5s_average_accuracy_classic.sagews

1+1

2

load('5s_accuracy_dependencies.sage')

improved=(5189/1360, -7/136, 547/340)
classic = (34/10,0,4/10)

def get_slice_accuracy(sl):
    ss = sl.original_structure
    return get_accuracies(name,ss)

# Get accuracy for classic and for improved.
# Takes ~40 min
n=len(names)
classic_accuracy = {}
improved_accuracy = {}
for i,(sname,name) in enumerate(zip(sobj_names,names)):
    stname = "5s/st_tuples/"+name+"_ss.pkl"
    P = RNAPolytope.construct_from_file(sname)
    classic_accuracy[name] = []
    for sl in get_slice_containing_pt(P,classic):
        classic_accuracy[name].extend(get_slice_accuracy(sl))
    improved_accuracy[name] = []
    for sl in get_slice_containing_pt(P,improved):
        improved_accuracy[name].extend(get_slice_accuracy(sl))
    print timestamp("Finished averaging [{0}/{1}] {2}".format(i+1,n,name))

[2017-09-14 15:05:44.496260] Finished averaging [1/88] d.5.e.I.illecebrosus_X00993 [2017-09-14 15:06:04.873153] Finished averaging [2/88] d.5.e.G.riobambae_M74438 [2017-09-14 15:06:29.754576] Finished averaging [3/88] d.5.a.P.aerophilum_AE009942 [2017-09-14 15:06:51.680383] Finished averaging [4/88] d.5.e.G.biloba_M10433 [2017-09-14 15:07:16.355802] Finished averaging [5/88] d.5.a.P.occultum_M21086 [2017-09-14 15:07:43.535841] Finished averaging [6/88] d.5.a.M.fervidus_M26976 [2017-09-14 15:08:09.946434] Finished averaging [7/88] d.5.e.P.reticulata_X01550 [2017-09-14 15:08:33.604065] Finished averaging [8/88] d.5.e.L.edodes_AB030581 [2017-09-14 15:08:58.461764] Finished averaging [9/88] d.5.e.A.immersus.1_X99087 [2017-09-14 15:09:23.133870] Finished averaging [10/88] d.5.a.H.mediterranei.1_X14441 [2017-09-14 15:09:45.426209] Finished averaging [11/88] d.5.e.C.elegans.2_Z82085 [2017-09-14 15:10:10.925285] Finished averaging [12/88] d.5.e.H.sapiens.2_V00589 [2017-09-14 15:10:34.615212] Finished averaging [13/88] d.5.e.R.norvegicus.1_X83746 [2017-09-14 15:10:58.771417] Finished averaging [14/88] d.5.a.P.woesei_X15329 [2017-09-14 15:11:19.627945] Finished averaging [15/88] d.5.e.F.hygrometrica_X80212 [2017-09-14 15:11:41.234117] Finished averaging [16/88] d.5.e.B.neritina_X06834 [2017-09-14 15:12:05.297159] Finished averaging [17/88] d.5.e.O.mykiss_J01861 [2017-09-14 15:12:30.029893] Finished averaging [18/88] d.5.e.C.quinquecirrha_K02348 [2017-09-14 15:12:50.370363] Finished averaging [19/88] d.5.b.E.coli_V00336 [2017-09-14 15:13:13.533487] Finished averaging [20/88] d.5.e.C.vicina_X01129 [2017-09-14 15:13:40.652666] Finished averaging [21/88] d.5.a.T.celer.1_X07692 [2017-09-14 15:14:06.600786] Finished averaging [22/88] d.5.e.R.norvegicus.2_X83747 [2017-09-14 15:14:35.375552] Finished