# hub.solver.do_solver.sgs_policies

Domain specification

Domain

Only show finetuned characteristics Simplify signatures

# BasePolicyMethod

Base options to define Scheduling policies

# FOLLOW_GANTT BasePolicyMethod

Strictly return scheduling policy based on the gantt chart. Based on the time stored in the state, task are started at the right time.

# SGS_INDEX_FREEDOM BasePolicyMethod

At a given state, look for the first task "TASK" in the permutation that is not started or scheduled yet, If it's not available to start yet, some other task are considered candidates based their "ordering" closeness to the starting time of "TASK", the policy will consider starting task that are close to the one that was first expected. delta_index_freedom is the parameter that impacts this setting.

# SGS_PRECEDENCE BasePolicyMethod

At a given state, look for the first available task in an ordered permutation that is start-able and do it. If no activity is launchable, just advance in time.

# SGS_READY BasePolicyMethod

Same as SGS_PRECEDENCE, one of those 2 will be in deprecation soon.

# SGS_STRICT BasePolicyMethod

At a given state, look for the first task in the permutation that is not started or scheduled yet. If it's not available to start yet, we advance in time until it is.

WARNING

This will only work when the permutation of tasks fulfills the precedence constraints.

# SGS_TIME_FREEDOM BasePolicyMethod

At a given state, look for the first task "TASK" in the permutation that is not started or scheduled yet, If it's not available to start yet, some other task are considered candidates based their time closeness to the starting time of "TASK", the policy will consider starting task that are close to the one that was first expected. delta_time_freedom is the parameter that impacts this setting.

# PolicyMethodParams

Wrapped params for scheduling policy parameters, see BasePolicyMethod for more details

# Constructor PolicyMethodParams

PolicyMethodParams(
  base_policy_method: BasePolicyMethod,
  delta_time_freedom = 10,
  delta_index_freedom = 10
)

Initialize self. See help(type(self)) for accurate signature.

# complete_with_default_hyperparameters Hyperparametrizable

complete_with_default_hyperparameters(
  kwargs: dict[str, Any],
  names: Optional[list[str]] = None
)

Add missing hyperparameters to kwargs by using default values

Args: kwargs: keyword arguments to complete (e.g. for __init__, init_model, or solve) names: names of the hyperparameters to add if missing. By default, all available hyperparameters.

Returns: a new dictionary, completion of kwargs

# copy_and_update_hyperparameters Hyperparametrizable

copy_and_update_hyperparameters(
  names: Optional[list[str]] = None,
  **kwargs_by_name: dict[str, Any]
) -> list[Hyperparameter]

Copy hyperparameters definition of this class and update them with specified kwargs.

This is useful to define hyperparameters for a child class for which only choices of the hyperparameter change for instance.

Args: names: names of hyperparameters to copy. Default to all. **kwargs_by_name: for each hyperparameter specified by its name, the attributes to update. If a given hyperparameter name is not specified, the hyperparameter is copied without further update.

Returns:

# get_default_hyperparameters Hyperparametrizable

get_default_hyperparameters(
  names: Optional[list[str]] = None
) -> dict[str, Any]

Get hyperparameters default values.

Args: names: names of the hyperparameters to choose. By default, all available hyperparameters will be suggested.

Returns: a mapping between hyperparameter's name_in_kwargs and its default value (None if not specified)

# get_hyperparameter Hyperparametrizable

get_hyperparameter(
  name: str
) -> Hyperparameter

Get hyperparameter from given name.

# get_hyperparameters_by_name Hyperparametrizable

get_hyperparameters_by_name(
) -> dict[str, Hyperparameter]

Mapping from name to corresponding hyperparameter.

# get_hyperparameters_names Hyperparametrizable

get_hyperparameters_names(
) -> list[str]

List of hyperparameters names.

# suggest_hyperparameter_with_optuna Hyperparametrizable

suggest_hyperparameter_with_optuna(
  trial: optuna.trial.Trial,
  name: str,
  prefix: str,
  **kwargs
) -> Any

Suggest hyperparameter value during an Optuna trial.

This can be used during Optuna hyperparameters tuning.

Args: trial: optuna trial during hyperparameters tuning name: name of the hyperparameter to choose prefix: prefix to add to optuna corresponding parameter name (useful for disambiguating hyperparameters from subsolvers in case of meta-solvers) **kwargs: options for optuna hyperparameter suggestions

Returns:

kwargs can be used to pass relevant arguments to

trial.suggest_float()
trial.suggest_int()
trial.suggest_categorical()

For instance it can

add a low/high value if not existing for the hyperparameter or override it to narrow the search. (for float or int hyperparameters)
add a step or log argument (for float or int hyperparameters, see optuna.trial.Trial.suggest_float())
override choices for categorical or enum parameters to narrow the search

# suggest_hyperparameters_with_optuna Hyperparametrizable

suggest_hyperparameters_with_optuna(
  trial: optuna.trial.Trial,
  names: Optional[list[str]] = None,
  kwargs_by_name: Optional[dict[str, dict[str, Any]]] = None,
  fixed_hyperparameters: Optional[dict[str, Any]] = None,
  prefix: str
) -> dict[str, Any]

Suggest hyperparameters values during an Optuna trial.

Args: trial: optuna trial during hyperparameters tuning names: names of the hyperparameters to choose. By default, all available hyperparameters will be suggested. If fixed_hyperparameters is provided, the corresponding names are removed from names. kwargs_by_name: options for optuna hyperparameter suggestions, by hyperparameter name fixed_hyperparameters: values of fixed hyperparameters, useful for suggesting subbrick hyperparameters, if the subbrick class is not suggested by this method, but already fixed. Will be added to the suggested hyperparameters. prefix: prefix to add to optuna corresponding parameters (useful for disambiguating hyperparameters from subsolvers in case of meta-solvers)

Returns: mapping between the hyperparameter name and its suggested value. If the hyperparameter has an attribute name_in_kwargs, this is used as the key in the mapping instead of the actual hyperparameter name. the mapping is updated with fixed_hyperparameters.

kwargs_by_name[some_name] will be passed as **kwargs to suggest_hyperparameter_with_optuna(name=some_name)

# PolicyRCPSP

Policy object containing results of scheduling solver policy.

