mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 12:28:10 +08:00
Richard Liaw
90 lines
2.3 KiB
Python
90 lines
2.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import logging
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import numpy as np
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logger = logging.getLogger(__name__)
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class sample_from(object):
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"""Specify that tune should sample configuration values from this function.
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Arguments:
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func: An callable function to draw a sample from.
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"""
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def __init__(self, func):
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self.func = func
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def __str__(self):
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return "tune.sample_from({})".format(str(self.func))
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def __repr__(self):
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return "tune.sample_from({})".format(repr(self.func))
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def function(func):
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logger.warning(
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"DeprecationWarning: wrapping {} with tune.function() is no "
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"longer needed".format(func))
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return func
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def uniform(*args, **kwargs):
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"""Wraps tune.sample_from around ``np.random.uniform``.
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``tune.uniform(1, 10)`` is equivalent to
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``tune.sample_from(lambda _: np.random.uniform(1, 10))``
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"""
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return sample_from(lambda _: np.random.uniform(*args, **kwargs))
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def loguniform(min_bound, max_bound, base=10):
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"""Sugar for sampling in different orders of magnitude.
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Args:
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min_bound (float): Lower boundary of the output interval (1e-4)
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max_bound (float): Upper boundary of the output interval (1e-2)
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base (float): Base of the log. Defaults to 10.
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"""
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logmin = np.log(min_bound) / np.log(base)
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logmax = np.log(max_bound) / np.log(base)
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def apply_log(_):
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return base**(np.random.uniform(logmin, logmax))
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return sample_from(apply_log)
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def choice(*args, **kwargs):
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"""Wraps tune.sample_from around ``np.random.choice``.
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``tune.choice(10)`` is equivalent to
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``tune.sample_from(lambda _: np.random.choice(10))``
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"""
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return sample_from(lambda _: np.random.choice(*args, **kwargs))
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def randint(*args, **kwargs):
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"""Wraps tune.sample_from around ``np.random.randint``.
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``tune.randint(10)`` is equivalent to
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``tune.sample_from(lambda _: np.random.randint(10))``
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"""
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return sample_from(lambda _: np.random.randint(*args, **kwargs))
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def randn(*args, **kwargs):
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"""Wraps tune.sample_from around ``np.random.randn``.
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``tune.randn(10)`` is equivalent to
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``tune.sample_from(lambda _: np.random.randn(10))``
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"""
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return sample_from(lambda _: np.random.randn(*args, **kwargs))
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