# krcf
## Project Description
Python bindings for the [aws/random-cut-forest-by-aws](https://github.com/aws/random-cut-forest-by-aws) library, providing a fast and efficient implementation of Random Cut Forests for anomaly detection.
Key features:
- Fast anomaly detection using Random Cut Forests
- Attribution and scoring for detected anomalies
- Support for shingling and advanced configuration
## API Usage
### Basic Example
```python
from krcf import RandomCutForest, RandomCutForestOptions
# Define forest options
options: RandomCutForestOptions = {
"dimensions": 3, # Number of features in each data point
"shingle_size": 2, # Shingle size for time series
"output_after": 1, # Number of points before output is ready
}
forest = RandomCutForest(options)
# Update the forest with new data points
forest.update([1.0, 2.0, 3.0])
forest.update([2.0, 3.0, 4.0])
# Compute anomaly score
score = forest.score([2.0, 3.0, 4.0])
print("Anomaly score:", score)
# >>> Anomaly score: 1.0
# Get attribution vector
attr = forest.attribution([2.0, 3.0, 4.0])
print("Attribution:", attr)
# >>> Attribution: {'high': [0.3333333333333335, 0.3333333333333335, 0.3333333333333335, 0.0, 0.0, 0.0], 'low': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}
```
### API Reference
#### RandomCutForest(options)
Create a new Random Cut Forest instance.
- `options`: A dictionary or `RandomCutForestOptions` specifying configuration (see below).
#### Methods
- `update(point: Sequence[float]) -> None`: Update the forest with a new data point.
- `score(point: Sequence[float]) -> float`: Compute the anomaly score for a data point.
- `displacement_score(point: Sequence[float]) -> float`: Compute the displacement score.
- `attribution(point: Sequence[float]) -> dict`: Get the attribution vector for a data point.
- `density(point: Sequence[float]) -> float`: Compute the density estimate for a data point.
- `near_neighbor(point: Sequence[float], percentile: int) -> list`: Find near neighbors for a data point.
- `is_output_ready() -> bool`: Check if the forest is ready to output scores.
#### RandomCutForestOptions
Options for configuring the forest (all except `dimensions` and `shingle_size` are optional):
- `dimensions` (int): Number of features in each data point (required)
- `shingle_size` (int): Shingle size for time series (required)
- `num_trees` (int): Number of trees in the forest
- `sample_size` (int): Sample size for each tree
- `output_after` (int): Number of points before output is ready
- `random_seed` (int): Random seed for reproducibility
- ...and more advanced options
For more details, see the docstrings in the code or the API reference.
Raw data
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"description": "# krcf\n\n## Project Description\n\nPython bindings for the [aws/random-cut-forest-by-aws](https://github.com/aws/random-cut-forest-by-aws) library, providing a fast and efficient implementation of Random Cut Forests for anomaly detection.\n\nKey features:\n\n- Fast anomaly detection using Random Cut Forests\n- Attribution and scoring for detected anomalies\n- Support for shingling and advanced configuration\n\n## API Usage\n\n### Basic Example\n\n```python\nfrom krcf import RandomCutForest, RandomCutForestOptions\n\n# Define forest options\noptions: RandomCutForestOptions = {\n \"dimensions\": 3, # Number of features in each data point\n \"shingle_size\": 2, # Shingle size for time series\n \"output_after\": 1, # Number of points before output is ready\n}\nforest = RandomCutForest(options)\n\n# Update the forest with new data points\nforest.update([1.0, 2.0, 3.0])\nforest.update([2.0, 3.0, 4.0])\n\n# Compute anomaly score\nscore = forest.score([2.0, 3.0, 4.0])\nprint(\"Anomaly score:\", score)\n# >>> Anomaly score: 1.0\n\n# Get attribution vector\nattr = forest.attribution([2.0, 3.0, 4.0])\nprint(\"Attribution:\", attr)\n# >>> Attribution: {'high': [0.3333333333333335, 0.3333333333333335, 0.3333333333333335, 0.0, 0.0, 0.0], 'low': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}\n```\n\n### API Reference\n\n#### RandomCutForest(options)\n\nCreate a new Random Cut Forest instance.\n\n- `options`: A dictionary or `RandomCutForestOptions` specifying configuration (see below).\n\n#### Methods\n\n- `update(point: Sequence[float]) -> None`: Update the forest with a new data point.\n- `score(point: Sequence[float]) -> float`: Compute the anomaly score for a data point.\n- `displacement_score(point: Sequence[float]) -> float`: Compute the displacement score.\n- `attribution(point: Sequence[float]) -> dict`: Get the attribution vector for a data point.\n- `density(point: Sequence[float]) -> float`: Compute the density estimate for a data point.\n- `near_neighbor(point: Sequence[float], percentile: int) -> list`: Find near neighbors for a data point.\n- `is_output_ready() -> bool`: Check if the forest is ready to output scores.\n\n#### RandomCutForestOptions\n\nOptions for configuring the forest (all except `dimensions` and `shingle_size` are optional):\n\n- `dimensions` (int): Number of features in each data point (required)\n- `shingle_size` (int): Shingle size for time series (required)\n- `num_trees` (int): Number of trees in the forest\n- `sample_size` (int): Sample size for each tree\n- `output_after` (int): Number of points before output is ready\n- `random_seed` (int): Random seed for reproducibility\n- ...and more advanced options\n\nFor more details, see the docstrings in the code or the API reference.\n\n",
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