krcf
Project Description
Python bindings for the 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
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 orRandomCutForestOptionsspecifying 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 forestsample_size(int): Sample size for each treeoutput_after(int): Number of points before output is readyrandom_seed(int): Random seed for reproducibility- ...and more advanced options
For more details, see the docstrings in the code or the API reference.
Metadata
Release files for krcf 0.4.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| krcf-0.4.0.tar.gz | 113.8 kB | Details |
Built distributions (wheels)
Total release size: 14.4 MB
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| Tags | CPython 3.10 Linux glibc 2.28+ ARM64 abi3 |
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