def test_memory_usage(self):
import tracemalloc
import inspect
from corpus import InMemoryDocument, InMemoryCorpus
from suffixarray import SuffixArray
corpus = InMemoryCorpus()
corpus.add_document(
InMemoryDocument(0, {
"a": "o o\n\n\no\n\no",
"b": "o o\no \no"
}))
corpus.add_document(InMemoryDocument(1, {"a": "ba", "b": "b bab"}))
corpus.add_document(InMemoryDocument(2, {"a": "o o O o", "b": "o o"}))
corpus.add_document(InMemoryDocument(3, {"a": "oO" * 10000, "b": "o"}))
corpus.add_document(
InMemoryDocument(4, {
"a": "cbab o obab O ",
"b": "o o " * 10000
}))
tracemalloc.start()
snapshot1 = tracemalloc.take_snapshot()
engine = SuffixArray(corpus, ["a", "b"], self._normalizer,
self._tokenizer)
snapshot2 = tracemalloc.take_snapshot()
tracemalloc.stop()
for statistic in snapshot2.compare_to(snapshot1, "filename"):
if statistic.traceback[0].filename == inspect.getfile(SuffixArray):
self.assertLessEqual(statistic.size_diff, 2000000,
"Memory usage seems excessive.")
def test_multiple_fields(self):
from corpus import InMemoryDocument, InMemoryCorpus
from suffixarray import SuffixArray
corpus = InMemoryCorpus()
corpus.add_document(
InMemoryDocument(0, {
"field1": "a b c",
"field2": "b c d"
}))
corpus.add_document(InMemoryDocument(1, {
"field1": "x",
"field2": "y"
}))
corpus.add_document(InMemoryDocument(2, {
"field1": "y",
"field2": "z"
}))
engine0 = SuffixArray(corpus, ["field1", "field2"], self._normalizer,
self._tokenizer)
engine1 = SuffixArray(corpus, ["field1"], self._normalizer,
self._tokenizer)
engine2 = SuffixArray(corpus, ["field2"], self._normalizer,
self._tokenizer)
self._process_query_and_verify_winner(engine0, "b c", [0], 2)
self._process_query_and_verify_winner(engine0, "y", [1, 2], 1)
self._process_query_and_verify_winner(engine1, "x", [1], 1)
self._process_query_and_verify_winner(engine1, "y", [2], 1)
self._process_query_and_verify_winner(engine1, "z", [], None)
self._process_query_and_verify_winner(engine2, "z", [2], 1)
def assignment_a_inverted_index_1():
# Use these throughout below.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
# Dump postings for a dummy two-document corpus.
print("INDEXING...")
corpus = InMemoryCorpus()
corpus.add_document(InMemoryDocument(0, {"body": "this is a Test"}))
corpus.add_document(InMemoryDocument(1, {"body": "test TEST prØve"}))
index = InMemoryInvertedIndex(corpus, ["body"], normalizer, tokenizer)
for (term, expected) in zip(index.get_terms("PRøvE wtf tesT"), [[(1, 1)], [], [(0, 1), (1, 2)]]):
print(term)
assert term in ["prøve", "wtf", "test"]
postings = list(index[term])
for posting in postings:
print(posting)
assert len(postings) == len(expected)
assert [(p.document_id, p.term_frequency) for p in postings] == expected
print(index)
# Document counts should be correct.
assert index.get_document_frequency("wtf") == 0
assert index.get_document_frequency("test") == 2
assert index.get_document_frequency("prøve") == 1
def assignment_a():
# Use these throughout below.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
# Dump postings for a dummy two-document corpus.
print("INDEXING...")
corpus = InMemoryCorpus()
corpus.add_document(InMemoryDocument(0, {"body": "this is a Test"}))
corpus.add_document(InMemoryDocument(1, {"body": "test TEST prØve"}))
index = InMemoryInvertedIndex(corpus, ["body"], normalizer, tokenizer)
for (term, expected) in zip(index.get_terms("PRøvE wtf tesT"),
[[(1, 1)], [], [(0, 1), (1, 2)]]):
print(term)
assert term in ["prøve", "wtf", "test"]
postings = list(index.get_postings_iterator(term))
for posting in postings:
print(posting)
assert len(postings) == len(expected)
assert [(p.document_id, p.term_frequency)
for p in postings] == expected
print(index)
# Again, for a slightly bigger corpus.
print("LOADING...")
corpus = InMemoryCorpus("data/mesh.txt")
print("INDEXING...")
