If None: Obtains the str() representation of each transformer as its name.īatch_size ( int) – If not None, evaluation is conducted in batches of batch_size topics. Names ( list) – List of names for each retrieval system when presenting the results.ĭefault=None. Qrels – Either a path to a qrels file or a pandas.Dataframe with columns=Įval_metrics ( list) – Which evaluation metrics to use. Topics – Either a path to a topics file or a pandas.Dataframe with columns= The transformers must have “qid”, “docno”, “score”, “rank” columns. (or more) of your systems, a results dataframe can also be used here. Retr_systems ( list) – A list of transformers to evaluate. (using pt.Utils.evaluate()) for each system. Then the named trec_eval evaluation measures are computed In essence, each transformer is applied on Identical evaluation measures computed using the same qrels. Experiment ( ) ¶Īllows easy comparison of multiple retrieval transformer pipelines using a common set of topics, and The main way to achieve this is using pt.Experiment().

The evaluation metrics are calculated by the pytrec_eval library,Ī Python wrapper around the widely-used trec_eval evaluation tool. Metrics based on known relevant documents (obtained from a set relevance assessments, also known as qrels). Pipeline over a set of queries, and evaluating the outcome using standard information retrieval evaluation PyTerrier aims to make it easy to conduct an information retrieval experiment, namely, to run a transformer pyterrier.new - Creating new dataframes.pyterrier.anserini - Anserini/Lucene Support.

Sigmaplot 11 do not test bonferroni