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

Tutustu luonnollisen kielen käsittelyn (NLP) perusteisiin oppimalla keskeiset tekstin esikäsittelytekniikat ja menetelmät tekstidatan esittämiseen. Saat käytännön kokemusta työkaluista, joilla puhdistetaan, analysoidaan ja tulkitaan tekstuaalista tietoa. Kehitä taidot, joilla muutat raakakielen jäsennellyiksi oivalluksiksi, ja luot vahvan pohjan tekoälyn ja koneoppimisen edistyneille sovelluksille.

Perehdy tekstin esikäsittelyn perusteisiin raakatekstin valmistelussa analyysia varten. Opiskele tekstin tokenisointia, stop-sanojen suodatusta sekä tokenisoinnin mukauttamista säännöllisten lausekkeiden avulla.

Opi, miten sanoja voidaan palauttaa perusmuotoonsa stemmauksen ja lemmatisaation avulla. Hallitse sanaluokkien tunnistus tekstin kieliopillisen kontekstin rikastamiseksi ja sovella sanaluokkatietoista lemmatisaatiota.

Opi, miten teksti voidaan esittää numeroina vektoriavaruusmallien avulla. Saat käytännön kokemusta toteuttamalla ja mukauttamalla kahta suosittua vektoriavaruusmallia: sanapussimalli ja TF-IDF.

Hanki vankka ymmärrys sanaesityksistä ja siitä, miten ne tallentavat semanttista merkitystä. Tutustu Word2Vecin CBoW- ja Skip-gram-arkkitehtuureihin ja toteuta ne itse.

Haaste: Sanaluokkien Tunnistus

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Haaste: Sanaluokkien Tunnistus

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