ai-starter-kit

Importable boilerplate for machine learning / deep learning projects — reusable code wrapped around common libraries (like NLTK and spaCy) so you don't have to rewrite the same setup every time. Install it, import what you need.

Install

pip install ai-starter-kit

Some modules need extra dependencies beyond the core install — see each module's own README, linked below, for what's required and how to install it.

Available modules

Module Description

ask.text NLP preprocessing and description: TextProcessor chains lowercasing, noise/punctuation removal, tokenization, stopword removal, stemming, and lemmatization via NLTK. SocialTextPreprocessor extends it for noisy, code-switched (Filipino/English) social-media text — URLs/@mentions/#hashtags, emoji, letter elongation, leetspeak, irregular casing. PosAnalyzer (spaCy), NgramModel (NLTK), and CodeSwitchAnalyzer (NLTK) describe text instead of transforming it — POS tagging, morphology, N-gram modeling, and code-switching ratios as pandas DataFrames.

ask.image Synthetic grayscale test-image generation: SyntheticImageGenerator builds a reproducible six-image complexity gradient (flat, sharp-edged, smooth, textured, noisy, mixed) plus an FFT/DWT localization pair, one image at a time or all at once, from fully configurable recipes.

Module	Description
`ask.text`	NLP preprocessing and description: `TextProcessor` chains lowercasing, noise/punctuation removal, tokenization, stopword removal, stemming, and lemmatization via NLTK. `SocialTextPreprocessor` extends it for noisy, code-switched (Filipino/English) social-media text — URLs/@mentions/#hashtags, emoji, letter elongation, leetspeak, irregular casing. `PosAnalyzer` (spaCy), `NgramModel` (NLTK), and `CodeSwitchAnalyzer` (NLTK) describe text instead of transforming it — POS tagging, morphology, N-gram modeling, and code-switching ratios as pandas DataFrames.
`ask.image`	Synthetic grayscale test-image generation: `SyntheticImageGenerator` builds a reproducible six-image complexity gradient (flat, sharp-edged, smooth, textured, noisy, mixed) plus an FFT/DWT localization pair, one image at a time or all at once, from fully configurable recipes.

More modules will be added as the project grows.

Development

To set up a local environment for running the test suite:

# Windows
powershell -ExecutionPolicy Bypass -File .\scripts\bootstrap.ps1

# macOS/Linux
./scripts/bootstrap.sh

This creates a .venv, installs the package in editable mode, and pulls in dev/test dependencies (nltk, pytest). Then activate it and run the tests:

# Windows
. .\.venv\Scripts\Activate.ps1
pytest -v

# macOS/Linux
source .venv/bin/activate
pytest -v

Contributing

New modules are welcome. Add a package under src/ask/<name>/, cover it with tests and its own README.md, then open a PR.

License

MIT

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