Add column-level evaluation layer and rank proposals in propose

[yhong123]

Resolved #112

Summary

Adds a column-level evaluation layer that scores and ranks candidate generators against real data, and uses it to improve the propose command in configure-generators.

• New datafaker.evaluators package: ColumnEvaluator samples a real column, picks an evaluation profile (short text / free text / categorical / identifier) from its statistics, and scores each candidate proposer's synthetic output against it using novelty, diversity, and statistical fidelity metrics (metrics.py, statistical_fidelity.py, feature_extractors.py, distribution_builders.py).
• proposal_ranking.py: normalises the heterogeneous raw fit scores into a probabilistic ranking and truncates the displayed list with entropy-based cutoff, so propose no longer dumps every candidate unranked.
• propose command (interactive/generators.py): now surfaces ranked proposals (capped at MAX_PROPOSERS_SHOWN = 10, propose all still shows everything) and filters out continuous-float proposers for genuinely Integer-typed columns, since inserting a non-whole float into an Integer column is backend-dependent.
• IncrementProposer (proposers/sequence.py): a new proposer for integer primary keys that continues the real data's max value, reusing the same generic.column_value_provider.increment mechanism make.py already uses as its default for integer PKs. (Works towards Add a UniqueSequenceProposer for unique/ID columns — no current proposer guarantees non-duplicate values #134)
• dialects.py: adds WordCount/SentenceCount (and related) SQL expression helpers, with Postgres/DuckDB and MSSQL compilation, used by the new feature extractors.
• Docs: new builtin_generators.rst and choosing_a_generator.rst, glossary entries for Generator/Proposer/Role, and TOC updates. (Work towards https://github.com/alan-turing-institute/DataMatryoshka/issues/14)

Implements the "Enhancement to generators selection layer" half of #112 (keyword/ranking improvements to propose, without changing generator/fit logic) plus the groundwork column-level fidelity metrics for the evaluation layer described there. The evaluate command and table/dataset-level utility & privacy metrics from #112 are not part of this PR -- a new issue will be created for this.

Further details on the generator evaluation and ranking methods can be found here: https://safehr-data.github.io/datafaker/builtin_generators.html#evaluating-and-ranking-proposals

Note

@tim-band your original fit-value table is left in the codebase alongside the new ranked table — I didn't want to remove it without your sign-off. Specifically, this PR does not touch:

• interactive/generators.py:1072-1090 — the old per-proposer loop that prints PROPOSE_GENERATOR_SAMPLE_TEXT using prop.fit(-1)
• Proposer.fit in proposers/base.py:140, which that loop calls

Once you've reviewed and are happy with the ranking table (RANKED_SAMPLE_TEXT) replacing this output, let me know and I'll remove both in a follow-up — or if you'd rather keep both tables shown side by side permanently, let me know that too and I'll leave them as-is.

Test plan

• poetry run pytest tests/test_evaluators_*.py — 169 passed
• Exercise configure-generators → propose interactively against a real table to confirm ranked output and propose all still work as expected