Recipe 35: Semantic feature extraction #35

Recipe 35 of 60 · Level 3 (Intermediate) · Evaluation & optimization · Noul

Use case

Convert text into predefined semantic features with independent judgments and compare a classifier trained on those features with a fixed baseline.

What to build

The folder recipes/35-semantic-feature-extraction/ with notebook.ipynb, README.md, and fixtures/, meeting the recipe contract. Start from python tools/new_recipe.py 35 and the template in recipes/_template/.

Build notes

• A predefined list of semantic features. One independent Noul per feature gives a feature vector per text.
• Train a simple classifier on the vectors and compare with a fixed baseline on a held-out split.
• Keep the feature list, the split, and the baseline fixed before looking at test results.

Decision design

• Noul. Write each proposition as a statement that is true or false. One independent question per label or concern, sent together in one request. Noul has no confidence field, so each threshold is chosen from examples and evaluated.

Level 3 scope

Multiple judgments or stages, structured evidence, deterministic composition, and task-level evaluation.

Acceptance criteria

• recipes/35-semantic-feature-extraction/notebook.ipynb runs top to bottom offline, with no key and no network, and its outputs are committed
• Live mode is opt-in (JEV_COOKBOOK_LIVE=1 with TYPESAFE_API_KEY) and uses the same question definitions as the offline run
• Fixtures are synthetic, include the hard cases named in the build notes, carry gold labels, and pass the fixture validator
• Every response fixture records its provenance, and the notebook states which mode it ran in
• Evaluation: Held-out accuracy of both classifiers, stated as a pipeline check when responses are synthetic.
• The deterministic composition lives in a Python function with tests in tests/
• Evaluation is at task level, not only per question
• No statement about Jev's quality, latency, or cost appears without recorded live inference; in a synthetic run, metrics are labelled as pipeline checks
• recipes/35-semantic-feature-extraction/README.md says what the recipe teaches, how to run it, and what was and was not measured live
• The pull request changes only recipes/35-semantic-feature-extraction/ and CI is green

Dependencies

• Blocked by Foundation: CI executes every notebook offline and validates fixtures #69 (CI executes every notebook offline and validates fixtures)
• Builds on Recipe 06: Multiple topic labels #6 (Multiple topic labels). Merge those first and reuse their patterns; do not copy their fixtures.
• Tracked in Epic: Level 3 · Intermediate recipes #77

Sources

Read these before writing the questions.

• S02 · TypeSafe AI: Primitives (Questions): Choice, Noul, Score, independent questions, and dependent follow-up calls.
• S03 · TypeSafe AI: Confidence: Distribution-derived confidence and task-specific thresholds informed by observed outcomes.

Out of scope

• Changes to shared code, the catalog, or the root README. If the shared package is missing something, open a foundation issue and link it here.
• Real side effects of any kind. Actions are simulated.