bayesmark/builtin_opt/scikit_optimizer.py [54:162]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self.skopt = SkOpt( dimensions, n_initial_points=n_initial_points, base_estimator=base_estimator, acq_func=acq_func, acq_optimizer="auto", acq_func_kwargs={}, acq_optimizer_kwargs={}, ) @staticmethod def get_sk_dimensions(api_config, transform="normalize"): """Help routine to setup skopt search space in constructor. Take api_config as argument so this can be static. """ # The ordering of iteration prob makes no difference, but just to be # safe and consistnent with space.py, I will make sorted. param_list = sorted(api_config.keys()) sk_dims = [] round_to_values = {} for param_name in param_list: param_config = api_config[param_name] param_type = param_config["type"] param_space = param_config.get("space", None) param_range = param_config.get("range", None) param_values = param_config.get("values", None) # Some setup for case that whitelist of values is provided: values_only_type = param_type in ("cat", "ordinal") if (param_values is not None) and (not values_only_type): assert param_range is None param_values = np.unique(param_values) param_range = (param_values[0], param_values[-1]) round_to_values[param_name] = interp1d( param_values, param_values, kind="nearest", fill_value="extrapolate" ) if param_type == "int": # Integer space in sklearn does not support any warping => Need # to leave the warping as linear in skopt. sk_dims.append(Integer(param_range[0], param_range[-1], transform=transform, name=param_name)) elif param_type == "bool": assert param_range is None assert param_values is None sk_dims.append(Integer(0, 1, transform=transform, name=param_name)) elif param_type in ("cat", "ordinal"): assert param_range is None # Leave x-form to one-hot as per skopt default sk_dims.append(Categorical(param_values, name=param_name)) elif param_type == "real": # Skopt doesn't support all our warpings, so need to pick # closest substitute it does support. prior = "log-uniform" if param_space in ("log", "logit") else "uniform" sk_dims.append(Real(param_range[0], param_range[-1], prior=prior, transform=transform, name=param_name)) else: assert False, "type %s not handled in API" % param_type return sk_dims, round_to_values def suggest(self, n_suggestions=1): """Get a suggestion from the optimizer. Parameters ---------- n_suggestions : int Desired number of parallel suggestions in the output Returns ------- next_guess : list of dict List of `n_suggestions` suggestions to evaluate the objective function. Each suggestion is a dictionary where each key corresponds to a parameter being optimized. """ # First get list of lists from skopt.ask() next_guess = self.skopt.ask(n_points=n_suggestions) # Then convert to list of dicts next_guess = [dict(zip(self.dimensions_list, x)) for x in next_guess] # Now do the rounding, custom rounding is not supported in skopt. Note # that there is not nec a round function for each dimension here. for param_name, round_f in self.round_to_values.items(): for xx in next_guess: xx[param_name] = round_f(xx[param_name]) return next_guess def observe(self, X, y): """Send an observation of a suggestion back to the optimizer. Parameters ---------- X : list of dict-like Places where the objective function has already been evaluated. Each suggestion is a dictionary where each key corresponds to a parameter being optimized. y : array-like, shape (n,) Corresponding values where objective has been evaluated """ # Supposedly skopt can handle blocks, but not sure about interface for # that. Just do loop to be safe for now. for xx, yy in zip(X, y): # skopt needs lists instead of dicts xx = [xx[dim_name] for dim_name in self.dimensions_list] # Just ignore, any inf observations we got, unclear if right thing if np.isfinite(yy): self.skopt.tell(xx, yy) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - example_opt_root/scikit_optimizer.py [38:146]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - self.skopt = SkOpt( dimensions, n_initial_points=n_initial_points, base_estimator=base_estimator, acq_func=acq_func, acq_optimizer="auto", acq_func_kwargs={}, acq_optimizer_kwargs={}, ) @staticmethod def get_sk_dimensions(api_config, transform="normalize"): """Help routine to setup skopt search space in constructor. Take api_config as argument so this can be static. """ # The ordering of iteration prob makes no difference, but just to be # safe and consistnent with space.py, I will make sorted. param_list = sorted(api_config.keys()) sk_dims = [] round_to_values = {} for param_name in param_list: param_config = api_config[param_name] param_type = param_config["type"] param_space = param_config.get("space", None) param_range = param_config.get("range", None) param_values = param_config.get("values", None) # Some setup for case that whitelist of values is provided: values_only_type = param_type in ("cat", "ordinal") if (param_values is not None) and (not values_only_type): assert param_range is None param_values = np.unique(param_values) param_range = (param_values[0], param_values[-1]) round_to_values[param_name] = interp1d( param_values, param_values, kind="nearest", fill_value="extrapolate" ) if param_type == "int": # Integer space in sklearn does not support any warping => Need # to leave the warping as linear in skopt. sk_dims.append(Integer(param_range[0], param_range[-1], transform=transform, name=param_name)) elif param_type == "bool": assert param_range is None assert param_values is None sk_dims.append(Integer(0, 1, transform=transform, name=param_name)) elif param_type in ("cat", "ordinal"): assert param_range is None # Leave x-form to one-hot as per skopt default sk_dims.append(Categorical(param_values, name=param_name)) elif param_type == "real": # Skopt doesn't support all our warpings, so need to pick # closest substitute it does support. prior = "log-uniform" if param_space in ("log", "logit") else "uniform" sk_dims.append(Real(param_range[0], param_range[-1], prior=prior, transform=transform, name=param_name)) else: assert False, "type %s not handled in API" % param_type return sk_dims, round_to_values def suggest(self, n_suggestions=1): """Get a suggestion from the optimizer. Parameters ---------- n_suggestions : int Desired number of parallel suggestions in the output Returns ------- next_guess : list of dict List of `n_suggestions` suggestions to evaluate the objective function. Each suggestion is a dictionary where each key corresponds to a parameter being optimized. """ # First get list of lists from skopt.ask() next_guess = self.skopt.ask(n_points=n_suggestions) # Then convert to list of dicts next_guess = [dict(zip(self.dimensions_list, x)) for x in next_guess] # Now do the rounding, custom rounding is not supported in skopt. Note # that there is not nec a round function for each dimension here. for param_name, round_f in self.round_to_values.items(): for xx in next_guess: xx[param_name] = round_f(xx[param_name]) return next_guess def observe(self, X, y): """Send an observation of a suggestion back to the optimizer. Parameters ---------- X : list of dict-like Places where the objective function has already been evaluated. Each suggestion is a dictionary where each key corresponds to a parameter being optimized. y : array-like, shape (n,) Corresponding values where objective has been evaluated """ # Supposedly skopt can handle blocks, but not sure about interface for # that. Just do loop to be safe for now. for xx, yy in zip(X, y): # skopt needs lists instead of dicts xx = [xx[dim_name] for dim_name in self.dimensions_list] # Just ignore, any inf observations we got, unclear if right thing if np.isfinite(yy): self.skopt.tell(xx, yy) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -