def get_benchmark_pareto(iteration_id: int) -> List[creator.Individual]:
db_iter = MongoDBConnection.get_benchmarks_iterations_db()
iter_obj = db_iter.find_one({'_id': iteration_id})
paretos_gridfs = MongoDBConnection.get_paretos_gridfs()
paretos = []
for pareto_file in iter_obj['paretos']:
with paretos_gridfs.get(pareto_file) as f:
paretos.append(pickle.loads(f.read()))
return paretos
def clear_population_performace_iteration(dataset_id=None):
find_d = {}
if dataset_id is not None:
find_d['dataset_id'] = dataset_id
db = MongoDBConnection.get_performance_populations_db()
iteration = db.find(find_d)
paretos_gridfs = MongoDBConnection.get_paretos_gridfs()
for it in iteration:
for pareto_file in it['paretos']:
paretos_gridfs.delete(pareto_file)
db.remove(find_d)
def clear_benchmark_iteration(dataset_id=None, benchmark_id=None):
find_d = {}
if dataset_id is not None:
find_d['dataset_id'] = dataset_id
if benchmark_id is not None:
find_d['benchmark_id'] = benchmark_id
db = MongoDBConnection.get_benchmarks_iterations_db()
iteration = db.find(find_d)
paretos_gridfs = MongoDBConnection.get_paretos_gridfs()
for it in iteration:
for pareto_file in it['paretos']:
paretos_gridfs.delete(pareto_file)
db.remove(find_d)
def clear_benchmark_performace_iteration(population_iteration=None,
bmk_settings=None):
find_d = {}
if population_iteration is not None:
find_d['population_iteration'] = population_iteration
if bmk_settings is not None:
find_d['bmk_settings'] = bmk_settings
db = MongoDBConnection.get_benchmark_performance_db()
iteration = db.find(find_d)
paretos_gridfs = MongoDBConnection.get_paretos_gridfs()
for it in iteration:
for pareto_file in it['paretos']:
paretos_gridfs.delete(pareto_file)
db.remove(find_d)
def save_population_performance_iteration(
dataset_id: str, duration: List[str],
population: List[List[creator.Individual]]):
db = MongoDBConnection.get_paretos_gridfs()
paretos_pickles = [0] * len(population)
for n_snp, actual_pareto in enumerate(population):
with db.new_file() as f:
paretos_pickles[n_snp] = f._id
binary_data = Binary(pickle.dumps(actual_pareto, protocol=2),
subtype=128)
f.write(binary_data)
db = MongoDBConnection.get_performance_populations_db()
result = db.insert_one({
"dataset_id": dataset_id,
"duration": duration,
"paretos": paretos_pickles,
"version": "v1"
})
return result.inserted_id
def save_benchmark_iteration(dataset_id: str, iteration_id: str,
benchmark_id: str, duration: List[str], paretos):
db = MongoDBConnection.get_paretos_gridfs()
paretos_pickles = [0] * len(paretos)
for n_snp, actual_pareto in enumerate(paretos):
with db.new_file() as f:
paretos_pickles[n_snp] = f._id
binary_data = Binary(pickle.dumps(actual_pareto, protocol=2),
subtype=128)
f.write(binary_data)
db = MongoDBConnection.get_benchmarks_iterations_db()
result = db.insert_one({
"dataset_id": dataset_id,
"iteration_id": iteration_id,
"benchmark_id": benchmark_id,
"duration": duration,
"paretos": paretos_pickles,
"version": "v1"
})
return result.inserted_id
def save_iteration(dataset_id: str, settings_id: str,
sp_generations: List[List[float]], paretos,
execution_info: Dict):
db = MongoDBConnection.get_paretos_gridfs()
paretos_pickles = [0] * len(paretos)
for n_snp, actual_pareto in enumerate(paretos):
with db.new_file() as f:
paretos_pickles[n_snp] = f._id
binary_data = Binary(pickle.dumps(actual_pareto, protocol=2),
subtype=128)
f.write(binary_data)
db = MongoDBConnection.get_iterations_db()
result = db.insert_one({
"dataset_id": dataset_id,
"settings_id": settings_id,
"execution_info": execution_info,
"snapshots": sp_generations,
"paretos": paretos_pickles,
"version": "v2"
})
return result.inserted_id