# Nonconform Package Audit for 1.0.0 Release

**Date:** 2025-08-22
**Auditor:** Gemini Senior Python Developer/Architect

## Executive Summary

Overall, the `nonconform` package demonstrates a strong foundation with a logical architecture, good modularity, and a clear purpose. The use of a strategy pattern for conformalization is a major strength, promoting flexibility and extensibility. The project adheres to many Python best practices, including a modern `pyproject.toml` setup and a solid testing structure.

The primary recommendations for solidifying the 1.0.0 API focus on **enhancing API consistency**, **improving discoverability through `__init__.py` files**, **formalizing error handling**, and **ensuring comprehensive docstrings with type hinting**. The codebase is clean and well-organized, making these improvements straightforward to implement.

---

## 1. Overall Architecture and Project Structure

This area is generally very strong. The project is well-organized and follows modern Python standards.

*   **Project Layout:**
    *   **[+] Logical Structure:** The directory structure is logical and scalable. The separation of `nonconform` (source), `docs`, and `tests` is clean. The breakdown within `nonconform` into `estimation`, `strategy`, and `utils` is excellent and clearly communicates the architecture.
    *   **[+] Pythonic Conventions:** While not using a `src` layout, the current structure (`<root>/nonconform`) is a perfectly valid and common convention. It's simple and effective for a library of this scope.
    *   **[+] Configuration:** The use of `pyproject.toml` is the modern standard, and it is well-configured.

*   **Modularity and Reusability:**
    *   **[+] Excellent Modularity:** The code is well-segmented. The core architectural pattern, where `ConformalDetector` classes accept a `Strategy` object, is a standout feature. This is a classic example of the **Strategy Pattern**, which promotes loose coupling and high cohesion. Detectors define *what* they do (conformal prediction), while strategies define *how* the calibration is performed. This is highly reusable and extensible.
    *   **[+] Loose Coupling:** The `estimation` and `strategy` modules are loosely coupled. Detectors depend on the `BaseStrategy` interface, not concrete implementations, which is excellent.

*   **`__init__.py` Usage:**
    *   **[-] Opportunity for Improvement:** The `__init__.py` files are underutilized. They are crucial for defining a package's public API. By explicitly importing the key public classes and functions into the `__init__.py` files, you can create a more stable and user-friendly API. This prevents users from importing from deep, internal modules, which might change in future releases.

    **Recommendation:**
    Use your `__init__.py` files to create a clear public API. For example:

    ```python
    # In nonconform/estimation/__init__.py
    from .standard_conformal import StandardConformalDetector
    from .weighted_conformal import WeightedConformalDetector
    from .extreme_conformal import ExtremeConformalDetector

    __all__ = [
        "StandardConformalDetector",
        "WeightedConformalDetector",
        "ExtremeConformalDetector",
    ]
    ```
    This would allow a user to write `from nonconform.estimation import StandardConformalDetector`, which is cleaner and more stable.

---

## 2. API Design and Stability (for version 1.0.0)

The API is generally well-designed, but there are opportunities for increased consistency and clarity.

*   **Clarity and Intuitiveness:**
    *   **[+] Good Naming:** Class names like `StandardConformalDetector`, `WeightedConformalDetector`, `Split`, and `Bootstrap` are clear and intuitive.
    *   **[-] `predict()` Method Signature:** The `predict(raw=False)` parameter is slightly ambiguous. `raw=True` returning scores and `raw=False` returning p-values is reasonable, but the naming could be more explicit to improve readability.

    **Recommendation:**
    Consider adding more descriptive method names as aliases for improved clarity.
    ```python
    class BaseConformalDetector:
        # ...
        def predict(self, X, raw=False):
            # current implementation
            ...

        def predict_p_values(self, X):
            """Predicts conformal p-values for the given data."""
            return self.predict(X, raw=False)

        def predict_scores(self, X):
            """Predicts raw non-conformity scores for the given data."""
            return self.predict(X, raw=True)
    ```
    This makes user code extremely readable (e.g., `detector.predict_p_values(X)`) and adheres to the "explicit is better than implicit" principle.

*   **Consistency:**
    *   **[+] Consistent Pattern:** The `fit()`/`predict()` interface is consistent with the scikit-learn ecosystem, which is a huge plus for usability.
    *   **[-] Strategy Parameter Naming:** The parameters in different strategies could be harmonized for greater consistency (e.g., `n_calib` vs. `k` vs. `n_bootstraps`). While not critical, a review could be beneficial.

*   **Flexibility and Extensibility:**
    *   **[+] Excellent Extensibility:** The strategy pattern is the biggest win here. Users can easily write their own calibration strategies by inheriting from a `BaseStrategy` class. This is a hallmark of a well-designed, extensible library.

