def test_intersect(self):
import pyterrier.transformer as ptt
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "q1texta", "doc1", 5, "body text"]],
columns=["qid", "query", "docno", "score", "body"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "q1textb", "doc2", 10, "body text"],
["q1", "q1textb", "doc1", 10, "body text"]],
columns=["qid", "query", "docno", "score", "body"]))
combined = mock1 & mock2
# we dont need an input, as both Identity transformers will return anyway
rtr = combined.transform(None)
self.assertEqual(1, len(rtr))
self.assertTrue("q1" in rtr["qid"].values)
self.assertTrue("doc1" in rtr["docno"].values)
self.assertFalse("doc2" in rtr["docno"].values)
# in case we have different values for query for the same (qid, docno), we use the left one
self.assertTrue("q1texta" in rtr["query"].values)
self.assertTrue("q1textb" not in rtr["query"].values)
for col in ["qid", "query", "docno", "body"]:
self.assertTrue(col in rtr.columns, "%s not found in cols" % col)
for col in ["rank", "score"]:
self.assertFalse(col in rtr.columns, "%s found in cols" % col)
def test_plus_more_cols(self):
import pyterrier.transformer as ptt
from pyterrier.model import add_ranks
mock1 = ptt.UniformTransformer(
add_ranks(
pd.DataFrame([["q1", "a query", "doc1", 5]],
columns=["qid", "query", "docno", "score"])))
mock2 = ptt.UniformTransformer(
add_ranks(
pd.DataFrame([["q1", "a query", "doc1", 10]],
columns=["qid", "query", "docno", "score"])))
combined = mock1 + mock2
# we dont need an input, as both Identity transformers will return anyway
rtr = combined.transform(None)
self.assertEqual(1, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc1", rtr.iloc[0]["docno"])
self.assertEqual(15, rtr.iloc[0]["score"])
bad_columns = [
"rank_x", "rank_y", "rank_r", "query_x", "query_y", "query_R",
"score_x", "score_y", "score_r"
]
for bad in bad_columns:
self.assertFalse(bad in rtr.columns,
"column %s in returned dataframe" % bad)
def test_concatenate(self):
import numpy as np
import pyterrier.transformer as ptt
mock1 = ptt.UniformTransformer(
pd.DataFrame(
[["q1", "d2", 2, 4.9, np.array([1, 2])],
["q1", "d3", 1, 5.1, np.array([1, 2])]],
columns=["qid", "docno", "rank", "score", "bla"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame(
[["q1", "d1", 1, 4.9, np.array([1, 1])],
["q1", "d3", 2, 5.1, np.array([1, 2])]],
columns=["qid", "docno", "rank", "score", "bla"]))
cutpipe = mock1 ^ mock2
rtr = cutpipe.transform(None)
self.assertEqual(3, len(rtr))
row0 = rtr.iloc[0]
self.assertEqual("d3", row0["docno"])
self.assertEqual(5.1, row0["score"])
row1 = rtr.iloc[1]
self.assertEqual("d2", row1["docno"])
self.assertEqual(4.9, row1["score"])
row2 = rtr.iloc[2]
self.assertEqual("d1", row2["docno"])
self.assertEqual(4.9 - 0.0001, row2["score"])
def test_mul(self):
import pyterrier.transformer as ptt
mock = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
for comb in [mock * 10, 10 * mock]:
rtr = comb.transform(None)
self.assertEqual(1, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc1", rtr.iloc[0]["docno"])
self.assertEqual(50, rtr.iloc[0]["score"])
import pyterrier.transformer as ptt
from pyterrier.model import add_ranks
mock = ptt.UniformTransformer(
add_ranks(
pd.DataFrame([["q1", "doc1", 5], ["q1", "doc2", 10]],
columns=["qid", "docno", "score"])))
rtr = mock.search("bla", qid="q1")
self.assertEqual(2, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc2", rtr.iloc[0]["docno"])
self.assertEqual(pt.model.FIRST_RANK, rtr.iloc[0]["rank"])
rtr = (-1 * mock).search("bla", qid="q1")
self.assertEqual(2, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc1", rtr.iloc[0]["docno"])
self.assertEqual(pt.model.FIRST_RANK, rtr.iloc[0]["rank"])
def test_feature_union(self):
mock_input = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
