cb_test = cb.Pool(data.X_test, data.y_test) if data.task == 'Ranking': cb_test.set_group_id(data.groups) preds = self.model.predict(cb_test) metric = self.eval(data, preds) return metric def model_name(self): name = "catboost_" use_cpu = "gpu_" if self.use_gpu else "cpu_" nr = str(self.num_rounds) + "_" return name + use_cpu + nr + str(self.max_depth) if __name__ == "__main__": X, y, groups = du.get_yahoo() dataset = Dataset(name='yahoo', task='Ranking', metric='NDCG', get_func=du.get_yahoo) print(dataset.X_train.shape) print(dataset.y_test.shape) t_start = time.time() xgbModel = CatboostModel() xgbModel.use_gpu = False xgbModel.run_model(data=dataset) eplased = time.time() - t_start print("--------->> " + str(eplased))
def cifar(): return Dataset(name='cifar10', task='Multiclass classification', metric='Accuracy', get_func=du.get_cifar10)
def news20(): return Dataset(name='news20', task='Multiclass classification', metric='Accuracy', get_func=du.get_news20)
def log1p(): return Dataset(name='log1p', task='Regression', metric='RMSE', get_func=du.get_log1p)
def higgs(): return Dataset(name='higgs', task='Regression', metric='RMSE', get_func=du.get_higgs)
def __init__(self, data_func, name, task, metric): self.data = Dataset(data_func()) self.name = name self.task = task self.metric = metric
def _predict(self, data): pred = self.model.predict(data.X_test) metric = self.eval(data, pred) return metric def model_name(self): name = "thundergbm_" use_cpu = "gpu_" if self.use_gpu else "cpu_" nr = str(self.num_rounds) + "_" return name + use_cpu + nr + str(self.max_depth) if __name__ == "__main__": # X, y = du.get_higgs() dataset = Dataset(name='higgs', task='Regression', metric='RMSE', get_func=du.get_realsim) print(dataset.X_train.shape) print(dataset.y_test.shape) t_start = time.time() tgmModel = ThunderGBMModel() tgmModel.tree_method = 'hist' tgmModel.run_model(data=dataset) eplased = time.time() - t_start print("--------->> " + str(eplased))
'Adam', loss=sm.losses.bce_jaccard_loss, metrics=metrics, ) def test_preprocessing(image, mask): sample = {} image = cv2.resize(image, (cfg.image_size, cfg.image_size), interpolation=cv2.INTER_NEAREST) mask = cv2.resize(mask, (cfg.image_size, cfg.image_size), interpolation=cv2.INTER_NEAREST) sample["image"] = image sample["mask"] = mask # cv2.imshow("image", image) # cv2.imshow("mask", mask) # cv2.waitKey(0) return sample # Dataset for testation images test_dataset = Dataset(TEST_IMAGES, TEST_MASKS, classes=cfg.CLASSES, preprocessing=test_preprocessing) test_dataloader = Dataloader(test_dataset, batch_size=1, shuffle=False) scores = model.evaluate_generator(test_dataloader, verbose=1) print("Loss: {:.5}".format(scores[0])) for metric, value in zip(metrics, scores[1:]): print("mean {}: {:.5}".format(metric.__name__, value))