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Improve cohesion and consistency in the codebase #4

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@stochastic-sisyphus stochastic-sisyphus commented Dec 9, 2024

Make the codebase cohesive and consistent by addressing various issues.

  • src/cluster_manager.py:

    • Define self.method attribute in the ClusterManager class constructor.
    • Add a docstring to the perform_clustering method.
  • src/main.py:

    • Remove unused imports ThreadPoolExecutor and ProcessPoolExecutor.
    • Add a docstring to the main function.
  • requirements.txt:

    • Remove unused dependencies beautifulsoup4 and lxml.
  • src/data_loader.py:

    • Add docstrings to the DataLoader class and its methods.
  • src/main_with_training.py:

    • Verify and correct the import from utils.logging_config import setup_logging.

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Summary by Sourcery

Enhance code cohesion and consistency by adding docstrings to various methods and classes, removing unused imports and dependencies, and correcting import statements.

Enhancements:

  • Define the 'self.method' attribute in the 'ClusterManager' class constructor to improve code clarity.
  • Remove unused imports 'ThreadPoolExecutor' and 'ProcessPoolExecutor' from 'src/main.py' to clean up the code.
  • Verify and correct the import statement in 'src/main_with_training.py' for better accuracy.

Documentation:

  • Add docstrings to the 'perform_clustering' method in 'src/cluster_manager.py'.
  • Add docstrings to the 'main' function in 'src/main.py'.
  • Add comprehensive docstrings to the 'DataLoader' class and its methods in 'src/data_loader.py'.

Chores:

  • Remove unused dependencies 'beautifulsoup4' and 'lxml' from 'requirements.txt'.

Make the codebase cohesive and consistent by addressing various issues.

* **src/cluster_manager.py**:
  - Define `self.method` attribute in the `ClusterManager` class constructor.
  - Add a docstring to the `perform_clustering` method.

* **src/main.py**:
  - Remove unused imports `ThreadPoolExecutor` and `ProcessPoolExecutor`.
  - Add a docstring to the `main` function.

* **requirements.txt**:
  - Remove unused dependencies `beautifulsoup4` and `lxml`.

* **src/data_loader.py**:
  - Add docstrings to the `DataLoader` class and its methods.

* **src/main_with_training.py**:
  - Verify and correct the import `from utils.logging_config import setup_logging`.

---

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sourcery-ai bot commented Dec 9, 2024

Reviewer's Guide by Sourcery

This PR focuses on improving code quality through better documentation and cleanup. The changes include adding comprehensive docstrings to classes and methods, removing unused dependencies, and making minor code improvements. The implementation primarily involves adding documentation strings, removing unnecessary imports and dependencies, and fixing a small initialization issue in the ClusterManager class.

Updated class diagram for DataLoader

classDiagram
    class DataLoader {
        +Dict config
        +Logger logger
        +int batch_size
        +__init__(config: Dict[str, Any])
        +load_all_datasets() Dict[str, pd.DataFrame]
        +load_scisummnet(path: str) Optional[pd.DataFrame]
        +load_scisummnet_dataset() pd.DataFrame
    }
    note for DataLoader "Added docstrings to class and methods"
Loading

Updated class diagram for ClusterManager

classDiagram
    class ClusterManager {
        +config
        +clusterer
        +method
        +__init__(config)
        +perform_clustering(embeddings)
    }
    note for ClusterManager "Added 'method' attribute initialization"
Loading

File-Level Changes

Change Details Files
Added comprehensive docstrings to the DataLoader class and its methods
  • Added class-level docstring with attributes documentation
  • Enhanced method docstrings with Args and Returns sections
  • Added detailed documentation for load_scisummnet method
  • Improved documentation for load_all_datasets and load_scisummnet_dataset methods
src/data_loader.py
Removed unused dependencies and imports
  • Removed beautifulsoup4 and lxml dependencies
  • Removed unused ThreadPoolExecutor and ProcessPoolExecutor imports
requirements.txt
src/main.py
Enhanced ClusterManager class initialization
  • Added self.method attribute initialization in constructor
  • Added docstring to perform_clustering method
src/cluster_manager.py
Added documentation to main function
  • Added docstring describing the main function's purpose
src/main.py

