diff --git a/extra_tests/regression_fits/central_16.exportgrid b/extra_tests/regression_fits/central_16.exportgrid
index 503dbac86cfee4f0c535d52bc9bb41f56c37a896..f7a1c4867c02388e8732658afb6bf72505ef6227 100644
--- a/extra_tests/regression_fits/central_16.exportgrid
+++ b/extra_tests/regression_fits/central_16.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
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q20: 2.7224999999999997
replica: 16
diff --git a/extra_tests/regression_fits/central_16.json b/extra_tests/regression_fits/central_16.json
index ea2c90be7e3ab77d7d15c54b16f15829edb489b5..c5e586f79ea43442d84f0a247dcb57bd5e03ce97 100644
--- a/extra_tests/regression_fits/central_16.json
+++ b/extra_tests/regression_fits/central_16.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1099,
- "erf_tr": 2.6700053215026855,
- "erf_vl": 2.596088409423828,
- "chi2": 2.1046974658966064,
+ "erf_tr": 2.670098304748535,
+ "erf_vl": 2.5964081287384033,
+ "chi2": 2.1027894020080566,
"pos_state": "POS_VETO",
"arc_lengths": [
- 1.3207831327275608,
- 1.4752541158950256,
- 1.1834874152294614,
- 1.0011426742289826,
- 1.1960979802612568
+ 1.320814433685272,
+ 1.4723886173965648,
+ 1.1834960676986725,
+ 1.0011427382272051,
+ 1.196111193543841
],
"integrability": [
- 0.0009203169902344266,
- 0.0009203169902324282,
- 4.8332353002611406e-05,
- 0.46257528662681513,
- 0.006963513907974672
+ 0.0009203124209320734,
+ 0.0009203124209312685,
+ 4.8332128016359555e-05,
+ 0.4625752046704281,
+ 0.006963436899239506
],
"timing": {
"walltime": {
- "Total": 18.85464835166931,
+ "Total": 20.482789754867554,
"start": 0.0,
- "replica_set": 0.3591601848602295,
- "replica_fitted": 18.854573249816895,
- "replica_set_to_replica_fitted": 18.495413064956665
+ "replica_set": 0.38495421409606934,
+ "replica_fitted": 20.482685327529907,
+ "replica_set_to_replica_fitted": 20.097731113433838
},
"cputime": {
- "Total": 20.889074,
+ "Total": 22.8096241,
"start": 0.0,
- "replica_set": 0.6770135999999995,
- "replica_fitted": 20.888997200000002,
- "replica_set_to_replica_fitted": 20.211983600000003
+ "replica_set": 0.6998853,
+ "replica_fitted": 22.809518,
+ "replica_set_to_replica_fitted": 22.1096327
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/diagonal_45.exportgrid b/extra_tests/regression_fits/diagonal_45.exportgrid
index eb4155994c88e7d29169b0a564d27a36ffffbef3..74c4ca69424091836cedd9cd722abbbf188e1a75 100644
--- a/extra_tests/regression_fits/diagonal_45.exportgrid
+++ b/extra_tests/regression_fits/diagonal_45.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
-- [0.0, 0.0, -2.306413173675537, 0.16342006623744965, 0.1369123011827469, 0.13695918023586273,
- -16.513973236083984, 0.13711097836494446, 0.1370655596256256, 0.16317586600780487,
- -2.306413173675537, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.233588218688965, 0.16312028467655182, 0.13555052876472473, 0.13560384511947632,
- -16.522781372070312, 0.13577169179916382, 0.13572008907794952, 0.16285233199596405,
- -2.2335877418518066, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.1630680561065674, 0.16308163106441498, 0.13440610468387604, 0.1344667226076126,
- -16.530052185058594, 0.13465234637260437, 0.13459381461143494, 0.16278767585754395,
- -2.1630680561065674, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.094780921936035, 0.1633024513721466, 0.13347531855106354, 0.13354429602622986,
- -16.535680770874023, 0.13374963402748108, 0.1336831897497177, 0.16297999024391174,
- -2.094780921936035, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.0286550521850586, 0.16378167271614075, 0.13275523483753204, 0.13283362984657288,
- -16.53955841064453, 0.1330607533454895, 0.1329854130744934, 0.1634279191493988,
- -2.0286550521850586, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.9646214246749878, 0.16451886296272278, 0.13224317133426666, 0.13233228027820587,
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- -1.9646217823028564, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.902613878250122, 0.16551391780376434, 0.13193675875663757, 0.13203807175159454,
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-- [0.0, 0.0, -1.8425673246383667, 0.16676653921604156, 0.13183334469795227, 0.1319485306739807,
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-- [0.0, 0.0, -1.784420371055603, 0.16827784478664398, 0.13193151354789734, 0.13206246495246887,
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-- [0.0, 0.0, -1.7281112670898438, 0.17004846036434174, 0.1322292536497116, 0.13237814605236053,
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-- [0.0, 0.0, -1.6735825538635254, 0.17207995057106018, 0.13272537291049957, 0.1328946053981781,
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-- [0.0, 0.0, -1.620776653289795, 0.1743740290403366, 0.13341856002807617, 0.13361097872257233,
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-- [0.0, 0.0, -1.5696386098861694, 0.17693264782428741, 0.13430769741535187, 0.13452641665935516,
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- -1.5696388483047485, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.5201154947280884, 0.1797586977481842, 0.13539232313632965, 0.1356409341096878,
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- -1.5201157331466675, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.4721556901931763, 0.18285460770130157, 0.13667136430740356, 0.1369539350271225,
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-- [0.0, 0.0, -1.4257094860076904, 0.18622396886348724, 0.13814470171928406, 0.13846586644649506,
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- -1.4257092475891113, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.3807276487350464, 0.18987040221691132, 0.13981199264526367, 0.14017699658870697,
- -16.396047592163086, 0.14095360040664673, 0.14061658084392548, 0.18879978358745575,
- -1.3807276487350464, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.3371635675430298, 0.19379813969135284, 0.1416732519865036, 0.14208810031414032,
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-- [0.0, 0.0, -1.2949720621109009, 0.1980116367340088, 0.14372847974300385, 0.1441999077796936,
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-- [0.0, 0.0, -1.2145310640335083, 0.20731577277183533, 0.14842133224010468, 0.14902999997138977,
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-- [0.0, 0.0, -1.1761982440948486, 0.21241798996925354, 0.15106002986431122, 0.1517515480518341,
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-- [0.0, 0.0, -1.139068841934204, 0.21782775223255157, 0.15389341115951538, 0.15467901527881622,
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- -1.139068841934204, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.103104829788208, 0.22355204820632935, 0.15692228078842163, 0.1578146368265152,
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-- [0.0, 0.0, -1.0682686567306519, 0.22959774732589722, 0.1601468175649643, 0.16116033494472504,
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- -1.0682686567306519, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.034523844718933, 0.23597198724746704, 0.1635671705007553, 0.1647181361913681,
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- -1.0345240831375122, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -1.001834750175476, 0.24268263578414917, 0.1671835035085678, 0.16849036514759064,
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-- [0.0, 0.0, -0.9701670408248901, 0.2497376650571823, 0.17099551856517792, 0.17247915267944336,
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-- [0.0, 0.0, -0.9394879937171936, 0.25714513659477234, 0.17500263452529907, 0.17668667435646057,
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-- [0.0, 0.0, -0.90976482629776, 0.2649139165878296, 0.17920413613319397, 0.18111521005630493,
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-- [0.0, 0.0, -0.8809667229652405, 0.27305278182029724, 0.1835986077785492, 0.18576692044734955,
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-- [0.0, 0.0, -0.8530634641647339, 0.28157079219818115, 0.1881842017173767, 0.19064372777938843,
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-- [0.0, 0.0, -0.826025664806366, 0.29047688841819763, 0.19295808672904968, 0.19574719667434692,
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-- [0.0, 0.0, -0.7998253703117371, 0.29978057742118835, 0.19791695475578308, 0.201078861951828,
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-- [0.0, 0.0, -0.7744348645210266, 0.30949079990386963, 0.20305605232715607, 0.2066393792629242,
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-- [0.0, 0.0, -0.7498273849487305, 0.31961631774902344, 0.2083694487810135, 0.21242891252040863,
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-- [0.0, 0.0, -0.7259777784347534, 0.3301658630371094, 0.21385003626346588, 0.2184470146894455,
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-- [0.0, 0.0, -0.7028614282608032, 0.3411475419998169, 0.21948905289173126, 0.22469229996204376,
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-- [0.0, 0.0, -0.6804541945457458, 0.35256877541542053, 0.22527535259723663, 0.23116189241409302,
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-- [0.0, 0.0, -0.6587333083152771, 0.36443546414375305, 0.23119564354419708, 0.23785144090652466,
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-- [0.0, 0.0, -0.6376766562461853, 0.37675273418426514, 0.23723410069942474, 0.24475494027137756,
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-- [0.0, 0.0, -0.6172631978988647, 0.38952362537384033, 0.2433719038963318, 0.25186434388160706,
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-- [0.0, 0.0, -0.5974729657173157, 0.40274932980537415, 0.24958674609661102, 0.2591688632965088,
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-- [0.0, 0.0, -0.5782867670059204, 0.4164275825023651, 0.2558523714542389, 0.2666545808315277,
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-- [0.0, 0.0, -0.5596864819526672, 0.4305534362792969, 0.2621384263038635, 0.27430441975593567,
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-- [0.0, 0.0, -0.5416556000709534, 0.4451172649860382, 0.26840940117836, 0.2820965051651001,
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-- [0.0, 0.0, -0.5241780877113342, 0.46010416746139526, 0.27462488412857056, 0.2900046408176422,
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-- [0.0, 0.0, -0.5072401762008667, 0.4754934012889862, 0.2807384729385376, 0.2979968190193176,
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-- [0.0, 0.0, -0.4908289611339569, 0.49125581979751587, 0.2866973876953125, 0.3060343563556671,
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-- [0.0, 0.0, -0.47493332624435425, 0.507353663444519, 0.29244264960289, 0.31407156586647034,
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-- [0.0, 0.0, -0.45954430103302, 0.5237386226654053, 0.29790911078453064, 0.32205507159233093,
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- -0.45954424142837524, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.4446544051170349, 0.540349006652832, 0.30302464962005615, 0.3299221098423004,
- -9.011687278747559, 0.3638976514339447, 0.35255956649780273, 0.5131489038467407,
- -0.4446544051170349, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.43025892972946167, 0.5571088194847107, 0.3077119290828705, 0.3376009166240692,
- -8.4452543258667, 0.3754422068595886, 0.3639998137950897, 0.5273197889328003, -0.4302588701248169,
+- [0.0, 0.0, -2.3064141273498535, 0.16341926157474518, 0.13691148161888123, 0.13695837557315826,
+ -16.51400375366211, 0.13711017370224, 0.13706474006175995, 0.1631750613451004, -2.3064141273498535,
0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.4163551330566406, 0.573925256729126, 0.3118888735771179, 0.3450101912021637,
- -7.851329803466797, 0.38711515069007874, 0.37582919001579285, 0.5413332581520081,
- -0.4163551330566406, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.4029432535171509, 0.5906860828399658, 0.3154708445072174, 0.3520587980747223,
