-
Notifications
You must be signed in to change notification settings - Fork 1
/
prep.sh
1786 lines (1468 loc) · 87.4 KB
/
prep.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/bin/bash
zcat PtAPkgQR.s0 | cut -d\; -f4- | perl -e 'while(<STDIN>){chop();@m=sort split(/;/);for $i (0..$#m){$a{$m[$i]}++;for $j (($i+1)..$#m){$n{$m[$i]}{$m[$j]}++;$n{$m[$j]}{$m[$i]}++}}};for $i (keys %a){for $j (keys %a){ print "$i;$j;$a{$i};$a{$j};$n{$i}{$j}\n" if ($i cmp $j)<0 && $a{$j}>5000 && $a{$i} > 5000 }}' | gzip > crosstab.gz
x=read.table("crosstab.gz", sep=";",quote="",comment.char="")
names(x)=c("a","b","na","nb","nab")
x$mn = apply(x[,c("na","nb")],1,min)
x$mor = x$mn/(x$nab+1);
x$tot=x$na+x$nb-x$nab;
x$ind=(x$na/x$tot * x$nb/x$tot);
x$pab = x$nab/x$tot;
x$or = x$ind/(1-x$ind)*(1-x$pab)/x$pab
#x=x[x$na>1000&x$nb>1000&x$or>3,]
myftestl = function(y){
y=as.integer(y)
res = fisher.test(matrix(c(y[1]-y[3], y[3], y[3], y[2]-y[3]),ncol=2))
res$conf.int[1];
}
myftestu = function(y){
y=as.integer(y)
res = fisher.test(matrix(c(y[1]-y[3], y[3], y[3], y[2]-y[3]),ncol=2))
res$conf.int[2];
}
x$orl = apply(x[,3:5],1,myftestl)
x$oru = apply(x[,3:5],1,myftestu)
quantile(x$oru)
y = x[x$a=='tidyr'&x$b=='readr',3:5]
fisher.test(matrix(as.integer(c(y[1]-y[3], y[3], y[3], y[2]-y[3])),ncol=2))
Fisher's Exact Test for Count Data
data: matrix(as.integer(c(y[1] - y[3], y[3], y[3], y[2] - y[3])), ncol = 2)
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
13.59858 13.99002
sample estimates:
odds ratio
13.7906
#prepare data mapping projects/time/author/apis for the following languages
for LA in jl pl R F Go Scala Rust Cs PY ipy JS C java rb
do zcat PtaPkgQ$LA.*.gz | lsort 500G -t\| | uniq | gzip > PtaPkgQ$LA.s
zcat PtaPkgQ$LA.s | perl -ane 'chop();($p,$t,$a,@ms) = split(/;/); for $m (@ms){print "$p;$m\n"}' | lsort 500G -t\; -k1,2 -u | gzip > P2Pkg$LA.s
zcat PtaPkgQ$LA.s | perl -ane 'chop();($p,$t,$a,@ms) = split(/;/); for $m (@ms){print "$a;$m\n"}' | lsort 500G -t\; -k1,2 -u | gzip > a2Pkg$LA.s
done
zcat PtaPkgQPY.s | grep -iE 'systemml|cntk|opennn|pandas|numpy|tensorflow|random|sklearn|gensim|nltk|scipy|skimage|datacube|matplotlib|face_recognition|fastai|keras|torch|basicnn|DecisionTree|baseline_cnn|pyaicnn|mtcnn_detector|nnclf|cnn|clustering|svm|caffe|scikit|mlib|torch|theano|veles|h2o' | cut -d\; -f1 | uniq | gzip > b.gz
zcat PtaPkgQPY.s | perl ~/lookup/grepField.perl b.gz 1 | gzip > PtaPkgQPYml.s
#try on several small languages 'F', 'R', 'jl', 'pl', 'ipy'
(time python3 fit.py F R jl pl ipy) &
#one iteration takes 5 hr on da4 (see /da4_data/play/api)
# lets look at the second iteration
import gzip,collections,gensim.models.doc2vec,math
from gensim.models import Doc2Vec
mod = Doc2Vec.load ("doc2vec.QFRjlplipy.2")
mod = Doc2Vec.load ("doc2vec.QFipy.7")
#get most similar packages to language, project, author
mod.wv.similar_by_vector(mod.docvecs['R'])
it1-7: [('extrafont', 0.9955250024795532), ('csnorm', 0.9952453374862671), ('knitr', 0.9948492050170898), ('stringr', 0.9943090081214905), ('matrixStats', 0.9934355020523071), ('building.h', 0.9933176636695862), ('scam', 0.9915322065353394), ('gridExtra', 0.9907146096229553), ('shinystan', 0.9894420504570007), ('esprdbfile.h', 0.9891785979270935)]
mod.wv.similar_by_vector(mod.docvecs['cran_tidyquery'])
it7: [('HuffmanDecoder.jl', 0.46050825715065), ('general.fh', 0.4211460053920746), ('OPN', 0.4155998229980469), ('qubarqu_nInJququbar_465_Sq1_specs.h', 0.41047632694244385), ('arcgis.geocode', 0.4010382294654846), ('dataset_export.jl', 0.39810460805892944), ('NQS_Header', 0.3891255855560303), ('mapStats', 0.3890906870365143), ('curlib', 0.38832515478134155), ('cctk_Faces.h', 0.38744601607322693)]
ii1-3: [('HuffmanDecoder.jl', 0.46050825715065), ('general.fh', 0.4211460053920746), ('bokeh.palettes.all_palettes', 0.4210362434387207), ('OPN', 0.4131404757499695), ('qubarqu_nInJququbar_465_Sq1_specs.h', 0.41047632694244385), ('flask_pymongo.PyMongo', 0.39284461736679077), ('mapStats', 0.3890906870365143), ('curlib', 0.38832515478134155), ('cctk_Faces.h', 0.38744601607322693), ('PhageR', 0.38713282346725464)]
mod.wv.similar_by_vector(mod.docvecs['Yannick Spill <[email protected]>']);
it1-7: [('csnorm', 0.9973132610321045), ('extrafont', 0.9968918561935425), ('stringr', 0.9961603283882141), ('matrixStats', 0.9951667785644531), ('jiebaRD', 0.9946102499961853), ('knitr', 0.9934378862380981), ('flowCore', 0.9919644594192505), ('rhdf5', 0.9909929037094116), ('mgcv', 0.9899401664733887), ('scam', 0.9892599582672119)]
#get most similar languages, projects,authors to language, project, author
mod.docvecs.most_similar('R');
it7: [('F', 0.9947392344474792), ('Yannick Spill <[email protected]>', 0.9929205179214478), ('AsaEE_ESP-rSource', 0.992476224899292), ('jhand <jhand@7d53e970-de11-0410-8a54-3d01b9da36cf>', 0.9924437999725342), ('2DegreesInvesting_PortCheck', 0.990721583366394), ('Clare2D <[email protected]>', 0.9905468225479126), ('Taylor Posey <[email protected]>', 0.9884799718856812), ('tinaGNAW <[email protected]>', 0.9880185127258301), ('Paul Fischer <[email protected]>', 0.987598180770874), ('12379Monty_scRNASeq', 0.9872961044311523)]
it:1-3: ('F', 0.9947392344474792), ('Yannick Spill <[email protected]>', 0.9929205179214478), ('AsaEE_ESP-rSource', 0.992476224899292), ('jhand <jhand@7d53e970-de11-0410-8a54-3d01b9da36cf>', 0.9924437999725342), ('2DegreesInvesting_PortCheck', 0.990721583366394), ('Clare2D <[email protected]>', 0.9905468225479126), ('Taylor Posey <[email protected]>', 0.9884799718856812), ('tinaGNAW <[email protected]>', 0.9880185127258301), ('Paul Fischer <[email protected]>', 0.987598180770874), ('12379Monty_scRNASeq', 0.9872961044311523)]
mod.docvecs.most_similar('cran_tidyquery')
it2:[('kungeinus_Prediction_Assignment_Writeup', 0.49396124482154846), ('parserpro_db_update', 0.48060914874076843), ('adisarid <[email protected]>', 0.47186779975891113), ('danthemango <[email protected]>', 0.4652522802352905), ('arnarg_plex_exporter', 0.46454471349716187), ('colin-combe_CLMS-UI', 0.4438340663909912), ('alanaw1_CulturalHitchhiking', 0.4377615451812744), ('lavanyaj09_BE223A', 0.4339529275894165), ('jnarhan_Kaggle-Pneumonia', 0.43245214223861694), ('gxe778_Trajectory-Inference-Methods-applied-on-early-cell-lines-from-human-embryo', 0.42638999223709106)]
it1:[('parserpro_db_update', 0.4985978603363037), ('kungeinus_Prediction_Assignment_Writeup', 0.49396124482154846), ('adisarid <[email protected]>', 0.47186779975891113), ('danthemango <[email protected]>', 0.4674111604690552), ('arnarg_plex_exporter', 0.46454471349716187), ('PeterHenell_goora', 0.4478600025177002), ('colin-combe_CLMS-UI', 0.44383400678634644), ('danthemango_ClientRG', 0.4412115514278412), ('alanaw1_CulturalHitchhiking', 0.4377615451812744), ('jnarhan_Kaggle-Pneumonia', 0.43245214223861694)]
mod.docvecs.most_similar('Yannick Spill <[email protected]>')
