analyze_titles

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case4-hn/analyze_titles@1",
    kwargs={'anchor_utc': 'replace-with-value', 'min_support': 0, 'remap': {}, 'snapshot': {}, 'stop_words': [], 'tag_rules': {}, 'window_hours': 0.0},
    trust=True,
)

Review before running. Public bundles contain executable code. Verify the publisher and exact content hash; a virtual environment isolates dependencies, not the operating system.

Evidence

Evidence-backed concepts

• Analyze titles
• Counts describe supplied snapshot
• Not all hacker news activity
• Stop words
• Tag rules
• Window hours
• Min support
• Anchor utc

• v113 Oct 202610 downloads

• hn_topic_digestTurn supplied Hacker News records into an explainable HTML digest in one call: normalize records, apply explicit topic rules, and render a reading list. Returns a notebook-safe HTML fragment by default; full_document=True produces a standalone document. No live fetch or bundled dataset. Runs in the caller's Python environment.
• fetch_snapshotFetch and normalize a bounded sample of real Hacker News stories through the official API. Explicit feed, item limit, timeout, retries and total time budget. Requires network access and httpx in the caller's environment; returns records and acquisition provenance. No bundled dataset or fallback data.
• normalize_storiesValidate Hacker News records, normalize UTC timestamps and HTTP(S) links, and deduplicate by record identity. Input data stays with the caller.
• render_digestRender a supplied topic analysis as an escaped responsive HTML fragment with container-scoped CSS, coverage notes, window counts and a reading list. Safe for inline notebook display by default; full_document=True produces a standalone HTML file.
• find_rulesSearch numeric classification rules with a dataset and target column. Skip unsupported columns and return their names and reasons in unusable_columns, alongside the model, predictions and holdout metrics.