def __init__(self, num_process: int, design_netlist: PathLike,
root_dir: PathLike = None) -> None:
if root_dir is None:
self.root_dir: Path = Path(NgSpiceWrapper.BASE_TMP_DIR).resolve()
else:
self.root_dir: Path = Path(root_dir).resolve()
self.num_process: int = num_process
self.base_design_name: str = Path(design_netlist).stem
self.gen_dir: Path = self.root_dir / f'designs_{self.base_design_name}'
self.gen_dir.mkdir(parents=True, exist_ok=True)
with open(design_netlist, 'r') as raw_file:
self.content = raw_file.read()
# get/create cache file
self.cache_path = self.gen_dir / 'cache.yaml'
self.cache: Dict[str, Tuple[int, str]]
self.updated_cache = False
if self.cache_path.exists():
self.cache = read_yaml(self.cache_path) or {}
stat = os.stat(str(self.cache_path))
self.last_cache_mtime = stat[-1]
else:
self.cache = {}
self.last_cache_mtime = 0
# atexit takes care of saving the current cache content in case of an error
atexit.register(self._write_cache)
def _write_cache(self):
if self.updated_cache:
# read the yaml if cache file already exists and has been modified since last time visited
if self.cache_path.exists():
stat = os.stat(str(self.cache_path))
if self.last_cache_mtime < stat[-1]:
current_cache = read_yaml(self.cache_path)
else:
current_cache = {}
else:
current_cache = {}
current_cache.update(self.cache)
# print(f'Saving cache for {self.base_design_name} ....')
write_yaml(self.cache_path, current_cache)
# update last mtime stamp after updating cache file
self.last_cache_mtime = os.stat(str(self.cache_path))[-1]
self.updated_cache = False
def __init__(self, spec_file: str = '', spec_dict: Optional[Mapping[str, Any]] = None,
**kwargs) -> None:
LoggingBase.__init__(self)
if spec_file:
specs = read_yaml(spec_file)
else:
specs = spec_dict
self.specs = specs
params = specs['params']
try:
self.work_dir = params['work_dir']
except KeyError:
unique_name = time.strftime('%Y%m%d%H%M%S')
self.work_dir = Path(specs['root_dir']) / f'random_{unique_name}'
write_yaml(self.work_dir / 'params.yaml', specs, mkdir=True)
self.ndim = params['ndim']
self.goal = params['goal_value']
self.mode = params['mode']
self.input_scale = params['input_scale']
eval_fn = params['eval_fn']
try:
self.fn = registered_functions[eval_fn]
except KeyError:
raise ValueError(f'{eval_fn} is not a valid benchmark function')
# hacky version of passing input vectors around
self.input_vectors_norm = [np.linspace(start=-1.0, stop=1.0, dtype='float32',
num=100) for _ in range(self.ndim)]
self.input_vectors = [self.input_scale * vec for vec in self.input_vectors_norm]
# TODO: remove this hacky way of keeping track of delta
self.delta = self.input_vectors_norm[0][-1] - self.input_vectors_norm[0][-2]
import argparse
from utils.file import read_yaml
from utils.pdb import register_pdb_hook
register_pdb_hook()
import logging
logging.basicConfig(level=logging.INFO)
from bbbo.explorer.bo import BOExplorer
def _parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('spec', type=str, help='input yaml file.')
