Source code for ray.tune.sample

import logging
from copy import copy
from inspect import signature
from math import isclose
from typing import Any, Callable, Dict, List, Optional, Sequence, Union

import numpy as np

logger = logging.getLogger(__name__)


class Domain:
    """Base class to specify a type and valid range to sample parameters from.

    This base class is implemented by parameter spaces, like float ranges
    (``Float``), integer ranges (``Integer``), or categorical variables
    (``Categorical``). The ``Domain`` object contains information about
    valid values (e.g. minimum and maximum values), and exposes methods that
    allow specification of specific samplers (e.g. ``uniform()`` or
    ``loguniform()``).

    """
    sampler = None
    default_sampler_cls = None

    def cast(self, value):
        """Cast value to domain type"""
        return value

    def set_sampler(self, sampler, allow_override=False):
        if self.sampler and not allow_override:
            raise ValueError("You can only choose one sampler for parameter "
                             "domains. Existing sampler for parameter {}: "
                             "{}. Tried to add {}".format(
                                 self.__class__.__name__, self.sampler,
                                 sampler))
        self.sampler = sampler

    def get_sampler(self):
        sampler = self.sampler
        if not sampler:
            sampler = self.default_sampler_cls()
        return sampler

    def sample(self, spec=None, size=1):
        sampler = self.get_sampler()
        return sampler.sample(self, spec=spec, size=size)

    def is_grid(self):
        return isinstance(self.sampler, Grid)

    def is_function(self):
        return False

    def is_valid(self, value: Any):
        """Returns True if `value` is a valid value in this domain."""
        raise NotImplementedError

    @property
    def domain_str(self):
        return "(unknown)"


class Sampler:
    def sample(self,
               domain: Domain,
               spec: Optional[Union[List[Dict], Dict]] = None,
               size: int = 1):
        raise NotImplementedError


class BaseSampler(Sampler):
    def __str__(self):
        return "Base"


class Uniform(Sampler):
    def __str__(self):
        return "Uniform"


class LogUniform(Sampler):
    def __init__(self, base: float = 10):
        self.base = base
        assert self.base > 0, "Base has to be strictly greater than 0"

    def __str__(self):
        return "LogUniform"


class Normal(Sampler):
    def __init__(self, mean: float = 0., sd: float = 0.):
        self.mean = mean
        self.sd = sd

        assert self.sd > 0, "SD has to be strictly greater than 0"

    def __str__(self):
        return "Normal"


class Grid(Sampler):
    """Dummy sampler used for grid search"""

    def sample(self,
               domain: Domain,
               spec: Optional[Union[List[Dict], Dict]] = None,
               size: int = 1):
        return RuntimeError("Do not call `sample()` on grid.")


class Float(Domain):
    class _Uniform(Uniform):
        def sample(self,
                   domain: "Float",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):
            assert domain.lower > float("-inf"), \
                "Uniform needs a lower bound"
            assert domain.upper < float("inf"), \
                "Uniform needs a upper bound"
            items = np.random.uniform(domain.lower, domain.upper, size=size)
            return items if len(items) > 1 else domain.cast(items[0])

    class _LogUniform(LogUniform):
        def sample(self,
                   domain: "Float",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):
            assert domain.lower > 0, \
                "LogUniform needs a lower bound greater than 0"
            assert 0 < domain.upper < float("inf"), \
                "LogUniform needs a upper bound greater than 0"
            logmin = np.log(domain.lower) / np.log(self.base)
            logmax = np.log(domain.upper) / np.log(self.base)

            items = self.base**(np.random.uniform(logmin, logmax, size=size))
            return items if len(items) > 1 else domain.cast(items[0])

    class _Normal(Normal):
        def sample(self,
                   domain: "Float",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):
            assert not domain.lower or domain.lower == float("-inf"), \
                "Normal sampling does not allow a lower value bound."
            assert not domain.upper or domain.upper == float("inf"), \
                "Normal sampling does not allow a upper value bound."
            items = np.random.normal(self.mean, self.sd, size=size)
            return items if len(items) > 1 else domain.cast(items[0])

