diff --git a/extra_tests/regression_fits/central_16.json b/extra_tests/regression_fits/central_16.json index 456fc26110..39aa4f3cb3 100644 --- a/extra_tests/regression_fits/central_16.json +++ b/extra_tests/regression_fits/central_16.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 29.638298988342285, + "Total": 32.32825946807861, "start": 0.0, - "replica_set": 0.2522115707397461, - "replica_fitted": 29.6381618976593, - "replica_set_to_replica_fitted": 29.385950326919556 + "replica_set": 0.23800349235534668, + "replica_fitted": 32.32805871963501, + "replica_set_to_replica_fitted": 32.09005522727966 }, "cputime": { - "Total": 31.201514887000002, + "Total": 33.586156003, "start": 0.0, - "replica_set": 0.5735468639999999, - "replica_fitted": 31.201376138, - "replica_set_to_replica_fitted": 30.627829274 + "replica_set": 0.23583456399999925, + "replica_fitted": 33.585950811000004, + "replica_set_to_replica_fitted": 33.350116247 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - 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"nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/flavour_29.json b/extra_tests/regression_fits/flavour_29.json index c5ae2dc291..045396ff1e 100644 --- a/extra_tests/regression_fits/flavour_29.json +++ b/extra_tests/regression_fits/flavour_29.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 7.07843017578125, + "Total": 7.731718301773071, "start": 0.0, - "replica_set": 0.2522542476654053, - "replica_fitted": 7.078283309936523, - "replica_set_to_replica_fitted": 6.826029062271118 + "replica_set": 0.2266373634338379, + "replica_fitted": 7.731366872787476, + "replica_set_to_replica_fitted": 7.504729509353638 }, "cputime": { - "Total": 7.514604531, + "Total": 7.901237213, "start": 0.0, - "replica_set": 0.5747256410000006, - "replica_fitted": 7.514455934000001, - "replica_set_to_replica_fitted": 6.939730293 + "replica_set": 0.22565210800000024, + "replica_fitted": 7.900883664, + "replica_set_to_replica_fitted": 7.675231556 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/no_csr_613.json b/extra_tests/regression_fits/no_csr_613.json index cc65f0829f..29319b0842 100644 --- a/extra_tests/regression_fits/no_csr_613.json +++ b/extra_tests/regression_fits/no_csr_613.json @@ -77,24 +77,24 @@ ], "timing": { "walltime": { - "Total": 27.847703218460083, + "Total": 31.898317337036133, "start": 0.0, - "replica_set": 0.2785060405731201, - "replica_fitted": 27.847578287124634, - "replica_set_to_replica_fitted": 27.569072246551514 + "replica_set": 0.2562828063964844, + "replica_fitted": 31.898224592208862, + "replica_set_to_replica_fitted": 31.641941785812378 }, "cputime": { - "Total": 28.899172986, + "Total": 32.496953049, "start": 0.0, - "replica_set": 0.5995310260000002, - "replica_fitted": 28.899045308, - "replica_set_to_replica_fitted": 28.299514282 + "replica_set": 0.25330967699999984, + "replica_fitted": 32.496857621, + "replica_set_to_replica_fitted": 32.243547944 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/no_lagrange_27.json b/extra_tests/regression_fits/no_lagrange_27.json index 513cdee089..720997b4b6 100644 --- a/extra_tests/regression_fits/no_lagrange_27.json +++ b/extra_tests/regression_fits/no_lagrange_27.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 33.39091753959656, + "Total": 37.980634450912476, "start": 0.0, - "replica_set": 0.2545652389526367, - "replica_fitted": 33.390822649002075, - "replica_set_to_replica_fitted": 33.13625741004944 + "replica_set": 0.22922587394714355, + "replica_fitted": 37.98039174079895, + "replica_set_to_replica_fitted": 37.75116586685181 }, "cputime": { - "Total": 34.736605786000005, + "Total": 39.138274702000004, "start": 0.0, - "replica_set": 0.566752718, - "replica_fitted": 34.736509046, - "replica_set_to_replica_fitted": 34.169756328 + "replica_set": 0.22799105100000006, + "replica_fitted": 39.138029595, + "replica_set_to_replica_fitted": 38.910038543999995 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/no_msr_92.json b/extra_tests/regression_fits/no_msr_92.json index c464443992..bd743bd266 100644 --- a/extra_tests/regression_fits/no_msr_92.json +++ b/extra_tests/regression_fits/no_msr_92.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 41.017494440078735, + "Total": 46.0448694229126, "start": 0.0, - "replica_set": 0.2511754035949707, - "replica_fitted": 41.01731038093567, - "replica_set_to_replica_fitted": 40.7661349773407 + "replica_set": 0.23608732223510742, + "replica_fitted": 46.04460954666138, + "replica_set_to_replica_fitted": 45.80852222442627 }, "cputime": { - "Total": 42.594483392, + "Total": 47.628624913, "start": 0.0, - "replica_set": 0.5723506120000001, - "replica_fitted": 42.594297225, - "replica_set_to_replica_fitted": 42.021946613 + "replica_set": 0.2344786430000001, + "replica_fitted": 47.628361752, + "replica_set_to_replica_fitted": 47.393883109 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/no_sumrules_18.json b/extra_tests/regression_fits/no_sumrules_18.json index 6af8e57862..92081e8302 100644 --- a/extra_tests/regression_fits/no_sumrules_18.json +++ b/extra_tests/regression_fits/no_sumrules_18.