From 30cd59059a87cf5ef84ed9e0a93595cc64f6f70f Mon Sep 17 00:00:00 2001 From: Kobi Felton Date: Sun, 25 Jun 2023 19:05:52 +0100 Subject: [PATCH] Remove GPy and SOBO --- poetry.lock | 4946 +++++++++++++++---------------- pyproject.toml | 4 +- summit/strategies/__init__.py | 4 - summit/strategies/sobo.py | 453 --- summit/tests/test_runner.py | 5 +- summit/tests/test_strategies.py | 186 +- 6 files changed, 2427 insertions(+), 3171 deletions(-) delete mode 100644 summit/strategies/sobo.py diff --git a/poetry.lock b/poetry.lock index 18796121..3b055e6e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,26 +1,37 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + [[package]] name = "about-time" version = "4.2.1" description = "Easily measure timing and throughput of code blocks, with beautiful human friendly representations." -category = "main" optional = false python-versions = ">=3.7, <4" +files = [ + {file = "about-time-4.2.1.tar.gz", hash = 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b/pyproject.toml index d5fb6931..ad0466e5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,9 +36,7 @@ torch = "^1.11.0" skorch = "^0.11.0" cython = "^0.29.21" -# Dependencies for TSEMO and SOBO -GPy = "^1.9" -gpyopt = "^1.2.6" +# Dependencies for TSEMO pyrff = "^2.0.1" pymoo = "^0.6.0" diff --git a/summit/strategies/__init__.py b/summit/strategies/__init__.py index 3507bac0..90201ed5 100644 --- a/summit/strategies/__init__.py +++ b/summit/strategies/__init__.py @@ -5,7 +5,6 @@ from .tsemo import TSEMO from .neldermead import NelderMead from .snobfit import SNOBFIT -from .sobo import SOBO from .multitask import MTBO, STBO from .deep_reaction_optimizer import DRO from .entmoot import ENTMOOT @@ -20,7 +19,6 @@ "ENTMOOT", "MTBO", "STBO", - "SOBO", "DRO", "strategy_from_dict", ] + base_all @@ -35,8 +33,6 @@ def strategy_from_dict(d): return TSEMO.from_dict(d) elif d["name"] == "GRYFFIN": raise ValueError("Gryffin is now deprecated.") - elif d["name"] == "SOBO": - return SOBO.from_dict(d) elif d["name"] == "SNOBFIT": return SNOBFIT.from_dict(d) elif d["name"] == "NelderMead": diff --git a/summit/strategies/sobo.py b/summit/strategies/sobo.py deleted file mode 100644 index 53e9854c..00000000 --- a/summit/strategies/sobo.py +++ /dev/null @@ -1,453 +0,0 @@ -from .base import Strategy -from .random import LHS -from summit.domain import * -from summit.utils.dataset import DataSet - - -import numpy as np -import pandas as pd -from abc import ABC, abstractmethod - - -class SOBO(Strategy): - """Single-objective Bayesian Optimization (SOBO) - - This is a general BO method since it is a wrapper around GPyOpt. - - Parameters - ---------- - domain: :class:`~summit.domain.Domain` - The Summit domain describing the optimization problem. - transform : :class:`~summit.strategies.base.Transform`, optional - A transform object. By default no transformation will be done - on the input variables or objectives. - gp_model_type: string, optional - The GPy Gaussian Process model type. See notes for options. - By default, gaussian processes with the Matern 5.2 kernel will be used. - use_descriptors : bool, optional - Whether to use descriptors of categorical variables. Defaults to False. - acquisition_type: string, optional - The acquisition function type from GPyOpt. See notes for options. - By default, Excpected Improvement (EI). - optimizer_type: string, optional - The internal optimizer used in GPyOpt for maximization of the acquisition function. - By default, lfbgs will be used. - evaluator_type: string, optional - The evaluator type used for batch mode (how multiple points are chosen in one iteration). - By default, thompson sampling will be used. - kernel: :class:`~GPy.kern.kern`, optional - The kernel used in the GP. - By default a Matern 5.2 kernel (GPy object) will be used. - exact_feval: boolean, optional - Whether