    default_sampler_cls = _Uniform

    def __init__(self, lower: Optional[float], upper: Optional[float]):
        # Need to explicitly check for None
        self.lower = lower if lower is not None else float("-inf")
        self.upper = upper if upper is not None else float("inf")

    def cast(self, value):
        return float(value)

    def uniform(self):
        if not self.lower > float("-inf"):
            raise ValueError(
                "Uniform requires a lower bound. Make sure to set the "
                "`lower` parameter of `Float()`.")
        if not self.upper < float("inf"):
            raise ValueError(
                "Uniform requires a upper bound. Make sure to set the "
                "`upper` parameter of `Float()`.")
        new = copy(self)
        new.set_sampler(self._Uniform())
        return new

    def loguniform(self, base: float = 10):
        if not self.lower > 0:
            raise ValueError(
                "LogUniform requires a lower bound greater than 0."
                f"Got: {self.lower}. Did you pass a variable that has "
                "been log-transformed? If so, pass the non-transformed value "
                "instead.")
        if not 0 < self.upper < float("inf"):
            raise ValueError(
                "LogUniform requires a upper bound greater than 0. "
                f"Got: {self.lower}. Did you pass a variable that has "
                "been log-transformed? If so, pass the non-transformed value "
                "instead.")
        new = copy(self)
        new.set_sampler(self._LogUniform(base))
        return new

    def normal(self, mean=0., sd=1.):
        new = copy(self)
        new.set_sampler(self._Normal(mean, sd))
        return new

    def quantized(self, q: float):
        if self.lower > float("-inf") and not isclose(self.lower / q,
                                                      round(self.lower / q)):
            raise ValueError(
                f"Your lower variable bound {self.lower} is not divisible by "
                f"quantization factor {q}.")
        if self.upper < float("inf") and not isclose(self.upper / q,
                                                     round(self.upper / q)):
            raise ValueError(
                f"Your upper variable bound {self.upper} is not divisible by "
                f"quantization factor {q}.")

        new = copy(self)
        new.set_sampler(Quantized(new.get_sampler(), q), allow_override=True)
        return new

    def is_valid(self, value: float):
        return self.lower <= value <= self.upper

    @property
    def domain_str(self):
        return f"({self.lower}, {self.upper})"


class Integer(Domain):
    class _Uniform(Uniform):
        def sample(self,
                   domain: "Integer",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):
            items = np.random.randint(domain.lower, domain.upper, size=size)
            return items if len(items) > 1 else domain.cast(items[0])

    class _LogUniform(LogUniform):
        def sample(self,
                   domain: "Integer",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):
            assert domain.lower > 0, \
                "LogUniform needs a lower bound greater than 0"
            assert 0 < domain.upper < float("inf"), \
                "LogUniform needs a upper bound greater than 0"
            logmin = np.log(domain.lower) / np.log(self.base)
            logmax = np.log(domain.upper) / np.log(self.base)

            items = self.base**(np.random.uniform(logmin, logmax, size=size))
            items = np.floor(items).astype(int)
            return items if len(items) > 1 else domain.cast(items[0])

    default_sampler_cls = _Uniform

    def __init__(self, lower, upper):
        self.lower = lower
        self.upper = upper

    def cast(self, value):
        return int(value)

    def quantized(self, q: int):
        new = copy(self)
        new.set_sampler(Quantized(new.get_sampler(), q), allow_override=True)
        return new

    def uniform(self):
        new = copy(self)
        new.set_sampler(self._Uniform())
        return new

    def loguniform(self, base: float = 10):
        if not self.lower > 0:
            raise ValueError(
                "LogUniform requires a lower bound greater than 0."
                f"Got: {self.lower}. Did you pass a variable that has "
                "been log-transformed? If so, pass the non-transformed value "
                "instead.")
        if not 0 < self.upper < float("inf"):
            raise ValueError(
                "LogUniform requires a upper bound greater than 0. "
                f"Got: {self.lower}. Did you pass a variable that has "
                "been log-transformed? If so, pass the non-transformed value "
                "instead.")
        new = copy(self)
        new.set_sampler(self._LogUniform(base))
        return new

