In [1]:
from vahun.LogReader import LogReader
import pandas as pd
log=LogReader()
table=log.get_full_table()
table.to_csv('/mnt/store/velkey/result_full.tsv',sep='\t')
print(table)
Experiment Encoded_len Uniq_words \
0 uniq_variational_top_uni__20170426181649 20 200000
1 uniq_variational_top_uni__20170426181649 20 200000
2 uniq_autoencoder_top_uni__20170426181705 20 200000
3 uniq_autoencoder_top_uni__20170426181705 20 200000
4 uniq_autoencoder_top_uni__20170426182211 40 200000
5 uniq_autoencoder_top_uni__20170426182211 40 200000
6 uniq_variational_top_uni__20170426182341 40 200000
7 uniq_variational_top_uni__20170426182341 40 200000
8 uniq_autoencoder_top_uni__20170426182714 60 200000
9 uniq_autoencoder_top_uni__20170426182714 60 200000
10 uniq_variational_top_uni__20170426182915 60 200000
11 uniq_variational_top_uni__20170426182915 60 200000
12 uniq_autoencoder_top_uni__20170426182925 20 200000
13 uniq_autoencoder_top_uni__20170426182925 20 200000
14 uniq_autoencoder_top_uni__20170426183223 80 200000
15 uniq_autoencoder_top_uni__20170426183223 80 200000
16 uniq_autoencoder_top_uni__20170426183350 20 200000
17 uniq_autoencoder_top_uni__20170426183350 20 200000
18 uniq_variational_top_uni__20170426183613 80 200000
19 uniq_variational_top_uni__20170426183613 80 200000
20 uniq_autoencoder_top_uni__20170426183722 100 200000
21 uniq_autoencoder_top_uni__20170426183722 100 200000
22 uniq_autoencoder_top_uni__20170426183820 20 200000
23 uniq_autoencoder_top_uni__20170426183820 20 200000
24 uniq_autoencoder_top_uni__20170426184232 120 200000
25 uniq_autoencoder_top_uni__20170426184232 120 200000
26 uniq_autoencoder_top_uni__20170426184251 20 200000
27 uniq_autoencoder_top_uni__20170426184251 20 200000
28 uniq_variational_top_uni__20170426184258 100 200000
29 uniq_variational_top_uni__20170426184258 100 200000
... ... ... ...
2173 uniq_autoencoder_segmented__20170510160419 540 273178
2174 uniq_autoencoder_segmented__20170510160419 540 273178
2175 uniq_autoencoder_CV__20170510160829 540 259159
2176 uniq_autoencoder_CV__20170510160829 540 259159
2177 uniq_variational_segmented__20170510161520 540 273178
2178 uniq_variational_segmented__20170510161520 540 273178
2179 uniq_variational_CV__20170510161931 540 259159
2180 uniq_variational_CV__20170510161931 540 259159
2181 uniq_autoencoder_segmented__20170510162954 560 273178
2182 uniq_autoencoder_segmented__20170510162954 560 273178
2183 uniq_autoencoder_CV__20170510163416 560 259159
2184 uniq_autoencoder_CV__20170510163416 560 259159
2185 uniq_variational_segmented__20170510164110 560 273178
2186 uniq_variational_segmented__20170510164110 560 273178
2187 uniq_variational_CV__20170510164542 560 259159
2188 uniq_variational_CV__20170510164542 560 259159
2189 uniq_autoencoder_segmented__20170510165548 580 273178
2190 uniq_autoencoder_segmented__20170510165548 580 273178
2191 uniq_autoencoder_CV__20170510170030 580 259159
2192 uniq_autoencoder_CV__20170510170030 580 259159
2193 uniq_variational_segmented__20170510170704 580 273178
2194 uniq_variational_segmented__20170510170704 580 273178
2195 uniq_variational_CV__20170510171129 580 259159
2196 uniq_variational_CV__20170510171129 580 259159
2197 uniq_autoencoder_segmented__20170510172219 600 273178
2198 uniq_autoencoder_segmented__20170510172219 600 273178
2199 uniq_autoencoder_CV__20170510172552 600 259159
2200 uniq_autoencoder_CV__20170510172552 600 259159
2201 uniq_variational_segmented__20170510173338 600 273178