it2:[('R', 0.992920458316803), ('3schwartz_SpecialeScrAndFun', 0.9900994300842285), ('Francois <[email protected]>', 0.9879549145698547), ('215ALab4_lab4', 0.9871786832809448), ('12379Monty_scRNASeq', 0.986408531665802), ('12379Monty <[email protected]>', 0.9862282872200012), ('3wen_elus', 0.9861852526664734), ('52North_tamis', 0.9858419299125671), ('tinaGNAW <[email protected]>', 0.9855629205703735), ('2DegreesInvesting_PortCheck', 0.984999418258667)]
it1:[('3DGenomes_binless', 0.9939588904380798), ('R', 0.9929205179214478), ('3schwartz_SpecialeScrAndFun', 0.9900994300842285), ('Francois <[email protected]>', 0.9879549145698547), ('215ALab4_lab4', 0.9871785640716553), ('12379Monty_scRNASeq', 0.986408531665802), ('12379Monty <[email protected]>', 0.986228346824646), ('3wen_elus', 0.9861852526664734), ('52North_tamis', 0.9858419299125671), ('tinaGNAW <[email protected]>', 0.9855630397796631)]
#get most similar packages to a package
mod.wv.most_similar('pandas')
[('song_data.songs', 0.6327548623085022), ('context.plot.plot.plot_points.plot_points', 0.6035584211349487), ('ax.storage.sqa_store.save.save_experiment', 0.585919976234436), ('emperor', 0.5831856727600098), ('geograph.term_profile.get_term_profile', 0.5705782175064087), ('negmas.apps.scml.utils.anac2019_world', 0.5701258778572083), ('pymove.conversions', 0.5696786642074585), ('learning_curve.learning_curve', 0.5638871192932129), ('ax.Data', 0.5624120831489563), ('starutils.populations.Raghavan_BinaryPopulation', 0.5612468123435974)]
mod.wv.most_similar('numpy')
[('tigre.utilities.plotimg.plotimg', 0.6538034677505493), ('cs231n.classifiers.linear_classifier.LinearSVM', 0.6469931602478027), ('Test_data.data_loader.load_head_phantom', 0.6307787895202637), ('PsyNeuLink.Components.Projections.TransmissiveProjections.MappingProjection.MappingProjection', 0.6261978149414062), ('section3_1_heatingday', 0.6204843521118164), ('tigre.Utilities.plotproj.ppslice', 0.617354154586792), ('tigre.demos.Test_data.data_loader.load_head_phantom', 0.6111389994621277), ('hmtk.hazard.HMTKHazardCurve', 0.6096319556236267), ('pyshtools.spectralanalysis.SHBias', 0.6086275577545166), ('agentnet.learning.n_step', 0.6063860058784485)]
mod.wv.most_similar('ggplot2')
it7: [('bsearchtools', 0.8243459463119507), ('cp_common_uses.h', 0.8239778876304626), ('PTRACERS_FIELDS.h', 0.8211128115653992), ('filnames.h', 0.8199384212493896), ('jelira.h', 0.8191816210746765), ('soilsnow.h', 0.8181809186935425), ('parmhor.h', 0.8178006410598755), ('da_transform_xtoy_pilot_adj.inc', 0.8176625967025757), ('ebbyeb.blk', 0.8171699047088623), ('SCHROD', 0.8164928555488586)]
it2: [('nortest', 0.9667873382568359), ('reshape2', 0.9574425220489502), ('tidyr', 0.9566653966903687), ('partykit', 0.9542033076286316), ('purrr', 0.9534142017364502), ('knitr', 0.9527696371078491), ('aim2_parameters.h', 0.9526889324188232), ('matrixStats', 0.9519108533859253), ('FlowSOM', 0.9512166976928711), ('Obspars.com', 0.9505057334899902)]
it1: ('tidyr', 0.9896135330200195), ('reshape2', 0.9894652366638184), ('nortest', 0.9879751205444336), ('dplyr', 0.9831089973449707), ('knitr', 0.9812949895858765), ('partykit', 0.9795119762420654), ('RColorBrewer', 0.9769814610481262), ('purrr', 0.9767657518386841), ('lubridate', 0.9766140580177307), ('data.table', 0.9752072095870972)]
mod.wv.most_similar('keras_learn')
it2:[('tensorbayes.nputils.log_sum_exp', 0.7950129508972168), ('neural_network_decision_tree.nn_decision_tree', 0.774245023727417), ('TechnicalAnalysis.TechnicalAnalysis', 0.768464207649231), ('models.naive_convnet.NaiveConvColoringModel', 0.762176513671875), ('model_VAE.VAE_mnist', 0.761232316493988), ('batch_generator.dir.DirIterator', 0.7570154070854187), ('optimizer.learing_rate_scheduling', 0.7539881467819214), ('models.yolov3_gpu_head.inference.restore_model', 0.7510530948638916), ('flickr8k_parse', 0.7491798400878906), ('ppo.NNValueFunction', 0.7474846839904785)]
it1:[('tensorbayes.nputils.log_sum_exp', 0.8044129610061646), ('models.yolov3_gpu_head.inference.restore_model', 0.7988119125366211), ('model_VAE.VAE_mnist', 0.7955332398414612), ('antTrainEnv_class.antTrainEnv_class', 0.784370481967926), ('models.naive_convnet.NaiveConvColoringModel', 0.7827848196029663), ('batch_generator.dir.DirIterator', 0.7793075442314148), ('neural_network_decision_tree.nn_decision_tree', 0.7765824198722839), ('TechnicalAnalysis.TechnicalAnalysis', 0.7733845710754395), ('envs.economy.jesusfv', 0.773059606552124), ('model.audio_u_net_dnn', 0.7707473039627075)]
#no most similar language,project.author from package, need to write a function
# get a doc vector based on the set of words and find most closely related terms
mod.wv.similar_by_vector(mod.infer_vector(['ggplot2','data.table']))
[('analyticlab.LaTeX', 0.9497971534729004), ('knitr', 0.9475012421607971), ('lubridate', 0.9463158845901489), ('mafdecls.fh', 0.9458824992179871), ('tidyr', 0.9457533359527588), ('ggthemes', 0.9443897008895874), ('demos.sampling_freq_demo1', 0.9438977837562561), ('meyer.basic_constructs.MRest', 0.9436166286468506), ('errquit.fh', 0.9411042332649231), ('scam', 0.9407658576965332)]
mod.wv.most_similar('data.table')
[('tidyr', 0.9825125336647034), ('scam', 0.9800063967704773), ('knitr', 0.9797146320343018), ('lubridate', 0.9793548583984375), ('purrr', 0.976327657699585), ('espriou.h', 0.9762163758277893), ('plant.h', 0.9760875105857849), ('gksenu.h', 0.9760852456092834), ('ggthemes', 0.9758648872375488), ('g01wsl.h', 0.9758262634277344)]
#similarities among languages
for la in ('F', 'R', 'jl', 'pl', 'ipy'):
for lb in ('F', 'R', 'jl', 'pl', 'ipy'):
print (la+":"+lb+" "+str(mod.docvecs.distance(la,lb))
it7:
F:R 0.005260765552520752
F:jl 0.5538360178470612
F:pl 0.26047611236572266
F:ipy 0.2711484432220459
R:jl 0.5432112514972687
R:pl 0.25471168756484985
R:ipy 0.2826273441314697
jl:pl 0.4799261689186096
jl:ipy 0.7023965418338776
pl:ipy 0.3867550492286682
it2:
F:R 0.005260765552520752
F:jl 0.5120232105255127
F:pl 0.18782222270965576
F:ipy 0.24761343002319336
R:jl 0.5035586059093475
R:pl 0.1807081699371338
R:ipy 0.2535156011581421
jl:pl 0.429531455039978
jl:ipy 0.8230383545160294
pl:ipy 0.4515225887298584
it1:
F:R 0.005260765552520752
F:jl 0.518935889005661
F:pl 0.160944402217865
F:ipy 0.2259724736213684
R:jl 0.5141101777553558
R:pl 0.14899468421936035
R:ipy 0.23450106382369995
jl:pl 0.46918046474456787
jl:ipy 0.8400852829217911
pl:ipy 0.41011691093444824
#measure distance between package and project/author/language
def dist (a, b):
av = mod.wv.get_vector(a)
bv = mod.docvecs[b]
return (sum(av*bv)/math.sqrt(sum(av*av)*sum(bv*bv)))
# save document and word vectors
f = open('outDocs','w')
for t in mod.docvecs.doctags.keys():
f.write(t)
for v in mod.docvecs[t]:
f.write(';'+"{:1.12e}".format(v))
f.write('\n')
f.close()
f = open('outWords','w')
for t in mod.wv.vocab.keys():
f.write(t)
for v in mod.wv[t]:
f.write(';'+"{:1.12e}".format(v))
f.write('\n')
f.close()