index = InMemoryInvertedIndex(corpus, ["body"], normalizer, tokenizer)
for (term, expected_length) in [("hydrogen", 8), ("hydrocephalus", 2)]:
print(term)
for posting in index.get_postings_iterator(term):
print(posting)
assert len(list(index.get_postings_iterator(term))) == expected_length
# Test that we merge posting lists correctly. Note implicit test for case- and whitespace robustness.
print("MERGING...")
merger = PostingsMerger()
and_query = ("HIV pROtein", "AND", [11316, 11319, 11320, 11321])
or_query = ("water Toxic", "OR",
[3078, 8138, 8635, 9379, 14472, 18572, 23234, 23985] +
[i for i in range(25265, 25282)])
for (query, operator, expected_document_ids) in [and_query, or_query]:
print(re.sub("\W+", " " + operator + " ", query))
terms = list(index.get_terms(query))
assert len(terms) == 2
postings = [
index.get_postings_iterator(terms[i]) for i in range(len(terms))
]
merged = {
"AND": merger.intersection,
"OR": merger.union
}[operator](postings[0], postings[1])
documents = [
corpus.get_document(posting.document_id) for posting in merged
]
print(*documents, sep="\n")
assert len(documents) == len(expected_document_ids)
assert [d.get_document_id()
for d in documents] == expected_document_ids
def assignment_e_naivebayes_2():
# Use these throughout below. These are really language-specific functions, so it's a huge
# simplification to use these for a language identifier.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
results = []
# Callback for receiving results. Received scores are log-probabilities.
def match_collector(match: dict):
results.append(match)
print("*** WINNER", match["score"], match["category"])
# Replicate Example 13.1 on pages 241 and 242 in the textbook.
china = InMemoryCorpus()
china.add_document(InMemoryDocument(0,
{"body": "Chinese Beijing Chinese"}))
china.add_document(
InMemoryDocument(1, {"body": "Chinese Chinese Shanghai"}))
china.add_document(InMemoryDocument(2, {"body": "Chinese Macao"}))
not_china = InMemoryCorpus()
not_china.add_document(InMemoryDocument(0,
{"body": "Tokyo Japan Chinese"}))
training_set = {"china": china, "not china": not_china}
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
buffer = "Chinese Chinese Chinese Tokyo Japan"
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert len(results) == 2
assert results[0]["category"] == "china"
assert results[1]["category"] == "not china"
assert math.isclose(math.exp(results[0]["score"]), 0.0003, abs_tol=0.00001)
assert math.isclose(math.exp(results[1]["score"]), 0.0001, abs_tol=0.00005)
def test_access_documents(self):
from corpus import InMemoryDocument, InMemoryCorpus
corpus = InMemoryCorpus()
corpus.add_document(InMemoryDocument(0, {"body": "this is a Test"}))
corpus.add_document(InMemoryDocument(1, {"title": "prØve", "body": "en to tre"}))
self.assertEqual(corpus.size(), 2)
self.assertListEqual([d.document_id for d in corpus], [0, 1])
self.assertListEqual([corpus[i].document_id for i in range(0, corpus.size())], [0, 1])
self.assertListEqual([corpus.get_document(i).document_id for i in range(0, corpus.size())], [0, 1])
def setUp(self):
from normalization import BrainDeadNormalizer
from tokenization import BrainDeadTokenizer
from corpus import InMemoryDocument, InMemoryCorpus
from invertedindex import InMemoryInvertedIndex
from ranking import BetterRanker
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
corpus = InMemoryCorpus()
corpus.add_document(
InMemoryDocument(0, {
"title": "the foo",
"static_quality_score": 0.9
}))
corpus.add_document(
InMemoryDocument(1, {
"title": "the foo",
"static_quality_score": 0.2
}))
corpus.add_document(
InMemoryDocument(2, {
"title": "the foo foo",
"static_quality_score": 0.2
}))
corpus.add_document(InMemoryDocument(3, {"title": "the bar"}))
corpus.add_document(InMemoryDocument(4, {"title": "the bar bar"}))
corpus.add_document(InMemoryDocument(5, {"title": "the baz"}))
corpus.add_document(InMemoryDocument(6, {"title": "the baz"}))
