pucktrick 0.6.0.1

A python library for error generation in dataset for machine learning

Pucktrick

Pucktrick is a Python library that provides utility functions for introducing errors in your dataframe. The library's name is based on Puck. Puck is the name of the elf in the “A Midsummer Night’s Dream” of William Shakespeare, who is very famous for causing trouble and playing tricks on mortals and other fairies alike.

Features

Pucktrick is organized in modules, one for each error type. Each module includes a main function (or a class injector) that receives as parameters the dataset to modify, the strategy dictionary, and the original dataset if mode="extended". Functions return two parameters: an error code (0 for success, 1 for failure/no modifications) and the generated dataset.

The Strategy Configuration

The core of Pucktrick is the strategy configuration, which is passed as a JSON object or a Python dictionary. It allows you to precisely define the error model.

Base Parameters

{
  "affected_features": ["column1", "column2"],
  "selection_criteria": "all",
  "percentage": 0.2,
  "mode": "new",
  "perturbate_data": {
    "sampling": "random"
  }
}

• affected_features: A list of strings specifying the columns to be corrupted.
• selection_criteria: A predicate (e.g., "age > 30") to target specific rows, or "all" to target the entire dataset.
• percentage: A float (0.0 to 1.0) indicating the proportion of targeted rows to corrupt.
• mode:
  • "new": Applies errors to a clean dataset.
  • "extended": Incrementally adds errors to a previously corrupted dataset, reading the original_df to avoid double-corrupting rows.
• perturbate_data: A dictionary containing the noise injection logic.
  • sampling: How rows are chosen ("random", "uniform", "normal", "exponential").

Modules & Specific Configurations

1. Missing (missing.py)

Replaces values with NaN. Specifics: No special parameters required in perturbate_data.

2. Outliers (outliers.py)

Injects outliers using a 3-sigma rule for continuous numeric data, domain expansion for categorical integers, or specific string tokens for text. Specifics: No special parameters required in perturbate_data.

3. Duplicated (duplicated.py)

Duplicates existing rows and optionally applies text transformations. Specifics: Set "function" in the main strategy to apply text transformations like "shuffle_words", "abbreviate_text", "replace_punctuation", "remove_replace", or "upper_lower".

4. Noisy (noisy.py)

Adds random noise or a systematic shift to data (numeric, string, or datetime). Specifics: In perturbate_data, set "distribution": "shift" to apply systematic shifting. You must provide a "param" dictionary:

• "shift_value": Numeric value to add (or days for dates).
• "shift_unit": "absolute" or "std" (standard deviations).
• "shift_sign": "positive", "negative", or "random". (Use "distribution": "random" for standard uniform noise).

5. Labels (labels.py)

Flips labels for binary or multi-class classification. Specifics: For multi-class labels in perturbate_data, set "noise_model" to:

• "NCAR" (Noise Completely At Random): Uniform random flip.
• "NAR" (Noise At Random): Class-dependent flip. Provide "flip_distribution" in param.
• "NNAR" (Nearest Neighbor At Random): Flips labels of instances close to decision boundaries. Provide "features_for_similarity" in param.

Version

version 0.6.0.1

• Codebase fully refactored using Object-Oriented Programming (OOP) with the Template Method Pattern.
• Added systematic shift ("distribution": "shift") error type to the noisy module.
• Standardized the strategy interface and improved the extended mode logic across all modules.

version 0.5.1

• add multiclass definition

version 0.5

• add strategy, a JSON file where it is possible to create an error model by specifying the affected features (from one to many), a selection criterion, a Boolean predicate that specifies a subset of the rows to be corrupted, the mode, the percentage, the distribution function for injection errors.

version 0.4

• errortype added: missing values

version 0.3 -error type added: duplicated

version 0.2

• error type inserted: outliers

version 0.1

• error type inserted: noisy error and inconsistency labels

Installation

You can install pucktrick using pip:

pip install pucktrick

References

Contributing

We welcome contributions from the community. To contribute:

Fork the repository Create a new branch (git checkout -b feature/your-feature) Commit your changes (git commit -am 'Add new feature') Push to the branch (git push origin feature/your-feature) Create a new Pull Request Please ensure your code adheres to our coding standards and includes appropriate tests.

License This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) - see the LICENSE file for details.

Acknowledgements Thanks to the contributors and open-source community for their support.