From 7d18e3e45f84c90585c6580c212d4f88f9fe483c Mon Sep 17 00:00:00 2001 From: RoyStegeman Date: Wed, 29 May 2024 17:16:41 +0100 Subject: [PATCH 1/5] add also [1] to selected_poinst + include enpoint if 1 --- n3fit/src/n3fit/scaler.py | 49 ++++++++++++++++++++++++--------------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/n3fit/src/n3fit/scaler.py b/n3fit/src/n3fit/scaler.py index 2129f24d7f..c21086cd19 100644 --- a/n3fit/src/n3fit/scaler.py +++ b/n3fit/src/n3fit/scaler.py @@ -1,4 +1,4 @@ -from typing import Callable, List, Optional +from typing import Callable, Optional import numpy as np import numpy.typing as npt @@ -6,7 +6,7 @@ def generate_scaler( - input_list: List[npt.NDArray], interpolation_points: Optional[int] = None + input_list: list[npt.NDArray], interpolation_points: Optional[int] = None ) -> Callable: """ Generate the scaler function that applies feature scaling to the input data. @@ -27,43 +27,54 @@ def generate_scaler( input_arr_size = input_arr.size # Define an evenly spaced grid in the domain [0,1] - # force_set_smallest is used to make sure the smallest point included in the scaling is - # 1e-9, to prevent trouble when saving it to the LHAPDF grid + # force_set_smallest is used to make sure the smallest point included in the scaling is 1e-9, to + # prevent trouble when saving it to the LHAPDF grid force_set_smallest = input_arr.min() > 1e-9 + include_endpoint = ( + 1.0 in input_arr + ) # if 1.0 is in the xgrid it should also be 1.0 in the output xgrid if force_set_smallest: new_xgrid = np.linspace( - start=1 / input_arr_size, stop=1.0, endpoint=False, num=input_arr_size + start=1 / input_arr_size, stop=1.0, endpoint=include_endpoint, num=input_arr_size ) else: - new_xgrid = np.linspace(start=0, stop=1.0, endpoint=False, num=input_arr_size) + new_xgrid = np.linspace(start=0, stop=1.0, endpoint=include_endpoint, num=input_arr_size) - # When mapping the FK xgrids onto our new grid, we need to consider degeneracies among - # the x-values in the FK grids + # When mapping the FK xgrids onto our new grid, we need to consider degeneracies among the x-values + # in the FK grids unique, counts = np.unique(input_arr, return_counts=True) - map_to_complete = [] + map_to = [] for cumsum_ in np.cumsum(counts): # Make sure to include the smallest new_xgrid value, such that we have a point at # x<=1e-9 - map_to_complete.append(new_xgrid[cumsum_ - counts[0]]) - map_to_complete = np.array(map_to_complete) - map_from_complete = unique + map_to.append(new_xgrid[cumsum_ - counts[0]]) + map_to = np.array(map_to) + map_from = unique # If needed, set feature_scaling(x=1e-9)=0 if force_set_smallest: - map_from_complete = np.insert(map_from_complete, 0, 1e-9) - map_to_complete = np.insert(map_to_complete, 0, 0.0) + map_from = np.insert(map_from, 0, 1e-9) + map_to = np.insert(map_to, 0, 0.0) # Select the indices of the points that will be used by the interpolator - onein = map_from_complete.size / (int(interpolation_points) - 1) + onein = map_from.size / (int(interpolation_points - 1)) selected_points = [round(i * onein - 1) for i in range(1, int(interpolation_points))] if selected_points[0] != 0: selected_points = [0] + selected_points - map_from = map_from_complete[selected_points] - map_from = np.log(map_from) - map_to = map_to_complete[selected_points] + selected_points += [1] # add also this one since 1e-9 is just an outlier + # make a mask of which pints to keep + mask = np.zeros(len(map_from), dtype=bool) + mask[selected_points] = True + + # apply the mask and lot the input + masked_map_from = map_from[mask] + log_masked_map_from = np.log(masked_map_from) + masked_map_to = map_to[mask] + + # construct the scaler try: - scaler = PchipInterpolator(map_from, map_to) + scaler = PchipInterpolator(log_masked_map_from, masked_map_to) except ValueError as e: raise ValueError( "interpolation_points is larger than the number of unique input x-values" From f934deb20e28bc0cf42f0290505d02b86c7b9bb7 Mon Sep 17 00:00:00 2001 From: RoyStegeman Date: Mon, 30 Sep 2024 15:56:11 +0200 Subject: [PATCH 2/5] fix photon computation with feature scaling --- n3fit/src/n3fit/model_trainer.