Source code for deephyper.skopt.optimizer.base

Abstraction for optimizers.

It is sufficient that one re-implements the base estimator.

import warnings
import numbers

    from import Iterable
except ImportError:
    from collections import Iterable

from ..callbacks import check_callback
from ..callbacks import VerboseCallback
from .optimizer import Optimizer
from ..utils import eval_callbacks

[docs]def base_minimize( func, dimensions, base_estimator, n_calls=100, n_random_starts=None, n_initial_points=10, initial_point_generator="random", acq_func="EI", acq_optimizer="lbfgs", x0=None, y0=None, random_state=None, verbose=False, callback=None, n_points=10000, n_restarts_optimizer=5, xi=0.01, kappa=1.96, n_jobs=1, model_queue_size=None, ): """Base optimizer class Parameters ---------- func : callable Function to minimize. Should take a single list of parameters and return the objective value. If you have a search-space where all dimensions have names, then you can use :func:`deephyper.skopt.utils.use_named_args` as a decorator on your objective function, in order to call it directly with the named arguments. See `use_named_args` for an example. dimensions : list, shape (n_dims,) List of search space dimensions. Each search dimension can be defined either as - a `(lower_bound, upper_bound)` tuple (for `Real` or `Integer` dimensions), - a `(lower_bound, upper_bound, "prior")` tuple (for `Real` dimensions), - as a list of categories (for `Categorical` dimensions), or - an instance of a `Dimension` object (`Real`, `Integer` or `Categorical`). .. note:: The upper and lower bounds are inclusive for `Integer` dimensions. base_estimator : sklearn regressor Should inherit from `sklearn.base.RegressorMixin`. In addition, should have an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`. n_calls : int, default: 100 Maximum number of calls to `func`. An objective function will always be evaluated this number of times; Various options to supply initialization points do not affect this value. n_random_starts : int, default: None Number of evaluations of `func` with random points before approximating it with `base_estimator`. .. deprecated:: 0.8 use `n_initial_points` instead. n_initial_points : int, default: 10 Number of evaluations of `func` with initialization points before approximating it with `base_estimator`. Initial point generator can be changed by setting `initial_point_generator`. initial_point_generator : str, InitialPointGenerator instance, \ default: `"random"` Sets a initial points generator. Can be either - `"random"` for uniform random numbers, - `"sobol"` for a Sobol' sequence, - `"halton"` for a Halton sequence, - `"hammersly"` for a Hammersly sequence, - `"lhs"` for a latin hypercube sequence, - `"grid"` for a uniform grid sequence acq_func : string, default: `"EI"` Function to minimize over the posterior distribution. Can be either - `"LCB"` for lower confidence bound, - `"EI"` for negative expected improvement, - `"PI"` for negative probability of improvement. - `"EIps"` for negated expected improvement per second to take into account the function compute time. Then, the objective function is assumed to return two values, the first being the objective value and the second being the time taken in seconds. - `"PIps"` for negated probability of improvement per second. The return type of the objective function is assumed to be similar to that of `"EIps"` acq_optimizer : string, `"sampling"` or `"lbfgs"`, default: `"lbfgs"` Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing `acq_func` with `acq_optimizer`. - If set to `"sampling"`, then `acq_func` is optimized by computing `acq_func` at `n_points` randomly sampled points and the smallest value found is used. - If set to `"lbfgs"`, then - The `n_restarts_optimizer` no. of points which the acquisition function is least are taken as start points. - `"lbfgs"` is run for 20 iterations with these points as initial points to find local minima. - The optimal of these local minima is used to update the prior. x0 : list, list of lists or `None` Initial input points. - If it is a list of lists, use it as a list of input points. If no corresponding outputs `y0` are supplied, then len(x0) of total calls to the objective function will be spent evaluating the points in `x0`. If the corresponding outputs are provided, then they will be used together with evaluated points during a run of the algorithm to construct a surrogate. - If it is a list, use it as a single initial input point. The algorithm will spend 1 call to evaluate the initial point, if the outputs are not provided. - If it is `None`, no initial input points are used. y0 : list, scalar or `None` Objective values at initial input points. - If it is a list, then it corresponds to evaluations of the function at each element of `x0` : the i-th element of `y0` corresponds to the function evaluated at the i-th element of `x0`. - If it is a scalar, then it corresponds to the evaluation of the function at `x0`. - If it is None and `x0` is provided, then the function is evaluated at each element of `x0`. random_state : int, RandomState