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 36.68100190162659, + "Total": 41.40428566932678, "start": 0.0, - "replica_set": 0.41010546684265137, - "replica_fitted": 36.6807975769043, - "replica_set_to_replica_fitted": 36.270692110061646 + "replica_set": 0.2421131134033203, + "replica_fitted": 41.40400695800781, + "replica_set_to_replica_fitted": 41.16189384460449 }, "cputime": { - "Total": 37.817408023, + "Total": 42.813383816, "start": 0.0, - "replica_set": 0.7309529969999993, - "replica_fitted": 37.817202128999995, - "replica_set_to_replica_fitted": 37.086249132 + "replica_set": 0.24098446600000045, + "replica_fitted": 42.813101980999996, + "replica_set_to_replica_fitted": 42.572117514999995 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/no_vsr_54.json b/extra_tests/regression_fits/no_vsr_54.json index 1451179f58..acb7939d0c 100644 --- a/extra_tests/regression_fits/no_vsr_54.json +++ b/extra_tests/regression_fits/no_vsr_54.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 30.62160563468933, + "Total": 33.98991370201111, "start": 0.0, - "replica_set": 0.25388574600219727, - "replica_fitted": 30.621519565582275, - "replica_set_to_replica_fitted": 30.367633819580078 + "replica_set": 0.2610938549041748, + "replica_fitted": 33.98975157737732, + "replica_set_to_replica_fitted": 33.728657722473145 }, "cputime": { - "Total": 31.731461988999996, + "Total": 35.629525277, "start": 0.0, - "replica_set": 0.5682404839999995, - "replica_fitted": 31.731373433, - "replica_set_to_replica_fitted": 31.163132949 + "replica_set": 0.25395271799999986, + "replica_fitted": 35.629360870999996, + "replica_set_to_replica_fitted": 35.375408152999995 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/normal_fit_72.json b/extra_tests/regression_fits/normal_fit_72.json index 720e3acdc3..adb403c767 100644 --- a/extra_tests/regression_fits/normal_fit_72.json +++ b/extra_tests/regression_fits/normal_fit_72.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 37.61420464515686, + "Total": 41.181039333343506, "start": 0.0, - "replica_set": 0.2624022960662842, - "replica_fitted": 37.61404800415039, - "replica_set_to_replica_fitted": 37.351645708084106 + "replica_set": 0.22890329360961914, + "replica_fitted": 41.180768728256226, + "replica_set_to_replica_fitted": 40.951865434646606 }, "cputime": { - "Total": 39.01239065, + "Total": 41.631527645, "start": 0.0, - "replica_set": 0.581023987, - "replica_fitted": 39.012231764, - "replica_set_to_replica_fitted": 38.431207777 + "replica_set": 0.227995653999999, + "replica_fitted": 41.631252642, + "replica_set_to_replica_fitted": 41.403256988 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/polarized_evol.yml b/extra_tests/regression_fits/polarized_evol.yml index 3d02c566b4..cea9ccd333 100644 --- a/extra_tests/regression_fits/polarized_evol.yml +++ b/extra_tests/regression_fits/polarized_evol.yml @@ -31,7 +31,7 @@ nnseed: 1865683875 mcseed: 746898422 genrep: true -save: "weights_polevol.h5" +load: "weights_polevol.weights.h5" parameters: nodes_per_layer: [25, 20, 4] @@ -39,9 +39,9 @@ parameters: initializer: glorot_normal optimizer: clipnorm: 6.073e-6 - learning_rate: 2.621e-3 + learning_rate: 2.621e-4 optimizer_name: Nadam - epochs: 20000 + epochs: 1100 positivity: initial: 1 multiplier: diff --git a/extra_tests/regression_fits/polarized_evol_34.exportgrid b/extra_tests/regression_fits/polarized_evol_34.exportgrid index 24f571985e..b0228e9f06 100644 --- a/extra_tests/regression_fits/polarized_evol_34.exportgrid +++ b/extra_tests/regression_fits/polarized_evol_34.exportgrid @@ -1,577 +1,583 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, 0.0, -0.2108040153980255, -0.2110241949558258, -0.2110241949558258, -1.6818376779556274, - -0.2110241949558258, -0.2110241949558258, -0.2108040153980255, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, -0.2019251435995102, -0.20216001570224762, -0.20216001570224762, - -1.6203995943069458, -0.20216001570224762, -0.20216001570224762, -0.2019251435995102, +- [0.0, 0.0, 0.0, -0.10375979542732239, -0.10418701171875, -0.10418701171875, -0.6372790932655334, + -0.10418701171875, -0.10418701171875, -0.10375979542732239, 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, -0.096921905875206, -0.09737326204776764, -0.09737326204776764, + -0.6048541069030762, -0.09737326204776764, -0.09737326204776764, -0.096921905875206, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, -0.19326776266098022, -0.19351829588413239, -0.19351829588413239, - -1.5603023767471313, -0.19351829588413239, -0.19351829588413239, -0.19326776266098022, +- [0.0, 0.0, 0.0, -0.0903250128030777, -0.09080172330141068, -0.09080172330141068, + -0.5734948515892029, -0.09080172330141068, -0.09080172330141068, -0.0903250128030777, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, -0.1848253458738327, -0.18509255349636078, -0.18509255349636078, - 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1.0276677130605094e-05, -2.1830899186170427e-06, 0.0022078112233430147, 6.069192386348732e-05, +- [0.0, 0.0, 0.0, -0.00045975667308084667, 0.007658129092305899, -0.0006001081201247871, + -0.0008465482969768345, -0.0006001081201247871, 0.007658129092305899, -0.00045975667308084667, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 6.825141463195905e-05, 0.0017902569379657507, 3.424088208703324e-05, - 9.177236279356293e-06, 3.424088208703324e-05, 0.0017902569379657507, 6.825141463195905e-05, +- [0.0, 0.0, 0.0, -0.00037157683982513845, 0.0066325790248811245, -0.0004690028727054596, + -0.0006943886983208358, -0.0004690028727054596, 0.0066325790248811245, -0.00037157683982513845, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 7.230781920952722e-05, 0.001430833712220192, 5.9207934100413695e-05, - 7.860242476454005e-06, 5.9207934100413695e-05, 