Format ml.py with black

Author: vepadulano

analyses/cms-open-data-ttbar/ml.py

@@ -75,6 +75,7 @@ class MLHistoConf:
     for i in range(len(feature_names))
 ]
 
+
 def load_cpp(max_n_jets=6):
     # the default value of max_n_jets is the same as in the reference implementation
     # https://github.com/iris-hep/analysis-grand-challenge
@@ -91,12 +92,13 @@ def load_cpp(max_n_jets=6):
         model_even_path = "models/bdt_even.root"
         model_odd_path = "models/bdt_odd.root"
 
-    ROOT.gInterpreter.Declare(f"#include \"{cpp_source}\"")
+    ROOT.gInterpreter.Declare(f'#include "{cpp_source}"')
     # Initialize FastForest models.
     # Our BDT models have 20 input features according to the AGC documentation
     # https://agc.readthedocs.io/en/latest/taskbackground.html#machine-learning-component
 
-    ROOT.gInterpreter.ProcessLine(f"""
+    ROOT.gInterpreter.ProcessLine(
+        f"""
         #ifndef AGC_MODELS
         #define AGC_MODELS
         const static TMVA::Experimental::RBDT model_even{{"feven", "{model_even_path}"}};
@@ -105,7 +107,8 @@ def load_cpp(max_n_jets=6):
         const static auto permutations = get_permutations_dict(max_n_jets);
         #endif
         """
-        )
+    )
+
 
 def define_features(df: ROOT.RDataFrame) -> ROOT.RDataFrame:
     return df.Define(
@@ -131,6 +134,7 @@ def define_features(df: ROOT.RDataFrame) -> ROOT.RDataFrame:
         """,
     )
 
+
 def predict_proba(df: ROOT.RDataFrame) -> ROOT.RDataFrame:
     """get probability scores for every permutation in event"""
 
@@ -147,6 +151,7 @@ def predict_proba(df: ROOT.RDataFrame) -> ROOT.RDataFrame:
         """,
     )
 
+
 def infer_output_ml_features(df: ROOT.RDataFrame) -> ROOT.RDataFrame:
     """
     Choose for each feature the best candidate with the highest probability score.