krcf 0.4.0

AWS Random Cut Forest for Python

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 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.