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Merged
scarlehoff merged 3 commits into from
Dec 1, 2022
Merged

Train on 1-point datasets #1636

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ec0f497
when the trvl would leave always a full dataset out, get one point wi…
scarlehoff Nov 30, 2022
0165cba
Update fitbot.yml
scarlehoff Dec 1, 2022
9b845d3
explicitly select a rng from numpy
scarlehoff Dec 1, 2022
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2 changes: 1 addition & 1 deletion .github/workflows/fitbot.yml
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Expand Up @@ -10,7 +10,7 @@ on:
env:
N3FIT_MAXNREP: 20 # total number of replicas to fit
POSTFIT_NREP: 16 # requested replicas for postfit
REFERENCE_SET: NNBOT-ecb18dcb3-2022-09-21 # reference set for vp
REFERENCE_SET: NNBOT-90875c07e-2022-11-30 # reference set for vp
CONDA_PY: 39

jobs:
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56 changes: 28 additions & 28 deletions n3fit/src/n3fit/tests/regressions/quickcard_1.json
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Expand Up @@ -20,8 +20,8 @@
},
{
"fl": "v3",
"smallx": 0.30309754610061646,
"largex": 2.6213722229003906,
"smallx": 0.3030211925506592,
"largex": 2.621304512023926,
"trainable": true
},
{
Expand Down Expand Up @@ -51,45 +51,45 @@
],
"stop_epoch": 1000,
"best_epoch": 999,
"erf_tr": 38.022315979003906,
"erf_vl": 31.481945037841797,
"chi2": 22.705814361572266,
"erf_tr": 39.057010650634766,
"erf_vl": 29.909780502319336,
"chi2": 22.705690383911133,
"pos_state": "POS_VETO",
"arc_lengths": [
1.103731936457393,
1.505681943929697,
1.2027105035135508,
1.426451562695196,
1.1901165080869252
1.103742133208361,
1.5056703837007153,
1.2027024122156913,
1.4182762310320054,
1.190134533856863
],
"integrability": [
0.002630652510560605,
0.0026306525105581624,
0.00013975353567441395,
3.6658432483673087,
0.004028247582030353
0.002630715302072484,
0.002630715302073705,
0.00013720474453138287,
3.665958881378174,
0.004028137103890828
],
"timing": {
"walltime": {
"Total": 12.020201921463013,
"Total": 22.385475635528564,
"start": 0.0,
"replica_set": 0.003970146179199219,
"replica_fitted": 12.02015495300293,
"replica_set_to_replica_fitted": 12.01618480682373
"replica_set": 0.025483131408691406,
"replica_fitted": 22.385230779647827,
"replica_set_to_replica_fitted": 22.359747648239136
},
"cputime": {
"Total": 13.138117000000001,
"Total": 27.547072900000003,
"start": 0.0,
"replica_set": 0.029436999999999713,
"replica_fitted": 13.138062999999999,
"replica_set_to_replica_fitted": 13.108626
"replica_set": 1.4402541279999994,
"replica_fitted": 27.546810807999996,
"replica_set_to_replica_fitted": 26.106556679999997
}
},
"version": {
"keras": "2.4.0",
"tensorflow": "2.4.1, mkl=False",
"numpy": "1.21.2",
"nnpdf": "4.0.4.221+gc94f7414b-dev",
"validphys": "4.0.4.221+gc94f7414b-dev"
"keras": "2.7.0",
"tensorflow": "2.7.0, mkl=False",
"numpy": "1.21.5",
"nnpdf": "4.0.5.295+g34eba1986-dev",
"validphys": "4.0.5.295+g34eba1986-dev"
}
}
88 changes: 44 additions & 44 deletions n3fit/src/n3fit/tests/regressions/quickcard_2.json
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Expand Up @@ -2,94 +2,94 @@
"preprocessing": [
{
"fl": "sng",
"smallx": 1.1001018285751343,
"largex": 2.6947875022888184,
"smallx": 1.099926233291626,
"largex": 2.694965362548828,
"trainable": true
},
{
"fl": "g",
"smallx": 0.9447894096374512,
"largex": 1.6324347257614136,
"smallx": 0.9449679851531982,
"largex": 1.6326133012771606,
"trainable": true
},
{
"fl": "v",
"smallx": 0.7402390837669373,
"largex": 1.7163281440734863,
"smallx": 0.7404175400733948,
"largex": 1.7165066003799438,
"trainable": true
},
{
"fl": "v3",
"smallx": 0.2104424387216568,
"largex": 1.3539683818817139,
"smallx": 0.21026656031608582,
"largex": 1.3537929058074951,
"trainable": true
},
{
"fl": "v8",
"smallx": 0.7599998712539673,
"largex": 2.40140700340271,
