| |
| |
| |
| |
|
|
| import unittest |
|
|
| import numpy as np |
| import yaml |
|
|
| from pythainlp.benchmarks import word_tokenization |
|
|
| with open("./tests/data/sentences.yml", "r", encoding="utf8") as stream: |
| TEST_DATA = yaml.safe_load(stream) |
|
|
|
|
| class BenchmarksTestCaseX(unittest.TestCase): |
| def test_preprocessing(self): |
| self.assertIsNotNone( |
| word_tokenization.preprocessing( |
| txt="ทดสอบ การ ทำ ความสะอาด ข้อมูล<tag>ok</tag>" |
| ) |
| ) |
|
|
| def test_benchmark_not_none(self): |
| self.assertIsNotNone( |
| word_tokenization.benchmark( |
| ["วัน", "จัน", "ทร์", "สี", "เหลือง"], |
| ["วัน", "จันทร์", "สี", "เหลือง"], |
| ) |
| ) |
|
|
| def test_binary_representation(self): |
| sentence = "อากาศ|ร้อน|มาก|ครับ" |
| rept = word_tokenization._binary_representation(sentence) |
|
|
| self.assertEqual( |
| [1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0], rept.tolist() |
| ) |
|
|
| def test_compute_stats(self): |
| for pair in TEST_DATA["sentences"]: |
| exp, act = pair["expected"], pair["actual"] |
|
|
| result = word_tokenization.compute_stats( |
| word_tokenization.preprocessing(exp), |
| word_tokenization.preprocessing(act), |
| ) |
|
|
| self.assertIsNotNone(result) |
|
|
| def test_benchmark(self): |
| expected = [] |
| actual = [] |
| for pair in TEST_DATA["sentences"]: |
| expected.append(pair["expected"]) |
| actual.append(pair["actual"]) |
|
|
| df = word_tokenization.benchmark(expected, actual) |
|
|
| self.assertIsNotNone(df) |
|
|
| def test_count_correctly_tokenised_words(self): |
| for d in TEST_DATA["binary_sentences"]: |
| sample = np.array(list(d["actual"])).astype(int) |
| ref_sample = np.array(list(d["expected"])).astype(int) |
|
|
| sb = list(word_tokenization._find_word_boundaries(sample)) |
| rb = list(word_tokenization._find_word_boundaries(ref_sample)) |
|
|
| |
| correctly_tokenized_words = ( |
| word_tokenization._find_words_correctly_tokenised(rb, sb) |
| ) |
|
|
| self.assertEqual( |
| np.sum(correctly_tokenized_words), d["expected_count"] |
| ) |
|
|
| def test_words_correctly_tokenised(self): |
| r = [(0, 2), (2, 10), (10, 12)] |
| s = [(0, 10), (10, 12)] |
|
|
| expected = "01" |
|
|
| labels = word_tokenization._find_words_correctly_tokenised(r, s) |
| self.assertEqual(expected, "".join(np.array(labels).astype(str))) |
|
|
| def test_flatten_result(self): |
| result = {"key1": {"v1": 6}, "key2": {"v2": 7}} |
|
|
| actual = word_tokenization._flatten_result(result) |
| self.assertEqual(actual, {"key1:v1": 6, "key2:v2": 7}) |
|
|
