Offline Detectors

High-level Python classes for batch change-point detection. All follow the fit/predict pattern.

Pelt

PELT (Pruned Exact Linear Time) detector.

Constructor

cpd.Pelt(
    model: str = "l2",
    min_segment_len: int = 2,
    jump: int = 1,
    max_change_points: int | None = None,
)

Parameter	Type	Default	Description
model	str	"l2"	Cost model. Accepts "l2", "normal", "normal_full_cov"
min_segment_len	int	2	Minimum number of samples in each segment
jump	int	1	Stride over candidate split points. Increase to trade precision for speed
max_change_points	int | None	None	Hard upper bound on the number of change points returned

Methods

fit(values) -> Pelt

Precompute cost cache on the input signal.

• values: array-like of float32 or float64. Shape (n,) for univariate, (n, d) for multivariate.

• Returns: self (for method chaining)

• Raises: TypeError if input dtype is not float32/float64; RuntimeError if input contains NaN

predict(*, pen=None, n_bkps=None) -> OfflineChangePointResult

Run PELT detection and return results.

• pen: float | str | None – Penalty value. A float for manual penalty, "bic" for BIC, "aic" for AIC

• n_bkps: int | None – Exact number of change points to detect

• Returns: OfflineChangePointResult

• Raises: RuntimeError if fit() was not called first

Note

Exactly one of pen or n_bkps must be provided.

Example

import numpy as np
import cpd

x = np.concatenate([np.zeros(50), np.full(50, 5.0), np.full(50, -2.0)])
result = cpd.Pelt(model="l2", min_segment_len=2).fit(x).predict(n_bkps=2)
print(result.breakpoints)  # [50, 100, 150]

---

Binseg

Binary Segmentation detector.

Constructor

cpd.Binseg(
    model: str = "l2",
    min_segment_len: int = 2,
    jump: int = 1,
    max_change_points: int | None = None,
    max_depth: int | None = None,
)

Parameter	Type	Default	Description
model	str	"l2"	Cost model. Accepts "l2", "normal", "normal_full_cov"
min_segment_len	int	2	Minimum segment length
jump	int	1	Candidate stride
max_change_points	int | None	None	Hard cap on detected change points
max_depth	int | None	None	Maximum recursion depth for the binary split tree

Methods

fit(values) -> Binseg

Same interface as Pelt.fit().

predict(*, pen=None, n_bkps=None) -> OfflineChangePointResult

Same interface as Pelt.predict().

Example

result = cpd.Binseg(model="l2").fit(x).predict(n_bkps=3)

Warning

BinSeg may miss closely spaced changes (masking). Check diagnostics warnings. If masking is indicated, prefer WBS via detect_offline(pipeline=...).

---

Fpop

Functional Pruning Optimal Partitioning detector. L2 cost only.

Constructor

cpd.Fpop(
    min_segment_len: int = 2,
    jump: int = 1,
    max_change_points: int | None = None,
)

Parameter	Type	Default	Description
min_segment_len	int	2	Minimum segment length
jump	int	1	Candidate stride
max_change_points	int | None	None	Hard cap

Note

FPOP does not accept a model parameter because it is restricted to L2 cost.

Methods

fit(values) -> Fpop

Same interface as Pelt.fit().

predict(*, pen=None, n_bkps=None) -> OfflineChangePointResult

Same interface as Pelt.predict().

Example

result = cpd.Fpop(min_segment_len=2).fit(x).predict(pen="bic")