    def is_valid(self, value: int):
        return self.lower <= value <= self.upper

    @property
    def domain_str(self):
        return f"({self.lower}, {self.upper})"


class Categorical(Domain):
    class _Uniform(Uniform):
        def sample(self,
                   domain: "Categorical",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):

            items = np.random.choice(domain.categories, size=size).tolist()
            return items if len(items) > 1 else domain.cast(items[0])

    default_sampler_cls = _Uniform

    def __init__(self, categories: Sequence):
        self.categories = list(categories)

    def uniform(self):
        new = copy(self)
        new.set_sampler(self._Uniform())
        return new

    def grid(self):
        new = copy(self)
        new.set_sampler(Grid())
        return new

    def __len__(self):
        return len(self.categories)

    def __getitem__(self, item):
        return self.categories[item]

    def is_valid(self, value: Any):
        return value in self.categories

    @property
    def domain_str(self):
        return f"{self.categories}"


class Function(Domain):
    class _CallSampler(BaseSampler):
        def sample(self,
                   domain: "Function",
                   spec: Optional[Union[List[Dict], Dict]] = None,
                   size: int = 1):
            if domain.pass_spec:
                items = [
                    domain.func(spec[i] if isinstance(spec, list) else spec)
                    for i in range(size)
                ]
            else:
                items = [domain.func() for i in range(size)]

            return items if len(items) > 1 else domain.cast(items[0])

    default_sampler_cls = _CallSampler

    def __init__(self, func: Callable):
        sig = signature(func)

        pass_spec = True  # whether we should pass `spec` when calling `func`
        try:
            sig.bind({})
        except TypeError:
            pass_spec = False

        if not pass_spec:
            try:
                sig.bind()
            except TypeError as exc:
                raise ValueError(
                    "The function passed to a `Function` parameter must be "
                    "callable with either 0 or 1 parameters.") from exc

        self.pass_spec = pass_spec
        self.func = func

    def is_function(self):
        return True

    def is_valid(self, value: Any):
        return True  # This is user-defined, so lets not assume anything

    @property
    def domain_str(self):
        return f"{self.func}()"


class Quantized(Sampler):
    def __init__(self, sampler: Sampler, q: Union[float, int]):
        self.sampler = sampler
        self.q = q

        assert self.sampler, "Quantized() expects a sampler instance"

    def get_sampler(self):
        return self.sampler

    def sample(self,
               domain: Domain,
               spec: Optional[Union[List[Dict], Dict]] = None,
               size: int = 1):
        values = self.sampler.sample(domain, spec, size)
        quantized = np.round(np.divide(values, self.q)) * self.q
        if not isinstance(quantized, np.ndarray):
            return domain.cast(quantized)
        return list(quantized)


# TODO (krfricke): Remove tune.function
def function(func):
    logger.warning(
        "DeprecationWarning: wrapping {} with tune.function() is no "
        "longer needed".format(func))
    return func