- -7.231403350830078, 0.3988494873046875, 0.38806071877479553, 0.5550732016563416,
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+- [0.0, 0.0, -0.0023801245260983706, -0.004178548697382212, 0.001718891435302794,
+ -0.00043504752102307975, -0.0006637468468397856, 0.00024275736359413713, 0.0025104505475610495,
+ 0.005312446970492601, -0.002380124991759658, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0019002200569957495, -0.0032489041332155466, 0.001236251788213849,
+ -0.0003201733634341508, -0.00040523792267777026, 0.00025036538136191666, 0.00187912886030972,
+ 0.004154152702540159, -0.0019002199405804276, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.001466176239773631, -0.0024312061723321676, 0.0008406996494159102,
+ -0.000227685333811678, -0.00022885504586156458, 0.00024247635155916214, 0.0013537395279854536,
+ 0.0031284098513424397, -0.001466176239773631, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0010797035647556186, -0.0017269651871174574, 0.0005283903446979821,
+ -0.00015532266115769744, -0.00011619392171269283, 0.0002199259033659473, 0.0009267281857319176,
+ 0.002237997017800808, -0.0010797035647556186, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0007430942496284842, -0.0011378999333828688, 0.000294725236017257,
+ -0.00010055329039460048, -5.056637019151822e-05, 0.00018424182781018317, 0.0005903392448090017,
+ 0.0014863689430058002, -0.000743094424251467, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00045952864456921816, -0.0006661344086751342, 0.00013407092774286866,
+ -6.05537679803092e-05, -1.7248572476091795e-05, 0.000137948285555467, 0.00033664226066321135,
+ 0.000877973681781441, -0.00045952864456921816, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0002337401092518121, -0.0003146260278299451, 3.926815406884998e-05,
+ -3.222027589799836e-05, -3.7726092614320805e-06, 8.517834794474766e-05, 0.00015769166930112988,
+ 0.00041894998867064714, -0.00023374013835564256, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -7.37723967176862e-05, -8.833003812469542e-05, 4.6725278934900416e-07,
+ -1.2370160220598336e-05, -2.787792823255586e-07, 3.3221844205399975e-05, 4.615620491676964e-05,
+ 0.00011895166971953586, -7.377240399364382e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
q20: 2.7224999999999997
replica: 45
diff --git a/extra_tests/regression_fits/diagonal_45.json b/extra_tests/regression_fits/diagonal_45.json
index fd464acbda07ec8140be4dc4411456908eecfea1..4fd13215e71293f2e4662aab0f258bab39a4ff81 100644
--- a/extra_tests/regression_fits/diagonal_45.json
+++ b/extra_tests/regression_fits/diagonal_45.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1099,
- "erf_tr": 3.3770620822906494,
+ "erf_tr": 3.3770642280578613,
"erf_vl": 3.2323102951049805,
- "chi2": 2.229722023010254,
+ "chi2": 2.2297263145446777,
"pos_state": "POS_VETO",
"arc_lengths": [
- 1.318226912266057,
- 1.5444128182135202,
- 1.179107474257061,
- 1.0015316669311096,
- 1.2179101304602167
+ 1.3182265509206665,
+ 1.5444147441704428,
+ 1.1791074624024902,
+ 1.0015316549599111,
+ 1.2179114648035199
],
"integrability": [
- 0.0008988641238855122,
- 0.0008988641238834028,
- 4.724650636855898e-05,
- 0.46261481940746196,
- 0.0070505824987761034
+ 0.0008988639456246617,
+ 0.0008988639456223302,
+ 4.7246536836742514e-05,
+ 0.4626150503754609,
+ 0.007050594722385117
],
"timing": {
"walltime": {
- "Total": 18.131428241729736,
+ "Total": 19.21068048477173,
"start": 0.0,
- "replica_set": 0.3876476287841797,
- "replica_fitted": 18.131293058395386,
- "replica_set_to_replica_fitted": 17.743645429611206
+ "replica_set": 0.3876974582672119,
+ "replica_fitted": 19.2105815410614,
+ "replica_set_to_replica_fitted": 18.82288408279419
},
"cputime": {
- "Total": 20.1227844,
+ "Total": 21.2651539,
"start": 0.0,
- "replica_set": 0.704001100000001,
- "replica_fitted": 20.1226482,
- "replica_set_to_replica_fitted": 19.4186471
+ "replica_set": 0.7038960999999997,
+ "replica_fitted": 21.265053100000003,
+ "replica_set_to_replica_fitted": 20.561157
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/feature_scaling_81.json b/extra_tests/regression_fits/feature_scaling_81.json
index 72f073386b70c6a94766431dc1d42490ace8c7d6..620be972b5d9781f07c5251248018cba215826ff 100644
--- a/extra_tests/regression_fits/feature_scaling_81.json
+++ b/extra_tests/regression_fits/feature_scaling_81.json
@@ -71,24 +71,24 @@
],
"timing": {
"walltime": {
- "Total": 18.211228370666504,
+ "Total": 19.78455400466919,
"start": 0.0,
- "replica_set": 0.39786696434020996,
- "replica_fitted": 18.211086988449097,
- "replica_set_to_replica_fitted": 17.813220024108887
+ "replica_set": 0.3843960762023926,
+ "replica_fitted": 19.78445529937744,
+ "replica_set_to_replica_fitted": 19.40005922317505
},
"cputime": {
- "Total": 20.0817579,
+ "Total": 21.797165300000003,
"start": 0.0,
- "replica_set": 0.7005326000000007,
- "replica_fitted": 20.081615399999997,
- "replica_set_to_replica_fitted": 19.381082799999998
+ "replica_set": 0.7009340999999996,
+ "replica_fitted": 21.797065099999998,
+ "replica_set_to_replica_fitted": 21.096131
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/flavour_29.exportgrid b/extra_tests/regression_fits/flavour_29.exportgrid
index 192fb76349f5a0a2c441b138cb2d1d9cc670587f..73362ea913bc94c4b1ff73a4f92a3bb0f3681662 100644
--- a/extra_tests/regression_fits/flavour_29.exportgrid
+++ b/extra_tests/regression_fits/flavour_29.exportgrid
@@ -87,9 +87,9 @@ pdfgrid:
- [0.0, 0.0, -0.14895430207252502, 0.24588581919670105, 0.3680756986141205, 0.10286284238100052,
-1.6412948369979858, 0.16512326896190643, 0.4689398407936096, 0.244236558675766,
-0.14895425736904144, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.15229447185993195, 0.24562674760818481, 0.37209370732307434, 0.10177303850650787,
- -1.5791362524032593, 0.16366641223430634, 0.4733952283859253, 0.24422100186347961,
- -0.15229450166225433, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.15229448676109314, 0.24562668800354004, 0.37209367752075195, 0.10177300125360489,
+ -1.5791362524032593, 0.16366635262966156, 0.4733951985836029, 0.24422094225883484,
+ -0.15229453146457672, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.15574216842651367, 0.24535757303237915, 0.37624379992485046, 0.10064376890659332,
-1.515177845954895, 0.1621570885181427, 0.47799140214920044, 0.24419888854026794,
-0.15574213862419128, 0.0, 0.0, 0.0]
@@ -153,11 +153,11 @@ pdfgrid:
- [0.0, 0.0, -0.25425276160240173, 0.2364824414253235, 0.4980448782444, 0.07307767868041992,
0.21004416048526764, 0.12294769287109375, 0.6101816296577454, 0.2408454716205597,
-0.2542527914047241, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.2609139382839203, 0.23570077121257782, 0.5066717863082886, 0.07188008725643158,
- 0.3206326365470886, 0.1208682730793953, 0.6193326711654663, 0.24034471809864044,
+- [0.0, 0.0, -0.2609139382839203, 0.23570077121257782, 0.5066717863082886, 0.07188010215759277,
+ 0.3206326365470886, 0.12086828798055649, 0.6193326711654663, 0.24034471809864044,
-0.2609139680862427, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.2677140533924103, 0.234847292304039, 0.5155699253082275, 0.07077652961015701,
- 0.43332526087760925, 0.1188407614827156, 0.6287373900413513, 0.23976650834083557,
+- [0.0, 0.0, -0.2677140533924103, 0.234847292304039, 0.5155699253082275, 0.07077653706073761,
+ 0.43332526087760925, 0.1188407689332962, 0.6287373900413513, 0.23976650834083557,
-0.2677140533924103, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.27462679147720337, 0.2339085042476654, 0.524726927280426, 0.06978268921375275,
0.5479415655136108, 0.11687824130058289, 0.6383780837059021, 0.23909516632556915,
@@ -177,8 +177,8 @@ pdfgrid:
- [0.0, 0.0, -0.3092227876186371, 0.22725816071033478, 0.5732590556144714, 0.06697443127632141,
1.1380325555801392, 0.10848188400268555, 0.6886995434761047, 0.23356397449970245,
-0.3092227876186371, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.3156188428401947, 0.225355863571167, 0.5830909609794617, 0.06692209094762802,
- 1.255548119544983, 0.10715459287166595, 0.6986900568008423, 0.23181523382663727,
+- [0.0, 0.0, -0.3156188428401947, 0.2253558486700058, 0.5830909609794617, 0.06692209094762802,
+ 1.255548119544983, 0.10715459287166595, 0.6986900568008423, 0.23181524872779846,
-0.3156188428401947, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.32159385085105896, 0.22319084405899048, 0.5927444100379944, 0.06704737991094589,
1.370976209640503, 0.10595400631427765, 0.7084061503410339, 0.22976996004581451,
@@ -192,8 +192,8 @@ pdfgrid:
- [0.0, 0.0, -0.33569902181625366, 0.21482810378074646, 0.6193123459815979, 0.06837309151887894,
1.6935606002807617, 0.10304156690835953, 0.7344648241996765, 0.22152163088321686,
-0.3356989622116089, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.33871889114379883, 0.21134883165359497, 0.6269066333770752, 0.06905965507030487,
- 1.789278507232666, 0.10225197672843933, 0.741624116897583, 0.21798917651176453,
+- [0.0, 0.0, -0.33871889114379883, 0.21134881675243378, 0.62690669298172, 0.06905965507030487,
+ 1.789278507232666, 0.10225197672843933, 0.7416241765022278, 0.21798916161060333,
-0.33871889114379883, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.34073489904403687, 0.20751141011714935, 0.6336542963981628, 0.06981994956731796,
1.8772753477096558, 0.10151956230401993, 0.7477944493293762, 0.2140510380268097,
@@ -315,11 +315,11 @@ pdfgrid:
- [0.0, 0.0, -0.08735264092683792, 0.033624690026044846, 0.3773176074028015, 0.04446864128112793,
0.9675965309143066, 0.04508081451058388, 0.42720267176628113, 0.030822429805994034,
-0.08735264092683792, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0824020653963089, 0.03150033950805664, 0.36699178814888, 0.04302497208118439,
- 0.9228903651237488, 0.04344648867845535, 0.4153457283973694, 0.028661807999014854,
+- [0.0, 0.0, -0.0824020653963089, 0.031500332057476044, 0.36699178814888, 0.043024979531764984,
+ 0.9228903651237488, 0.04344649612903595, 0.4153457283973694, 0.02866179868578911,
-0.08240208029747009, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.07766158133745193, 0.029486387968063354, 0.3567582666873932, 0.04159330949187279,
- 0.8793783783912659, 0.04183930158615112, 0.40360790491104126, 0.02662038616836071,
+- [0.0, 0.0, -0.07766158133745193, 0.029486380517482758, 0.3567582666873932, 0.041593313217163086,
+ 0.8793783783912659, 0.04183930531144142, 0.40360790491104126, 0.026620378717780113,
-0.07766158133745193, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.07312697917222977, 0.027578741312026978, 0.34662488102912903, 0.040175627917051315,
0.8370882868766785, 0.04026041179895401, 0.3919968903064728, 0.024693578481674194,
@@ -363,9 +363,9 @@ pdfgrid:
- [0.0, 0.0, -0.030635468661785126, 0.010702569968998432, 0.22663074731826782, 0.023699302226305008,
0.40207329392433167, 0.022679267451167107, 0.2552758455276489, 0.008126295171678066,
-0.030635468661785126, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.02844409830868244, 0.009889432229101658, 0.21844594180583954, 0.022617358714342117,
- 0.3771832585334778, 0.021568574011325836, 0.24599450826644897, 0.007366144564002752,
- -0.02844409830868244, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.02844409830868244, 0.009889423847198486, 0.21844594180583954, 0.022617347538471222,
+ 0.3771832585334778, 0.02156856842339039, 0.24599450826644897, 0.007366136647760868,
+ -0.02844410203397274, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.02637934312224388, 0.00912958849221468, 0.21042375266551971, 0.021564824506640434,
0.3534272015094757, 0.02049296349287033, 0.23690243065357208, 0.006661758758127689,
-0.02637934312224388, 0.0, 0.0, 0.0]
@@ -522,9 +522,9 @@ pdfgrid:
- [0.0, 0.0, -2.928620233433321e-05, 4.5309374399948865e-05, 0.006419737357646227,
0.00022756221005693078, 0.0007320509757846594, 0.0001349138474324718, 0.007175615523010492,
-5.0080634537152946e-05, -2.9286195058375597e-05, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.2459134925156832e-05, 3.9259328332263976e-05, 0.005591817665845156,
- 0.00019066590175498277, 0.0005718927131965756, 0.000109249354864005, 0.006249779369682074,
