alloygbm

v0.12.8 suspicious
4.0
Medium Risk

Rust-first gradient boosting for regression, classification, and ranking with time-aware validation and Python bindings

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential code obfuscation and lacks detailed author information, raising concerns about its transparency and integrity.

  • Potential code obfuscation through pickling/unpickling
  • Sparse author information
Per-check LLM notes
  • Obfuscation: The observed patterns suggest potential code obfuscation through the use of pickling and unpickling, which can be used to hide logic or evade simple static analysis.
  • Credentials: No suspicious patterns for credential harvesting were detected.
  • Metadata: The author's information is sparse, indicating potential lack of transparency, but no other red flags are present.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 41 test file(s) found

  • 41 test file(s) detected (e.g. test_classifier_and_metrics.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://alloygbm.readthedocs.io/en/latest/
  • Detailed PyPI description (20513 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 194 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in LGA-Personal/AlloyGBM
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • rankers.append(pickle.loads(blob)) first_params = rankers[0].get_params(
  • a(X_train) restored = pickle.loads(pickle.dumps(clf)) restored_preds = restored.predict
  • .predict(X[:3]) m2 = pickle.loads(pickle.dumps(m)) pred_after = m2.predict(X[:3])
  • rnings), 0) model2 = pickle.loads(data) self.assertIsNone(model2.objective) #
  • ob = pickle.dumps(m) m2 = pickle.loads(blob) pred_after = np.asarray(m2.predict(X)) np.tes
  • le.dumps(cont) restored = pickle.loads(blob) restored_preds = np.asarray(restored.predict(X[:5]
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • ERSION_CHECK"] = "1" subprocess.run( [ sys.executable,
  • wheel = wheels[-1] subprocess.run( [ sys.executable,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository LGA-Personal/AlloyGBM appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with alloygbm 
Create a predictive analytics tool using the 'alloygbm' Python package. This tool will predict customer churn in a telecommunications company based on historical data. The application should include the following steps and features:

1. **Data Collection**: Start by collecting a dataset of customer information including service usage details, contract terms, payment methods, customer demographics, and whether they churned or not.
2. **Data Preprocessing**: Clean and preprocess the data to handle missing values, encode categorical variables, and normalize numerical features.
3. **Model Training**: Use 'alloygbm' to train a gradient boosting model. Since 'alloygbm' supports time-aware validation, incorporate this feature to ensure that your model is robust over different time periods. Split your data into training and validation sets, ensuring that the validation set is temporally after the training set.
4. **Model Evaluation**: Evaluate the model's performance using appropriate metrics such as AUC-ROC, precision, recall, and F1-score. Discuss the importance of these metrics in the context of churn prediction.
5. **Interactive Prediction**: Implement a simple user interface where users can input customer data, and the model predicts whether the customer is likely to churn. This interface could be a command-line interface (CLI) or a basic web application.
6. **Feature Importance Analysis**: Utilize 'alloygbm' capabilities to analyze which features contribute most significantly to predicting customer churn. Visualize these results for better understanding.
7. **Documentation**: Provide comprehensive documentation explaining how to install 'alloygbm', how to prepare the data, how to train the model, and how to use the prediction interface. Include examples and best practices.

The goal is to create a fully functional, documented mini-application that demonstrates the power and flexibility of 'alloygbm' in real-world scenarios. This project will serve as both a learning tool and a practical solution for businesses looking to reduce customer churn.

💬 Discussion Feed

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