mirror of
https://github.com/wassname/ray.git
synced 2026-06-29 19:00:58 +08:00
Richard Liaw
89 lines
2.4 KiB
Python
89 lines
2.4 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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The use of function arguments in tune configs must be disambiguated by
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either wrapped the function in tune.sample_from() or tune.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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class function(object):
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"""Wraps `func` to make sure it is not expanded during resolution.
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The use of function arguments in tune configs must be disambiguated by
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either wrapped the function in tune.sample_from() or tune.function().
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Arguments:
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func: A function literal.
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"""
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def __init__(self, func):
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self.func = func
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def __call__(self, *args, **kwargs):
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return self.func(*args, **kwargs)
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def __str__(self):
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return "tune.function({})".format(str(self.func))
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def __repr__(self):
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return "tune.function({})".format(repr(self.func))
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def uniform(*args, **kwargs):
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"""A wrapper around np.random.uniform."""
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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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"""A wrapper around np.random.choice."""
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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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"""A wrapper around np.random.randint."""
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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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"""A wrapper around np.random.randn."""
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return sample_from(lambda _: np.random.randn(*args, **kwargs))
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