Zaktualizuj 'evaluate.py'

evaluate.py

@@ -7,7 +7,6 @@ from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_sc
 from sklearn.preprocessing import StandardScaler
 import torch.nn.functional as F
 
-# Definicja modelu
 class ANN_Model(nn.Module):
     def __init__(self,input_features=82,hidden1=20,hidden2=20,out_features=3):
         super().__init__()
@@ -20,10 +19,8 @@ class ANN_Model(nn.Module):
         x=self.out(x)
         return x
 
-# Wczytanie danych
 data = pd.read_csv("./Sales.csv")
 
-# Przygotowanie danych
 data["Profit_Category"] = pd.cut(data["Profit"], bins=[-np.inf, 500, 1000, np.inf], labels=[0, 1, 2])
 bike = data.loc[:, ['Customer_Age', 'Customer_Gender', 'Country','State', 'Product_Category', 'Sub_Category', 'Profit_Category']]  
 bikes = pd.get_dummies(bike, columns=['Country', 'State', 'Product_Category', 'Sub_Category', 'Customer_Gender'])
@@ -37,30 +34,24 @@ y_test = y_test.astype(np.float32)
 X_test=torch.FloatTensor(X_test)
 y_test=torch.LongTensor(y_test)
 
-# Wczytanie modelu
 model = torch.load("classificationn_model.pt")
 
-# Funkcja do obliczania predykcji
 def calculate_predictions(model, X):
     with torch.no_grad():
         outputs = model(X)
         _, predicted = torch.max(outputs.data, 1)
     return predicted
 
-# Obliczenie predykcji
 y_pred = calculate_predictions(model, X_test)
 y_pred_np = y_pred.numpy()
 
-# Zapisanie predykcji do pliku
 np.savetxt("predictions.txt", y_pred_np, fmt='%d')
 
-# Obliczenie metryk
 accuracy = accuracy_score(y_test.numpy(), y_pred_np)
 f1 = f1_score(y_test.numpy(), y_pred_np, average='micro')
 precision = precision_score(y_test.numpy(), y_pred_np, average='micro')
 recall = recall_score(y_test.numpy(), y_pred_np, average='micro')
 
-# Zapisanie metryk do pliku
 with open("metrics.txt", "w") as f:
     f.write(f"Accuracy: {accuracy}\n")
     f.write(f"F1 Score: {f1}\n")