import unittest
import re
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer


# Helper functions
def _require_success(test_case, condition, failure_message):
    if not condition:
        test_case._testMethodName = failure_message
        test_case.fail()

def _check_success(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
    else:
        test_case._testMethodName = failure_message
        test_case.fail()


class TestUserCode(unittest.TestCase):
    # Test that everything is imported correctly
    def test_imports(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "word_tokenize"), 
            "`word_tokenize` is not declared"
        )
        _require_success(
            self,
            user_code.word_tokenize == word_tokenize,
            "`word_tokenize` is declared, but it's not `nltk.tokenize.word_tokenize`"
        )
        _require_success(
            self, 
            hasattr(user_code, "stopwords"), 
            "`stopwords` is not declared"
        )
        _require_success(
            self,
            user_code.stopwords == stopwords,
            "`stopwords` is declared, but it's not `nltk.corpus.stopwords`"
        )
        _require_success(
            self, 
            hasattr(user_code, "PorterStemmer"), 
            "`PorterStemmer` is not declared"
        )
        _require_success(
            self,
            user_code.PorterStemmer == PorterStemmer,
            "`PorterStemmer` is declared, but it's not `nltk.stem.PorterStemmer`"
        )
        _require_success(
            self,
            hasattr(user_code, "nltk"),
            "`nltk` is not declared"
        )
        _require_success(
            self,
            user_code.nltk == nltk,
            "`nltk` is declared, but it's not `nltk` library"
        )
        self._testMethodName = "imports are correct"

    # Test that required NLTK resources are downloaded
    def test_nltk_downloads(self):
        import user_code
        _require_success(
            self,
            hasattr(user_code, "nltk"),
            "`nltk` is not declared"
        )
        _require_success(
            self,
            user_code.nltk == nltk,
            "`nltk` is declared, but it's not `nltk` library"
        )
        with open("user_code.py", "r") as f:
            user_code_text = f.read()
            _require_success(
                self,
                re.search(r"nltk *\. *download *\( *'punkt_tab' *\)", user_code_text),
                "Code for downloading `'punkt_tab'` tokenizer shouldn't be modified"
            )
            _require_success(
                self,
                re.search(r"nltk *\. *download *\( *'stopwords' *\)", user_code_text),
                "Code for downloading `'stopwords'` shouldn't be modified"
            )
        self._testMethodName = "NLTK resources are downloaded"

    # Test original text
    def test_text(self):
        import user_code
        expected = "Despite the pouring rain, the overwhelming sense of joy and accomplishment made the day unforgettable!"
        _require_success(
            self, 
            hasattr(user_code, "text"), 
            "`text` is not declared"
        )
        _check_success(
            self,
            user_code.text == expected,
            "`text` contains correct value",
            "`text` shouldn't be modified"
        )

    # Test lowercase conversion
    def test_text_lower(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "text"), 
            "`text` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.text, str), 
            "`text` is not a string"
        )
        _require_success(
            self, 
            hasattr(user_code, "text_lower"), 
            "`text_lower` is not declared"
        )
        _check_success(
            self,
            user_code.text_lower == user_code.text.lower(),
            "`text_lower` is computed correctly",
            "`text_lower` is not the lowercase of `text`"
        )

    # Test tokenization of text_lower
    def test_tokens(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "text_lower"), 
            "`text_lower` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.text_lower, str), 
            "`text_lower` is not a string"
        )
        _require_success(
            self, 
            hasattr(user_code, "tokens"), 
            "`tokens` is not declared"
        )
        _check_success(
            self,
            user_code.tokens == word_tokenize(user_code.text_lower),
            "`tokens` is computed correctly",
            "`text_lower` is not correctly tokenized into words"
        )

    # Test stop_words set
    def test_stop_words(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "stop_words"), 
            "`stop_words` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.stop_words, set), 
            "`stop_words` is not a set"
        )
        _check_success(
            self,
            user_code.stop_words == set(stopwords.words('english')),
            "`stop_words` contains correct English stopwords set",
            "`stop_words` doesn't contain English stopwords"
        )