- -4.378720041131601e-05, -2.2459140382125042e-05, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -2.2459134925156832e-05, 3.9259066397789866e-05, 0.005591817665845156,
+ 0.00019066604727413505, 0.0005718927131965756, 0.00010924947127932683, 0.006249779369682074,
+ -4.378743324195966e-05, -2.2459140382125042e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, -1.7001650121528655e-05, 3.3831860491773114e-05, 0.0048376163467764854,
0.0001584526035003364, 0.0004413191054482013, 8.740081830183044e-05, 0.005406470969319344,
-3.7972240534145385e-05, -1.7001650121528655e-05, 0.0, 0.0, 0.0]
diff --git a/extra_tests/regression_fits/flavour_29.json b/extra_tests/regression_fits/flavour_29.json
index 871aa030f779fd344b9974817cd927afe7ad5cbf..d222928a79f4544b8ea1dc455826cdfe0ff87f09 100644
--- a/extra_tests/regression_fits/flavour_29.json
+++ b/extra_tests/regression_fits/flavour_29.json
@@ -56,11 +56,11 @@
"chi2": 3.895918846130371,
"pos_state": "POS_PASS",
"arc_lengths": [
- 1.1046911082279192,
+ 1.1046911095057348,
1.2122842009645642,
- 0.99064649188045,
+ 0.9906464918872098,
0.990491641076893,
- 0.9907081134354656
+ 0.9907081134568303
],
"integrability": [
0.4676428288221359,
@@ -71,24 +71,24 @@
],
"timing": {
"walltime": {
- "Total": 7.520705223083496,
+ "Total": 8.190976619720459,
"start": 0.0,
- "replica_set": 0.3701026439666748,
- "replica_fitted": 7.520532608032227,
- "replica_set_to_replica_fitted": 7.150429964065552
+ "replica_set": 0.39362668991088867,
+ "replica_fitted": 8.190709829330444,
+ "replica_set_to_replica_fitted": 7.797083139419556
},
"cputime": {
- "Total": 8.128277999999998,
+ "Total": 8.850825799999999,
"start": 0.0,
- "replica_set": 0.6848363999999991,
- "replica_fitted": 8.1281039,
- "replica_set_to_replica_fitted": 7.443267500000001
+ "replica_set": 0.7093926999999995,
+ "replica_fitted": 8.8505578,
+ "replica_set_to_replica_fitted": 8.141165100000002
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/no_lagrange_27.exportgrid b/extra_tests/regression_fits/no_lagrange_27.exportgrid
index 44109e223dd942759b15c9cf13e5aaf33dca38d1..5459329ed833588614ca1de6d80c6d6dff69579f 100644
--- a/extra_tests/regression_fits/no_lagrange_27.exportgrid
+++ b/extra_tests/regression_fits/no_lagrange_27.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
-- [0.0, 0.0, -2.2586069107055664, 0.03173479065299034, 0.004537232685834169, 0.004585680551826954,
- -16.76595687866211, 0.004740157630294561, 0.004693238064646721, 0.03148813918232918,
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+ -6.071380539651727e-06, -2.5896267175085086e-07, 3.59088990080636e-05, 4.902789441985078e-05,
+ 0.00012389066978357732, -6.996076263021678e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
q20: 2.7224999999999997
replica: 27
diff --git a/extra_tests/regression_fits/no_lagrange_27.json b/extra_tests/regression_fits/no_lagrange_27.json
index ac8acfd4df7353610e404104e7444203a4f5b10e..05764ec3b6915e847757169c4db8566d0e0a4fe8 100644
--- a/extra_tests/regression_fits/no_lagrange_27.json
+++ b/extra_tests/regression_fits/no_lagrange_27.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1098,
- "erf_tr": 2.8000152111053467,
- "erf_vl": 2.48862624168396,
- "chi2": 2.0644352436065674,
+ "erf_tr": 2.800015926361084,
+ "erf_vl": 2.4886276721954346,
+ "chi2": 2.0644304752349854,
"pos_state": "POS_PASS",
"arc_lengths": [
- 1.3221020624883704,
- 1.4870359495902816,
- 1.1735315998209264,
- 1.0005949415170394,
- 1.2219866415152165
+ 1.322102073510827,
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"integrability": [
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],
"timing": {
"walltime": {
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"start": 0.0,
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- "replica_fitted": 22.51093602180481,
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},
"cputime": {
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- "replica_fitted": 24.3749498,
- "replica_set_to_replica_fitted": 23.6999039
+ "replica_set": 0.6909512000000007,
+ "replica_fitted": 26.2492338,
+ "replica_set_to_replica_fitted": 25.5582826
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/no_msr_92.exportgrid b/extra_tests/regression_fits/no_msr_92.exportgrid
index 89cf717c7876b04366c1b78c644a808550cb49c2..f0306e57aa132115e4b8e750be3ad5e8b9e20be7 100644
--- a/extra_tests/regression_fits/no_msr_92.exportgrid
+++ b/extra_tests/regression_fits/no_msr_92.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
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-- [0.0, 0.0, -0.013674077577888966, -0.023846520110964775, 0.020004812628030777, -0.005049556493759155,
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-- [0.0, 0.0, -0.012582679279148579, -0.021891547366976738, 0.018040094524621964, -0.004455766174942255,
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-- [0.0, 0.0, -0.011533675715327263, -0.020000899210572243, 0.016181407496333122, -0.003901462769135833,
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+- [0.0, 0.0, -0.012582681141793728, -0.021891547366976738, 0.018040092661976814, -0.00445577222853899,
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+- [0.0, 0.0, -0.011533675715327263, -0.020000899210572243, 0.01618140935897827, -0.003901464631780982,
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-0.011533675715327263, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.010527149774134159, -0.018178429454565048, 0.014428914524614811, -0.003386775264516473,
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-- [0.0, 0.0, -0.009563203901052475, -0.016427697613835335, 0.012782623060047626, -0.0029116959776729345,
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-0.009563203901052475, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.008641955442726612, -0.014752035960555077, 0.01124237384647131, -0.002476038644090295,
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-0.008641955442726612, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.00776355154812336, -0.013154524378478527, 0.009807839058339596, -0.0020794698502868414,
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- -0.007763552013784647, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.006928154267370701, -0.01163798663765192, 0.008478476665914059, -0.0017214847030118108,
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- -0.006928154733031988, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.006135987583547831, -0.010205047205090523, 0.007253575604408979, -0.0014014129992574453,
- -0.00011711879778886214, 0.002465923549607396, 0.01178644597530365, 0.01652795635163784,
- -0.006135988514870405, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.005387313663959503, -0.008858118206262589, 0.006132219452410936, -0.001118405838496983,
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- -0.0053873141296207905, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.004682451486587524, -0.007599393371492624, 0.005113252904266119, -0.0008714196737855673,
- -6.799191032769158e-05, 0.002080336445942521, 0.008460698649287224, 0.012511028908193111,
- -0.004682451486587524, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0040218099020421505, -0.006430904380977154, 0.004195300862193108,
- -0.0006592236459255219, -4.986461499356665e-05, 0.0018871986540034413, 0.007036625407636166,
- 0.010693294927477837, -0.004021809436380863, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0034058766905218363, -0.005354492925107479, 0.0033766995184123516,
- -0.00048037435044534504, -3.54275616700761e-05, 0.001693193567916751, 0.005763906054198742,
- 0.009005282074213028, -0.0034058764576911926, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0028352648951113224, -0.004371851682662964, 0.002655508229508996,
- -0.00033319980138912797, -2.422377838229295e-05, 0.0014982132706791162, 0.004636466968804598,
- 0.007449144963175058, -0.0028352648951113224, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.002310738433152437, -0.00348453875631094, 0.002029455965384841, -0.00021578144514933228,
- -1.5798506865394302e-05, 0.0013025117805227637, 0.003648084355518222, 0.00602727709338069,
- -0.002310738433152437, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0018332540057599545, -0.0026939930394291878, 0.0014958955580368638,
- -0.00012592221901286393, -9.705579941510223e-06, 0.0011067718733102083, 0.002792386570945382,
- 0.00474237697198987, -0.001833254238590598, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0014040326932445168, -0.002001587999984622, 0.0010517496848478913,
- -6.111822585808113e-05, -5.514274107554229e-06, 0.0009122192277573049, 0.002062898362055421,
- 0.0035975940991193056, -0.0014040324604138732, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0010246436577290297, -0.0014086529845371842, 0.0006934131379239261,
- -1.8502421880839393e-05, -2.816112100845203e-06, 0.000720771960914135, 0.0014530456392094493,
+- [0.0, 0.0, -0.007763550616800785, -0.013154526241123676, 0.009807837195694447, -0.002079472178593278,
+ -0.00018627381359692663, 0.002856339793652296, 0.015786651521921158, 0.021041834726929665,
+ -0.007763550616800785, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0069281551986932755, -0.01163798663765192, 0.00847847480326891, -0.0017214857507497072,
+ -0.00014898776134941727, 0.002659984165802598, 0.013699496164917946, 0.018723605200648308,
+ -0.006928155664354563, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.006135987583547831, -0.010205047205090523, 0.007253575138747692, -0.0014014142798259854,
+ -0.00011711881234077737, 0.0024659258779138327, 0.011786448769271374, 0.01652795635163784,
+ -0.006135987117886543, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.005387314595282078, -0.008858118206262589, 0.006132219452410936, -0.0011184061877429485,
+ -9.026244515553117e-05, 0.0022730196360498667, 0.010042058303952217, 0.014456531032919884,
+ -0.005387313663959503, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.004682452417910099, -0.007599392905831337, 0.005113252904266119, -0.0008714196737855673,
+ -6.799190305173397e-05, 0.002080336445942521, 0.008460698649287224, 0.012511029839515686,
+ -0.004682452417910099, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.004021809436380863, -0.006430904846638441, 0.004195300862193108, -0.0006592244026251137,
+ -4.986461499356665e-05, 0.001887199585326016, 0.00703662633895874, 0.010693296790122986,
+ -0.0040218099020421505, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.003405876224860549, -0.005354493856430054, 0.003376698587089777, -0.0004803749034181237,
+ -3.542756530805491e-05, 0.0016931940335780382, 0.005763906054198742, 0.009005283005535603,
+ -0.0034058764576911926, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0028352646622806787, -0.004371852148324251, 0.0026555077638477087,
+ -0.000333200121531263, -2.422377838229295e-05, 0.0014982144348323345, 0.0046364679001271725,
+ 0.007449145894497633, -0.0028352646622806787, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0023107382003217936, -0.003484538523480296, 0.0020294557325541973,
+ -0.00021578175073955208, -1.5798506865394302e-05, 0.0013025120133534074, 0.003648084821179509,
+ 0.006027276162058115, -0.002310738433152437, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0018332538893446326, -0.0026939930394291878, 0.001495894743129611,
+ -0.00012592299026437104, -9.705582669994328e-06, 0.001106772106140852, 0.0027923868037760258,
+ 0.00474237697198987, -0.0018332538893446326, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0014040326932445168, -0.0020015884656459093, 0.0010517494520172477,
+ -6.111852417234331e-05, -5.51427456230158e-06, 0.0009122190531343222, 0.0020628981292247772,
+ 0.003597594564780593, -0.0014040326932445168, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0010246435413137078, -0.0014086529845371842, 0.0006934128468856215,
+ -1.8502623788663186e-05, -2.816112100845203e-06, 0.0007207721355371177, 0.0014530461048707366,
0.002596649108454585, -0.0010246435413137078, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0006971846451051533, -0.0009165707742795348, 0.0004166447906754911,
