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 Custom Objective and Evaluation Metric 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 argparse
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


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]]] = {}
              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
    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()
              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
    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},
              evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],

    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')

    ax1.plot(x, rmsle_evals['dtrain']['rmsle'], label='train-native-RMSLE')
    ax1.plot(x, rmsle_evals['dtest']['rmsle'], label='test-native-RMSLE')

    ax2.plot(x, py_rmsle_evals['dtrain']['PyRMSLE'], label='train-PyRMSLE')
    ax2.plot(x, py_rmsle_evals['dtest']['PyRMSLE'], label='test-PyRMSLE')

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)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description='Arguments for custom RMSLE objective function demo.')
        help='Set to 0 to disable plotting the evaluation history.')
    args = parser.parse_args()

Total running time of the script: ( 0 minutes 0.000 seconds)

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