From 1f128005fd755a8d3ff7eeaaebe6113b76ba721c Mon Sep 17 00:00:00 2001 From: Lucie Date: Wed, 18 Oct 2023 17:40:35 +0200 Subject: [PATCH 1/3] feat(metadata): add support for computing simple metadata --- poetry.lock | 627 ++++++++---------- pyproject.toml | 2 +- saga_llm_evaluation_ml/model/helpers/utils.py | 48 ++ tests/test_helpers.py | 52 ++ 4 files changed, 396 insertions(+), 333 deletions(-) create mode 100644 tests/test_helpers.py diff --git a/poetry.lock b/poetry.lock index fedd483..0871a7c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -225,6 +225,17 @@ torch = ">=1.0.0" tqdm = ">=4.31.1" transformers = ">=3.0.0" +[[package]] +name = "better-profanity" +version = "0.7.0" +description = "Blazingly fast cleaning swear words (and their leetspeak) in strings" +optional = false +python-versions = "==3.*" +files = [ + {file = "better_profanity-0.7.0-py3-none-any.whl", hash = "sha256:bd4c529ea6aa2db1aaa50524be1ed14d0fe5c664f1fd88c8bc388c7e9f9f00e8"}, + {file = 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VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains." +optional = false +python-versions = "*" +files = [ + {file = "vaderSentiment-3.3.2-py2.py3-none-any.whl", hash = "sha256:3bf1d243b98b1afad575b9f22bc2cb1e212b94ff89ca74f8a23a588d024ea311"}, + {file = "vaderSentiment-3.3.2.tar.gz", hash = "sha256:5d7c06e027fc8b99238edb0d53d970cf97066ef97654009890b83703849632f9"}, +] + +[package.dependencies] +requests = "*" + [[package]] name = "wasabi" version = "1.1.2" @@ -4460,4 +4423,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.11" -content-hash = "3af2e94f11b8599784f52919ebd45bae147919f2b116b199448d95d4346f6e02" +content-hash = "d7ce36a5bc5e6db1a799f1a8b294de5aabf27df40181c07529e2fdb0f4304308" diff --git a/pyproject.toml b/pyproject.toml index aaa4d48..fc71f8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,10 +13,10 @@ evaluate = "^0.4.1" scikit-learn = "^1.3.1" mauve-text = "^0.3.0" bert-score = "^0.3.13" -torch = ">=2.0.0, !=2.0.1, !=2.1.0" tensorflow = "^2.14.0" bleurt = {git = "https://github.com/google-research/bleurt.git"} tensorflow-macos = {version = "2.14.0", platform = "darwin"} +elemeta = "^1.1.2" [tool.poetry.dev-dependencies] pylint = "^2.13" diff --git a/saga_llm_evaluation_ml/model/helpers/utils.py b/saga_llm_evaluation_ml/model/helpers/utils.py index 2c34e51..0476d38 100644 --- a/saga_llm_evaluation_ml/model/helpers/utils.py +++ b/saga_llm_evaluation_ml/model/helpers/utils.py @@ -1,7 +1,55 @@ import json +from elemeta.nlp.runners.metafeature_extractors_runner import ( + MetafeatureExtractorsRunner, +) +from elemeta.nlp.extractors.high_level.word_regex_matches_count import ( + WordRegexMatchesCount, +) +from elemeta.nlp.extractors.high_level.regex_match_count import RegexMatchCount def load_json(path): with open(path) as json_file: o_file = json_file.read() return json.loads(o_file) + + +class MetadataExtractor: + def __init__(self): + self.metadata_extractor = MetafeatureExtractorsRunner() + + def addWordRegexMatchesCount(self, regex_rule, name=None): + """ + Adds a regex rule to the metadata extractor. + For a given regex return the number of words matching the regex. + + Args: + regex_rule (str): regex rule to add + """ + self.metadata_extractor.add_metafeature_extractor( + WordRegexMatchesCount(regex=regex_rule, name=name) + ) + + def addRegexMatchCount(self, regex_rule, name=None): + """ + Adds a regex rule to the metadata extractor. + For