- 5.217917077970924e-06, -1.2325160696491366e-06, 0.0005353152519091964, 0.0009562804480083287,
- 0.001744152745231986, -0.0006971846451051533, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0004245503223501146, -0.000526891031768173, 0.00021636142628267407,
- 1.3864630091120489e-05, -4.22743568151418e-07, 0.00036015562363900244, 0.0005662395269609988,
- 0.0010460608173161745, -0.0004245503223501146, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.00021101518359500915, -0.00024158033193089068, 8.629779767943546e-05,
- 1.1947875464102253e-05, -9.295886371774031e-08, 0.0002019731909967959, 0.0002772264997474849,
- 0.0005106807802803814, -0.00021101518359500915, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -6.37346674920991e-05, -6.366728484863415e-05, 1.830075416364707e-05,
- 4.955160875397269e-06, -6.905087612807392e-09, 7.228499453049153e-05, 8.571602666052058e-05,
- 0.00015133171109482646, -6.373466021614149e-05, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0006971845286898315, -0.0009165707742795348, 0.00041664481977932155,
+ 5.217855232331203e-06, -1.2325161833359743e-06, 0.0005353152519091964, 0.0009562805062159896,
+ 0.001744152745231986, -0.0006971845286898315, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0004245503223501146, -0.0005268911481834948, 0.00021636132441926748,
+ 1.3864486390957609e-05, -4.2274362499483686e-07, 0.00036015553632751107, 0.0005662395269609988,
+ 0.0010460607009008527, -0.0004245503223501146, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00021101518359500915, -0.0002415803464828059, 8.629779040347785e-05,
+ 1.1947884559049271e-05, -9.295886371774031e-08, 0.00020197330741211772, 0.0002772265870589763,
+ 0.0005106808384880424, -0.00021101518359500915, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -6.373464566422626e-05, -6.3667306676507e-05, 1.8300721421837807e-05,
+ 4.955135409545619e-06, -6.905088945075022e-09, 7.228501635836437e-05, 8.57160339364782e-05,
+ 0.00015133175475057214, -6.373464566422626e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
q20: 2.7224999999999997
replica: 92
diff --git a/extra_tests/regression_fits/no_msr_92.json b/extra_tests/regression_fits/no_msr_92.json
index c87ab8a66d499df780975f67b42e3f4c552d8cf2..2c90ee4bf53234a004033b7a619ef6bdb81e2475 100644
--- a/extra_tests/regression_fits/no_msr_92.json
+++ b/extra_tests/regression_fits/no_msr_92.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1099,
- "erf_tr": 6.576577663421631,
- "erf_vl": 5.858455181121826,
- "chi2": 3.330012321472168,
+ "erf_tr": 6.576578617095947,
+ "erf_vl": 5.858458518981934,
+ "chi2": 3.3300085067749023,
"pos_state": "POS_PASS",
"arc_lengths": [
- 1.337021614754758,
- 0.9939697379044121,
- 1.1791486314894875,
- 1.0013385222517424,
- 1.1751097571490132
+ 1.3370216884066382,
+ 0.9939697374308255,
+ 1.1791486738858517,
+ 1.0013385257857281,
+ 1.1751098009017222
],
"integrability": [
- 0.000991988403256927,
- 0.000991988403257052,
- 4.665317624120979e-05,
- 0.4762776866555213,
- 0.007486370741389338
+ 0.0009919884178089394,
+ 0.0009919884178089672,
+ 4.6653176695957144e-05,
+ 0.4762776866555214,
+ 0.0074863710906354144
],
"timing": {
"walltime": {
- "Total": 31.462430477142334,
+ "Total": 33.80535387992859,
"start": 0.0,
- "replica_set": 0.3577704429626465,
- "replica_fitted": 31.46228003501892,
- "replica_set_to_replica_fitted": 31.104509592056274
+ "replica_set": 0.3920779228210449,
+ "replica_fitted": 33.8051962852478,
+ "replica_set_to_replica_fitted": 33.41311836242676
},
"cputime": {
- "Total": 33.54510500000001,
+ "Total": 36.0601982,
"start": 0.0,
- "replica_set": 0.6758694999999992,
- "replica_fitted": 33.5449531,
- "replica_set_to_replica_fitted": 32.869083599999996
+ "replica_set": 0.7084465000000009,
+ "replica_fitted": 36.0600387,
+ "replica_set_to_replica_fitted": 35.3515922
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/no_sumrules_18.exportgrid b/extra_tests/regression_fits/no_sumrules_18.exportgrid
index feb23099dadaa86acde871990a6449977b60d376..0f7c0b80800084d5e4b2415c0553bc5a4a983a5f 100644
--- a/extra_tests/regression_fits/no_sumrules_18.exportgrid
+++ b/extra_tests/regression_fits/no_sumrules_18.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
-- [0.0, 0.0, -2.4447743892669678, 0.48206278681755066, 0.4548843801021576, 0.4549284875392914,
- -0.536359429359436, 0.4549674689769745, 0.45492371916770935, 0.4819997251033783,
- -2.4447743892669678, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.3672444820404053, 0.47239094972610474, 0.44413238763809204, 0.44418248534202576,
- -0.5363330841064453, 0.4442256689071655, 0.44417592883110046, 0.4723217785358429,
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0.01656525954604149, -0.014742772094905376, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.01361055951565504, -0.0010818762239068747, 0.025648197159171104, -0.0007491065771318972,
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-- [0.0, 0.0, -0.012520892545580864, -0.0009709763107821345, 0.023124700412154198,
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-- [0.0, 0.0, -0.011473842896521091, -0.0008586078183725476, 0.020746421068906784,
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-- [0.0, 0.0, -0.010469482280313969, -0.0007466551032848656, 0.018511874601244926,
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-- [0.0, 0.0, -0.009507897309958935, -0.0006368624744936824, 0.01641940325498581, 1.6126299669849686e-05,
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-- [0.0, 0.0, -0.008589194156229496, -0.0005307944375090301, 0.014467175118625164,
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-- [0.0, 0.0, -0.007713504135608673, -0.0004298663407098502, 0.012653215788304806,
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-- [0.0, 0.0, -0.006880981381982565, -0.00033533747773617506, 0.010975317098200321,
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-- [0.0, 0.0, -0.006091827526688576, -0.0002482963027432561, 0.009431138634681702,
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-- [0.0, 0.0, -0.005346294026821852, -0.0001696867257123813, 0.008018126711249352,
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-- [0.0, 0.0, -0.004644683562219143, -0.00010028399265138432, 0.006733530201017857,
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-- [0.0, 0.0, -0.003987387288361788, -4.070678187417798e-05, 0.005574400071054697,
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- 0.004524869844317436, -0.003987387288361788, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.003374875523149967, 8.593724487582222e-06, 0.004537553060799837, 0.0005699180183000863,
- -3.522588303894736e-05, 0.000802720314823091, 0.004820843227207661, 0.003834234084933996,
+- [0.0, 0.0, -0.013610557653009892, -0.0010818780865520239, 0.025648195296525955,
+ -0.0007491090800613165, -0.0005395213956944644, 0.0002802256203722209, 0.02739117667078972,
+ 0.015298159793019295, -0.013610557653009892, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.012520893476903439, -0.0009709767764434218, 0.023124700412154198,
+ -0.0005292660207487643, -0.00046352503704838455, 0.00039054660010151565, 0.02465588040649891,
+ 0.014079790562391281, -0.012520893476903439, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.011473842896521091, -0.000858604849781841, 0.020746422931551933, -0.00032793081481941044,
+ -0.0003945711359847337, 0.0004923323285765946, 0.022086335346102715, 0.012909713201224804,
+ -0.011473842896521091, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.01046948041766882, -0.0007466535898856819, 0.018511874601244926, -0.0001459098421037197,
+ -0.0003325043071527034, 0.0005841317470185459, 0.01967979222536087, 0.011787580326199532,
+ -0.01046948041766882, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00950789824128151, -0.000636859389487654, 0.01641940139234066, 1.6129017240018584e-05,
+ -0.00027711346046999097, 0.0006646282854489982, 0.01743335649371147, 0.010713132098317146,
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+- [0.0, 0.0, -0.008589192293584347, -0.0005307936226017773, 0.014467176049947739,
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+ 0.009686189703643322, -0.008589192293584347, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.007713505066931248, -0.0004298672720324248, 0.012653215788304806,
+ 0.000278440595138818, -0.00018527508655097336, 0.0007873277645558119, 0.013408350758254528,
+ 0.00870667863637209, -0.007713505066931248, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.006880980916321278, -0.00033533660462126136, 0.01097531896084547,
+ 0.00037821682053618133, -0.00014817983901593834, 0.0008277642191387713, 0.011623110622167587,
+ 0.007774599362164736, -0.006880980916321278, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.006091827526688576, -0.0002482955460436642, 0.009431136772036552,
+ 0.0004570341552607715, -0.00011647686187643558, 0.000853387638926506, 0.009984671138226986,
+ 0.006890085060149431, -0.006091827526688576, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.005346294026821852, -0.00016968604177236557, 0.008018126711249352,
+ 0.0005150959477759898, -8.9762601419352e-05, 0.0008637795690447092, 0.00848926417529583,
+ 0.006053365301340818, -0.005346294026821852, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.004644683096557856, -0.00010028349788626656, 0.006733530666679144,
+ 0.0005528092151507735, -6.76117415423505e-05, 0.0008587409975007176, 0.0071329413913190365,
+ 0.005264793056994677, -0.004644683562219143, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.003987387288361788, -4.0706745494389907e-05, 0.005574400536715984,
+ 0.0005707965465262532, -4.958324643666856e-05, 0.0008382962550967932, 0.005911581218242645,
+ 0.004524869378656149, -0.003987387754023075, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.003374875523149967, 8.593972779635806e-06, 0.004537553060799837, 0.0005699180183000863,
+ -3.522587940096855e-05, 0.0008027204894460738, 0.004820843227207661, 0.00383423431776464,
-0.003374875523149967, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.002807742217555642, 4.734005779027939e-05, 0.0036195728462189436,
- 0.0005512997158803046, -2.4084714823402464e-05, 0.0007525734254159033, 0.0038561932742595673,
- 0.003193708835169673, -0.002807741519063711, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0022867275401949883, 7.543749961769208e-05, 0.0028167918790131807,
- 0.0005163657478988171, -1.570707536302507e-05, 0.0006887360941618681, 0.0030128811486065388,
+- [0.0, 0.0, -0.0028077424503862858, 4.734039248432964e-05, 0.0036195728462189436,
+ 0.0005512995994649827, -2.4084714823402464e-05, 0.000752573658246547, 0.0038561932742595673,
+ 0.0031937090680003166, -0.002807741751894355, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0022867273073643446, 7.543752872152254e-05, 0.0028167921118438244,
+ 0.000516365806106478, -1.570707536302507e-05, 0.0006887360359542072, 0.003012880915775895,
0.0026043171528726816, -0.0022867273073643446, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0018127619987353683, 9.29874659050256e-05, 0.0021252550650388002,
- 0.00046688542352057993, -9.648976629250683e-06, 0.0006124668871052563, 0.002285919850692153,
- 0.0020673221442848444, -0.0018127619987353683, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0013870391994714737, 0.00010031545389210805, 0.0015407162718474865,
- 0.0004050442366860807, -5.481878361024428e-06, 0.0005254875286482275, 0.0016701002605259418,
- 0.0015842977445572615, -0.0013870391994714737, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0010110917501151562, 9.800397674553096e-05, 0.001058576861396432,
- 0.00033353574690409005, -2.7994499305350473e-06, 0.00043009661021642387, 0.0011599527206271887,
- 0.0011572003131732345, -0.0010110917501151562, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0006869719945825636, 8.695988799445331e-05, 0.0006738805095665157,
- 0.00025571821606718004, -1.2251739462953992e-06, 0.00032936810748651624, 0.0007497863844037056,
