def test_save_classifier(db, datarun, model, metrics):
worker = Worker(db, datarun, models_dir=MODEL_DIR, metrics_dir=METRIC_DIR)
hp = db.get_hyperpartitions(datarun_id=worker.datarun.id)[0]
classifier = worker.db.start_classifier(hyperpartition_id=hp.id,
datarun_id=worker.datarun.id,
host='localhost',
hyperparameter_values=DT_PARAMS)
worker.db.complete_classifier = Mock()
worker.save_classifier(classifier.id, model, metrics)
worker.db.complete_classifier.assert_called()
with DBSession(worker.db):
clf = db.get_classifier(classifier.id)
loaded = load_model(clf, MODEL_DIR)
assert isinstance(loaded, Model)
assert loaded.method == model.method
assert loaded.random_state == model.random_state
assert load_metrics(clf, METRIC_DIR) == metrics
def test_save_classifier(db, datarun, model, metrics):
log_conf = LogConfig(model_dir=MODEL_DIR, metric_dir=METRIC_DIR)
worker = Worker(db, datarun, log_config=log_conf)
hp = db.get_hyperpartitions(datarun_id=worker.datarun.id)[0]
classifier = worker.db.start_classifier(hyperpartition_id=hp.id,
datarun_id=worker.datarun.id,
host='localhost',
hyperparameter_values=DT_PARAMS)
worker.db.complete_classifier = Mock()
worker.save_classifier(classifier.id, model, metrics)
worker.db.complete_classifier.assert_called()
with db_session(worker.db):
clf = db.get_classifier(classifier.id)
loaded = load_model(clf, MODEL_DIR)
assert type(loaded) == Model
assert loaded.method == model.method
assert loaded.random_state == model.random_state
assert load_metrics(clf, METRIC_DIR) == metrics
help='Only train on dataruns with these ids',
nargs='+')
parser.add_argument('--time', help='Number of seconds to run worker', type=int)
parser.add_argument(
'--choose-randomly',
action='store_true',
help='Choose dataruns to work on randomly (default = sequential order)')
parser.add_argument('--no-save',
dest='save_files',
default=True,
action='store_const',
const=False,
help="don't save models and metrics at all")
# parse arguments and load configuration
args = parser.parse_args()
sql_config, _, aws_config, log_config = load_config(**vars(args))
db = Database(**vars(sql_config))
with db_session(db): # keep a database session open to access the dataruns
## get all the classifier in the dataset
classifiers = db.get_classifiers()
## or
## get one classifier by the classifier ID
# classifier = db.get_classifier(classifier_id)
print("total {} classifiers".format(len(classifiers)))
for classifier in classifiers:
metrics = load_metrics(classifier, metric_dir="./metrics")
print(metrics)