зеркало из
https://github.com/ssciwr/AMMICO.git
synced 2025-10-30 05:26:05 +02:00
a520f48b87
* fixed text translation test, made it more flexible * added clearing cache after pytests to not overload ROM in Linux CI
210 строки
8.2 KiB
Python
210 строки
8.2 KiB
Python
import pytest
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import ammico.text as tt
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import spacy
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@pytest.fixture
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def set_testdict(get_path):
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testdict = {
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"IMG_3755": {
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"filename": get_path + "IMG_3755.jpg",
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},
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"IMG_3756": {
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"filename": get_path + "IMG_3756.jpg",
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},
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"IMG_3757": {
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"filename": get_path + "IMG_3757.jpg",
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},
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}
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return testdict
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LANGUAGES = ["de", "en", "en"]
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def test_TextDetector(set_testdict):
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for item in set_testdict:
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test_obj = tt.TextDetector(set_testdict[item])
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assert test_obj.subdict["text"] is None
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assert test_obj.subdict["text_language"] is None
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assert test_obj.subdict["text_english"] is None
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assert not test_obj.analyse_text
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def test_run_spacy(set_testdict, get_path):
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test_obj = tt.TextDetector(set_testdict["IMG_3755"], analyse_text=True)
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ref_file = get_path + "text_IMG_3755.txt"
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with open(ref_file, "r") as file:
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reference_text = file.read()
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test_obj.subdict["text_english"] = reference_text
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test_obj._run_spacy()
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assert isinstance(test_obj.doc, spacy.tokens.doc.Doc)
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def test_clean_text(set_testdict):
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nlp = spacy.load("en_core_web_md")
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doc = nlp("I like cats and fjejg")
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test_obj = tt.TextDetector(set_testdict["IMG_3755"])
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test_obj.doc = doc
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test_obj.clean_text()
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result = "I like cats and"
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assert test_obj.subdict["text_clean"] == result
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def test_init_revision_numbers_and_models():
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test_obj = tt.TextDetector({})
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# check the default options
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assert test_obj.model_summary == "sshleifer/distilbart-cnn-12-6"
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assert test_obj.model_sentiment == "distilbert-base-uncased-finetuned-sst-2-english"
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assert test_obj.model_ner == "dbmdz/bert-large-cased-finetuned-conll03-english"
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assert test_obj.revision_summary == "a4f8f3e"
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assert test_obj.revision_sentiment == "af0f99b"
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assert test_obj.revision_ner == "f2482bf"
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# provide non-default options
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model_names = ["facebook/bart-large-cnn", None, None]
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test_obj = tt.TextDetector({}, model_names=model_names)
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assert test_obj.model_summary == "facebook/bart-large-cnn"
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assert test_obj.model_sentiment == "distilbert-base-uncased-finetuned-sst-2-english"
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assert test_obj.model_ner == "dbmdz/bert-large-cased-finetuned-conll03-english"
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assert not test_obj.revision_summary
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assert test_obj.revision_sentiment == "af0f99b"
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assert test_obj.revision_ner == "f2482bf"
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revision_numbers = ["3d22493", None, None]
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test_obj = tt.TextDetector(
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{},
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model_names=model_names,
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revision_numbers=revision_numbers,
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)
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assert test_obj.model_summary == "facebook/bart-large-cnn"
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assert test_obj.model_sentiment == "distilbert-base-uncased-finetuned-sst-2-english"
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assert test_obj.model_ner == "dbmdz/bert-large-cased-finetuned-conll03-english"
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assert test_obj.revision_summary == "3d22493"
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assert test_obj.revision_sentiment == "af0f99b"
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assert test_obj.revision_ner == "f2482bf"
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# now test the exceptions
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with pytest.raises(ValueError):
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tt.TextDetector({}, analyse_text=1.0)
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with pytest.raises(ValueError):
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tt.TextDetector({}, model_names=1.0)
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with pytest.raises(ValueError):
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tt.TextDetector({}, revision_numbers=1.0)
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with pytest.raises(ValueError):
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tt.TextDetector({}, model_names=["something"])
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with pytest.raises(ValueError):
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tt.TextDetector({}, revision_numbers=["something"])
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@pytest.mark.gcv
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def test_analyse_image(set_testdict, set_environ):
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for item in set_testdict:
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test_obj = tt.TextDetector(set_testdict[item])
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test_obj.analyse_image()
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test_obj = tt.TextDetector(set_testdict[item], analyse_text=True)
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test_obj.analyse_image()
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@pytest.mark.gcv
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def test_get_text_from_image(set_testdict, get_path, set_environ):
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for item in set_testdict:
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test_obj = tt.TextDetector(set_testdict[item])
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test_obj.get_text_from_image()
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ref_file = get_path + "text_" + item + ".txt"
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with open(ref_file, "r", encoding="utf8") as file:
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reference_text = file.read()
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assert test_obj.subdict["text"] == reference_text
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def test_translate_text(set_testdict, get_path):
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for item, lang in zip(set_testdict, LANGUAGES):
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test_obj = tt.TextDetector(set_testdict[item])
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ref_file = get_path + "text_" + item + ".txt"
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trans_file = get_path + "text_translated_" + item + ".txt"
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with open(ref_file, "r", encoding="utf8") as file:
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reference_text = file.read()
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with open(trans_file, "r", encoding="utf8") as file:
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true_translated_text = file.read()
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test_obj.subdict["text"] = reference_text
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test_obj.translate_text()
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assert test_obj.subdict["text_language"] == lang
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translated_text = test_obj.subdict["text_english"].lower()
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for word in true_translated_text.lower():
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assert word in translated_text
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def test_remove_linebreaks():
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test_obj = tt.TextDetector({})
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test_obj.subdict["text"] = "This is \n a test."