- 0.0007885367376729846, -0.0006869719945825636, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0004175136564299464, 6.852885417174548e-05, 0.0003812584327533841,
- 0.00017586970352567732, -4.202087779958674e-07, 0.00022748237824998796, 0.0004337197751738131,
- 0.0004816175496671349, -0.0004175136564299464, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.00020690029487013817, 4.4744894694304094e-05, 0.00017493947234470397,
- 9.968574886443093e-05, -9.239793286042186e-08, 0.00013039048644714057, 0.00020585792663041502,
- 0.0002411100285826251, -0.00020690029487013817, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -6.212703738128766e-05, 1.9011475160368718e-05, 4.897643520962447e-05,
- 3.549793837009929e-05, -6.863166035486756e-09, 4.7500550863333046e-05, 6.0999274865025654e-05,
- 7.451765850419179e-05, -6.212703738128766e-05, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.001812762231566012, 9.298733493778855e-05, 0.0021252550650388002,
+ 0.0004668851906899363, -9.648977538745385e-06, 0.0006124664796516299, 0.002285919850692153,
+ 0.0020673214457929134, -0.001812762231566012, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0013870391994714737, 0.00010031535930465907, 0.0015407161554321647,
+ 0.0004050442366860807, -5.481877906277077e-06, 0.0005254874122329056, 0.00167010014411062,
+ 0.0015842976281419396, -0.0013870391994714737, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.001011091866530478, 9.80038385023363e-05, 0.00105857674498111, 0.00033353560138493776,
+ -2.799450840029749e-06, 0.00043009649380110204, 0.0011599526042118669, 0.0011572001967579126,
+ -0.0010110919829457998, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0006869718199595809, 8.696002623764798e-05, 0.0006738804513588548,
+ 0.00025571833248250186, -1.2251737189217238e-06, 0.00032936831121332943, 0.0007497864426113665,
+ 0.0007885368540883064, -0.0006869719945825636, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00041751362732611597, 6.852889055153355e-05, 0.000381258490961045,
+ 0.00017586973262950778, -4.2020872115244856e-07, 0.00022748242190573364, 0.000433719833381474,
+ 0.00048161757877096534, -0.00041751362732611597, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00020690028031822294, 4.474492743611336e-05, 0.00017493950144853443,
+ 9.968579252017662e-05, -9.23979257549945e-08, 0.00013039053010288626, 0.00020585798483807594,
+ 0.00024111005768645555, -0.0002069003094220534, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -6.212705193320289e-05, 1.9011475160368718e-05, 4.897643520962447e-05,
+ 3.549793837009929e-05, -6.863166479575966e-09, 4.7500550863333046e-05, 6.0999274865025654e-05,
+ 7.451765850419179e-05, -6.212704465724528e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
q20: 2.7224999999999997
replica: 18
diff --git a/extra_tests/regression_fits/no_sumrules_18.json b/extra_tests/regression_fits/no_sumrules_18.json
index df7888cb298528b6da7cd6dca2dcdf83e88bfa40..8e5f95e4ae134829db52c6c74e7f9242b6217ca0 100644
--- a/extra_tests/regression_fits/no_sumrules_18.json
+++ b/extra_tests/regression_fits/no_sumrules_18.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1099,
- "erf_tr": 5.3037943840026855,
- "erf_vl": 5.3994460105896,
- "chi2": 3.260413646697998,
+ "erf_tr": 5.303784370422363,
+ "erf_vl": 5.399442672729492,
+ "chi2": 3.2604198455810547,
"pos_state": "POS_PASS",
"arc_lengths": [
- 1.334838194261321,
- 0.9938354337963408,
- 1.0016686481364248,
- 0.9905760062890283,
- 1.0071507465033542
+ 1.3348380879587267,
+ 0.9938354367951591,
+ 1.0016686524387772,
+ 0.9905760067696133,
+ 1.0071507400671937
],
"integrability": [
- 0.00022732430443259122,
- 0.00022732430443293816,
- 1.0915415287984787e-05,
- 0.4772165119647979,
- 0.0018179939506805104
+ 0.000227324335355987,
+ 0.00022732433535575802,
+ 1.0915415316406496e-05,
+ 0.477216564118862,
+ 0.001817993746953725
],
"timing": {
"walltime": {
- "Total": 26.5335693359375,
+ "Total": 29.39768624305725,
"start": 0.0,
- "replica_set": 0.3634805679321289,
- "replica_fitted": 26.533432006835938,
- "replica_set_to_replica_fitted": 26.16995143890381
+ "replica_set": 0.4022369384765625,
+ "replica_fitted": 29.397472381591797,
+ "replica_set_to_replica_fitted": 28.995235443115234
},
"cputime": {
- "Total": 28.4186657,
+ "Total": 31.500726199999995,
"start": 0.0,
- "replica_set": 0.6786007000000005,
- "replica_fitted": 28.4185265,
- "replica_set_to_replica_fitted": 27.7399258
+ "replica_set": 0.7186190999999997,
+ "replica_fitted": 31.500510499999997,
+ "replica_set_to_replica_fitted": 30.7818914
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/no_vsr_54.exportgrid b/extra_tests/regression_fits/no_vsr_54.exportgrid
index b71ccbb37a429d46b3c938cee67688f8c1646075..acd72ec4e51694983eb846744912863c6ba3a604 100644
--- a/extra_tests/regression_fits/no_vsr_54.exportgrid
+++ b/extra_tests/regression_fits/no_vsr_54.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
-- [0.0, 0.0, -2.3827309608459473, 0.17211303114891052, 0.14561547338962555, 0.14566178619861603,
- -16.24198341369629, 0.14569853246212006, 0.14565253257751465, 0.17205394804477692,
- -2.3827309608459473, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.3074989318847656, 0.17170847952365875, 0.1441579908132553, 0.1442105770111084,
- -16.250526428222656, 0.14425119757652283, 0.14419899880886078, 0.17164362967014313,
- -2.3074984550476074, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.2346484661102295, 0.1715724766254425, 0.14292648434638977, 0.1429862529039383,
- -16.257551193237305, 0.14303116500377655, 0.1429719179868698, 0.1715013086795807,
- -2.2346484661102295, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.1641037464141846, 0.17170359194278717, 0.14191779494285583, 0.1419856697320938,
- -16.262956619262695, 0.14203542470932007, 0.14196811616420746, 0.171625554561615,
- -2.1641042232513428, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -2.0957915782928467, 0.1720997393131256, 0.1411278247833252, 0.14120490849018097,
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q20: 2.7224999999999997
replica: 54
diff --git a/extra_tests/regression_fits/no_vsr_54.json b/extra_tests/regression_fits/no_vsr_54.json
index 7541524c3069f33ea79a4c59d24904bbfa37bfa1..522ce699c61d18ae0c146dd13d7cf6d88c77f5b8 100644
--- a/extra_tests/regression_fits/no_vsr_54.json
+++ b/extra_tests/regression_fits/no_vsr_54.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1099,
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- "erf_vl": 4.0898518562316895,
- "chi2": 2.1583468914031982,
+ "erf_tr": 3.661821126937866,
+ "erf_vl": 4.091275691986084,
+ "chi2": 2.160372734069824,
"pos_state": "POS_VETO",
"arc_lengths": [
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],
"timing": {
"walltime": {
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+ "Total": 20.7750883102417,
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},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/normal_fit_72.exportgrid b/extra_tests/regression_fits/normal_fit_72.exportgrid
index 2932bc2ec4381a5821843f3c039c4de8e15974f5..96a485716a1b80367388aefb39cedd3d1ddba17d 100644
--- a/extra_tests/regression_fits/normal_fit_72.exportgrid
+++ b/extra_tests/regression_fits/normal_fit_72.exportgrid
@@ -1,590 +1,590 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
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+ 0.00039306041435338557, -0.0002221790491603315, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -6.951462273718789e-05, -7.091680163284764e-05, 9.051329470821656e-06,
+ -4.328504473960493e-06, -1.7165162091714592e-07, 3.573266803869046e-05, 4.914800956612453e-05,
+ 0.00011257095320615917, -6.951462273718789e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
q20: 2.7224999999999997
replica: 72
diff --git a/extra_tests/regression_fits/normal_fit_72.json b/extra_tests/regression_fits/normal_fit_72.json
index 9d12a53fe001f8681da151949c6b913ab35b7b25..5ba917395204010bd02392c12189f5b331ef4e81 100644
--- a/extra_tests/regression_fits/normal_fit_72.json
+++ b/extra_tests/regression_fits/normal_fit_72.json
@@ -49,46 +49,46 @@
"trainable": false
}
],
- "stop_epoch": 1688,
- "best_epoch": 386,
- "erf_tr": 3.8196349143981934,
- "erf_vl": 4.181242942810059,
- "chi2": 2.3859992027282715,
+ "stop_epoch": 1702,
+ "best_epoch": 400,
+ "erf_tr": 3.785813331604004,
+ "erf_vl": 4.147735118865967,
+ "chi2": 2.364035129547119,
"pos_state": "POS_PASS",
"arc_lengths": [
- 1.3172170310450748,
- 1.2737718180572153,
- 1.1682106289760015,
- 0.9969939105106653,
- 1.178227991809247
+ 1.3241317467975082,
+ 1.27799809169975,
+ 1.1699576063062493,
+ 0.9969979836415483,
+ 1.1936096402729792
],
"integrability": [
- 0.000922872146476017,
- 0.0009228721464754619,
- 6.687239761005159e-05,
- 0.528376147150993,
- 0.006918032886459646
+ 0.000914250413189821,
+ 0.0009142504131890439,
+ 6.752895887940369e-05,
+ 0.5550887137651436,
+ 0.007037573843261868
],
"timing": {
"walltime": {
- "Total": 25.596290588378906,
+ "Total": 27.138862133026123,
"start": 0.0,
- "replica_set": 0.4257495403289795,
- "replica_fitted": 25.596144676208496,
- "replica_set_to_replica_fitted": 25.170395135879517
+ "replica_set": 0.38606929779052734,
+ "replica_fitted": 27.13863968849182,
+ "replica_set_to_replica_fitted": 26.752570390701294
},
"cputime": {
- "Total": 28.3766029,
+ "Total": 30.034188999999998,
"start": 0.0,
- "replica_set": 0.7320899000000001,
- "replica_fitted": 28.376455399999998,
- "replica_set_to_replica_fitted": 27.6443655
+ "replica_set": 0.7043999000000003,
+ "replica_fitted": 30.0339653,
+ "replica_set_to_replica_fitted": 29.3295654
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/extra_tests/regression_fits/trainable_prepro_61.exportgrid b/extra_tests/regression_fits/trainable_prepro_61.exportgrid
index 781ff81c0d60578c34a414411411376a2c1092d8..d6c2703dadd70edd5fc4a160db79108138b73a2b 100644
--- a/extra_tests/regression_fits/trainable_prepro_61.exportgrid
+++ b/extra_tests/regression_fits/trainable_prepro_61.exportgrid
@@ -1,79 +1,79 @@
labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
pdfgrid:
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-- [0.0, 0.0, -1.9466972351074219, 0.644726037979126, 0.644425094127655, 0.644431471824646,
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-- [0.0, 0.0, -1.7774064540863037, 0.5878276228904724, 0.5874190926551819, 0.5874293446540833,
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@@ -81,9 +81,9 @@ pdfgrid:
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-- [0.0, 0.0, -0.9516870975494385, 0.308000773191452, 0.30352404713630676, 0.30391842126846313,
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@@ -97,8 +97,8 @@ pdfgrid:
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@@ -106,484 +106,484 @@ pdfgrid:
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-- [0.0, 0.0, -0.005186258815228939, -0.018198547884821892, 0.012194514274597168, 0.011420144699513912,
- -0.008654520846903324, -0.0033361692912876606, -0.0023385800886899233, 0.011588728986680508,
- -0.0051862578839063644, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.004440126474946737, -0.015778575092554092, 0.01049072202295065, 0.009906020946800709,
- -0.006904028356075287, -0.002946865512058139, -0.002197000663727522, 0.010146994143724442,
- -0.004440126474946737, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0037484238855540752, -0.013481860049068928, 0.008897373452782631,
- 0.008467930369079113, -0.005379220936447382, -0.002570768352597952, -0.002022573258727789,
- 0.008767697960138321, -0.0037484238855540752, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.003111243946477771, -0.011318105272948742, 0.007417107932269573, 0.007112081628292799,
- -0.004073586780577898, -0.002208887366577983, -0.0018213908188045025, 0.007455188315361738,
- -0.003111243946477771, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.005186258349567652, -0.01819855161011219, 0.012194515205919743, 0.011420145630836487,
+ -0.008654520846903324, -0.00333617115393281, -0.0023385819513350725, 0.011588731780648232,
+ -0.005186257418245077, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.004440127406269312, -0.015778575092554092, 0.01049072202295065, 0.009906021878123283,
