演示：定义自定义回归目标和评估指标

演示：定义自定义评估指标和目标函数。请注意，为了简化，以下示例中未使用权重。在此脚本中，我们实现平方对数误差 (SLE) 目标函数和 RMSLE 评估指标作为自定义函数，然后将其与 XGBoost 中的原生实现进行比较。

请参阅 自定义目标和评估指标 获取详细的分步详解，以及其他细节。

SLE 目标函数减少了训练数据集中异常值的影响，因此我们在此将其性能与标准平方误差进行比较。

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)