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@thvasilo @trivialfis
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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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