def run_trial_test(spec_file, spec_name=False):
spec = spec_util.get(spec_file, spec_name)
spec = spec_util.override_test_spec(spec)
spec_util.tick(spec, 'trial')
trial = Trial(spec)
trial_metrics = trial.run()
assert isinstance(trial_metrics, dict)
def run_trial_test(spec_file, spec_name=False):
spec = spec_util.get(spec_file, spec_name)
spec = util.override_test_spec(spec)
info_space = InfoSpace()
info_space.tick('trial')
trial = Trial(spec, info_space)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def run_trial_test(spec_file, spec_name=False, distributed=False):
spec = spec_util.get(spec_file, spec_name)
spec = util.override_test_spec(spec)
info_space = InfoSpace()
info_space.tick('trial')
if distributed:
spec['meta']['distributed'] = True
if os.environ.get('CI') != 'true': # CI has not enough CPU
spec['meta']['max_session'] = 2
trial = Trial(spec, info_space)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def generic_algo_test(spec, algo_name):
'''Need new InfoSpace() per trial otherwise session id doesn't tick correctly'''
trial = Trial(spec, info_space=InfoSpace())
trial_data = trial.run()
folders = [x for x in os.listdir('data/') if x.startswith(algo_name)]
assert len(folders) == 1
path = 'data/' + folders[0]
sess_data = util.read(path + '/' + algo_name + '_t0_s0_session_df.csv')
rewards = sess_data['0.2'].replace("reward", -1).astype(float)
print(f'rewards: {rewards}')
maxr = rewards.max()
'''Delete test data folder and trial'''
shutil.rmtree(path)
del trial
return maxr
def test_algo(spec_file, spec_name):
spec = spec_util.get(spec_file, spec_name)
spec = util.override_test_spec(spec)
trial = Trial(spec)
trial_data = trial.run()
def run_trial_test(spec_file, spec_name):
spec = spec_util.get(spec_file, spec_name)
spec = util.override_test_spec(spec)
trial = Trial(spec)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def test_base(spec_file, spec_name):
spec = spec_util.get(spec_file, spec_name)
spec['meta']['train_mode'] = True
trial = Trial(spec)
trial_data = trial.run()
def run_trial_test(spec_file, spec_name):
spec = spec_util.get(spec_file, spec_name)
spec = util.override_test_spec(spec)
trial = Trial(spec)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def test_trial(test_spec):
trial = Trial(test_spec)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def test_trial(test_spec):
spec_util.tick(test_spec, 'trial')
spec_util.save(test_spec, unit='trial')
trial = Trial(test_spec)
trial_metrics = trial.run()
assert isinstance(trial_metrics, dict)
def test_trial(test_spec, test_info_space):
test_info_space.tick('trial')
trial = Trial(test_spec, test_info_space)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def test_trial(test_spec):
trial = Trial(test_spec)
trial_data = trial.run()
# TODO trial data checker method
assert isinstance(trial_data, pd.DataFrame)
def test_trial(test_spec):
trial = Trial(test_spec)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)
def test_trial(test_spec, test_info_space):
test_info_space.tick('trial')
analysis.save_spec(test_spec, test_info_space, unit='trial')
trial = Trial(test_spec, test_info_space)
trial_data = trial.run()
assert isinstance(trial_data, pd.DataFrame)