mean ap': 0.12335753431869072, 'mean p1': 0.15551289083927591}
In [ ]:
Content source: jvbalen/cover_id
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