for la in F R jl ipy pl Cs Go PYml Rust Scala PY JS java rb; do zcat PtaPkgQ$la.s | perl -e 'while(<STDIN>){chop();($p,$t,$a)=split(/;/);$pre=0; $pre=1 if $t>= 1518784533+3600*24*365.25; $pn{$p}{$pre}++; $an{$a}{$pre}++;}; for my $p (keys %pn){print "p;$p;$pn{$p}{1};$pn{$p}{0}\n";} for my $a (keys %an){print "a;$a;$an{$a}{1};$an{$a}{0}\n";}' | gzip > PtaPkgQ$la.cnt; done &
for la in F jl R ipy pl Cs Go PYml Rust Scala PY PYml JS java rb; do
zcat PtaPkgQ$la.cnt| grep ^a | awk -F\; '{if($4>10 && $3>10)print $0}' > PtaPkgQ$la.cnt10
done
for la in F jl R ipy pl Cs Go PYml Rust Scala PY PYml JS java rb; do
zcat PtaPkgQ$la.cnt| grep ^p | awk -F\; '{if($4>100 && $3>100)print $0}' > PtaPkgQ$la.cnt100
done
for la in F jl R ipy pl Cs Go PYml Rust Scala PY PYml JS java rb; do zcat PtaPkgQ$la.s | perl ~/lookup/mp.perl 2 /da0_data/basemaps/gz/a2AQ.s | gzip > PtAPkgQ$la.s ; done &
for la in F R jl ipy pl Cs Go PYml Rust Scala PY JS java rb; do zcat PtAPkgQ$la.s | perl -e 'while(<STDIN>){chop();($p,$t,$a)=split(/;/);$pre=0; $pre=1 if $t>= 1518784533+3600*24*365.25; $pn{$p}{$pre}++; $an{$a}{$pre}++;}; for my $p (keys %pn){print "p;$p;$pn{$p}{1};$pn{$p}{0}\n";} for my $a (keys %an){print "a;$a;$an{$a}{1};$an{$a}{0}\n";}' | gzip > PtAPkgQ$la.cnt; done
for la in F jl R ipy pl Cs Go Rust Scala PY PYml JS java rb; do
zcat PtAPkgQ$la.cnt| grep ^a | awk -F\; '{if($4>10 && $3>10)print $0}' > PtAPkgQ$la.cnt10
done
for la in F jl R ipy pl Cs Go PYml Rust Scala PY JS java rb; do
zcat PtAPkgQ$la.cnt| grep ^p | awk -F\; '{if($4>100 && $3>100)print $0}' > PtAPkgQ$la.cntp100
done
for la in F jl R ipy pl Cs Go PYml Rust Scala PY JS java rb
do cut -d\; -f2 PtAPkgQ$la.cnt10
done | lsort 1G -u | gzip > au10.gz
for la in F jl R ipy pl Cs Go Rust Scala PY JS java rb
do zcat PtAPkgQ$la.s | perl ~/bin/grepField.perl au10.gz 3
done | gzip > all.a10.gz
for la in F jl R ipy pl Cs Go Rust Scala PY PYml JS java rb; do
zcat PtAPkgQ$la.cnt| grep ^a | awk -F\; '{if($4>100 && $3>100)print $0}' > PtAPkgQ$la.cnt100
done
for la in F jl R ipy pl Cs Go PYml Rust Scala PY JS java rb
do cut -d\; -f2 PtAPkgQ$la.cnt100
done | lsort 1G -u | gzip > au100.gz
for la in C F jl R ipy pl Cs Go Rust Scala PY JS java rb
do zcat PtAPkgQ$la.s | perl ~/bin/grepField.perl au100.gz 3
done | gzip > all.a100.gz
cat PRdata_new.csv | perl ~/lookup/mp.perl 0 /da0_data/basemaps/gz/a2AQ.s > PRdata_newA.csv
cut -d\; -f1 PRdata_newA.csv | lsort 1G -u | gzip > au.prs
for la in JS C F jl R ipy pl Cs Go Rust Scala PY java rb
do zcat /da4_data/play/api/PtAPkgQ$la.s | perl ~/bin/grepField.perl au.prs 3 | gzip > PtaPkgQ$la.prs.s
done
zcat *A*.cnt | grep ^a | awk -F\; '{print $4+$3";"$2}' | lsort 30G -t\; -rn |gzip > topA
zcat topA|awk -F\; '{if ($1>50000){print $2}}' | gzip | lsort 10G -u > topA.50K
zcat au100.gz | lsort 10G -t\; -k1,1 -u | join -t\; -v1 - <(cat topA.50K | lsort 1G -t\; -k1,1)| gzip > au100-50k.gz
zcat au10.gz | lsort 10G -t\; -k1,1 -u | join -t\; -v1 - <(cat topA.50K | lsort 1G -t\; -k1,1)| gzip > au10-50k.gz
for la in F jl R ipy pl Cs Go PYml Rust Scala PY JS java rb; do
zcat PtAPkgQ$la.s | perl ~/bin/grepField.perl au100-50K.gz 3 | gzip > PtAPkgQ$la.a100.s
done
#reproduce import2vec
import gzip,collections,gensim.models.doc2vec,math
from gensim.models import Doc2Vec, Word2Vec
mod = Doc2Vec.load ("doc2vec.QAJS.a100.1558784533.1")
#get most similar packages to language, project, author
mod.wv.most_similar('http')
import gzip,collections,gensim.models.doc2vec,math
from gensim.models import Doc2Vec
mod = Doc2Vec.load ("doc2vec.QAJS.a100.1558784533.5")
mod.wv.most_similar('http')
[('firebase-admin', 0.7661893367767334), ('koa-bodyparser', 0.733301043510437), ('mysql2', 0.7215847969055176), ('react-loadable', 0.6763203144073486), ('vuelidate', 0.665387749671936), ('vue-style-loader', 0.6605743765830994), ('marklar', 0.6552571654319763), ('ipfs-mdns', 0.6538937091827393), ('diap', 0.6523748636245728), ('jQuery', 0.6493134498596191)]
for f in ('doc2vec.QML.2', 'doc2vec.QFRjlipyml.1518784533.9', 'doc2vec.Qipy.9', 'doc2vec.QR.1518784533.9'):
mod = Doc2Vec.load (f)
mod.wv.most_similar('data.table')
mod.wv.similar_by_vector(mod.docvecs['R'])
mod.wv.similar_by_vector(mod.docvecs['R'])
zcat PtAPkgQJS.a100.s | grep ';http;' | wc -l
15375
zcat PtAPkgQJS.a100.s | grep ';http;' | grep -v ';https;' | wc -l
12852
zcat PtAPkgQJS.a100.s | grep ';https\b' > s &
zcat PtAPkgQJS.a100.s | grep ';http\b' > p &
wc -l p s
1239802 3189600 15785383911 s
1899811 4873782 18410486749 p
grep -v ';http\b' s | wc -l
56677
grep -v ';https\b' p | wc -l
716686
#doc2vec (binary, author+project)
f='doc2vec.PAPkgQR.a100b.9'
mod = Doc2Vec.load (f)
mod.wv.most_similar('data.table')
[('ggtree', 0.45562177896499634), ('datastorr', 0.45432671904563904), ('koRpus', 0.45120853185653687), ('emmeans', 0.45097100734710693), ('fansi', 0.44699180126190186), ('datasets', 0.442926287651062), ('ellipse', 0.4332200288772583), ('mlmRev', 0.43092772364616394), ('ddalpha', 0.4287028908729553), ('extrafont', 0.42746877670288086)]
mod.wv.most_similar('readr')
[('reshape2', 0.5896936655044556), ('rgdal', 0.5330021381378174), ('rlist', 0.5308029651641846), ('scales', 0.5262876749038696), ('slam', 0.5214910507202148), ('rstanarm', 0.5202317833900452), ('readstata13', 0.517525315284729), ('reshape', 0.5141236186027527), ('tidyverse', 0.511005163192749), ('pracma', 0.5025790929794312)]
#doc2vec (binary, author only)
f='doc2vecA01.20.1.20.3.PAPkgQR.a100b.10'
mod = Doc2Vec.load (f)
mod.wv.most_similar('data.table')
[('dplyr', 0.9619144797325134), ('devtools', 0.9410998821258545), ('stringr', 0.938940703868866), ('gridExtra', 0.9301508069038391), ('tidyverse', 0.9286336302757263), ('tidyr', 0.9281747341156006), ('ggplot2', 0.924401581287384), ('RColorBrewer', 0.9191794395446777), ('Hmisc', 0.9127570986747742), ('foreach', 0.9072998762130737)]
mod.wv.most_similar('readr')
[('tidyr', 0.9521045684814453), ('tidyverse', 0.9518229961395264), ('lubridate', 0.9460780024528503), ('ggthemes', 0.9348131418228149), ('rvest', 0.9196170568466187), ('scales', 0.912832498550415), ('RColorBrewer', 0.8977759480476379), ('gridExtra', 0.8954176902770996), ('corrplot', 0.8899544477462769), ('stringr', 0.8898525238037109)]
f='doc2vecA01.20.1.20.3.PAPkgQR.a100b.17'
mod = Doc2Vec.load (f)
mod.wv.most_similar('data.table')
[('dplyr', 0.9987048506736755), ('devtools', 0.9978946447372437), ('ggplot2', 0.9974669814109802), ('tidyr', 0.9973721504211426), ('knitr', 0.9970445036888123), ('reshape2', 0.996809720993042), ('tidyverse', 0.996529221534729), ('gridExtra', 0.9961731433868408), ('scales', 0.9960485696792603), ('plyr', 0.9955645203590393)]
mod.wv.most_similar('readr')
[('scales', 0.9980828166007996), ('tidyr', 0.9975719451904297), ('ggthemes', 0.9971051812171936), ('magrittr', 0.9970694780349731), ('lubridate', 0.9966671466827393), ('RColorBrewer', 0.9965049028396606), ('gridExtra', 0.9964038133621216), ('tidyverse', 0.9963114261627197), ('rpart', 0.9957792162895203), ('ggplot2', 0.9957766532897949)]
