util.py

import numpy as np
import sys
from time import time
from datetime import timedelta

def _wrap_index__in_verbose(iterations):
    """Yields the values in iterations printing the status on the stdout."""
    m = len(iterations)
    digits = len(str(m))
    progress = '\r [ {s:{d}} / {m} ] {s:3.0f}% - ? it/s'
    progress = progress.format(m=m, d=digits, s=0)
    sys.stdout.write(progress)
    beginning = time()
    sys.stdout.write(progress)
    for i, it in enumerate(iterations):
        yield it
        sec_left = ((m-i+1) * (time() - beginning)) / (i+1)
        time_left = str(timedelta(seconds=sec_left))[:7]
        progress = '\r [ {i:{d}} / {m} ]'.format(i=i+1, d=digits, m=m)
        progress += ' {p:3.0f}%'.format(p=100*(i+1)/m)
        progress += ' - {time_left} left '.format(time_left=time_left)
        sys.stdout.write(progress)

def _build_iteration_indexes(data_len, num_iterations,
                             verbose=False, random_generator=None,
                             use_epochs=False):
    """Returns an iterable with the indexes of the samples
    to pick at each iteration of the training.

    If random_generator is not None, it must be an instance
    of numpy.random.RandomState and it will be used
    to randomize the order of the samples."""
    if use_epochs:
        iterations_per_epoch = np.arange(data_len)
        if random_generator:
            random_generator.shuffle(iterations_per_epoch)
        iterations = np.tile(iterations_per_epoch, num_iterations)
    else:
        iterations = np.arange(num_iterations) % data_len
        if random_generator:
            random_generator.shuffle(iterations)
    if verbose:
        return _wrap_index__in_verbose(iterations)
    else:
        return iterations