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name: feature-extraction-and-images description: "Routes pyts feature extraction, image transforms, and singular-spectrum decomposition workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

feature-extraction-and-images

Use this sub-skill when a user wants to turn time series into feature vectors, images, or SSA components before fitting a downstream model.

What this covers

• Feature extraction transformers: BagOfPatterns, BOSS, ROCKET, ShapeletTransform, WEASEL.
• Image transforms: GramianAngularField, MarkovTransitionField, RecurrencePlot.
• Decomposition: SingularSpectrumAnalysis.
• Output shape, sparsity, flattening, and runtime-cost trade-offs for feature and image workflows.

What this excludes

• Dataset loading: use ../datasets-and-loaders/SKILL.md.
• Preprocessing and symbolic building blocks: use ../preprocessing-and-symbols/SKILL.md.
• Metrics and classifiers: use ../metrics-and-classifiers/SKILL.md.
• Multivariate wrappers such as JointRecurrencePlot and WEASELMUSE: use ../multivariate-workflows/SKILL.md.

Start here

1. Read references/workflows.md for the common feature/image recipes.
2. Read references/api-reference.md for the verified signatures and shape notes.
3. Read references/troubleshooting.md for kernel-size, sparse-output, and flattening issues.
4. Run scripts/smoke.py to confirm the installed package on a tiny feature/image/decomposition case.

Useful triggers

• "ROCKET on GunPoint"
• "convert a series to a Gramian Angular Field"
• "shapelet transform output shape"
• "BOSS or WEASEL feature extraction"
• "SSA on a time series"

Routing hints

• If the user needs a classifier after feature extraction, route there after you choose the transform and confirm the output shape.
• If the user needs the multivariate versions of these ideas, hand off to ../multivariate-workflows/SKILL.md instead of expanding this sub-skill.
• If the user only wants a quick feature smoke test, use the bundled smoke script and tiny arrays rather than a full benchmark.

Links

• Read references/workflows.md for recipes that chain feature extraction, images, and SSA.
• Read references/api-reference.md for the verified object signatures and key defaults.
• Read references/troubleshooting.md for kernel-size, shape, and sparsity failures.
• Run scripts/smoke.py for a quick installed-package check.