def test_save_load_update_model_manager():
# Local imports are recommended when using moto
from emmaa.model_tests import ModelManager, save_model_manager_to_s3, \
load_model_manager_from_s3, update_model_manager_on_s3
from emmaa.util import find_number_of_files_on_s3
client = setup_bucket(add_model=True)
# Should be None if no model manager
assert find_number_of_files_on_s3(TEST_BUCKET_NAME,
'results/test/model_manager_',
'.pkl') == 0
loaded_mm = load_model_manager_from_s3(model_name='test',
bucket=TEST_BUCKET_NAME)
assert loaded_mm is None
# Save a model manager and load it back
model = create_model()
mm = ModelManager(model)
save_model_manager_to_s3('test', mm, bucket=TEST_BUCKET_NAME)
loaded_mm = load_model_manager_from_s3(model_name='test',
bucket=TEST_BUCKET_NAME)
assert loaded_mm
assert find_number_of_files_on_s3(TEST_BUCKET_NAME,
'results/test/model_manager_',
'.pkl') == 1
# Update should create a new file if there's at least one second difference
time.sleep(1)
update_model_manager_on_s3('test', TEST_BUCKET_NAME)
assert find_number_of_files_on_s3(TEST_BUCKET_NAME,
'results/test/model_manager_',
'.pkl') == 2
def setup_bucket(add_model=False,
add_mm=False,
add_tests=False,
add_results=False,
add_model_stats=False,
add_test_stats=False):
"""
This function creates a new (local) bucket mocking S3 bucket at each call.
Then all calls to S3 are calling this bucket instead of real S3 bucket.
Depending on the test we might or might not need the bucket to contain
different files. For faster computation, only required files for the test
are generated and stored in the bucket. Files can be added by setting
corresponding arguments to True when calling this function.
"""
# Local imports are recommended when using moto
from emmaa.util import get_s3_client
from emmaa.model import save_config_to_s3
from emmaa.model_tests import ModelManager, save_model_manager_to_s3, \
StatementCheckingTest
# Create a mock s3 bucket
client = get_s3_client()
bucket = client.create_bucket(Bucket=TEST_BUCKET_NAME, ACL='public-read')
date_str = make_date_str()
emmaa_model = None
if add_model:
# Put config and model files into empty bucket
config_dict = {
'ndex': {
'network': 'a08479d1-24ce-11e9-bb6a-0ac135e8bacf'
},
'search_terms': [{
'db_refs': {
'HGNC': '20974'
},
'name': 'MAPK1',
'search_term': 'MAPK1',
'type': 'gene'
}],
'test': {
'test_corpus': 'simple_tests',
'default_test_corpus': 'simple_tests'
},
'human_readable_name':
'Test Model',
'assembly': [{
'function': 'filter_no_hypothesis'
}, {
'function': 'map_grounding'
}, {
'function': 'filter_grounded_only'
}, {
'function': 'filter_human_only'
}, {
'function': 'map_sequence'
}, {
'function': 'run_preassembly',
'kwargs': {
'return_toplevel': False
}
}, {
'function': 'filter_top_level'
}]
}
save_config_to_s3('test', config_dict, bucket=TEST_BUCKET_NAME)
emmaa_model = create_model()
emmaa_model.save_to_s3(bucket=TEST_BUCKET_NAME)
if add_mm:
# Add a ModelManager to bucket
if not emmaa_model:
emmaa_model = create_model()
mm = ModelManager(emmaa_model)
mm.date_str = date_str
mm.save_assembled_statements(upload_to_db=False,
bucket=TEST_BUCKET_NAME)
save_model_manager_to_s3('test', mm, bucket=TEST_BUCKET_NAME)
if add_tests:
tests = [
StatementCheckingTest(Activation(Agent('BRAF'), Agent('MAPK1')))
]
test_dict = {
'test_data': {
'description': 'Tests for functionality testing'
},
'tests': tests
}
client.put_object(Body=pickle.dumps(test_dict),
Bucket=TEST_BUCKET_NAME,
Key=f'tests/simple_tests.pkl')
if add_results:
client.put_object(
Body=json.dumps(previous_results, indent=1),
Bucket=TEST_BUCKET_NAME,
Key=f'results/test/results_simple_tests_{date_str}.json')
if add_model_stats:
client.put_object(Body=json.dumps(previous_model_stats, indent=1),
Bucket=TEST_BUCKET_NAME,
Key=f'model_stats/test/model_stats_{date_str}.json')
if add_test_stats:
client.put_object(
Body=json.dumps(previous_test_stats, indent=1),
Bucket=TEST_BUCKET_NAME,
Key=f'stats/test/test_stats_simple_tests_{date_str}.json')
return client
import argparse
from emmaa.model import EmmaaModel
from emmaa.model_tests import ModelManager, save_model_manager_to_s3
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Script to update ModelManager stored on Amazon S3.')
parser.add_argument('-m', '--model', help='Model name', required=True)
args = parser.parse_args()
model = EmmaaModel.load_from_s3(args.model)
mm = ModelManager(model, mode='s3')
mm.model.update_to_ndex()
mm.save_assembled_statements()
save_model_manager_to_s3(args.model, mm)
