How to get the predictions for regression task?

[ED20B012]

This is my simple code to load data from a csv and train the model for a regression task.
How do I retrieve the predictions so that I can visualise them and calculate metrics?
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import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from kan import * # Importing PyKAN

def get_csv_data(file_path, test_split=0.2, device="cpu"):
# Load the data from the CSV file
data = pd.read_csv(file_path)

# Separate features (X) and target (y)
target_column = data.columns[-1]
print(target_column)
X = data.drop(columns=[target_column]).values  # All columns except the target
y = data[target_column].values.reshape(-1, 1)  # Target column as a 2D array for scaling

# Scale features and target
scaler_X = StandardScaler()
scaler_y = StandardScaler()
X = scaler_X.fit_transform(X)
y = scaler_y.fit_transform(y).flatten()  # Flatten to keep y as a 1D array for PyTorch

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_split, random_state=0)

# Convert data to PyTorch tensors
dataset = {
    'train_input': torch.tensor(X_train, dtype=torch.float32).to(device),
    'train_label': torch.tensor(y_train, dtype=torch.float32).to(device),
    'test_input': torch.tensor(X_test, dtype=torch.float32).to(device),
    'test_label': torch.tensor(y_test, dtype=torch.float32).to(device)
}

# Return dataset and scalers for potential inverse transformation later
return dataset, scaler_X, scaler_y

Specify your CSV file path and target column

file_path = 'data.csv' # Replace with your CSV file path

target_column = 'target' # Replace with the name of the target column

Load the dataset and scalers

dataset, scaler_X, scaler_y = get_csv_data(file_path, test_split=0.2, device="cpu")

Define the KAN model

model = KAN(width=[dataset['train_input'].shape[1], 4, 1], grid=3, k=3, seed=0, device="cpu")

Train the model

model.fit(dataset, opt="LBFGS", steps=50)

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