_args = parser.parse_args()
return _args
if __name__ == '__main__':
_args = _parse_args()
spec = read_yaml(_args.spec)
explorer = BOExplorer(spec)
res = explorer.start()
exper_path = Path(sys.argv[1])
seed_paths = [x for x in exper_path.iterdir() if x.is_dir()]
for seed_path in seed_paths:
seed_name = seed_path.name
output_path = seed_path / 'pmap_output'
output_path.mkdir(exist_ok=True)
writer_path = output_path / 'writer'
if writer_path.exists():
shutil.rmtree(writer_path)
writer = SummaryWriter(log_dir=str(writer_path))
res = read_pickle(seed_path / 'res.pkl')
yaml_specs = read_yaml(seed_path / 'spec.yaml')
env = import_bb_env(yaml_specs['env'])
Xs = np.array(res['X'])
costs = np.array(res['y'])
vmax = 3
values = -np.log(costs + 10**(-vmax))
nsamples, xdim = Xs.shape
time_cnt = np.arange(nsamples)
params_names = list(env.params_vec.keys())
prefix = f'{seed_path}/{seed_name}'
for i in range(xdim):
xmin = env.input_bounds[0][i] * 0.9
def __init__(self,
spec_file: str = '',
spec_dict: Optional[Mapping[str, Any]] = None,
load: bool = False,
use_time_stamp: bool = True,
**kwargs) -> None:
"""
Parameters
----------
spec_file: str
spec_dict: Dict[str, Any]
some non-obvious fields
elite_criteria: str
'optim': from sorted x1, ..., xn choose p-quantile
'csp': constraint satisfaction is enough, from x1, ..., xn
choose p-quantile if it is worst than the constraint else choose all which are
better than the constraint
allow_repeated: bool
True to allow repeated samples to be added to the buffer, else all samples in buffer
will have equal likelihood when drawn from it.
on_policy: bool
True to allow on_policy sample usage, meaning that we won't use samples from
previous policies to train the current policy (samples are not drawn from
CacheBuffer)
load: bool
kwargs: Dict[str, Any]
"""
LoggingBase.__init__(self)
if spec_file:
specs = read_yaml(spec_file)
else:
specs = spec_dict
self.specs = specs
params = specs['params']
if load:
self.work_dir = Path(spec_file).parent
else:
suffix = params.get('suffix', '')
prefix = params.get('prefix', '')
if use_time_stamp:
unique_name = time.strftime('%Y%m%d%H%M%S')
unique_name = get_full_name(unique_name, prefix, suffix)
else:
unique_name = f'{prefix}' if prefix else ''
if suffix:
unique_name = f'{unique_name}_{suffix}' if unique_name else f'{suffix}'
self.work_dir = Path(specs['root_dir']) / f'{unique_name}'
write_yaml(self.work_dir / 'params.yaml', specs, mkdir=True)
self.load = load
self.seed = params['seed']
self.ndim = params['ndim']
self.nsamples = params['nsamples']
self.n_init_samples = params['n_init_samples']
self.niter = params['niter']
self.cut_off = params['cut_off']
self.input_scale = params['input_scale']
# goal has to always be positive if not we'll change mode and negate self.goal
self.goal = params['goal_value']
self.mode = params['mode']
self.allow_repeated = params.get('allow_repeated', False)
self.elite_criteria = params.get('elite_criteria', 'optim')
self.on_policy = params.get('on_policy', False)
if self.elite_criteria not in ['csp', 'optim']:
raise ValueError('invalid elite criteria: optim | csp')
# allow repeated does not make sense when sampling is on-policy (on-policy: T -> repeat: T)
self.allow_repeated = self.on_policy or self.allow_repeated
eval_fn = params['fn']
try:
fn = registered_functions[eval_fn]
self.fn = fn
except KeyError:
raise ValueError(f'{eval_fn} is not a valid benchmark function')
if self.goal < 0:
self.mode = 'le' if self.mode == 'ge' else 'ge'
self.fn = lambda x: -fn(x)
# hacky version of passing input vectors around
self.input_vectors_norm = [
np.linspace(start=-1.0, stop=1.0, dtype='float32', num=100)
for _ in range(self.ndim)
]
self.input_vectors = [
self.input_scale * vec for vec in self.input_vectors_norm
]
self.cem = CEM(self.input_vectors,
dist_type=params['base_fn'],
