import unittest
import re
import nltk
from gensim.models import Word2Vec
import pandas as pd
from scipy.sparse import csr_matrix


# 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, "pd"), 
            "`pd` is not declared"
        )
        _require_success(
            self,
            user_code.pd == pd,
            "`pd` is declared, but it's not `pandas` library"
        )
        _require_success(
            self, 
            hasattr(user_code, "Word2Vec"), 
            "`Word2Vec` is not declared"
        )
        _require_success(
            self,
            user_code.Word2Vec == Word2Vec,
            "`Word2Vec` is declared, but it's not `gensim.models.Word2Vec`"
        )
        self._testMethodName = "imports are correct"

    # Test original corpus
    def test_corpus(self):
        import user_code
        expected = pd.read_csv(
    'https://content-media-cdn.codefinity.com/courses/c68c1f2e-2c90-4d5d-8db9-1e97ca89d15e/section_3/chapter_4/example_corpus.csv')
        _require_success(
            self, 
            hasattr(user_code, "corpus"), 
            "`corpus` is not declared"
        )
        _check_success(
            self,
            expected.equals(user_code.corpus),
            "`corpus` contains correct value",
            "`corpus` shouldn't be modified"
        )

    # Test that sentences are correctly computed
    def test_sentences(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "corpus"), 
            "`corpus` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.corpus, pd.DataFrame), 
            "`corpus` is not a DataFrame"
        )
        _require_success(
            self, 
            "Document" in user_code.corpus.columns, 
            "`corpus` doesn't contain `'Document'` column"
        )
        _require_success(
            self, 
            hasattr(user_code, "sentences"), 
            "`sentences` is not declared"
        )
        sentences = user_code.corpus['Document'].str.split()
        _check_success(
            self,
            sentences.equals(user_code.sentences),
            "`sentences` is computed correctly",
            "`sentences` is not computed correctly"
        )
  
    # Test that model is declared correctly
    def test_model(self):
        import user_code
        _require_success(
            self, 
            hasattr(user_code, "sentences"), 
            "`sentences` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.sentences, pd.Series), 
            "`sentences` is not a `Series` object"
        )
        model = Word2Vec(user_code.sentences, vector_size=50, window=2, min_count=1, sg=1)
        _require_success(
            self, 
            hasattr(user_code, "model"), 
            "`model` is not declared"
        )
        _require_success(
            self, 
            isinstance(user_code.model, Word2Vec), 
            "`model` is not an instance of `Word2Vec`"
        )
        _require_success(
            self, 
            user_code.model.corpus_count == model.corpus_count and user_code.model.corpus_total_words == model.corpus_total_words, 
            "`model` should be initialized with `sentences`"
        )
        _require_success(
            self, 
            user_code.model.vector_size == model.vector_size, 
            "`model` should be initialized with `vector_size=50`"
        )
        _require_success(
            self, 
            user_code.model.window == model.window, 
            "`model` should be initialized with `window=2`"
        )
        _require_success(
            self, 
            user_code.model.min_count == model.min_count, 
            "`model` should be initialized with `min_count=1`"
        )
        _require_success(
            self, 
            user_code.model.sg == model.sg, 
            "`model` should be initialized with `sg=1`"
        )
        self._testMethodName = "`model` is defined correctly"

    # 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 *\( *model *\. *wv *\. *most_similar *\( *(?P<quote>[\\"'])bowl(?P=quote) *, *topn *= *3 *\) *\)", user_code_text),
            "print statement is correct",
            "print statement is not correct"
        )


if __name__ == '__main__':
    unittest.main()

test_main.py

Verken de basisprincipes van Natural Language Processing (NLP) door essentiële technieken voor tekstvoorbewerking en methoden voor het representeren van tekstdata te bestuderen. Doe praktische ervaring op met de tools die worden gebruikt om tekstuele informatie te reinigen, analyseren en interpreteren. Ontwikkel de vaardigheden die nodig zijn om ruwe taal om te zetten in gestructureerde inzichten, als solide basis voor geavanceerde toepassingen in kunstmatige intelligentie en machine learning.

Verdiep u in de grondbeginselen van tekstvoorbewerking om ruwe tekst voor analyse voor te bereiden. Leer hoe u tekst segmenteert, stopwoorden verwijdert en tokenisatie aanpast met reguliere expressies.

Ontdek hoe woorden kunnen worden teruggebracht tot hun basisvormen met behulp van stemming en lemmatizatie. Beheers het toekennen van woordsoorten om tekst te verrijken met grammaticale context en pas lemmatizatie toe met kennis van woordsoorten.

Leer hoe tekst kan worden weergegeven met getallen door middel van vectorruimte modellen. Doe praktische ervaring op met het implementeren en aanpassen van twee populaire vectorruimte modellen: bag-of-words en TF-IDF.

Verkrijg een grondig begrip van woordembeddings en hoe deze semantische betekenis vastleggen. Verken CBoW- en Skip-gram-architecturen die worden gebruikt in Word2Vec, en implementeer deze zelfstandig.

Uitdaging: Het Creëren van Woordembeddings

Oplossing

Awesome!

Uitdaging: Het Creëren van Woordembeddings

Oplossing