# Attributes

domain: scheduling domain where the policy will be applied
policy_method_params: params of the policy
permutation_task: list of tasks ids, representing a priority list for scheduling
modes_dictionnary: when relevant (multimode rcpsp for e.g) specifies in which mode a task is executed
schedule: when given, details the schedule to follow : this will be relevant for deterministic scheduling problems
resource_allocation: when relevant (multiskill problems for e.g), list the allocated (unitary) resources to the tasks
resource_allocation_priority: for each task, store a preference order for resources to be allocated to the task. Resource will be greedily allocated based on this priority

# Constructor PolicyRCPSP

PolicyRCPSP(
  domain: SchedulingDomain,
  policy_method_params: PolicyMethodParams,
  permutation_task: list[int],
  modes_dictionnary: dict[int, int],
  schedule: Optional[dict[int, dict[str, int]]] = None,
  resource_allocation: Optional[dict[int, list[str]]] = None,
  resource_allocation_priority: Optional[dict[int, list[str]]] = None
)

Initialize self. See help(type(self)) for accurate signature.

# get_next_action DeterministicPolicies

get_next_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Get the next deterministic action (from the solver's current policy).

# Parameters

observation: The observation for which next action is requested.

# Returns

The next deterministic action.

# get_next_action_distribution UncertainPolicies

get_next_action_distribution(
  self,
  observation: StrDict[D.T_observation]
) -> Distribution[StrDict[list[D.T_event]]]

Get the probabilistic distribution of next action for the given observation (from the solver's current policy).

# Parameters

observation: The observation to consider.

# Returns

The probabilistic distribution of next action.

# is_policy_defined_for Policies

is_policy_defined_for(
  self,
  observation: StrDict[D.T_observation]
) -> bool

Check whether the solver's current policy is defined for the given observation.

# Parameters

observation: The observation to consider.

# Returns

True if the policy is defined for the given observation memory (False otherwise).

# sample_action Policies

sample_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Sample an action for the given observation (from the solver's current policy).

# Parameters

observation: The observation for which an action must be sampled.

# Returns

The sampled action.

# _get_next_action DeterministicPolicies

_get_next_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Get the next deterministic action (from the solver's current policy).

# Parameters

observation: The observation for which next action is requested.

# Returns

The next deterministic action.

# _get_next_action_distribution UncertainPolicies

_get_next_action_distribution(
  self,
  observation: StrDict[D.T_observation]
) -> Distribution[StrDict[list[D.T_event]]]

Get the probabilistic distribution of next action for the given observation (from the solver's current policy).

# Parameters

observation: The observation to consider.

# Returns

The probabilistic distribution of next action.

# _is_policy_defined_for Policies

_is_policy_defined_for(
  self,
  observation: StrDict[D.T_observation]
) -> bool

Check whether the solver's current policy is defined for the given observation.

# Parameters

observation: The observation to consider.

# Returns

True if the policy is defined for the given observation memory (False otherwise).

# _sample_action Policies

_sample_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Sample an action for the given observation (from the solver's current policy).

# Parameters

observation: The observation for which an action must be sampled.

# Returns

The sampled action.

# next_action_follow_static_gantt

next_action_follow_static_gantt(
  policy_rcpsp: PolicyRCPSP,
  state: State,
  check_if_applicable: bool = False,
  **kwargs
)

Implements the policy with the parameters FOLLOW_GANTT (see its doc)

# next_action_sgs_first_task_precedence_ready

next_action_sgs_first_task_precedence_ready(
  policy_rcpsp: PolicyRCPSP,
  state: State,
  check_if_applicable: bool = False,
  **kwargs
)

Implements the policy with the parameters SGS_PRECEDENCE (see its doc)

# next_action_sgs_first_task_ready

next_action_sgs_first_task_ready(
  policy_rcpsp: PolicyRCPSP,
  state: State,
  check_if_applicable: bool = False,
  domain_sk_decide: Union[MultiModeRCPSP, SingleModeRCPSP] = None,
  **kwargs
)

Implements the policy with the parameters SGS_READY (see its doc)

# next_action_sgs_strict

next_action_sgs_strict(
  policy_rcpsp: PolicyRCPSP,
  state: State,
  check_if_applicable: bool = False,
  domain_sk_decide: Union[MultiModeRCPSP, SingleModeRCPSP] = None,
  **kwargs
)

Implements the policy with the parameters SGS_STRICT (see its doc)

# next_action_sgs_time_freedom

next_action_sgs_time_freedom(
  policy_rcpsp: PolicyRCPSP,
  state: State,
  check_if_applicable: bool = False,
  domain_sk_decide: Union[MultiModeRCPSP, SingleModeRCPSP] = None,
  delta_time_freedom: int = 10,
  **kwargs
)

Implements the policy with the parameters SGS_TIME_FREEDOM (see its doc)

# next_action_sgs_index_freedom

next_action_sgs_index_freedom(
  policy_rcpsp: PolicyRCPSP,
  state: State,
  check_if_applicable: bool = False,
  domain_sk_decide: Union[MultiModeRCPSP, SingleModeRCPSP] = None,
  delta_index_freedom: int = 10,
  **kwargs
)

Implements the policy with the parameters SGS_INDEX_FREEDOM (see its doc)