Solving xgboost.core.XGBoostError: b[20:58:45] C:\Users\Administrator\Desktop\xgboost\dmlc-core\s

Table of Contents

Solve xgboost.core.XGBoostError: b'[20:58:45] C:\Users\Administrator\Desktop\xgboost\dmlc-core\s

Cause of the problem

Solution

Method 1: Modify the file path

Method 2: Modify xgboost source code

Summarize

Solve xgboost.core.XGBoostError: b'[20:58:45] C:\Users\Administrator\Desktop\xgboost\dmlc-core\s

Recently, when using xgboost for machine learning, I encountered a problem: xgboost.core.XGBoostError: b'[20:58:45] C:\Users\Administrator\Desktop\xgboost\dmlc-core\ \src\io\local_filesys.cc:209: Check failed: allow_null \\
‘ In this article, I will introduce the cause and solution of this problem.

Cause of the problem

First, let’s analyze the cause of this error. When I use xgboost for training or prediction, xgboost reads the file based on the path of the dataset. However, when the file path contains Chinese or special characters, a path parsing error will occur, resulting in the above-mentioned XGBoostError.

Solution

To solve this problem, we can take the following two solutions.

Method 1: Modify the file path

The easiest way is to modify the file path so that it does not contain Chinese or special characters. For example, change the file path to ??"C:/Users/Administrator/Desktop/xgboost/dmlc-core/src/io/local_filesys.cc"??. In this way, xgboost can correctly parse the file path and no more errors will occur.

Method 2: Modify xgboost source code

If you cannot modify the file path, or need to handle a large number of file paths, you can try to modify the source code of xgboost to solve the problem. Here are the specific steps:

Open the ??local_filesys.cc?? file. The path of the file is ??"xgboost/dmlc-core/src/io/local_filesys.cc"?? .
Find the following code snippet:

cppCopy codebool FileExists(const URI & amp; path) {
    std::string fname = path.name;
    std::ifstream ifs(fname.c_str(), std::ios::in);
    if (ifs.fail()) {
        return false;
    } else {
        ifs.close();
        return true;
    }
}

Modify the code to:

cppCopy codebool FileExists(const URI & amp; path) {
    std::string fname = dmlc::GetPath(path);
    std::ifstream ifs(fname.c_str(), std::ios::in);
    if (ifs.fail()) {
        return false;
    } else {
        ifs.close();
        return true;
    }
}

Save the file and recompile xgboost. In this way, the problem can be solved. The modified code will use the ??dmlc::GetPath()?? function to obtain the file path. This function will encode Chinese or special characters in the path, thus avoiding path parsing errors.

Summary

This article explains how to resolve xgboost.core.XGBoostError. If the path contains Chinese or special characters, we can solve the problem by modifying the file path or modifying the source code of xgboost. Choose the appropriate method according to actual needs to ensure that you can successfully use xgboost for machine learning. I hope this article will be helpful to readers who encounter similar problems when using xgboost! If you have questions or other questions, please feel free to leave a comment below.

In this application scenario, our goal is to use xgboost to build a model to perform risk assessment and classification of customer data. The data set contains the customer’s personal information, financial status and other characteristics. We need to predict whether the customer is at risk of default based on these characteristics. In the code example, we will show how to use xgboost for data training and prediction, and solve the above problems.

pythonCopy codeimport pandas as pd
import xgboost as xgb
from sklearn.model_selection import train_test_split
# Read the training data set
df = pd.read_csv("data.csv") # Assume the data set file name is data.csv
# Divide features and labels
X = df.drop("label", axis=1)
y = df["label"]
# Divide training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
#Set xgboost model parameters
params = {
    'objective': 'binary:logistic',
    'eval_metric': 'auc',
    'max_depth': 5,
    'eta': 0.1,
    'subsample': 0.8,
    'colsample_bytree': 0.8,
    'min_child_weight': 1,
}
#Create DMatrix data structure
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
#Train model
model = xgb.train(params, dtrain, num_boost_round=100)
# predict
y_pred = model.predict(dtest)
# Evaluate the model
auc_score = roc_auc_score(y_test, y_pred)
print("AUC score:", auc_score)

In the above code example, we first use the Pandas library to read the dataset and separate features and labels. Then, the data set is divided into a training set and a test set by using the ??train_test_split?? function. Next, we set the parameters of the xgboost model and created the DMatrix data structure of xgboost. When training the model, we use the ??xgb.train?? function to pass in parameters for model training. Finally, we use the trained model to predict the test set and calculate the AUC as the model evaluation metric. I hope the above example code can help you solve the xgboost.core.XGBoostError problem and train and predict financial risk control models in practical applications. If you have any questions, please feel free to leave a comment below.

XGBoost is an efficient and flexible open source machine learning algorithm library that is widely used in data science and machine learning competitions. XGBoost stands for “eXtreme Gradient Boosting” and is an algorithm based on the gradient boosting framework. The main advantages of XGBoost include:

High performance and efficiency: XGBoost performs well in processing large-scale data. It uses parallelization technology and optimization strategies to effectively utilize multi-core CPUs and distributed computing resources.
Flexibility and scalability: XGBoost supports a variety of tasks and model types, including classification, regression, ranking, and recommendation. It can handle structured data and feature engineering, supports custom losses and evaluation metrics, and is easily integrated into existing machine learning pipelines.
Accuracy and generalization ability: XGBoost uses the gradient boosting algorithm, which can effectively reduce the bias and variance of the model and improve the accuracy and generalization ability of the model. At the same time, it also uses regularization technology and pruning strategies to prevent model overfitting.
Feature Importance Analysis: XGBoost provides convenient feature importance analysis tools that can help machine learning engineers and data scientists understand the data and identify the most important features.
Wide application fields: XGBoost has been successfully used in various fields, including financial risk control, advertising click-through rate prediction, recommendation systems, search ranking, etc. The core algorithm of XGBoost is the Gradient Boosting Machine (GBM), which gradually improves the prediction model by integrating multiple weak learners (usually decision trees). During each iteration, the model will pay more attention to those samples that are difficult to predict and the samples that the previous round of model predicted incorrectly, in order to further reduce the loss function. At the same time, XGBoost uses regularization technology and pruning strategies to prevent overfitting. Although XGBoost itself is developed in C++, it provides interfaces for multiple programming languages such as Python, R and Java. This makes it easy to use and integrate into various machine learning frameworks, such as scikit-learn and Spark MLlib. To sum up, XGBoost is a powerful, efficient, flexible and easy-to-use machine learning algorithm library with excellent performance and generalization capabilities. It is widely used in practical applications and has become one of the algorithms of choice for many data scientists and machine learning engineers.

The knowledge points of the article match the official knowledge files, and you can further learn related knowledge. C Skill Tree Home Page Overview 192381 people are learning the system