def test_fit_and_predict(self):
MAX_STEPS = 100
additional["max_steps"] = MAX_STEPS
iters_cnt = 5
max_iters = MaxItersConstraint({"max_iters": iters_cnt})
metric_logger = MetricLogger({"metric_names": ["logloss"]})
il = IterativeLearner(self.train_params, callbacks=[max_iters, metric_logger])
il.train(self.data)
metric_logs = il.get_metric_logs()
for k in range(self.kfolds):
self.assertEqual(
len(metric_logs[il.learners[k].uid]["train"]["logloss"]), iters_cnt
)
self.assertNotEqual(
len(metric_logs[il.learners[k].uid]["train"]["logloss"]), MAX_STEPS
)
def test_fit_and_predict(self):
MAX_STEPS = 10
additional["max_steps"] = MAX_STEPS
metric_logger = MetricLogger({"metric_names": ["logloss", "auc"]})
il = IterativeLearner(self.train_params, callbacks=[metric_logger])
il.train(self.data)
metric_logs = il.get_metric_logs()
self.assertEqual(
len(metric_logs[il.learners[0].uid]["train"]["logloss"]),
len(metric_logs[il.learners[0].uid]["train"]["auc"]),
)
self.assertEqual(
len(metric_logs[il.learners[0].uid]["train"]["logloss"]),
len(metric_logs[il.learners[0].uid]["iters"]),
)
self.assertEqual(
len(metric_logs[il.learners[0].uid]["train"]["logloss"]),
MAX_STEPS)