corpus.add_document(InMemoryDocument(7, {"title": "the baz baz"}))
index = InMemoryInvertedIndex(corpus, ["title"], normalizer, tokenizer)
self._ranker = BetterRanker(corpus, index)
def test_access_postings(self):
from corpus import InMemoryDocument, InMemoryCorpus
from invertedindex import InMemoryInvertedIndex
corpus = InMemoryCorpus()
corpus.add_document(InMemoryDocument(0, {"body": "this is a Test"}))
corpus.add_document(InMemoryDocument(1, {"body": "test TEST prØve"}))
index = InMemoryInvertedIndex(corpus, ["body"], self._normalizer,
self._tokenizer)
self.assertListEqual(list(index.get_terms("PRøvE wtf tesT")),
["prøve", "wtf", "test"])
self.assertListEqual([(p.document_id, p.term_frequency)
for p in index["prøve"]], [(1, 1)])
self.assertListEqual([(p.document_id, p.term_frequency)
for p in index.get_postings_iterator("wtf")], [])
self.assertListEqual([(p.document_id, p.term_frequency)
for p in index["test"]], [(0, 1), (1, 2)])
self.assertEqual(index.get_document_frequency("wtf"), 0)
self.assertEqual(index.get_document_frequency("prøve"), 1)
self.assertEqual(index.get_document_frequency("test"), 2)
def test_china_example_from_textbook(self):
import math
from corpus import InMemoryDocument, InMemoryCorpus
from naivebayesclassifier import NaiveBayesClassifier
china = InMemoryCorpus()
china.add_document(
InMemoryDocument(0, {"body": "Chinese Beijing Chinese"}))
china.add_document(
InMemoryDocument(1, {"body": "Chinese Chinese Shanghai"}))
china.add_document(InMemoryDocument(2, {"body": "Chinese Macao"}))
not_china = InMemoryCorpus()
not_china.add_document(
InMemoryDocument(0, {"body": "Tokyo Japan Chinese"}))
training_set = {"china": china, "not china": not_china}
classifier = NaiveBayesClassifier(training_set, ["body"],
self._normalizer, self._tokenizer)
results = []
classifier.classify("Chinese Chinese Chinese Tokyo Japan",
lambda m: results.append(m))
self.assertEqual(len(results), 2)
self.assertEqual(results[0]["category"], "china")
self.assertAlmostEqual(math.exp(results[0]["score"]), 0.0003, 4)
self.assertEqual(results[1]["category"], "not china")
self.assertAlmostEqual(math.exp(results[1]["score"]), 0.0001, 4)
def test_multiple_fields(self):
from corpus import InMemoryDocument, InMemoryCorpus
from invertedindex import InMemoryInvertedIndex
document = InMemoryDocument(
0, {
'felt1': 'Dette er en test. Test, sa jeg. TEST!',
'felt2': 'test er det',
'felt3': 'test TEsT',
})
corpus = InMemoryCorpus()
corpus.add_document(document)
index = InMemoryInvertedIndex(corpus, ['felt1', 'felt3'],
self._normalizer, self._tokenizer)
posting = next(index.get_postings_iterator('test'))
self.assertEqual(posting.document_id, 0)
self.assertEqual(posting.term_frequency, 5)
def assignment_a_inverted_index_3():
# tests that multiple fields are handled correctly
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
doc = InMemoryDocument(document_id=0, fields={
'felt 1': 'Dette er en test. Test, sa jeg. TEST!',
'felt 2': 'test er det',
'felt 3': 'test TEsT',
})
corpus = InMemoryCorpus()
corpus.add_document(doc)
index = InMemoryInvertedIndex(corpus, ['felt 1', 'felt 3'], normalizer, tokenizer)
p = next(index.get_postings_iterator('test'))
print(f"term-freq: {p.term_frequency} (correct is 5)")
assert p.document_id == 0
assert p.term_frequency == 5
def test_synthetic_corpus(self):
from itertools import product, combinations_with_replacement
from corpus import InMemoryDocument, InMemoryCorpus
from invertedindex import InMemoryInvertedIndex
from searchengine import SimpleSearchEngine
corpus = InMemoryCorpus()
words = ("".join(term) for term in product("bcd", "aei", "jkl"))
texts = (" ".join(word) for word in combinations_with_replacement(words, 3))
for text in texts:
corpus.add_document(InMemoryDocument(corpus.size(), {"a": text}))
engine = SimpleSearchEngine(corpus, InMemoryInvertedIndex(corpus, ["a"], self._normalizer, self._tokenizer))
epsilon = 0.0001
self._process_query_verify_matches("baj BAJ baj", engine,
