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import random
import numpy as np
import torch
from typing import List, Dict, Any
def set_seed(seed: int):
"""Set random seed for reproducibility."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def min_max_normalize_dataset(train_dataset: List[Dict[str, Any]],
val_dataset: List[Dict[str, Any]],
test_dataset: List[Dict[str, Any]]) -> tuple:
"""Normalize datasets using min-max normalization."""
# Get all labels from training set
train_labels = [data['label'] for data in train_dataset]
# Calculate min and max from training set
min_val = min(train_labels)
max_val = max(train_labels)
# Normalize all datasets
for dataset in [train_dataset, val_dataset, test_dataset]:
for data in dataset:
data['label'] = (data['label'] - min_val) / (max_val - min_val)
return train_dataset, val_dataset, test_dataset
def check_early_stopping(val_list: List[float],
optimize_func: callable,
patience: int = 10) -> bool:
"""Check if training should stop early."""
if len(val_list) < patience:
return False
best_val = optimize_func(val_list[:-patience])
return all(optimize_func([best_val, val]) == best_val
for val in val_list[-patience:]) |