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test reproducibility from retrain (#533)

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Chi Wang 2022-05-07 09:13:17 -07:00 committed by GitHub
parent bcdfdc8735
commit c1bb66980c
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1 changed files with 23 additions and 12 deletions
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35
test/automl/test_regression.py
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@ -34,7 +34,7 @@ class MyXGB2(XGBoostEstimator):
class TestRegression(unittest.TestCase):
def test_regression(self):
automl_experiment = AutoML()
automl = AutoML()
automl_settings = {
"time_budget": 2,
"task": "regression",
@ -45,22 +45,23 @@ class TestRegression(unittest.TestCase):
}
X_train, y_train = fetch_california_housing(return_X_y=True)
n = int(len(y_train) * 9 // 10)
automl_experiment.fit(
automl.fit(
X_train=X_train[:n],
y_train=y_train[:n],
X_val=X_train[n:],
y_val=y_train[n:],
**automl_settings
)
assert automl_experiment._state.eval_method == "holdout"
print(automl_experiment.predict(X_train))
print(automl_experiment.model)
print(automl_experiment.config_history)
print(automl_experiment.best_model_for_estimator("xgboost"))
print(automl_experiment.best_iteration)
print(automl_experiment.best_estimator)
assert automl._state.eval_method == "holdout"
y_pred = automl.predict(X_train)
print(y_pred)
print(automl.model.estimator)
print(automl.config_history)
print(automl.best_model_for_estimator("xgboost"))
print(automl.best_iteration)
print(automl.best_estimator)
print(get_output_from_log(automl_settings["log_file_name"], 1))
automl_experiment.retrain_from_log(
automl.retrain_from_log(
task="regression",
log_file_name=automl_settings["log_file_name"],
X_train=X_train,
@ -68,14 +69,24 @@ class TestRegression(unittest.TestCase):
train_full=True,
time_budget=1,
)
automl_experiment.retrain_from_log(
automl.retrain_from_log(
task="regression",
log_file_name=automl_settings["log_file_name"],
X_train=X_train,
y_train=y_train,
train_full=True,
time_budget=0,
)
automl = AutoML()
automl.retrain_from_log(
task="regression",
log_file_name=automl_settings["log_file_name"],
X_train=X_train[:n],
y_train=y_train[:n],
train_full=True,
)
print(automl.model.estimator)
y_pred2 = automl.predict(X_train)
assert (y_pred == y_pred2).all()
def test_sparse_matrix_regression(self):
X_train = scipy.sparse.random(300, 900, density=0.0001)