{"match_threshold": 1.0, "hit_count": 27},
(27, 9.0, [0]))
self._process_query_verify_matches("baj caj", engine,
{"match_threshold": 1.0, "hit_count": 100},
(27, None, None))
self._process_query_verify_matches("baj caj daj", engine,
{"match_threshold": 2/3 + epsilon, "hit_count": 100},
(79, None, None))
self._process_query_verify_matches("baj caj", engine,
{"match_threshold": 2/3 + epsilon, "hit_count": 100},
(100, 3.0, [0, 9, 207, 2514]))
self._process_query_verify_matches("baj cek dil", engine,
{"match_threshold": 1.0, "hit_count": 10},
(1, 3.0, [286]))
self._process_query_verify_matches("baj cek dil", engine,
{"match_threshold": 1.0, "hit_count": 10},
(1, None, None))
self._process_query_verify_matches("baj cek dil", engine,
{"match_threshold": 2/3 + epsilon, "hit_count": 80},
(79, 3.0, [13, 26, 273, 286, 377, 3107, 3198]))
self._process_query_verify_matches("baj xxx yyy", engine,
{"match_threshold": 2/3 + epsilon, "hit_count": 100},
(0, None, None))
self._process_query_verify_matches("baj xxx yyy", engine,
{"match_threshold": 2/3 - epsilon, "hit_count": 100},
(100, None, None))
def assignment_c_simplesearchengine_2():
# Use these throughout below.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
ranker = BrainDeadRanker()
# Used for comparing floating point numbers.
epsilon = 0.0001
# Create a dummy test corpus.
corpus = InMemoryCorpus()
words = (''.join(term) for term in product("bcd", "aei", "jkl"))
texts = (' '.join(word)
for word in combinations_with_replacement(words, 3))
for text in texts:
corpus.add_document(InMemoryDocument(corpus.size(), {'a': text}))
# What we're testing.
engine = SimpleSearchEngine(
corpus, InMemoryInvertedIndex(corpus, ["a"], normalizer, tokenizer))
# Where the callback will collect the matches.
results = []
# Callback that collects matches.
def collect(m):
results.append((m['score'], m['document'].document_id))
# Executes a query.
def search(q, t, n):
results.clear()
engine.evaluate(q, {
'match_threshold': t,
'hit_count': n
}, ranker, collect)
# Sorts the collected matches.
def sort_results():
results.sort(key=lambda e: e[1])
results.sort(key=lambda e: e[0], reverse=True)
# Test predicate.
def check_at(i, expected):
assert results[i] == expected
# Test predicate.
def check_range(indices, score, document_ids):
for i, d in zip(indices, document_ids):
check_at(i, (score, d))
# Test predicate.
def check_hits(n):
assert len(results) == n
# Run tests!
search('baj BAJ baj', 1.0, 27)
check_hits(27)
check_at(0, (9.0, 0))
sort_results()
check_range(range(1, 27), 6.0, range(1, 27))
search('baj caj', 1.0, 100)
check_hits(27)
search('baj caj daj', 2 / 3 + epsilon, 100)
check_hits(79)
search('baj caj', 2 / 3 + epsilon, 100)
check_hits(100)
sort_results()
check_at(0, (3.0, 0))
check_range(range(4, 12), 2.0, range(1, 9))
check_range(range(12, 29), 2.0, range(10, 27))
check_at(29, (2.0, 35))
check_at(78, (2.0, 2531))
search('baj cek dil', 1.0, 10)
check_hits(1)
check_at(0, (3.0, 286))
search('baj cek dil', 2 / 3 + epsilon, 80)
check_hits(79)
sort_results()
check_at(0, (3.0, 13))
check_at(1, (3.0, 26))
check_at(2, (3.0, 273))
search('baj xxx yyy', 2 / 3 + epsilon, 100)
check_hits(0)
search('baj xxx yyy', 2 / 3 - epsilon, 100)
check_hits(100)
def assignment_e():
# Use these throughout below. These are really language-specific functions, so it's a huge
# simplification to use these for a language identifier.
normalizer = BrainDeadNormalizer()
tokenizer = BrainDeadTokenizer()
results = []
# Callback for receiving results. Received scores are log-probabilities.
def match_collector(match: dict):
results.append(match)
print("*** WINNER", match["score"], match["category"])
# Use this as the training set for our language identifier.
print("LOADING...")
training_set = {
language: InMemoryCorpus("data/" + language + ".txt")
for language in ["en", "no", "da", "de"]
}
# Assess probabilities from the training set.
print("TRAINING...")
