def __init__(self,
param_ranges,
fixed_params,
model_folder,
override_w_fixed_params=True):
"""Instantiates model.
Args:
param_ranges: Discrete hyperparameter range for random search.
fixed_params: Fixed model parameters per experiment.
model_folder: Folder to store optimisation artifacts.
override_w_fixed_params: Whether to override serialsed fixed model
parameters with new supplied values.
"""
self.param_ranges = param_ranges
self._max_tries = 1000
self.results = pd.DataFrame()
self.fixed_params = fixed_params
self.saved_params = pd.DataFrame()
self.best_score = np.Inf
self.optimal_name = ""
# Setup
# Create folder for saving if its not there
self.hyperparam_folder = model_folder
utils.create_folder_if_not_exist(self.hyperparam_folder)
self._override_w_fixed_params = override_w_fixed_params
def reset_temp_folder(self):
"""Deletes and recreates folder with temporary Keras training outputs."""
print('Resetting temp folder...')
utils.create_folder_if_not_exist(self._temp_folder)
shutil.rmtree(self._temp_folder)
os.makedirs(self._temp_folder)
def __init__(self,
param_ranges,
fixed_params,
root_model_folder,
worker_number,
search_iterations=1000,
num_iterations_per_worker=5,
clear_serialised_params=False):
"""Instantiates optimisation manager.
This hyperparameter optimisation pre-generates #search_iterations
hyperparameter combinations and serialises them
at the start. At runtime, each worker goes through their own set of
parameter ranges. The pregeneration
allows for multiple workers to run in parallel on different machines without
resulting in parameter overlaps.
Args:
param_ranges: Discrete hyperparameter range for random search.
fixed_params: Fixed model parameters per experiment.
root_model_folder: Folder to store optimisation artifacts.
worker_number: Worker index definining which set of hyperparameters to
test.
search_iterations: Maximum numer of random search iterations.
num_iterations_per_worker: How many iterations are handled per worker.
clear_serialised_params: Whether to regenerate hyperparameter
combinations.
"""
max_workers = int(np.ceil(search_iterations / num_iterations_per_worker))
# Sanity checks
if worker_number > max_workers:
raise ValueError(
"Worker number ({}) cannot be larger than the total number of workers!"
.format(max_workers))
if worker_number > search_iterations:
raise ValueError(
"Worker number ({}) cannot be larger than the max search iterations ({})!"
.format(worker_number, search_iterations))
print("*** Creating hyperparameter manager for worker {} ***".format(
worker_number))
hyperparam_folder = os.path.join(root_model_folder, str(worker_number))
super().__init__(
param_ranges,
fixed_params,
hyperparam_folder,
override_w_fixed_params=True)
serialised_ranges_folder = os.path.join(root_model_folder, "hyperparams")
if clear_serialised_params:
print("Regenerating hyperparameter list")
if os.path.exists(serialised_ranges_folder):
shutil.rmtree(serialised_ranges_folder)
utils.create_folder_if_not_exist(serialised_ranges_folder)
self.serialised_ranges_path = os.path.join(
serialised_ranges_folder, "ranges_{}.csv".format(search_iterations))
self.hyperparam_folder = hyperparam_folder # override
self.worker_num = worker_number
self.total_search_iterations = search_iterations
self.num_iterations_per_worker = num_iterations_per_worker
self.global_hyperparam_df = self.load_serialised_hyperparam_df()
self.worker_search_queue = self._get_worker_search_queue()
