Merge branch 'master' into CMS_Z0J_13TEV

Author: enocera

.github/workflows/fitbot.yml

@@ -10,6 +10,8 @@ on:
 env:
   N3FIT_MAXNREP: 30 # total number of replicas to fit
   POSTFIT_NREP: 15 # requested minimum replicas for postfit
+  # IMPORTANT
+  # WHEN CHANGING THE REFERENCE SET, THE NEW REFERENCE MUST BE MANUALLY UPLOADED TO THE SERVER
   REFERENCE_SET: NNBOT-99108504e-2025-11-22 # reference set for exact results
   STABLE_REFERENCE_SET: NNBOT-99108504e-2025-11-22 # reference set for last tag
   PYTHONHASHSEED: "0"
@@ -80,16 +82,15 @@ jobs:
         conda activate nnpdfenv
         cd $RUNFOLDER
         postfit $POSTFIT_NREP $RUNCARD --at-least-nrep
-        res=$(vp-upload $RUNCARD 2>&1)
-        echo ${res}
-        while echo ${res} | grep ERROR >/dev/null
-        do
-          sleep 30s
-          res=$(vp-upload $RUNCARD 2>&1)
-        done
-        url=$( echo "${res}" | grep https )
-        echo "FIT_URL=$url" >> $GITHUB_ENV
-    # running validphys report
+        ln -s ${PWD}/${RUNCARD} ${CONDA_PREFIX}/share/NNPDF/results
+        tar -czf ${RUNCARD}.tar.gz ${RUNCARD}
+        echo "PATH_TO_SAVE=${PWD}/${RUNCARD}.tar.gz" >> ${GITHUB_ENV}
+    - name: Keep the fit as an artifact
+      if: ${{ !cancelled() }}
+      uses: actions/upload-artifact@v4
+      with:
+        name: ${{ env.RUNCARD }}.tar.gz
+        path: ${{ env.PATH_TO_SAVE }}
     - name: Building and upload report
       shell: bash -l {0}
       run: |
@@ -121,6 +122,6 @@ jobs:
             - Fit Name: ${{ env.RUNCARD }}
             - Fit Report wrt master: ${{ env.REPORT_URL }}
             - Fit Report wrt latest stable reference: ${{ env.REPORT_URL_STABLE }}
-            - Fit Data: ${{ env.FIT_URL }}
+            - Fit Data: fit data is kept as an artifact. Please, remember to upload it to the server if the reference is changed.
 
           Check the report **carefully**, and please  buy me a :coffee: , or better, a GPU :wink:!

extra_tests/regression_checks.py

@@ -17,7 +17,7 @@
 runcard_and_replicas = {
     "normal_fit": 72,
     "central": 16,
-    "diagonal": 45,
+    "no_diagonal": 45,
     "feature_scaling": 81,
     "flavour": 29,
     "no_msr": 92,

extra_tests/regression_fits/diagonal_45.exportgrid

@@ -31,7 +31,7 @@ genrep: True    # on = generate MC replicas, False = use real data
 trvlseed: 3
 nnseed: 2
 mcseed: 1
-diagonal_basis: True
+diagonal_basis: False
 
 load: "weights.weights.h5"
 

extra_tests/regression_fits/diagonal.yml …ra_tests/regression_fits/no_diagonal.ymlextra_tests/regression_fits/diagonal.yml renamed to extra_tests/regression_fits/no_diagonal.yml extra_tests/regression_fits/diagonal.yml renamed to extra_tests/regression_fits/no_diagonal.yml

