def retro_analyze_trials(predir):
'''Retro-analyze all trial level datas.'''
logger.info('Retro-analyzing trials from file')
from slm_lab.experiment.control import Trial
for filename in os.listdir(predir):
if filename.endswith('_trial_data.json'):
filepath = f'{predir}/{filename}'
tn = filename.replace('_trial_data.json', '').split('_')[-1]
trial_index = int(tn[1:])
# mock trial
spec, info_space = mock_info_space_spec(predir, trial_index)
trial = Trial(spec, info_space)
session_data_dict = session_data_dict_from_file(
predir, trial_index)
trial.session_data_dict = session_data_dict
trial_fitness_df = analyze_trial(trial)
# write trial_data that was written from ray search
fitness_vec = trial_fitness_df.iloc[0].to_dict()
fitness = calc_fitness(trial_fitness_df)
trial_data = util.read(filepath)
trial_data.update({
**fitness_vec,
'fitness': fitness,
'trial_index': trial_index,
})
util.write(trial_data, filepath)
def retro_analyze_trials(predir):
'''Retro-analyze all trial level datas.'''
logger.info('Retro-analyzing trials from file')
from slm_lab.experiment.control import Trial
filenames = ps.filter_(os.listdir(predir),
lambda filename: filename.endswith('_trial_df.csv'))
for idx, filename in enumerate(filenames):
filepath = f'{predir}/{filename}'
prepath = filepath.replace('_trial_df.csv', '')
spec, info_space = util.prepath_to_spec_info_space(prepath)
trial_index, _ = util.prepath_to_idxs(prepath)
trial = Trial(spec, info_space)
trial.session_data_dict = session_data_dict_from_file(
predir, trial_index, ps.get(info_space, 'ckpt'))
# zip only at the last
zip = (idx == len(filenames) - 1)
trial_fitness_df = analysis.analyze_trial(trial, zip)
# write trial_data that was written from ray search
trial_data_filepath = filepath.replace('_trial_df.csv',
'_trial_data.json')
if os.path.exists(trial_data_filepath):
fitness_vec = trial_fitness_df.iloc[0].to_dict()
fitness = analysis.calc_fitness(trial_fitness_df)
trial_data = util.read(trial_data_filepath)
trial_data.update({
**fitness_vec,
'fitness': fitness,
'trial_index': trial_index,
})
util.write(trial_data, trial_data_filepath)
def analyze_eval_trial(spec, info_space, predir):
'''Create a trial and run analysis to get the trial graph and other trial data'''
from slm_lab.experiment.control import Trial
trial = Trial(spec, info_space)
trial.session_data_dict = session_data_dict_from_file(
predir, trial.index, ps.get(info_space, 'ckpt'))
# don't zip for eval analysis, slow otherwise
analysis.analyze_trial(trial, zip=False)
def run_trial_test_dist(spec_file, spec_name=False):
spec = spec_util.get(spec_file, spec_name)
spec = spec_util.override_test_spec(spec)
info_space = InfoSpace()
info_space.tick('trial')
spec['meta']['distributed'] = True
spec['meta']['max_session'] = 2
trial = Trial(spec, info_space)
# manually run the logic to obtain global nets for testing to ensure global net gets updated
global_nets = trial.init_global_nets()
# only test first network
if ps.is_list(global_nets): # multiagent only test first
net = list(global_nets[0].values())[0]
else:
net = list(global_nets.values())[0]
session_datas = trial.parallelize_sessions(global_nets)
trial.session_data_dict = {data.index[0]: data for data in session_datas}
trial_data = analysis.analyze_trial(trial)
trial.close()
assert isinstance(trial_data, pd.DataFrame)
def retro_analyze_trials(predir):
'''Retro-analyze all trial level datas.'''
logger.info('Retro-analyzing trials from file')
from slm_lab.experiment.control import Trial
for filename in os.listdir(predir):
if filename.endswith('_trial_data.json'):
filepath = f'{predir}/{filename}'
tn = filename.replace('_trial_data.json', '').split('_')[-1]
trial_index = int(tn[1:])
# mock trial
spec, info_space = mock_info_space_spec(predir, trial_index)
trial = Trial(spec, info_space)
session_data_dict = session_data_dict_from_file(predir, trial_index)
trial.session_data_dict = session_data_dict
trial_fitness_df = analyze_trial(trial)
# write trial_data that was written from ray search
fitness_vec = trial_fitness_df.iloc[0].to_dict()
fitness = calc_fitness(trial_fitness_df)
trial_data = util.read(filepath)
trial_data.update({
**fitness_vec, 'fitness': fitness, 'trial_index': trial_index,
})
util.write(trial_data, filepath)
def analyze_eval_trial(spec, info_space, predir):
'''Create a trial and run analysis to get the trial graph and other trial data'''
from slm_lab.experiment.control import Trial
trial = Trial(spec, info_space)
trial.session_data_dict = session_data_dict_from_file(predir, trial.index)
analyze_trial(trial)
