autogen/test/test_training_log.py

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import os
import unittest
from tempfile import TemporaryDirectory
from sklearn.datasets import fetch_california_housing
from flaml import AutoML
from flaml.training_log import training_log_reader
class TestTrainingLog(unittest.TestCase):
def test_training_log(self, path="test_training_log.log"):
with TemporaryDirectory() as d:
filename = os.path.join(d, path)
# Run a simple job.
automl = AutoML()
automl_settings = {
"time_budget": 1,
"metric": "mse",
"task": "regression",
"log_file_name": filename,
"log_training_metric": True,
"mem_thres": 1024 * 1024,
"n_jobs": 1,
"model_history": True,
"train_time_limit": 0.01,
"verbose": 3,
"ensemble": True,
"keep_search_state": True,
}
X_train, y_train = fetch_california_housing(return_X_y=True)
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
automl._state._train_with_config(automl.best_estimator, automl.best_config)
# Check if the training log file is populated.
self.assertTrue(os.path.exists(filename))
with training_log_reader(filename) as reader:
count = 0
for record in reader.records():
print(record)
count += 1
self.assertGreater(count, 0)
automl_settings["log_file_name"] = None
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
automl._selected.update(None, 0)
automl = AutoML()
automl.fit(X_train=X_train, y_train=y_train, max_iter=0, task="regression")
def test_illfilename(self):
try:
self.test_training_log("/")
except IsADirectoryError:
print("IsADirectoryError happens as expected in linux.")
except PermissionError:
print("PermissionError happens as expected in windows.")