mlos_bench.tunables.covariant_group
===================================

.. py:module:: mlos_bench.tunables.covariant_group

.. autoapi-nested-parse::

   Tunable parameter definition.



Classes
-------

.. autoapisummary::

   mlos_bench.tunables.covariant_group.CovariantTunableGroup


Module Contents
---------------

.. py:class:: CovariantTunableGroup(name: str, config: dict)

   A collection of tunable parameters.

   Changing any of the parameters in the group incurs the same cost of the experiment.

   Create a new group of tunable parameters.

   :param name: Human-readable identifier of the tunable parameters group.
   :type name: str
   :param config: Python dict that represents a CovariantTunableGroup
                  (e.g., deserialized from JSON).
   :type config: dict


   .. py:method:: __contains__(tunable: str | mlos_bench.tunables.tunable.Tunable) -> bool


   .. py:method:: __eq__(other: object) -> bool

      Check if two CovariantTunableGroup objects are equal.

      :param other: A covariant tunable group object to compare to.
      :type other: CovariantTunableGroup

      :returns: **is_equal** -- True if two CovariantTunableGroup objects are equal.
      :rtype: bool



   .. py:method:: __getitem__(tunable: str | mlos_bench.tunables.tunable.Tunable) -> mlos_bench.tunables.tunable.TunableValue


   .. py:method:: __repr__() -> str

      Produce a human-readable version of the CovariantTunableGroup (mostly for
      logging).

      :returns: **string** -- A human-readable version of the CovariantTunableGroup.
      :rtype: str



   .. py:method:: __setitem__(tunable: str | mlos_bench.tunables.tunable.Tunable, tunable_value: mlos_bench.tunables.tunable.TunableValue | mlos_bench.tunables.tunable.Tunable) -> mlos_bench.tunables.tunable.TunableValue


   .. py:method:: copy() -> CovariantTunableGroup

      Deep copy of the CovariantTunableGroup object.

      :returns: **group** -- A new instance of the CovariantTunableGroup object
                that is a deep copy of the original one.
      :rtype: CovariantTunableGroup



   .. py:method:: equals_defaults(other: CovariantTunableGroup) -> bool

      Checks to see if the other CovariantTunableGroup is the same, ignoring the
      current values of the two groups' Tunables.

      :param other: A covariant tunable group object to compare to.
      :type other: CovariantTunableGroup

      :returns: **are_equal** -- True if the two CovariantTunableGroup objects' *metadata* are the same,
                False otherwise.
      :rtype: bool



   .. py:method:: get_current_cost() -> int

      Get the cost of the experiment given current tunable values.

      :returns: **cost** -- Cost of the experiment or 0 if parameters have not been updated.
      :rtype: int



   .. py:method:: get_names() -> collections.abc.Iterable[str]

      Get the names of all tunables in the group.



   .. py:method:: get_tunable(tunable: str | mlos_bench.tunables.tunable.Tunable) -> mlos_bench.tunables.tunable.Tunable

      Access the entire Tunable in a group (not just its value). Throw KeyError if the
      tunable is not in the group.

      :param tunable: Name of the tunable parameter.
      :type tunable: str

      :returns: An instance of the Tunable parameter.
      :rtype: Tunable



   .. py:method:: get_tunable_values_dict() -> dict[str, mlos_bench.tunables.tunable.TunableValue]

      Get current values of all tunables in the group as a dict.

      :returns: **tunables**
      :rtype: dict[str, TunableValue]



   .. py:method:: get_tunables() -> collections.abc.Iterable[mlos_bench.tunables.tunable.Tunable]

      Gets the set of tunables for this CovariantTunableGroup.

      :rtype: Iterable[Tunable]



   .. py:method:: is_defaults() -> bool

      Checks whether the currently assigned values of all tunables are at their
      defaults.

      :rtype: bool



   .. py:method:: is_updated() -> bool

      Check if any of the tunable values in the group has been updated.

      :returns: **is_updated** -- True if any of the tunable values in the group has been updated, False otherwise.
      :rtype: bool



   .. py:method:: reset_is_updated() -> None

      Clear the update flag.

      That is, state that running an experiment with the current values of the
      tunables in this group has no extra cost.



   .. py:method:: restore_defaults() -> None

      Restore all tunable parameters to their default values.



   .. py:property:: cost
      :type: int


      Get the cost of changing the values in the covariant group. This value is a
      constant. Use `get_current_cost()` to get the cost given the group update
      status.

      :returns: **cost** -- Cost of changing the values in the covariant group.
      :rtype: int


   .. py:property:: name
      :type: str


      Get the name of the covariant group.

      :returns: **name** -- Name (i.e., a string id) of the covariant group.
      :rtype: str