def load(self):
# Prevent double-loading the dataset
if self.is_loaded:
return
data_files = list(sorted(iterate_files(self.folder, pattern=f'.*{self.extension}')))
if len(data_files) == 0:
print('WARNING: No data files found.')
return
self._arrays = [np.load(data_file, mmap_mode='r') for data_file in data_files]
self._array_lengths = [int(len(arr) / len(self._fields)) for arr in self._arrays]
self.set_length(sum(self._array_lengths))
self._ids = list(range(self.length))
# Retrieve saved index or build sequential index if none given
index_file = os.path.join(self.folder, INDEX_FILE)
if os.path.exists(index_file):
self._index = read_by_file_suffix(index_file)
self._ids = list(sorted(self._index.keys()))
else:
for sample_id in self._ids:
self._index[sample_id] = self._get_array_index(sample_id)
self.set_loaded(True)
def create_multi_model_systems(
folder: str, model_type: str,
power_system_type: PowerType) -> List[RuntimeSystem]:
model_type = model_type.upper()
assert model_type in (
'SKIP_RNN', 'PHASED_RNN'), 'Unknown model type: {0}'.format(model_type)
runtime_systems: List[RuntimeSystem] = []
valid_results: List[ModelResults] = []
test_results: List[ModelResults] = []
model_paths: List[str] = []
for model_path in iterate_files(
folder,
pattern=r'model-{0}-.*model_best\.pkl\.gz'.format(model_type)):
model, dataset = restore_neural_network(model_path,
dataset_folder=dataset_folder)
if model_type == 'SKIP_RNN':
valid_result = execute_skip_rnn_model(model,
dataset,
series=DataSeries.VALID)
test_result = execute_skip_rnn_model(model,
dataset,
series=DataSeries.TEST)
else:
valid_result = execute_phased_rnn_model(model,
dataset,
series=DataSeries.VALID)
test_result = execute_phased_rnn_model(model,
dataset,
series=DataSeries.TEST)
valid_results.append(valid_result)
test_results.append(test_result)
model_paths.append(model_path)
# Concatenate the results from each model
test_results_concat = concat_model_results(test_results)
valid_results_concat = concat_model_results(valid_results)
power_system = make_power_system(num_levels=seq_length,
seq_length=seq_length,
model_type=model.model_type,
power_type=power_system_type)
under_budget = RuntimeSystem(test_results=test_results_concat,
valid_results=valid_results_concat,
system_type=SystemType.FIXED_UNDER_BUDGET,
model_path=model_paths[0],
dataset_folder=dataset_folder,
seq_length=seq_length,
num_levels=len(model_paths),
num_classes=num_classes,
power_system=power_system)
runtime_systems.append(under_budget)
return runtime_systems
def data_generator(data_folder: str) -> Iterable[Dict[str, Any]]:
for data_file in iterate_files(data_folder, pattern=r'.*jsonl.gz'):
for sample in read_by_file_suffix(data_file):
# indices = list(range(len(sample[INPUTS])))
# sampled_indices = np.sort(np.random.choice(indices, size=seq_length, replace=False))
# sample[INPUTS] =
yield sample
def load(self):
if self.is_loaded:
return # Prevent double loading
data_files = sorted(iterate_files(self.folder, pattern=f'.*{self.extension}'))
for data_file in data_files:
self._dataset.extend(read_by_file_suffix(data_file))
self.set_length(len(self._dataset))
self._ids = list(range(self.length))
self.set_loaded(True)
def count_samples(data_folder: str, file_type: str, num_fields: int):
"""
Counts the number of samples in the given archive.
"""
count = 0
for data_file in iterate_files(data_folder, pattern=f'.*\.{file_type}'):
data = read_by_file_suffix(data_file)
if file_type == 'npz':
count += int(len(data) / num_fields)
else:
count += sum((1 for _ in data))
print(f'Total number of samples: {count}')
def merge_datasets(folders: List[str], output_folder: str, file_prefix: str,
file_suffix: str, chunk_size: int):
with DataWriter(output_folder,
file_prefix=file_prefix,
file_suffix=file_suffix,
chunk_size=chunk_size) as writer:
data_files = chain(*(iterate_files(folder, pattern=f'.*{file_suffix}')
for folder in folders))
sample_id = 0
for data_file in data_files:
for sample in read_by_file_suffix(data_file):
sample[SAMPLE_ID] = sample_id
writer.add(sample)
sample_id += 1
if (sample_id + 1) % chunk_size == 0:
print('Completed {0} samples.'.format(sample_id + 1),
end='\r')
print()
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument(
'--input-folder',
type=str,
required=True,
help='Folder containing RNN models (of the same type) to measure.')