"largex": 2.401231050491333,
"trainable": true
},
{
"fl": "t3",
"smallx": 1.4329228401184082,
"largex": 2.284966230392456,
"smallx": 1.4331011772155762,
"largex": 2.2847883701324463,
"trainable": true
},
{
"fl": "t8",
"smallx": 1.0496479272842407,
"largex": 1.7641903162002563,
"smallx": 1.0498263835906982,
"largex": 1.7643688917160034,
"trainable": true
},
{
"fl": "cp",
"smallx": 0.23785176873207092,
"largex": 2.786799430847168,
"smallx": 0.23803016543388367,
"largex": 2.786621570587158,
"trainable": true
}
],
"stop_epoch": 313,
"best_epoch": 11,
"erf_tr": 3.859008312225342,
"erf_vl": 3.781097650527954,
"chi2": 2.1904516220092773,
"stop_epoch": 302,
"best_epoch": 0,
"erf_tr": 3.74715518951416,
"erf_vl": 3.685098171234131,
"chi2": 2.187250852584839,
"pos_state": "POS_PASS",
"arc_lengths": [
1.9062157860541882,
1.1675613091142847,
1.1346750403691772,
1.5211630991705873,
1.099993511588776
1.9042910513554256,
1.1674365527313044,
1.1344623143618382,
1.5301224176894808,
1.1008704792768709
],
"integrability": [
0.025609320495277865,
0.025609320495276755,
2.8907650913900795e-05,
16.2582106590271,
0.028889004141091856
0.02566919755190339,
0.025669197551905665,
2.9297336424871645e-05,
16.31229305267334,
0.028826652094720906
],
"timing": {
"walltime": {
"Total": 7.219811916351318,
"Total": 13.346972227096558,
"start": 0.0,
"replica_set": 0.00436091423034668,
"replica_fitted": 7.219769716262817,
"replica_set_to_replica_fitted": 7.215408802032471
"replica_set": 0.005179166793823242,
"replica_fitted": 13.346774339675903,
"replica_set_to_replica_fitted": 13.34159517288208
},
"cputime": {
"Total": 8.290757,
"Total": 18.004598894000004,
"start": 0.0,
"replica_set": 0.03159199999999984,
"replica_fitted": 8.290709999999999,
"replica_set_to_replica_fitted": 8.259117999999999
"replica_set": 0.29500279599999857,
"replica_fitted": 18.004358473000003,
"replica_set_to_replica_fitted": 17.709355677000005
}
},
"version": {
"keras": "2.4.0",
"tensorflow": "2.4.1, mkl=False",
"numpy": "1.21.2",
"nnpdf": "4.0.4.220+g894138a9a",
"validphys": "4.0.4.220+g894138a9a"
"keras": "2.7.0",
"tensorflow": "2.7.0, mkl=False",
"numpy": "1.21.5",
"nnpdf": "4.0.5.295+g34eba1986-dev",
"validphys": "4.0.5.295+g34eba1986-dev"
}
}
}
10 changes: 7 additions & 3 deletions validphys2/src/validphys/n3fit_data.py
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Expand Up @@ -84,19 +84,23 @@ def tr_masks(data, replica_trvlseed):
nameseed = int(hashlib.sha256(str(data).encode()).hexdigest(), 16) % 10**8
nameseed += replica_trvlseed
# TODO: update this to new random infrastructure.
np.random.seed(nameseed)
rng = np.random.Generator(np.random.PCG64(nameseed))
trmask_partial = []
for dataset in data.datasets:
# TODO: python commondata will not require this rubbish.
# all data if cuts are None
cuts = dataset.cuts
ndata = len(cuts.load()) if cuts else dataset.commondata.ndata
frac = dataset.frac
trmax = int(frac * ndata)
# 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)
mask = np.concatenate(
[np.ones(trmax, dtype=np.bool), np.zeros(ndata - trmax, dtype=np.bool)]
)
np.random.shuffle(mask)
rng.shuffle(mask)
trmask_partial.append(mask)
return _TrMasks(str(data), replica_trvlseed, trmask_partial)

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6 changes: 4 additions & 2 deletions validphys2/src/validphys/tests/conftest.py
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Expand Up @@ -50,9 +50,11 @@ def tmp(tmpdir):
HESSIAN_PDF = "NNPDF40_nnlo_as_01180_hessian"
THEORYID = 162
FIT = "NNPDF40_nnlo_lowprecision"
FIT_3REPLICAS = "Basic_runcard_3replicas_lowprec"
FIT_3REPLICAS = "Basic_runcard_3replicas_lowprec_221130"
FIT_ITERATED = "NNPDF40_nnlo_low_precision_iterated"
PSEUDODATA_FIT = "pseudodata_test_fit_n3fit_220330"
PSEUDODATA_FIT = "pseudodata_test_fit_n3fit_221130"



base_config = dict(
pdf=PDF,
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