syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl module

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRange(name)[source]

Bases: object

property name: str

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(cand_ndarray)[source]

Return type:: Union[str, int, float]

ndarray_size()[source]

Return type:: int

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.scale_from_zero_one(value, lower_bound, upper_bound, scaling, lower_internal, upper_internal)[source]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeContinuous(name, lower_bound, upper_bound, scaling, active_lower_bound=None, active_upper_bound=None)[source]

Bases: HyperparameterRange

Real valued hyperparameter. If active_lower_bound and/or active_upper_bound are given, the feasible interval for values of new configs is reduced, but data can still contain configs with values in [lower_bound, upper_bound], and internal encoding is done w.r.t. this original range.

Parameters:

name (str) – Name of hyperparameter
lower_bound (float) – Lower bound (included)
upper_bound (float) – Upper bound (included)
scaling (Scaling) – Determines internal representation, whereby parameter = scaling(internal).
active_lower_bound (Optional[float]) – See above
active_upper_bound (Optional[float]) – See above

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeInteger(name, lower_bound, upper_bound, scaling, active_lower_bound=None, active_upper_bound=None)[source]

Bases: HyperparameterRange

Integer valued hyperparameter. Both bounds are included in the valid values. Under the hood generates a continuous range from lower_bound - 0.5 to upper_bound + 0.5. See docs for continuous hyperparameter for more information.

Parameters:

name (str) – Name of hyperparameter
lower_bound (int) – Lower bound (integer, included)
upper_bound (int) – Upper bound (integer, included)
scaling (Scaling) – Determines internal representation, whereby parameter = scaling(internal).
active_lower_bound (Optional[int]) – See above
active_upper_bound (Optional[int]) – See above

property scaling: Scaling

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeFiniteRange(name, lower_bound, upper_bound, size, scaling, cast_int=False)[source]

Bases: HyperparameterRange

Finite range numerical hyperparameter, see FiniteRange. Internally, we use an int with linear scaling.

Note: Different to HyperparameterRangeContinuous, we require that lower_bound < upper_bound and size >=2.

Parameters:

name (str) – Name of hyperparameter
lower_bound (float) – Lower bound (included)
upper_bound (float) – Upper bound (included)
size (int) – Number of values in range
scaling (Scaling) – Determines internal representation, whereby parameter = scaling(internal).
cast_int (bool) – If True, values are cast to int

property scaling: Scaling

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeCategorical(name, choices)[source]

Bases: HyperparameterRange

Base class for categorical hyperparameter.

Parameters:

name (str) – Name of hyperparameter
choices (Tuple[Any, ...]) – Values parameter can take

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeCategoricalNonBinary(name, choices, active_choices=None)[source]

Bases: HyperparameterRangeCategorical

Can take on discrete set of values. We use one-hot encoding internally. If the value range has size 2, it is more efficient to use HyperparameterRangeCategoricalBinary.

Parameters:

name (str) – Name of hyperparameter
choices (Tuple[Any, ...]) – Values parameter can take
active_choices (Optional[Tuple[Any, ...]]) – If given, must be nonempty subset of choices.

ndarray_size()[source]

Return type:: int

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(cand_ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeCategoricalBinary(name, choices, active_choices=None)[source]

Bases: HyperparameterRangeCategorical

Here, the value range must be of size 2. The internal encoding is a single int, so 1 instead of 2 dimensions.

Parameters:

name (str) – Name of hyperparameter
choices (Tuple[Any, ...]) – Values parameter can take (must be size 2)
active_choices (Optional[Tuple[Any, ...]]) – If given, must be nonempty subset of choices.

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(cand_ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeOrdinalEqual(name, choices, active_choices=None)[source]

Bases: HyperparameterRangeCategorical

Ordinal hyperparameter, equal distance encoding. See also Ordinal.

Parameters:

name (str) – Name of hyperparameter
choices (Tuple[Any, ...]) – Values parameter can take
active_choices (Optional[Tuple[Any, ...]]) – If given, must be nonempty contiguous subsequence of choices.

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(cand_ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangeOrdinalNearestNeighbor(name, choices, log_scale=False, active_choices=None)[source]

Bases: HyperparameterRangeCategorical

Ordinal hyperparameter, nearest neighbour encoding. See also OrdinalNearestNeighbor.

Parameters:

name (str) – Name of hyperparameter
choices (Tuple[Any, ...]) – Values parameter can take (numerical values, strictly increasing, size >= 2)
log_scale (bool) – If True, nearest neighbour done in log (choices must be positive)
active_choices (Optional[Tuple[Any, ...]]) – If given, must be nonempty contiguous subsequence of choices.

property log_scale: bool

to_ndarray(hp)[source]

Return type:: ndarray

from_ndarray(cand_ndarray)[source]

Return type:: Union[str, int, float]

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]

class syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.HyperparameterRangesImpl(config_space, name_last_pos=None, value_for_last_pos=None, active_config_space=None, prefix_keys=None)[source]

Bases: HyperparameterRanges

Basic implementation of HyperparameterRanges.

Parameters:

config_space (Dict[str, Any]) – Configuration space
name_last_pos (Optional[str]) – See HyperparameterRanges, optional
value_for_last_pos – See HyperparameterRanges, optional
active_config_space (Optional[Dict[str, Any]]) – See HyperparameterRanges, optional
prefix_keys (Optional[List[str]]) – See HyperparameterRanges, optional

property ndarray_size: int

Returns:: Dimensionality of encoded vector returned by to_ndarray

to_ndarray(config)[source]

Map configuration to [0, 1] encoded vector

Parameters:: config (Dict[str, Union[int, float, str]]) – Configuration to encode
Return type:: ndarray
Returns:: Encoded vector

from_ndarray(enc_config)[source]

Maps encoded vector back to configuration (can involve rounding)

The encoded vector enc_config need to be in the image of to_ndarray. In fact, any [0, 1] valued vector of dimensionality ndarray_size is allowed.

Parameters:: enc_config (ndarray) – Encoded vector
Return type:: Dict[str, Union[int, float, str]]
Returns:: Configuration corresponding to encoded vector

property encoded_ranges: Dict[str, Tuple[int, int]]

Encoded ranges are [0, 1] or closed subintervals thereof, in case active_config_space is used.

Returns:: Ranges of hyperparameters in the encoded ndarray representation

get_ndarray_bounds()[source]

Return type:: List[Tuple[float, float]]
Returns:: List of (lower, upper) bounds for each dimension in encoded vector representation.

syne_tune.optimizer.schedulers.searchers.utils.hp_ranges_impl.decode_extended_features(features_ext, resource_attr_range)[source]

Given matrix of features from extended configs, corresponding to ExtendedConfiguration, split into feature matrix from normal configs and resource values.

Parameters:

features_ext (ndarray) – Matrix of features from extended configs
resource_attr_range (Tuple[int, int]) – (r_min, r_max)

Return type:

(ndarray, ndarray)

Returns:

(features, resources)