XGBoost segmentation fault

[tejas-kale]

Hi Tushar. Thanks for sharing the package. I am facing an issue with the line self.temp1 = XGBClassifier().fit(self.X, self.y).feature_importances_ in the method base_tree() of models.py. I can't get much out of the error which says:

UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
  warnings.warn(label_encoder_deprecation_msg, UserWarning)

Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)

The surprising thing is that when I run XGBClassifier().fit(X, y) on the same data in an IPython console, it runs fine.

I am using the same script as provided in the README:

import torch
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from XBNet.training_utils import training,predict
from XBNet.models import XBNETClassifier
from XBNet.run import run_XBNET

# Experiment with Iris data directly from sklearn.
# iris = load_iris()
# data = pd.DataFrame(iris.data)
# data.columns = iris.feature_names
# data.loc[:, 'type'] = iris.target
# X, y = data.iloc[:, :-1], data.iloc[:, -1]
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

data = pd.read_csv('test/Iris.csv')
print(data.shape)
x_data = data[data.columns[:-1]]
print(x_data.shape)
y_data = data[data.columns[-1]]
le = LabelEncoder()
y_data = np.array(le.fit_transform(y_data))
print(le.classes_)

X_train,X_test,y_train,y_test = train_test_split(x_data.to_numpy(),y_data,test_size = 0.3,random_state = 0)
model = XBNETClassifier(X_train,y_train,2)

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

m,acc, lo, val_ac, val_lo = run_XBNET(X_train,X_test,y_train,y_test,model,criterion,optimizer,32,300)
print(predict(m,x_data.to_numpy()[0,:]))

Any idea what could be going wrong here? Following are some details of my system that might be useful:

• OS: macOS 11.5.2
• Python 3.8.11
• XGBoost version: 1.4.2
• XBNet: 1.3.1

[tusharsarkar3]

Hey! Sorry for the late reply. This problem was also reported by another Mac OS user, so we're probing into it to see how it can be solved.