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211c37c | 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 | import os, time, joblib
import numpy as np
from typing import Any
try:
import torch
HAS_TORCH = True
except ImportError:
HAS_TORCH = False
def save_model(model: Any, path: str, model_type: str = "sklearn"):
os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
if model_type == "pytorch" and HAS_TORCH:
torch.save(model, path)
print(f" [Saved PyTorch model → {path}]")
else:
joblib.dump(model, path)
print(f" [Saved model → {path}]")
def load_model(path: str, model_type: str = "sklearn") -> Any:
if model_type == "pytorch" and HAS_TORCH:
return torch.load(path, map_location="cpu")
return joblib.load(path)
def save_pipeline(pipeline: Any, path: str):
joblib.dump(pipeline, path)
print(f" [Saved pipeline → {path}]")
class Timer:
def __init__(self): self._start = time.time()
def elapsed(self) -> float: return time.time() - self._start
def remaining(self, budget: float) -> float: return max(0.0, budget - self.elapsed())
def is_expired(self, budget: float) -> bool: return self.elapsed() >= budget
def set_seed(seed: int = 42):
import random
random.seed(seed)
np.random.seed(seed)
if HAS_TORCH:
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
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