def make_model(self):
"""Make an EmmaaModel and upload it along with the config to S3."""
config = self.make_config()
em = EmmaaModel(self.name, config)
em.stmts = self.stmts
ndex_uuid = em.upload_to_ndex()
config['ndex'] = {'network': ndex_uuid}
save_config_to_s3(self.name, config)
em.save_to_s3()
def create_upload_model(model_name, full_name, indra_stmts, ndex_id=None):
"""Make and upload an EMMAA model from a list of INDRA Statements.
Parameters
----------
short_name : str
Short name of the model to use on S3.
full_name : str
Human-readable model name to use in EMMAA dashboard.
indra_stmts : list of indra.statement
INDRA Statements to be used to populate the EMMAA model.
ndex_id : str
UUID of the network corresponding to the model on NDex. If provided,
the NDex network will be updated with the latest model content.
If None (default), a new network will be created and the UUID stored
in the model config files on S3.
"""
emmaa_stmts = to_emmaa_stmts(indra_stmts, datetime.datetime.now(), [])
# Get updated CX content for the INDRA Statements
cxa = CxAssembler(indra_stmts)
cx_str = cxa.make_model()
# If we don't have an NDex ID, create network and upload to Ndex
if ndex_id is None:
ndex_id = cxa.upload_model(private=False)
print(f'NDex ID for {model_name} is {ndex_id}.')
# If the NDEx ID is provided, update the existing network
else:
ndex_client.update_network(cx_str, ndex_id)
# Create the config dictionary
config_dict = {'ndex': {'network': ndex_id}, 'search_terms': []}
# Create EMMAA model
emmaa_model = EmmaaModel(model_name, config_dict)
emmaa_model.add_statements(emmaa_stmts)
# Upload model to S3 with config as YAML and JSON
emmaa_model.save_to_s3()
s3_client = boto3.client('s3')
config_json = json.dumps(config_dict)
s3_client.put_object(Body=config_json.encode('utf8'),
Key='models/%s/config.json' % model_name,
Bucket='emmaa')
config_json = json.dumps(config_dict)
s3_client.put_object(Body=config_json.encode('utf8'),
Key='models/%s/config.json' % model_name,
Bucket='emmaa')
def update_cancer(cancer_type):
"""Update the model for the given cancer.
A JSON config file must be present for the given cancer type, located in
the models/<cancer_type>/config.json.
Parameters
----------
cancer_type : str
A short string which is the name of the cancer, and corresponds to a
directory in the models directory, as described above.
"""
print(cancer_type)
with open(f'models/{cancer_type}/prior_stmts.pkl', 'rb') as fh:
stmts = pickle.load(fh)
config = json.load(open(f'models/{cancer_type}/config.json', 'r'))
em = EmmaaModel(cancer_type, config)
ess = [EmmaaStatement(st, datetime.datetime.now(), []) for st in stmts]
em.add_statements(ess)
em.save_to_s3()
return
def create_upload_model(model_name, indra_stmts, config_file):
"""Make and upload an EMMAA model from a list of INDRA Statements.
Parameters
----------
model_name : str
Name of the model to use on S3.
indra_stmts : list of indra.statement
INDRA Statements to be used to populate the EMMAA model.
config_file : str
Path to the local config.json file.
"""
emmaa_stmts = to_emmaa_stmts(indra_stmts, datetime.datetime.now(), [],
{'internal': True})
# Load config information
with open(config_file, 'rt') as f:
config_json = json.load(f)
# If there is no ndex entry in the config, create a new network and update
# the config file with the NDex network ID
if 'ndex' not in config_json:
cxa = CxAssembler(indra_stmts)
cx_str = cxa.make_model()
ndex_id = cxa.upload_model(private=False)
print(f'NDex ID for {model_name} is {ndex_id}.')
config_json['ndex'] = {'network': ndex_id}
updated_config_file = f'{config_file}.updated'
with open(updated_config_file, 'wt') as f:
json.dump(config_json, f, indent=2)
# If the NDEx ID is provided we don't need to update the existing network
# because this will occur as part of the model assembly/update procedure
# on EMMAA itself.
# Create the config dictionary
# Create EMMAA model
emmaa_model = EmmaaModel(model_name, config_json)
emmaa_model.add_statements(emmaa_stmts)
# Upload model to S3
emmaa_model.save_to_s3()
# Upload config JSON
s3_client = boto3.client('s3')
save_config_to_s3(model_name, config_json)
def make_model(self, estmts, upload_to_s3=False):
"""Return, and optionally upload to S3 an initial EMMAA Model.
Parameters
----------
estmts : list of emmaa.statement.EmmaaStatement
A list of prior EMMAA Statements to initialize the model with.
upload_to_s3 : Optional[bool]
If True, the model and the config are uploaded to S3, otherwise
the model object is just returned without upload. Default: False
Returns
-------
emmaa.model.EmmaaModel
The EMMAA Model object constructed from the generated config
and the given EMMAA Statements.
"""
from emmaa.model import EmmaaModel
config = self.make_config(upload_to_s3=upload_to_s3)
model = EmmaaModel(name=self.name, config=config)
model.add_statements(estmts)
if upload_to_s3:
model.save_to_s3()
return model