0.001430833712220192, 7.230781920952722e-05, +- [0.0, 0.0, 0.0, -0.00029121898114681244, 0.0057054986245930195, -0.00035662937443703413, + -0.0005646670470014215, -0.00035662937443703413, 0.0057054986245930195, -0.00029121898114681244, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 7.319133874261752e-05, 0.0011247150832787156, 7.437743624905124e-05, - 6.471938831964508e-06, 7.437743624905124e-05, 0.0011247150832787156, 7.319133874261752e-05, +- [0.0, 0.0, 0.0, -0.00021916035620961338, 0.00487104058265686, -0.0002611011441331357, + -0.0004547596618067473, -0.0002611011441331357, 0.00487104058265686, -0.00021916035620961338, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 7.127166463760659e-05, 0.0008671666728332639, 8.135695679811761e-05, - 5.121544290886959e-06, 8.135695679811761e-05, 0.0008671666728332639, 7.127166463760659e-05, +- [0.0, 0.0, 0.0, -0.00015570360119454563, 0.004123591352254152, -0.00018074382387567312, + -0.0003622868680395186, -0.00018074382387567312, 0.004123591352254152, -0.00015570360119454563, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 6.69557266519405e-05, 0.0006535447901114821, 8.169592911144719e-05, - 3.885663772962289e-06, 8.169592911144719e-05, 0.0006535447901114821, 6.69557266519405e-05, +- [0.0, 0.0, 0.0, -0.00010097896301886067, 0.003457761136814952, -0.00011407534475438297, + -0.000285094982245937, -0.00011407534475438297, 0.003457761136814952, -0.00010097896301886067, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 6.068560105632059e-05, 0.00047931031440384686, 7.688115874771029e-05, - 2.813033688653377e-06, 7.688115874771029e-05, 0.00047931031440384686, 6.068560105632059e-05, +- [0.0, 0.0, 0.0, -5.4951367928879336e-05, 0.0028683540876954794, -5.9777670685434714e-05, + -0.00022123436792753637, -5.9777670685434714e-05, 0.0028683540876954794, -5.4951367928879336e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 5.293423237162642e-05, 0.00034003969631157815, 6.832309009041637e-05, - 1.9286119368189247e-06, 6.832309009041637e-05, 0.00034003969631157815, 5.293423237162642e-05, +- [0.0, 0.0, 0.0, -1.7429461877327412e-05, 0.0023503636475652456, -1.666152456891723e-05, + -0.00016894661530386657, -1.666152456891723e-05, 0.0023503636475652456, -1.7429461877327412e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 4.420247205416672e-05, 0.00023143182625062764, 5.73495781281963e-05, - 1.2378916380839655e-06, 5.73495781281963e-05, 0.00023143182625062764, 4.420247205416672e-05, +- [0.0, 0.0, 0.0, 1.1927386367460713e-05, 0.0018989337841048837, 1.636104025237728e-05, + -0.00012664461974054575, 1.636104025237728e-05, 0.0018989337841048837, 1.1927386367460713e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 3.50138325302396e-05, 0.00014932222256902605, 4.5193904952611774e-05, - 7.308884732992738e-07, 4.5193904952611774e-05, 0.00014932222256902605, 3.50138325302396e-05, +- [0.0, 0.0, 0.0, 3.359601032570936e-05, 0.0015093344263732433, 4.030536729260348e-05, + -9.289818990509957e-05, 4.030536729260348e-05, 0.0015093344263732433, 3.359601032570936e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 2.5908362658810802e-05, 8.968992187874392e-05, 3.2982257835101336e-05, - 3.8603562302341743e-07, 3.2982257835101336e-05, 8.968992187874392e-05, 2.5908362658810802e-05, +- [0.0, 0.0, 0.0, 4.818113302462734e-05, 0.001176935387775302, 5.615298869088292e-05, + -6.642034713877365e-05, 5.615298869088292e-05, 0.001176935387775302, 4.818113302462734e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 1.743440589052625e-05, 4.867513052886352e-05, 2.171693995478563e-05, - 1.7395628049143852e-07, 2.171693995478563e-05, 4.867513052886352e-05, 1.743440589052625e-05, +- [0.0, 0.0, 0.0, 5.640164090436883e-05, 0.0008971710922196507, 6.487464270321652e-05, + -4.605400681612082e-05, 6.487464270321652e-05, 0.0008971710922196507, 5.640164090436883e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 1.0136009223060682e-05, 2.2591579181607813e-05, 1.2253504792170133e-05, - 6.116002992939684e-08, 1.2253504792170133e-05, 2.2591579181607813e-05, 1.0136009223060682e-05, +- [0.0, 0.0, 0.0, 5.907891318202019e-05, 0.0006655191536992788, 6.745915743522346e-05, + -3.0761824746150523e-05, 6.745915743522346e-05, 0.0006655191536992788, 5.907891318202019e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 4.5318156480789185e-06, 7.944236131152138e-06, 5.2655418585345615e-06, - 1.3716577207389946e-08, 5.2655418585345615e-06, 7.944236131152138e-06, 4.5318156480789185e-06, +- [0.0, 0.0, 0.0, 5.712369238608517e-05, 0.0004774775879923254, 6.493427645182237e-05, + -1.9616374629549682e-05, 6.493427645182237e-05, 0.0004774775879923254, 5.712369238608517e-05, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 1.0725657375587616e-06, 1.4470747373707127e-06, 1.1806446309492458e-06, - 1.031158713971081e-09, 1.1806446309492458e-06, 1.4470747373707127e-06, 1.0725657375587616e-06, +- [0.0, 0.0, 0.0, 5.152165613253601e-05, 0.0003285413549747318, 5.838176002725959e-05, + -1.1792317309300415e-05, 5.838176002725959e-05, 0.0003285413549747318, 5.152165613253601e-05, + 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, 4.331897071097046e-05, 0.0002141948789358139, 4.8946971219265833e-05, + -6.56032307233545e-06, 4.8946971219265833e-05, 0.0002141948789358139, 4.331897071097046e-05, + 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, 3.360482878633775e-05, 0.00012989896640647203, 3.7837718991795555e-05, + -3.2820951219036942e-06, 