def test_scoring_manual(self):
input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]],
columns=["qid", "query", "docno", "body"])
from pyterrier.batchretrieve import TextScorer
scorer = TextScorer(wmodel="Tf")
rtr = scorer(input)
self.assertEqual(1, len(rtr))
self.assertTrue("score" in rtr.columns)
self.assertEqual(2, rtr.iloc[0]["score"])
scorer = TextScorer(wmodel="org.terrier.python.TestModel$TFOverN")
rtr = scorer(input)
self.assertEqual(1, len(rtr))
self.assertTrue("score" in rtr.columns)
self.assertEqual(2, rtr.iloc[0]["score"]) # tf / numdocs
def test_scoring_manual_empty(self):
input = pd.DataFrame([["q1", "fox", "d1", ""]],
columns=["qid", "query", "docno", "body"])
from pyterrier.batchretrieve import TextScorer
scorer = TextScorer(wmodel="Tf")
rtr = scorer(input)
self.assertEqual(1, len(rtr))
self.assertTrue("score" in rtr.columns)
self.assertEqual(0, rtr.iloc[0]["score"])
def test_scoring_manual_background(self):
input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]],
columns=["qid", "query", "docno", "body"])
from pyterrier.batchretrieve import TextScorer
scorer = TextScorer(
wmodel="Tf",
background_index=pt.get_dataset("vaswani").get_index())
rtr = scorer(input)
self.assertEqual(1, len(rtr))
self.assertTrue("score" in rtr.columns)
self.assertEqual(2, rtr.iloc[0]["score"])
index_background = pt.IndexFactory.of(
pt.get_dataset("vaswani").get_index())
scorer = TextScorer(wmodel="org.terrier.python.TestModel$TFOverN",
background_index=index_background)
rtr = scorer(input)
self.assertEqual(1, len(rtr))
self.assertTrue("score" in rtr.columns)
self.assertEqual(
2 /
index_background.getCollectionStatistics().getNumberOfDocuments(),
rtr.iloc[0]["score"])