Created
March 2, 2022 19:37
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XGBoost with Tensorboard
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| import datetime | |
| import xgboost as xgb | |
| from tensorboardX import SummaryWriter | |
| from sklearn.datasets import load_boston | |
| from sklearn.model_selection import train_test_split | |
| class TensorBoardCallback(xgb.callback.TrainingCallback): | |
| def __init__(self, experiment: str = None, data_name: str = None): | |
| self.experiment = experiment or "logs" | |
| self.data_name = data_name or "test" | |
| self.datetime_ = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") | |
| self.log_dir = f"runs/{self.experiment}/{self.datetime_}" | |
| self.train_writer = SummaryWriter(log_dir=os.path.join(self.log_dir, "train/")) | |
| if self.data_name: | |
| self.test_writer = SummaryWriter( | |
| log_dir=os.path.join(self.log_dir, f"{self.data_name}/") | |
| ) | |
| def after_iteration( | |
| self, model, epoch: int, evals_log: xgb.callback.TrainingCallback.EvalsLog | |
| ) -> bool: | |
| if not evals_log: | |
| return False | |
| for data, metric in evals_log.items(): | |
| for metric_name, log in metric.items(): | |
| score = log[-1][0] if isinstance(log[-1], tuple) else log[-1] | |
| if data == "train": | |
| self.train_writer.add_scalar(metric_name, score, epoch) | |
| else: | |
| self.test_writer.add_scalar(metric_name, score, epoch) | |
| return False | |
| X, y = load_boston(return_X_y=True) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100) | |
| dtrain = xgb.DMatrix(X_train, label=y_train) | |
| dtest = xgb.DMatrix(X_test, label=y_test) | |
| params = {'objective':'reg:squarederror', 'eval_metric': 'rmse'} | |
| bst = xgb.train(params, dtrain, num_boost_round=100, evals=[(dtrain, 'train'), (dtest, 'test')], | |
| callbacks=[TensorBoardCallback(experiment='exp_1', data_name='test')]) |
Author
darenr
commented
Mar 2, 2022
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