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/n3fit/src/n3fit/model_trainer.py b/n3fit/src/n3fit/model_trainer.py index 5b52422dda..d92e7cf51d 100644 --- a/n3fit/src/n3fit/model_trainer.py +++ b/n3fit/src/n3fit/model_trainer.py @@ -936,7 +936,10 @@ def hyperparametrizable(self, params): ) if photons: - pdf_model.get_layer("add_photon").register_photon(xinput.input.tensor_content) + if self._scaler: # select only the non-scaled input + pdf_model.get_layer("add_photon").register_photon(xinput.input.tensor_content[:,:,1:]) + else: + pdf_model.get_layer("add_photon").register_photon(xinput.input.tensor_content) # Model generation joins all the different observable layers # together with pdf model generated above From 1bf91367a68c312d12b6674e9b9fa85f2374fffa Mon Sep 17 00:00:00 2001 From: Redo regressions bot Date: Wed, 16 Oct 2024 09:24:04 +0000 Subject: [PATCH 3/5] Automatically regenerated regressions from PR 2165, branch update_fs. --- extra_tests/regression_fits/central_16.json | 22 +- extra_tests/regression_fits/diagonal_45.json | 22 +- .../feature_scaling_81.exportgrid | 1168 ++++++++--------- .../regression_fits/feature_scaling_81.json | 48 +- extra_tests/regression_fits/flavour_29.json | 22 +- .../regression_fits/multi_dense_316.json | 22 +- extra_tests/regression_fits/no_csr_613.json | 22 +- .../regression_fits/no_lagrange_27.json | 22 +- extra_tests/regression_fits/no_msr_92.json | 22 +- .../regression_fits/no_sumrules_18.json | 22 +- extra_tests/regression_fits/no_vsr_54.json | 22 +- .../regression_fits/normal_fit_72.json | 22 +- .../regression_fits/polarized_evol_34.json | 22 +- .../regression_fits/trainable_prepro_61.json | 22 +- 14 files changed, 740 insertions(+), 740 deletions(-) diff --git a/extra_tests/regression_fits/central_16.json b/extra_tests/regression_fits/central_16.json index f3fa15cbbd..f4da97612e 100644 --- a/extra_tests/regression_fits/central_16.json +++ b/extra_tests/regression_fits/central_16.json @@ -71,25 +71,25 @@ ], "timing": { "walltime": { - "Total": 10.90460753440857, + "Total": 12.018421173095703, "start": 0.0, - "replica_set": 0.22848939895629883, - "replica_fitted": 10.904474258422852, - "replica_set_to_replica_fitted": 10.675984859466553 + "replica_set": 0.23227167129516602, + "replica_fitted": 12.018356561660767, + "replica_set_to_replica_fitted": 11.7860848903656 }, "cputime": { - "Total": 12.365805662, + "Total": 13.647957485, "start": 0.0, - "replica_set": 0.22668013299999945, - "replica_fitted": 12.365671202000001, - "replica_set_to_replica_fitted": 12.138991069000003 + "replica_set": 0.23026263799999924, + "replica_fitted": 13.647891120999997, + "replica_set_to_replica_fitted": 13.417628482999998 } }, "version": { - "keras": "3.4.1", + "keras": "3.6.0", "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post1210.dev0+d36bcce78", - "validphys": "4.0.9.post1210.dev0+d36bcce78" + "nnpdf": "4.0.9.post1366.dev0+f934deb20", + "validphys": "4.0.9.post1366.dev0+f934deb20" } } diff --git a/extra_tests/regression_fits/diagonal_45.json b/extra_tests/regression_fits/diagonal_45.json index 93d0845574..81ec009cbd 100644 --- a/extra_tests/regression_fits/diagonal_45.json +++ b/extra_tests/regression_fits/diagonal_45.json @@ -71,25 +71,25 @@ ], "timing": { "walltime": { - "Total": 10.154094696044922, + "Total": 10.89296817779541, "start": 0.0, - "replica_set": 0.22886013984680176, - "replica_fitted": 10.154032945632935, - "replica_set_to_replica_fitted": 9.925172805786133 + "replica_set": 0.2278742790222168, + "replica_fitted": 10.892873764038086, + "replica_set_to_replica_fitted": 10.66499948501587 }, "cputime": { - "Total": 11.552391722, + "Total": 12.396652105999998, "start": 0.0, - "replica_set": 0.22704437600000027, - "replica_fitted": 11.552328112999998, - "replica_set_to_replica_fitted": 11.325283737 + "replica_set": 0.227109338, + "replica_fitted": 