the function evaluations are exact (True) or noisy (False). - By default: False. - ARD: boolean, optional - Whether automatic relevance determination should be applied (True). - By default: True. - standardize_outputs: boolean, optional - Whether the outputs should be standardized (True). - By default: True. - - Examples - -------- - >>> from summit.domain import * - >>> from summit.strategies import SOBO - >>> domain = Domain() - >>> domain += ContinuousVariable(name='temperature', description='reaction temperature in celsius', bounds=[50, 100]) - >>> domain += CategoricalVariable(name='flowrate_a', description='flow of reactant a in mL/min', levels=[1,2,3,4,5]) - >>> domain += ContinuousVariable(name='flowrate_b', description='flow of reactant b in mL/min', bounds=[0.1, 0.5]) - >>> domain += ContinuousVariable(name="yld", description='yield of reaction', bounds=[0,100], is_objective=True) - >>> strategy = SOBO(domain) - >>> next_experiments = strategy.suggest_experiments(5) - - Notes - ---------- - - Gaussian Process (GP) model - GP: standard Gaussian Process - - GP_MCMC: Gaussian Process with prior in hyperparameters - - sparseGP: sparse Gaussian Process - - warpedGP: warped Gaussian Process - - InputWarpedGP: input warped Gaussian Process - - RF: random forest (scikit-learn) - - Acquisition function type - EI: expected improvement - - EI_MCMC: integrated expected improvement (requires GP_MCMC model) (https://dash.harvard.edu/bitstream/handle/1/11708816/snoek-bayesopt-nips-2012.pdf?sequence%3D1) - - LCB: lower confidence bound - - LCB_MCMC: integrated GP-Lower confidence bound (requires GP_MCMC model) - - MPI: maximum probability of improvement - - MPI_MCMC: maximum probability of improvement (requires GP_MCMC model) - - LP: local penalization - - ES: entropy search - - This implementation uses the python package GPyOpt provided by - the Machine Learning Group of the University of Sheffield. - - Github repository: https://github.com/SheffieldML/GPyOpt - - - """ - - def __init__( - self, - domain, - transform=None, - gp_model_type=None, - acquisition_type=None, - optimizer_type=None, - evaluator_type=None, - **kwargs - ): - from GPy.kern import Matern52 - - Strategy.__init__(self, domain, transform=transform, **kwargs) - - self.use_descriptors = kwargs.get("use_descriptors", False) - # TODO: notation - discrete in our model (e.g., catalyst type) = categorical? - self.input_domain = [] - for v in self.domain.variables: - if not v.is_objective: - if isinstance(v, ContinuousVariable): - self.input_domain.append( - { - "name": v.name, - "type": v.variable_type, - "domain": (v.bounds[0], v.bounds[1]), - } - ) - elif isinstance(v, CategoricalVariable): - if not self.use_descriptors: - self.input_domain.append( - { - "name": v.name, - "type": "categorical", - "domain": tuple(self.categorical_wrapper(v.levels)), - } - ) - elif v.ds is not None and self.use_descriptors: - if v.ds is None: - raise ValueError( - "No descriptors provided for variable: {}".format( - v.name - ) - ) - descriptor_names = v.ds.data_columns - descriptors = np.asarray( - [ - v.ds.loc[:, [l]].values.tolist() - for l in v.ds.data_columns - ] - ) - for j, d in enumerate(descriptors): - self.input_domain.append( - { - "name": descriptor_names[j], - "type": "continuous", - "domain": ( - np.min(np.asarray(d)), - np.max(np.asarray(d)), - ), - } - ) - elif v.ds is None and self.use_descriptors: - raise ValueError( - "Cannot use descriptors because none are provided." - ) - # TODO: GPyOpt currently does not support mixed-domains w/ bandit inputs, there is a PR for this though - else: - raise TypeError("Unknown variable type.") - - # TODO: how to handle equality constraints? Could we remove '==' from constraint types as each equality - # constraint reduces the degrees of freedom? - if self.domain.constraints is not None: - constraints = self.constr_wrapper(self.domain) - self.constraints = [ - { - "name": "constr_" + str(i), - "constraint": c[0] - if c[1] in ["<=", "<"] - else "(" + c[0] + ")*(-1)", - } - for i, c in enumerate(constraints) - if not (c[1] == "==") - ] - else: - self.constraints = None - - self.input_dim = len(self.domain.input_variables) - - if gp_model_type in [ - "GP", - "GP_MCMC", - "sparseGP", - "warpedGP", - "InputWarpedGP", - "RF", - ]: - self.gp_model_type = gp_model_type - else: - self.gp_model_type = "GP" # default model type is a standard Gaussian Process (from GPy package) - - if acquisition_type in [ - "EI", - "EI_MCMC", - "LCB", - "LCB_MCMC", - "MPI", - "MPI_MCMC", - "LP", - "ES", - ]: - self.acquisition_type = acquisition_type - else: - self.acquisition_type = ( - "EI" # default acquisition function is expected utility improvement - ) - - """ - Method for optimization of acquisition function - lbfgs: Limited-memory Broyden–Fletcher–Goldfarb–Shanno, - DIRECT: Dividing Rectangles, - CMA: covariance matrix adaption - """ - if optimizer_type in ["lbfgs", "DIRECT", "CMA"]: - self.optimizer_type = optimizer_type - else: - self.optimizer_type = "lbfgs" # default optimizer: lbfgs - - if evaluator_type in [ - "sequential", - "random", - "local_penalization", - "thompson_sampling", - ]: - self.evaluator_type = evaluator_type - else: - self.evaluator_type = "random" - - # specify GPy kernel: # https://gpy.readthedocs.io/en/deploy/GPy.kern.html#subpackages - self.kernel = kwargs.get("kernel", Matern52(self.input_dim)) - # Are function values exact (w/o noise)? - self.exact_feval = kwargs.get("exact_feval", False) - # automatic relevance determination - self.ARD = kwargs.get("ARD", True) - # Standardization of outputs? - self.standardize_outputs = kwargs.get("standardize_outputs", True) - self.prev_param = None - - def suggest_experiments( - self, num_experiments=1, prev_res: DataSet = None, **kwargs - ): - """Suggest experiments using GPyOpt single-objective Bayesian Optimization - - Parameters - ---------- - num_experiments: int, optional - The number of experiments (i.e., samples) to generate. Default is 1. - prev_res: :class:`~summit.utils.data.DataSet`, optional - Dataset with data from previous experiments of previous iteration. - If no data is passed, then random sampling will - be used to suggest an initial design. - - Returns - ------- - next_experiments : :class:`~summit.utils.data.DataSet` - A Dataset object with the suggested experiments - - """ - import GPyOpt - - param = None - xbest = np.zeros(self.domain.num_continuous_dimensions()) - obj = self.domain.output_variables[0] - objective_dir = -1.0 if obj.maximize else 1.0 - fbest = float("inf") - - # Suggest random initial design - if prev_res is None: - """lhs design does not consider constraints - lhs = LHS(self.domain) - next_experiments = lhs.suggest_experiments((num_experiments)) - return next_experiments, None, float("inf"), None - """ - feasible_region = GPyOpt.Design_space( - space=self.input_domain, constraints=self.constraints - ) - request = GPyOpt.experiment_design.initial_design( - "random", feasible_region, num_experiments - ) - else: - # Get inputs and outputs - inputs, outputs = self.transform.transform_inputs_outputs( - prev_res, - categorical_method="descriptors" if self.use_descriptors else None, - ) - - # Set up maximization and minimization by converting maximization to minimization problem - for v in self.domain.variables: - if v.is_objective and v.maximize: - outputs[v.name] = -1 * outputs[v.name] - if isinstance(v, CategoricalVariable): - if not self.use_descriptors: - inputs[v.name] = self.categorical_wrapper( - inputs[v.name], v.levels - ) - - inputs = inputs.to_numpy().astype(float) - outputs = outputs.to_numpy() - - if self.prev_param is not None: - X_step = self.prev_param[0].astype(float) - Y_step = self.prev_param[1].astype(float) - - X_step = np.vstack((X_step, inputs)) - Y_step = np.vstack((Y_step, outputs)) - - else: - X_step = inputs - Y_step = outputs - - sobo_model = GPyOpt.methods.BayesianOptimization( - f=None, - domain=self.input_domain, - constraints=self.constraints, - model_type=self.gp_model_type, - kernel=self.kernel, - acquisition_type=self.acquisition_type, - acquisition_optimizer_type=self.optimizer_type, - normalize_Y=self.standardize_outputs, - batch_size=num_experiments, - evaluator_type=self.evaluator_type, - maximize=False, - ARD=self.ARD, - exact_feval=self.exact_feval, - X=X_step, - Y=Y_step, - ) - request = sobo_model.suggest_next_locations() - - # Store parameters (history of suggested points and function evaluations) - param = [X_step, Y_step] - - fbest = np.min(Y_step) - xbest = X_step[np.argmin(Y_step)] - - # Generate DataSet object with variable values of next - next_experiments = None - transform_descriptors = False - if request is not None and len(request) != 0: - next_experiments = {} - i_inp = 0 - for v in self.domain.variables: - if not v.is_objective: - if isinstance(v, CategoricalVariable): - if v.ds is None or not self.use_descriptors: - cat_list = [] - for j, entry in enumerate(request[:, i_inp]): - cat_list.append( - self.categorical_unwrap(entry, v.levels) - ) - next_experiments[v.name] = np.asarray(cat_list) - i_inp += 1 - else: - descriptor_names = v.ds.data_columns - for d in descriptor_names: - next_experiments[d] = request[:, i_inp] - i_inp += 1 - transform_descriptors = True - else: - next_experiments[v.name] = request[:, i_inp] - i_inp += 1 - next_experiments = DataSet.from_df(pd.DataFrame(data=next_experiments)) - next_experiments[("strategy", "METADATA")] = "Single-objective BayOpt" - - self.fbest = objective_dir * fbest - self.xbest = xbest - self.prev_param = param - - # Do any necessary transformation back - next_experiments = self.transform.un_transform( - next_experiments, - categorical_method="descriptors" if self.use_descriptors else None, - ) - - return next_experiments - - def reset(self): - """Reset the internal parameters""" - self.prev_param = None - - def to_dict(self): - if self.prev_param is not None: - param = [ - self.prev_param[0].astype(float).tolist(), - self.prev_param[1].astype(float).tolist(), - ] - else: - param = None - - strategy_params = dict( - prev_param=param, - use_descriptors=self.use_descriptors, - gp_model_type=self.gp_model_type, - acquisition_type=self.acquisition_type, - optimizer_type=self.optimizer_type, - evaluator_type=self.evaluator_type, - kernel=self.kernel.to_dict(), - exact_feval=self.exact_feval, - ARD=self.ARD, - standardize_outputs=self.standardize_outputs, - ) - - return super().to_dict(**strategy_params) - - @classmethod - def from_dict(cls, d): - from GPy.kern import Kern - - # Get kernel - kernel = d["strategy_params"].get("kernel") - if kernel is not None: - kernel = Kern.from_dict(kernel) - d["strategy_params"]["kernel"] = kernel - - # Setup SOBO - sobo = super().from_dict(d) - param = d["strategy_params"]["prev_param"] - if param is not None: - param = [np.array(param[0]), np.array(param[1])] - sobo.prev_param = param - return sobo - - def constr_wrapper(self, summit_domain): - v_input_names = [v.name for v in summit_domain.variables if not v.is_objective] - gpyopt_constraints = [] - for c in summit_domain.constraints: - tmp_c = c.lhs - for v_input_index, v_input_name in enumerate(v_input_names): - v_gpyopt_name = "x[:," + str(v_input_index) + "]" - tmp_c = tmp_c.replace(v_input_name, v_gpyopt_name) - gpyopt_constraints.append([tmp_c, c.constraint_type]) - return gpyopt_constraints - - def categorical_wrapper(self, categories, reference_categories=None): - if not reference_categories: - return [i for i, _ in enumerate(categories)] - else: - return [reference_categories.index(c) for c in categories] - - def