average_coeff=params.get('average_coeff', 1),
gauss_sigma=params.get('gauss_sigma', None))
self.buffer = CacheBuffer(self.mode,
self.goal,
self.cut_off,
with_frequencies=self.allow_repeated)
self.buffer_temp = {}
self.fvals = SortedList()
def main(specs, force_replot=False):
nsamples = specs['nsamples']
root_dir = Path(specs.get('root_dir', ''))
prefix = specs.get('prefix', '')
method = specs.get('method', 'pca')
seed = specs.get('seed', 10)
solution_only = specs.get('solution_only', False)
samples_list, labels_list = [], []
init_pop_list, pop_labels_list = [], []
label_map = {}
work_dir = root_dir / 'model_comparison'
datasets_path = work_dir / 'datasets'
datasets_path.parent.mkdir(exist_ok=True, parents=True)
sol_all = 'sol' if solution_only else 'all'
dataset_suf = f'n{nsamples}_' + sol_all
fig_name = get_full_name('comparison', prefix,
f'{method}_{sol_all}_s{seed}')
# try reading the cache set
try:
cache = read_pickle(work_dir / 'cache.pickle')
except FileNotFoundError:
cache = set()
# find a unique fname based on the content of spec file
spec_immutable = to_immutable(specs)
for index in itertools.count():
fig_path = work_dir / f'{fig_name}_{index}.png'
# increment index if fig_path exists and spec is new
if not fig_path.exists() or force_replot:
break
else:
if spec_immutable in cache:
print('nothing is new')
exit()
cache.add(spec_immutable)
# noinspection PyUnboundLocalVariable
fig_title = str(fig_path.stem)
for label, (label_str, model_str) in enumerate(specs['models'].items()):
data_path = datasets_path / f'{model_str}_{dataset_suf}.pickle'
if data_path.exists():
print(f'loading dataset {label}: {label_str}')
content = read_pickle(data_path)
samples = content['samples']
else:
print(f'sampling model {label} : {label_str}')
model_path = root_dir / model_str / 'params.yaml'
model_specs = read_yaml(model_path)
alg_cls_str = model_specs.pop('alg_class')
alg_cls = cast(Type[LoggingBase], import_class(alg_cls_str))
alg = alg_cls(model_path, load=True)
# noinspection PyUnresolvedReferences
samples = alg.load_and_sample(nsamples,
only_positive=solution_only)
print(f'saving into {str(data_path)}')
write_pickle(data_path, dict(samples=samples))
labels = np.ones(shape=samples.shape[0]) * label
label_map[label] = label_str
# content = read_pickle(root_dir / model_str / 'init_buffer.pickle')
# init_pop = list(map(lambda x: x.item, content['init_buffer'].db_set.keys()))
# init_pop_list += init_pop
# pop_labels_list.append(np.ones(shape=len(init_pop)) * label)
# noinspection PyUnresolvedReferences
samples_list.append(samples)
labels_list.append(labels)
samples = np.concatenate(samples_list, axis=0)
labels = np.concatenate(labels_list, axis=0)
# pops = np.stack(init_pop_list, axis=0)
# pop_labels = np.concatenate(pop_labels_list, axis=0)
if method == 'pca':
pca_scatter2d(samples,
labels,
label_map,
fpath=fig_path,
alpha=0.5,
title=fig_title,
edgecolors='none',
s=10)
elif method == 'tsne':
# import matplotlib.pyplot as plt
# plt.close()
# _, axes = plt.subplots(2, 1)
# tsne_scatter2d(samples, labels, label_map, seed=seed, ax=axes[0], alpha=0.5,
# title=fig_title, edgecolors='none', s=10)
tsne_scatter2d(samples,
labels,
label_map,
seed=seed,
fpath=fig_path,
alpha=0.5,
title=fig_title,
edgecolors='none',
s=10)
# tsne_scatter2d(pops, pop_labels, label_map, seed=seed, ax=axes[1], alpha=0.5,
# title=fig_title, edgecolors='none', s=10)
# plt.tight_layout()
# plt.savefig(fig_path)
else:
raise ValueError(
'invalid dimensionality reduction, valid options are {"pca"| "tsne"}'
)
# update cache
write_pickle(work_dir / 'cache.pickle', cache)
"""A script that maps old modules to new modules for pickle compatible imports and re-saves the
data"""
import pickle
import sys
import torch
from pathlib import Path
from importlib import import_module
from utils.pdb import register_pdb_hook
