pyterrier-pisa 0.0.2

A PyTerrier interface to the PISA search engine

PyTerrier PISA

PyTerrier bindings for the PISA search engine.

Getting Started

These bindings are only available for cpython 3.7-3.10 on manylinux2010_x86_64 platforms. They can be installed via pip:

pip install pyterrier_pisa

Indexing

You can easily index corpora from PyTerrier datasets:

import pyterrier as pt
if not pt.started():
  pt.init()
from pyterrier_pisa import PisaIndex

# from a dataset
dataset = pt.get_dataset('irds:msmarco-passage')
index = PisaIndex('./msmarco-passage-pisa')
index.index(dataset.get_corpus_iter())

Since PISA does not support multiple fields, you will need to have all the text you want to index in a single field. By default, it uses the "text" field, but this can be overridden with text_field.

dataset = pt.get_dataset('irds:cord19')
index = PisaIndex('./cord19-pisa', text_field='title_and_abstract')
# create a new field called title_and_abstract, from the title and abstract text
index_pipeline = pt.apply.title_and_abstract(lambda r: f'{r["title"]} {r["abstract"]}') >> index
index_pipeline.index(dataset.get_corpus_iter())

PisaIndex accepts various other options to configure the indexing process. Most notable are:

• stemmer: Which stemmer to use? Options: porter2 (default), krovetz, none
• threads: How many threads to use for indexing? Default: 8

# E.g.,
index = PisaIndex('./cord19-pisa', stemmer='krovetz', threads=32)

For some collections you can download pre-built indices from data.terrier.org. PISA indices are prefixed with pisa_.

index = PisaIndex.from_dataset('trec-covid', 'pisa_unstemmed')

Retrieval

From an index, you can build retrieval transformers:

dph = index.dph()
bm25 = index.bm25(k1=1.2, b=0.4)
pl2 = index.pl2(c=1.0)
qld = index.qld(mu=1000.)

These retrievers support all the typical pipeline operations.

Search:

bm25.search('covid symptoms')
#     qid           query     docno     score
# 0     1  covid symptoms  a6avr09j  6.273450
# 1     1  covid symptoms  hdxs9dgu  6.272374
# 2     1  covid symptoms  zxq7dl9t  6.272374
# ..   ..             ...       ...       ...
# 999   1  covid symptoms  m8wggdc7  4.690651

Batch retrieval:

print(dph(dataset.get_topics('title')))
#       qid                     query     docno     score
# 0       1        coronavirus origin  8ccl9aui  9.329109
# 1       1        coronavirus origin  es7q6c90  9.260190
# 2       1        coronavirus origin  8l411r1w  8.862670
# ...    ..                       ...       ...       ...
# 49999  50  mrna vaccine coronavirus  eyitkr3s  5.610429

Experiment:

from pyterrier.measures import *
pt.Experiment(
  [dph, bm25, pl2, qld],
  dataset.get_topics('title'),
  dataset.get_qrels(),
  [nDCG@10, P@5, P(rel=2)@5, 'mrt'],
  names=['dph', 'bm25', 'pl2', 'qld']
)
#    name   nDCG@10    P@5  P(rel=2)@5       mrt
# 0   dph  0.623450  0.720       0.548  1.101846
# 1  bm25  0.624923  0.728       0.572  0.880318
# 2   pl2  0.536506  0.632       0.456  1.123883
# 3   qld  0.570032  0.676       0.504  0.974924

You can also build a retrieval transformer from PisaRetrieve:

from pyterrier_pisa import PisaRetrieve
# from index path:
bm25 = PisaRetrieve('./cord19-pisa', scorer='bm25', bm25_k1=1.2, bm25_b=0.4)
# from dataset
bm25 = PisaRetrieve.from_dataset('trec-covid', 'pisa_unstemmed', scorer='bm25', bm25_k1=1.2, bm25_b=0.4)

FAQ

What retrieval functions are supported?

• "dph". Parameters: (none)
• "bm25". Parameters: k1, b
• "pl2". Parameters: c
• "qld". Parameters: mu

How do I index [some other type of data]?

PisaIndex accepts an iterator over dicts, each of which containing a docno field and a text field. All you need to do is coerce the data into that format and you're set.

Examples:

# any iterator
def iter_docs():
  for i in range(100):
    yield {'docno': str(i), 'text': f'document {i}'}
index = PisaIndex('./dummy-pisa')
index.index(iter_docs())

# from a dataframe
import pandas as pd
docs = pd.DataFrame([
  ('1', 'test doc'),
  ('2', 'another doc'),
], columns=['docno', 'text'])
index = PisaIndex('./dummy-pisa-2')
index.index(docs.to_dict(orient="records"))

Can I build a doc2query index?

You can use PisaIndex with any document rewriter, such as doc2query or DeepCT. All you need to do is build an indexing pipeline. For example:

pip install --upgrade git+https://github.com/terrierteam/pyterrier_doc2query.git
wget https://git.uwaterloo.ca/jimmylin/doc2query-data/raw/master/T5-passage/t5-base.zip
unzip t5-base.zip

doc2query = Doc2Query(out_attr="exp_terms", batch_size=8)
dataset = pt.get_dataset('irds:vaswani')
index = PisaIndex('./vaswani-doc2query-pisa')
index_pipeline = doc2query >> pt.apply.text(lambda r: f'{r["text"]} {r["exp_terms"]}') >> index
index_pipeline.index(dataset.get_corpus_iter())

What are the supported index encodings and query algorithms?

Right now only PisaIndexEncoding.block_simdbp and PisaQueryAlgorithm.block_max_wand are supported. Feel free to submit a PR to support other encodings/algorithms!

References

• [Mallia19]: Antonio Mallia, Michal Siedlaczek, Joel Mackenzie, Torsten Suel. PISA: Performant Indexes and Search for Academia. Proceedings of the Open-Source IR Replicability Challenge. http://ceur-ws.org/Vol-2409/docker08.pdf
• [Macdonald21]: Craig Macdonald, Nicola Tonellotto, Sean MacAvaney, Iadh Ounis. PyTerrier: Declarative Experimentation in Python from BM25 to Dense Retrieval. Proceedings of CIKM 2021. https://dl.acm.org/doi/abs/10.1145/3459637.3482013

Credits

• Sean MacAvaney, University of Glasgow