12.396556207, + "replica_set_to_replica_fitted": 12.169446869000001 } }, "version": { - "keras": "3.4.1", + "keras": "3.6.0", "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post1210.dev0+d36bcce78", - "validphys": "4.0.9.post1210.dev0+d36bcce78" + "nnpdf": "4.0.9.post1366.dev0+f934deb20", + "validphys": "4.0.9.post1366.dev0+f934deb20" } } diff --git a/extra_tests/regression_fits/feature_scaling_81.exportgrid b/extra_tests/regression_fits/feature_scaling_81.exportgrid index a0f860d664..b372c06374 100644 --- a/extra_tests/regression_fits/feature_scaling_81.exportgrid +++ b/extra_tests/regression_fits/feature_scaling_81.exportgrid @@ -1,593 +1,593 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, 0.997036337852478, 0.42249229550361633, 0.42429763078689575, 0.4242978096008301, - 0.06217102333903313, 0.4243163764476776, 0.4243168234825134, 0.42282232642173767, - 0.997036337852478, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.9651403427124023, 0.4109576344490051, 0.41293981671333313, 0.41294005513191223, - 0.06504461914300919, 0.41296008229255676, 0.41296058893203735, 0.411327064037323, - 0.9651403427124023, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.9341227412223816, 0.399787962436676, 0.40196430683135986, 0.40196457505226135, - 0.06804516911506653, 0.4019862413406372, 0.40198689699172974, 0.4002014696598053, - 0.9341227412223816, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.9039687514305115, 0.38896551728248596, 0.3913551867008209, 0.3913554549217224, - 0.07117819786071777, 0.391378790140152, 0.39137953519821167, 0.3894282281398773, - 0.903968870639801, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.8746635913848877, 0.3784729540348053, 0.3810969591140747, 0.38109734654426575, - 0.07444944232702255, 0.3811224102973938, 0.38112327456474304, 0.3789907395839691, - 0.8746635913848877, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.8461903929710388, 0.368294358253479, 0.3711756765842438, 0.3711761236190796, - 0.07786491513252258, 0.37120306491851807, 0.3712041676044464, 0.3688736855983734, - 0.8461903929710388, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.8185340166091919, 0.3584141433238983, 0.36157819628715515, 0.3615787923336029, - 0.08143085986375809, 0.36160770058631897, 0.3616090416908264, 0.3590623736381531, - 0.8185340166091919, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.7916759252548218, 0.34881800413131714, 0.3522926867008209, 0.35229334235191345, - 0.08515381067991257, 0.3523244261741638, 0.35232606530189514, 0.3495432436466217, - 0.7916759252548218, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.7656001448631287, 0.33949288725852966, 0.34330880641937256, 0.343309611082077, - 0.08904051035642624, 0.34334293007850647, 0.34334489703178406, 0.34030428528785706, - 0.7656000256538391, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.7402871251106262, 0.3304246664047241, 0.33461540937423706, 0.33461645245552063, - 0.09309809654951096, 0.33465203642845154, 0.33465445041656494, 0.33133217692375183, - 0.7402870059013367, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.7157198786735535, 0.3216009736061096, 0.3262036144733429, 0.3262048661708832, - 0.09733391553163528, 0.3262428343296051, 0.3262457549571991, 0.32261601090431213, - 0.715719997882843, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.6918805837631226, 0.31300872564315796, 0.318063884973526, 0.31806543469429016, - 0.10175569355487823, 0.3181059956550598, 0.31810954213142395, 0.3141440451145172, - 0.6918805837631226, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.6687508821487427, 0.30463558435440063, 0.3101879954338074, 0.3101898729801178, - 0.10637152940034866, 0.310232937335968, 0.3102372884750366, 0.30590519309043884, - 0.6687508821487427, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.6463136672973633, 0.2964702546596527, 0.3025690019130707, 0.30257126688957214, - 0.11118967831134796, 0.30261698365211487, 0.3026222288608551, 0.29788997769355774, - 0.6463136672973633, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.6245508193969727, 