[docs]def sample_from(func: Callable[[Dict], Any]): """Specify that tune should sample configuration values from this function. Arguments: func: An callable function to draw a sample from. """ return Function(func)
[docs]def uniform(lower: float, upper: float): """Sample a float value uniformly between ``lower`` and ``upper``. Sampling from ``tune.uniform(1, 10)`` is equivalent to sampling from ``np.random.uniform(1, 10))`` """ return Float(lower, upper).uniform()
[docs]def quniform(lower: float, upper: float, q: float): """Sample a quantized float value uniformly between ``lower`` and ``upper``. Sampling from ``tune.uniform(1, 10)`` is equivalent to sampling from ``np.random.uniform(1, 10))`` The value will be quantized, i.e. rounded to an integer increment of ``q``. Quantization makes the upper bound inclusive. """ return Float(lower, upper).uniform().quantized(q)
[docs]def loguniform(lower: float, upper: float, base: float = 10): """Sugar for sampling in different orders of magnitude. Args: lower (float): Lower boundary of the output interval (e.g. 1e-4) upper (float): Upper boundary of the output interval (e.g. 1e-2) base (int): Base of the log. Defaults to 10. """ return Float(lower, upper).loguniform(base)
[docs]def qloguniform(lower: float, upper: float, q: float, base: float = 10): """Sugar for sampling in different orders of magnitude. The value will be quantized, i.e. rounded to an integer increment of ``q``. Quantization makes the upper bound inclusive. Args: lower (float): Lower boundary of the output interval (e.g. 1e-4) upper (float): Upper boundary of the output interval (e.g. 1e-2) q (float): Quantization number. The result will be rounded to an integer increment of this value. base (int): Base of the log. Defaults to 10. """ return Float(lower, upper).loguniform(base).quantized(q)
[docs]def choice(categories: Sequence): """Sample a categorical value. Sampling from ``tune.choice([1, 2])`` is equivalent to sampling from ``np.random.choice([1, 2])`` """ return Categorical(categories).uniform()
[docs]def randint(lower: int, upper: int): """Sample an integer value uniformly between ``lower`` and ``upper``. ``lower`` is inclusive, ``upper`` is exclusive. Sampling from ``tune.randint(10)`` is equivalent to sampling from ``np.random.randint(10)`` .. versionchanged:: 1.5.0 When converting Ray Tune configs to searcher-specific search spaces, the lower and upper limits are adjusted to keep compatibility with the bounds stated in the docstring above. """ return Integer(lower, upper).uniform()
[docs]def lograndint(lower: int, upper: int, base: float = 10): """Sample an integer value log-uniformly between ``lower`` and ``upper``, with ``base`` being the base of logarithm. ``lower`` is inclusive, ``upper`` is exclusive. .. versionchanged:: 1.5.0 When converting Ray Tune configs to searcher-specific search spaces, the lower and upper limits are adjusted to keep compatibility with the bounds stated in the docstring above. """ return Integer(lower, upper).loguniform(base)
[docs]def qrandint(lower: int, upper: int, q: int = 1): """Sample an integer value uniformly between ``lower`` and ``upper``. ``lower`` is inclusive, ``upper`` is also inclusive (!). The value will be quantized, i.e. rounded to an integer increment of ``q``. Quantization makes the upper bound inclusive. .. versionchanged:: 1.5.0 When converting Ray Tune configs to searcher-specific search spaces, the lower and upper limits are adjusted to keep compatibility with the bounds stated in the docstring above. """ return Integer(lower, upper).uniform().quantized(q)
[docs]def qlograndint(lower: int, upper: int, q: int, base: float = 10): """Sample an integer value log-uniformly between ``lower`` and ``upper``, with ``base`` being the base of logarithm. ``lower`` is inclusive, ``upper`` is also inclusive (!). The value will be quantized, i.e. rounded to an integer increment of ``q``. Quantization makes the upper bound inclusive. .. versionchanged:: 1.5.0 When converting Ray Tune configs to searcher-specific search spaces, the lower and upper limits are adjusted to keep compatibility with the bounds stated in the docstring above. """ return Integer(lower, upper).loguniform(base).quantized(q)
[docs]def randn(mean: float = 0., sd: float = 1.): """Sample a float value normally with ``mean`` and ``sd``. Args: mean (float): Mean of the normal distribution. Defaults to 0. sd (float): SD of the normal distribution. Defaults to 1. """ return Float(None, None).normal(mean, sd)
[docs]def qrandn(mean: float, sd: float, q: float): """Sample a float value normally with ``mean`` and ``sd``. The value will be quantized, i.e. rounded to an integer increment of ``q``. Args: mean (float): Mean of the normal distribution. sd (float): SD of the normal distribution. q (float): Quantization number. The result will be rounded to an integer increment of this value. """ return Float(None, None).normal(mean, sd).quantized(q)