normalize_stories

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case4-hn/normalize_stories@2",
    kwargs={'records': 'replace-with-value', 'source_info': 'replace-with-value'},
    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

• Normalize stories
• Acquisition provenance
• Quality counts
• Normalize hn records
• Preserve missing popularity values
• Merge overlapping feed membership
• Deduplicate story i ds
• Source info

• v231 Oct 20268 downloads
• v113 Oct 202611 downloads

• predict_topicsApply a supplied fitted HN topic model without fitting vocabulary or components. Returns topic assignments, automatic keyword tags, unknown-topic diagnostics and unchanged observed popularity values.
• hn_topic_digestFetch real HN stories, normalize them, apply the required immutable embedded trained model, count observed attention and render notebook-safe HTML. Training is a separate operation. Published implementation executes locally in embedded mode.
• fetch_snapshotCollect real HN stories from date-filtered Algolia search or current Firebase feeds. Explicit time range, overlapping feeds, request/item/time budgets and collection timestamp; no substitute data. Returns normalized rows and coverage diagnostics.
• evaluate_topics_temporallySplit HN stories chronologically, fit vocabulary and topics only on earlier titles, then inspect later-title coverage and reconstruction. No supervised accuracy or future-popularity forecast is claimed.
• rule_monitorMonitor coverage and missing values. Pass optional target_col for labelled quality metrics.