from utils.file import read_yaml
from utils.importlib import import_class
register_pdb_hook()
config_file = Path(__file__).parent / 'compatibilty.yaml'
module_specs = read_yaml(config_file)
import_as = module_specs['import_as']
from_import = module_specs['from_import']
for key, val in import_as.items():
sys.modules[key] = import_module(val)
for key, val in from_import.items():
sys.modules[key] = import_class(val)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device(
'cpu')
for work_dir in Path('data').iterdir():
if work_dir.is_dir():
for file in work_dir.iterdir():
try:
from utils.pdb import register_pdb_hook
from utils.importlib import import_class
from utils.file import read_yaml
register_pdb_hook()
def parse_arguments() -> argparse.Namespace:
parser = argparse.ArgumentParser('Test black box environments')
parser.add_argument('spec_file',
type=str,
help='the spec file for the black box environment')
return parser.parse_args()
if __name__ == '__main__':
_args = parse_arguments()
specs = read_yaml(_args.spec_file)
engine = import_class(specs['bb_engine'])(specs=specs['bb_engine_params'])
# just generates the designs
designs_empty = engine.generate_rand_designs(n=2, evaluate=False)
# evaluates the design
designs_populated = engine.evaluate(designs_empty, do_interpet=True)
# generates the design and also evaluates it
designs = engine.generate_rand_designs(n=2, evaluate=True)
pdb.set_trace()
labels,
label_map,
seed=seed,
fpath=fig_path,
alpha=0.5,
title=fig_title,
edgecolors='none',
s=10)
# tsne_scatter2d(pops, pop_labels, label_map, seed=seed, ax=axes[1], alpha=0.5,
# title=fig_title, edgecolors='none', s=10)
# plt.tight_layout()
# plt.savefig(fig_path)
else:
raise ValueError(
'invalid dimensionality reduction, valid options are {"pca"| "tsne"}'
)
# update cache
write_pickle(work_dir / 'cache.pickle', cache)
if __name__ == '__main__':
_args = parse_arguments()
# noinspection PyUnresolvedReferences
fpath = _args.spec_fpath
force_replot = _args.force
specs = read_yaml(fpath)
main(specs, force_replot)
'If True loads the results from root_dir in spec_file and continues '
'training')
parsed_args = parser.parse_args()
return parsed_args
if __name__ == '__main__':
# wierd issue with matplotlib backend https://github.com/matplotlib/matplotlib/issues/8795
multiprocessing.set_start_method('spawn')
_args = parse_arguments()
# noinspection PyUnresolvedReferences
fpath = _args.spec_fpath
specs = read_yaml(fpath)
alg_cls_str = specs['alg_class']
alg_cls = cast(Type[AlgBase], import_class(alg_cls_str))
params = specs['params']
root_dir = Path('data', f'{params["prefix"]}_{params["suffix"]}')
specs['root_dir'] = str(root_dir)
processes = []
for seed_iter in range(_args.nseeds):
spec_seed = deepcopy(specs)
seed = (seed_iter + 1) * 10
spec_seed['params']['seed'] = seed
spec_seed['params']['prefix'] = f's{seed}'
spec_seed['params']['suffix'] = ''
def import_bb_env(env_yaml_str: Union[str, Path]) -> EvaluationEngineBase:
specs = read_yaml(env_yaml_str)
env = import_class(specs['bb_engine'])(specs=specs['bb_engine_params'])
return env
parsed_args = parser.parse_args()
return parsed_args
if __name__ == '__main__':
_args = parse_arguments()
root_dir = Path(_args.save_path).absolute()
root_dir.mkdir(parents=True, exist_ok=True)
perf_list, df_list = [], []
keys = [Path(path).stem for path in _args.folders]
for key, path_str in zip(keys, _args.folders):
path = Path(path_str)
perf_list.append(read_yaml(path / 'avg_performance.yaml'))
df_tmp = pd.read_hdf(path / 'df.hdf5', 'df')
df_tmp['name'] = key
df_list.append(df_tmp)
perf_df = pd.DataFrame(perf_list, index=keys)
perf_df.to_excel(root_dir / 'perf_cmp.xlsx')
print(perf_df)
df = pd.concat(df_list, ignore_index=True, sort=False)
plot_exp(df, 'sample_cnt', 'n_sols_in_buffer', 'name',
