train_topic_model

Inputs

Outputs

from spl import SPLClient

result = SPLClient.embedded().call(
    "splime://@splime.project/case4-hn/train_topic_model@1",
    kwargs={'max_features': 0, 'min_df': 0, 'min_documents': 0, 'n_topics': 0, 'random_state': 0, 'snapshot': {}, 'stop_words': []},
    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

• Train topic model
• Portable JSON vocabulary
• Idf weights
• Learned components
• Content identity
• Fit tf idf
• Automatic top term names
• No rule generated labels

• v131 Oct 20263 downloads

• 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.
• tag_titlesAssign HN titles to topics with the immutable trained model embedded as a required native graph dependency. Automatic names use learned top terms; no caller model file or retraining. Returns hn-topics-v1 predictions.
• 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.
• analyze_titlesApply explicit topic rules and compare document shares in adjacent equal-duration windows. Counts describe the supplied snapshot, not all Hacker News activity.