def runner(self):
self.root.setup() # Loading configuration (if exists)
db_url = self.root.db_url
dir_plot = self.args.directory
config = yaml.load(self.args.config)
db_engine = create_engine(db_url)
return AuditionRunner(config, db_engine, dir_plot)
def test_Audition():
with testing.postgresql.Postgresql() as postgresql:
db_engine = create_engine(postgresql.url())
ensure_db(db_engine)
init_engine(db_engine)
num_model_groups = 10
model_types = [
"classifier type {}".format(i) for i in range(0, num_model_groups)
]
model_groups = [
ModelGroupFactory(model_type=model_type)
for model_type in model_types
]
train_end_times = [
datetime(2013, 1, 1),
datetime(2014, 1, 1),
datetime(2015, 1, 1),
datetime(2016, 1, 1),
]
models = [
ModelFactory(model_group_rel=model_group,
train_end_time=train_end_time)
for model_group in model_groups
for train_end_time in train_end_times
]
metrics = [
("precision@", "100_abs"),
("recall@", "100_abs"),
("precision@", "50_abs"),
("recall@", "50_abs"),
("fpr@", "10_pct"),
]
class ImmediateEvalFactory(EvaluationFactory):
evaluation_start_time = factory.LazyAttribute(
lambda o: o.model_rel.train_end_time)
for model in models:
for (metric, parameter) in metrics:
ImmediateEvalFactory(model_rel=model,
metric=metric,
parameter=parameter)
session.commit()
with tempfile.TemporaryDirectory() as td:
with mock.patch('os.getcwd') as mock_getcwd:
mock_getcwd.return_value = td
AuditionRunner(config_dict=config,
db_engine=db_engine,
directory=td).run()
assert len(os.listdir(os.getcwd())) == 6
def runner(self):
db_url = self.root.db_url
dir_plot = self.args.directory
config = yaml.load(self.args.config)
db_engine = create_engine(db_url)
return AuditionRunner(config, db_engine, dir_plot)