for f in ('doc2vec.20.30.3.PAPkgQR.a100b.19','doc2vec.20.3.3.PAPkgQR.a100b.19','doc2vec.40.30.3.PAPkgQR.a100b.19', 'doc2vec.40.3.3.PAPkgQR.a100b.19', 'doc2vec.80.30.3.PAPkgQR.a100b.19', 'doc2vec.80.3.3.PAPkgQR.a100b.19', 'doc2vec.120.30.3.PAPkgQR.a100b.19', 'doc2vec.120.3.3.PAPkgQR.a100b.19'):
mod = Doc2Vec.load (f)
#mod.wv.most_similar('data.table')
mod.wv.most_similar('readr')
[('RColorBrewer', 0.9983182549476624), ('scales', 0.998315155506134), ('tidyverse', 0.9982290267944336), ('magrittr', 0.9981428980827332), ('gridExtra', 0.9980251789093018), ('reshape2', 0.9979166388511658), ('knitr', 0.9977608919143677), ('lubridate', 0.997739315032959), ('tidyr', 0.9972754120826721), ('e1071', 0.9971935749053955)]
[('knitr', 0.9893307685852051), ('parallel', 0.9887491464614868), ('data.table', 0.9852929711341858), ('DESeq2', 0.9849643707275391), ('ggplot2', 0.9848182201385498), ('devtools', 0.9846794605255127), ('tidyr', 0.9845887422561646), ('rmarkdown', 0.9837965965270996), ('readxl', 0.9831156134605408), ('tools', 0.9823206663131714)]
[('lubridate', 0.9961996078491211), ('scales', 0.9947738647460938), ('knitr', 0.9944191575050354), ('RColorBrewer', 0.994251012802124), ('magrittr', 0.9941045045852661), ('reshape2', 0.9939345121383667), ('gridExtra', 0.9935609102249146), ('tidyr', 0.9930714964866638), ('ggthemes', 0.9929649233818054), ('readxl', 0.9924424886703491)]
[('tidyr', 0.9897856116294861), ('magrittr', 0.9842157959938049), ('lubridate', 0.9824410676956177), ('tidyverse', 0.9819707870483398), ('scales', 0.9788222908973694), ('RColorBrewer', 0.976738452911377), ('knitr', 0.9757811427116394), ('dplyr', 0.973181426525116), ('reshape2', 0.9710521697998047), ('gridExtra', 0.9706953763961792)]
[('tidyverse', 0.9914058446884155), ('lubridate', 0.9820787906646729), ('ggplot2', 0.981021523475647), ('reshape2', 0.9763184189796448), ('dplyr', 0.96944260597229), ('stringr', 0.9606151580810547), ('scales', 0.9509478807449341), ('ggthemes', 0.9472478032112122), ('rmarkdown', 0.9466075897216797), ('gridExtra', 0.946486234664917)]
[('tidyr', 0.979855477809906), ('tidyverse', 0.9716714024543762), ('dplyr', 0.9697237014770508), ('lubridate', 0.9694627523422241), ('magrittr', 0.9675484895706177), ('ggplot2', 0.9638528823852539), ('knitr', 0.9620657563209534), ('scales', 0.959942638874054), ('data.table', 0.9549976587295532), ('gridExtra', 0.9542347192764282)]
[('magrittr', 0.9877380132675171), ('lubridate', 0.983036994934082), ('tidyverse', 0.9830121994018555), ('scales', 0.9816074967384338), ('knitr', 0.9798682928085327), ('devtools', 0.9794678688049316), ('RColorBrewer', 0.9773341417312622), ('jsonlite', 0.9698127508163452), ('readxl', 0.9695264101028442), ('ggthemes', 0.9690042734146118)]
[('tidyr', 0.9725443720817566), ('dplyr', 0.9542319774627686), ('data.table', 0.9513483047485352), ('magrittr', 0.9475299119949341), ('ggplot2', 0.9475277662277222), ('jsonlite', 0.9473656415939331), ('lubridate', 0.9440580606460571), ('tidyverse', 0.9436643123626709), ('stringr', 0.9377952814102173), ('devtools', 0.9372730255126953)]
for f in ('doc2vecA.20.30.3.PAPkgQR.a100b.19','doc2vecA.20.3.3.PAPkgQR.a100b.19','doc2vecA.40.30.3.PAPkgQR.a100b.19', 'doc2vecA.40.3.3.PAPkgQR.a100b.19', 'doc2vecA.80.30.3.PAPkgQR.a100b.19', 'doc2vecA.80.3.3.PAPkgQR.a100b.19', 'doc2vecA.120.30.3.PAPkgQR.a100b.19', 'doc2vecA.120.3.3.PAPkgQR.a100b.19'):
mod = Doc2Vec.load (f)
#mod.wv.most_similar('data.table')
mod.wv.most_similar('readr')
[('magrittr', 0.9991831183433533), ('tidyverse', 0.9989697933197021), ('gridExtra', 0.9982430934906006), ('tidyr', 0.9982177019119263), ('scales', 0.9980565309524536), ('jsonlite', 0.9979439973831177), ('data.table', 0.9979092478752136), ('reshape2', 0.9978592395782471), ('knitr', 0.9976184368133545), ('stringi', 0.9969038367271423)]
[('tidyverse', 0.9947269558906555), ('tidyr', 0.9942873120307922), ('magrittr', 0.9933727979660034), ('lubridate', 0.9930378794670105), ('knitr', 0.9925771951675415), ('gridExtra', 0.9892634749412537), ('scales', 0.9878233671188354), ('RColorBrewer', 0.9874245524406433), ('devtools', 0.9865281581878662), ('dplyr', 0.9864441752433777)]
[('tidyr', 0.9988787174224854), ('magrittr', 0.9987964630126953), ('knitr', 0.9982733726501465), ('scales', 0.9981702566146851), ('lubridate', 0.9979217648506165), ('gridExtra', 0.9973997473716736), ('ggthemes', 0.9967532157897949), ('reshape2', 0.9961109161376953), ('rpart.plot', 0.995823860168457), ('tidyverse', 0.9956599473953247)]
[('tidyr', 0.9956086277961731), ('tidyverse', 0.9895380735397339), ('magrittr', 0.9875149726867676), ('dplyr', 0.9868128299713135), ('jsonlite', 0.9848260879516602), ('lubridate', 0.9833686947822571), ('devtools', 0.9828656315803528), ('data.table', 0.9797936677932739), ('gridExtra', 0.9791477918624878), ('ggplot2', 0.979009747505188)]
[('scales', 0.997490644454956), ('tidyverse', 0.9954097270965576), ('e1071', 0.9951643943786621), ('knitr', 0.9948922991752625), ('cluster', 0.9930667877197266), ('devtools', 0.9929601550102234), ('rpart.plot', 0.9902037382125854), ('RColorBrewer', 0.9877432584762573), ('ggfortify', 0.9876832365989685), ('DBI', 0.9824402928352356)]
[('tidyr', 0.9836821556091309), ('dplyr', 0.9753010272979736), ('magrittr', 0.9750666618347168), ('lubridate', 0.9709521532058716), ('tidyverse', 0.9692929983139038), ('ggplot2', 0.9688870906829834), ('knitr', 0.9656769037246704), ('jsonlite', 0.965003490447998), ('devtools', 0.9634120464324951), ('RColorBrewer', 0.960491955280304)]
[('magrittr', 0.991066038608551), ('dplyr', 0.9904848337173462), ('ggplot2', 0.9897637963294983), ('lubridate', 0.9878308773040771), ('knitr', 0.9876624941825867), ('data.table', 0.9873613119125366), ('reshape2', 0.9868736267089844), ('tidyverse', 0.9868265390396118), ('gridExtra', 0.9866074919700623), ('stringr', 0.9863458871841431)]
[('tidyr', 0.982008695602417), ('dplyr', 0.9632465243339539), ('magrittr', 0.9609993696212769), ('ggplot2', 0.9522542953491211), ('lubridate', 0.9505354762077332), ('tidyverse', 0.9469977021217346), ('data.table', 0.9440603256225586), ('devtools', 0.9428290128707886), ('stringr', 0.9426917433738708), ('jsonlite', 0.9396312832832
#W2V
f='word2vec.20.1.3.PAPkgQR.a100b'
mod = Word2Vec.load (f)
mod.most_similar('data.table')
[('devtools', 0.9298685789108276), ('ggplot2', 0.9192495346069336), ('dplyr', 0.9015513062477112), ('reshape2', 0.8996330499649048), ('RColorBrewer', 0.8813983201980591), ('gridExtra', 0.876798689365387), ('knitr', 0.8706860542297363), ('scales', 0.8666546940803528), ('readr', 0.86326003074646), ('magrittr', 0.8588861227035522)]
mod.most_similar('readr')
[('tidyr', 0.9588572978973389), ('magrittr', 0.9268977642059326), ('dplyr', 0.9099311828613281), ('tidyverse', 0.875988781452179), ('patchwork', 0.8729138374328613), ('data.table', 0.86326003074646), ('knitr', 0.8573061227798462), ('forcats', 0.8519827723503113), ('stringi', 0.8486554622650146), ('jsonlite', 0.8443582057952881)]
