[tmva] Implement linter suggestions in Python files

guitargeek · guitargeek · commit a33ba343887a · 2025-12-01T10:39:13.000+01:00
diff --git a/tmva/pymva/test/generatePyTorchModelClassification.py b/tmva/pymva/test/generatePyTorchModelClassification.py
@@ -2,11 +2,7 @@
 from torch import nn
 
 # Define model
-model = nn.Sequential(
-                nn.Linear(4, 64),
-                nn.ReLU(),
-                nn.Linear(64, 2),
-                nn.Softmax(dim=1))
+model = nn.Sequential(nn.Linear(4, 64), nn.ReLU(), nn.Linear(64, 2), nn.Softmax(dim=1))
 
 # Construct loss function and Optimizer.
 criterion = torch.nn.MSELoss()
@@ -33,8 +29,8 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 
             # print train statistics
             running_train_loss += train_loss.item()
-            if i % 32 == 31:    # print every 32 mini-batches
-                print(f"[{epoch+1}, {i+1}] train loss: {running_train_loss / 32 :.3f}")
+            if i % 32 == 31:  # print every 32 mini-batches
+                print(f"[{epoch + 1}, {i + 1}] train loss: {running_train_loss / 32:.3f}")
                 running_train_loss = 0.0
 
         if schedule:
@@ -51,23 +47,23 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 
             curr_val = running_val_loss / len(val_loader)
             if save_best:
-               if best_val==None:
-                   best_val = curr_val
-               best_val = save_best(model, curr_val, best_val)
+                if best_val is None:
+                    best_val = curr_val
+                best_val = save_best(model, curr_val, best_val)
 
             # print val statistics per epoch
-            print(f"[{epoch+1}] val loss: {curr_val :.3f}")
+            print(f"[{epoch + 1}] val loss: {curr_val:.3f}")
             running_val_loss = 0.0
 
-    print(f"Finished Training on {epoch+1} Epochs!")
+    print(f"Finished Training on {epoch + 1} Epochs!")
 
     return model
 
 
 def predict(model, test_X, batch_size=32):
     # Set to eval mode
     model.eval()
-   
+
     test_dataset = torch.utils.data.TensorDataset(torch.Tensor(test_X))
     test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
@@ -78,13 +74,12 @@ def predict(model, test_X, batch_size=32):
             outputs = model(X)
             predictions.append(outputs)
         preds = torch.cat(predictions)
-   
+
     return preds.numpy()
 
 
 load_model_custom_objects = {"optimizer": optimizer, "criterion": criterion, "train_func": fit, "predict_func": predict}
 
 # Store model to file
 m = torch.jit.script(model)
-torch.jit.save(m,"PyTorchModelClassification.pt")
-
+torch.jit.save(m, "PyTorchModelClassification.pt")
diff --git a/tmva/pymva/test/generatePyTorchModelMulticlass.py b/tmva/pymva/test/generatePyTorchModelMulticlass.py
@@ -2,11 +2,7 @@
 from torch import nn
 
 # Define model
-model = nn.Sequential(
-                nn.Linear(4, 64),
-                nn.ReLU(),
-                nn.Linear(64, 4),
-                nn.Softmax(dim=1))
+model = nn.Sequential(nn.Linear(4, 64), nn.ReLU(), nn.Linear(64, 4), nn.Softmax(dim=1))
 
 # Construct loss function and Optimizer.
 criterion = nn.CrossEntropyLoss()
@@ -34,7 +30,7 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 
             # print train statistics
             running_train_loss += train_loss.item()
-            if i % 4 == 3:    # print every 4 mini-batches
+            if i % 4 == 3:  # print every 4 mini-batches
                 print(f"[{epoch+1}, {i+1}] train loss: {running_train_loss / 4 :.3f}")
                 running_train_loss = 0.0
 
@@ -53,9 +49,9 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 
             curr_val = running_val_loss / len(val_loader)
             if save_best:
-               if best_val==None:
-                   best_val = curr_val
-               best_val = save_best(model, curr_val, best_val)
+                if best_val is None:
+                    best_val = curr_val
+                best_val = save_best(model, curr_val, best_val)
 
             # print val statistics per epoch
             print(f"[{epoch+1}] val loss: {curr_val :.3f}")
@@ -69,7 +65,7 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 def predict(model, test_X, batch_size=32):
     # Set to eval mode
     model.eval()
-   
+
     test_dataset = torch.utils.data.TensorDataset(torch.Tensor(test_X))
     test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
@@ -80,13 +76,12 @@ def predict(model, test_X, batch_size=32):
             outputs = model(X)
             predictions.append(outputs)
         preds = torch.cat(predictions)
-   
+
     return preds.numpy()
 
 
 load_model_custom_objects = {"optimizer": optimizer, "criterion": criterion, "train_func": fit, "predict_func": predict}
 
 # Store model to file
 m = torch.jit.script(model)
-torch.jit.save(m,"PyTorchModelMulticlass.pt")
-
+torch.jit.save(m, "PyTorchModelMulticlass.pt")
diff --git a/tmva/pymva/test/generatePyTorchModelRegression.py b/tmva/pymva/test/generatePyTorchModelRegression.py
@@ -2,10 +2,7 @@
 from torch import nn
 
