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logs_SemanticGraph_delta_1_cutoff_25_minedge_1.pkl.txt
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logs_SemanticGraph_delta_1_cutoff_25_minedge_1.pkl.txt
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ReadSemanticGraph_delta_1_cutoff_25_minedge_1.pkl
1.1) Create training data for 2018
in create_training_data_biased
Create Graph for 2018
Create Graph for 2019
len(vertex_large_degs): 55898
edge progress (0.06484818458557129sec): 0.01M/1.0M 21 9979
edge progress (0.06588006019592285sec): 0.02M/1.0M 43 19957
edge progress (0.06421232223510742sec): 0.03M/1.0M 70 29930
edge progress (0.06021261215209961sec): 0.04M/1.0M 94 39906
edge progress (0.05998826026916504sec): 0.05M/1.0M 121 49879
edge progress (0.06822562217712402sec): 0.06M/1.0M 142 59858
edge progress (0.0638573169708252sec): 0.07M/1.0M 165 69835
edge progress (0.06000161170959473sec): 0.08M/1.0M 185 79815
edge progress (0.0755164623260498sec): 0.09M/1.0M 204 89796
edge progress (0.0644383430480957sec): 0.1M/1.0M 221 99779
edge progress (0.0640716552734375sec): 0.11M/1.0M 234 109766
edge progress (0.060009002685546875sec): 0.12M/1.0M 259 119741
edge progress (0.06399178504943848sec): 0.13M/1.0M 275 129725
edge progress (0.0599668025970459sec): 0.14M/1.0M 292 139708
edge progress (0.060338735580444336sec): 0.15M/1.0M 312 149688
edge progress (0.0751185417175293sec): 0.16M/1.0M 331 159669
edge progress (0.0642843246459961sec): 0.17M/1.0M 357 169643
edge progress (0.06284260749816895sec): 0.18M/1.0M 380 179620
edge progress (0.06494879722595215sec): 0.19M/1.0M 410 189590
edge progress (0.06571531295776367sec): 0.2M/1.0M 431 199569
edge progress (0.062458038330078125sec): 0.21M/1.0M 453 209547
edge progress (0.06451058387756348sec): 0.22M/1.0M 478 219522
edge progress (0.07614994049072266sec): 0.23M/1.0M 507 229493
edge progress (0.06226754188537598sec): 0.24M/1.0M 525 239475
edge progress (0.05921769142150879sec): 0.25M/1.0M 547 249453
edge progress (0.05694723129272461sec): 0.26M/1.0M 570 259430
edge progress (0.06397104263305664sec): 0.27M/1.0M 588 269412
edge progress (0.060208797454833984sec): 0.28M/1.0M 615 279385
edge progress (0.05911898612976074sec): 0.29M/1.0M 634 289366
edge progress (0.06592631340026855sec): 0.3M/1.0M 657 299343
edge progress (0.06779289245605469sec): 0.31M/1.0M 682 309318
edge progress (0.06774735450744629sec): 0.32M/1.0M 700 319300
edge progress (0.058289527893066406sec): 0.33M/1.0M 713 329287
edge progress (0.06008100509643555sec): 0.34M/1.0M 730 339270
edge progress (0.07601642608642578sec): 0.35M/1.0M 751 349249
edge progress (0.060005903244018555sec): 0.36M/1.0M 765 359235
edge progress (0.06000852584838867sec): 0.37M/1.0M 791 369209
edge progress (0.056077003479003906sec): 0.38M/1.0M 813 379187
edge progress (0.05999755859375sec): 0.39M/1.0M 832 389168
edge progress (0.06406188011169434sec): 0.4M/1.0M 854 399146
edge progress (0.06000256538391113sec): 0.41M/1.0M 878 409122
edge progress (0.07192683219909668sec): 0.42M/1.0M 903 419097
edge progress (0.07200026512145996sec): 0.43M/1.0M 919 429081
edge progress (0.0600743293762207sec): 0.44M/1.0M 938 439062
edge progress (0.06000089645385742sec): 0.45M/1.0M 956 449044
edge progress (0.07557296752929688sec): 0.46M/1.0M 981 459019
edge progress (0.06754207611083984sec): 0.47M/1.0M 1002 468998
edge progress (0.06625819206237793sec): 0.48M/1.0M 1019 478981
edge progress (0.06399989128112793sec): 0.49M/1.0M 1039 488961
edge progress (0.06249856948852539sec): 0.5M/1.0M 1061 498939
edge progress (22.713175535202026sec): 0.51M/1.0M 10000 500000
edge progress (25.72417402267456sec): 0.52M/1.0M 20000 500000
edge progress (26.498239278793335sec): 0.53M/1.0M 30000 500000
edge progress (25.585718393325806sec): 0.54M/1.0M 40000 500000
edge progress (29.749469995498657sec): 0.55M/1.0M 50000 500000
edge progress (27.331865072250366sec): 0.56M/1.0M 60000 500000
edge progress (25.530134677886963sec): 0.57M/1.0M 70000 500000
