def testCatboostNoEarlyStop(self): estimator_list = [create_catboost(), create_catboost()] config = self.base_params(estimator_list=estimator_list) config["early_stopping"] = False trainable = _Trainable(config) trainable.train() assert not any(trainable.saved_models) trainable.stop()
def testWarmStart(self): # Hard to get introspection so we just test that it runs. config = self.base_params([LogisticRegression(), LogisticRegression()]) config["early_stopping"] = True trainable = _Trainable(config) trainable.train() trainable.train() trainable.stop()
def testNoPartialFit(self): config = self.base_params([SGDClassifier(), SGDClassifier()]) config["early_stopping"] = False trainable = _Trainable(config) trainable.train() assert not hasattr(trainable.estimator_list[0], "t_") trainable.train() assert not hasattr(trainable.estimator_list[0], "t_") trainable.stop()
def testLGBMNoEarlyStop(self): config = self.base_params( estimator_list=[create_lightgbm(), create_lightgbm()]) config["early_stopping"] = False trainable = _Trainable(config) trainable.train() assert not any(trainable.saved_models) trainable.stop()
def testPartialFit(self): config = self.base_params([SGDClassifier(), SGDClassifier()]) config["early_stopping"] = True trainable = _Trainable(config) trainable.train() assert trainable.estimator_list[0].t_ > 0 previous_t = trainable.estimator_list[0].t_ trainable.train() assert trainable.estimator_list[0].t_ > previous_t trainable.stop()
def testWarmStart(self): # Hard to get introspection so we just test that it runs. estimator_list = [LogisticRegression(), LogisticRegression()] config = self.base_params(estimator_list) config["early_stopping"] = True config["early_stop_type"] = get_early_stop_type( estimator_list[0], True) trainable = _Trainable(config) trainable.train() trainable.train() trainable.stop()
def testXGBoostEarlyStop(self): config = self.base_params( estimator_list=[create_xgboost(), create_xgboost()]) config["early_stopping"] = True trainable = _Trainable(config) trainable.train() assert all(trainable.saved_models) trainable.train() assert all(trainable.saved_models) trainable.stop()
def testXGBoostEarlyStop(self): estimator_list = [create_xgboost(), create_xgboost()] config = self.base_params(estimator_list=estimator_list) config["early_stopping"] = True config["early_stop_type"] = get_early_stop_type( estimator_list[0], True) trainable = _Trainable(config) trainable.train() assert all(trainable.saved_models) trainable.train() assert all(trainable.saved_models) trainable.stop()
def testLGBMEarlyStop(self): config = self.base_params( estimator_list=[create_lightgbm(), create_lightgbm()]) config["early_stopping"] = True config["early_stop_type"] = get_early_stop_type( config["estimator_list"][0], True) trainable = _Trainable(config) trainable.train() assert all(trainable.saved_models) trainable.train() assert all(trainable.saved_models) trainable.stop()
def testPartialFit(self): estimator_list = [SGDClassifier(), SGDClassifier()] config = self.base_params(estimator_list) config["early_stopping"] = True config["early_stop_type"] = get_early_stop_type( estimator_list[0], True) trainable = _Trainable(config) trainable.train() assert trainable.estimator_list[0].t_ > 0 previous_t = trainable.estimator_list[0].t_ trainable.train() assert trainable.estimator_list[0].t_ > previous_t trainable.stop()
def test_basic_train(self): config = self.base_params(estimator_list=[SVC(), SVC()]) trainable = _Trainable(config) trainable.train() trainable.stop()