averaging [23/88] d.5.a.M.thermolithotrophicus.2_M34911 [2017-09-14 15:14:59.579235] Finished averaging [24/88] d.5.e.E.kokanica_X06996 [2017-09-14 15:15:25.134489] Finished averaging [25/88] d.5.e.P.radiata_Z33604 [2017-09-14 15:15:46.606917] Finished averaging [26/88] d.5.e.A.aquaticus_AJ238321 [2017-09-14 15:16:10.524965] Finished averaging [27/88] d.5.a.M.jannaschii.rrnB_U67518 [2017-09-14 15:16:36.891947] Finished averaging [28/88] d.5.e.B.vulgaris_X05059 [2017-09-14 15:17:02.933724] Finished averaging [29/88] d.5.e.Q.petraea_AJ242949 [2017-09-14 15:17:24.886062] Finished averaging [30/88] d.5.e.A.tabira_AB015591 [2017-09-14 15:17:48.104989] Finished averaging [31/88] d.5.e.P.coxalis_Y14281 [2017-09-14 15:18:13.965116] Finished averaging [32/88] d.5.e.I.iguana_M10817 [2017-09-14 15:18:36.524177] Finished averaging [33/88] d.5.e.M.fossilis_V00647 [2017-09-14 15:19:01.122857] Finished averaging [34/88] d.5.e.B.taurus_X57170 [2017-09-14 15:19:23.547032] Finished averaging [35/88] d.5.b.G.stearothermophilus.4_M25591 [2017-09-14 15:19:50.477719] Finished averaging [36/88] d.5.e.A.magnoliae_X01518 [2017-09-14 15:20:15.491809] Finished averaging [37/88] d.5.e.Hemicentrotus.sp_X01004 [2017-09-14 15:20:36.750976] Finished averaging [38/88] d.5.e.S.aggregatum_X06104 [2017-09-14 15:21:02.240803] Finished averaging [39/88] d.5.a.H.salinarum.2_X03407 [2017-09-14 15:21:22.460975] Finished averaging [40/88] d.5.e.H.oculata_X01536 [2017-09-14 15:21:44.020495] Finished averaging [41/88] d.5.e.L.japonicum_D00076 [2017-09-14 15:22:05.801997] Finished averaging [42/88] d.5.e.A.pernyi_X13035 [2017-09-14 15:22:30.810544] Finished averaging [43/88] d.5.e.G.arboreum_U31855 [2017-09-14 15:22:55.513319] Finished averaging [44/88] d.5.a.H.marismortui_AF034620 [2017-09-14 15:23:17.841638] Finished averaging [45/88] d.5.e.B.vorax_K02343 [2017-09-14 15:23:43.096685] Finished averaging [46/88] d.5.e.D.melanogaster.1_M25016 [2017-09-14 15:24:07.668947] Finished averaging [47/88] d.5.e.M.salmoides_L49397 [2017-09-14 15:24:30.414213] Finished averaging [48/88] d.5.e.P.gouldii_X00994 [2017-09-14 15:24:55.372167] Finished averaging [49/88] d.5.e.B.napus_X05060 [2017-09-14 15:25:18.136831] Finished averaging [50/88] d.5.e.H.japonica_V00475 [2017-09-14 15:25:41.642548] Finished averaging [51/88] d.5.e.O.sativa.1_M18171 [2017-09-14 15:26:06.603389] Finished averaging [52/88] d.5.b.G.stearothermophilus.2_AJ251080 [2017-09-14 15:26:26.773667] Finished averaging [53/88] d.5.b.A.tumefaciens_X02627 [2017-09-14 15:26:52.696002] Finished averaging [54/88] d.5.e.R.norvegicus.3_X83748 [2017-09-14 15:27:18.640655] Finished averaging [55/88] d.5.e.L.usitatissimum_X01531 [2017-09-14 