---

name: pyts description: "Routes pyts time-series loading, preprocessing, symbolic encoding, feature extraction, metrics, classification, and multivariate workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

pyts

Use this skill when a user wants to work with the pyts package for time series classification, transformation, metrics, or multivariate wrappers.

Quick install

For a fresh environment, install the runtime package and the verification tools:

python -m pip install -e .
python -m pip install 'scikit-learn<1.6' pytest

The verified snapshot for this repo needed scikit-learn<1.6 so the DTW and classifier workflows remain compatible. If you already have a working install, keep the compatible scikit-learn version and run the smoke script below.

Quick check

Run the bundled smoke helper from this skill tree:

python scripts/pyts_smoke.py --mode core
python scripts/pyts_smoke.py --mode all

Use --mode datasets, --mode symbolic, --mode features, --mode metrics, or --mode multivariate when you want a focused installed-package check for a single workflow family. Each sub-skill also ships a local wrapper at sub-skills/<id>/scripts/smoke.py that runs the matching mode from inside that sub-skill directory.

Route map

datasets-and-loaders

Use when the task is to load bundled toy data, inspect UCR/UEA metadata, make synthetic data, or discuss cached versus network-backed dataset helpers.

preprocessing-and-symbols

Use when the task is to scale, impute, discretize, approximate, or build symbolic/bag-of-words representations from univariate series.

feature-extraction-and-images

Use when the task is to extract feature vectors, build time-series images, or decompose series with SSA before a downstream model.

metrics-and-classifiers

Use when the task is to compare series with DTW/BOSS, choose a region or lower bound, or fit a pyts classifier such as KNeighborsClassifier(metric='dtw').

multivariate-workflows

Use when the input is 3D and the task is to wrap a univariate pyts estimator, work with WEASELMUSE, or build multivariate recurrence images.

How to navigate

1. Start from the user's data shape and desired output.
2. Go to the most specific sub-skill above.
3. Read the sub-skill's workflow and troubleshooting reference before choosing parameters.
4. If the task crosses sub-skill boundaries, keep the data-shape or model choice in the upstream sub-skill and hand off to the downstream one only when the intermediate representation is clear.

Shared references

• Read references/repo-provenance.md when checking whether this skill still matches the current checkout or before refreshing it.
• Read references/package-and-build.md or references/package-build-metadata.json when you need the verified distribution metadata, build backend, optional extras, or compatibility pin.
• Read references/troubleshooting.md for cross-cutting install/import/version issues, especially the scikit-learn compatibility note for DTW.
• Read scripts/pyts_smoke.py when you need a quick reproducible installed- package check for one workflow family or all of them.
• Read the route-specific sub-skills/*/scripts/smoke.py wrappers when you want a single workflow-family smoke check from the owning sub-skill.

Examples of natural requests

• "Load GunPoint and tell me the shapes"
• "Impute missing values and discretize a time series"
• "Build a ROCKET or shapelet feature matrix"
• "Compute DTW with a Sakoe-Chiba band"
• "Wrap a classifier for BasicMotions"

Notes for future agents

• The repo exposes no public CLI of its own.
• This skill is for the installed package, not the original checkout's docs or examples.
• If dtw starts failing with a force_all_finite validation error, use the troubleshooting reference; the verified snapshot required a compatible scikit-learn pin.