0.28850021958351135, 0.29519930481910706, 0.2952020764350891, - 0.11621898412704468, 0.2952505648136139, 0.29525697231292725, 0.2900877594947815, - 0.6245508193969727, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.6034457087516785, 0.28071296215057373, 0.28807175159454346, 0.28807514905929565, - 0.12146853655576706, 0.2881262004375458, 0.28813400864601135, 0.2824878692626953, - 0.6034457087516785, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.5829800963401794, 0.27309685945510864, 0.2811805307865143, 0.28118473291397095, - 0.12694790959358215, 0.2812385857105255, 0.2812480032444, 0.27508124709129333, 0.5829800963401794, +- [0.0, 0.0, 0.959721565246582, 0.4762605130672455, 0.4780727028846741, 0.4780729115009308, + 0.05299321934580803, 0.47810158133506775, 0.47810202836990356, 0.4765755236148834, + 0.9597216844558716, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.9295225143432617, 0.46291476488113403, 0.4649040997028351, 0.4649043381214142, + 0.05544901266694069, 0.46493569016456604, 0.46493619680404663, 0.4632672965526581, + 0.9295225143432617, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.9001472592353821, 0.4499861001968384, 0.4521700143814087, 0.4521702826023102, + 0.058014146983623505, 0.45220455527305603, 0.45220518112182617, 0.45038074254989624, + 0.9001472592353821, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.8715824484825134, 0.43745455145835876, 0.4398520290851593, 0.43985238671302795, + 0.060693420469760895, 0.4398898482322693, 0.439890593290329, 0.43789616227149963, + 0.8715826272964478, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.8438156843185425, 0.42530301213264465, 0.42793509364128113, 0.42793554067611694, + 0.06349188089370728, 0.42797648906707764, 0.42797744274139404, 0.4257972836494446, + 0.8438156843185425, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.8168322443962097, 0.4135119616985321, 0.41640186309814453, 0.4164023697376251, + 0.0664147213101387, 0.4164470136165619, 0.4164481461048126, 0.4140649735927582, + 0.8168322443962097, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.7906173467636108, 0.4020662009716034, 0.4052390158176422, 0.40523964166641235, + 0.0694674625992775, 0.4052883982658386, 0.40528979897499084, 0.4026850163936615, + 0.7906172275543213, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.7651553750038147, 0.39094847440719604, 0.3944321870803833, 0.39443302154541016, + 0.0726558119058609, 0.39448609948158264, 0.39448782801628113, 0.3916407823562622, + 0.7651553750038147, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.7404308915138245, 0.38014453649520874, 0.3839697241783142, 0.3839706778526306, + 0.07598567754030228, 0.3840285837650299, 0.38403069972991943, 0.38091903924942017, + 0.7404307723045349, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.716427743434906, 0.3696388006210327, 0.3738390803337097, 0.37384024262428284, + 0.07946334779262543, 0.3739033639431, 0.3739059269428253, 0.37050530314445496, 0.716427743434906, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.5631388425827026, 0.26563969254493713, 0.27452006936073303, 0.27452516555786133, - 0.13266703486442566, 0.27458155155181885, 0.2745929956436157, 0.26785799860954285, - 0.5631387233734131, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.543904185295105, 0.25832897424697876, 0.26808491349220276, 0.268091082572937, - 0.13863633573055267, 0.2681500017642975, 0.268163800239563, 0.2608086168766022, - 0.543904185295105, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.5252611041069031, 0.2511517405509949, 0.2618698179721832, 0.2618773877620697, - 0.14486664533615112, 0.26193857192993164, 0.2619553506374359, 0.253923237323761, - 0.5252611041069031, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.5071935057640076, 0.24409537017345428, 0.2558709383010864, 0.2558801472187042, - 0.15136928856372833, 0.2559433877468109, 0.2559637129306793, 0.2471928745508194, - 0.5071935057640076, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.48968639969825745, 0.23714585602283478, 0.2500835955142975, 0.2500948905944824, - 0.15815626084804535, 0.25015974044799805, 0.25018438696861267, 0.24060741066932678, - 0.4896864593029022, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.47272536158561707, 0.23028963804244995, 0.24450476467609406, 0.24451856315135956, - 0.1652398258447647, 0.2445845603942871, 0.24461443722248077, 0.2341577708721161, - 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b/extra_tests/regression_fits/feature_scaling_81.json index 2959ecdc86..f8a062636c 100644 --- a/extra_tests/regression_fits/feature_scaling_81.json +++ b/extra_tests/regression_fits/feature_scaling_81.json @@ -51,45 +51,45 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 2.6529221534729004, - "erf_vl": 2.1242189407348633, - "chi2": 1.4108729362487793, + "erf_tr": 2.803447723388672, + "erf_vl": 2.2027039527893066, + "chi2": 1.5391007661819458, "pos_state": "POS_VETO", "arc_lengths": [ - 1.9705476983138794, - 1.512302243077699, - 1.260815752013667, - 1.1146985688924977, - 2.925545376397096 + 1.9466023456684678, + 1.3799482754266563, + 1.2471559596177706, + 1.1163689372359673, + 2.615530925864889 ], "integrability": [ - 0.0038688021886626456, - 0.0038688021886626456, - 2.285109604843738e-05, - 0.05472337594255805, - 0.006996966199948718 + 0.003927234502043547, + 0.0039272345020434085, + 2.453492459170148e-05, + 0.055360530037432854, + 0.006458622578065065 ], "timing": { 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a/extra_tests/regression_fits/flavour_29.json b/extra_tests/regression_fits/flavour_29.json index fb78f5b325..c4d7bed4a3 100644 --- a/extra_tests/regression_fits/flavour_29.json +++ b/extra_tests/regression_fits/flavour_29.json @@ -71,25 +71,25 @@ ], "timing": { "walltime": { - "Total": 4.325552940368652, + "Total": 4.7116029262542725, "start": 0.0, - "replica_set": 0.22845125198364258, - "replica_fitted": 4.325416326522827, - "replica_set_to_replica_fitted": 4.096965074539185 + "replica_set": 0.22231054306030273, + "replica_fitted": 4.711346387863159, + "replica_set_to_replica_fitted": 4.4890358448028564 }, "cputime": { - "Total": 4.471941011, + "Total": 4.862365402999999, "start": 0.0, - "replica_set": 0.226811251, - "replica_fitted": 4.471802753, - "replica_set_to_replica_fitted": 4.244991502 + "replica_set": 0.2213146479999999, + "replica_fitted": 4.862106009999999, + "replica_set_to_replica_fitted": 4.640791361999999 } }, "version": { - "keras": "3.4.1", + "keras": "3.6.0", 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e83ba97eb6..f83d20b956 100644 --- a/extra_tests/regression_fits/normal_fit_72.json +++ b/extra_tests/regression_fits/normal_fit_72.json @@ -71,25 +71,25 @@ ], "timing": { "walltime": { - "Total": 16.169139862060547, + "Total": 17.19196391105652, "start": 0.0, - "replica_set": 0.24132728576660156, - "replica_fitted": 16.168989896774292, - "replica_set_to_replica_fitted": 15.92766261100769 + "replica_set": 0.2349693775177002, + "replica_fitted": 17.191868543624878, + "replica_set_to_replica_fitted": 16.956899166107178 }, "cputime": { - "Total": 18.279248535, + "Total": 19.64416452, "start": 0.0, - "replica_set": 0.2395706509999993, - "replica_fitted": 18.279097574, - "replica_set_to_replica_fitted": 18.039526923000004 + "replica_set": 0.23289664700000046, + "replica_fitted": 19.644066357, + "replica_set_to_replica_fitted": 19.41116971 } }, "version": { - "keras": "3.4.1", + "keras": "3.6.0", "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post1210.dev0+d36bcce78", - "validphys": "4.0.9.post1210.dev0+d36bcce78" + "nnpdf": "4.0.9.post1366.dev0+f934deb20", + "validphys": "4.0.9.post1366.dev0+f934deb20" } } diff --git a/extra_tests/regression_fits/polarized_evol_34.json b/extra_tests/regression_fits/polarized_evol_34.json index e988f0c8a1..a4703d8d8b 100644 --- a/extra_tests/regression_fits/polarized_evol_34.json +++ b/extra_tests/regression_fits/polarized_evol_34.json @@ -47,25 +47,25 @@ ], "timing": { "walltime": { - "Total": 