categorical_unwrap(self, gpyopt_level, categories): - return categories[int(gpyopt_level)] diff --git a/summit/tests/test_runner.py b/summit/tests/test_runner.py index 26995085..87e5067b 100644 --- a/summit/tests/test_runner.py +++ b/summit/tests/test_runner.py @@ -91,7 +91,7 @@ def stop(self): assert r.experiment.data.shape[0] == int(batch_size * iterations + 10) -@pytest.mark.parametrize("strategy", [SOBO, SNOBFIT, NelderMead, Random, LHS]) +@pytest.mark.parametrize("strategy", [SNOBFIT, NelderMead, Random, LHS]) @pytest.mark.parametrize( "experiment", [ @@ -118,7 +118,7 @@ def test_runner_so_integration(strategy, experiment): os.remove("test_save.json") -@pytest.mark.parametrize("strategy", [SOBO, SNOBFIT, NelderMead, Random, LHS, TSEMO]) +@pytest.mark.parametrize("strategy", [SNOBFIT, NelderMead, Random, LHS, TSEMO]) @pytest.mark.parametrize( "experiment", [ @@ -137,7 +137,6 @@ def test_runner_mo_integration(strategy, experiment): exp = experiment if experiment.__class__.__name__ == "ReizmanSuzukiEmulator" and strategy not in [ - SOBO, TSEMO, ]: # only run on strategies that work with categorical variables direclty diff --git a/summit/tests/test_strategies.py b/summit/tests/test_strategies.py index 8b7c3328..a67cfa21 100644 --- a/summit/tests/test_strategies.py +++ b/summit/tests/test_strategies.py @@ -534,99 +534,99 @@ def test_nm3D(maximize, x_start, constraint, plot=False): fig, ax = hartmann3D.plot(polygons=polygons_points) -@pytest.mark.parametrize( - "batch_size, max_num_exp, maximize, constraint", - [ - [1, 200, True, True], - [4, 200, False, True], - [1, 200, True, False], - [4, 200, False, False], - ], -) -def test_sobo( - batch_size, max_num_exp, maximize, constraint, test_num_improve_iter=50, plot=False -): - hartmann3D = Hartmann3D(maximize=maximize, constraints=constraint) - strategy = SOBO(domain=hartmann3D.domain, kernel=GPy.kern.Matern32(3)) - - # run SOBO loop for fixed number of iteration - num_iter = max_num_exp // batch_size # maximum number of iterations - max_stop = ( - 80 // batch_size - ) # allowed number of consecutive iterations w/o improvement - min_stop = ( - 20 // batch_size - ) # minimum number of iterations before algorithm is stopped - nstop = 0 - - num_improve_iter = 0 - fbestold = float("inf") - next_experiments = None - pb = progress_bar(range(num_iter)) - for i in pb: - next_experiments = strategy.suggest_experiments( - num_experiments=batch_size, prev_res=next_experiments - ) - - # This is the part where experiments take place - next_experiments = hartmann3D.run_experiments(next_experiments) - - fbest = strategy.fbest * -1.0 if maximize else strategy.fbest - xbest = strategy.xbest - if fbest < fbestold: - if fbest < 0.99 * fbestold or i < min_stop: - nstop = 0 - num_improve_iter += 1 - else: - nstop += 1 - fbestold = fbest - pb.comment = f"Best f value: {fbest} at point: {xbest}" - print("\n") - else: - nstop += 1 - if nstop >= max_stop: - print( - "Stopping criterion reached. No improvement in last " - + str(max_stop) - + " iterations." - ) - break - if fbest < -3.85: - print("Stopping criterion reached. Function value below -3.85.") - break - if num_improve_iter >= test_num_improve_iter: - print( - "Requirement to improve fbest in at least {} satisfied, test stopped.".format( - test_num_improve_iter - ) - ) - break - - xbest = np.around(xbest.astype(float), decimals=3) - fbest = np.around(fbest, decimals=3) - print("Optimal setting: " + str(xbest) + " with outcome: " + str(fbest)) - # Extrema of test function without constraint: glob_min = -3.86 at (0.114,0.556,0.853) - assert fbest <= -3.84 and fbest >= -3.87 - - # Test saving and loading - strategy.save("sobo_test.json") - strategy_2 = SOBO.load("sobo_test.json") - os.remove("sobo_test.json") - - if strategy.prev_param is not None: - # assert np.isclose(strategy.prev_param[0], strategy_2.prev_param[0]) - # assert