3.7837718991795555e-05, 0.00012989896640647203, 3.360482878633775e-05, + 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, 2.3491662432206795e-05, 7.109759462764487e-05, 2.6312056434107944e-05, + -1.4074875025471556e-06, 2.6312056434107944e-05, 7.109759462764487e-05, 2.3491662432206795e-05, + 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, 1.4088440366322175e-05, 3.3226398954866454e-05, 1.5646359315724112e-05, + -4.728907185835851e-07, 1.5646359315724112e-05, 3.3226398954866454e-05, 1.4088440366322175e-05, + 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, 6.461486464104382e-06, 1.1738436114683282e-05, 7.074640961945988e-06, + -1.0173076248065627e-07, 7.074640961945988e-06, 1.1738436114683282e-05, 6.461486464104382e-06, + 0.0, 0.0, 0.0, 0.0] +- [0.0, 0.0, 0.0, 1.5614931498930673e-06, 2.141876848327229e-06, 1.666849925641145e-06, + -7.360528631039642e-09, 1.666849925641145e-06, 2.141876848327229e-06, 1.5614931498930673e-06, 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, 0.0] q20: 1.0 diff --git a/extra_tests/regression_fits/polarized_evol_34.json b/extra_tests/regression_fits/polarized_evol_34.json index c17a2c6fd9..c185258ef1 100644 --- a/extra_tests/regression_fits/polarized_evol_34.json +++ b/extra_tests/regression_fits/polarized_evol_34.json @@ -25,46 +25,46 @@ "trainable": false } ], - "stop_epoch": 11034, - "best_epoch": 9032, - "erf_tr": 1.259786605834961, - "erf_vl": 3.418194532394409, - "chi2": 1.2056642770767212, + "stop_epoch": 435, + "best_epoch": 323, + "erf_tr": 1.2238341569900513, + "erf_vl": 3.0599794387817383, + "chi2": 1.109000325202942, "pos_state": "POS_PASS", "arc_lengths": [ - 1.0456830117585252, - 1.0220538806553736, + 1.0326780832672715, + 1.014154549694492, 0.9899487587939698, 0.9899487587939698, 0.9899487587939698 ], "integrability": [ - 2.3592239273284576e-16, - 5.551115123125783e-17, - 1.249000902703301e-16, - 0.005192503915168359, - 3.0531133177191805e-16 + 1.0061396160665481e-16, + 1.734723475976807e-17, + 3.122502256758253e-17, + 0.008682500803843137, + 7.979727989493313e-17 ], "timing": { "walltime": { - "Total": 298.4062387943268, + "Total": 18.630010843276978, "start": 0.0, - "replica_set": 0.2670104503631592, - "replica_fitted": 298.40571093559265, - "replica_set_to_replica_fitted": 298.1387004852295 + "replica_set": 0.249741792678833, + "replica_fitted": 18.62974452972412, + "replica_set_to_replica_fitted": 18.380002737045288 }, "cputime": { - "Total": 306.350081155, + "Total": 19.099821179000003, "start": 0.0, - "replica_set": 0.5725485690000003, - "replica_fitted": 306.349551839, - "replica_set_to_replica_fitted": 305.77700327 + "replica_set": 0.24700718099999985, + "replica_fitted": 19.099551677, + "replica_set_to_replica_fitted": 18.852544496 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post498.dev0+84a8ed01e", - "validphys": "4.0.9.post498.dev0+84a8ed01e" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/single_dense_316.json b/extra_tests/regression_fits/single_dense_316.json index f009047b54..97bad55b75 100644 --- a/extra_tests/regression_fits/single_dense_316.json +++ b/extra_tests/regression_fits/single_dense_316.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 30.499980926513672, + "Total": 34.44913601875305, "start": 0.0, - "replica_set": 0.24414348602294922, - "replica_fitted": 30.499889612197876, - "replica_set_to_replica_fitted": 30.255746126174927 + "replica_set": 0.25393104553222656, + "replica_fitted": 34.448877811431885, + "replica_set_to_replica_fitted": 34.19494676589966 }, "cputime": { - "Total": 31.149875994999995, + "Total": 35.914583166, "start": 0.0, - "replica_set": 0.5663533699999999, - "replica_fitted": 31.149782650999995, - "replica_set_to_replica_fitted": 30.583429280999997 + "replica_set": 0.24511515099999936, + "replica_fitted": 35.914323130999996, + "replica_set_to_replica_fitted": 35.669207979999996 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/trainable_prepro_61.json b/extra_tests/regression_fits/trainable_prepro_61.json index 85dd251a30..5c426e7b05 100644 --- a/extra_tests/regression_fits/trainable_prepro_61.json +++ b/extra_tests/regression_fits/trainable_prepro_61.json @@ -71,24 +71,24 @@ ], "timing": { "walltime": { - "Total": 31.86877465248108, + "Total": 34.79690074920654, "start": 0.0, - "replica_set": 0.26708412170410156, - "replica_fitted": 31.86863875389099, - "replica_set_to_replica_fitted": 31.60155463218689 + "replica_set": 0.24268245697021484, + "replica_fitted": 34.79669666290283, + "replica_set_to_replica_fitted": 34.55401420593262 }, "cputime": { - "Total": 32.985740084, + "Total": 36.326103099, "start": 0.0, - "replica_set": 0.5635142529999992, - "replica_fitted": 32.985602307, - "replica_set_to_replica_fitted": 32.422088054 + "replica_set": 0.24133399599999983, + "replica_fitted": 36.325896654, + "replica_set_to_replica_fitted": 36.084562657999996 } }, "version": { "tensorflow": "2.16.1, mkl=??", "numpy": "1.26.4", - "nnpdf": "4.0.9.post446.dev0+264ada234", - "validphys": "4.0.9.post446.dev0+264ada234" + "nnpdf": "4.0.9.post576.dev0+47a077fe1", + "validphys": "4.0.9.post576.dev0+47a077fe1" } } diff --git a/extra_tests/regression_fits/weights_polevol.weights.h5 b/extra_tests/regression_fits/weights_polevol.weights.h5 new file mode 100644 index 0000000000..351d53d501 Binary files /dev/null and b/extra_tests/regression_fits/weights_polevol.weights.h5 differ diff --git a/n3fit/src/n3fit/layers/msr_normalization.py b/n3fit/src/n3fit/layers/msr_normalization.py index 6e64f2b6b8..7695d4f11f 100644 --- a/n3fit/src/n3fit/layers/msr_normalization.py +++ b/n3fit/src/n3fit/layers/msr_normalization.py @@ -45,37 +45,43 @@ # The following lays out the SR for Polarised PDFs TSR_COMPONENTS = ['t3', 't8'] TSR_DENOMINATORS = {'t3': 't3', 't8': 't8'} -# Sum Rules defined as in PDG 2023 +# Sum Rules defined as in PDG 2023 (cv, std) TSR_CONSTANTS = {'t3': 1.2756, 't8': 0.5850} TSR_CONSTANTS_UNC = {'t3': 0.0013, 't8': 0.025} -def sample_tsr(v: dict, e: dict, t: list, nr: int) -> list: +def sample_tsr(sampler, component) -> list: """ Sample the Triplets sum rules according to the PDG uncertainties. Parameters ---------- - v: dict - dictionary that maps the triplet component to its PDG value - e: dict - dictionary that maps the triplet component to its denominator - t: list - list of triplet component for which SR should be applied - nr: int - number of replicas that are fitter simultaneously + sampler: np.random.Generator + Numpy Generator from which to sample the TSR + c: str + Component for which to sample from according to TSR_CONSTANTS Returns ------- - list: - list of sum rule values sampled according to a normal distribution + float: + value of the sum rule for this replica for this component + sampled according to a normal distribution """ - return [np.random.normal(v[c], e[c], size=nr) for c in t] + cv = TSR_CONSTANTS[component] + std = TSR_CONSTANTS_UNC[component] + return sampler.normal(cv, std) class MSR_Normalization(MetaLayer): """ Computes the normalisation factors for the sum rules of the PDFs. + + Parameters + ---------- + mode: str + The type of sum rule to apply, it can be one of ALL, MSR, VSR, TSR, ALLBUTCSR. + replica_seeds: List[int] + A list of seed-per-replica. Used to sample the polarized sum rules. """ _msr_enabled = False @@ -83,7 +89,7 @@ class MSR_Normalization(MetaLayer): _tsr_enabled = False _csr_enabled = False - def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs): + def __init__(self, mode: str = "ALL", replica_seeds=None, **kwargs): if mode == True or mode.upper() == "ALL": self._msr_enabled = True self._vsr_enabled = True @@ -99,7 +105,11 @@ def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs): else: raise ValueError(f"Mode {mode} not accepted for sum rules") - self.replicas = replicas + if replica_seeds is None: + self._replicas = 1 + else: + self._replicas = len(replica_seeds) + indices = [] self.divisor_indices = [] if self._msr_enabled: @@ -109,14 +119,20 @@ def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs): self.divisor_indices += [IDX[VSR_DENOMINATORS[c]] for c in VSR_COMPONENTS] indices += [IDX[c] for c in VSR_COMPONENTS] self.vsr_factors = op.numpy_to_tensor( - [np.repeat(VSR_CONSTANTS[c], replicas) for c in VSR_COMPONENTS] + [np.repeat(VSR_CONSTANTS[c], self._replicas) for c in VSR_COMPONENTS] ) if self._tsr_enabled: + if replica_seeds is None: + raise ValueError("To use sum_rules=TSR a list of seeds must be provided") + self.divisor_indices += [IDX[TSR_DENOMINATORS[c]] for c in TSR_COMPONENTS] indices += [IDX[c] for c in TSR_COMPONENTS] - self.tsr_factors = op.numpy_to_tensor( - sample_tsr(TSR_CONSTANTS, TSR_CONSTANTS_UNC, TSR_COMPONENTS, replicas) - ) + + tsr_factors_per_replica = [] + for seed in replica_seeds: + sampler = np.random.default_rng(seed) + tsr_factors_per_replica.append([sample_tsr(sampler, c) for c in TSR_COMPONENTS]) + self.tsr_factors = op.numpy_to_tensor(np.array(tsr_factors_per_replica).T) if self._csr_enabled: # modified vsr for V, V24, V35 @@ -126,7 +142,7 @@ def __init__(self, mode: str = "ALL", replicas: int = 1, **kwargs): self.divisor_indices += [IDX[NOV15_DENOMINATORS[c]] for c in NOV15_COMPONENTS] indices += [IDX[c] for c in NOV15_COMPONENTS] self.vsr_factors = op.numpy_to_tensor( - [np.repeat(NOV15_CONSTANTS[c], replicas) for c in NOV15_COMPONENTS] + [np.repeat(NOV15_CONSTANTS[c], self._replicas) for c in NOV15_COMPONENTS] ) # Need this extra dimension for the scatter_to_one operation self.indices = [[i] for i in indices] @@ -183,7 +199,7 @@ def call(self, pdf_integrated, photon_integral): # Fill in the rest of the flavours with 1 num_flavours = y.shape[0] norm_constants = op.scatter_to_one( - numerators / divisors, indices=self.indices, output_shape=(num_flavours, self.replicas) + numerators / divisors, indices=self.indices, output_shape=(num_flavours, self._replicas) ) return op.batchit(op.transpose(norm_constants), batch_dimension=1) diff --git a/n3fit/src/n3fit/model_gen.py b/n3fit/src/n3fit/model_gen.py index 92467c44f7..da6910faca 100644 --- a/n3fit/src/n3fit/model_gen.py +++ b/n3fit/src/n3fit/model_gen.py @@ -483,7 +483,7 @@ def pdfNN_layer_generator( dropout: float rate of dropout layer by layer impose_sumrule: str - whether to impose sumrules on the output pdf and which one to impose (All, MSR, VSR) + whether to impose sumrules on the output pdf and which one to impose (All, MSR, VSR, TSR) scaler: callable Function to apply to the input. If given the input to the model will be a (1, None, 2) tensor where dim [:,:,0] is scaled @@ -526,7 +526,6 @@ def pdfNN_layer_generator( if regularizer_args is None: regularizer_args = dict() - number_of_layers = len(nodes) # The number of nodes in the last layer is equal to the number of fitted flavours last_layer_nodes = nodes[-1] # (== len(flav_info)) @@ -586,7 +585,7 @@ def pdfNN_layer_generator( # Normalization and sum rules if impose_sumrule: sumrule_layer, integrator_input = generate_msr_model_and_grid( - fitbasis=fitbasis, mode=impose_sumrule, scaler=scaler, replicas=num_replicas + fitbasis=fitbasis, mode=impose_sumrule, scaler=scaler, replica_seeds=seed ) model_input["integrator_input"] = integrator_input else: diff --git a/n3fit/src/n3fit/msr.py b/n3fit/src/n3fit/msr.py index 88c1fe56fe..a9a30190c2 100644 --- a/n3fit/src/n3fit/msr.py +++ b/n3fit/src/n3fit/msr.py @@ -1,6 +1,7 @@ """ The constraint module include functions to impose the momentum sum rules on the PDFs """ + import logging from typing import Callable, Optional @@ -19,7 +20,7 @@ def generate_msr_model_and_grid( mode: str = "ALL", nx: int = int(2e3), scaler: Optional[Callable] = None, - replicas: int = 1, + replica_seeds: Optional[list] = None, ) -> MetaModel: """ Generates a model that applies the sum rules to the PDF. @@ -53,6 +54,7 @@ def generate_msr_model_and_grid( - values: the integration grid - input: the input layer of the integration grid """ + replicas = len(replica_seeds) # 0. Prepare input layers to MSR model pdf_x = Input(shape=(replicas, None, output_dim), batch_size=1, name="pdf_x") pdf_xgrid_integration = Input( @@ -92,7 +94,7 @@ def generate_msr_model_and_grid( photon_integral = Input(shape=(replicas, 1), batch_size=1, name='photon_integral') # 6. Compute the normalization factor - normalization_factor = MSR_Normalization(mode, replicas, name="msr_weights")( + normalization_factor = MSR_Normalization(mode, replica_seeds, name="msr_weights")( pdf_integrated, photon_integral ) diff --git a/n3fit/src/n3fit/performfit.py b/n3fit/src/n3fit/performfit.py index 6c9f76c123..04703ef924 100644 --- a/n3fit/src/n3fit/performfit.py +++ b/n3fit/src/n3fit/performfit.py @@ -9,7 +9,6 @@ import numpy as np import n3fit.checks - from n3fit.vpinterface import N3PDF log = logging.getLogger(__name__) @@ -103,8 +102,8 @@ def performfit( fitbasis: str Valid basis which the fit is to be ran in. Available bases can be found in :py:mod:`validphys.pdfbases`. - sum_rules: bool - Whether to impose sum rules in fit. By default set to True + sum_rules: str + Whether to impose sum rules in fit. By default set to True="ALL" parameters: dict Mapping containing parameters which define the network architecture/fitting methodology. diff --git a/n3fit/src/n3fit/tests/regressions/quickcard_pol.yml b/n3fit/src/n3fit/tests/regressions/quickcard_pol.yml index 4f88b4ba3c..94bc90414f 100644 --- a/n3fit/src/n3fit/tests/regressions/quickcard_pol.yml +++ b/n3fit/src/n3fit/tests/regressions/quickcard_pol.yml @@ -38,26 +38,26 @@ parameters: activation_per_layer: [tanh, tanh, linear] initializer: glorot_normal optimizer: - clipnorm: 6.073e-6 - learning_rate: 2.621e-3 - optimizer_name: Nadam - epochs: 2000 + optimizer_name: 'RMSprop' + learning_rate: 0.00001 + clipnorm: 1e-4 + epochs: 600 positivity: - initial: 1 - multiplier: - threshold: 1e-7 + multiplier: 1.05 + threshold: 1e-5 integrability: - initial: 1 - multiplier: + multiplier: 1.5 threshold: 1e-2 stopping_patience: 0.1 layer_type: dense dropout: 0.0 - threshold_chi2: 3.5 + threshold_chi2: 5.0 + fitting: + savepseudodata: false fitbasis: POLARIZED_EVOL - sum_rules: False + sum_rules: TSR basis: - {fl: sng, trainable: false, smallx: [1.094, 1.118], largex: [1.46, 3.003]} - {fl: g, trainable: false, smallx: [0.8189, 1.844], largex: [2.591, 5.697]} @@ -67,7 +67,7 @@ fitting: ########################################################### positivity: posdatasets: - - {dataset: NNPDF_POS_2P24GEV_XGL-POLARIZED, maxlambda: 1e8} + - {dataset: NNPDF_POS_2P24GEV_XGL-POLARIZED, maxlambda: 1e5} ############################################################ integrability: @@ -76,3 +76,4 @@ integrability: ############################################################ debug: true +double_precision: true diff --git a/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.exportgrid b/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.exportgrid index 98481b6091..7f85331078 100644 --- a/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.exportgrid +++ b/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.exportgrid @@ -1,513 +1,528 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, 0.0, 0.39660704135894775, 0.40793853998184204, 0.3857048749923706, 0.039867423474788666, - 0.3857048749923706, 0.40793853998184204, 0.39660704135894775, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.38579651713371277, 0.3975309431552887, 0.3745315372943878, 0.04044530540704727, - 0.3745315372943878, 0.3975309431552887, 0.38579651713371277, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.37524905800819397, 0.3874014914035797, 0.36361005902290344, 0.041019849479198456, - 0.36361005902290344, 0.3874014914035797, 0.37524905800819397, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.36495718359947205, 0.37754306197166443, 0.3529326319694519, 0.041589923202991486, - 0.3529326319694519, 0.37754306197166443, 0.36495718359947205, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.3549138605594635, 0.3679494857788086, 0.3424920439720154, 0.04215466231107712, - 0.3424920439720154, 0.3679494857788086, 0.3549138605594635, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.34511226415634155, 0.35861438512802124, 0.3322811424732208, 0.042712464928627014, - 0.3322811424732208, 0.35861438512802124, 0.34511226415634155, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.3355458378791809, 0.34953200817108154, 0.322293221950531, 