8.643769264221191, + "Total": 9.182395219802856, "start": 0.0, - "replica_set": 0.23259401321411133, - "replica_fitted": 8.643662691116333, - "replica_set_to_replica_fitted": 8.411068677902222 + "replica_set": 0.23716163635253906, + "replica_fitted": 9.182160377502441, + "replica_set_to_replica_fitted": 8.944998741149902 }, "cputime": { - "Total": 9.363861112999999, + "Total": 9.966191528, "start": 0.0, - "replica_set": 0.23084108100000034, - "replica_fitted": 9.363752581, - "replica_set_to_replica_fitted": 9.1329115 + "replica_set": 0.23521285599999953, + "replica_fitted": 9.965954095, + "replica_set_to_replica_fitted": 9.730741239 } }, "version": { - "keras": "3.4.1", + "keras": "3.6.0", "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post1210.dev0+d36bcce78", - "validphys": "4.0.9.post1210.dev0+d36bcce78" + "nnpdf": "4.0.9.post1366.dev0+f934deb20", + "validphys": "4.0.9.post1366.dev0+f934deb20" } } diff --git a/extra_tests/regression_fits/trainable_prepro_61.json b/extra_tests/regression_fits/trainable_prepro_61.json index 627cf8be93..52aec8f19f 100644 --- a/extra_tests/regression_fits/trainable_prepro_61.json +++ b/extra_tests/regression_fits/trainable_prepro_61.json @@ -71,25 +71,25 @@ ], "timing": { "walltime": { - "Total": 11.894519090652466, + "Total": 12.690647840499878, "start": 0.0, - "replica_set": 0.23260784149169922, - "replica_fitted": 11.894434690475464, - "replica_set_to_replica_fitted": 11.661826848983765 + "replica_set": 0.23492431640625, + "replica_fitted": 12.690513372421265, + "replica_set_to_replica_fitted": 12.455589056015015 }, "cputime": { - "Total": 13.421419582999999, + "Total": 14.288013259, "start": 0.0, - "replica_set": 0.23085444399999933, - "replica_fitted": 13.421333412, - "replica_set_to_replica_fitted": 13.190478968 + "replica_set": 0.23258636100000007, + "replica_fitted": 14.287876923999999, + "replica_set_to_replica_fitted": 14.055290563 } }, "version": { - "keras": "3.4.1", + "keras": "3.6.0", "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post1210.dev0+d36bcce78", - "validphys": "4.0.9.post1210.dev0+d36bcce78" + "nnpdf": "4.0.9.post1366.dev0+f934deb20", + "validphys": "4.0.9.post1366.dev0+f934deb20" } } From e64c35fff6002f611204db6d3ffb439f14329a10 Mon Sep 17 00:00:00 2001 From: RoyStegeman Date: Wed, 16 Oct 2024 11:40:25 +0200 Subject: [PATCH 4/5] trigger ci test --- nnprofile_example.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/nnprofile_example.yaml b/nnprofile_example.yaml index 5e675579bc..11aedc648a 100644 --- a/nnprofile_example.yaml +++ b/nnprofile_example.yaml @@ -13,3 +13,4 @@ hyperscan_path: hyperscan validphys_cache_path: vp-cache # With these options downloaded theories will go to ~/.local/share/NNPDF/theories + From ad1c1e07909be69081a7584318ba8ff0aae3c0e0 Mon Sep 17 00:00:00 2001 From: RoyStegeman Date: Wed, 16 Oct 2024 11:54:44 +0200 Subject: [PATCH 5/5] implement review comments --- n3fit/src/n3fit/scaler.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/n3fit/src/n3fit/scaler.py b/n3fit/src/n3fit/scaler.py index c21086cd19..65deb2a5b4 100644 --- a/n3fit/src/n3fit/scaler.py +++ b/n3fit/src/n3fit/scaler.py @@ -30,9 +30,8 @@ def generate_scaler( # force_set_smallest is used to make sure the smallest point included in the scaling is 1e-9, to # prevent trouble when saving it to the LHAPDF grid force_set_smallest = input_arr.min() > 1e-9 - include_endpoint = ( - 1.0 in input_arr - ) # if 1.0 is in the xgrid it should also be 1.0 in the output xgrid + # if 1.0 is in the xgrid it should also be 1.0 in the output xgrid + include_endpoint = 1.0 in input_arr if force_set_smallest: new_xgrid = np.linspace( start=1 / input_arr_size, stop=1.0, endpoint=include_endpoint, num=input_arr_size @@ -67,7 +66,7 @@ def generate_scaler( mask = np.zeros(len(map_from), dtype=bool) mask[selected_points] = True - # apply the mask and lot the input + # apply the mask and log the input masked_map_from = map_from[mask] log_masked_map_from = np.log(masked_map_from) masked_map_to = map_to[mask]