 # Define model
-model = nn.Sequential(
-                nn.Linear(2, 64),
-                nn.Tanh(),
-                nn.Linear(64, 1))
+model = nn.Sequential(nn.Linear(2, 64), nn.Tanh(), nn.Linear(64, 1))
 
 # Construct loss function and Optimizer.
 criterion = torch.nn.MSELoss()
@@ -32,7 +29,7 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 
             # print train statistics
             running_train_loss += train_loss.item()
-            if i % 32 == 31:    # print every 32 mini-batches
+            if i % 32 == 31:  # print every 32 mini-batches
                 print(f"[{epoch+1}, {i+1}] train loss: {running_train_loss / 32 :.3f}")
                 running_train_loss = 0.0
 
@@ -50,9 +47,9 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 
             curr_val = running_val_loss / len(val_loader)
             if save_best:
-               if best_val==None:
-                   best_val = curr_val
-               best_val = save_best(model, curr_val, best_val)
+                if best_val is None:
+                    best_val = curr_val
+                best_val = save_best(model, curr_val, best_val)
 
             # print val statistics per epoch
             print(f"[{epoch+1}] val loss: {curr_val :.3f}")
@@ -66,7 +63,7 @@ def fit(model, train_loader, val_loader, num_epochs, batch_size, optimizer, crit
 def predict(model, test_X, batch_size=32):
     # Set to eval mode
     model.eval()
-   
+
     test_dataset = torch.utils.data.TensorDataset(torch.Tensor(test_X))
     test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
@@ -77,12 +74,12 @@ def predict(model, test_X, batch_size=32):
             outputs = model(X)
             predictions.append(outputs)
         preds = torch.cat(predictions)
-   
+
     return preds.numpy()
 
 
 load_model_custom_objects = {"optimizer": optimizer, "criterion": criterion, "train_func": fit, "predict_func": predict}
 
 # Store model to file
 m = torch.jit.script(model)
-torch.jit.save(m,"PyTorchModelRegression.pt")
+torch.jit.save(m, "PyTorchModelRegression.pt")
diff --git a/tutorials/machine_learning/TMVA_SOFIE_Inference.py b/tutorials/machine_learning/TMVA_SOFIE_Inference.py
@@ -14,9 +14,8 @@
 ### \macro_output
 ### \author Lorenzo Moneta
 
-import ROOT
 import numpy as np
-
+import ROOT
 
 # check if the input file exists
 modelFile = "Higgs_trained_model.h5"
diff --git a/tutorials/machine_learning/TMVA_SOFIE_Models.py b/tutorials/machine_learning/TMVA_SOFIE_Models.py
@@ -13,18 +13,16 @@
 ### \macro_output
 ### \author Lorenzo Moneta
 
-import ROOT
-from os.path import exists
-
-
-## generate and train Keras models with different architectures
+import os
 
 import numpy as np
-from tensorflow.keras.models import Sequential
+import ROOT
+from sklearn.model_selection import train_test_split
 from tensorflow.keras.layers import Dense
+from tensorflow.keras.models import Sequential
 from tensorflow.keras.optimizers import Adam
 
-from sklearn.model_selection import train_test_split
+## generate and train Keras models with different architectures
 
 def CreateModel(nlayers = 4, nunits = 64):
    model = Sequential()
@@ -101,7 +99,6 @@ def GenerateModelCode(modelFile, generatedHeaderFile):
 
 generatedHeaderFile = "Higgs_Model.hxx"
 #need to remove existing header file since we are appending on same one
-import os
 if (os.path.exists(generatedHeaderFile)):
    weightFile = "Higgs_Model.root"
    print("removing existing files", generatedHeaderFile,weightFile)
diff --git a/tutorials/machine_learning/TMVA_SOFIE_RDataFrame.py b/tutorials/machine_learning/TMVA_SOFIE_RDataFrame.py
@@ -10,13 +10,13 @@
 ### \macro_output
 ### \author Lorenzo Moneta
 
-import ROOT
 from os.path import exists
 
+import ROOT
 
 # check if the input file exists
 modelFile = "Higgs_trained_model.h5"
-modelName = "Higgs_trained_model";
+modelName = "Higgs_trained_model"
 
 if not exists(modelFile):
     raise FileNotFoundError("You need to run TMVA_Higgs_Classification.C to generate the Keras trained model")
@@ -43,7 +43,7 @@
 h2 = df2.Define("DNN_Value", "sofie_functor(rdfslot_,m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb)").Histo1D(("h_bkg", "", 100, 0, 1),"DNN_Value")
 
 # run over the input data once, combining both RDataFrame graphs.
-ROOT.RDF.RunGraphs([h1, h2]);
+ROOT.RDF.RunGraphs([h1, h2])
 
 print("Number of signal entries",h1.GetEntries())
 print("Number of background entries",h2.GetEntries())
