def run(arguments):
azure_info_path = arguments.get('--azure-info', None)
valid_data_dir = test.expand_data_path(arguments['VALID_DATA_PATH'],
azure_info_path)
test_data_dir = test.expand_data_path(arguments['TEST_DATA_PATH'],
azure_info_path)
model_paths = RichPath.create(
arguments['MODEL_PATH'],
azure_info_path=azure_info_path).get_filtered_files_in_dir('*.pkl.gz')
alpha = float(args['--alpha'])
with Pool(int(arguments['--processes'])) as pool:
results = pool.map(
functools.partial(test.compute_evaluation_metrics,
arguments=arguments,
azure_info_path=azure_info_path,
valid_data_dirs=valid_data_dir,
test_data_dirs=test_data_dir,
return_results=True,
languages=['java'],
test_valid=False), model_paths)
docstring_mrrs = [x['java'][0] for x in results]
func_name_mrrs = [x['java'][1] for x in results]
docstring_confidence = get_confidence_interval(docstring_mrrs, alpha)
func_name_confidence = get_confidence_interval(func_name_mrrs, alpha)
print(
f'{alpha*100}% confidence interval for mrr using docstring as the query: {docstring_confidence}'
)
print(
f'{alpha*100}% confidence interval for mrr using function name as the query: {func_name_confidence}'
)
def run(arguments):
max_num_examples = int(arguments.get('--max-num-examples')) if arguments.get('--max-num-examples') else None
azure_info_path = arguments.get('--azure-info', None)
test_data_dirs = expand_data_path(arguments['DATA_PATH'], azure_info_path)
if arguments['--hypers-override'] is not None:
hypers_override = json.loads(arguments['--hypers-override'])
elif arguments['--hypers-override-file'] is not None:
with open(arguments['--hypers-override-file']) as f:
hypers_override = json.load(f)
else:
hypers_override = {}
model_path = RichPath.create(arguments['MODEL_PATH'], azure_info_path=azure_info_path)
tester = MrrSearchTester(model_path, test_batch_size=int(arguments['--test-batch-size']),
distance_metric=arguments['--distance-metric'], hypers_override=hypers_override)
# Load dataset
if arguments['--standard-dataset'] or arguments['--method2code-dataset']:
data = model_test.get_dataset_from(test_data_dirs, use_func_names=arguments['--method2code-dataset'])
else:
raise Exception(f'No dataset option seems to have been passed in.')
generate_html_error_report(tester=tester,
data=data,
max_num_examples=max_num_examples,
outfile=arguments['OUT_FILE'],
filter_language=arguments.get('--language-to-analyze'))
def run(arguments):
azure_info_path = arguments.get('--azure-info', None)
# if you do not pass arguments for train/valid/test data default to files checked into repo.
if not arguments['VALID_DATA_PATH']:
dir_path = Path(__file__).parent.absolute()
print(dir_path)
arguments['VALID_DATA_PATH'] = str(dir_path / 'data_dirs_valid.txt')
arguments['TEST_DATA_PATH'] = str(dir_path / 'data_dirs_test.txt')
valid_data_dirs = test.expand_data_path(arguments['VALID_DATA_PATH'],
azure_info_path)
test_data_dirs = test.expand_data_path(arguments['TEST_DATA_PATH'],
azure_info_path)
test.compute_evaluation_metrics(
RichPath.create(arguments['MODEL_PATH'],
azure_info_path=azure_info_path), arguments,
azure_info_path, valid_data_dirs, test_data_dirs)
def run(arguments, tag_in_vcs=False) -> None:
azure_info_path = arguments.get('--azure-info', None)
testrun = arguments.get('--testrun')
no_eval = arguments.get('--no-eval')
max_files_per_dir = arguments.get('--max-files-per-dir')
dir_path = Path(__file__).parent.absolute()