15:27:47.051849] Finished averaging [56/88] d.5.a.M.vacuolata_X62864 [2017-09-14 15:28:09.407796] Finished averaging [57/88] d.5.e.L.geniculatus_X00020 [2017-09-14 15:28:35.103131] Finished averaging [58/88] d.5.a.Sulfolobus.sp.B12_X05870 [2017-09-14 15:28:59.782087] Finished averaging [59/88] d.5.e.H.sapiens.1_Z75742 [2017-09-14 15:29:29.530099] Finished averaging [60/88] d.5.a.N.magadii_X72495 [2017-09-14 15:29:55.911024] Finished averaging [61/88] d.5.e.D.mauritiana_X87874 [2017-09-14 15:30:19.706611] Finished averaging [62/88] d.5.a.H.morrhuae.2_X72588 [2017-09-14 15:30:43.471464] Finished averaging [63/88] d.5.e.H.sanguinea_X01535 [2017-09-14 15:31:05.887440] Finished averaging [64/88] d.5.e.A.domesticus_M16074 [2017-09-14 15:31:28.115566] Finished averaging [65/88] d.5.b.G.stearothermophilus.3_M24839 [2017-09-14 15:31:49.731470] Finished averaging [66/88] d.5.e.C.elegans.1_X06102 [2017-09-14 15:32:15.039032] Finished averaging [67/88] d.5.e.H.sapiens.3_X71802 [2017-09-14 15:32:39.001553] Finished averaging [68/88] d.5.e.G.gnemon_X06995 [2017-09-14 15:33:05.434241] Finished averaging [69/88] d.5.e.O.sativa.2_M18170 [2017-09-14 15:33:27.788430] Finished averaging [70/88] d.5.e.P.brevicirris_X00996 [2017-09-14 15:33:51.251811] Finished averaging [71/88] d.5.e.E.gracilis.2_X01484 [2017-09-14 15:34:15.505118] Finished averaging [72/88] d.5.e.M.glyptostroboides_M10432 [2017-09-14 15:34:42.007367] Finished averaging [73/88] d.5.e.P.tetraurelia_J01872 [2017-09-14 15:35:03.961757] Finished averaging [74/88] d.5.e.N.viridescens_X06097 [2017-09-14 15:35:25.637755] Finished averaging [75/88] d.5.e.A.salina_Y00128 [2017-09-14 15:35:49.551195] Finished averaging [76/88] d.5.a.H.salinarum.1_M10691 [2017-09-14 15:36:10.852719] Finished averaging [77/88] d.5.e.A.equina_X05233 [2017-09-14 15:36:38.790848] Finished averaging [78/88] d.5.e.P.hybrida.2_X07929 [2017-09-14 15:37:01.020510] Finished averaging [79/88] d.5.e.L.luteus_Z93433 [2017-09-14 15:37:26.658621] Finished averaging [80/88] d.5.a.M.formicicum_X62859 [2017-09-14 15:37:52.099264] Finished averaging [81/88] d.5.e.E.eurystomus_X13718 [2017-09-14 15:38:24.825862] Finished averaging [82/88] d.5.a.M.barkeri_M36188 [2017-09-14 15:38:51.451532] Finished averaging [83/88] d.5.a.D.mobilis_X07545 [2017-09-14 15:39:16.192431] Finished averaging [84/88] d.5.e.H.sapiens.4_X51545 [2017-09-14 15:39:40.271680] Finished averaging [85/88] d.5.e.C.carpio_AB015590 [2017-09-14 15:40:04.489801] Finished averaging [86/88] d.5.a.T.acidophilum_X02709 [2017-09-14 15:40:28.475300] Finished averaging [87/88] d.5.a.H.morrhuae.1_J01865 [2017-09-14 15:40:49.427189] Finished averaging [88/88] d.5.b.M.genitalium_U39694