root_dir / 'nsols_sample_cnt.png', _args.legends)
plot_with_goal(df,
'sample_cnt',
'top_20',
'name',
def __init__(self,
spec_file: str = '',
spec_dict: Optional[Mapping[str, Any]] = None,
load: bool = False,
use_time_stamp: bool = True,
init_buffer_path=None,
**kwargs) -> None:
LoggingBase.__init__(self)
if spec_file:
specs = read_yaml(spec_file)
else:
specs = spec_dict
self.specs = specs
params = specs['params']
if load:
self.work_dir = Path(spec_file).parent
else:
suffix = params.get('suffix', '')
prefix = params.get('prefix', '')
if use_time_stamp:
unique_name = time.strftime('%Y%m%d%H%M%S')
unique_name = get_full_name(unique_name, prefix, suffix)
else:
unique_name = f'{prefix}' if prefix else ''
if suffix:
unique_name = f'{unique_name}_{suffix}' if unique_name else f'{suffix}'
self.work_dir = Path(specs['root_dir']) / f'{unique_name}'
write_yaml(self.work_dir / 'params.yaml', specs, mkdir=True)
self.load = load
self.seed = params.get('seed', 10)
self.ndim = params['ndim']
self.bsize = params['batch_size']
self.hiddens = params['hidden_list']
self.niter = params['niter']
self.goal = params['goal_value']
self.mode = params['mode']
self.viz_rate = self.niter // 10
self.lr = params['lr']
self.nepochs = params['nepochs']
self.nsamples = params['nsamples']
self.n_init_samples = params['n_init_samples']
self.init_nepochs = params['init_nepochs']
self.cut_off = params['cut_off']
self.beta = params['beta']
self.nr_mix = params['nr_mix']
self.base_fn = params['base_fn']
self.only_pos = params['only_positive']
# whether to run 1000 epochs of training for the later round of iteration
self.full_training = params['full_training_last']
self.input_scale = params['input_scale']
self.fixed_sigma = params.get('fixed_sigma', None)
self.on_policy = params.get('on_policy', False)
self.problem_type = params.get('problem_type', 'csp')
self.allow_repeated = params.get('allow_repeated', False)
self.allow_repeated = self.on_policy or self.allow_repeated
self.important_sampling = params.get('important_sampling', False)
self.visited_dist: Optional[nn.Module] = None
self.visited_fixed_sigma = params.get('visited_fixed_sigma', None)
self.visited_nr_mix = params.get('visited_nr_mix', None)
self.explore_coeff = params.get('explore_coeff', None)
self.nepoch_visited = params.get('nepoch_visited', -1)
self.normalize_weight = params.get('normalize_weight', True)
self.add_ent_before_norm = params.get(
'add_entropy_before_normalization', False)
self.weight_type = params.get('weight_type', 'ind')
self.model_visited = self.explore_coeff is not None or self.important_sampling
if self.model_visited and self.nepoch_visited == -1:
raise ValueError(
'nepoch_visited should be specified when a model is '
'learning visited states')
self.init_buffer_paths = init_buffer_path
eval_fn = params['eval_fn']
try:
self.fn = registered_functions[eval_fn]
except KeyError:
raise ValueError(f'{eval_fn} is not a valid benchmark function')
self.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
print(f'device: {self.device}')
self.cpu = torch.device('cpu')
self.model: Optional[nn.Module] = None
self.buffer = None
self.opt = None
# hacky version of passing input vectors around
self.input_vectors_norm = [
np.linspace(start=-1.0, stop=1.0, dtype='float32', num=100)
for _ in range(self.ndim)
]
self.input_vectors = [
self.input_scale * vec for vec in self.input_vectors_norm
]
# TODO: remove this hacky way of keeping track of delta
self.delta = self.input_vectors_norm[0][-1] - self.input_vectors_norm[
0][-2]
# keep track of lo and hi for indicies
self.params_min = np.array([0] * self.ndim)
self.params_max = np.array([len(x) - 1 for x in self.input_vectors])
self.fvals = SortedList()
def _read_cache(self) -> Dict[ImmutableSortedDict, str]:
if self._cache_fname.exists():
return read_yaml(self._cache_fname)
return {}
