def test_automl_default_classification():
for data_id in [
179,
4135,
]:
dataset = fetch_openml(data_id=data_id, as_frame=True)
dataset.target = dataset.target.astype('category').cat.codes
if len(dataset.data) < 2000:
crop = len(dataset.data)
else:
crop = 2000
X_train, X_test, y_train, y_test = train_test_split(
dataset.data[:crop],
dataset.target[:crop],
test_size=0.2,
random_state=RANDOM_SEED,
)
model = AutoMLClassifier(random_state=RANDOM_SEED, )
model.fit(X_train, y_train, timeout=600)
predicts = model.predict(X_test)
score = round(sklearn.metrics.roc_auc_score(y_test, predicts), 4)
assert score is not None
assert 0.5 < score <= 1
model.save('AutoML_model_1', folder=TMP_FOLDER)
model_new = AutoMLClassifier(random_state=RANDOM_SEED, )
model_new = model_new.load('AutoML_model_1', folder=TMP_FOLDER)
predicts = model_new.predict(X_test)
score2 = round(sklearn.metrics.roc_auc_score(y_test, predicts), 4)
assert score2 is not None
assert 0.5 < score2 <= 1
assert (score - score2) == 0.
def load_model(model_name, folder):
model = AutoMLClassifier()
model = model.load(model_name, folder=folder)
return(model)