151 lines
4.8 KiB
Python
151 lines
4.8 KiB
Python
import contextlib
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import itertools
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import multiprocessing
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import os
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import pickle
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import urllib
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from pathlib import Path
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import quapy as qp
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import numpy as np
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from joblib import Parallel, delayed
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def _get_parallel_slices(n_tasks, n_jobs=-1):
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if n_jobs == -1:
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n_jobs = multiprocessing.cpu_count()
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batch = int(n_tasks / n_jobs)
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remainder = n_tasks % n_jobs
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return [slice(job * batch, (job + 1) * batch + (remainder if job == n_jobs - 1 else 0)) for job in range(n_jobs)]
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def map_parallel(func, args, n_jobs):
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"""
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Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then
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func is applied in two parallel processes to args[0:50] and to args[50:99]
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"""
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args = np.asarray(args)
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slices = _get_parallel_slices(len(args), n_jobs)
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results = Parallel(n_jobs=n_jobs)(
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delayed(func)(args[slice_i]) for slice_i in slices
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)
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return list(itertools.chain.from_iterable(results))
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def parallel(func, args, n_jobs):
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"""
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A wrapper of multiprocessing:
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Parallel(n_jobs=n_jobs)(
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delayed(func)(args_i) for args_i in args
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)
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that takes the quapy.environ variable as input silently
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"""
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def func_dec(environ, *args):
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qp.environ = environ
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return func(*args)
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return Parallel(n_jobs=n_jobs)(
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delayed(func_dec)(qp.environ, args_i) for args_i in args
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)
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@contextlib.contextmanager
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def temp_seed(seed):
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"""
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Can be used in a "with" context to set a temporal seed without modifying the outer numpy's current state. E.g.:
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with temp_seed(random_seed):
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# do any computation depending on np.random functionality
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:param seed: the seed to set within the "with" context
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"""
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state = np.random.get_state()
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np.random.seed(seed)
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try:
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yield
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finally:
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np.random.set_state(state)
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def download_file(url, archive_filename):
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def progress(blocknum, bs, size):
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total_sz_mb = '%.2f MB' % (size / 1e6)
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current_sz_mb = '%.2f MB' % ((blocknum * bs) / 1e6)
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print('\rdownloaded %s / %s' % (current_sz_mb, total_sz_mb), end='')
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print("Downloading %s" % url)
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urllib.request.urlretrieve(url, filename=archive_filename, reporthook=progress)
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print("")
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def download_file_if_not_exists(url, archive_path):
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if os.path.exists(archive_path):
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return
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create_if_not_exist(os.path.dirname(archive_path))
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download_file(url,archive_path)
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def create_if_not_exist(path):
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os.makedirs(path, exist_ok=True)
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def get_quapy_home():
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home = os.path.join(str(Path.home()), 'quapy_data')
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os.makedirs(home, exist_ok=True)
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return home
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def create_parent_dir(path):
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parentdir = Path(path).parent
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if parentdir:
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os.makedirs(parentdir, exist_ok=True)
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def save_text_file(path, text):
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create_parent_dir(path)
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with open(text, 'wt') as fout:
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fout.write(text)
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def pickled_resource(pickle_path:str, generation_func:callable, *args):
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"""
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Allows for fast reuse of resources that are generated only once by calling generation_func(*args). The next times
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this function is invoked, it loads the pickled resource. Example:
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def some_array(n):
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return np.random.rand(n)
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pickled_resource('./my_array.pkl', some_array, 10) # the resource does not exist: it is created by some_array(10)
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pickled_resource('./my_array.pkl', some_array, 10) # the resource exists: it is loaded from './my_array.pkl'
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:param pickle_path: the path where to save (first time) and load (next times) the resource
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:param generation_func: the function that generates the resource, in case it does not exist in pickle_path
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:param args: any arg that generation_func uses for generating the resources
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:return: the resource
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"""
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if pickle_path is None:
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return generation_func(*args)
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else:
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if os.path.exists(pickle_path):
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return pickle.load(open(pickle_path, 'rb'))
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else:
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instance = generation_func(*args)
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os.makedirs(str(Path(pickle_path).parent), exist_ok=True)
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pickle.dump(instance, open(pickle_path, 'wb'), pickle.HIGHEST_PROTOCOL)
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return instance
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class EarlyStop:
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def __init__(self, patience, lower_is_better=True):
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self.PATIENCE_LIMIT = patience
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self.better = lambda a,b: a<b if lower_is_better else a>b
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self.patience = patience
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self.best_score = None
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self.best_epoch = None
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self.STOP = False
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self.IMPROVED = False
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def __call__(self, watch_score, epoch):
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self.IMPROVED = (self.best_score is None or self.better(watch_score, self.best_score))
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if self.IMPROVED:
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self.best_score = watch_score
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self.best_epoch = epoch
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self.patience = self.PATIENCE_LIMIT
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else:
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self.patience -= 1
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if self.patience <= 0:
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self.STOP = True |