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

Udforsk grundlæggende principper inden for Natural Language Processing (NLP) ved at lære essentielle teknikker til tekstforbehandling og metoder til repræsentation af tekstdata. Opnå praktisk erfaring med værktøjer til at rense, analysere og fortolke tekstuel information. Udvikl færdigheder til at omdanne råt sprog til strukturerede indsigter og skab et solidt fundament for avancerede anvendelser inden for kunstig intelligens og maskinlæring.

Dyk ned i de grundlæggende principper for tekstforbehandling for at klargøre rå tekst til analyse. Lær hvordan man opdeler tekst i tokens, filtrerer stopord fra og tilpasser tokenisering med regulære udtryk.

Opdag, hvordan ord kan reduceres til deres grundformer ved hjælp af stamme- og lemmatisering. Behersk ordklassemærkning for at berige tekst med grammatisk kontekst og anvend ordklassebevidst lemmatisering.

Lær, hvordan tekst kan repræsenteres med tal ved hjælp af vektorrumsmodeller. Få praktisk erfaring med at implementere og tilpasse to populære vektorrumsmodeller: bag of words og TF-IDF.

Opnå en solid forståelse af ordindlejringer og hvordan de indfanger semantisk betydning. Udforsk CBoW- og Skip-gram-arkitekturer anvendt i Word2Vec, og implementer dem selv.

Udfordring: Oprettelse af Word Embeddings

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Awesome!

Udfordring: Oprettelse af Word Embeddings

Løsning