A set of python modules for anomaly detection
Project description
kenchi
This is a set of python modules for anomaly detection.
Requirements
Python (>=3.5)
matplotlib (>=2.0.2)
numpy (>=1.11.2)
pandas (>=0.20.3)
scikit-learn (>=0.18.0)
scipy (>=0.18.1)
Installation
You can install via pip
pip install kenchi
or conda.
conda install -c y_ohr_n kenchi
Usage
import matplotlib.pyplot as plt
import numpy as np
from kenchi.datasets import make_blobs_with_outliers
from kenchi.outlier_detection import GaussianOutlierDetector
train_size = 1000
test_size = 100
n_outliers = 10
n_features = 10
centers = np.zeros((1, n_features))
random_state = np.random.RandomState(0)
# Generate the training data
X_train, _ = make_blobs_with_outliers(
n_inliers = train_size,
n_outliers = 0,
n_features = n_features,
centers = centers,
random_state = random_state
)
# Generate the test data that contains outliers
X_test, _ = make_blobs_with_outliers(
n_inliers = test_size - n_outliers,
n_outliers = n_outliers,
n_features = n_features,
centers = centers,
shuffle = False,
random_state = random_state
)
# Fit the model according to the given training data
det = GaussianOutlierDetector().fit(X_train)
# Plot anomaly scores for test samples
det.plot_anomaly_score(X_test)
plt.show()
License
The MIT License (MIT)
Copyright (c) 2017 Kon
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