2202 uniq_variational_CV__20170510173725 600 259159
Variational Uniq Layernum Train_char_acc Valid_char_acc \
0 True True 2 0.699399 0.698318
1 True True 2 0.699329 0.698235
2 False True 2 0.696825 0.696345
3 False True 2 0.695728 0.695835
4 False True 2 0.766488 0.765912
5 False True 2 0.765310 0.764845
6 True True 2 0.766905 0.765960
7 True True 2 0.766304 0.765430
8 False True 2 0.815684 0.814975
9 False True 2 0.815565 0.815110
10 True True 2 0.812797 0.812333
11 True True 2 0.812692 0.812187
12 False True 4 0.793466 0.792432
13 False True 4 0.800906 0.799380
14 False True 2 0.848580 0.847882
15 False True 2 0.849468 0.848598
16 False True 4 0.802851 0.801817
17 False True 4 0.807402 0.805930
18 True True 2 0.847447 0.846897
19 True True 2 0.847630 0.846988
20 False True 2 0.879867 0.878848
21 False True 2 0.879648 0.878698
22 False True 4 0.805708 0.804340
23 False True 4 0.804753 0.803462
24 False True 2 0.901023 0.899970
25 False True 2 0.900562 0.899625
26 False True 4 0.809498 0.808140
27 False True 4 0.807624 0.806068
28 True True 2 0.877074 0.876700
29 True True 2 0.877083 0.876517
... ... ... ... ... ...
2173 False True 2 0.881281 0.850677
2174 False True 2 0.880760 0.850059
2175 False True 2 0.860798 0.843756
2176 False True 2 0.860869 0.843773
2177 True True 2 0.840781 0.805949
2178 True True 2 0.840884 0.806110
2179 True True 2 0.824895 0.809824
2180 True True 2 0.824951 0.809931
2181 False True 2 0.881498 0.850936
2182 False True 2 0.882108 0.851210
2183 False True 2 0.861339 0.844330
2184 False True 2 0.861618 0.844521
2185 True True 2 0.841010 0.806205
2186 True True 2 0.840983 0.805790
2187 True True 2 0.824666 0.809342
2188 True True 2 0.824900 0.809662
2189 False True 2 0.882199 0.851333
2190 False True 2 0.882069 0.851454
2191 False True 2 0.862265 0.845041
2192 False True 2 0.875390 0.844917
2193 True True 2 0.840470 0.805808
2194 True True 2 0.840613 0.805630
2195 True True 2 0.841646 0.809758
2196 True True 2 0.841475 0.809552
2197 False True 2 0.882566 0.851680
2198 False True 2 0.882607 0.851546
2199 False True 2 0.875575 0.844993
2200 False True 2 0.875777 0.844976
2201 True True 2 0.840522 0.805695
2202 True True 2 0.841503 0.810038
Test_char_acc Test_word_acc Test_Leven_avg Train_Leven_avg \
0 0.701747 0.000750 5.956950 5.995712
1 0.701557 0.000950 5.958300 5.997044
2 0.694913 0.000550 6.089100 6.050719
3 0.694362 0.000550 6.099150 6.072012
4 0.764868 0.007200 4.694850 4.663012
5 0.763818 0.007150 4.715600 4.687056
6 0.768895 0.007150 4.619850 4.650450
7 0.768230 0.006500 4.629150 4.664187
8 0.813835 0.024700 3.719150 3.682937
9 0.814045 0.024500 3.715550 3.685225
10 0.814685 0.024500 3.700400 3.734394
11 0.814473 0.024850 3.704350 3.735806
12 0.792952 0.014100 4.130750 4.121738
13 0.801033 0.016850 3.972850 3.975375
14 0.846850 0.054400 3.060100 3.025913
15 0.847935 0.055650 3.038550 3.008200
16 0.802702 0.018450 3.938000 3.935069
17 0.806933 0.020900 3.854450 3.845844
18 0.849120 0.054650 3.010050 3.039200
19 0.849380 0.053200 3.006500 3.037288
20 0.878408 0.113900 2.430850 2.401425
21 0.878255 0.112650 2.433900 2.405931
22 0.805593 0.020100 3.880050 3.878594
23 0.804272 0.019250 3.907550 3.897744
24 0.899540 0.175500 2.008150 1.978163
25 0.899203 0.173900 2.014650 1.987344
26 0.809435 0.023500 3.804350 3.802719
27 0.807183 0.020000 3.847100 3.839081
28 0.878865 0.110450 2.418050 2.447331
29 0.879000 0.110500 2.420500 2.446737
... ... ... ... ...
