import contextlib import itertools import multiprocessing import os import pickle import urllib from pathlib import Path import numpy as np from joblib import Parallel, delayed def get_parallel_slices(n_tasks, n_jobs=-1): if n_jobs == -1: n_jobs = multiprocessing.cpu_count() batch = int(n_tasks / n_jobs) remainder = n_tasks % n_jobs return [slice(job * batch, (job + 1) * batch + (remainder if job == n_jobs - 1 else 0)) for job in range(n_jobs)] def parallelize(func, args, n_jobs): args = np.asarray(args) slices = get_parallel_slices(len(args), n_jobs) results = Parallel(n_jobs=n_jobs)( delayed(func)(args[slice_i]) for slice_i in slices ) return list(itertools.chain.from_iterable(results)) @contextlib.contextmanager def temp_seed(seed): state = np.random.get_state() np.random.seed(seed) try: yield finally: np.random.set_state(state) def download_file(url, archive_filename): def progress(blocknum, bs, size): total_sz_mb = '%.2f MB' % (size / 1e6) current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6) print('\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb), end='') print("Downloading %s" % url) urllib.request.urlretrieve(url, filename=archive_filename, reporthook=progress) print("") def download_file_if_not_exists(url, archive_path): if os.path.exists(archive_path): return create_if_not_exist(os.path.dirname(archive_path)) download_file(url,archive_path) def create_if_not_exist(path): os.makedirs(path, exist_ok=True) def get_quapy_home(): home = os.path.join(str(Path.home()), 'quapy_data') os.makedirs(home, exist_ok=True) return home def create_parent_dir(path): os.makedirs(Path(path).parent, exist_ok=True) def pickled_resource(pickle_path:str, generation_func:callable, *args): if pickle_path is None: return generation_func(*args) else: if os.path.exists(pickle_path): return pickle.load(open(pickle_path, 'rb')) else: instance = generation_func(*args) os.makedirs(str(Path(pickle_path).parent), exist_ok=True) pickle.dump(instance, open(pickle_path, 'wb'), pickle.HIGHEST_PROTOCOL) return instance class EarlyStop: def __init__(self, patience, lower_is_better=True): self.PATIENCE_LIMIT = patience self.better = lambda a,b: ab self.patience = patience self.best_score = None self.best_epoch = None self.STOP = False self.IMPROVED = False def __call__(self, watch_score, epoch): self.IMPROVED = (self.best_score is None or self.better(watch_score, self.best_score)) if self.IMPROVED: self.best_score = watch_score self.best_epoch = epoch self.patience = self.PATIENCE_LIMIT else: self.patience -= 1 if self.patience <= 0: self.STOP = True