monotone_constraints not working with xgb.regressor (python) about xgboost

[niltecedu]

Hey @trivialfis

Here are the feature names:

['t_2m:C_53.77_1.702', 't_2m:C_53.84_1.767', 't_2m:C_53.9_1.832', 't_2m:C_53.97_1.897', 't_2m:C_54.03_1.962', 't_2m:C_54.1_2.027', 'wind_speed_10m:ms_53.77_1.702', 'wind_speed_10m:ms_53.84_1.767', 'wind_speed_10m:ms_53.9_1.832', 'wind_speed_10m:ms_53.97_1.897', 'wind_speed_10m:ms_54.03_1.962', 'wind_speed_10m:ms_54.1_2.027', 'wind_speed_100m:ms_53.77_1.702', 'wind_speed_100m:ms_53.84_1.767', 'wind_speed_100m:ms_53.9_1.832', 'wind_speed_100m:ms_53.97_1.897', 'wind_speed_100m:ms_54.03_1.962', 'wind_speed_100m:ms_54.1_2.027', 'wind_dir_10m:d_53.77_1.702', 'wind_dir_10m:d_53.84_1.767', 'wind_dir_10m:d_53.9_1.832', 'wind_dir_10m:d_53.97_1.897', 'wind_dir_10m:d_54.03_1.962', 'wind_dir_10m:d_54.1_2.027', 'wind_dir_100m:d_53.77_1.702', 'wind_dir_100m:d_53.84_1.767', 'wind_dir_100m:d_53.9_1.832', 'wind_dir_100m:d_53.97_1.897', 'wind_dir_100m:d_54.03_1.962', 'wind_dir_100m:d_54.1_2.027', 'precip_1h:mm_53.77_1.702', 'precip_1h:mm_53.84_1.767', 'precip_1h:mm_53.9_1.832', 'precip_1h:mm_53.97_1.897', 'precip_1h:mm_54.03_1.962', 'precip_1h:mm_54.1_2.027', 'relative_humidity_2m:p_53.77_1.702', 'relative_humidity_2m:p_53.84_1.767', 'relative_humidity_2m:p_53.9_1.832', 'relative_humidity_2m:p_53.97_1.897', 'relative_humidity_2m:p_54.03_1.962', 'relative_humidity_2m:p_54.1_2.027', 'Wind_MWh_credit', 'windlimit']

Here is the constraints python value being passed to the regressor object

{'wind_speed_10m:ms_53.77_1.702': 1, 'wind_speed_10m:ms_53.84_1.767': 1, 'wind_speed_10m:ms_53.9_1.832': 1, 'wind_speed_10m:ms_53.97_1.897': 1, 'wind_speed_10m:ms_54.03_1.962': 1, 'wind_speed_10m:ms_54.1_2.027': 1, 'wind_speed_100m:ms_53.77_1.702': 1, 'wind_speed_100m:ms_53.84_1.767': 1, 'wind_speed_100m:ms_53.9_1.832': 1, 'wind_speed_100m:ms_53.97_1.897': 1, 'wind_speed_100m:ms_54.03_1.962': 1, 'wind_speed_100m:ms_54.1_2.027': 1, 'windlimit': 1, 'relative_humidity_2m:p_53.77_1.702': -1, 'relative_humidity_2m:p_53.84_1.767': -1, 'relative_humidity_2m:p_53.9_1.832': -1, 'relative_humidity_2m:p_53.97_1.897': -1, 'relative_humidity_2m:p_54.03_1.962': -1, 'relative_humidity_2m:p_54.1_2.027': -1}

[trivialfis]

Hi, could you please provide some info for us to reproduce:

• The actual Python value of the constraint parameter.
• The list of feature names.

