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Author: jeroenjanssens

README.md

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+# scikit-sos
+
+scikit-sos is a Python module for Stochastic Outlier Selection (SOS). It is compatible with scikit-learn. SOS is an unsupervised outlier selection algorithm. It uses the concept of affinity to compute an outlier probability for each data point.
+
+![SOS](https://github.com/jeroenjanssens/scikit-sos/raw/master/doc/sos.png)
+
+For more information about SOS, see the technical report: J.H.M. Janssens, F. Huszar, E.O. Postma, and H.J. van den Herik. [Stochastic Outlier Selection](https://github.com/jeroenjanssens/sos/blob/master/doc/sos-ticc-tr-2012-001.pdf?raw=true). Technical Report TiCC TR 2012-001, Tilburg University, Tilburg, the Netherlands, 2012.
+
+## Install
+
+Using pip:
+
+```bash
+pip install scikit-sos
+```
+
+Using uv (recommended for fast installation):
+
+```bash
+# Install uv if not already installed
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Install scikit-sos
+uv pip install scikit-sos
+```
+
+## Development
+
+This project uses modern Python tooling:
+
+- **uv** for fast package management
+- **ruff** for linting and formatting
+- **mypy** for type checking
+- **pytest** for testing
+
+To set up a development environment:
+
+```bash
+# Clone repository
+git clone https://github.com/jeroenjanssens/scikit-sos.git
+cd scikit-sos
+
+# Create virtual environment and install with dev dependencies
+uv venv
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+uv pip install -e ".[dev]"
+
+# Install pre-commit hooks
+pre-commit install
+```
+
+Run tests:
+
+```bash
+pytest
+```
+
+Run linting:
+
+```bash
+ruff check .
+```
+
+Run formatting:
+
+```bash
+ruff format .
+```
+
+Run type checking:
+
+```bash
+mypy sksos
+```
+
+### Type Hints
+
+This package includes full type hints for better IDE support:
+
+```python
+from sksos import SOS
+import numpy as np
+from numpy.typing import NDArray
+
+# Type hints work automatically
+detector: SOS = SOS(perplexity=20)
+data: NDArray = np.array([[1, 2], [3, 4]])
+scores: NDArray = detector.predict(data)
+```
+
+## Usage
+
+```python
+>>> import pandas as pd
+>>> from sksos import SOS
+>>> iris = pd.read_csv("http://bit.ly/iris-csv")
+>>> X = iris.drop("Name", axis=1).values
+>>> detector = SOS()
+>>> iris["score"] = detector.predict(X)
+>>> iris.sort_values("score", ascending=False).head(10)
+     SepalLength  SepalWidth  PetalLength  PetalWidth             Name     score
+41           4.5         2.3          1.3         0.3      Iris-setosa  0.981898
+106          4.9         2.5          4.5         1.7   Iris-virginica  0.964381
+22           4.6         3.6          1.0         0.2      Iris-setosa  0.957945
+134          6.1         2.6          5.6         1.4   Iris-virginica  0.897970
+24           4.8         3.4          1.9         0.2      Iris-setosa  0.871733
+114          5.8         2.8          5.1         2.4   Iris-virginica  0.831610
+62           6.0         2.2          4.0         1.0  Iris-versicolor  0.821141
+108          6.7         2.5          5.8         1.8   Iris-virginica  0.819842
+44           5.1         3.8          1.9         0.4      Iris-setosa  0.773301
+100          6.3         3.3          6.0         2.5   Iris-virginica  0.765657
+```
+
+## Command Line Interface
+
+This module also includes a command-line tool called `sos`. To illustrate, we apply SOS with a perplexity of 10 to the Iris dataset:
+
+```bash
+$ curl -sL http://bit.ly/iris-csv |
+> tail -n +2 | cut -d, -f1-4 |
+> sos -p 10 |
+> sort -nr | head
+0.98189840
+0.96438132
+0.95794492
+0.89797043
+0.87173299
+0.83161045
+0.82114072
+0.81984209
+0.77330148
+0.76565738
+```
+
+Adding a threshold causes SOS to output 0s and 1s instead of outlier probabilities. If we set the threshold to 0.8 then we see that out of the 150 data points, 8 are selected as outliers:
+
+```bash
+$ curl -sL http://bit.ly/iris-csv |
+> tail -n +2 | cut -d, -f1-4 |
+> sos -p 10 -t 0.8 |
+> paste -sd+ | bc
+8
+```
+
+## License
+
+All software in this repository is distributed under the terms of the BSD Simplified License. The full license is in the LICENSE file.