0.04326194152235985, - 0.322293221950531, 0.34953200817108154, 0.3355458378791809, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.3262074589729309, 0.34069588780403137, 0.31252098083496094, 0.04380181431770325, - 0.31252098083496094, 0.34069588780403137, 0.3262074589729309, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.31709104776382446, 0.33210065960884094, 0.30295804142951965, 0.044330257922410965, - 0.30295804142951965, 0.33210065960884094, 0.31709104776382446, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.30818983912467957, 0.32374027371406555, 0.2935974895954132, 0.04484551027417183, - 0.2935974895954132, 0.32374027371406555, 0.30818983912467957, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2994975745677948, 0.31560930609703064, 0.284432977437973, 0.04534568265080452, - 0.284432977437973, 0.31560930609703064, 0.2994975745677948, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2910081148147583, 0.3077023923397064, 0.2754581868648529, 0.04582885280251503, - 0.2754581868648529, 0.3077023923397064, 0.2910081148147583, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2827152907848358, 0.3000141680240631, 0.26666679978370667, 0.046292465180158615, - 0.26666679978370667, 0.3000141680240631, 0.2827152907848358, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2746129333972931, 0.2925393879413605, 0.2580527663230896, 0.04673432186245918, - 0.2580527663230896, 0.2925393879413605, 0.2746129333972931, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2666946053504944, 0.28527238965034485, 0.24960952997207642, 0.04715166613459587, - 0.24960952997207642, 0.28527238965034485, 0.2666946053504944, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2589549124240875, 0.27820876240730286, 0.24133175611495972, 0.04754175990819931, - 0.24133175611495972, 0.27820876240730286, 0.2589549124240875, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2513878345489502, 0.2713433504104614, 0.23321352899074554, 0.047901492565870285, - 0.23321352899074554, 0.2713433504104614, 0.2513878345489502, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.243987575173378, 0.264671266078949, 0.2252492755651474, 0.048227567225694656, - 0.2252492755651474, 0.264671266078949, 0.243987575173378, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.23674798011779785, 0.2581873834133148, 0.2174331247806549, 0.04851628094911575, - 0.2174331247806549, 0.2581873834133148, 0.23674798011779785, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2296636700630188, 0.2518872320652008, 0.20975996553897858, 0.048763956874608994, - 0.20975996553897858, 0.2518872320652008, 0.2296636700630188, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.22272826731204987, 0.24576535820960999, 0.2022240161895752, 0.04896653816103935, - 0.2022240161895752, 0.24576535820960999, 0.22272826731204987, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.21593685448169708, 0.23981784284114838, 0.19482089579105377, 0.04911932349205017, - 0.19482089579105377, 0.23981784284114838, 0.21593685448169708, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.2092830389738083, 0.23403921723365784, 0.18754491209983826, 0.04921770468354225, - 0.18754491209983826, 0.23403921723365784, 0.2092830389738083, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.20276133716106415, 0.22842493653297424, 0.1803915649652481, 0.049256447702646255, - 0.1803915649652481, 0.22842493653297424, 0.20276133716106415, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.196366086602211, 0.22297023236751556, 0.17335622012615204, 0.049230076372623444, - 0.17335622012615204, 0.22297023236751556, 0.196366086602211, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.19009104371070862, 0.21766968071460724, 0.16643394529819489, 0.04913284629583359, - 0.16643394529819489, 0.21766968071460724, 0.19009104371070862, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.18393108248710632, 0.21251900494098663, 0.15962110459804535, 0.048958152532577515, - 0.15962110459804535, 0.21251900494098663, 0.18393108248710632, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.17787986993789673, 0.20751254260540009, 0.1529131829738617, 0.04869934171438217, - 0.1529131829738617, 0.20751254260540009, 0.17787986993789673, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.17193184792995453, 0.20264536142349243, 0.14630666375160217, 0.04834909364581108, - 0.14630666375160217, 0.20264536142349243, 0.17193184792995453, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.16608072817325592, 0.19791164994239807, 0.13979768753051758, 0.04789994657039642, - 0.13979768753051758, 0.19791164994239807, 0.16608072817325592, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.16032099723815918, 0.1933063417673111, 0.13338352739810944, 0.047343283891677856, - 0.13338352739810944, 0.1933063417673111, 0.16032099723815918, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.15464632213115692, 0.18882323801517487, 0.12706103920936584, 0.04667053371667862, - 0.12706103920936584, 0.18882323801517487, 0.15464632213115692, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.14905080199241638, 0.1844564527273178, 0.12082799524068832, 0.04587254300713539, - 0.12082799524068832, 0.1844564527273178, 0.14905080199241638, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.1435280442237854, 0.18019931018352509, 0.11468220502138138, 0.04493902623653412, - 0.11468220502138138, 0.18019931018352509, 0.1435280442237854, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.1380719691514969, 