*   **Error Handling:**
    *   **[-] Needs Formalizing:** The library should raise meaningful and specific exceptions. For example, if `predict()` is called before `fit()`, the library should raise a custom `NotFittedError` rather than a generic `AttributeError`. This helps users debug their own code more effectively.

---

## 3. Pythonic Code Quality and Best Practices

The code quality is high, demonstrating modern Python practices.

*   **[+] PEP 8 Compliance & Tooling:** The code is cleanly formatted with `black` and linted with `ruff`, as configured in `pyproject.toml`. This shows a strong commitment to code quality.
*   **[+] The Zen of Python:** The code embodies Pythonic principles like "Explicit is better than implicit" (e.g., ABCs, Enums) and "Simple is better than complex" (e.g., clean separation of concerns).
*   **[+] Readability and Maintainability:** The code is highly readable due to descriptive names and a logical structure.
*   **[+] Idiomatic Python:** The use of abstract base classes, enums, decorators, and modern type hinting is excellent.

---

## 4. Documentation and Usability

Documentation is a very strong point for this package.

*   **[+] Docstrings:** The source files reviewed contain excellent, NumPy-style docstrings that clearly explain classes, methods, and parameters.
*   **[+] README:** The `README.md` is comprehensive, providing a clear project overview, installation instructions, and easy-to-follow usage examples. The inclusion of citations is a great touch for a scientific package.
*   **[+] Type Hinting:** Type hints are used effectively throughout the code I've seen, improving clarity and enabling static analysis.

---

## 5. Testing and Robustness

The project appears to have a solid testing strategy.

*   **[+] Test Structure:** The `tests/` directory, with its `functional` and `unit` subdirectories, is a standard and effective way to organize tests. The test file names suggest a good mapping from tests to source code.
*   **[?] Test Quality:** Without viewing the content of the tests, coverage and quality cannot be fully assessed. However, the structure is sound and implies a good testing discipline.

---

## 6. Packaging and Distribution

The packaging and distribution setup is modern and robust.

*   **[+] `pyproject.toml`:** The `pyproject.toml` file is excellent. It uses `hatchling`, defines rich metadata, and correctly specifies optional dependencies. The inclusion of tool configurations (`black`, `ruff`) is a best practice.
*   **[+] Versioning:** The version is correctly managed in `nonconform/__init__.py` and dynamically loaded by the build system.

---

## Final Actionable Recommendations for 1.0.0

1.  **Solidify the Public API with `__init__.py` (High Priority):** This is the most critical step for a 1.0 release. Explicitly import all public classes and functions into the relevant `__init__.py` files and use `__all__`. This creates a stable API contract with your users.
2.  **Introduce Custom Exceptions (High Priority):** Create a `nonconform/exceptions.py` module and define specific exceptions (e.g., `NotFittedError`, `InvalidParameterError`). This makes the library much easier for others to build upon.
3.  **Enhance API Explicitness (Medium Priority):** Consider adding `predict_p_values()` and `predict_scores()` as aliases for `predict(raw=...)` to improve code readability for your users.
4.  **Final Docstring and Type Hint Review (Medium Priority):** Perform a final review of all public-facing APIs to ensure documentation is complete and accurate for the 1.0.0 release.
5.  **Update Classifier in `pyproject.toml` (Low Priority):** For the official 1.0.0 release, remember to update the development status classifier to `"Development Status :: 5 - Production/Stable"`.

## Final Audit Summary Table

| Area | Rating | Summary & Key Recommendations |
| :--- | :--- | :--- |
| **1. Architecture & Structure** | **Excellent** | Logical, modular, and scalable. The strategy pattern is a highlight. **Action:** Use `__init__.py` files to define a stable public API. |
| **2. API Design & Stability** | **Good** | The `fit/predict` interface is intuitive. **Actions:** Improve consistency (e.g., `predict` method naming) and introduce custom exceptions for robust error handling. |
| **3. Code Quality & Best Practices** | **Excellent** | The code is clean, modern, and idiomatic Python. The use of `black` and `ruff` ensures high quality and consistency. No major recommendations here. |
| **4. Documentation & Usability** | **Excellent** | The README is comprehensive, and docstrings are well-written. The use of Sphinx with autoapi is great. **Action:** Ensure all public APIs are fully documented before release. |
| **5. Testing & Robustness** | **Good** | The test structure is sound. (Cannot assess coverage, but the setup is professional). No recommendations without viewing test code. |
| **6. Packaging & Distribution** | **Excellent** | `pyproject.toml` is well-configured with modern tools, optional dependencies, and clear metadata. **Action:** Update the development status classifier to "Production/Stable" for the release. |