+ -0.006904028356075287, -0.0029468650463968515, -0.0021970001980662346, 0.010146994143724442,
+ -0.004440127406269312, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0037484238855540752, -0.013481860980391502, 0.008897374384105206,
+ 0.008467930369079113, -0.005379220470786095, -0.0025707699824124575, -0.002022574422881007,
+ 0.00876769982278347, -0.0037484238855540752, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0031112441793084145, -0.011318106204271317, 0.0074171083979308605,
+ 0.007112081628292799, -0.004073587246239185, -0.00220888783223927, -0.0018213912844657898,
+ 0.007455189246684313, -0.0031112441793084145, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.0025288208853453398, -0.009297503158450127, 0.0060528917238116264,
0.005845014471560717, -0.0029785633087158203, -0.0018624247750267386, -0.0015996773727238178,
0.006214385852217674, -0.0025288208853453398, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0020015647169202566, -0.007430892903357744, 0.0048081353306770325,
- 0.0046736979857087135, -0.0020834477618336678, -0.0015328616136685014, -0.0013638486852869391,
- 0.005050962325185537, -0.0020015647169202566, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0015301386592909694, -0.005730059463530779, 0.003686889773234725,
- 0.0036057105753570795, -0.001375332591123879, -0.001222081365995109, -0.001120609580539167,
- 0.00397163350135088, -0.0015301386592909694, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.002001565182581544, -0.0074308933690190315, 0.004808136261999607,
+ 0.004673698917031288, -0.0020834477618336678, -0.0015328616136685014, -0.0013638486852869391,
+ 0.005050962790846825, -0.002001565182581544, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0015301386592909694, -0.005730059929192066, 0.0036868900060653687,
+ 0.0036057105753570795, -0.0013753327075392008, -0.0012220815988257527, -0.0011206098133698106,
+ 0.003971633967012167, -0.0015301386592909694, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.0011155317770317197, -0.0042080446146428585, 0.002694044029340148,
0.002649407833814621, -0.0008389418944716454, -0.0009325185092166066, -0.0008770745480433106,
0.0029845056124031544, -0.0011155317770317197, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0007592430920340121, -0.002879832172766328, 0.0018357853405177593,
- 0.0018143567722290754, -0.0004564860137179494, -0.0006674372707493603, -0.000641007733065635,
- 0.0020997701212763786, -0.0007592432084493339, 0.0, 0.0, 0.0]
-- [0.0, 0.0, -0.0004635540535673499, -0.0017633828101679683, 0.0011202655732631683,
- 0.001111964462324977, -0.00020737350860144943, -0.0004313824465498328, -0.0004212285275571048,
- 0.001330826198682189, -0.000463553995359689, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0007592430920340121, -0.0028798324055969715, 0.0018357854569330812,
+ 0.0018143568886443973, -0.0004564860137179494, -0.0006674373871646821, -0.0006410078494809568,
+ 0.0020997703541070223, -0.0007592432084493339, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.000463553995359689, -0.0017633828101679683, 0.0011202655732631683,
+ 0.001111964462324977, -0.00020737350860144943, -0.0004313824756536633, -0.0004212286148685962,
+ 0.0013308260822668672, -0.0004635539080481976, 0.0, 0.0, 0.0]
- [0.0, 0.0, -0.00023213210806716233, -0.0008818600908853114, 0.0005590427899733186,
0.0005568687338382006, -6.779083923902363e-05, -0.0002311105781700462, -0.00022847899526823312,
0.0006966661894693971, -0.00023213210806716233, 0.0, 0.0, 0.0]
- [0.0, 0.0, -7.16399954399094e-05, -0.00026940598036162555, 0.00017080882389564067,
- 0.00017058996309060603, -9.925640370056499e-06, -7.798225124133751e-05, -7.772142998874187e-05,
+ 0.00017058996309060603, -9.925639460561797e-06, -7.798225124133751e-05, -7.772142998874187e-05,
0.0002281411871081218, -7.16399954399094e-05, 0.0, 0.0, 0.0]
- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
q20: 2.7224999999999997
diff --git a/extra_tests/regression_fits/trainable_prepro_61.json b/extra_tests/regression_fits/trainable_prepro_61.json
index db5b3f5ba8ba0304c21e7cd6092cef7c9a922dd7..8442536b24b4c96a928b48bda1354d8cd71a1aa3 100644
--- a/extra_tests/regression_fits/trainable_prepro_61.json
+++ b/extra_tests/regression_fits/trainable_prepro_61.json
@@ -51,44 +51,44 @@
],
"stop_epoch": 1100,
"best_epoch": 1099,
- "erf_tr": 83.88552856445312,
- "erf_vl": 88.89352416992188,
- "chi2": 59.15236282348633,
+ "erf_tr": 83.88551330566406,
+ "erf_vl": 88.89353942871094,
+ "chi2": 59.15235900878906,
"pos_state": "POS_VETO",
"arc_lengths": [
- 1.0440971720839172,
- 1.5280262072729438,
- 1.0033084215844166,
- 0.9979762330411598,
- 1.4192811360785857
+ 1.0440971854791903,
+ 1.5280261822454786,
+ 1.0033084216442367,
+ 0.9979762378240656,
+ 1.4192810423200604
],
"integrability": [
- 0.11098538525402664,
- 0.11098538525402585,
- 0.0002027638274733623,
- 0.00843445386271846,
- 0.0018140712927551261
+ 0.11098538525402746,
+ 0.11098538525402579,
+ 0.00020276382838291251,
+ 0.008434453979133893,
+ 0.0018140712345475762
],
"timing": {
"walltime": {
- "Total": 19.603114128112793,
+ "Total": 21.60775899887085,
"start": 0.0,
- "replica_set": 0.3588278293609619,
- "replica_fitted": 19.60303783416748,
- "replica_set_to_replica_fitted": 19.24421000480652
+ "replica_set": 0.3911454677581787,
+ "replica_fitted": 21.60759949684143,
+ "replica_set_to_replica_fitted": 21.216454029083252
},
"cputime": {
- "Total": 21.8009634,
+ "Total": 23.8026402,
"start": 0.0,
- "replica_set": 0.6759696999999996,
- "replica_fitted": 21.800885700000002,
- "replica_set_to_replica_fitted": 21.124916
+ "replica_set": 0.7076198999999992,
+ "replica_fitted": 23.8024797,
+ "replica_set_to_replica_fitted": 23.094859800000002
}
},
"version": {
"tensorflow": "2.15.0, mkl=??",
"numpy": "1.24.4",
- "nnpdf": "4.0.8.post297.dev0+234efa01e",
- "validphys": "4.0.8.post297.dev0+234efa01e"
+ "nnpdf": "4.0.8.post339.dev0+bc76939f2",
+ "validphys": "4.0.8.post339.dev0+bc76939f2"
}
}
\ No newline at end of file
diff --git a/n3fit/src/n3fit/backends/keras_backend/operations.py b/n3fit/src/n3fit/backends/keras_backend/operations.py
index 5d7638537f74bf85c6c751695e92db264258500a..fe62dfd24e5c5c6dc88fdc1c18d15213dbf76076 100644
--- a/n3fit/src/n3fit/backends/keras_backend/operations.py
+++ b/n3fit/src/n3fit/backends/keras_backend/operations.py
@@ -212,6 +212,12 @@ def flatten(x):
return tf.reshape(x, (-1,))
+@tf.function
+def reshape(x, shape):
+ """reshape tensor x"""
+ return tf.reshape(x, shape)
+
+
def boolean_mask(*args, **kwargs):
"""
Applies a boolean mask to a tensor
diff --git a/n3fit/src/n3fit/checks.py b/n3fit/src/n3fit/checks.py
index 36ae24d52cc6c603b29d596eded289bc1bcdb46a..e1df9e28653f133f905964e827ec8a2de57d911b 100644
--- a/n3fit/src/n3fit/checks.py
+++ b/n3fit/src/n3fit/checks.py
@@ -380,11 +380,6 @@ def check_consistent_parallel(parameters, parallel_models, same_trvl_per_replica
"""
if not parallel_models:
return
- if not same_trvl_per_replica:
- raise CheckError(
- "Replicas cannot be run in parallel with different training/validation "
- " masks, please set `same_trvl_per_replica` to True in the runcard"
- )
if parameters.get("layer_type") != "dense":
raise CheckError("Parallelization has only been tested with layer_type=='dense'")
diff --git a/n3fit/src/n3fit/layers/losses.py b/n3fit/src/n3fit/layers/losses.py
index 4315919907385cced18021a376601ad824ea05ad..1d330ef8f59fe64251d40e621ac87949b1d0ce48 100644
--- a/n3fit/src/n3fit/layers/losses.py
+++ b/n3fit/src/n3fit/layers/losses.py
@@ -8,6 +8,7 @@
"""
import numpy as np
+
from n3fit.backends import MetaLayer
from n3fit.backends import operations as op
@@ -38,9 +39,6 @@ class LossInvcovmat(MetaLayer):
"""
def __init__(self, invcovmat, y_true, mask=None, covmat=None, **kwargs):
- # If we have a diagonal matrix, padd with 0s and hope it's not too heavy on memory
- if len(invcovmat.shape) == 1:
- invcovmat = np.diag(invcovmat)
self._invcovmat = op.numpy_to_tensor(invcovmat)
self._covmat = covmat
self._y_true = op.numpy_to_tensor(y_true)
@@ -56,9 +54,7 @@ class LossInvcovmat(MetaLayer):
"""Transform the inverse covmat and the mask into
weights of the layers"""
init = MetaLayer.init_constant(self._invcovmat)
- self.kernel = self.builder_helper(
- "invcovmat", (self._ndata, self._ndata), init, trainable=False
- )
+ self.kernel = self.builder_helper("invcovmat", self._invcovmat.shape, init, trainable=False)
mask_shape = (1, 1, self._ndata)
if self._mask is None:
init_mask = MetaLayer.init_constant(np.ones(mask_shape))
@@ -79,17 +75,24 @@ class LossInvcovmat(MetaLayer):
self.mask.assign(new_mask)
def call(self, y_pred, **kwargs):
- tmp_raw = self._y_true - y_pred
+ obs_diff_raw = self._y_true - y_pred
# TODO: most of the time this is a y * I multiplication and can be skipped
# benchmark how much time (if any) is lost in this in actual fits for the benefit of faster kfolds
- tmp = op.op_multiply([tmp_raw, self.mask])
- if tmp.shape[1] == 1:
- # einsum is not well suited for CPU, so use tensordot if not multimodel
- right_dot = op.tensor_product(self.kernel, tmp[0, 0, :], axes=1)
- res = op.tensor_product(tmp[0, :, :], right_dot, axes=1)
+ obs_diff = op.op_multiply([obs_diff_raw, self.mask])
+
+ # The experimental loss doesn't depend on replicas, so it doesn't have a replica axis and
+ # must be treated separately
+ experimental_loss = len(self.kernel.shape) == 2
+ one_replica = obs_diff.shape[1] == 1
+
+ if one_replica: # einsum is not well suited for CPU, so use tensordot if single replica
+ kernel = self.kernel if experimental_loss else self.kernel[0]
+ right_dot = op.tensor_product(kernel, obs_diff[0, 0, :], axes=1)
+ loss = op.tensor_product(obs_diff[0, :, :], right_dot, axes=1)
else:
- res = op.einsum("bri, ij, brj -> r", tmp, self.kernel, tmp)
- return res
+ einstr = "bri, ij, brj -> r" if experimental_loss else "bri, rij, brj -> r"
+ loss = op.einsum(einstr, obs_diff, self.kernel, obs_diff)
+ return loss
class LossLagrange(MetaLayer):
diff --git a/n3fit/src/n3fit/layers/mask.py b/n3fit/src/n3fit/layers/mask.py
index 877df3b502965af07ce01f816ccf20de7a3d7cd6..2b0bff6c00f27ff466b1f6d55f8f0b76a7367ad8 100644
--- a/n3fit/src/n3fit/layers/mask.py
+++ b/n3fit/src/n3fit/layers/mask.py
@@ -1,10 +1,12 @@
+from numpy import count_nonzero
+
from n3fit.backends import MetaLayer
from n3fit.backends import operations as op
class Mask(MetaLayer):
"""
- This layers applies a boolean mask to a rank-1 input tensor.
+ This layers applies a boolean mask to an input tensor.
The mask admit a multiplier for all outputs which will be internally
saved as a weight so it can be updated during trainig.
@@ -14,34 +16,50 @@ class Mask(MetaLayer):
Parameters
----------
- bool_mask: np.array
+ bool_mask: np.array of shape (n_replicas, n_features)
numpy array with the boolean mask to be applied
c: float
constant multiplier for every output
- axis: int
- axis in which to apply the mask
"""
- def __init__(self, bool_mask=None, c=None, axis=None, **kwargs):
+ def __init__(self, bool_mask=None, c=None, **kwargs):
if bool_mask is None:
self.mask = None
+ self.last_dim = -1
else:
self.mask = op.numpy_to_tensor(bool_mask, dtype=bool)
+ self.last_dim = count_nonzero(bool_mask[0, ...])
self.c = c
- self.axis = axis
+ self.masked_output_shape = None
super().__init__(**kwargs)
def build(self, input_shape):
if self.c is not None:
initializer = MetaLayer.init_constant(value=self.c)
- self.kernel = self.builder_helper(
- "mask", (1,), initializer, trainable=False
- )
+ self.kernel = self.builder_helper("mask", (1,), initializer, trainable=False)
+ # Make sure reshape will succeed: set the last dimension to the unmasked data length and before-last to
+ # the number of replicas
+ if self.mask is not None:
+ self.masked_output_shape = [-1 if d is None else d for d in input_shape]
+ self.masked_output_shape[-1] = self.last_dim
+ self.masked_output_shape[-2] = self.mask.shape[-2]
super(Mask, self).build(input_shape)
def call(self, ret):
+ """
+ Apply the mask to the input tensor, and multiply by the constant if present.