#lsi
python3 fitXtl.py PAPkgQR.a100.s3
records:20700
data.table;1.0
hierinf;0.96815044
MixtureInf;0.9635873
data.cube;0.9635765
macrobenchmark;0.9634209
RcppAPT;0.96161354
sykdomspulscompartmentalinfluenza;0.9587247
JFuncs;0.9568475
antaresWeeklyMargin;0.95673203
readr;0.99999994
stuko;0.9718676
imrParsers;0.97154045
ctsmr;0.9688772
targetscan.Hs.eg.db;0.9636713
wyntonquery;0.9597609
scdhlm;0.9505869
farms;0.9332409
sde;0.9197796
python3 fitXl.py PAPkgQR.a100.s3
records:20700
data.table;0.9999999
macrobenchmark;0.9698489
data.cube;0.96984845
MixtureInf;0.96984583
RcppAPT;0.9698335
hierinf;0.96799344
antaresWeeklyMargin;0.96222705
antaresRead;0.9620175
spatialdatatable;0.9574404
readr;1.0
ctsmr;0.9638993
imrParsers;0.96388185
stuko;0.9625468
wyntonquery;0.9605134
targetscan.Hs.eg.db;0.95924646
scdhlm;0.92792475
stlcsb;0.9269278
NameNeedle;0.92489654
#JS
f='doc2vecA.30.100.3.PAPkgQJS.0.b.1'
mod = Doc2Vec.load (f)
mod.wv.most_similar('http')
import gzip,collections,gensim.models.doc2vec,math
from gensim.models import Doc2Vec, Word2Vec
python3 fitXw.py PAPkgQR.s1 1 100 50 20 100 200
mod = Word2Vec(docs,sg=dm,size=vs, window=ws, negative=ns, min_count=mc, workers=cores,iter=iter)
mod.save("word2vec."+str(dm)+"."+str(vs)+"."+str(ws)+"."+str(ns)+"."+str(mc)+"."+str(iter)+"."+lst)
f='word2vec.100.50.20.100.200.0.PAPkgQR.s1' #garbage sg=0
f='word2vec.100.50.20.100.200.1.PAPkgQR.s1' #decent sg=1
>>> mod.most_similar('data.table')
[('dplyr', 0.8867905139923096), ('stringr', 0.8839394450187683), ('plyr', 0.8830145001411438), ('magrittr', 0.8689486384391785), ('magclass', 0.8649991154670715), ('readr', 0.8637726902961731), ('tidyr', 0.8611730337142944), ('lubridate', 0.8585350513458252), ('gWidgetsWWW2', 0.8545087575912476), ('lucode', 0.852830171585083)]
>>> mod.most_similar('readr')
[('magrittr', 0.9195265769958496), ('tidyr', 0.8952105641365051), ('dplyr', 0.8914402723312378), ('ggplot2', 0.8837970495223999), ('stringr', 0.8687031865119934), ('data.table', 0.8637727499008179), ('plotly', 0.8532767295837402), ('magclass', 0.852198600769043), ('lucode', 0.8505121469497681), ('plyr', 0.841021716594696)]
f='word2vec.1.100.50.20.100.200.PAPkgQR.s1' # OK
mod = Word2Vec.load (f)
mod.most_similar('data.table')
mod.most_similar('readr')
[('dplyr', 0.838019609451294), ('stringr', 0.7983173131942749), ('plyr', 0.7869787216186523), ('ggplot2', 0.7796253561973572), ('magrittr', 0.7705162167549133), ('reshape2', 0.7650773525238037), ('tidyr', 0.7562291622161865), ('readr', 0.7183531522750854), ('scales', 0.7113677263259888), ('tidyverse', 0.7113019227981567)]
>>> mod.most_similar('readr')
[('dplyr', 0.8551141023635864), ('magrittr', 0.8088136911392212), ('tidyr', 0.8009055852890015), ('stringr', 0.7933487892150879), ('ggplot2', 0.7563961148262024), ('data.table', 0.7183531522750854), ('tidyverse', 0.7072793841362), ('readxl', 0.6874278783798218), ('plotly', 0.6379566192626953), ('scales', 0.6288162469863892)]
f='word2vec.0.100.50.20.100.200.PAPkgQR.s1'
mod = Word2Vec.load (f)
mod.most_similar('data.table') # OK
mod.most_similar('readr')
[('dplyr', 0.6934608221054077), ('stringr', 0.6257824301719666), ('plyr', 0.6186755895614624), ('readr', 0.5545837879180908), ('tidyr', 0.5514156818389893), ('ggplot2', 0.5356006622314453), ('reshape2', 0.5290185809135437), ('lubridate', 0.5161994695663452), ('scales', 0.5158300399780273), ('magrittr', 0.4615442454814911)]
>>> mod.most_similar('readr')
[('dplyr', 0.6838119626045227), ('stringr', 0.6082045435905457), ('tidyverse', 0.5811571478843689), ('tidyr', 0.5662992596626282), ('data.table', 0.5545837879180908), ('lubridate', 0.531836748123169), ('magrittr', 0.5190625190734863), ('ggplot2', 0.4508250951766968), ('readxl', 0.42567265033721924), ('forcats', 0.3814961314201355)]
f='word2vec.20.1.3.PAPkgQJS.0.b'
mod.most_similar('http')
[('color-namer', 0.8891410827636719), ('easyyoutubedownload', 0.8820397257804871), ('socketio', 0.8606370091438293), ('https', 0.858386754989624), ('ffmetadata', 0.8539266586303711), ('tress', 0.8495419025421143), ('lwip', 0.8478525280952454), ('data-utils', 0.847440242767334), ('render', 0.8417345285415649), ('sharedb-mingo-memory', 0.836542546749115)]
mod.most_similar('https')
[('http', 0.858386754989624), ('google-search-scraper', 0.8478592038154602), ('restc', 0.8463708758354187), ('lwip', 0.8457597494125366), ('sanitize', 0.8455460071563721), ('skipper', 0.842998206615448), ('dom-parser', 0.842816948890686), ('mongoose-auto-increment', 0.8401623368263245), ('guid', 0.8306612968444824), ('easyyoutubedownload', 0.8301177024841309)]
####
#all tl - tfidf + lsi
python3 fitXtl.py PAPkgQ.all1.a100.0.s2
data.table;1.0
lubridate;0.9246081
magrittr;0.92408097
glmnet;0.90353286
dplyr;0.8974145
reshape;0.86968565
tibble;0.8680916
shinydashboard;0.8583525
shinythemes;0.85728675
readr;1.0
synapser;0.915734
RJSONIO;0.9078114
stringr;0.9041679
dplyr;0.8992814
tidyr;0.8970244
gridGraphics;0.8929982
tibble;0.8897572
magrittr;0.8888842
https;1.0000001
facebook-chat-api;0.9842278
shrink-ray;0.9786956
@sanity/mutator;0.9766238
groq;0.9766238
mead;0.9766238
sse-channel;0.9766238
epoll;0.976463
promise-mysql;0.97581285
python3 fitXl.py PAPkgQ.all1.a100.0.s2
data.table;1.0
yum;0.9950353
string.split;0.9948384
report;0.9942483
dbus.mainloop.glib.DBusGMainLoop;0.9939707
Reporter;0.99381834
ScanView;0.99381834
startfile;0.99381834
webbrowser._iscommand;0.99381834
readr;0.99999994
cfa/chrony_conf;0.9650319
yast2/target_file" # required to cfa work on changed scr;0.9650319
all;0.96063083
y2ntp_client/dialog/add_pool;0.9601435
deep_merge/core;0.95998293
yast/logger;0.9599484
cwm/dialog;0.95994604
cwm/popup;0.95994604
https;1.0000001
@sanity/mutator;0.94792485
groq;0.94792485
mead;0.94792485
sse-channel;0.94792485
pretty-log;0.9450143
@luminati-io/socksv5;0.9416811
hutil;0.9416811
python3 fitXtl.py PAPkgQ.all1.a100.s2
records:1614998
data.table;1.0
DESeq2;0.989663
RColorBrewer;0.9851666
reshape2;0.98426837
gplots;0.9818575
cowplot;0.9815432
ggplot2;0.9800583
lme4;0.9800026
ggrepel;0.9798994
readr;1.0
dplyr;0.9935278
GGally;0.99229115
magrittr;0.9897382
dendextend;0.9892954
pROC;0.9880314
pandas ;0.98731506
rdkit.Chem.PandasTools;0.986886
allensdk.core.cell_types_cache.CellTypesCache;0.98675674
https;0.99999994
express-fileupload;0.9932468
express-promise-router;0.9930251
mongodb;0.9930065
express-validator;0.9927011
passport-local-mongoose;0.9923985
body-parse;0.9923428
monk;0.992233
multer-s3;0.99221814
####################################
#Market basket:
####################################
zcat PAPkgQ.all1.a100.0.s2 | cut -d\; -f3- > tr
R --no-save
library(arules);
tr = read.transactions("tr",sep=";",quote="");
summary(tr);
itemFrequencyPlot(tr, topN=15);
res = apriori(tr, parameter = list(support=0.006, confidence = 0.25, minlen=2,maxtime=200));
summary(res);
set of 2399140 rules
rule length distribution (lhs + rhs):sizes
2
2399140
Min. 1st Qu. Median Mean 3rd Qu. Max.
