lerot.ranker¶

class lerot.ranker.DeterministicRankingFunction(ranker_arg_str, ties, feature_count, init='random', sample='sample_unit_sphere')[source]¶

Bases: lerot.ranker.AbstractRankingFunction.AbstractRankingFunction

document_count()[source]¶

getDocs(numdocs=None)[source]¶: Copied from StatelessRankingFunction.

get_document_probability(docid)[source]¶: get probability of producing doc as the next document drawn

init_ranking(query)[source]¶

next()[source]¶: produce the next document

next_det()[source]¶

next_random()[source]¶: produce a random next document

rm_document(docid)[source]¶: remove doc from list of available docs and adjust probabilities

class lerot.ranker.ModelRankingFunction[source]¶

Bases: lerot.ranker.StatelessRankingFunction.StatelessRankingFunction

add_doc_for_query(query, doc)[source]¶

init_ranking(query)[source]¶

update_weights(new_weights)[source]¶

class lerot.ranker.ProbabilisticRankingFunction(ranker_arg_str, ties, feature_count, init='random', sample='sample_unit_sphere')[source]¶

Bases: lerot.ranker.AbstractRankingFunction.AbstractRankingFunction

document_count()[source]¶

getDocs(numdocs=None)[source]¶: Copied from StatelessRankingFunction.

get_document_probability(docid)[source]¶: get probability of producing doc as the next document drawn

get_ranking()[source]¶

init_ranking(query)[source]¶

next()[source]¶: produce the next document by random sampling, or deterministically

next_det()[source]¶

next_random()[source]¶: produce a random next document

rm_document(docid)[source]¶: remove doc from list of available docs and adjust probabilities

class lerot.ranker.StatelessRankingFunction(ranker_arg_str, ties, feature_count, init='random', sample='sample_unit_sphere')[source]¶

Bases: lerot.ranker.AbstractRankingFunction.AbstractRankingFunction

document_count()[source]¶

getDocs(numdocs=None)[source]¶: More efficient and less error-prone version of getDocs.

init_ranking(query)[source]¶

Initialize ranking for particular query.

Since AbstractRankingFunction has a next() function that changes a state, we need to have a support for that. You need to set self.docs and the only stateful object self.doc_idx

next()[source]¶

next_det()[source]¶

next_random()[source]¶

rm_document(doc)[source]¶

verticals(length=None)[source]¶

class lerot.ranker.SyntheticDeterministicRankingFunction(synthetic_docs)[source]¶

Bases: lerot.ranker.StatelessRankingFunction.StatelessRankingFunction

Synthetic deterministic ranker.

get_document_probability(doc)[source]¶: Get probability of producing doc as the next document drawn.

init_ranking(query)[source]¶

update_weights(new_weights)[source]¶

class lerot.ranker.SyntheticProbabilisticRankingFunction(ranker_arg_str, ties='random')[source]¶

Bases: lerot.ranker.ProbabilisticRankingFunction.ProbabilisticRankingFunction

Synthetic ranker for use in this experiment only

get_document_probability(docid)[source]¶: get probability of producing doc as the next document drawn

init_ranking(synthetic_docids)[source]¶

rm_document(docid)[source]¶: remove doc from list of available docs, adjust probabilities

update_weights(new_weights)[source]¶: not required under synthetic data