def assignment_b_suffixarray_1():
# Use these throughout below.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
# Prepare for some suffix array lookups.
print("LOADING...")
corpus = InMemoryCorpus("data/cran.xml")
print("INDEXING...")
engine = SuffixArray(corpus, ["body"], normalizer, tokenizer)
results = []
hit_count = 5
# Callback for receiving matches.
def match_collector(match):
results.append(match)
print("*** WINNER", match["score"], match["document"])
# Define the actual test queries.
test1 = ("visc", 11, [328]) # Look for {'viscous', 'viscosity', ...}.
test2 = ("Of A", 10, [946]) # Test robustness for case and whitespace.
test3 = ("", 0, []) # Safety feature: Match nothing instead of everything.
test4 = ("approximate solution", 3, [1374, 159]) # Multiple winners.
# Test that the simple occurrence ranking works. Be robust towards how ties are resolved.
for (query, winner_score,
winner_document_ids) in [test1, test2, test3, test4]:
print("SEARCHING for '" + query + "'...")
results.clear()
engine.evaluate(query, {
"debug": False,
"hit_count": hit_count
}, match_collector)
if winner_document_ids:
assert results[0]["score"] == winner_score
assert results[0]["document"].document_id in winner_document_ids
assert len(results) <= hit_count
else:
assert len(results) == 0
def assignment_b_suffixarray_2():
# For testing.
class TestNormalizer(Normalizer):
_table = str.maketrans({'Ø': 'O'})
def canonicalize(self, buffer: str) -> str:
return buffer
def normalize(self, token: str) -> str:
return token.upper().translate(self._table)
# For testing.
class TestDocument(Document):
def __init__(self, document_id: int, a: str, b: str):
self._document_id = document_id
self._a = a
self._b = b
def get_document_id(self) -> int:
return self._document_id
def get_field(self, field_name: str, default: str) -> str:
if field_name == "a":
return self._a
if field_name == "b":
return self._b
return default
# For testing.
class TestCorpus(Corpus):
def __init__(self):
self._docs = []
self._docs.append(TestDocument(len(self._docs), "ø o\n\n\nø\n\no", "ø o\nø \no"))
self._docs.append(TestDocument(len(self._docs), "ba", "b bab"))
self._docs.append(TestDocument(len(self._docs), "ø o Ø o", "ø o"))
self._docs.append(TestDocument(len(self._docs), "øO" * 10000, "o"))
self._docs.append(TestDocument(len(self._docs), "cbab o øbab Ø ", "ø o " * 10000))
def __iter__(self):
return iter(self._docs)
def size(self) -> int:
return len(self._docs)
def get_document(self, document_id: int) -> Document:
return self._docs[document_id]
# Run the tests!
for fields in [("b",), ("a", "b")]:
# Create the suffix array over the given set of fields. Measure memory usage. If memory usage is
# excessive, most likely the implementation is copying strings or doing other silly stuff instead
# of working with buffer indices. The naive reference implementation is not in any way optimized,
# and uses about 1.5 MB of memory on this corpus.
tracemalloc.start()
snapshot1 = tracemalloc.take_snapshot()
engine = SuffixArray(TestCorpus(), fields, TestNormalizer(), BrainDeadTokenizer())
snapshot2 = tracemalloc.take_snapshot()
for statistic in snapshot2.compare_to(snapshot1, "filename"):
if statistic.traceback[0].filename == inspect.getfile(SuffixArray):
assert statistic.size_diff < 2000000, f"Memory usage is {statistic.size_diff}"
tracemalloc.stop()
results = []
def process(m):
results.append((m['document'].document_id, m['score']))
expected_results = {
('b',): (
('bab', [(1, 1)]),
('ø o', [(4, 19999), (0, 3), (2, 1)]),
('o O', [(4, 19999), (0, 3), (2, 1)]),
('oooooo', []),
('o o o o', [(4, 19997), (0, 1)]),
),
('a', 'b'): (
('bab', [(1, 1)]),
('ø o', [(4, 20000), (0, 6), (2, 4)]),
('o O', [(4, 20000), (0, 6), (2, 4)]),
('oøØOøO', [(3, 1), ]),
('o o o o', [(4, 19997), (0, 2), (2, 1)]),
)
}
for query, expected in expected_results[fields]:
results.clear()
engine.evaluate(query, {'hit_count': 10}, process)
assert results == expected
def assignment_b_suffixarray_2():
# For testing.
class TestNormalizer(Normalizer):
_table = str.maketrans({'Ø': 'O'})
def canonicalize(self, buffer: str) -> str:
return buffer
def normalize(self, token: str) -> str:
return token.upper().translate(self._table)