np.isclose(strategy.prev_param[1], strategy_2.prev_param[1]) - assert strategy.use_descriptors == strategy_2.use_descriptors - assert strategy.gp_model_type == strategy_2.gp_model_type - assert strategy.acquisition_type == strategy_2.acquisition_type - assert strategy.optimizer_type == strategy_2.optimizer_type - assert strategy.evaluator_type == strategy_2.evaluator_type - assert strategy.kernel.to_dict() == strategy_2.kernel.to_dict() - assert strategy.exact_feval == strategy_2.exact_feval - assert strategy.ARD == strategy_2.ARD - assert strategy.standardize_outputs == strategy_2.standardize_outputs - if plot: - fig, ax = hartmann3D.plot() +# @pytest.mark.parametrize( +# "batch_size, max_num_exp, maximize, constraint", +# [ +# [1, 200, True, True], +# [4, 200, False, True], +# [1, 200, True, False], +# [4, 200, False, False], +# ], +# ) +# def test_sobo( +# batch_size, max_num_exp, maximize, constraint, test_num_improve_iter=50, plot=False +# ): +# hartmann3D = Hartmann3D(maximize=maximize, constraints=constraint) +# strategy = SOBO(domain=hartmann3D.domain, kernel=GPy.kern.Matern32(3)) + +# # run SOBO loop for fixed number of iteration +# num_iter = max_num_exp // batch_size # maximum number of iterations +# max_stop = ( +# 80 // batch_size +# ) # allowed number of consecutive iterations w/o improvement +# min_stop = ( +# 20 // batch_size +# ) # minimum number of iterations before algorithm is stopped +# nstop = 0 + +# num_improve_iter = 0 +# fbestold = float("inf") +# next_experiments = None +# pb = progress_bar(range(num_iter)) +# for i in pb: +# next_experiments = strategy.suggest_experiments( +# num_experiments=batch_size, prev_res=next_experiments +# ) + +# # This is the part where experiments take place +# next_experiments = hartmann3D.run_experiments(next_experiments) + +# fbest = strategy.fbest * -1.0 if maximize else strategy.fbest +# xbest = strategy.xbest +# if fbest < fbestold: +# if fbest < 0.99 * fbestold or i < min_stop: +# nstop = 0 +# num_improve_iter += 1 +# else: +# nstop += 1 +# fbestold = fbest +# pb.comment = f"Best f value: {fbest} at point: {xbest}" +# print("\n") +# else: +# nstop += 1 +# if nstop >= max_stop: +# print( +# "Stopping criterion reached. No improvement in last " +# + str(max_stop) +# + " iterations." +# ) +# break +# if fbest < -3.85: +# print("Stopping criterion reached. Function value below -3.85.") +# break +# if num_improve_iter >= test_num_improve_iter: +# print( +# "Requirement to improve fbest in at least {} satisfied, test stopped.".format( +# test_num_improve_iter +# ) +# ) +# break + +# xbest = np.around(xbest.astype(float), decimals=3) +# fbest = np.around(fbest, decimals=3) +# print("Optimal setting: " + str(xbest) + " with outcome: " + str(fbest)) +# # Extrema of test function without constraint: glob_min = -3.86 at (0.114,0.556,0.853) +# assert fbest <= -3.84 and fbest >= -3.87 + +# # Test saving and loading +# strategy.save("sobo_test.json") +# strategy_2 = SOBO.load("sobo_test.json") +# os.remove("sobo_test.json") + +# if strategy.prev_param is not None: +# # assert np.isclose(strategy.prev_param[0], strategy_2.prev_param[0]) +# # assert np.isclose(strategy.prev_param[1], strategy_2.prev_param[1]) +# assert strategy.use_descriptors == strategy_2.use_descriptors +# assert strategy.gp_model_type == strategy_2.gp_model_type +# assert strategy.acquisition_type == strategy_2.acquisition_type +# assert strategy.optimizer_type == strategy_2.optimizer_type +# assert strategy.evaluator_type == strategy_2.evaluator_type +# assert strategy.kernel.to_dict() == strategy_2.kernel.to_dict() +# assert strategy.exact_feval == strategy_2.exact_feval +# assert strategy.ARD == strategy_2.ARD +# assert strategy.standardize_outputs == strategy_2.standardize_outputs +# if plot: +# fig, ax = hartmann3D.plot() @pytest.mark.parametrize(