2173 0.848145 0.018600 3.037100 2.374388
2174 0.847746 0.019100 3.045075 2.384787
2175 0.843383 0.032895 3.132307 2.784012
2176 0.843485 0.033096 3.130279 2.782595
2177 0.803365 0.003300 3.932675 3.184375
2178 0.803596 0.002900 3.927900 3.182300
2179 0.808991 0.011366 3.819727 3.501771
2180 0.809015 0.010890 3.819226 3.500698
2181 0.848243 0.018400 3.035150 2.370050
2182 0.848641 0.019400 3.027175 2.357838
2183 0.843739 0.033696 3.125222 2.773204
2184 0.844159 0.034247 3.116811 2.767609
2185 0.803851 0.003225 3.922775 3.179725
2186 0.803550 0.003275 3.928900 3.180262
2187 0.808893 0.010740 3.821755 3.506327
2188 0.809060 0.010815 3.818325 3.501671
2189 0.848595 0.019275 3.028075 2.356025
2190 0.849228 0.019925 3.015450 2.358613
2191 0.844566 0.034472 3.108649 2.754669
2192 0.844505 0.033822 3.109901 2.492189
2193 0.803306 0.002725 3.933725 3.190575
2194 0.803435 0.003175 3.931200 3.187712
2195 0.809256 0.011691 3.814570 3.166667
2196 0.808778 0.010815 3.823783 3.170259
2197 0.849081 0.019550 3.018375 2.348688
2198 0.849150 0.019575 3.017000 2.347850
2199 0.844608 0.033696 3.107798 2.488509
2200 0.844874 0.034673 3.102466 2.484454
2201 0.803397 0.002550 3.931950 3.189525
2202 0.809045 0.011240 3.818701 3.169658
Valid_Leven_avg Layers
0 6.018550 [20, 960]
1 6.020550 [20, 960]
2 6.061100 [20, 960]
3 6.070100 [20, 960]
4 4.674750 [40, 960]
5 4.696250 [40, 960]
6 4.673700 [40, 960]
7 4.687600 [40, 960]
8 3.697450 [60, 960]
9 3.695050 [60, 960]
10 3.749500 [60, 960]
11 3.749550 [60, 960]
12 4.141350 [220, 20, 220, 960]
13 4.005900 [220, 20, 220, 960]
14 3.039850 [80, 960]
15 3.026000 [80, 960]
16 3.955300 [260, 20, 260, 960]
17 3.875050 [260, 20, 260, 960]
18 3.057450 [80, 960]
19 3.058150 [80, 960]
20 2.421850 [100, 960]
21 2.425000 [100, 960]
22 3.905600 [300, 20, 300, 960]
23 3.921850 [300, 20, 300, 960]
24 1.999150 [120, 960]
25 2.005800 [120, 960]
26 3.829400 [340, 20, 340, 960]
27 3.870550 [340, 20, 340, 960]
28 2.465600 [100, 960]
29 2.465200 [100, 960]
... ... ...
2173 2.986450 [540, 980]
2174 2.998800 [540, 980]
2175 3.124822 [540, 980]
2176 3.124496 [540, 980]
2177 3.880975 [540, 980]
2178 3.877650 [540, 980]
2179 3.803029 [540, 980]
2180 3.800926 [540, 980]
2181 2.981275 [560, 980]
2182 2.975800 [560, 980]
2183 3.113356 [560, 980]
2184 3.109526 [560, 980]
2185 3.875825 [560, 980]
2186 3.884125 [560, 980]
2187 3.812743 [560, 980]
2188 3.806359 [560, 980]
2189 2.973350 [580, 980]
2190 2.970900 [580, 980]
2191 3.099136 [580, 980]
2192 3.101590 [580, 980]
2193 3.883650 [580, 980]
2194 3.887325 [580, 980]
2195 3.804356 [580, 980]
2196 3.808537 [580, 980]
2197 2.966400 [600, 980]
2198 2.969025 [600, 980]
2199 3.099987 [600, 980]
2200 3.100463 [600, 980]
2201 3.886025 [600, 980]
2202 3.798924 [600, 980]
[2203 rows x 14 columns]
Content source: evelkey/vahun
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