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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.

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)

Source distribution for krcf 0.4.0
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Built distributions (wheels)

Table of built distributions (wheels) for krcf 0.4.0
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krcf-0.4.0-pp311-pypy311_pp73-win_amd64.whl PyPy 3.11 PyPy 3.11 7.3 Windows x86-64 Details
krcf-0.4.0-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.28+ x86-64 Details
krcf-0.4.0-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl PyPy 3.11 PyPy 3.11 7.3 Linux glibc 2.28+ ARM64 Details
krcf-0.4.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl PyPy 3.11 PyPy 3.11 7.3 macOS 11.0+ ARM64 Details
krcf-0.4.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl PyPy 3.11 PyPy 3.11 7.3 macOS 10.15+ x86-64 Details
krcf-0.4.0-cp314-cp314t-win_arm64.whl CPython 3.14 CPython 3.14 free-threading Windows ARM64 Details
krcf-0.4.0-cp314-cp314t-win_amd64.whl CPython 3.14 CPython 3.14 free-threading Windows x86-64 Details
krcf-0.4.0-cp314-cp314t-musllinux_1_2_x86_64.whl CPython 3.14 CPython 3.14 free-threading Linux musl 1.2+ x86-64 Details
krcf-0.4.0-cp314-cp314t-musllinux_1_2_aarch64.whl CPython 3.14 CPython 3.14 free-threading Linux musl 1.2+ ARM64 Details
krcf-0.4.0-cp314-cp314t-manylinux_2_28_x86_64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ x86-64 Details
krcf-0.4.0-cp314-cp314t-manylinux_2_28_aarch64.whl CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ ARM64 Details
krcf-0.4.0-cp314-cp314t-macosx_11_0_arm64.whl CPython 3.14 CPython 3.14 free-threading macOS 11.0+ ARM64 Details
krcf-0.4.0-cp314-cp314t-macosx_10_15_x86_64.whl CPython 3.14 CPython 3.14 free-threading macOS 10.15+ x86-64 Details
krcf-0.4.0-cp310-abi3-win_arm64.whl CPython 3.10 abi3 Windows ARM64 Details
krcf-0.4.0-cp310-abi3-win_amd64.whl CPython 3.10 abi3 Windows x86-64 Details
krcf-0.4.0-cp310-abi3-musllinux_1_2_x86_64.whl CPython 3.10 abi3 Linux musl 1.2+ x86-64 Details
krcf-0.4.0-cp310-abi3-musllinux_1_2_riscv64.whl CPython 3.10 abi3 Linux musl 1.2+ RISC-V 64 Details
krcf-0.4.0-cp310-abi3-musllinux_1_2_aarch64.whl CPython 3.10 abi3 Linux musl 1.2+ ARM64 Details
krcf-0.4.0-cp310-abi3-manylinux_2_31_riscv64.whl CPython 3.10 abi3 Linux glibc 2.31+ RISC-V 64 Details
krcf-0.4.0-cp310-abi3-manylinux_2_28_x86_64.whl CPython 3.10 abi3 Linux glibc 2.28+ x86-64 Details
krcf-0.4.0-cp310-abi3-manylinux_2_28_aarch64.whl CPython 3.10 abi3 Linux glibc 2.28+ ARM64 Details
krcf-0.4.0-cp310-abi3-macosx_11_0_arm64.whl CPython 3.10 abi3 macOS 11.0+ ARM64 Details
krcf-0.4.0-cp310-abi3-macosx_10_15_x86_64.whl CPython 3.10 abi3 macOS 10.15+ x86-64 Details

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