kenchi 0.10.0

Dependencies

• Required dependencies

  1. numpy>=1.13.3 (BSD 3-Clause License)

  2. scikit-learn>=0.20.0 (BSD 3-Clause License)

  3. scipy>=0.19.1 (BSD 3-Clause License)

• Optional dependencies

  1. matplotlib>=2.1.2 (PSF-based License)

  2. networkx>=2.2 (BSD 3-Clause License)

Installation

You can install via pip

pip install kenchi

or conda.

conda install -c y_ohr_n kenchi

Algorithms

• Outlier detection

  1. FastABOD [8]

  2. LOF [2] (scikit-learn wrapper)

  3. KNN [1], [12]

  4. OneTimeSampling [14]

  5. HBOS [5]

• Novelty detection

  1. OCSVM [13] (scikit-learn wrapper)

  2. MiniBatchKMeans

  3. IForest [10] (scikit-learn wrapper)

  4. PCA

  5. GMM (scikit-learn wrapper)

  6. KDE [11] (scikit-learn wrapper)

  7. SparseStructureLearning [6]

Examples

import matplotlib.pyplot as plt
import numpy as np
from kenchi.datasets import load_pima
from kenchi.outlier_detection import *
from kenchi.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

np.random.seed(0)

scaler = StandardScaler()

detectors = [
    FastABOD(novelty=True, n_jobs=-1), OCSVM(),
    MiniBatchKMeans(), LOF(novelty=True, n_jobs=-1),
    KNN(novelty=True, n_jobs=-1), IForest(n_jobs=-1),
    PCA(), KDE()
]

# Load the Pima Indians diabetes dataset.
X, y = load_pima(return_X_y=True)
X_train, X_test, _, y_test = train_test_split(X, y)

# Get the current Axes instance
ax = plt.gca()

for det in detectors:
    # Fit the model according to the given training data
    pipeline = make_pipeline(scaler, det).fit(X_train)

    # Plot the Receiver Operating Characteristic (ROC) curve
    pipeline.plot_roc_curve(X_test, y_test, ax=ax)

# Display the figure
plt.show()

References