2 2 2 2 2 2
summary of quality measures:
support confidence lift count
Min. :0.006015 Min. :0.2500 Min. : 0.9964 Min. : 304
1st Qu.:0.009576 1st Qu.:0.4796 1st Qu.: 9.8844 1st Qu.: 484
Median :0.016778 Median :0.7383 Median : 11.6512 Median : 848
Mean :0.023824 Mean :0.6956 Mean : 22.7509 Mean :1204
3rd Qu.:0.029994 3rd Qu.:0.9292 3rd Qu.: 20.4901 3rd Qu.:1516
Max. :0.162001 Max. :1.0000 Max. :161.9968 Max. :8188
mining info:
data ntransactions support confidence
tr 50543 0.006 0.25
inspect(sort(res, by="lift")[1:7])
lhs rhs support confidence lift count
[1] {mdast-util-compact} => {is-alphanumeric} 0.006172962 1 161.9968 312
[2] {is-alphanumeric} => {mdast-util-compact} 0.006172962 1 161.9968 312
[3] {setuptools.extern.six.moves.urllib.parse} => {setuptools.extern.six.moves.html_parser} 0.006212532 1 160.9650 314
[4] {setuptools.extern.six.moves.html_parser} => {setuptools.extern.six.moves.urllib.parse} 0.006212532 1 160.9650 314
[5] {setuptools.extern.six.moves.urllib.parse} => {setuptools.extern.pyparsing.ZeroOrMore} 0.006212532 1 160.9650 314
[6] {setuptools.extern.pyparsing.ZeroOrMore} => {setuptools.extern.six.moves.urllib.parse} 0.006212532 1 160.9650 314
[7] {setuptools.extern.six.moves.urllib.parse} => {setuptools.extern.pyparsing.ParseException} 0.006212532 1 160.9650 314
res85 = apriori(tr, parameter = list(support=0.006, confidence = 0.85, minlen=2,maxtime=200))
inspect(sort(res85, by="lift")[1:7])
lhs rhs support confidence lift count
[1] {functools.update_wrapper,
memcache} => {pylibmc} 0.006014681 1.0000000 165.1732 304
[2] {memcache,
pprint.pformat} => {pylibmc} 0.006014681 0.9967213 164.6317 304
[3] {dummy_threading,
memcache} => {pylibmc} 0.006014681 0.9934641 164.0936 304
[4] {code,
memcache} => {pylibmc} 0.006014681 0.9902280 163.5591 304
[5] {decimal,
memcache} => {pylibmc} 0.006014681 0.9902280 163.5591 304
[6] {datetime.date,
memcache} => {pylibmc} 0.006014681 0.9902280 163.5591 304
[7] {sw-toolbox,
trim-newlines} => {serviceworker-cache-polyfill} 0.006074036 1.0000000 163.0419 307
res850.1 = apriori(tr, parameter = list(support=0.1, confidence = 0.85, minlen=2,maxlen=5,maxtime=200))
inspect(sort(res850.1, by="lift")[1:7])
lhs rhs support
[1] {ajv,fast-deep-equal} => {json-schema-traverse} 0.1002711
[2] {json-schema-traverse} => {fast-deep-equal} 0.1003700
[3] {fast-deep-equal} => {json-schema-traverse} 0.1003700
[4] {ajv,json-schema-traverse} => {fast-deep-equal} 0.1002711
[5] {is-buffer,kind-of,source-map} => {repeat-string} 0.1000930
[6] {is-buffer,repeat-string,source-map} => {kind-of} 0.1000930
[7] {is-buffer,kind-of} => {repeat-string} 0.1006865
confidence lift count
[1] 1.0000000 9.961175 5068
[2] 0.9998029 9.943534 5073
[3] 0.9982290 9.943534 5073
[4] 0.9998027 9.943532 5068
[5] 0.9964546 9.861720 5059
[6] 0.9996048 9.854306 5059
[7] 0.9953061 9.850354 5089
res850.05 = apriori(tr, parameter = list(support=0.05, confidence = 0.85, minlen=2,maxlen=3,maxtime=200))
inspect(sort(res850.05, by="lift")[1:7])
lhs rhs support confidence
[1] {homedir-polyfill,isexe} => {parse-passwd} 0.05037295 1.0000000
[2] {homedir-polyfill,semver} => {parse-passwd} 0.05047188 1.0000000
[3] {parse-passwd} => {homedir-polyfill} 0.05051145 1.0000000
[4] {homedir-polyfill} => {parse-passwd} 0.05051145 0.9996085
[5] {is-glob,parse-passwd} => {homedir-polyfill} 0.05009596 1.0000000
[6] {is-extglob,parse-passwd} => {homedir-polyfill} 0.05013553 1.0000000
[7] {is-extendable,parse-passwd} => {homedir-polyfill} 0.05017510 1.0000000
lift count
[1] 19.79749 2546
[2] 19.79749 2551
[3] 19.78974 2553
[4] 19.78974 2553
[5] 19.78974 2532
[6] 19.78974 2534
[7] 19.78974 2536
####################################
####################################
####################################
####################################
####################################
#do evaluation: get diffs, new apis, projects, authors
a100 - authors that had between 100 and 25K blobs changed
for i in {0..31}; do zcat PAPkgQ.all2.a100.$i.s[24] | perl -e 'while(<STDIN>){chop(); ($p,$la,$a,@ms)=split(/;/);for $m (@ms){$m =~ s/^\s+//; $m =~ s/\s+$//; $k{"$p;$la"}{$m}++}};while (($p, $v)=each %k){@ms=sort keys %{$v}; print "$p;".(join ";", @ms)."\n";}' | gzip > PAPkgQ.all2.a100.$i.sPA; done &
for i in {0..31}; do perl cmp.perl $i | gzip > PAPkgQ.all2.a100.$i.sPD; done
#do author api prediction
for i in {0..31}; do zcat PAPkgQ.all2.a100.$i.s2; done | perl -e 'while(<STDIN>){chop(); ($p,$la,$a,@ms)=split(/;/);for $m (@ms){$m =~ s/^\s+//; $m =~ s/\s+$//; $k{"$a;$la"}{$m}++}};while (($p, $v)=each %k){@ms=sort keys %{$v}; print "$p;".(join ";", @ms)."\n";}' | gzip > PAPkgQ.all2.a100.$i.sA2
for i in {0..31}; do zcat PAPkgQ.all2.a100.$i.s[24]; done | perl -e 'while(<STDIN>){chop(); ($p,$la,$a,@ms)=split(/;/);for $m (@ms){$m =~ s/^\s+//; $m =~ s/\s+$//; $k{"$a;$la"}{$m}++}};while (($p, $v)=each %k){@ms=sort keys %{$v}; print "$p;".(join ";", @ms)."\n";}' | gzip > PAPkgQ.all2.a100.$i.sAA
perl cmpA.perl | gzip > PAPkgQ.all2.a100.sAD
zcat APPkgQ.all2.a100.s2.0.gz | cut -d\; -f1 | gzip > APPkgQall2.a100.0.s2.a
zcat PAPkgQ.all2.a100.sAD | perl ~/lookup/grepField.perl APPkgQ.all2.a100.0.s2.a 2 | gzip > APPkgQ.all2.a100.sAD.0
###########
# it looks as if the doc2vec overfits after the first iteration
###########
for (i in 0:17){
x = read.table(paste("out.",i,".1",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.50927680 0.09067491 0.10093593 0.18090885 0.19335262
[1] 1.00000000 0.44869976 0.08790081 0.09649559 0.08724058 0.09609265
[1] 2.00000000 0.42212822 0.08993144 0.09809864 0.06047655 0.06825218
[1] 3.00000000 0.40641982 0.09011352 0.09808722 0.04716476 0.05433207
[1] 4.00000000 0.39646346 0.09043100 0.09829863 0.03969543 0.04650274
[1] 5.00000000 0.39025141 0.09065208 0.09841595 0.03526486 0.04181648
[1] 6.00000000 0.38500585 0.09046420 0.09818283 0.03178368 0.03820169
[1] 7.00000000 0.38154876 0.09033579 0.09799816 0.02943122 0.03567698
[1] 8.00000000 0.37869745 0.09019635 0.09783633 0.02834118 0.03452642
[1] 9.00000000 0.37661727 0.09015368 0.09777063 0.02703326 0.03317761
[1] 10.000000 0.37553708 0.09035486 0.09793701 0.02664744 0.03276708
[1] 11.000000 0.37455881 0.09032891 0.09790877 0.02617130 0.03224815
[1] 12.000000 0.37403331 0.09060562 0.09817693 0.02633169 0.03245005
[1] 13.000000 0.49103977 0.06520619 0.07053818 0.06103873 0.06537175
[1] 14.000000 0.52866034 0.04765265 0.05206281 0.04579535 0.04934915
[1] 15.000000 0.54368915 0.04360702 0.04800054 0.00185246 0.005180105
[1] 16.000000 0.54855054 0.04256544 0.0469666 -0.02046332 -0.01718081
[1] 17.000000 0.54995806 0.04208497 0.0464946 -0.02926308 -0.02602597
for (i in 0:14){
x = read.table(paste("out.",i,".0",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean);
print (c(i, mean(a[,1]),t.test(a[,1]-a[,2])$conf.int[1:2], t.test(a[,2]-a[,3])$conf.int[1:2]));
}
[1] 0.00000000 0.50449317 0.08559275 0.09438839 0.18037605 0.19138137
[1] 1.00000000 0.44622477 0.08535391 0.09280849 0.08754807 0.09538888
[1] 2.00000000 0.41799572 0.08583718 0.09302845 0.06024928 0.06713268
[1] 3.00000000 0.40280881 0.08656080 0.09359742 0.04696934 0.05334605
[1] 4.00000000 0.39329022 0.08686626 0.09380557 0.03994251 0.04601570
[1] 5.00000000 0.38565571 0.08581838 0.09273360 0.03513862 0.04099686
[1] 6.00000000 0.38082778 0.08592939 0.09279577 0.03214265 0.03786785
[1] 7.00000000 0.37717005 0.08587037 0.09270433 0.03005095 0.03572047
[1] 8.00000000 0.37450212 0.08571395 0.09252385 0.02864018 0.03423129
[1] 9.00000000 0.37289926 0.08587650 0.09266644 0.02735387 0.03286991
[1] 10.000000 0.37146300 0.08569699 0.09247655 0.02718105 0.03267695
[1] 11.000000 0.37067109 0.08585872 0.09263314 0.02689559 0.03244588