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
# Classify some previously unseen text fragments.
print("CLASSIFYING...")
for (buffer, language) in [
("Mon tro om det riktige språket identifiseres? Dette er norsk bokmål, forøvrig.",
"no"),
("I don't believe that the number of tokens exceeds a billion.", "en"),
("De danske drenge drikker snaps!", "da"),
("Der Kriminalpolizei! Haben sie angst?", "de")
]:
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert results[0]["category"] == language
# For demonstration purposes, replicate Example 13.1 on pages 241 and 242 in the textbook.
china = InMemoryCorpus()
china.add_document(InMemoryDocument(0,
{"body": "Chinese Beijing Chinese"}))
china.add_document(
InMemoryDocument(1, {"body": "Chinese Chinese Shanghai"}))
china.add_document(InMemoryDocument(2, {"body": "Chinese Macao"}))
not_china = InMemoryCorpus()
not_china.add_document(InMemoryDocument(0,
{"body": "Tokyo Japan Chinese"}))
training_set = {"china": china, "not china": not_china}
classifier = NaiveBayesClassifier(training_set, ["body"], normalizer,
tokenizer)
buffer = "Chinese Chinese Chinese Tokyo Japan"
print(buffer)
results.clear()
classifier.classify(buffer, match_collector)
assert len(results) == 2
assert results[0]["category"] == "china"
assert results[1]["category"] == "not china"
assert math.isclose(math.exp(results[0]["score"]), 0.0003, abs_tol=0.00001)
assert math.isclose(math.exp(results[1]["score"]), 0.0001, abs_tol=0.00005)
def test_simple_search_engine():
from itertools import product, combinations_with_replacement
from tokenization import BrainDeadTokenizer
from normalization import BrainDeadNormalizer
from corpus import InMemoryCorpus, InMemoryDocument
from invertedindex import InMemoryInvertedIndex
from searchengine import SimpleSearchEngine
from ranking import BrainDeadRanker
Ɛ = 0.0001
corpus = InMemoryCorpus()
for txt in (' '.join(w) for w in combinations_with_replacement(
list(''.join(t) for t in product(
'bcd',
'aei',
'jkl',
)), 3)):
corpus.add_document(InMemoryDocument(corpus.size(), {'a': txt}))
engine = SimpleSearchEngine(
corpus,
InMemoryInvertedIndex(corpus, ('a', ), BrainDeadNormalizer(),
BrainDeadTokenizer()))
results = []
def search(q, r, n):
results.clear()
def match(m):
results.append((m['score'], m['document'].document_id))
print('searching "' + q + '" at threshold', r, '…')
engine.evaluate(q, {
'recall_threshold': r,
'hit_count': n
}, BrainDeadRanker(), match)
def sort_results():
results.sort(key=lambda e: e[1])
results.sort(key=lambda e: e[0], reverse=True)
def check_at(i, expected):
if results[i] != expected:
print('FAILED, EXPECTED ', expected, ' RESULT', i, ' was',
results[i])
def check_range(indices, score, docrange):
for i, d in zip(indices, docrange):
check_at(i, (score, d))
def check_hits(n):
if len(results) != n:
print('FAILED, expected', n, 'results, got', len(results))
search('baj BAJ baj', 1.0, 27)
check_hits(27)
check_at(0, (9.0, 0))
sort_results()
check_range(range(1, 27), 6.0, range(1, 27))
search('baj CAj', 1.0, 100)
check_hits(27)
search('baj caj daj', 2 / 3 + Ɛ, 100)
check_hits(79)
search('baj caj', 2 / 3 + Ɛ, 100) # her
check_hits(100)
sort_results()
check_at(0, (3.0, 0))
check_range(range(4, 12), 2.0, range(1, 9))
check_range(range(12, 29), 2.0, range(10, 27))
check_at(29, (2.0, 35))
check_at(78, (2.0, 2531))
search('baj cek dil', 1.0, 10)
check_hits(1)
check_at(0, (3.0, 286))
search('baj cek dil', 2 / 3 + Ɛ, 80)
check_hits(79)
sort_results()
check_at(0, (3.0, 13))
check_at(1, (3.0, 26))
check_at(2, (3.0, 273))
search('baj xxx yyy', 2 / 3 + Ɛ, 100)
check_hits(0)
search('baj xxx yyy', 2 / 3 - Ɛ, 100)
check_hits(100)
def get_document(self, document_id: int) -> Document:
return InMemoryDocument(
document_id,
{'body': inverted_index.get_doc_body(document_id)})