a given regex return the number of matches it has in the text. + + Args: + regex_rule (str): regex rule to add + """ + self.metadata_extractor.add_metafeature_extractor( + RegexMatchCount(regex=regex_rule, name=name) + ) + + def compute(self, text): + """ + Computes metadata from a text using elemeta library and returns a dictionary of metadata. + + Args: + text (str): text to extract metadata from + + Returns: + dict: dictionary of metadata + """ + return self.metadata_extractor.run(text) diff --git a/tests/test_helpers.py b/tests/test_helpers.py new file mode 100644 index 0000000..9d3f244 --- /dev/null +++ b/tests/test_helpers.py @@ -0,0 +1,52 @@ +import unittest + +from saga_llm_evaluation_ml.model.helpers.utils import MetadataExtractor + + +class TestMetadataExtractor(unittest.TestCase): + def test_extract_metadata(self): + """Tests that the MetadataExtractor class extracts the correct metadata.""" + text = "The cat sat on the mat." + extractor = MetadataExtractor() + metadata = extractor.compute(text) + + # Test a few metadata values + self.assertEqual(metadata["text_length"], 23) + self.assertEqual(metadata["unique_word_ratio"], 1) + self.assertEqual(metadata["unique_word_count"], 6) + self.assertEqual(metadata["emoji_count"], 0) + self.assertEqual(metadata["number_count"], 0) + self.assertEqual(metadata["sentence_count"], 1) + self.assertEqual(metadata["stop_words_count"], 3) + self.assertEqual(metadata["syllable_count"], 6) + self.assertEqual(metadata["sentence_avg_length"], 23) + + def test_add_regex(self): + """Tests that the MetadataExtractor class extracts the correct metadata when regex rules are added.""" + text = "The cat sat on the mat." + extractor = MetadataExtractor() + extractor.addWordRegexMatchesCount("the") + extractor.addRegexMatchCount("the") + metadata = extractor.compute(text) + + # Test a few metadata values + self.assertEqual(metadata["text_length"], 23) + self.assertEqual(metadata["unique_word_ratio"], 1) + self.assertEqual(metadata["unique_word_count"], 6) + self.assertEqual(metadata["emoji_count"], 0) + self.assertEqual(metadata["number_count"], 0) + self.assertEqual(metadata["sentence_count"], 1) + self.assertEqual(metadata["stop_words_count"], 3) + self.assertEqual(metadata["syllable_count"], 6) + self.assertEqual(metadata["sentence_avg_length"], 23) + self.assertEqual(metadata["word_regex_matches_count"], 1) + self.assertEqual(metadata["regex_match_count"], 1) + + len_metadata = len(metadata) + + # Check that the metadata is longer when multiple regex rules are added + extractor.addWordRegexMatchesCount("cat", name="word_regex_matches_count_cat") + extractor.addRegexMatchCount("cat", name="regex_match_count_cat") + metadata = extractor.compute(text) + + self.assertGreater(len(metadata), len_metadata) From 74b2078d0bd306880271a2045d75215eb8e1973d Mon Sep 17 00:00:00 2001 From: Lucie Date: Wed, 18 Oct 2023 17:47:04 +0200 Subject: [PATCH 2/3] fix(ci): attempt for fixing ci --- poetry.lock | 392 ++++++++++++++++++++++++++++--------------------- pyproject.toml | 1 + 2 files changed, 222 insertions(+), 171 deletions(-) diff --git a/poetry.lock b/poetry.lock index 0871a7c..e52b93c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -274,7 +274,7 @@ uvloop = ["uvloop (>=0.15.2)"] [[package]] 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MetafeatureExtractorsRunner, ) from elemeta.nlp.extractors.high_level.word_regex_matches_count import (