[1] 12.000000 0.37011623 0.08606692 0.09284306 0.02672185 0.03223387
[1] 13.000000 0.48766617 0.06082190 0.06551233 0.06231336 0.06603381
[1] 14.000000 0.52486957 0.04465219 0.04876194 0.04607511 0.04923602
[1] 15.000000 0.53952873 0.04020725 0.04428654 0.00192170 0.005003169
for (i in 0:17){
x = read.table(paste("out.",i,".3",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean);
print (c(i, mean(a[,1]),t.test(a[,1]-a[,2])$conf.int[1:2], t.test(a[,2]-a[,3])$conf.int[1:2]));
}
[1] 0.00000000 0.51228422 0.08423109 0.09421903 0.18961387 0.20200018
[1] 1.00000000 0.45414432 0.08676788 0.09509846 0.09162590 0.10043595
[1] 2.00000000 0.42591008 0.08840818 0.09638070 0.06372909 0.07150438
[1] 3.00000000 0.41047527 0.08921806 0.09701840 0.04994462 0.05715492
[1] 4.00000000 0.40040532 0.08941896 0.09710931 0.04211002 0.04891717
[1] 5.00000000 0.39285663 0.08880580 0.09644760 0.03752624 0.04411451
[1] 6.00000000 0.38795940 0.08885461 0.09644423 0.03407689 0.04049291
[1] 7.00000000 0.38439066 0.08893528 0.09646439 0.03221137 0.03851050
[1] 8.00000000 0.38162160 0.08886911 0.09637950 0.03080327 0.03703007
[1] 9.00000000 0.37985002 0.08880199 0.09627510 0.02974078 0.03586236
[1] 10.000000 0.37892498 0.08915898 0.09660508 0.02977044 0.03595569
[1] 11.000000 0.37798348 0.08910305 0.09654585 0.02966858 0.03579533
[1] 12.000000 0.37741404 0.08938410 0.09682284 0.02925239 0.03542766
[1] 13.000000 0.49398193 0.06519341 0.07050073 0.06147903 0.06576454
[1] 14.000000 0.53190766 0.04968853 0.05420056 0.04470187 0.04835752
[1] 15.0000000 0.54696112 0.04496077 0.0494012 -0.0003606 0.0030147588
[1] 16.000000 0.55164709 0.04354439 0.0479578 -0.0215186 -0.01821583
[1] 17.000000 0.55291163 0.04290569 0.0472929 -0.0290859 -0.02576941
[1] 18.00000000 0.55315557 0.04267271 0.04704862 -0.03195601 -0.02864087
for (i in 0:12){
x = read.table(paste("out.",i,".2",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.49785506 0.07669858 0.08579238 0.18172363 0.19316974
[1] 1.00000000 0.44261004 0.07811053 0.08591582 0.09038415 0.09859895
[1] 2.00000000 0.41638081 0.08035304 0.08781969 0.06356984 0.07085234
[1] 3.00000000 0.40178666 0.08130004 0.08858155 0.05072390 0.05746753
[1] 4.00000000 0.39176749 0.08122574 0.08844958 0.04328591 0.04975070
[1] 5.00000000 0.38515961 0.08153330 0.08867910 0.03890856 0.04517659
[1] 6.00000000 0.38081594 0.08198846 0.08907676 0.03558160 0.04174849
[1] 7.00000000 0.37792258 0.08253919 0.08957826 0.03355376 0.03962063
[1] 8.00000000 0.37481701 0.08189974 0.08893458 0.03209322 0.03801004
[1] 9.00000000 0.37295145 0.08193768 0.08894917 0.03135597 0.03722180
[1] 10.000000 0.37174513 0.08208734 0.08907893 0.03071343 0.03660824
[1] 11.000000 0.37082015 0.08216424 0.08914824 0.03024330 0.03610591
[1] 12.000000 0.37022541 0.08241546 0.08939464 0.03013885 0.03599387
for (i in 0:13){
x = read.table(paste("out.",i,".4",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.50201980 0.07987561 0.08916248 0.18060162 0.19158181
[1] 1.00000000 0.44391281 0.08300614 0.09081614 0.08638836 0.09425660
[1] 2.00000000 0.41660976 0.08470465 0.09220151 0.06000864 0.06696254
[1] 3.00000000 0.40158190 0.08521869 0.09254401 0.04658003 0.05299151
[1] 4.00000000 0.39212684 0.08571876 0.09294464 0.03974222 0.04593746
[1] 5.00000000 0.38514603 0.08543596 0.09260558 0.03490646 0.04089176
[1] 6.00000000 0.38027639 0.08524057 0.09237278 0.03206088 0.03785095
[1] 7.00000000 0.37701049 0.08545423 0.09254990 0.03061003 0.03634893
[1] 8.00000000 0.37440156 0.08519508 0.09225728 0.02834649 0.03394094
[1] 9.00000000 0.37249239 0.08519084 0.09222946 0.02764028 0.03322387
[1] 10.000000 0.37108960 0.08493468 0.09197349 0.02752469 0.03306693
[1] 11.000000 0.37028994 0.08498972 0.09201713 0.02748844 0.03302571
[1] 12.000000 0.36986789 0.08519832 0.09221641 0.02678472 0.03228770
[1] 13.000000 0.48675934 0.06097694 0.06585658 0.06026705 0.06401915
for (i in 0:14){
x = read.table(paste("out.",i,".5",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.50267151 0.08600955 0.09632277 0.17849202 0.19114564
[1] 1.00000000 0.44218779 0.08239234 0.09104467 0.08791098 0.09704787
[1] 2.00000000 0.41486685 0.08387749 0.09206987 0.06065273 0.06858225
[1] 3.00000000 0.39991191 0.08414283 0.09208297 0.04886143 0.05630194
[1] 4.00000000 0.38959406 0.08372344 0.09153724 0.04083363 0.04786534
[1] 5.00000000 0.38285499 0.08351071 0.09122461 0.03697885 0.04380045
[1] 6.00000000 0.37839685 0.08361938 0.09124596 0.03406560 0.04067783
[1] 7.00000000 0.37465376 0.08337533 0.09096221 0.03179169 0.03826294
[1] 8.00000000 0.37260513 0.08378574 0.09132284 0.03066911 0.03704302
[1] 9.00000000 0.37041890 0.08355600 0.09106802 0.02981507 0.03618065
[1] 10.000000 0.36934699 0.08388878 0.09137486 0.02955678 0.03593405
[1] 11.000000 0.36870231 0.08412754 0.09160140 0.02956867 0.03589636
[1] 12.000000 0.36819066 0.08449028 0.09195601 0.02890331 0.03517397
[1] 13.000000 0.48408286 0.06204632 0.06726454 0.06454006 0.06901382
[1] 14.000000 0.51933219 0.04638277 0.05088548 0.05114478 0.05489391
for (i in 0:15){
x = read.table(paste("out.",i,".6",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.50194590 0.07802904 0.08722640 0.18307867 0.19440272
[1] 1.00000000 0.44191769 0.07892995 0.08676495 0.08899817 0.09697803
[1] 2.00000000 0.41517657 0.08203017 0.08954691 0.06153415 0.06861204
[1] 3.00000000 0.39924938 0.08213974 0.08957466 0.04791676 0.05440171
[1] 4.00000000 0.39020489 0.08310714 0.09043139 0.04041467 0.04652071
[1] 5.00000000 0.38355582 0.08362871 0.09086342 0.03575334 0.04174329
[1] 6.00000000 0.37902522 0.08404463 0.09122902 0.03312081 0.03896850
[1] 7.00000000 0.37519196 0.08374163 0.09090996 0.03071490 0.03646530
[1] 8.00000000 0.37258031 0.08369725 0.09084405 0.02923016 0.03489168
[1] 9.00000000 0.37086885 0.08370747 0.09083969 0.02901673 0.03465719
[1] 10.000000 0.3696916 0.08377138 0.09089033 0.02787652 0.03347392
[1] 11.000000 0.3689507 0.08393354 0.09104211 0.02782000 0.03341200
[1] 12.000000 0.3681606 0.08404009 0.09115488 0.02761429 0.03315016
[1] 13.000000 0.4852373 0.06116680 0.06610586 0.06109427 0.06482499
[1] 14.000000 0.5228279 0.04592885 0.05020548 0.04461072 0.04784755
[1] 15.000000 0.5373017 0.04143105 0.04569960 0.00103443 0.004078491
for (i in 0:15){
x = read.table(paste("out.",i,".7",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.50396787 0.07947480 0.08936423 0.18162355 0.19398814
[1] 1.00000000 0.44450430 0.07831268 0.08675250 0.09060640 0.09934813
[1] 2.00000000 0.41814557 0.08194505 0.08995940 0.06255808 0.07034907
[1] 3.00000000 0.40236771 0.08241262 0.09026814 0.04968048 0.05684223
[1] 4.00000000 0.39252629 0.08289184 0.09064631 0.04213635 0.04892226
[1] 5.00000000 0.38564438 0.08284525 0.09052531 0.03695521 0.04354209
[1] 6.00000000 0.38066470 0.08303947 0.09068435 0.03391318 0.04034644
[1] 7.00000000 0.37736891 0.08346400 0.09106224 0.03174895 0.03805528
[1] 8.00000000 0.37510235 0.08374403 0.09130274 0.03073049 0.03696002
[1] 9.00000000 0.37266407 0.08352685 0.09108348 0.02966367 0.03585700
[1] 10.000000 0.3716950 0.08391375 0.09144281 0.02928881 0.03547189
[1] 11.000000 0.3704244 0.08384022 0.09137753 0.02868524 0.03480612
[1] 12.000000 0.3697260 0.08412862 0.09166525 0.02847076 0.03464066
[1] 13.000000 0.4842518 0.06187893 0.06720188 0.06334633 0.06755593
[1] 14.000000 0.5215503 0.04675143 0.05130571 0.05097473 0.05456828
[1] 15.000000 0.5357240 0.04265098 0.04710515 0.00803823 0.011404840
#lets look at the performance by language
i=7
x = read.table("out.gz",sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3, x$V2), mean,na.rm=T);
print (apply(a, c(2,3),mean,na.rm=T));
print (apply(a, c(2,3),mean,na.rm=T));
C Cs F Go JS PY PYml
0 0.3571373 0.5189378 0.2278115 0.3515255 0.3386374 0.3755978 0.1295043
1 0.3874602 0.5061611 0.4108476 0.4010556 0.3477729 0.3159948 0.3361127
2 0.2280350 0.2268121 0.2768938 0.2245667 0.2239590 0.2281462 0.2301409
R Rust Scala ipy java jl pl
0 0.4039998 0.2178510 0.3608376 0.3047590 0.3847986 0.3876581 0.2629120
1 0.4546809 0.2959062 0.3415147 0.3921432 0.3510327 0.4497684 0.3492407
2 0.2343047 0.2165532 0.2121240 0.2311565 0.2275455 0.1987026 0.2134258
rb
0 0.3405758
1 0.3314413
2 0.2217781
###################################################################
# lets do project api prediction: what apis project will introduce?
for (i in 0:3){
x = read.table(paste("outP.",i,".0",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
}
[1] 0.00000000 0.50220484 0.03750815 0.04034000 0.06212110 0.06565460
[1] 1.00000000 0.42417264 0.03249091 0.03481417 0.03105006 0.03386180
[1] 3.00000000 0.37842667 0.03051798 0.03257089 0.01372966 0.01610825
i=0
x = read.table(paste("outP.",i,".0",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
[1] 0.00000000 0.50220484 0.03750815 0.04034000 0.06212110 0.06565460
a = tapply(x$V5, list(x$V1, x$V3, x$V2), mean,na.rm=T);
print (apply(a, c(2,3),mean,na.rm=T));
C Cs F Go JS PY PYml
0 0.4038616 0.5008812 0.3221772 0.4173387 0.4109708 0.3785951 0.2043879
1 0.4206692 0.5185743 0.4671599 0.4672491 0.4405812 0.3692081 0.3704627
2 0.3976459 0.4082502 0.3635964 0.3969003 0.3989239 0.3974906 0.3968144
R Rust Scala ipy java jl pl
0 0.4202732 0.3414117 0.3948754 0.3510758 0.3808703 0.4874737 0.3557524
1 0.4765751 0.4091097 0.4168996 0.4032985 0.3810110 0.5234631 0.4483392
2 0.3942742 0.3935141 0.3863154 0.3974065 0.3947928 0.3813477 0.3979783
rb
0 0.4213470
1 0.4464474
2 0.3960902
#does having more dimensions help (no diff)
outP300.0.0.gz
i=0
x = read.table(paste("outP300.",i,".0.gz",sep=""),sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(i, mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
[1] 0.00000000 0.50253115 0.03709295 0.03992245 0.06241946 0.06595332
a = tapply(x$V5, list(x$V1, x$V3, x$V2), mean,na.rm=T);
print (apply(a, c(2,3),mean,na.rm=T));
C Cs F Go JS PY PYml
0 0.4059530 0.4996751 0.3033180 0.4175354 0.4083223 0.3767376 0.1888257
1 0.4243197 0.5202884 0.4710519 0.4708684 0.4424237 0.3692582 0.3695199
2 0.3964593 0.4094849 0.3811432 0.3971758 0.3992997 0.3972929 0.3973700
R Rust Scala ipy java jl pl
0 0.4065591 0.3391558 0.3871812 0.3421830 0.3797190 0.4773927 0.3497007
1 0.4641753 0.4128587 0.4137810 0.4012505 0.3801242 0.5147258 0.4495260
2 0.3940716 0.3881544 0.3837782 0.3972767 0.3947031 0.3729810 0.3981757
rb
0 0.4173108
1 0.4443876
2 0.3973568
# Do project similarity prediction
#can we predict new authors for a project?
x = read.table("outPA.gz",sep=";",quote="",comment.char="");
a = tapply(x$V4, list(x$V1,x$V2), mean,na.rm=T);
print (apply(a, 2, mean, na.rm=T));
0.5455169 0.4287622 0.3155700
#can we predict new projects for an author?
x = read.table("outAP.gz",sep=";",quote="",comment.char="");
a = tapply(x$V4, list(x$V1,x$V2), mean,na.rm=T);
print (apply(a, 2,mean,na.rm=T));
0.6459501 0.3842832 0.2867446
##################################################
#fit an overall model instead of on 1/32 of the data
# first model without projects
xA = read.table("outA.gz",sep=";",quote="",comment.char="");
aA = tapply(xA$V5, list(xA$V1, xA$V3), mean,na.rm=T);
print (c(mean(aA[,1],na.rm=T),t.test(aA[,1]-aA[,2],na.rm=T)$conf.int[1:2], t.test(aA[,2]-aA[,3],na.rm=T)$conf.int[1:2]));
[1] 0.45467152 0.05725422 0.05858767 0.14957094 0.15119570
aA = tapply(xA$V5, list(xA$V1, xA$V3, xA$V2), mean,na.rm=T);
print (apply(aA, c(2,3),mean,na.rm=T));
C Cs F Go JS PY PYml
0 0.3372032 0.4443690 0.2833624 0.3148605 0.3272042 0.3324191 0.1087180
1 0.3779736 0.4413446 0.3941996 0.3667553 0.3732069 0.2934193 0.3089333
2 0.2410490 0.2515277 0.2318679 0.2307177 0.2393430 0.2431279 0.2432443
R Rust Scala ipy java jl pl
0 0.3576188 0.2324307 0.3308925 0.2608123 0.3621232 0.4033044 0.2310343
1 0.4388650 0.3207504 0.3580214 0.3403137 0.3427869 0.4454717 0.3475361
2 0.2498877 0.2302580 0.2312663 0.2464753 0.2441400 0.2346712 0.2402494
rb
0 0.3001650
1 0.3266216
2 0.2365377
# now model with projects
x = read.table("out.gz",sep=";",quote="",comment.char="");
a = tapply(x$V5, list(x$V1, x$V3), mean,na.rm=T);
print (c(mean(a[,1],na.rm=T),t.test(a[,1]-a[,2],na.rm=T)$conf.int[1:2], t.test(a[,2]-a[,3],na.rm=T)$conf.int[1:2]));
[1] 0.42057762 0.06031639 0.06163822 0.12221982 0.12375388
a = tapply(x$V5, list(x$V1, x$V3, x$V2), mean,na.rm=T);
print (apply(a, c(2,3),mean,na.rm=T));
C Cs F Go JS PY PYml
0 0.2971185 0.4002471 0.2653794 0.2841707 0.2934137 0.3129954 0.08763108
1 0.3305545 0.3918358 0.3886875 0.3296505 0.3354792 0.2624989 0.28077446
2 0.2307742 0.2443252 0.2345744 0.2193816 0.2295895 0.2320019 0.23174294
R Rust Scala ipy java jl pl
0 0.3279675 0.2043637 0.3113372 0.2359361 0.3330258 0.3766405 0.2024342
1 0.4009443 0.2859266 0.3292285 0.3048130 0.3068544 0.4191881 0.3123098
2 0.2368511 0.2168607 0.2182584 0.2336722 0.2350468 0.2188272 0.2332850
rb
0 0.2739018
1 0.2968572
2 0.2278331
# Do collaborator (new cop-project) similarity prediction?
#See how lsi works
# Do all the evaluations using tfidf + lsi
python3 fitXtl.py PAPkgQ.a100.s2
python3 measureTL.py PAPkgQ.all1.a100.s2 | gzip > outTL.gz
python3 fitXtl.py PAPkgQ.all1.a100.s2 200
python3 measureTL.py PAPkgQ.all1.a100.s2.200 | gzip > outTL200.gz
#pure LSI
python3 fitXl.py PAPkgQ.all1.a100.s2 200
python3 measureL.py PAPkgQ.all1.a100.s2 200 | gzip > PAPkgQ.all1.a100.s2.200.l.gz
python3 measureLw.py PAPkgQ.all1.a100.s2.200 200 | gzip > PAPkgQ.all1.a100.s2.200.lw.gz &
x = read.table("PAPkgQ.all1.a100.s2.200.l.gz",sep=";",quote="",comment.char="");