mock_f1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
mock_f2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 50]],
columns=["qid", "docno", "score"]))
# test using direct instantiation, as well as using the ** operator
for pipeline in [
mock_input >> ptt.FeatureUnionPipeline(mock_f1, mock_f2),
mock_input >> mock_f1**mock_f2
]:
# we dont need an input, as both Identity transformers will return anyway
rtr = pipeline.transform(None)
self.assertEqual(1, len(rtr))
self.assertTrue("qid" in rtr.columns)
self.assertTrue("docno" in rtr.columns)
self.assertTrue("score" in rtr.columns)
self.assertTrue("features" in rtr.columns)
self.assertTrue("q1" in rtr["qid"].values)
self.assertTrue("doc1" in rtr["docno"].values)
import numpy as np
self.assertTrue(
np.array_equal(np.array([10, 50]), rtr.iloc[0]["features"]))
def test_concatenate(self):
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "d2", 1, 4.9], ["q1", "d3", 2, 5.1]],
columns=["qid", "docno", "rank", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "d1", 1, 4.9], ["q1", "d3", 2, 5.1]],
columns=["qid", "docno", "rank", "score"]))
cutpipe = mock1 ^ mock2
rtr = cutpipe.transform(None)
self.assertEqual(3, len(rtr))
def test_plus(self):
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
combined = mock1 + mock2
# we dont need an input, as both Identity transformers will return anyway
rtr = combined.transform(None)
self.assertEqual(1, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc1", rtr.iloc[0]["docno"])
self.assertEqual(15, rtr.iloc[0]["score"])
def test_intersect(self):
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc2", 10], ["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
combined = mock1 & mock2
# we dont need an input, as both Identity transformers will return anyway
rtr = combined.transform(None)
self.assertEqual(1, len(rtr))
self.assertTrue("q1" in rtr["qid"].values)
self.assertTrue("doc1" in rtr["docno"].values)
self.assertFalse("doc2" in rtr["docno"].values)
def test_rank_cutoff(self):
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "d2", 1, 5.1], ["q1", "d3", 2, 5.1]],
columns=["qid", "docno", "rank", "score"]))
cutpipe = mock1 % 1
rtr = cutpipe.transform(None)
self.assertEqual(1, len(rtr))
def test_differing_queries(self):
topics = pd.DataFrame([["q1", "q1"], ["q2", "q1"]],
columns=["qid", "query"])
res1 = pd.DataFrame([["q1", "d1", 1.0]],
columns=["qid", "docno", "score"])
res2 = pd.DataFrame([["q1", "d1", 1.0], ["q2", "d1", 2.0]],
columns=["qid", "docno", "score"])
qrels = pd.DataFrame([["q1", "d1", 1], ["q2", "d1", 1]],
columns=["qid", "docno", "label"])
with warnings.catch_warnings(record=True) as w:
pt.pipelines.Experiment(
[ptt.UniformTransformer(res1),
ptt.UniformTransformer(res2)],
topics,
qrels, ["map"],
baseline=0)
assert "missing" in str(w[-1].message)
def test_feature_union_multi(self):
mock0 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 0]],
columns=["qid", "docno", "score"]))
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
mock3 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 15]],
columns=["qid", "docno", "score"]))
mock12a = mock1**mock2
mock123a = mock1**mock2**mock3
mock123b = mock12a**mock3
self.assertEqual(2, len(mock12a.models))
self.assertEqual(2, len(mock12a.models))
ptt.setup_rewrites()
mock123_simple = mock123a.compile()
self.assertIsNotNone(mock123_simple)
self.assertEqual(
"FeatureUnionPipeline(UniformTransformer(), UniformTransformer(), UniformTransformer())",
mock123_simple.__repr__())
#
#mock123a, mock123b
self.assertEqual(3, len(mock123_simple.models))
for expression in [mock123_simple]:
# we dont need an input, as both Identity transformers will return anyway
rtr = (mock0 >> expression).transform(None)
self.assertIsNotNone(rtr)
self.assertEqual(1, len(rtr))
self.assertTrue("qid" in rtr.columns)
