import unittest
import re
import nltk
from nltk.tokenize import word_tokenize
from nltk import pos_tag
import pandas as pd


# 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, "pos_tag"), 
            "`pos_tag` is not declared"
        )
        _require_success(
            self,
            user_code.pos_tag == pos_tag,
            "`pos_tag` is declared, but it's not `nltk.pos_tag`"
        )
        _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 *\( *'averaged_perceptron_tagger_eng' *\)", user_code_text),
                "Code for downloading `'averaged_perceptron_tagger_eng'` is not provided"
            )
        self._testMethodName = "NLTK resources are downloaded"

    # Test original text
    def test_text(self):
        import user_code
        expected = "The central bank raised interest rates by 0.5 percentage points yesterday."
        _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 pos tagging results
    def test_tagged_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, "tagged_tokens"), 
            "`tagged_tokens` is not declared"
        )
        _check_success(
            self,
            user_code.tagged_tokens == pos_tag(user_code.tokens),
            "`tagged_tokens` is computed correctly",
            "`tokens` are not correctly pos tagged"
        )

    # Test print statement
    def test_dataframe_print(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"
        )
        with open("user_code.py", "r") as f:
            user_code_text = f.read()
        _check_success(
            self,
            re.search(r"print *\( *pd *. *DataFrame *\( *tagged_tokens *, *columns *= *\[ *'Token', *'POS tag' *\] *\) *\)", user_code_text),
            "print statement is correct",
            "print statement is either incorrect, or original parts were modified"
        )


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

test_main.py

Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.

Dive into the fundamentals of text preprocessing to prepare raw text for analysis. Learn how to tokenize text, filter out stop words, and customize tokenization with regular expressions.

Discover how words can be reduced to their base forms using stemming and lemmatization. Master part-of-speech tagging to enrich text with grammatical context and apply POS-aware lemmatization.

Learn how text can be represented with numbers using vector space models. Get hands-on experience by implementing and customizing two popular vector space models: bag of words and TF-IDF.

Gain a solid understanding of word embeddings and how they capture semantic meaning. Explore CBoW and Skip-gram architectures used in Word2Vec, and implement them on your own.

Challenge: Part of Speech Tagging

Solution

Awesome!

Challenge: Part of Speech Tagging

Solution

Awesome!