[trivialfis]

Hi, I tried to create a reproducer based on the parameters, but couldn't see the error:

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.datasets import make_regression

alpha = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])

mono = {
    "wind_speed_10m:ms_53.77_1.702": 1,
    "wind_speed_10m:ms_53.84_1.767": 1,
    "wind_speed_10m:ms_53.9_1.832": 1,
    "wind_speed_10m:ms_53.97_1.897": 1,
    "wind_speed_10m:ms_54.03_1.962": 1,
    "wind_speed_10m:ms_54.1_2.027": 1,
    "wind_speed_100m:ms_53.77_1.702": 1,
    "wind_speed_100m:ms_53.84_1.767": 1,
    "wind_speed_100m:ms_53.9_1.832": 1,
    "wind_speed_100m:ms_53.97_1.897": 1,
    "wind_speed_100m:ms_54.03_1.962": 1,
    "wind_speed_100m:ms_54.1_2.027": 1,
    "windlimit": 1,
    "relative_humidity_2m:p_53.77_1.702": -1,
    "relative_humidity_2m:p_53.84_1.767": -1,
    "relative_humidity_2m:p_53.9_1.832": -1,
    "relative_humidity_2m:p_53.97_1.897": -1,
    "relative_humidity_2m:p_54.03_1.962": -1,
    "relative_humidity_2m:p_54.1_2.027": -1,
}

fname = [
    "t_2m:C_53.77_1.702",
    "t_2m:C_53.84_1.767",
    "t_2m:C_53.9_1.832",
    "t_2m:C_53.97_1.897",
    "t_2m:C_54.03_1.962",
    "t_2m:C_54.1_2.027",
    "wind_speed_10m:ms_53.77_1.702",
    "wind_speed_10m:ms_53.84_1.767",
    "wind_speed_10m:ms_53.9_1.832",
    "wind_speed_10m:ms_53.97_1.897",
    "wind_speed_10m:ms_54.03_1.962",
    "wind_speed_10m:ms_54.1_2.027",
    "wind_speed_100m:ms_53.77_1.702",
    "wind_speed_100m:ms_53.84_1.767",
    "wind_speed_100m:ms_53.9_1.832",
    "wind_speed_100m:ms_53.97_1.897",
    "wind_speed_100m:ms_54.03_1.962",
    "wind_speed_100m:ms_54.1_2.027",
    "wind_dir_10m:d_53.77_1.702",
    "wind_dir_10m:d_53.84_1.767",
    "wind_dir_10m:d_53.9_1.832",
    "wind_dir_10m:d_53.97_1.897",
    "wind_dir_10m:d_54.03_1.962",
    "wind_dir_10m:d_54.1_2.027",
    "wind_dir_100m:d_53.77_1.702",
    "wind_dir_100m:d_53.84_1.767",
    "wind_dir_100m:d_53.9_1.832",
    "wind_dir_100m:d_53.97_1.897",
    "wind_dir_100m:d_54.03_1.962",
    "wind_dir_100m:d_54.1_2.027",
    "precip_1h:mm_53.77_1.702",
    "precip_1h:mm_53.84_1.767",
    "precip_1h:mm_53.9_1.832",
    "precip_1h:mm_53.97_1.897",
    "precip_1h:mm_54.03_1.962",
    "precip_1h:mm_54.1_2.027",
    "relative_humidity_2m:p_53.77_1.702",
    "relative_humidity_2m:p_53.84_1.767",
    "relative_humidity_2m:p_53.9_1.832",
    "relative_humidity_2m:p_53.97_1.897",
    "relative_humidity_2m:p_54.03_1.962",
    "relative_humidity_2m:p_54.1_2.027",
    "Wind_MWh_credit",
    "windlimit",
]

n_features = len(fname)

X, y = make_regression(256, n_features)
X_df = pd.DataFrame(X, columns=fname)

xgb_regressor_wind = xgb.XGBRegressor(
    objective="reg:quantileerror",
    quantile_alpha=alpha,
    # n_jobs=1,
    colsample_bytree=0.8,
    gamma=0.3,
    learning_rate=0.02,
    max_depth=4,
    n_estimators=800,
    subsample=0.6,
    min_child_weight=3,
    monotone_constraints=mono,
)
xgb_regressor_wind.fit(X_df, y)