instance, or None (default) Set random state to something other than None for reproducible results. verbose : boolean, default: False Control the verbosity. It is advised to set the verbosity to True for long optimization runs. callback : callable, list of callables, optional If callable then `callback(res)` is called after each call to `func`. If list of callables, then each callable in the list is called. n_points : int, default: 10000 If `acq_optimizer` is set to `"sampling"`, then `acq_func` is optimized by computing `acq_func` at `n_points` randomly sampled points. n_restarts_optimizer : int, default: 5 The number of restarts of the optimizer when `acq_optimizer` is `"lbfgs"`. xi : float, default: 0.01 Controls how much improvement one wants over the previous best values. Used when the acquisition is either `"EI"` or `"PI"`. kappa : float, default: 1.96 Controls how much of the variance in the predicted values should be taken into account. If set to be very high, then we are favouring exploration over exploitation and vice versa. Used when the acquisition is `"LCB"`. n_jobs : int, default: 1 Number of cores to run in parallel while running the lbfgs optimizations over the acquisition function and given to the base_estimator. Valid only when `acq_optimizer` is set to "lbfgs". or when the base_estimator supports n_jobs as parameter and was given as string. Defaults to 1 core. If `n_jobs=-1`, then number of jobs is set to number of cores. model_queue_size : int or None, default: None Keeps list of models only as long as the argument given. In the case of None, the list has no capped length. Returns ------- res : `OptimizeResult`, scipy object The optimization result returned as a OptimizeResult object. Important attributes are: - `x` [list]: location of the minimum. - `fun` [float]: function value at the minimum. - `models`: surrogate models used for each iteration. - `x_iters` [list of lists]: location of function evaluation for each iteration. - `func_vals` [array]: function value for each iteration. - `space` [Space]: the optimization space. - `specs` [dict]`: the call specifications. - `rng` [RandomState instance]: State of the random state at the end of minimization. For more details related to the OptimizeResult object, refer """ specs = {"args": locals(), "function": "base_minimize"} acq_optimizer_kwargs = { "n_points": n_points, "n_restarts_optimizer": n_restarts_optimizer, "n_jobs": n_jobs, } acq_func_kwargs = {"xi": xi, "kappa": kappa} # Initialize optimization # Suppose there are points provided (x0 and y0), record them # check x0: list-like, requirement of minimal points if x0 is None: x0 = [] elif not isinstance(x0[0], (list, tuple)): x0 = [x0] if not isinstance(x0, list): raise ValueError("`x0` should be a list, but got %s" % type(x0)) # Check `n_random_starts` deprecation first if n_random_starts is not None: warnings.warn( ( "n_random_starts will be removed in favour of " "n_initial_points. It overwrites n_initial_points." ), DeprecationWarning, ) n_initial_points = n_random_starts if n_initial_points <= 0 and not x0: raise ValueError("Either set `n_initial_points` > 0," " or provide `x0`") # check y0: list-like, requirement of maximal calls if isinstance(y0, Iterable): y0 = list(y0) elif isinstance(y0, numbers.Number): y0 = [y0] required_calls = n_initial_points + (len(x0) if not y0 else 0) if n_calls < required_calls: raise ValueError("Expected `n_calls` >= %d, got %d" % (required_calls, n_calls)) # calculate the total number of initial points n_initial_points = n_initial_points + len(x0) # Build optimizer # create optimizer class optimizer = Optimizer( dimensions, base_estimator, n_initial_points=n_initial_points, initial_point_generator=initial_point_generator, n_jobs=n_jobs, acq_func=acq_func, acq_optimizer=acq_optimizer, random_state=random_state, model_queue_size=model_queue_size, acq_optimizer_kwargs=acq_optimizer_kwargs, acq_func_kwargs=acq_func_kwargs, ) # check x0: element-wise data type, dimensionality assert all(isinstance(p, Iterable) for p in x0) if not all(len(p) == for p in x0): raise RuntimeError( "Optimization space (%s) and initial points in x0 " "use inconsistent dimensions." % ) # check callback callbacks = check_callback(callback) if verbose: callbacks.append( VerboseCallback( n_init=len(x0) if not y0 else 0, n_random=n_initial_points, n_total=n_calls, ) ) # Record provided points # create return object result = None # evaluate y0 if only x0 is provided if x0 and y0 is None: y0 = list(map(func, x0)) n_calls -= len(y0) # record through tell function if x0: if not (isinstance(y0, Iterable) or isinstance(y0, numbers.Number)): raise ValueError( "`y0` should be an iterable or a scalar, got %s" % type(y0) ) if len(x0) != len(y0): raise ValueError("`x0` and `y0` should have the same length") result = optimizer.tell(x0, y0) result.specs = specs if eval_callbacks(callbacks, result): return result # Optimize for n in range(n_calls): next_x = optimizer.ask() next_y = func(next_x) result = optimizer.tell(next_x, next_y) result.specs = specs if eval_callbacks(callbacks, result): break return result