evaluate result not nan

init.py

@@ -5,14 +5,17 @@ import numpy as np
 from sklearn.model_selection import train_test_split
 import matplotlib
 from pathlib import Path
+import math
 
 # Inicjalizacja danych
-file_exists = exists('./df_atp.csv')
+file_exists = exists("./df_atp.csv")
 if not file_exists:
-    subprocess.run(["kaggle", "datasets", "download", "-d", "hakeem/atp-and-wta-tennis-data"])
+    subprocess.run(
+        ["kaggle", "datasets", "download", "-d", "hakeem/atp-and-wta-tennis-data"]
+    )
     subprocess.run(["unzip", "-o", "atp-and-wta-tennis-data.zip"])
-atp_data = pd.read_csv('df_atp.csv')
-print(atp_data)
+
+atp_data = pd.read_csv("df_atp.csv")
 
 # Średnia ilość gemów w pierwszym secie zwycięzców meczu
 print(atp_data[["Winner", "W1"]].mean())
@@ -30,25 +33,25 @@ print(atp_data[["Winner", "W1"]].std())
 print(atp_data[["Winner", "W1"]].median())
 
 # Zmiana nazwy nienazwanej kolumny
-atp_data.rename(columns={'Unnamed: 0':'ID'}, inplace=True)
+atp_data.rename(columns={"Unnamed: 0": "ID"}, inplace=True)
 
 # Jak często kto był zwycięzcą
 print(atp_data.groupby("Winner")["ID"].nunique())
 
 # Normalizacja rund -1: Finał, -2: Półfinał, -3: Ćwiartka, -4: Każdy z każdym
 # 1: pierwsza runda, 2: druga runda, 3: trzecia runda, 4: czwarta runda
-atp_data.loc[atp_data["Round"] == 'The Final', "Round"] = -1
-atp_data.loc[atp_data["Round"] == 'Semifinals', "Round"] = -2
-atp_data.loc[atp_data["Round"] == 'Quarterfinals', "Round"] = -3
-atp_data.loc[atp_data["Round"] == 'Round Robin', "Round"] = -4
-atp_data.loc[atp_data["Round"] == '1st Round', "Round"] = 1
-atp_data.loc[atp_data["Round"] == '2nd Round', "Round"] = 2
-atp_data.loc[atp_data["Round"] == '3rd Round', "Round"] = 3
-atp_data.loc[atp_data["Round"] == '4th Round', "Round"] = 4
+atp_data.loc[atp_data["Round"] == "The Final", "Round"] = -1
+atp_data.loc[atp_data["Round"] == "Semifinals", "Round"] = -2
+atp_data.loc[atp_data["Round"] == "Quarterfinals", "Round"] = -3
+atp_data.loc[atp_data["Round"] == "Round Robin", "Round"] = -4
+atp_data.loc[atp_data["Round"] == "1st Round", "Round"] = 1
+atp_data.loc[atp_data["Round"] == "2nd Round", "Round"] = 2
+atp_data.loc[atp_data["Round"] == "3rd Round", "Round"] = 3
+atp_data.loc[atp_data["Round"] == "4th Round", "Round"] = 4
 print(atp_data["Round"])
 
 # Czyszczenie: W polu z datą zamienimy ######## na pustego stringa
-atp_data.loc[atp_data["Date"] == '########', "Date"] = ''
+atp_data.loc[atp_data["Date"] == "########", "Date"] = ""
 print(atp_data["Date"])
 
 # Podział na podzbiory: trenujący, testowy, walidujący w proporcjach 6:2:2
@@ -62,6 +65,6 @@ print("\nElements of dev set: " + str(len(atp_dev)))
 print("\nElements of train set: " + str(len(atp_train)))
 
 # Stworzenie plików z danymi trenującymi i testowymi
-atp_test.to_csv('atp_test.csv', encoding="utf-8", index=False)
-atp_dev.to_csv('atp_dev.csv', encoding="utf-8", index=False)  
-atp_train.to_csv('atp_train.csv', encoding="utf-8", index=False)
+atp_test.to_csv("atp_test.csv", encoding="utf-8", index=False)
+atp_dev.to_csv("atp_dev.csv", encoding="utf-8", index=False)
+atp_train.to_csv("atp_train.csv", encoding="utf-8", index=False)

neutral_network.py

@@ -11,13 +11,15 @@ default_epochs = 4
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+
 class AtpDataset(Dataset):
     def __init__(self, file_name):
-        df = pd.read_csv(file_name)
+        df = pd.read_csv(file_name, usecols=["AvgL", "AvgW"])
+        df = df.dropna()
 
         # Loser avg and Winner avg
-        x = df.iloc[:, 4].values
-        y = df.iloc[:, 3].values
+        x = df.iloc[:, 1].values
+        y = df.iloc[:, 0].values
 
         self.x_train = torch.from_numpy(x)
         self.y_train = torch.from_numpy(y)
@@ -76,9 +78,9 @@ def test(dataloader, model, loss_fn):
 
 
 def setup_args():
-    args_parser = argparse.ArgumentParser(prefix_chars='-')
-    args_parser.add_argument('-b', '--batchSize', type=int, default=default_batch_size)
-    args_parser.add_argument('-e', '--epochs', type=int, default=default_epochs)
+    args_parser = argparse.ArgumentParser(prefix_chars="-")
+    args_parser.add_argument("-b", "--batchSize", type=int, default=default_batch_size)
+    args_parser.add_argument("-e", "--epochs", type=int, default=default_epochs)
     return args_parser.parse_args()
 
 
@@ -87,8 +89,8 @@ print(f"Using {device} device")
 args = setup_args()
 batch_size = args.batchSize
 
-plant_test = AtpDataset('atp_test.csv')
-plant_train = AtpDataset('atp_train.csv')
+plant_test = AtpDataset("atp_test.csv")
+plant_train = AtpDataset("atp_train.csv")
 
 train_dataloader = DataLoader(plant_train, batch_size=batch_size)
 test_dataloader = DataLoader(plant_test, batch_size=batch_size)
@@ -111,5 +113,5 @@ for t in range(epochs):
     test(test_dataloader, model, loss_fn)
 print("Finish!")
 
-torch.save(model.state_dict(), './model.zip')
+torch.save(model.state_dict(), "./model.zip")
 print("Model saved in ./model.zip file.")