+
+ Parameters
+ ----------
+ ret: Tensor of shape (batch_size, n_replicas, n_features)
+
+ Returns
+ -------
+ Tensor of shape (batch_size, n_replicas, n_features)
+ """
if self.mask is not None:
- ret = op.boolean_mask(ret, self.mask, axis=self.axis)
+ flat_res = op.boolean_mask(ret, self.mask, axis=1)
+ ret = op.reshape(flat_res, shape=self.masked_output_shape)
if self.c is not None:
ret = ret * self.kernel
return ret
diff --git a/n3fit/src/n3fit/model_gen.py b/n3fit/src/n3fit/model_gen.py
index 5c338135a1e09bfcdfb1769aa66bdbd433e466b7..219bbdfc11ebd9b160c28b6c05404e907bdcc708 100644
--- a/n3fit/src/n3fit/model_gen.py
+++ b/n3fit/src/n3fit/model_gen.py
@@ -32,6 +32,7 @@ from n3fit.layers import (
AddPhoton,
FkRotation,
FlavourToEvolution,
+ Mask,
ObsRotation,
Preprocessing,
losses,
@@ -56,6 +57,7 @@ class ObservableWrapper:
name: str
observables: list
+ trvl_mask_layer: Mask
dataset_xsizes: list
invcovmat: np.array = None
covmat: np.array = None
@@ -69,8 +71,22 @@ class ObservableWrapper:
"""Generates the corresponding loss function depending on the values the wrapper
was initialized with"""
if self.invcovmat is not None:
+ if self.rotation:
+ # If we have a matrix diagonal only, padd with 0s and hope it's not too heavy on memory
+ invcovmat_matrix = np.eye(self.invcovmat.shape[-1]) * self.invcovmat[..., np.newaxis]
+ if self.covmat is not None:
+ covmat_matrix = np.eye(self.covmat.shape[-1]) * self.covmat[..., np.newaxis]
+ else:
+ covmat_matrix = self.covmat
+ else:
+ covmat_matrix = self.covmat
+ invcovmat_matrix = self.invcovmat
loss = losses.LossInvcovmat(
- self.invcovmat, self.data, mask, covmat=self.covmat, name=self.name
+ invcovmat_matrix,
+ self.data,
+ mask,
+ covmat=covmat_matrix,
+ name=self.name
)
elif self.positivity:
loss = losses.LossPositivity(name=self.name, c=self.multiplier)
@@ -95,10 +111,15 @@ class ObservableWrapper:
else:
output_layers = [obs(pdf) for obs in self.observables]
- # Finally concatenate all observables (so that experiments are one single entitiy)
- ret = op.concatenate(output_layers, axis=2)
+ # Finally concatenate all observables (so that experiments are one single entity)
+ ret = op.concatenate(output_layers, axis=-1)
+
if self.rotation is not None:
ret = self.rotation(ret)
+
+ if self.trvl_mask_layer is not None:
+ ret = self.trvl_mask_layer(ret)
+
return ret
def __call__(self, pdf_layer, mask=None):
@@ -108,7 +129,14 @@ class ObservableWrapper:
def observable_generator(
- spec_dict, positivity_initial=1.0, integrability=False
+ spec_dict,
+ mask_array=None,
+ training_data=None,
+ validation_data=None,
+ invcovmat_tr=None,
+ invcovmat_vl=None,
+ positivity_initial=1.0,
+ integrability=False,
): # pylint: disable=too-many-locals
"""
This function generates the observable models for each experiment.
@@ -157,9 +185,7 @@ def observable_generator(
spec_name = spec_dict["name"]
dataset_xsizes = []
model_inputs = []
- model_obs_tr = []
- model_obs_vl = []
- model_obs_ex = []
+ model_observables = []
# The first step is to compute the observable for each of the datasets
for dataset in spec_dict["datasets"]:
# Get the generic information of the dataset
@@ -180,50 +206,14 @@ def observable_generator(
# list of validphys.coredata.FKTableData objects
# these will then be used to check how many different pdf inputs are needed
# (and convolutions if given the case)
-
- if spec_dict["positivity"]:
- # Positivity (and integrability, which is a special kind of positivity...)
- # enters only at the "training" part of the models
- obs_layer_tr = Obs_Layer(
- dataset.fktables_data,
- dataset.training_fktables(),
- operation_name,
- name=f"dat_{dataset_name}",
- )
- obs_layer_ex = obs_layer_vl = None
- elif spec_dict.get("data_transformation_tr") is not None:
- # Data transformation needs access to the full array of output data
- obs_layer_ex = Obs_Layer(
- dataset.fktables_data,
- dataset.fktables(),
- operation_name,
- name=f"exp_{dataset_name}",
- )
- obs_layer_tr = obs_layer_vl = obs_layer_ex
- else:
- obs_layer_tr = Obs_Layer(
- dataset.fktables_data,
- dataset.training_fktables(),
- operation_name,
- name=f"dat_{dataset_name}",
- )
- obs_layer_ex = Obs_Layer(
- dataset.fktables_data,
- dataset.fktables(),
- operation_name,
- name=f"exp_{dataset_name}",
- )
- obs_layer_vl = Obs_Layer(
- dataset.fktables_data,
- dataset.validation_fktables(),
- operation_name,
- name=f"val_{dataset_name}",
- )
+ obs_layer = Obs_Layer(
+ dataset.fktables_data, dataset.fktables(), operation_name, name=f"dat_{dataset_name}"
+ )
# If the observable layer found that all input grids are equal, the splitting will be None
# otherwise the different xgrids need to be stored separately
# Note: for pineappl grids, obs_layer_tr.splitting should always be None
- if obs_layer_tr.splitting is None:
+ if obs_layer.splitting is None:
xgrid = dataset.fktables_data[0].xgrid
model_inputs.append(xgrid)
dataset_xsizes.append(len(xgrid))
@@ -232,9 +222,7 @@ def observable_generator(
model_inputs += xgrids
dataset_xsizes.append(sum([len(i) for i in xgrids]))
- model_obs_tr.append(obs_layer_tr)
- model_obs_vl.append(obs_layer_vl)
- model_obs_ex.append(obs_layer_ex)
+ model_observables.append(obs_layer)
# Check whether all xgrids of all observables in this experiment are equal
# if so, simplify the model input
@@ -245,11 +233,25 @@ def observable_generator(
# Reshape all inputs arrays to be (1, nx)
model_inputs = np.concatenate(model_inputs).reshape(1, -1)
- full_nx = sum(dataset_xsizes)
+ # Make the mask layers...
+ if mask_array is not None:
+ tr_mask_layer = Mask(mask_array, name=f"trmask_{spec_name}")
+ vl_mask_layer = Mask(~mask_array, name=f"vlmask_{spec_name}")
+ else:
+ tr_mask_layer = None
+ vl_mask_layer = None
+
+ # Make rotations of the final data (if any)
+ if spec_dict.get("data_transformation") is not None:
+ obsrot = ObsRotation(spec_dict.get("data_transformation"))
+ else:
+ obsrot = None
+
if spec_dict["positivity"]:
out_positivity = ObservableWrapper(
spec_name,
- model_obs_tr,
+ model_observables,
+ tr_mask_layer,
dataset_xsizes,
multiplier=positivity_initial,
positivity=not integrability,
@@ -259,38 +261,33 @@ def observable_generator(
layer_info = {
"inputs": model_inputs,
"output_tr": out_positivity,
- "experiment_xsize": full_nx,
+ "experiment_xsize": sum(dataset_xsizes),
}
# For positivity we end here
return layer_info
- # Generate the loss function and rotations of the final data (if any)
- if spec_dict.get("data_transformation_tr") is not None:
- obsrot_tr = ObsRotation(spec_dict.get("data_transformation_tr"))
- obsrot_vl = ObsRotation(spec_dict.get("data_transformation_vl"))
- else:
- obsrot_tr = None
- obsrot_vl = None
-
out_tr = ObservableWrapper(
spec_name,
- model_obs_tr,
+ model_observables,
+ tr_mask_layer,
dataset_xsizes,
- invcovmat=spec_dict["invcovmat"],
- data=spec_dict["expdata"],
- rotation=obsrot_tr,
+ invcovmat=invcovmat_tr,
+ data=training_data,
+ rotation=obsrot,
)
out_vl = ObservableWrapper(
f"{spec_name}_val",
- model_obs_vl,
+ model_observables,
+ vl_mask_layer,
dataset_xsizes,
- invcovmat=spec_dict["invcovmat_vl"],
- data=spec_dict["expdata_vl"],
- rotation=obsrot_vl,
+ invcovmat=invcovmat_vl,
+ data=validation_data,
+ rotation=obsrot,
)
out_exp = ObservableWrapper(
f"{spec_name}_exp",
- model_obs_ex,
+ model_observables,
+ None,
dataset_xsizes,
invcovmat=spec_dict["invcovmat_true"],
covmat=spec_dict["covmat"],
@@ -303,7 +300,7 @@ def observable_generator(
"output": out_exp,
"output_tr": out_tr,
"output_vl": out_vl,
- "experiment_xsize": full_nx,
+ "experiment_xsize": sum(dataset_xsizes),
}
return layer_info
diff --git a/n3fit/src/n3fit/model_trainer.py b/n3fit/src/n3fit/model_trainer.py
index acbcc8b3cd854a3532147e314eca483c9ebd0d9b..59acff377a17c47ce8e5963a44c93d897b3be7b3 100644
--- a/n3fit/src/n3fit/model_trainer.py
+++ b/n3fit/src/n3fit/model_trainer.py
@@ -15,9 +15,8 @@ import logging
import numpy as np
from n3fit import model_gen
-from n3fit.backends import MetaModel, callbacks, clear_backend_state
+from n3fit.backends import NN_LAYER_ALL_REPLICAS, MetaModel, callbacks, clear_backend_state
from n3fit.backends import operations as op
-from n3fit.backends import NN_LAYER_ALL_REPLICAS
import n3fit.hyper_optimization.penalties
import n3fit.hyper_optimization.rewards
from n3fit.scaler import generate_scaler
@@ -143,15 +142,10 @@ class ModelTrainer:
list with the replicas ids to be fitted
"""
# Save all input information
- self.exp_info = exp_info
- if pos_info is None:
- pos_info = []
- self.pos_info = pos_info
- self.integ_info = integ_info
- if self.integ_info is not None:
- self.all_info = exp_info + pos_info + integ_info
- else:
- self.all_info = exp_info + pos_info
+ self.exp_info = list(exp_info)
+ self.pos_info = [] if pos_info is None else pos_info
+ self.integ_info = [] if integ_info is None else integ_info
+ self.all_info = self.exp_info[0] + self.pos_info + self.integ_info
self.flavinfo = flavinfo
self.fitbasis = fitbasis
self._nn_seeds = nnseeds
@@ -219,6 +213,7 @@ class ModelTrainer:
"posdatasets": [],
}
self.experimental = {"output": [], "expdata": [], "ndata": 0, "model": None, "folds": []}
+ self.tr_masks = []
self._fill_the_dictionaries()
@@ -268,7 +263,7 @@ class ModelTrainer:
- ``name``: names of the experiment
- ``ndata``: number of experimental points
"""
- for exp_dict in self.exp_info:
+ for exp_dict in self.exp_info[0]:
self.training["expdata"].append(exp_dict["expdata"])
self.validation["expdata"].append(exp_dict["expdata_vl"])
self.experimental["expdata"].append(exp_dict["expdata_true"])
@@ -293,10 +288,10 @@ class ModelTrainer:
self.training["posdatasets"].append(pos_dict["name"])
self.validation["expdata"].append(pos_dict["expdata"])
self.validation["posdatasets"].append(pos_dict["name"])
- if self.integ_info is not None:
- for integ_dict in self.integ_info:
- self.training["expdata"].append(integ_dict["expdata"])
- self.training["integdatasets"].append(integ_dict["name"])
+
+ for integ_dict in self.integ_info:
+ self.training["expdata"].append(integ_dict["expdata"])
+ self.training["integdatasets"].append(integ_dict["name"])
def _xgrid_generation(self):
"""
@@ -483,7 +478,7 @@ class ModelTrainer:
self.experimental[key] = []
############################################################################
- # # Parametizable functions #
+ # # Parameterizable functions #
# #
# The functions defined in this block accept a 'params' dictionary which #
# defines the fit and the behaviours of the Neural Networks #
@@ -523,12 +518,44 @@ class ModelTrainer:
self._reset_observables()
log.info("Generating layers")
+ # We need to transpose Experimental data, stacking over replicas
+ experiment_data = {
+ "trmask": [],
+ "expdata": [],
+ "expdata_vl": [],
+ "invcovmat": [],
+ "invcovmat_vl": [],
+ }
+
+ # Loop over datasets
+ for i in range(len(self.exp_info[0])):
+ # Loop over data fields
+ for key, value in experiment_data.items():
+ replica_data = []
+ # Loop over replicas
+ for replica in self.exp_info:
+ if key in ["expdata", "expdata_vl"]:
+ # Save the data with shape (ndata) instead of (1, ndata)
+ replica_data.append(replica[i][key][0])
+ else:
+ replica_data.append(replica[i][key])
+ # Stack
+ value.append(np.stack(replica_data))
+
# Now we need to loop over all dictionaries (First exp_info, then pos_info and integ_info)
- for exp_dict in self.exp_info:
+ for i, exp_dict in enumerate(self.exp_info[0]):
if not self.mode_hyperopt:
log.info("Generating layers for experiment %s", exp_dict["name"])
- exp_layer = model_gen.observable_generator(exp_dict)
+ # Stacked tr-vl mask array for all replicas for this dataset
+ exp_layer = model_gen.observable_generator(
+ exp_dict,
