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evaluate_metabo_svm.py
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evaluate_metabo_svm.py
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# Copyright (c) 2019 Robert Bosch GmbH
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# ******************************************************************
# evaluate_metabo_svm.py
# Reproduce results from MetaBO paper on SVM-hyperparameter optimization
# For convenience, we provide the pretrained weights resulting from the experiments described in the paper.
# These weights can be reproduced using train_metabo_svm.py
# ******************************************************************
# Note: due to licensing issues, the datasets used in this experiment cannot be shipped with the MetaBO package.
# However, you can download the datasets yourself from https://github.com/nicoschilling/ECML2016
# Put the folder "data/svm" from this repository into metabo/environment/hpo/data
import os
from metabo.eval.evaluate import eval_experiment
from metabo.eval.plot_results import plot_results
from metabo.environment.hpo.prepare_data import prepare_hpo_data
from metabo.policies.taf.generate_taf_data_hpo import generate_taf_data_hpo
from gym.envs.registration import register, registry
from datetime import datetime
# set evaluation parameters
afs_to_evaluate = ["MetaBO", "TAF-ME", "TAF-RANKING", "EI"]
rootdir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "metabo")
logpath = os.path.join(rootdir, "iclr2020", "hpo", "MetaBO-SVM-v0")
savepath = os.path.join(logpath, "eval", datetime.strftime(datetime.now(), "%Y-%m-%d-%H-%M-%S"))
n_workers = 1
n_episodes = 15 # 15 test sets
prepare_hpo_data(model="svm", datapath=os.path.join(rootdir, "environment", "hpo", "data", "svm"))
# evaluate all afs
for af in afs_to_evaluate:
# set af-specific parameters
if af == "MetaBO":
features = ["posterior_mean", "posterior_std", "timestep", "budget", "x"]
pass_X_to_pi = False
n_init_samples = 0
load_iter = 363 # determined via leave-one-out cross-validation on the training set
deterministic = True
policy_specs = {} # will be loaded from the logfiles
elif af == "TAF-ME" or af == "TAF-RANKING":
generate_taf_data_hpo(model="svm", datapath=os.path.join(rootdir, "environment", "hpo", "processed"))
features = ["posterior_mean", "posterior_std", "incumbent", "timestep", "x"]
pass_X_to_pi = True
n_init_samples = 0
load_iter = None # does only apply for MetaBO
deterministic = None # does only apply for MetaBO
policy_specs = {"TAF_datafile": os.path.join("policies", "taf", "taf_svm_M_35_N_143.pkl")}
else:
features = ["posterior_mean", "posterior_std", "incumbent", "timestep"]
pass_X_to_pi = False
n_init_samples = 0 # no initial design for discrete domain
load_iter = None # does only apply for MetaBO
deterministic = None # does only apply for MetaBO
if af == "EI":
policy_specs = {}
elif af == "Random":
policy_specs = {}
else:
raise ValueError("Unknown AF!")
# define environment
env_spec = {
"env_id": "MetaBO-SVM-v0",
"D": 2,
"f_type": "HPO",
"f_opts": {
"hpo_data_file": os.path.join(rootdir, "environment", "hpo", "processed", "svm", "objectives.pkl"),
"hpo_gp_hyperparameters_file": os.path.join(rootdir, "environment", "hpo", "processed", "svm",
"gp_hyperparameters.pkl"),
"hpo_datasets_file": os.path.join(rootdir, "environment", "hpo", "processed", "svm",
"test_datasets_iclr2020.txt"),
"draw_random_datasets": False, # present each test function once
"min_regret": 0.0},
"features": features,
"T": 20,
"n_init_samples": n_init_samples,
"pass_X_to_pi": pass_X_to_pi,
# GP hyperparameters will be set individually for each new function, the parameters were determined off-line
# via type-2-ML on all available data
"kernel_lengthscale": None,
"kernel_variance": None,
"noise_variance": None,
"use_prior_mean_function": True,
"local_af_opt": False, # discrete domain
"cardinality_domain": 143,
"reward_transformation": "none",
}
# register gym environment
if env_spec["env_id"] in registry.env_specs:
del registry.env_specs[env_spec["env_id"]]
register(
id=env_spec["env_id"],
entry_point="metabo.environment.metabo_gym:MetaBO",
max_episode_steps=env_spec["T"],
reward_threshold=None,
kwargs=env_spec
)
# define evaluation run
eval_spec = {
"env_id": env_spec["env_id"],
"env_seed_offset": 100,
"policy": af,
"logpath": logpath,
"load_iter": load_iter,
"deterministic": deterministic,
"policy_specs": policy_specs,
"savepath": savepath,
"n_workers": n_workers,
"n_episodes": n_episodes,
"T": env_spec["T"],
}
# perform evaluation
print("Evaluating {} on {}...".format(af, env_spec["env_id"]))
eval_experiment(eval_spec)
print("Done! Saved result in {}".format(savepath))
print("**********************\n\n")
# plot (plot is saved to savepath)
print("Plotting...")
plot_results(path=savepath, logplot=True)
print("Done! Saved plot in {}".format(savepath))