edge progress (26.77740502357483sec): 0.58M/1.0M 80000 500000
edge progress (25.561452627182007sec): 0.59M/1.0M 90000 500000
edge progress (25.819833993911743sec): 0.6M/1.0M 100000 500000
edge progress (26.280385494232178sec): 0.61M/1.0M 110000 500000
edge progress (25.249505043029785sec): 0.62M/1.0M 120000 500000
edge progress (25.679540157318115sec): 0.63M/1.0M 130000 500000
edge progress (25.33589196205139sec): 0.64M/1.0M 140000 500000
edge progress (25.005971908569336sec): 0.65M/1.0M 150000 500000
edge progress (26.317193746566772sec): 0.66M/1.0M 160000 500000
edge progress (25.338908672332764sec): 0.67M/1.0M 170000 500000
edge progress (26.289684534072876sec): 0.68M/1.0M 180000 500000
edge progress (25.889450550079346sec): 0.69M/1.0M 190000 500000
edge progress (25.843690156936646sec): 0.7M/1.0M 200000 500000
edge progress (25.84011697769165sec): 0.71M/1.0M 210000 500000
edge progress (27.25341534614563sec): 0.72M/1.0M 220000 500000
edge progress (25.426647424697876sec): 0.73M/1.0M 230000 500000
edge progress (25.88405418395996sec): 0.74M/1.0M 240000 500000
edge progress (26.456032752990723sec): 0.75M/1.0M 250000 500000
edge progress (25.795044660568237sec): 0.76M/1.0M 260000 500000
edge progress (25.97072410583496sec): 0.77M/1.0M 270000 500000
edge progress (25.750391483306885sec): 0.78M/1.0M 280000 500000
edge progress (26.49947500228882sec): 0.79M/1.0M 290000 500000
edge progress (26.130467653274536sec): 0.8M/1.0M 300000 500000
edge progress (28.020081520080566sec): 0.81M/1.0M 310000 500000
edge progress (26.1533625125885sec): 0.82M/1.0M 320000 500000
edge progress (26.790212154388428sec): 0.83M/1.0M 330000 500000
edge progress (25.57495403289795sec): 0.84M/1.0M 340000 500000
edge progress (24.881906509399414sec): 0.85M/1.0M 350000 500000
edge progress (25.52126932144165sec): 0.86M/1.0M 360000 500000
edge progress (25.86682391166687sec): 0.87M/1.0M 370000 500000
edge progress (26.03582525253296sec): 0.88M/1.0M 380000 500000
edge progress (26.76496911048889sec): 0.89M/1.0M 390000 500000
edge progress (26.314584255218506sec): 0.9M/1.0M 400000 500000
edge progress (25.39731740951538sec): 0.91M/1.0M 410000 500000
edge progress (26.094245433807373sec): 0.92M/1.0M 420000 500000
edge progress (26.727577686309814sec): 0.93M/1.0M 430000 500000
edge progress (26.444162130355835sec): 0.94M/1.0M 440000 500000
edge progress (25.902159214019775sec): 0.95M/1.0M 450000 500000
edge progress (25.562679767608643sec): 0.96M/1.0M 460000 500000
edge progress (24.899943113327026sec): 0.97M/1.0M 470000 500000
edge progress (26.555999517440796sec): 0.98M/1.0M 480000 500000
edge progress (25.86628270149231sec): 0.99M/1.0M 490000 500000
nearly done here
Shuffle training data...
1.2) Compute 15 network properties for training data y=2016
Computing properties for Training data
compute_all_properties_of_list progress: (296.1826319694519sec) 0.0M/0.9M
compute_all_properties_of_list progress: (6.4221861362457275sec) 0.01M/0.9M
compute_all_properties_of_list progress: (4.454283952713013sec) 0.02M/0.9M
compute_all_properties_of_list progress: (3.695507049560547sec) 0.03M/0.9M
compute_all_properties_of_list progress: (3.1045408248901367sec) 0.04M/0.9M
compute_all_properties_of_list progress: (2.7167091369628906sec) 0.05M/0.9M
compute_all_properties_of_list progress: (2.694498300552368sec) 0.06M/0.9M
compute_all_properties_of_list progress: (2.349384069442749sec) 0.07M/0.9M
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compute_all_properties_of_list progress: (2.064234495162964sec) 0.1M/0.9M
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Computing properties for Test data
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1.3) Train Neural Network
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1.4) Computes the AUC for training and test data using calculate_ROC.
2.1) Computes the 15 properties for the 2019 data.
compute all properties for evaluation
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