args = parser.parse_args()
total_time = timedelta()
times_list: List[datetime] = []
model_count = 0
for train_log_path in iterate_files(
args.input_folder, pattern=r'.*model-train-log-.*\.pkl\.gz'):
train_log = read_by_file_suffix(train_log_path)
if 'start_time' not in train_log:
match = TIME_REGEX.match(train_log_path)
start_date = datetime.strptime(match.group(1), '%Y-%m-%d-%H-%M-%S')
times_list.append(start_date)
else:
start_time, end_time = train_log['start_time'], train_log[
'end_time']
start_date = datetime.strptime(start_time, '%Y-%m-%d-%H-%M-%S')
end_date = datetime.strptime(end_time, '%Y-%m-%d-%H-%M-%S')
# Load the target data-set
dataset = get_dataset(dataset_type='standard', data_folder=args.dataset_folder)
# Unpack the power system type
power_type = PowerType[args.sensor_type.upper()]
# Load the adaptive model results and controllers
adaptive_result_dict: Dict[str, ModelResults] = dict()
adaptive_system_dict: Dict[str, RuntimeSystem] = dict()
power_system_dict: Dict[str, PowerSystem] = dict()
# Expand the model paths by unpacking directories
model_paths: List[str] = []
for model_path in args.adaptive_model_paths:
if os.path.isdir(model_path):
model_paths.extend(iterate_files(model_path, pattern=r'.*model-SAMPLE_RNN-.*'))
model_paths.extend(iterate_files(model_path, pattern=r'.*model-BUDGET_RNN-.*'))
else:
model_paths.append(model_path)
for model_path in model_paths:
model, _ = restore_neural_network(model_path=model_path,
dataset_folder=args.dataset_folder)
seq_length = model.metadata[SEQ_LENGTH]
num_classes = model.metadata[NUM_CLASSES]
num_levels = model.num_outputs
# Get the validation and test results
valid_results = execute_adaptive_model(model=model,
dataset=dataset,
series=DataSeries.VALID)
def get_results(
input_folders: List[str], noise_generator: NoiseGenerator, model_type: str
) -> Dict[str, DefaultDict[float, Dict[str, List[ModelResult]]]]:
"""
Gets the results for all models in the given folder with the given power shift value.
Args:
input_folders: A list of input folders containing model results.
target_shift: The power shift to extract results from
Returns:
A dictionary of the following format.
Key: Dataset Name
Value: A dictionary of the format below.
Key: Budget
Value: Dictionary of Model Name -> List of accuracy values.
"""
# Create the key for this series
noise_key = str(noise_generator)
baseline_mode = 'under_budget'
fixed_type = 'fixed_{0}'.format(baseline_mode)
model_results: Dict[str, DefaultDict[float, Dict[str,
List[float]]]] = dict()
for folder in input_folders:
for file_name in iterate_files(folder, pattern=r'.*\.jsonl\.gz'):
model_info = get_model_and_type(file_name)
if model_info is None:
continue
system_type, model_name, dataset_name = model_info
# Initialize new dataset entry
dataset_name = normalize_dataset_name(dataset_name)
if dataset_name not in model_results:
model_results[dataset_name] = defaultdict(dict)
# Skip all systems which don't match the criteria
if system_type.lower() not in ('adaptive', fixed_type,
'randomized'):
continue
# Read the test log and get the accuracy for each budget matching the provided shift
test_log = list(read_by_file_suffix(file_name))[0]
noise_test_log = test_log[noise_key]
for log_entry in noise_test_log.values():
budget = log_entry['BUDGET']
# Get the accuracy and power
accuracy = log_entry['ACCURACY']
power = log_entry['AVG_POWER']
valid_accuracy = log_entry.get('VALID_ACCURACY')
model_result = ModelResult(power=power,
accuracy=accuracy,
validation_accuracy=valid_accuracy)
system_name = log_entry.get(
'SYSTEM_NAME', '{0} {1}'.format(system_type,
model_name)).upper()
# Append accuracy to the adaptive model results
if system_name not in model_results[dataset_name][budget]:
model_results[dataset_name][budget][system_name] = []
model_results[dataset_name][budget][system_name].append(
model_result)
return model_results
'--dataset-folder',
type=str,
required=True,
help='Folder containing the dataset used for training and evaluation.')
args = parser.parse_args()
dataset = get_dataset(dataset_type='standard',
data_folder=args.dataset_folder)
dataset.dataset[DataSeries.TRAIN].load()
train_size = dataset.dataset[DataSeries.TRAIN].length
training_iterations = 0
model_count = 0
for train_log_path in iterate_files(args.input_folder,
pattern=r'model-train-log.*pkl.gz'):
match = MODEL_NAME_REGEX.match(train_log_path)
assert match is not None, 'Could not match {0}'.format(train_log_path)
# Get the batch size from the hyperparameters
name = match.group(1)
save_folder, _ = os.path.split(train_log_path)
hypers_path = os.path.join(
save_folder,
'model-hyper-params-{0}_model_best.pkl.gz'.format(name))
hypers = read_by_file_suffix(hypers_path)
batch_size = hypers['batch_size']
batches_per_epoch = int(math.ceil(train_size / batch_size))
assert os.path.exists(
train_folder), f'The folder {train_folder} does not exist!'
valid_folder = os.path.join(data_folder, VALID)
assert os.path.exists(
valid_folder), f'The folder {valid_folder} does not exist!'
test_folder = os.path.join(data_folder, TEST)
assert os.path.exists(
test_folder), f'The folder {test_folder} does not exist!'
# Unpack and Validate params files (to fail fast)
params_files: List[str] = []
for params_file in args.params_files:
if os.path.isdir(params_file):
params_files.extend(iterate_files(params_file, pattern=r'.*json'))
else:
params_files.append(params_file)
for params_file in params_files:
assert os.path.exists(
params_file), f'The file {params_file} does not exist!'
assert params_file.endswith(
'.json'), f'The params file must be a JSON.'
trials = max(args.trials, 1)
num_models = trials * len(params_files)
# Create save folder (if necessary)
base_save_folder = args.save_folder
make_dir(base_save_folder)