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test_obj.subdict["text_english"] = "This is \n another\n test."
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test_obj.remove_linebreaks()
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assert test_obj.subdict["text"] == "This is a test."
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assert test_obj.subdict["text_english"] == "This is another test."
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@pytest.mark.win_skip
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def test_text_summary(get_path):
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mydict = {}
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test_obj = tt.TextDetector(mydict, analyse_text=True)
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ref_file = get_path + "example_summary.txt"
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with open(ref_file, "r", encoding="utf8") as file:
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reference_text = file.read()
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mydict["text_english"] = reference_text
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test_obj.text_summary()
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reference_summary = " I’m sorry, but I don’t want to be an emperor"
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assert mydict["text_summary"] == reference_summary
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def test_text_sentiment_transformers():
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mydict = {}
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test_obj = tt.TextDetector(mydict, analyse_text=True)
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mydict["text_english"] = "I am happy that the CI is working again."
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test_obj.text_sentiment_transformers()
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assert mydict["sentiment"] == "POSITIVE"
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assert mydict["sentiment_score"] == pytest.approx(0.99, 0.02)
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@pytest.mark.win_skip
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def test_text_ner():
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mydict = {}
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test_obj = tt.TextDetector(mydict, analyse_text=True)
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mydict["text_english"] = "Bill Gates was born in Seattle."
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test_obj.text_ner()
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assert mydict["entity"] == ["Bill Gates", "Seattle"]
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assert mydict["entity_type"] == ["PER", "LOC"]
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@pytest.mark.win_skip
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def test_PostprocessText(set_testdict, get_path):
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reference_dict = "THE\nALGEBRAIC\nEIGENVALUE\nPROBLEM\nDOM\nNVS TIO\nMINA\nMonographs\non Numerical Analysis\nJ.. H. WILKINSON"
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reference_df = "Mathematische Formelsammlung\nfür Ingenieure und Naturwissenschaftler\nMit zahlreichen Abbildungen und Rechenbeispielen\nund einer ausführlichen Integraltafel\n3., verbesserte Auflage"
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img_numbers = ["IMG_3755", "IMG_3756", "IMG_3757"]
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for image_ref in img_numbers:
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ref_file = get_path + "text_" + image_ref + ".txt"
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with open(ref_file, "r") as file:
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reference_text = file.read()
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set_testdict[image_ref]["text_english"] = reference_text
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obj = tt.PostprocessText(mydict=set_testdict)
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test_dict = obj.list_text_english[2].replace("\r", "")
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assert test_dict == reference_dict
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for key in set_testdict.keys():
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set_testdict[key].pop("text_english")
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with pytest.raises(ValueError):
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tt.PostprocessText(mydict=set_testdict)
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obj = tt.PostprocessText(use_csv=True, csv_path=get_path + "test_data_out.csv")
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# make sure test works on windows where end-of-line character is \r\n
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test_df = obj.list_text_english[0].replace("\r", "")
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assert test_df == reference_df
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with pytest.raises(ValueError):
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tt.PostprocessText(use_csv=True, csv_path=get_path + "test_data_out_nokey.csv")
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with pytest.raises(ValueError):
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tt.PostprocessText()
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def test_analyse_topic(get_path):
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_, topic_df, most_frequent_topics = tt.PostprocessText(
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use_csv=True, csv_path=get_path + "topic_analysis_test.csv"
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).analyse_topic()
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# since this is not deterministic we cannot be sure we get the same result twice
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assert len(topic_df) == 2
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assert topic_df["Name"].iloc[0] == "0_the_feat_of_is"
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assert most_frequent_topics[0][0][0] == "the"
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