def test_save_and_load(self):
self.assertTrue(
"Private" in list(self.data["train"]["X"]["workclass"]))
early_stop = EarlyStopping({"metric": {"name": "logloss"}})
metric_logger = MetricLogger({"metric_names": ["logloss", "auc"]})
il = IterativeLearner(self.train_params,
callbacks=[early_stop, metric_logger])
il.train(self.data)
y_predicted = il.predict(self.data["train"]["X"])
metric = Metric({"name": "logloss"})
loss_1 = metric(self.data["train"]["y"], y_predicted)
json_desc = il.to_json()
il2 = IterativeLearner(self.train_params, callbacks=[])
self.assertTrue(il.uid != il2.uid)
il2.from_json(json_desc)
self.assertTrue(il.uid == il2.uid)
y_predicted_2 = il2.predict(self.data["train"]["X"])
loss_2 = metric(self.data["train"]["y"], y_predicted_2)
assert_almost_equal(loss_1, loss_2)
uids = [i.uid for i in il.learners]
uids2 = [i.uid for i in il2.learners]
for u in uids:
self.assertTrue(u in uids2)
def test_save_and_load(self):
il = IterativeLearner(self.train_params, callbacks=[])
il.train(self.data)
metric = Metric({"name": "logloss"})
loss = metric(self.y, il.predict(self.X))
json_desc = il.to_json()
il2 = IterativeLearner(json_desc.get("params"), callbacks=[])
self.assertTrue(il.uid != il2.uid)
il2.from_json(json_desc)
self.assertTrue(il.uid == il2.uid)
loss2 = metric(self.y, il2.predict(self.X))
assert_almost_equal(loss, loss2)
uids = [i.uid for i in il.learners]
uids2 = [i.uid for i in il2.learners]
for u in uids:
self.assertTrue(u in uids2)