# For testing.
class TestDocument(Document):
def __init__(self, document_id: int, a: str, b: str):
self._document_id = document_id
self._a = a
self._b = b
def get_document_id(self) -> int:
return self._document_id
def get_field(self, field_name: str, default: str) -> str:
if field_name == "a":
return self._a
if field_name == "b":
return self._b
return default
# For testing.
class TestCorpus(Corpus):
def __init__(self):
self._docs = []
self._docs.append(
TestDocument(len(self._docs), "ø o\n\n\nø\n\no",
"ø o\nø \no"))
self._docs.append(TestDocument(len(self._docs), "ba", "b bab"))
self._docs.append(TestDocument(len(self._docs), "ø o Ø o", "ø o"))
self._docs.append(TestDocument(len(self._docs), "øO" * 10000, "o"))
self._docs.append(
TestDocument(len(self._docs), "cbab o øbab Ø ",
"ø o " * 10000))
def __iter__(self):
return iter(self._docs)
def size(self) -> int:
return len(self._docs)
def get_document(self, document_id: int) -> Document:
return self._docs[document_id]
# Run the tests!
for fields in [("b", ), ("a", "b")]:
engine = SuffixArray(TestCorpus(), fields, TestNormalizer(),
BrainDeadTokenizer())
results = []
def process(m):
results.append((m['document'].document_id, m['score']))
expected_results = {
('b', ): (
('bab', [(1, 1)]),
('ø o', [(4, 19999), (0, 3), (2, 1)]),
('o O', [(4, 19999), (0, 3), (2, 1)]),
('oooooo', []),
('o o o o', [(4, 19997), (0, 1)]),
),
('a', 'b'): (
('bab', [(1, 1)]),
('ø o', [(4, 20000), (0, 6), (2, 4)]),
('o O', [(4, 20000), (0, 6), (2, 4)]),
('oøØOøO', [
(3, 1),
]),
('o o o o', [(4, 19997), (0, 2), (2, 1)]),
)
}
for query, expected in expected_results[fields]:
results.clear()
engine.evaluate(query, {'hit_count': 10}, process)
assert results == expected
def assignment_b():
# Use these throughout below.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
# Prepare for some suffix array lookups.
print("LOADING...")
corpus = InMemoryCorpus("data/cran.xml")
print("INDEXING...")
engine = SuffixArray(corpus, ["body"], normalizer, tokenizer)
results = []
hit_count = 5
# Callback for receiving matches.
def match_collector(match):
results.append(match)
print("*** WINNER", match["score"], match["document"])
# Define the actual test queries.
test1 = ("visc", 11, [328]) # Look for {'viscous', 'viscosity', ...}.
test2 = ("Of A", 10, [946]) # Test robustness for case and whitespace.
test3 = ("", 0, []) # Safety feature: Match nothing instead of everything.
test4 = ("approximate solution", 3, [1374, 159]) # Multiple winners.
# Test that the simple occurrence ranking works. Be robust towards how ties are resolved.
for (query, winner_score,
winner_document_ids) in [test1, test2, test3, test4]:
print("SEARCHING for '" + query + "'...")
results.clear()
engine.evaluate(query, {
"debug": False,
"hit_count": hit_count
}, match_collector)
if winner_document_ids:
assert results[0]["score"] == winner_score
assert results[0]["document"].document_id in winner_document_ids
assert len(results) <= hit_count
else:
assert len(results) == 0
# Simple test of using a trie-encoded dictionary for efficiently locating substrings in a buffer.
trie = Trie()
for s in [
"romerike", "apple computer", "norsk", "norsk ørret", "sverige",
"ørret", "banan"
]:
trie.add(s, tokenizer)
finder = StringFinder(trie, tokenizer)
buffer = "det var en gang en norsk ørret fra romerike som likte abba fra sverige"
print("SCANNING...")
results.clear()
finder.scan(buffer, lambda m: results.append(m))
print("Buffer \"" + buffer + "\" contains", results)
assert [m["match"] for m in results
] == ["norsk", "norsk ørret", "ørret", "romerike", "sverige"]
# Find all MeSH terms that occur verbatim in some selected Cranfield documents! Since MeSH
# documents are medical terms and the Cranfield documents have technical content, the
# overlap probably isn't that big.
print("LOADING...")
mesh = InMemoryCorpus("data/mesh.txt")
cranfield = InMemoryCorpus("data/cran.xml")
print("BUILDING...")
trie = Trie()
for d in mesh:
trie.add(d["body"] or "", tokenizer)
finder = StringFinder(trie, tokenizer)
print("SCANNING...")
for (document_id,
expected_matches) in [(0, ["wing", "wing"]),
(3, ["solutions", "skin", "friction"]),
(1254, ["electrons", "ions"])]:
document = cranfield[document_id]
buffer = document["body"] or ""
results.clear()
finder.scan(buffer, lambda m: results.append(m))
print("Cranfield document", document, "contains MeSH terms", results)
assert [m["match"] for m in results] == expected_matches