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5faf2eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import pandas as pd
import torch
class RegressionDataset:
def __init__(self, path: str, file_name: str = 'train.csv', delimiter: str = ',', header: int = 0,
encoding: str = 'utf-8', target_col: str = None):
"""
Initializes the RegressionDataset by loading data from a CSV file.
Args:
path (str): Path to the directory containing the CSV file.
file_name (str): Name of the CSV file. Defaults to 'train.csv'.
delimiter (str): Delimiter used in the CSV file. Defaults to ','.
header (int): Row number to use as the column names. Defaults to 0.
encoding (str): Encoding of the CSV file. Defaults to 'utf-8'.
target_col (str): Name of the target column. If None, the last column is used.
"""
self.data = pd.read_csv(f"{path}/{file_name}", delimiter=delimiter, header=header, encoding=encoding)
if self.data.empty:
raise ValueError("CSV file is empty.")
if target_col is None:
target_col = self.data.columns[-1]
if target_col not in self.data.columns:
raise ValueError(f"CSV must contain a column named '{target_col}'.")
self.X = self.data.drop(columns=[target_col]).apply(pd.to_numeric, errors='coerce').values
self.y = pd.to_numeric(self.data[target_col], errors='coerce').values
def get_data(self):
"""
Returns the data as PyTorch tensors (X, y).
"""
X_tensor = torch.tensor(self.X, dtype=torch.float32)
y_tensor = torch.tensor(self.y, dtype=torch.float32)
return X_tensor, y_tensor
def get_numpy(self):
"""
Returns the data as NumPy arrays (useful for sympy and R² calculations).
"""
return self.X, self.y
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