Fix some typos for imaging ML benchmark

benchmarks/imaging_shower_ML/config.yml

@@ -7,7 +7,7 @@ ml_shower:epi_separation:
     - |
       if [[ ${DETECTOR} =~ athena
          || ${DETECTOR} =~ ecce && ${DETECTOR_CONFIG} =~ imaging ]] ; then
-        pip3 install -r rbenchmarks/imaging_shower_ML/equirements.txt
+        pip3 install -r benchmarks/imaging_shower_ML/requirements.txt
         python3 benchmarks/imaging_shower_ML/run_benchmark.py -t imcal_epi -n 1000 --pmin 1.8 --pmax 2.2 --nocut-samples
       fi
 

benchmarks/imaging_shower_ML/scripts/epcut_scan.py

 """
     A script to scan the optimized cut on layer and E/p.
-    It scan all the possible ScFi layers (20 in the EPIC brycecanyon configuration)
-    The results give the best cut (highest pion rejection) on [layer, E/p] with a targeted electron efficiency
+    It groups [event, layer] and scan the E/p cut layer-wise (e.g., EPIC brycecanyon configuration has 16 ScFi layers)
+    The best 2D cut (highest rejection power) on [layer, E/p] that satisfies required effiiency will be given
 
     Chao Peng (ANL)
     2022/11/13

benchmarks/imaging_shower_ML/scripts/ml_training.py

@@ -131,7 +131,7 @@ if __name__ == '__main__':
 
     if args.seed > 0:
         np.random.seed(args.seed)
-        tf.random.seed(args.seed)
+        tf.random.set_seed(args.seed)
 
     df = pd.read_hdf(args.data_store, key=imcal_info.ml_data_key)
     # NOTE: assumed event index is exactly the same as df
@@ -274,7 +274,8 @@ if __name__ == '__main__':
                 ),
             test=OrderedDict(
                 sample_size=count_pid_labels(ytest, pids),
-                efficiencies=OrderedDict([(labels.name, eff) for labels, eff in effs])
+                efficiencies=OrderedDict([(labels.name, eff) for labels, eff in effs]),
+                rejections=OrderedDict([(labels.name, 1./eff) for labels, eff in effs]),
                 ),
             )
     res_json = json.dumps(result, indent=4, cls=NpEncoder)