Diagnostics

Rich metadata returned with every offline detection result.

Diagnostics

Accessible via result.diagnostics.

Properties

Property	Type	Description
n	int	Number of observations in the input
d	int	Number of dimensions (1 for univariate)
schema_version	int	JSON schema version marker
engine_version	str | None	Rust engine version string
runtime_ms	int | None	Detection runtime in milliseconds
notes	list[str]	Informational notes (e.g., penalty path results)
warnings	list[str]	Warning messages (e.g., masking risk)
algorithm	str	Algorithm name (e.g., "pelt", "binseg")
cost_model	str	Cost model name (e.g., "l2", "normal")
seed	int | None	RNG seed (for WBS and randomized methods)
repro_mode	str	Reproducibility mode ("strict", "balanced", "fast")
thread_count	int | None	Number of threads used
blas_backend	str | None	BLAS backend name (None for default BLAS-free wheels)
cpu_features	list[str] | None	Detected CPU features (e.g., ["avx2", "fma"])
build	BuildInfo | None	Build provenance and adapter context (omitted when unavailable)
params_json	Any | None	Algorithm-specific parameters as JSON
pruning_stats	PruningStats | None	Pruning statistics (PELT/FPOP only)
missing_policy_applied	str | None	Missing data policy used
missing_fraction	float | None	Fraction of missing values in input
effective_sample_count	int | None	Count of non-missing observations

Example

result = cpd.Pelt(model="l2").fit(x).predict(pen="bic")
diag = result.diagnostics

print(f"Algorithm: {diag.algorithm}")
print(f"Cost model: {diag.cost_model}")
print(f"Runtime: {diag.runtime_ms}ms")
print(f"Repro mode: {diag.repro_mode}")

if diag.pruning_stats:
    print(f"Candidates pruned: {diag.pruning_stats.candidates_pruned}")

if diag.warnings:
    for w in diag.warnings:
        print(f"Warning: {w}")

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BuildInfo

Optional build provenance object exposed via result.diagnostics.build.

Properties

Property	Type	Description
git_sha	str | None	Build-time commit SHA (best effort)
git_dirty	bool | None	Build-time dirty flag (best effort)
rustc_version	str | None	Rust compiler version used for build
target_triple	str | None	Rust target triple
profile	str | None	Cargo profile (debug, release, etc.)
features	list[str] | None	Enabled adapter/runtime feature flags
abi	str | None	Adapter ABI context (for Python: pyo3-abi3-py39)

---

PruningStats

Pruning statistics for PELT and FPOP detectors.

Properties

Property	Type	Description
candidates_considered	int	Total number of candidate split points evaluated
candidates_pruned	int	Number of candidates removed by the pruning rule

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SegmentStats

Per-segment statistics included in the result when available.

Properties

Property	Type	Description
start	int	Segment start index (inclusive)
end	int	Segment end index (exclusive)
mean	list[float] | None	Per-dimension mean values
variance	list[float] | None	Per-dimension variance values
count	int	Number of observations in the segment
missing_count	int	Number of missing values in the segment

Example

if result.segments:
    for seg in result.segments:
        print(f"Segment [{seg.start}, {seg.end}): "
              f"mean={seg.mean}, count={seg.count}")