Getting started

This section will help you get started with Skchange by providing a variety of examples that demonstrate the library’s main capabilities. For a deeper understanding of Skchange’s core concepts, please refer to the Concepts section.

Installation

pip install skchange

To make full use of the library, you can install the optional Numba dependency. This will speed up the computation of the algorithms in Skchange, often by as much as 10-100 times.

pip install skchange[numba]

Change detection

Detect changes in the mean

import plotly.io as pio

pio.renderers.default = "notebook"

from skchange.change_detectors import SeededBinarySegmentation
from skchange.change_scores import CUSUM
from skchange.datasets import generate_piecewise_normal_data
from skchange.utils.plotting import plot_detections

x = generate_piecewise_normal_data(
    means=[0, 10, 0, -3, 5, 1],
    lengths=[30, 5, 15, 50, 60, 40],
    seed=0,
)
detector = SeededBinarySegmentation(
    CUSUM(),
    penalty=5,
)
change_points = detector.fit_predict(x)

plot_detections(x, change_points).show()
print(change_points)

   ilocs
0     30
1     35
2     50
3    100
4    160

Detect changes in a continuous piecewise linear trend

from skchange.change_scores import ContinuousLinearTrendScore
from skchange.datasets import generate_continuous_piecewise_linear_data

x = generate_continuous_piecewise_linear_data(
    slopes=[0, 1, -0.5, 0.5, 0.1],
    lengths=[30, 20, 50, 60, 40],
    seed=1,
)
detector = SeededBinarySegmentation(
    ContinuousLinearTrendScore(),
    penalty=20,
    selection_method="narrowest",
)
change_points = detector.fit_predict(x)

plot_detections(x, change_points).show()
print(change_points)

   ilocs
0     27
1     49
2     98
3    163

Detect sparse changes in a high-dimensional mean vector

from skchange.change_detectors import MovingWindow
from skchange.change_scores import ESACScore

x = generate_piecewise_normal_data(
    means=[0, 5, 10],
    lengths=50,
    n_segments=3,
    n_variables=100,
    proportion_affected=[1.0, 0.1, 0.01],
    randomise_affected_variables=True,
    seed=3,
)
detector = MovingWindow(
    ESACScore(),
    bandwidth=[10, 20, 30, 40],
)
change_points = detector.fit_predict(x)

plot_detections(x, change_points).show()
print(change_points)

   ilocs
0     50
1    100

Detect changes in a linear regression model

from skchange.change_detectors import PELT
from skchange.costs import LinearRegressionCost
from skchange.datasets import generate_piecewise_regression_data
from skchange.utils.plotting import plot_scatter_segmentation

x, feature_cols, target_cols = generate_piecewise_regression_data(
    lengths=50,
    n_segments=3,
    n_features=1,
    n_targets=1,
    seed=2,
)
detector = PELT(
    LinearRegressionCost(target_cols[0], feature_cols),
    penalty=10,
)
change_points = detector.fit_predict(x)
plot_scatter_segmentation(
    x, change_points, x_var=feature_cols[0], y_var=target_cols[0]
).show()
print(change_points)

   ilocs
0     50
1    100

Segment anomaly detection

Detect segment anomalies in the mean

from skchange.anomaly_detectors import CAPA
from skchange.anomaly_scores import L2Saving

x = generate_piecewise_normal_data(
    means=[0, 4, 0, 10, 0, -3],
    lengths=[100, 30, 50, 5, 50, 40],
    seed=5,
)
detector = CAPA(L2Saving())
anomalies = detector.fit_predict(x)
plot_detections(x, anomalies).show()
print(anomalies)

        ilocs  labels
0  [100, 130)       1
1  [180, 185)       2
2  [235, 275)       3

Detect segment anomalies in multivariate data and identify the anomalous variables

from skchange.compose.penalised_score import PenalisedScore

x = generate_piecewise_normal_data(
    means=[0, [8.0, 0.0, 0.0], 0, [2.0, 3.0, 5.0]],
    lengths=[100, 20, 130, 50],
    seed=1,
)

score = L2Saving()
penalty = [15, 20, 25]  # Different penalty per amount of affected variables
penalised_score = PenalisedScore(score, penalty)
detector = CAPA(penalised_score, find_affected_components=True)
anomalies = detector.fit_predict(x)

plot_detections(x, anomalies, "subplot-line").show()
print(anomalies)

        ilocs  labels   icolumns
0  [100, 120)       1        [0]
1  [250, 300)       2  [2, 1, 0]

Detect segment anomalies in the covariance matrix

import numpy as np

from skchange.anomaly_detectors import CAPA
from skchange.costs import MultivariateGaussianCost

baseline_cov = np.array([[1, 0.9], [0.9, 1]])
anomalous_cov = np.eye(baseline_cov.shape[0])
x = generate_piecewise_normal_data(
    means=0,
    variances=[baseline_cov, anomalous_cov, baseline_cov],
    lengths=[100, 100, 100],
    seed=8,
)

detector = CAPA(
    MultivariateGaussianCost((0, baseline_cov)),
    min_segment_length=10,
)
anomalies = detector.fit_predict(x)
plot_scatter_segmentation(x, anomalies).show()
print(anomalies)

       ilocs  labels
0  [98, 204)       1