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Hey @stochastic-sisyphus - I've reviewed your changes - here's some feedback:

Overall Comments:

  • In data_loader.py, removing the explicit return None in load_scisummnet_dataset() could lead to an implicit None return, which conflicts with the function's return type annotation of pd.DataFrame. Consider keeping the explicit return None or updating the return type annotation to Optional[pd.DataFrame].
Here's what I looked at during the review
  • 🟡 General issues: 1 issue found
  • 🟢 Security: all looks good
  • 🟢 Testing: all looks good
  • 🟡 Complexity: 1 issue found
  • 🟢 Documentation: all looks good

Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.

@@ -130,7 +151,12 @@ def load_scisummnet(self, path: str) -> Optional[pd.DataFrame]:
return None

def load_scisummnet_dataset(self) -> pd.DataFrame:
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issue: Return type annotation should be Optional[pd.DataFrame] since the function can return None on error

@@ -70,7 +83,15 @@ def load_all_datasets(self) -> Dict[str, pd.DataFrame]:
return datasets

def load_scisummnet(self, path: str) -> Optional[pd.DataFrame]:
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issue (complexity): Consider consolidating the two ScisummNet loading methods into a single parameterized function.

The duplicate implementations of ScisummNet loading can be consolidated into a single method while maintaining all functionality:

def load_scisummnet(self, path: Optional[str] = None, log_level: str = 'error') -> Optional[pd.DataFrame]:
    """
    Load ScisummNet dataset from specified path or config.

    Args:
        path (Optional[str]): Override path to dataset. Uses config path if None.
        log_level (str): Logging level to use ('error' or 'warning')

    Returns:
        Optional[pd.DataFrame]: DataFrame containing the loaded data.
    """
    try:
        dataset_path = path or self.config['data']['scisummnet_path']
        self.logger.info(f"Loading ScisummNet dataset from {dataset_path}...")

        top1000_dir = Path(dataset_path) / 'top1000_complete'
        if not top1000_dir.exists():
            log_fn = self.logger.error if log_level == 'error' else self.logger.warning
            log_fn(f"Directory not found: {top1000_dir}")
            return None

        # Rest of implementation...

This consolidation:

  1. Eliminates code duplication
  2. Maintains different logging behaviors via the log_level parameter
  3. Preserves all existing functionality
  4. Makes the relationship between the two use cases explicit

@@ -20,7 +28,12 @@ def __init__(self, config: Dict[str, Any]):
self.config['data']['scisummnet_path'] = str(project_root / self.config['data']['scisummnet_path'])

def load_all_datasets(self) -> Dict[str, pd.DataFrame]:
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issue (code-quality): Low code quality found in DataLoader.load_all_datasets - 25% (low-code-quality)


ExplanationThe quality score for this function is below the quality threshold of 25%.
This score is a combination of the method length, cognitive complexity and working memory.

How can you solve this?

It might be worth refactoring this function to make it shorter and more readable.

  • Reduce the function length by extracting pieces of functionality out into
    their own functions. This is the most important thing you can do - ideally a
    function should be less than 10 lines.
  • Reduce nesting, perhaps by introducing guard clauses to return early.
  • Ensure that variables are tightly scoped, so that code using related concepts
    sits together within the function rather than being scattered.

* **TextPreprocessor class**
  - Add class-level docstring describing the class and its attributes
  - Add docstring to the `__init__` method

* **DomainAgnosticPreprocessor class**
  - Add class-level docstring describing the class and its attributes
…ribute in `__init__()` method

* Add a docstring to the `ClusterManager` class to describe its purpose.
* Define `self.method` attribute in the `__init__()` method using the configuration provided.
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