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QuaPy/quapy/util.py

100 lines
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Python
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import itertools
import multiprocessing
from joblib import Parallel, delayed
import contextlib
import numpy as np
import urllib
import os
from pathlib import Path
import pickle
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 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: a<b if lower_is_better else a>b
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