LightGBM 预测负值

Question

My LightGBM regressor model returns negative values.

For XGBoost there is objective='count:poisson' hyperparameter in order to prevent returning negative predicitons.

Is there any chance to do this?

Github issue => https://github.com/microsoft/LightGBM/issues/5629

Answer 1

LightGBM also supports poisson regression. For example, consider the following Python code.

import lightgbm as lgb
import numpy as np
from matplotlib import pyplot

# random Poisson-distributed target and one informative feature
y = np.random.poisson(lam=15.0, size=1_000)
X = y + np.random.normal(loc=10.0, scale=2.0, size=(y.shape[0], ))
X = X.reshape(-1, 1)

# fit a Poisson regression model
reg = lgb.LGBMRegressor(
    objective="poisson",
    n_estimators=150,
    min_data=1
)
reg.fit(X, y)

# get predictions
preds = reg.predict(X)

print("summary of predicted values")
print(f"  * min: {round(np.min(preds), 3)}")
print(f"  * max: {round(np.max(preds), 3)}")

# compare predicted distribution to the empirical one
bins = np.linspace(0, 30, 50)
pyplot.hist(y, bins, alpha=0.5, label='actual')
pyplot.hist(preds, bins, alpha=0.5, label='predicted')
pyplot.legend(loc='upper right')
pyplot.show()

This example uses Python 3.10 and lightgbm==3.3.3 .

However... I don't recommend using Poisson regression just to achieve "no negative predictions". The Poisson loss function is intended to be used for cases where you believe your target is Poisson-distributed, eg it looks like counts of events observed over some regular interval like time or space.

Other options you might consider to try to achieve the behavior "never predict a negative number from LightGBM regression":

• write a custom objective function in one of the interfaces that support it, like the R or Python package
• post-process LightGBM's predictions, recoding negative values to 0
• pre-process the target variable such that there are no negative values (eg dropping such observations, re-scaling, taking the absolute value)

Answer 2

LightGBM also facilitates an objective parameter which can be set to 'poisson' . Follow this link for more information.

An example for LGBMRegressor (scikit-learn API):

from lightgbm import LGBMRegressor


regressor = LGBMRegressor(objective='poisson')