updating documentation

deephyper/search/hps/_cbo.py

@@ -107,7 +107,7 @@ class CBO(Search):
         moo_lower_bounds (list, optional): List of lower bounds on the interesting range of objective values. Must be the same length as the number of obejctives. Defaults to ``None``, i.e., no bounds. Can bound only a single objective by providing ``None`` for all other values. For example, ``moo_lower_bounds=[None, 0.5, None]`` will explore all tradeoffs for the objectives at index 0 and 2, but only consider scores for objective 1 that exceed 0.5.
         moo_scalarization_strategy (str, optional): Scalarization strategy used in multiobjective optimization. Can be a value in ``["Linear", "Chebyshev", "AugChebyshev", "PBI", "Quadratic"]``. Defaults to ``"Chebyshev"``. Typically, randomized methods should be used to capture entire Pareto front, unless there is a known target solution a priori. Additional details on each scalarization can be found in :mod:`deephyper.skopt.moo`.
         moo_scalarization_weight (list, optional): Scalarization weights to be used in multiobjective optimization with length equal to the number of objective functions. Defaults to ``None`` for randomized weights. Only set if you want to fix the scalarization weights for a multiobjective HPS.
-        scheduler (dict, callable, optional): a function to manage the value of ``kappa, xi`` with iterations. Defaults to ``None`` which does not use any scheduler. The periodic exponential decay scheduler can be used with  ``scheduler={"type": "periodic-exp-decay", "period": 30, "rate": 0.1}``. The scheduler can also be a callable function with signature ``scheduler(i, eta_0, **kwargs)`` where ``i`` is the current iteration, ``eta_0`` is the initial value of ``[kappa, xi]`` and ``kwargs`` are other fixed parameters of the function.
+        scheduler (dict, callable, optional): a function to manage the value of ``kappa, xi`` with iterations. Defaults to ``None`` which does not use any scheduler. The periodic exponential decay scheduler can be used with  ``scheduler={"type": "periodic-exp-decay", "period": 30, "rate": 0.1}``. The scheduler can also be a callable function with signature ``scheduler(i, eta_0, **kwargs)`` where ``i`` is the current iteration, ``eta_0`` is the initial value of ``[kappa, xi]`` and ``kwargs`` are other fixed parameters of the function. Instead of fixing the decay ``"rate"`` the final ``kappa`` or ``xi` can be used ``{"type": "periodic-exp-decay", "period": 25, "kappa_final": 1.96}``.
         objective_scaler (str, optional): a way to map the objective space to some other support for example to normalize it. Defaults to ``"auto"`` which automatically set it to "identity" for any surrogate model except "RF" which will use "quantile-uniform".
         stopper (Stopper, optional): a stopper to leverage multi-fidelity when evaluating the function. Defaults to ``None`` which does not use any stopper.
     """