0.1760452389717102, 0.10862235724925995, 0.04385991767048836, - 0.10862235724925995, 0.1760452389717102, 0.1380719691514969, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.13267634809017181, 0.17198719084262848, 0.10264764726161957, 0.04262389615178108, - 0.10264764726161957, 0.17198719084262848, 0.13267634809017181, 0.0, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.0, 0.12733465433120728, 0.16801732778549194, 0.09675760567188263, 0.04121933877468109, - 0.09675760567188263, 0.16801732778549194, 0.12733465433120728, 0.0, 0.0, 0.0, 0.0] -- 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a/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.json b/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.json index 293b4151c5..f683eac8d8 100644 --- a/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.json +++ b/n3fit/src/n3fit/tests/regressions/quickcard_pol_1.json @@ -25,46 +25,46 @@ "trainable": false } ], - "stop_epoch": 253, - "best_epoch": 51, - "erf_tr": 1.8872156143188477, - "erf_vl": 2.174168348312378, - "chi2": 0.9632681012153625, + "stop_epoch": 600, + "best_epoch": 599, + "erf_tr": 2.5818292105708047, + "erf_vl": 1.9909996132247432, + "chi2": 1.319613746267476, "pos_state": "POS_PASS", "arc_lengths": [ - 1.039203994635079, - 0.9996606268390241, + 1.0207390340368907, + 1.0307402955168719, 0.9899487587939698, 0.9899487587939698, 0.9899487587939698 ], "integrability": [ - 1.6653345369377348e-16, - 6.938893903907228e-17, - 6.938893903907228e-17, - 0.0068895139265804095, - 4.163336342344337e-16 + 1.5265566588595902e-16, + 4.163336342344337e-17, + 4.163336342344337e-17, + 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0.9907265992237775, + 1.0232185373829865, + 0.9935004234019762, 0.9899487587939698, 0.9899487587939698, 0.9899487587939698 ], "integrability": [ - 3.885780586188048e-16, - 4.163336342344337e-17, - 4.163336342344337e-17, - 0.2757451906800269, - 3.885780586188048e-16 + 5.342948306008566e-16, + 1.1102230246251565e-16, + 1.942890293094024e-16, + 0.020493690636281092, + 1.942890293094024e-16 ], "timing": { "walltime": { - "Total": 11.740779161453247, + "Total": 8.025956392288208, "start": 0.0, - "replica_set": 0.5030853748321533, - "replica_fitted": 11.740601301193237, - "replica_set_to_replica_fitted": 11.237515926361084 + "replica_set": 0.2008500099182129, + "replica_fitted": 8.025688409805298, + "replica_set_to_replica_fitted": 7.824838399887085 }, "cputime": { - "Total": 12.942078551000002, + "Total": 10.633196927999997, "start": 0.0, - "replica_set": 1.013207317, - "replica_fitted": 12.941898041000002, - "replica_set_to_replica_fitted": 11.928690724 + "replica_set": 0.2002973319999981, + "replica_fitted": 10.632905010999998, + "replica_set_to_replica_fitted": 10.432607679 } }, "version": { "tensorflow": "2.16.1, mkl=??", - "numpy": "1.26.4", - "nnpdf": "4.0.9.post573.dev0+8966b140a.dirty", - "validphys": "4.0.9.post573.dev0+8966b140a.dirty" + "numpy": "1.24.0", + "nnpdf": "4.0.9.post574.dev0+412f8f857.dirty", + "validphys": "4.0.9.post574.dev0+412f8f857.dirty" } } diff --git a/n3fit/src/n3fit/tests/regressions/weights_pol_1.weights.h5 b/n3fit/src/n3fit/tests/regressions/weights_pol_1.weights.h5 index ba3a5875c9..5f7d2f80cb 100644 Binary files a/n3fit/src/n3fit/tests/regressions/weights_pol_1.weights.h5 and b/n3fit/src/n3fit/tests/regressions/weights_pol_1.weights.h5 differ diff --git a/n3fit/src/n3fit/tests/regressions/weights_pol_3.weights.h5 b/n3fit/src/n3fit/tests/regressions/weights_pol_3.weights.h5 index 96bd2a5615..3912417844 100644 Binary files a/n3fit/src/n3fit/tests/regressions/weights_pol_3.weights.h5 and b/n3fit/src/n3fit/tests/regressions/weights_pol_3.weights.h5 differ diff --git a/n3fit/src/n3fit/tests/test_fit.py b/n3fit/src/n3fit/tests/test_fit.py index b121802525..7b80065552 100644 --- a/n3fit/src/n3fit/tests/test_fit.py +++ b/n3fit/src/n3fit/tests/test_fit.py @@ -105,9 +105,9 @@ def check_fit_results( if key in equal_checks: assert_equal(value, reference, err_msg=err_msg) elif key in approx_checks: - assert_allclose(value, reference, err_msg=err_msg, rtol=rel_error) + assert_allclose(value, reference, err_msg=err_msg, rtol=rel_error, atol=1e-9) elif key in relaxed_checks: - assert_allclose(value, reference, err_msg=err_msg, rtol=rel_error * 10) + assert_allclose(value, reference, err_msg=err_msg, rtol=rel_error * 10, atol=1e-6) elif key == "preprocessing": for ref, cur in zip(reference, value): err_msg += f" - {ref['fl']}" diff --git a/n3fit/src/n3fit/tests/test_msr.py b/n3fit/src/n3fit/tests/test_msr.py index 9f76d3f421..d376956b3f 100644 --- a/n3fit/src/n3fit/tests/test_msr.py +++ b/n3fit/src/n3fit/tests/test_msr.py @@ -1,4 +1,5 @@ import numpy as np +import pytest from n3fit.backends import operations as op from n3fit.layers import MSR_Normalization @@ -76,3 +77,18 @@ def test_vsr(): ] ) np.testing.assert_allclose(output, known_output, rtol=1e-5) + + +def test_tsr(): + """Test the sum rules used in polarized fits""" + with pytest.raises(ValueError): + # Check that seeds _are_ needed + layer = MSR_Normalization(mode='TSR') + + layer = MSR_Normalization(mode="TSR", replica_seeds=[3]) + output = apply_layer_to_fixed_input(layer) + # They should _all_ be 1.0 except for entries 9 and 10 + known_output = np.ones((1, 1, 14)) + known_output[0, 0, 9] = 1.1133982 + known_output[0, 0, 10] = -0.9901034 + np.testing.assert_allclose(output, known_output, rtol=1e-5)