@@ -49,24 +49,24 @@
       "trainable": false
     }
   ],
-  "stop_epoch": 798,
-  "best_epoch": 686,
-  "erf_tr": 3.2565369606018066,
-  "erf_vl": 2.7407543659210205,
-  "chi2": 2.098212245725236,
-  "pos_state": "POS_PASS",
+  "stop_epoch": 1100,
+  "best_epoch": 1099,
+  "erf_tr": 3.6196327209472656,
+  "erf_vl": 4.072118759155273,
+  "chi2": 2.236768960952759,
+  "pos_state": "POS_VETO",
   "arc_lengths": [
-    1.3053929069568224,
-    1.368952528881263,
-    1.1780572526803577,
-    1.001460676089934,
-    1.1881297171260714
+    1.3254566831542567,
+    1.4902557302484747,
+    1.1771465807215509,
+    1.0008867970816,
+    1.174578069030533
   ],
   "integrability": [
-    0.0009263962419884564,
-    0.0012051404701207136,
-    4.5516787396227e-05,
-    0.46926869451999786,
-    0.007031772693154692
+    0.0009510074014508934,
+    0.0012195031449674931,
+    4.7982562820070296e-05,
+    0.46699107438325993,
+    0.007057319337037682
   ]
 }

extra_tests/regression_fits/no_diagonal_45.exportgrid

@@ -5,9 +5,6 @@
 description: Basic feature scaling
 
 ############################################################
-# frac: training fraction
-# ewk: apply ewk k-factors
-# sys: systematics treatment (see systypes)
 dataset_inputs:
 - {dataset: SLAC_NC_NOTFIXED_D_EM-F2, frac: 0.75, variant: legacy_dw}
 - {dataset: NMC_NC_NOTFIXED_EM-F2, frac: 0.75, variant: legacy_dw}
@@ -28,6 +25,7 @@ trvlseed: 1
 nnseed: 2
 mcseed: 3
 genrep: True     # true = generate MC replicas, false = use real data
+diagonal_basis: false
 
 save: 'weights.h5'
 
@@ -55,10 +53,6 @@ parameters: # This defines the parameter dictionary that is passed to the Model
   threshold_chi2: 5.0
 
 fitting:
-  # NN23(QED) = sng=0,g=1,v=2,t3=3,ds=4,sp=5,sm=6,(pht=7)
-  # EVOL(QED) = sng=0,g=1,v=2,v3=3,v8=4,t3=5,t8=6,(pht=7)
-  # EVOLS(QED)= sng=0,g=1,v=2,v8=4,t3=4,t8=5,ds=6,(pht=7)
-  # FLVR(QED) = g=0, u=1, ubar=2, d=3, dbar=4, s=5, sbar=6, (pht=7)
   fitbasis: NN31IC # EVOL (7), EVOLQED (8), etc.
   basis:
       # remeber to change the name of PDF accordingly with fitbasis

…a_tests/regression_fits/diagonal_45.json …ests/regression_fits/no_diagonal_45.jsonextra_tests/regression_fits/diagonal_45.json renamed to extra_tests/regression_fits/no_diagonal_45.json extra_tests/regression_fits/diagonal_45.json renamed to extra_tests/regression_fits/no_diagonal_45.json

@@ -103,6 +103,7 @@ trvlseed: 1
 nnseed: 2
 mcseed: 3
 genrep: false        # true = generate MC replicas, false = use real data
+diagonal_basis: false # true = use the diagonal basis
 
 parameters: # This defines the parameter dictionary that is passed to the Model Trainer
   nodes_per_layer: [35, 25, 8]

n3fit/runcards/examples/Basic_feature_scaling.yml

@@ -5,13 +5,12 @@
 description: Basic closure test runcard
 
 ############################################################
-# frac: training fraction
-# ewk: apply ewk k-factors
-# sys: systematics treatment (see systypes)
 dataset_inputs:
-- {dataset: NMC_NC_NOTFIXED_EM-F2, frac: 0.75, variant: legacy_dw}
-- {dataset: SLAC_NC_NOTFIXED_D_EM-F2, frac: 0.75, variant: legacy_dw}
-- {dataset: ATLAS_Z0J_8TEV_PT-M, frac: 0.75, cfac: [], variant: legacy_10}
+- {dataset: NMC_NC_NOTFIXED_EM-F2, variant: legacy_dw}
+- {dataset: SLAC_NC_NOTFIXED_D_EM-F2, variant: legacy_dw}
+- {dataset: ATLAS_Z0J_8TEV_PT-M, cfac: [], variant: legacy_10}
+
+diagonal_frac: 0.75
 
 ############################################################
 datacuts: