# InterDim
[](https://github.com/MShinkle/interdim/actions)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://MShinkle.github.io/interdim)
### Interactive Dimensionality Reduction, Clustering, and Visualization
InterDim is a Python package for interactive exploration of latent data dimensions. It wraps existing tools for dimensionality reduction, clustering, and data visualization in a streamlined interface, allowing for quick and intuitive analysis of high-dimensional data.
## Features
- Easy-to-use pipeline for dimensionality reduction, clustering, and visualization
- Interactive 3D scatter plots for exploring reduced data
- Support for various dimensionality reduction techniques (PCA, t-SNE, UMAP, etc.)
- Multiple clustering algorithms (K-means, DBSCAN, etc.)
- Customizable point visualizations for detailed data exploration
## Installation
You can install from [PyPI](https://pypi.org/project/interdim/) via `pip` (recommended):
```bash
pip install interdim
```
Or from source:
```bash
git clone https://github.com/MShinkle/interdim.git
cd interdim
pip install .
```
## Quick Start
Here's a basic example using the Iris dataset:
```python
from sklearn.datasets import load_iris
from interdim import InterDimAnalysis
iris = load_iris()
analysis = InterDimAnalysis(iris.data, true_labels=iris.target)
analysis.reduce(method='tsne', n_components=3)
analysis.cluster(method='kmeans', n_clusters=3)
analysis.show(n_components=3, point_visualization='bar')
```
This will reduce the Iris dataset to 3 dimensions using t-SNE, clusters the data using K-means, and displays an interactive 3D scatter plot with bar charts for each data point as you hover over them.
However, this is just a small example of what you can do with InterDim. You can use it to explore all sorts of data, including high-dimensional data like language model embeddings!
## Demo Notebooks
For more in-depth examples and use cases, check out our demo notebooks:
1. [Iris Species Analysis](https://github.com/MShinkle/interdim/blob/main/notebooks/IRIS_visualization.ipynb): Basic usage with the classic Iris dataset.
2. [DNN Latent Space Exploration](https://github.com/MShinkle/interdim/blob/main/notebooks/DNN_latents.ipynb): Visualizing deep neural network activations.
3. [LLM Token Analysis](https://github.com/MShinkle/interdim/blob/main/notebooks/LLM_token_embeddings.ipynb): Exploring language model token embeddings and layer activations.
## Documentation
For detailed API documentation and advanced usage, visit our [GitHub Pages](https://MShinkle.github.io/interdim).
## Contributing
We welcome discussion and contributions!
## License
InterDim is released under the BSD 3-Clause License. See the [LICENSE](https://github.com/MShinkle/interdim/blob/main/LICENSE) file for details.
## Contact
For questions and feedback, please [open an issue](https://github.com/MShinkle/interdim/issues) on GitHub.
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"description": "# InterDim\n[](https://github.com/MShinkle/interdim/actions)\n\n[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://MShinkle.github.io/interdim)\n\n### Interactive Dimensionality Reduction, Clustering, and Visualization\n\nInterDim is a Python package for interactive exploration of latent data dimensions. It wraps existing tools for dimensionality reduction, clustering, and data visualization in a streamlined interface, allowing for quick and intuitive analysis of high-dimensional data.\n\n## Features\n\n- Easy-to-use pipeline for dimensionality reduction, clustering, and visualization\n- Interactive 3D scatter plots for exploring reduced data\n- Support for various dimensionality reduction techniques (PCA, t-SNE, UMAP, etc.)\n- Multiple clustering algorithms (K-means, DBSCAN, etc.)\n- Customizable point visualizations for detailed data exploration\n\n## Installation\n\nYou can install from [PyPI](https://pypi.org/project/interdim/) via `pip` (recommended):\n\n```bash\npip install interdim\n```\n\nOr from source:\n\n```bash\ngit clone https://github.com/MShinkle/interdim.git\ncd interdim\npip install .\n```\n\n## Quick Start\n\nHere's a basic example using the Iris dataset:\n\n```python\nfrom sklearn.datasets import load_iris\nfrom interdim import InterDimAnalysis\n\niris = load_iris()\nanalysis = InterDimAnalysis(iris.data, true_labels=iris.target)\nanalysis.reduce(method='tsne', n_components=3)\nanalysis.cluster(method='kmeans', n_clusters=3)\nanalysis.show(n_components=3, point_visualization='bar')\n```\n\n\n\nThis will reduce the Iris dataset to 3 dimensions using t-SNE, clusters the data using K-means, and displays an interactive 3D scatter plot with bar charts for each data point as you hover over them.\n\nHowever, this is just a small example of what you can do with InterDim. You can use it to explore all sorts of data, including high-dimensional data like language model embeddings!\n\n## Demo Notebooks\n\nFor more in-depth examples and use cases, check out our demo notebooks:\n\n1. [Iris Species Analysis](https://github.com/MShinkle/interdim/blob/main/notebooks/IRIS_visualization.ipynb): Basic usage with the classic Iris dataset.\n\n\n2. [DNN Latent Space Exploration](https://github.com/MShinkle/interdim/blob/main/notebooks/DNN_latents.ipynb): Visualizing deep neural network activations.\n\n\n3. [LLM Token Analysis](https://github.com/MShinkle/interdim/blob/main/notebooks/LLM_token_embeddings.ipynb): Exploring language model token embeddings and layer activations.\n\n\n## Documentation\n\nFor detailed API documentation and advanced usage, visit our [GitHub Pages](https://MShinkle.github.io/interdim).\n\n## Contributing\n\nWe welcome discussion and contributions!\n\n## License\n\nInterDim is released under the BSD 3-Clause License. See the [LICENSE](https://github.com/MShinkle/interdim/blob/main/LICENSE) file for details.\n\n## Contact\n\nFor questions and feedback, please [open an issue](https://github.com/MShinkle/interdim/issues) on GitHub.\n",
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