0.0341637010676
]]
]
dboost_matrix_f = np.array(dboost_fscore_all)
dboost_avg_f = np.mean(dboost_matrix_f, axis=2)
#label_potential = [4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 78, 88, 98, 108, 118, 128, 138, 148, 158, 168, 178, 188, 198, 208, 218, 228, 238, 248, 258, 268, 278, 288, 298, 308, 318, 328, 338, 348, 358, 368, 378, 388, 398, 408, 418, 428, 438, 448, 458, 468, 478, 488, 498, 508, 518, 528, 538, 548, 558, 568, 578, 588, 598, 608, 618, 628, 638, 648, 658, 668, 678, 688, 698, 708, 718, 728, 738, 748, 758, 768, 778, 788, 798, 808, 818, 828, 838, 848, 858, 868, 878, 888, 898, 908, 918]
label_potential = [
4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 78, 88,
98, 108, 118, 128, 138, 148, 158, 168, 178, 188, 198, 208, 218, 228, 238,
248, 258, 268, 278, 288, 298, 308, 318, 328, 338, 348, 358, 368, 378, 388,
398, 408, 418, 428, 438, 448, 458, 468, 478, 488, 498, 508, 518, 528, 538,
548, 558, 568, 578, 588, 598, 608, 618, 628, 638, 648, 658, 668, 678, 688,
698, 708, 718, 728, 738, 748, 758, 768, 778, 788, 798, 808, 818, 828, 838,
848, 858, 868, 878, 888, 898, 908, 918, 928, 938, 948, 958, 968, 978, 988,
998, 1008, 1018, 1028, 1038
]
PlotterLatex(data,
label_potential,
fscore_metadata_no_svd_more_data,
dboost_models,
dboost_sizes,
dboost_avg_f,
nadeef_fscore,
openrefine_fscore,
None,
xmax=600,
filename="Hospital")
0.00631104603318
],
[
0.291690609844, 0.0548704825712, 0.114709605692, 0.0679864045995,
0.00631104603318
],
[
0.396137997093, 0.106538706836, 0.396137997093, 0.109184848579,
0.106538706836
],
[
0.395805130055, 0.396137997093, 0.206868755265, 0.396137997093,
0.438643682948
]]
]
dboost_matrix_f = np.array(dboost_fscore_all)
dboost_avg_f = np.mean(dboost_matrix_f, axis=2)
PlotterLatex(data,
label_potential,
fscore_metadata_no_svd_absolute_potential,
dboost_models,
dboost_sizes,
dboost_avg_f,
nadeef_fscore,
openrefine_fscore,
None,
xmax=200,
filename="Address")