tune.sample

Domain Objects

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()).

cast

def cast(value)

Cast value to domain type

is_valid

def is_valid(value: Any)

Returns True if value is a valid value in this domain.

Grid Objects

class Grid(Sampler)

Dummy sampler used for grid search

uniform

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))

quniform

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.

loguniform

def loguniform(lower: float, upper: float, base: float = 10)

Sugar for sampling in different orders of magnitude.

Arguments:

• 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.

qloguniform

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.

Arguments:

• 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.

choice

def choice(categories: Sequence)

Sample a categorical value. Sampling from tune.choice([1, 2]) is equivalent to sampling from np.random.choice([1, 2])

randint

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)

lograndint

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.

qrandint

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.

qlograndint

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.

randn

def randn(mean: float = 0.0, sd: float = 1.0)

Sample a float value normally with mean and sd.

Arguments:

• mean float - Mean of the normal distribution. Defaults to 0.
• sd float - SD of the normal distribution. Defaults to 1.

qrandn

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.

Arguments:

• mean - Mean of the normal distribution.
• sd - SD of the normal distribution.
• q - Quantization number. The result will be rounded to an integer increment of this value.