classic_accuracy[names[0]]

[0.6086956521739131]

from numpy import average as avg, median as med, std
avg_classic = []
for name in classic_accuracy:
    avg_classic.append(avg(classic_accuracy[name]))
print avg(avg_classic)
print std(avg_classic)
print avg_classic

0.695241310816 0.208183985316 [0.57894736842105265, 0.82339874120696033, 0.84637454355764208, 0.89411764705882357, 0.89583333333333337, 0.88059701492537312, 0.72730829420970267, 0.86301369863013688, 0.29333333333333333, 0.875, 0.75865677321156766, 0.86746987951807231, 0.75865677321156766, 0.60692541856925419, 0.75324675324675328, 0.20000000000000001, 0.68918918918918926, 0.875, 0.875, 0.15384615384615385, 0.8529411764705882, 0.74285714285714288, 0.30555555555555558, 0.88172043010752688, 0.88, 0.7567567567567568, 0.77333333333333332, 0.24324324324324326, 0.38356164383561642, 0.82339874120696033, 0.83561643835616439, 0.60869565217391308, 0.33809523809523812, 0.76824561403508773, 0.79934680547050407, 0.86111111111111116, 0.875, 0.77333333333333332, 0.84637454355764208, 0.49275362318840582, 0.81428571428571428, 0.76712328767123283, 0.88372093023255816, 0.86842105263157898, 0.82339874120696033, 0.82339874120696033, 0.82339874120696033, 0.26666666666666666, 0.78378378378378377, 0.81237237237237236, 0.78873239436619713, 0.56842298170980843, 0.85450292397660821, 0.71641791044776115, 0.36065573770491804, 0.78454106280193237, 0.64416315049226436, 0.23901895206243035, 0.47222222222222221, 0.8529411764705882, 0.75342465753424648, 0.73972602739726023, 0.58333333333333337, 0.38356164383561642, 0.1891891891891892, 0.87769961742564473, 0.29333333333333333, 0.69921227197346603, 0.70522522522522524, 0.20000000000000001, 0.77333333333333332, 0.78873239436619713, 0.76824561403508773, 0.75, 0.6901408450704225, 0.72941176470588232, 0.82339874120696044, 0.74626865671641796, 0.8571428571428571, 0.78378378378378377, 0.83225225225225219, 0.87769961742564473, 0.75, 0.82339874120696033, 0.87769961742564473, 0.79411764705882348, 0.38356164383561642, 0.70129870129870131]

avg_improved = []
for name in improved_accuracy:
    avg_improved.append(avg(improved_accuracy[name]))
print avg(avg_improved)
print std(avg_improved)
print avg_improved

0.722930338058 0.221618870643 [0.37585585585585579, 0.83472856418061891, 0.85835010060362171, 0.89411764705882357, 0.89583333333333337, 0.89393939393939392, 0.74824016563147, 0.875, 0.88977676306443421, 0.875, 0.76388888888888884, 0.86746987951807231, 0.76388888888888884, 0.60692541856925419, 0.76315789473684215, 0.83333333333333326, 0.79729729729729737, 0.88732394366197176, 0.88732394366197176, 0.15789473684210525, 0.8529411764705882, 0.74285714285714288, 0.0, 0.88172043010752688, 0.88, 0.7567567567567568, 0.78378378378378377, 0.24324324324324326, 0.88732394366197176, 0.83472856418061891, 0.83561643835616439, 0.60869565217391308, 0.91666666666666663, 0.7785585585585586, 0.8571428571428571, 0.87323943661971837, 0.88732394366197176, 0.78378378378378377, 0.85835010060362171, 0.24324324324324326, 0.38461538461538464, 0.0, 0.88372093023255816, 0.86842105263157898, 0.83472856418061891, 0.83472856418061891, 0.83472856418061891, 0.26315789473684209, 0.78378378378378377, 0.82339874120696033, 0.80000000000000004, 0.62700534759358284, 0.85450292397660821, 0.71641791044776115, 0.25, 0.76683716965046889, 0.63291139240506333, 0.24615384615384617, 0.84848484848484851, 0.8529411764705882, 0.75342465753424648, 0.3380281690140845, 0.59999999999999998, 0.88732394366197176, 0.27777777777777779, 0.88977676306443421, 0.21052631578947367, 0.69921227197346603, 0.71479698876959163, 0.83333333333333337, 0.78378378378378377, 0.78873239436619713, 0.7785585585585586, 0.75, 0.6901408450704225, 0.72941176470588232, 0.83472856418061891, 0.74626865671641796, 0.8571428571428571, 0.79452054794520544, 0.84357645316549423, 0.87769961742564473, 0.75, 0.83472856418061891, 0.88977676306443421, 0.79411764705882348, 0.88732394366197176, 0.70129870129870131]