# if you do not pass arguments for train/valid/test data default to files checked into repo.
if not arguments['TRAIN_DATA_PATH']:
arguments['TRAIN_DATA_PATH'] = str(dir_path / 'data_dirs_train.txt')
arguments['VALID_DATA_PATH'] = str(dir_path / 'data_dirs_valid.txt')
arguments['TEST_DATA_PATH'] = str(dir_path / 'data_dirs_test.txt')
train_data_dirs = test.expand_data_path(arguments['TRAIN_DATA_PATH'],
azure_info_path)
valid_data_dirs = test.expand_data_path(arguments['VALID_DATA_PATH'],
azure_info_path)
test_data_dirs = test.expand_data_path(arguments['TEST_DATA_PATH'],
azure_info_path)
# default model save location
if not arguments['SAVE_FOLDER']:
arguments['SAVE_FOLDER'] = str(dir_path.parent /
'resources/saved_models/')
save_folder = arguments['SAVE_FOLDER']
model_class = model_restore_helper.get_model_class_from_name(
arguments['--model'])
hyperparameters = model_class.get_default_hyperparameters()
# make name of wandb run = run_id (Doesn't populate yet)
hyperparameters['max_epochs'] = int(arguments.get('--max-num-epochs'))
if testrun:
hyperparameters['max_epochs'] = 2
if not max_files_per_dir:
max_files_per_dir = 1
# override hyperparams if flag is passed
hypers_override = arguments.get('--hypers-override')
if hypers_override is not None:
hyperparameters.update(json.loads(hypers_override))
elif arguments.get('--hypers-override-file') is not None:
with open(arguments.get('--hypers-override-file')) as f:
hyperparameters.update(json.load(f))
os.makedirs(save_folder, exist_ok=True)
if tag_in_vcs:
hyperparameters['git_commit'] = git_tag_run(run_name)
# turns off wandb if you don't want to log anything
if arguments.get('--dryrun'):
os.environ["WANDB_MODE"] = 'dryrun'
# save hyperparams to logging
# must filter out type=set from logging when as that is not json serializable
results = []
num_random_samples = int(arguments['--num-random-samples'])
if num_random_samples > 1:
random_data_dir = str(train_data_dirs[0]) + arguments['--run-name']
else:
random_data_dir = None
for i in range(num_random_samples):
run_name = make_run_id(arguments)
wandb.init(name=run_name,
config={
k: v
for k, v in hyperparameters.items()
if not isinstance(v, set)
})
wandb.config.update({
'model-class':
arguments['--model'],
'train_folder':
str(train_data_dirs),
'valid_folder':
str(valid_data_dirs),
'save_folder':
str(save_folder),
'test_folder':
str(test_data_dirs),
'CUDA_VISIBLE_DEVICES':
os.environ.get("CUDA_VISIBLE_DEVICES", 'Not Set'),
'run-name':
arguments.get('--run-name'),
'CLI-command':
' '.join(sys.argv)
})
if arguments.get('--evaluate-model'):
model_path = RichPath.create(arguments['--evaluate-model'])
else:
model_path = run_train(model_class,
train_data_dirs,
valid_data_dirs,
save_folder,
hyperparameters,
azure_info_path,
run_name,
arguments['--quiet'],
max_files_per_dir=max_files_per_dir,
parallelize=not (arguments['--sequential']),
random_sample_size=int(
args['--random-sample-size']),
random_data_dir_name=random_data_dir)
if num_random_samples == 1:
wandb.config['best_model_path'] = str(model_path)
wandb.save(str(model_path.to_local_path()))
if no_eval:
continue
# only limit files in test run if `--testrun` flag is passed by user.
elif testrun:
compute_evaluation_metrics(model_path, arguments, azure_info_path,
valid_data_dirs, test_data_dirs,
max_files_per_dir)
else:
compute_evaluation_metrics(model_path, arguments, azure_info_path,
valid_data_dirs, test_data_dirs)