self.assertTrue("docno" in rtr.columns)
self.assertTrue("score" in rtr.columns)
self.assertTrue("features" in rtr.columns)
self.assertTrue("q1" in rtr["qid"].values)
self.assertTrue("doc1" in rtr["docno"].values)
import numpy as np
self.assertTrue(
np.array_equal(np.array([5, 10, 15]), rtr.iloc[0]["features"]))
def test_feature_union(self):
import pyterrier.transformer as ptt
mock_input = ptt.UniformTransformer(
pd.DataFrame([["q1", "a query", "doc1", 5]],
columns=["qid", "query", "docno", "score"]))
mock_f1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "a query", "doc1", 10]],
columns=["qid", "query", "docno", "score"]))
mock_f2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "a query", "doc1", 50]],
columns=["qid", "query", "docno", "score"]))
def _test_expression(pipeline):
# check access to the objects
self.assertEqual(2, len(pipeline))
self.assertEqual(2, len(pipeline[1]))
# we dont need an input, as both Uniform transformers will return anyway
rtr = pipeline.transform(None)
self.assertEqual(1, len(rtr))
self.assertTrue("qid" in rtr.columns)
self.assertTrue("docno" in rtr.columns)
#self.assertTrue("score" in rtr.columns)
self.assertTrue("features" in rtr.columns)
bad_columns = [
"rank_x", "rank_y", "rank_r", "query_x", "query_y", "query_R",
"score_x", "score_y", "score_r", "features_x", "features_y"
]
print(rtr.columns)
for bad in bad_columns:
self.assertFalse(bad in rtr.columns,
"column %s in returned dataframe" % bad)
self.assertTrue("q1" in rtr["qid"].values)
self.assertTrue("doc1" in rtr["docno"].values)
import numpy as np
self.assertTrue(
np.array_equal(np.array([10, 50]), rtr.iloc[0]["features"]))
# test using direct instantiation, as well as using the ** operator
_test_expression(
mock_input >> ptt.FeatureUnionPipeline(mock_f1, mock_f2))
_test_expression(mock_input >> mock_f1**mock_f2)
def test_mul(self):
mock = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
for comb in [mock * 10, 10 * mock]:
rtr = comb.transform(None)
self.assertEqual(1, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc1", rtr.iloc[0]["docno"])
self.assertEqual(50, rtr.iloc[0]["score"])
def test_then_multi(self):
import pyterrier.transformer as ptt
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
mock3 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
mock4 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
combined12 = mock1 >> mock2
combined23 = mock2 >> mock3
combined123_a = combined12 >> mock3
combined123_b = mock1 >> mock2 >> mock3
combined123_c = mock2 >> combined23
combined123_a_C = combined123_a.compile()
combined123_b_C = combined123_b.compile()
combined123_c_C = combined123_c.compile()
self.assertEqual(2, len(combined12.models))
self.assertEqual(2, len(combined23.models))
self.assertEqual(2, len(combined12.models))
self.assertEqual(2, len(combined23.models))
for C in [combined123_a_C, combined123_b_C, combined123_c_C]:
self.assertEqual(3, len(C.models))
self.assertEqual(
"ComposedPipeline(UniformTransformer(), UniformTransformer(), UniformTransformer())",
C.__repr__())
# finally check recursive application
C4 = (mock1 >> mock2 >> mock3 >> mock4).compile()
self.assertEqual(
"ComposedPipeline(UniformTransformer(), UniformTransformer(), UniformTransformer(), UniformTransformer())",
C4.__repr__())
self.assertEqual(4, len(C4.models))
def test_plus_multi_rewrite(self):
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5]],
columns=["qid", "docno", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
mock3 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 15]],
columns=["qid", "docno", "score"]))
combined = mock1 + mock2 + mock3
for pipe in [combined, combined.compile()]:
# we dont need an input, as both Identity transformers will return anyway
rtr = pipe.transform(None)
self.assertEqual(1, len(rtr))
self.assertEqual("q1", rtr.iloc[0]["qid"])
