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5b2f805 Jun 26, 2019
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 '''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.rst 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 numpy as np import xgboost as xgb from typing import Tuple, Dict, List from time import time import matplotlib from matplotlib import pyplot as plt # 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, feval=rmsle, evals=[(dtrain, 'dtrain'), (dtest, 'dtest')], evals_result=results) return results if __name__ == '__main__': dtrain, dtest = generate_data() rmse_evals = native_rmse(dtrain, dtest) rmsle_evals = native_rmsle(dtrain, dtest) py_rmsle_evals = py_rmsle(dtrain, dtest) 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() plt.show() plt.close()
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