+ mask_array=experiment_data["trmask"][i],
+ training_data=experiment_data["expdata"][i],
+ validation_data=experiment_data["expdata_vl"][i],
+ invcovmat_tr=experiment_data["invcovmat"][i],
+ invcovmat_vl=experiment_data["invcovmat_vl"][i],
+ )
# Save the input(s) corresponding to this experiment
self.input_list.append(exp_layer["inputs"])
@@ -549,8 +576,17 @@ class ModelTrainer:
pos_initial, pos_multiplier = _LM_initial_and_multiplier(
all_pos_initial, all_pos_multiplier, max_lambda, positivity_steps
)
-
- pos_layer = model_gen.observable_generator(pos_dict, positivity_initial=pos_initial)
+ num_experiments = len(self.exp_info)
+ replica_masks = np.stack([pos_dict["trmask"]] * num_experiments)
+ training_data = np.stack([pos_dict["expdata"].flatten()] * num_experiments)
+
+ pos_layer = model_gen.observable_generator(
+ pos_dict,
+ positivity_initial=pos_initial,
+ mask_array=replica_masks,
+ training_data=training_data,
+ validation_data=training_data,
+ )
# The input list is still common
self.input_list.append(pos_layer["inputs"])
@@ -562,28 +598,27 @@ class ModelTrainer:
self.training["posinitials"].append(pos_initial)
# Finally generate the integrability penalty
- if self.integ_info is not None:
- for integ_dict in self.integ_info:
- if not self.mode_hyperopt:
- log.info("Generating integrability penalty for %s", integ_dict["name"])
-
- integrability_steps = int(epochs / PUSH_INTEGRABILITY_EACH)
- max_lambda = integ_dict["lambda"]
-
- integ_initial, integ_multiplier = _LM_initial_and_multiplier(
- all_integ_initial, all_integ_multiplier, max_lambda, integrability_steps
- )
-
- integ_layer = model_gen.observable_generator(
- integ_dict, positivity_initial=integ_initial, integrability=True
- )
- # The input list is still common
- self.input_list.append(integ_layer["inputs"])
-
- # The integrability all falls to the training
- self.training["output"].append(integ_layer["output_tr"])
- self.training["integmultipliers"].append(integ_multiplier)
- self.training["integinitials"].append(integ_initial)
+ for integ_dict in self.integ_info:
+ if not self.mode_hyperopt:
+ log.info("Generating integrability penalty for %s", integ_dict["name"])
+
+ integrability_steps = int(epochs / PUSH_INTEGRABILITY_EACH)
+ max_lambda = integ_dict["lambda"]
+
+ integ_initial, integ_multiplier = _LM_initial_and_multiplier(
+ all_integ_initial, all_integ_multiplier, max_lambda, integrability_steps
+ )
+
+ integ_layer = model_gen.observable_generator(
+ integ_dict, positivity_initial=integ_initial, integrability=True
+ )
+ # The input list is still common
+ self.input_list.append(integ_layer["inputs"])
+
+ # The integrability all falls to the training
+ self.training["output"].append(integ_layer["output_tr"])
+ self.training["integmultipliers"].append(integ_multiplier)
+ self.training["integinitials"].append(integ_initial)
# Store a reference to the interpolator as self._scaler
if interpolation_points:
diff --git a/n3fit/src/n3fit/performfit.py b/n3fit/src/n3fit/performfit.py
index ee38aad4ea401777ec1a2785370c13f76d85914b..6514029ca25f2d932e5fc2666de6e8c4c2a2df80 100644
--- a/n3fit/src/n3fit/performfit.py
+++ b/n3fit/src/n3fit/performfit.py
@@ -143,49 +143,40 @@ def performfit(
from n3fit.io.writer import WriterWrapper
from n3fit.model_trainer import ModelTrainer
- # Note: there are three possible scenarios for the loop of replicas:
- # 1.- Only one replica is being run, in this case the loop is only evaluated once
- # 2.- Many replicas being run, in this case each will have a replica_number, seed, etc
- # and they will be fitted sequentially
- # 3.- Many replicas being run in parallel. In this case the loop will be evaluated just once
- # but a model per replica will be generated
+ # Note that this can be run in sequence or in parallel
+ # To do both cases in the same loop, we uniformize the replica information as:
+ # - sequential: a list over replicas, each entry containing tuples of length 1
+ # - parallel: a list of length 1, containing tuples over replicas
#
- # In the main scenario (1) replicas_nnseed_fitting_data_dict is a list of just one element
- # case (3) is similar but the one element of replicas_nnseed_fitting_data_dict will be modified
- # to be (
- # [list of all replica idx],
- # one experiment with data=(replicas, ndata),
- # [list of all NN seeds]
- # )
- #
- n_models = len(replicas_nnseed_fitting_data_dict)
- if parallel_models and n_models != 1:
- replicas, replica_experiments, nnseeds = zip(*replicas_nnseed_fitting_data_dict)
- # Parse the experiments so that the output data contain information for all replicas
- # as the only different from replica to replica is the experimental training/validation data
- all_experiments = copy.deepcopy(replica_experiments[0])
- for i_exp in range(len(all_experiments)):
- training_data = []
- validation_data = []
- for i_rep in range(n_models):
- training_data.append(replica_experiments[i_rep][i_exp]['expdata'])
- validation_data.append(replica_experiments[i_rep][i_exp]['expdata_vl'])
- all_experiments[i_exp]['expdata'] = np.concatenate(training_data, axis=0)
- all_experiments[i_exp]['expdata_vl'] = np.concatenate(validation_data, axis=0)
+ # Add inner tuples
+ replicas_info = [
+ ((replica,), (experiment,), (nnseed,))
+ for replica, experiment, nnseed in replicas_nnseed_fitting_data_dict
+ ]
+
+ n_models = len(replicas_info)
+ if parallel_models:
+ # Move replicas from outer list to inner tuples
+ replicas, experiments, nnseeds = [], [], []
+
+ for replica, experiment, nnseed in replicas_info:
+ replicas.extend(replica)
+ experiments.extend(experiment)
+ nnseeds.extend(nnseed)
+
+ replicas_info = [(tuple(replicas), tuple(experiments), tuple(nnseeds))]
log.info(
"Starting parallel fits from replica %d to %d", replicas[0], replicas[0] + n_models - 1
)
- replicas_info = [(replicas, all_experiments, nnseeds)]
else:
- replicas_info = replicas_nnseed_fitting_data_dict
+ log.info(
+ "Starting sequential fits from replica %d to %d",
+ replicas[0],
+ replicas[0] + n_models - 1,
+ )
for replica_idxs, exp_info, nnseeds in replicas_info:
- if not parallel_models or n_models == 1:
- # Cases 1 and 2 above are a special case of 3 where the replica idx and the seed should
- # be a list of just one element
- replica_idxs = [replica_idxs]
- nnseeds = [nnseeds]
- log.info("Starting replica fit %d", replica_idxs[0])
+ log.info("Starting replica fit " + str(replica_idxs))
# Generate a ModelTrainer object
# this object holds all necessary information to train a PDF (up to the NN definition)
diff --git a/n3fit/src/n3fit/stopping.py b/n3fit/src/n3fit/stopping.py
index 8884e7f01b03a251d6dd6bb86a90d4128efcdee1..380c5445d7b321619c9b553bf40bd449d856ce96 100644
--- a/n3fit/src/n3fit/stopping.py
+++ b/n3fit/src/n3fit/stopping.py
@@ -384,7 +384,7 @@ class Stopping:
@property
def stop_epoch(self):
"""Epoch in which the fit is stopped"""
- return self._history.final_epoch + 1
+ return -1 if self._history.final_epoch is None else self._history.final_epoch + 1
@property
def positivity_status(self):
diff --git a/n3fit/src/n3fit/tests/test_layers.py b/n3fit/src/n3fit/tests/test_layers.py
index 1cae6c6c6a4ac534c3973c9ba4b146e34ca69e80..6ff4887d64623e73e0d13a4dc4f331b87f1c955f 100644
--- a/n3fit/src/n3fit/tests/test_layers.py
+++ b/n3fit/src/n3fit/tests/test_layers.py
@@ -240,8 +240,9 @@ def test_rotation_evol():
def test_mask():
"""Test the mask layer"""
- SIZE = 100
- fi = np.random.rand(SIZE)
+ batch_size, replicas, points = 1, 1, 100
+ shape = (batch_size, replicas, points)
+ fi = np.random.rand(*shape)
# Check that the multiplier works
vals = [0.0, 2.0, np.random.rand()]
for val in vals:
@@ -249,10 +250,10 @@ def test_mask():
ret = masker(fi)
np.testing.assert_allclose(ret, val * fi, rtol=1e-5)
# Check that the boolean works
- np_mask = np.random.randint(0, 2, size=SIZE, dtype=bool)
+ np_mask = np.random.randint(0, 2, size=shape[1:], dtype=bool)
masker = layers.Mask(bool_mask=np_mask)
ret = masker(fi)
- masked_fi = fi[np_mask]
+ masked_fi = fi[np.newaxis, :, np_mask]
np.testing.assert_allclose(ret, masked_fi, rtol=1e-5)
# Check that the combination works!
rn_val = vals[-1]
diff --git a/validphys2/src/validphys/filters.py b/validphys2/src/validphys/filters.py
index 931d5b42146c557050efdf61a9acf90fce71dd6e..566b372c6d0959b9b5b9b5f6fcdd8fc04e49d811 100644
--- a/validphys2/src/validphys/filters.py
+++ b/validphys2/src/validphys/filters.py
@@ -180,9 +180,7 @@ def _filter_real_data(filter_path, data):
return total_data_points, total_cut_data_points
-def _filter_closure_data(
- filter_path, data, fakepdf, fakenoise, filterseed, data_index, sep_mult
-):
+def _filter_closure_data(filter_path, data, fakepdf, fakenoise, filterseed, data_index, sep_mult):
"""
This function is accessed within a closure test only, that is, the fakedata
namespace has to be True (If fakedata = False, the _filter_real_data function
@@ -395,14 +393,7 @@ class Rule:
numpy_functions = {"sqrt": np.sqrt, "log": np.log, "fabs": np.fabs}
- def __init__(
- self,
- initial_data: dict,
- *,
- defaults: dict,
- theory_parameters: dict,
- loader=None,
- ):
+ def __init__(self, initial_data: dict, *, defaults: dict, theory_parameters: dict, loader=None):
self.dataset = None
self.process_type = None
self._local_variables_code = {}
@@ -453,13 +444,7 @@ class Rule:
self.rule_string = self.rule
self.defaults = defaults
self.theory_params = theory_parameters
- ns = {
- *self.numpy_functions,
- *self.defaults,
- *self.variables,
- "idat",
- "central_value",
- }
+ ns = {*self.numpy_functions, *self.defaults, *self.variables, "idat", "central_value"}
for k, v in self.local_variables.items():
try:
self._local_variables_code[k] = lcode = compile(
@@ -536,11 +521,7 @@ class Rule:
return eval(
self.rule,
self.numpy_functions,
- {
- **{"idat": idat, "central_value": central_value},
- **self.defaults,
- **ns,
- },
+ {**{"idat": idat, "central_value": central_value}, **self.defaults, **ns},
)
except Exception as e: # pragma: no cover
raise FatalRuleError(f"Error when applying rule {self.rule_string!r}: {e}") from e
diff --git a/validphys2/src/validphys/n3fit_data.py b/validphys2/src/validphys/n3fit_data.py
index 38f946248dae878ccbf6d8206c8fbba24e3681a1..ced700f98f6e62e4f24cb7e6c1b239211b224d9c 100644
--- a/validphys2/src/validphys/n3fit_data.py
+++ b/validphys2/src/validphys/n3fit_data.py
@@ -74,7 +74,7 @@ class _TrMasks(TupleComp):
yield m
-def tr_masks(data, replica_trvlseed):
+def tr_masks(data, replica_trvlseed, parallel_models=False, replica=1, replicas=(1,)):
"""Generate the boolean masks used to split data into training and
validation points. Returns a list of 1-D boolean arrays, one for each
dataset. Each array has length equal to N_data, the datapoints which
@@ -97,8 +97,16 @@ def tr_masks(data, replica_trvlseed):
# We do this so that a given dataset will always have the same number of points masked
trmax = int(ndata * frac)
if trmax == 0:
- # If that number is 0, then get 1 point with probability frac
- trmax = int(rng.random() < frac)
+ if parallel_models:
+ if replica == replicas[0]:
+ log.warning(
+ f'Single-datapoint dataset {dataset.name} encountered in parallel multi-replica fit: '
+ 'all replicas will include it in their training data'
+ )
+ trmax = 1
+ else:
+ # If that number is 0, then get 1 point with probability frac
+ trmax = int(rng.random() < frac)
mask = np.concatenate([np.ones(trmax, dtype=bool), np.zeros(ndata - trmax, dtype=bool)])
rng.shuffle(mask)
trmask_partial.append(mask)
@@ -311,6 +319,7 @@ def fitting_data_dict(
"folds": folds,
"data_transformation_tr": dt_trans_tr,
"data_transformation_vl": dt_trans_vl,
+ "data_transformation": dt_trans,
}
return dict_out