    # Test filtered_tokens filtering
    def test_filtered_tokens(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "tokens"), 
            "`tokens` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.tokens, list), 
            "`tokens` is not a list"
        )
        _require_success(
            self, 
            hasattr(user_code, "stop_words"), 
            "`stop_words` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.stop_words, set), 
            "`stop_words` is not a set"
        )
        _require_success(
            self, 
            hasattr(user_code, "filtered_tokens"), 
            "`filtered_tokens` is not declared"
        )
        expected = [tok for tok in user_code.tokens if tok not in user_code.stop_words]
        _check_success(
            self,
            user_code.filtered_tokens == expected,
            "`filtered_tokens` is computed correctly",
            "stop words in `tokens` are not correctly filtered out"
        )

    # Test stemmer instance
    def test_stemmer(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "stemmer"), 
            "`stemmer` is not declared"
        )
        _check_success(
            self,
            isinstance(user_code.stemmer, PorterStemmer),
            "`stemmer` is defined correctly",
            "`stemmer` is not an instance of `PorterStemmer`"
        )

    # Test stemming results
    def test_stemmed_tokens(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "stemmer"), 
            "`stemmer` is not declared"
        )
        _require_success(
            self,
            isinstance(user_code.stemmer, PorterStemmer),
            "`stemmer` is not an instance of `PorterStemmer`"
        )
        _require_success(
            self, 
            hasattr(user_code, "filtered_tokens"), 
            "`filtered_tokens` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.filtered_tokens, list), 
            "`filtered_tokens` is not a list"
        )
        _require_success(
            self, 
            hasattr(user_code, "stemmed_tokens"), 
            "`stemmed_tokens` is not declared"
        )
        expected = [user_code.stemmer.stem(tok) for tok in user_code.filtered_tokens]
        _check_success(
            self,
            user_code.stemmed_tokens == expected,
            "`stemmed_tokens` is computed correctly",
            "`filtered_tokens` are not correctly stemmed"
        )

    # Test print statement
    def test_print(self):
        with open("user_code.py", "r") as f:
            user_code_text = f.read()
        _check_success(
            self,
            re.search(r"print *\( *\\"Stemmed tokens:\\" *, *stemmed_tokens *\)", user_code_text),
            "print statement is correct",
            "print statement shouldn't be modified"
        )


if __name__ == '__main__':
    nltk.download('punkt_tab')
    nltk.download('stopwords')
    unittest.main()

test_main.py

Explorez les principes fondamentaux du traitement automatique du langage naturel (TALN) en apprenant les techniques essentielles de prétraitement du texte et les méthodes de représentation des données textuelles. Acquérez une expérience pratique avec les outils utilisés pour nettoyer, analyser et interpréter les informations textuelles. Développez les compétences nécessaires pour transformer le langage brut en informations structurées, établissant ainsi une base solide pour des applications avancées en intelligence artificielle et en apprentissage automatique.

Explorez les fondamentaux du prétraitement de texte afin de préparer le texte brut pour l'analyse. Découvrez la tokenisation du texte, l'élimination des mots vides et la personnalisation de la tokenisation à l'aide d'expressions régulières.

Découvrez comment les mots peuvent être réduits à leur forme de base grâce à la racinisation et à la lemmatisation.
Maîtrise de l'étiquetage des parties du discours pour enrichir le texte avec un contexte grammatical et application de la lemmatisation sensible au POS.

Découvrez comment le texte peut être représenté par des nombres à l'aide de modèles d'espace vectoriel. Expérimentez concrètement en implémentant et en personnalisant deux modèles d'espace vectoriel populaires : sac de mots et TF-IDF.

Acquérir une compréhension approfondie des représentations vectorielles de mots et de leur capacité à capturer la signification sémantique. Explorer les architectures CBoW et Skip-gram utilisées dans Word2Vec, et les mettre en œuvre soi-même.

Défi : Racinisation

Solution

Défi : Racinisation

Solution