self.assertEqual("doc1", rtr.iloc[0]["docno"])
self.assertEqual(30, rtr.iloc[0]["score"])
def test_maxmin_normalisation(self):
df = pd.DataFrame([
["q1", "doc1", 10], ["q1", "doc2", 2], ["q2", "doc1", 1], ["q3", "doc1", 0], ["q3", "doc2", 0]], columns=["qid", "docno", "score"])
mock_input = ptt.UniformTransformer(df)
pipe = mock_input >> ptp.PerQueryMaxMinScoreTransformer()
rtr = pipe.transform(None)
self.assertTrue("qid" in rtr.columns)
self.assertTrue("docno" in rtr.columns)
self.assertTrue("score" in rtr.columns)
thedict = rtr.set_index(['qid', 'docno']).to_dict()['score']
print(thedict)
self.assertEqual(1, thedict[("q1", "doc1")])
self.assertEqual(0, thedict[("q1", "doc2")])
self.assertEqual(0, thedict[("q2", "doc1")])
self.assertEqual(0, thedict[("q3", "doc1")])
self.assertEqual(0, thedict[("q3", "doc2")])
def test_feature_union_multi(self):
import pyterrier.transformer as ptt
mock0 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 0], ["q1", "doc2", 0]],
columns=["qid", "docno", "score"]))
mock1 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 5], ["q1", "doc2", 0]],
columns=["qid", "docno", "score"]))
mock2 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10], ["q1", "doc2", 0]],
columns=["qid", "docno", "score"]))
mock3 = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 15], ["q1", "doc2", 0]],
columns=["qid", "docno", "score"]))
mock3_empty = ptt.UniformTransformer(
pd.DataFrame([], columns=["qid", "docno", "score"]))
mock2_partial = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 10]],
columns=["qid", "docno", "score"]))
mock3_partial = ptt.UniformTransformer(
pd.DataFrame([["q1", "doc1", 15]],
columns=["qid", "docno", "score"]))
mock12a = mock1**mock2
mock123a = mock1**mock2**mock3
mock123b = mock12a**mock3
mock123a_manual = ptt.FeatureUnionPipeline(
ptt.FeatureUnionPipeline(mock1, mock2), mock3)
mock123b_manual = ptt.FeatureUnionPipeline(
mock1,
ptt.FeatureUnionPipeline(mock2, mock3),
)
mock123e = ptt.FeatureUnionPipeline(
mock1,
ptt.FeatureUnionPipeline(mock2, mock3_empty),
)
mock12e3 = ptt.FeatureUnionPipeline(
mock1,
ptt.FeatureUnionPipeline(mock3_empty, mock3),
)
mock123p = ptt.FeatureUnionPipeline(
mock1,
ptt.FeatureUnionPipeline(mock2, mock3_partial),
)
mock12p3 = ptt.FeatureUnionPipeline(
mock1,
ptt.FeatureUnionPipeline(mock2_partial, mock3),
)
self.assertEqual(2, len(mock12a.models))
self.assertEqual(2, len(mock12a.models))
ptt.setup_rewrites()
mock123_simple = mock123a.compile()
self.assertIsNotNone(mock123_simple)
self.assertEqual(
"FeatureUnionPipeline(UniformTransformer(), UniformTransformer(), UniformTransformer())",
mock123_simple.__repr__())
#
#
self.assertEqual(3, len(mock123_simple.models))
def _test_expression(expression):
# we dont need an input, as both Identity transformers will return anyway
rtr = (mock0 >> expression).transform(None)
#print(rtr)
self.assertIsNotNone(rtr)
self.assertEqual(2, len(rtr))
self.assertTrue("qid" in rtr.columns)
self.assertTrue("docno" in rtr.columns)
self.assertFalse("features_x" in rtr.columns)
self.assertFalse("features_y" in rtr.columns)
self.assertTrue("features" in rtr.columns)
self.assertTrue("q1" in rtr["qid"].values)
self.assertTrue("doc1" in rtr["docno"].values)
import numpy as np
self.assertTrue(
np.allclose(np.array([5, 10, 15]), rtr.iloc[0]["features"]))
_test_expression(mock123_simple)
_test_expression(mock123a)
_test_expression(mock123b)
_test_expression(mock123b)
with self.assertRaises(ValueError):
_test_expression(mock123e)
with self.assertRaises(ValueError):
_test_expression(mock12e3)
with warnings.catch_warnings(record=True) as w:
_test_expression(mock123p)
assert "Got number of results" in str(w[-1].message)
with warnings.catch_warnings(record=True) as w:
_test_expression(mock12p3)
assert "Got number of results" in str(w[-1].message)
