""" Demo for defining a custom regression objective and metric ========================================================== Demo for defining customized metric and objective. Notice that for simplicity reason weight is not used in following example. In this script, we implement the Squared Log Error (SLE) objective and RMSLE metric as customized functions, then compare it with native implementation in XGBoost. See :doc:`/tutorials/custom_metric_obj` for a step by step walkthrough, with other details. The `SLE` objective reduces impact of outliers in training dataset, hence here we also compare its performance with standard squared error. """ import argparse from time import time from typing import Dict, List, Tuple import matplotlib import numpy as np from matplotlib import pyplot as plt import xgboost as xgb # shape of generated data. kRows = 4096 kCols = 16 kOutlier = 10000 # mean of generated outliers kNumberOfOutliers = 64 kRatio = 0.7 kSeed = 1994 kBoostRound = 20 np.random.seed(seed=kSeed) def generate_data() -> Tuple[xgb.DMatrix, xgb.DMatrix]: '''Generate data containing outliers.''' x = np.random.randn(kRows, kCols) y = np.random.randn(kRows) y += np.abs(np.min(y)) # Create outliers for i in range(0, kNumberOfOutliers): ind = np.random.randint(0, len(y)-1) y[ind] += np.random.randint(0, kOutlier) train_portion = int(kRows * kRatio) # rmsle requires all label be greater than -1. assert np.all(y > -1.0) train_x: np.ndarray = x[: train_portion] train_y: np.ndarray = y[: train_portion] dtrain = xgb.DMatrix(train_x, label=train_y) test_x = x[train_portion:] test_y = y[train_portion:] dtest = xgb.DMatrix(test_x, label=test_y) return dtrain, dtest def native_rmse(dtrain: xgb.DMatrix, dtest: xgb.DMatrix) -> Dict[str, Dict[str, List[float]]]: '''Train using native implementation of Root Mean Squared Loss.''' print('Squared Error') squared_error = { 'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'tree_method': 'hist', 'seed': kSeed } start = time() results: Dict[str, Dict[str, List[float]]] = {} xgb.train(squared_error, dtrain=dtrain, num_boost_round=kBoostRound, evals=[(dtrain, 'dtrain'), (dtest, 'dtest')], evals_result=results) print('Finished Squared Error in:', time() - start, '\n') return results def native_rmsle(dtrain: xgb.DMatrix, dtest: xgb.DMatrix) -> Dict[str, Dict[str, List[float]]]: '''Train using native implementation of Squared Log Error.''' print('Squared Log Error') results: Dict[str, Dict[str, List[float]]] = {} squared_log_error = { 'objective': 'reg:squaredlogerror', 'eval_metric': 'rmsle', 'tree_method': 'hist', 'seed': kSeed } start = time() xgb.train(squared_log_error, dtrain=dtrain, num_boost_round=kBoostRound, evals=[(dtrain, 'dtrain'), (dtest, 'dtest')], evals_result=results) print('Finished Squared Log Error in:', time() - start) return results def py_rmsle(dtrain: xgb.DMatrix, dtest: xgb.DMatrix) -> Dict: '''Train using Python implementation of Squared Log Error.''' def gradient(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray: '''Compute the gradient squared log error.''' y = dtrain.get_label() return (np.log1p(predt) - np.log1p(y)) / (predt + 1) def hessian(predt: np.ndarray, dtrain: xgb.DMatrix) -> np.ndarray: '''Compute the hessian for squared log error.''' y = dtrain.get_label() return ((-np.log1p(predt) + np.log1p(y) + 1) / np.power(predt + 1, 2)) def squared_log(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[np.ndarray, np.ndarray]: '''Squared Log Error objective. A simplified version for RMSLE used as objective function. :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2` ''' predt[predt < -1] = -1 + 1e-6 grad = gradient(predt, dtrain) hess = hessian(predt, dtrain) return grad, hess def rmsle(predt: np.ndarray, dtrain: xgb.DMatrix) -> Tuple[str, float]: ''' Root mean squared log error metric. :math:`\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}` ''' y = dtrain.get_label() predt[predt < -1] = -1 + 1e-6 elements = np.power(np.log1p(y) - np.log1p(predt), 2) return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y))) results: Dict[str, Dict[str, List[float]]] = {} xgb.train({'tree_method': 'hist', 'seed': kSeed, 'disable_default_eval_metric': 1}, dtrain=dtrain, num_boost_round=kBoostRound, obj=squared_log, custom_metric=rmsle, evals=[(dtrain, 'dtrain'), (dtest, 'dtest')], evals_result=results) return results def plot_history(rmse_evals, rmsle_evals, py_rmsle_evals): fig, axs = plt.subplots(3, 1) ax0: matplotlib.axes.Axes = axs[0] ax1: matplotlib.axes.Axes = axs[1] ax2: matplotlib.axes.Axes = axs[2] x = np.arange(0, kBoostRound, 1) ax0.plot(x, rmse_evals['dtrain']['rmse'], label='train-RMSE') ax0.plot(x, rmse_evals['dtest']['rmse'], label='test-RMSE') ax0.legend() ax1.plot(x, rmsle_evals['dtrain']['rmsle'], label='train-native-RMSLE') ax1.plot(x, rmsle_evals['dtest']['rmsle'], label='test-native-RMSLE') ax1.legend() ax2.plot(x, py_rmsle_evals['dtrain']['PyRMSLE'], label='train-PyRMSLE') ax2.plot(x, py_rmsle_evals['dtest']['PyRMSLE'], label='test-PyRMSLE') ax2.legend() def main(args): dtrain, dtest = generate_data() rmse_evals = native_rmse(dtrain, dtest) rmsle_evals = native_rmsle(dtrain, dtest) py_rmsle_evals = py_rmsle(dtrain, dtest) if args.plot != 0: plot_history(rmse_evals, rmsle_evals, py_rmsle_evals) plt.show() if __name__ == "__main__": parser = argparse.ArgumentParser( description='Arguments for custom RMSLE objective function demo.') parser.add_argument( '--plot', type=int, default=1, help='Set to 0 to disable plotting the evaluation history.') args = parser.parse_args() main(args)