def test_main(mocker):
global TOKENIZATION_RESULT
args = {'modelName': 'modelName', 'modelStoreType': 'list', 'emailSubject': 'word1', 'emailBody': 'word2 word3',
'minTextLength': '0', 'labelProbabilityThreshold': '0', 'wordThreshold': '0', 'topWordsLimit': '10',
'returnError': 'true'}
mocker.patch.object(demisto, 'args', return_value=args)
mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand)
mocker.patch('demisto_ml.decode_model', return_value="Model", create=True)
mocker.patch('demisto_ml.filter_model_words', return_value=("text", 2), create=True)
mocker.patch('demisto_ml.explain_model_words', return_value={"Label": 'Valid',
'Probability': 0.7,
'PositiveWords': ['word1'],
'NegativeWords': ['word2']},
create=True)
TOKENIZATION_RESULT = {'originalText': '%s %s' % (args['emailSubject'], args['emailBody']),
'tokenizedText': '%s %s' % (args['emailSubject'], args['emailBody']),
'originalWordsToTokens': {'word1': ['word1'], 'word2': ['word2'], 'word3': ['word3']},
}
res = main()
correct_res = {'OriginalText': 'word1 word2 word3',
'Probability': 0.7, 'NegativeWords': ['word2'],
'TextTokensHighlighted': '<b>word1</b> word2 word3',
'PositiveWords': ['word1'], 'Label': 'Valid'}
assert res['Contents'] == correct_res
args['emailBodyHTML'] = args.pop('emailBody')
TOKENIZATION_RESULT = {'originalText': '%s %s' % (args['emailSubject'], args['emailBodyHTML']),
'tokenizedText': '%s %s' % (args['emailSubject'], args['emailBodyHTML']),
'originalWordsToTokens': {'word1': ['word1'], 'word2': ['word2'], 'word3': ['word3']},
}
main()
assert res['Contents'] == correct_res
def test_no_positive_words(mocker):
# make sure that if no positive words were found, TextTokensHighlighted output is equivalent to original text
global TOKENIZATION_RESULT
phishing_mock = PhishingModelMock()
d = {
"Label": 'Valid',
'Probability': 0.7,
'PositiveWords': [],
'NegativeWords': ['word2']
}
args = {
'modelName': 'modelName',
'modelStoreType': 'list',
'emailSubject': 'word1',
'emailBody': 'word2 word3',
'minTextLength': '0',
'labelProbabilityThreshold': '0',
'wordThreshold': '0',
'topWordsLimit': '10',
'returnError': 'true'
}
mocker.patch.object(demisto, 'args', return_value=args)
mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand)
phishing_mock = PhishingModelMock(("text", 2))
mocker.patch('demisto_ml.phishing_model_loads',
return_value=phishing_mock,
create=True)
mocker.patch.object(demisto,
'incidents',
return_value=[{
'isPlayground': True
}])
mocker.patch.object(phishing_mock,
'filter_model_words',
return_value=("text", 2),
create=True)
mocker.patch.object(phishing_mock,
'explain_model_words',
return_value=d,
create=True)
TOKENIZATION_RESULT = {
'originalText': '%s %s' % (args['emailSubject'], args['emailBody']),
'tokenizedText': '%s %s' % (args['emailSubject'], args['emailBody']),
'originalWordsToTokens': {
'word1': ['word1'],
'word2': ['word2'],
'word3': ['word3']
},
}
res = main()
assert res['Contents']['TextTokensHighlighted'] == TOKENIZATION_RESULT[
'originalText']
def test_main(mocker):
global TOKENIZATION_RESULT
phishing_mock = PhishingModelMock()
d = {
"Label": 'Valid',
'Probability': 0.7,
'PositiveWords': ['word1'],
'NegativeWords': ['word2']
}
args = {
'modelName': 'modelName',
'modelStoreType': 'list',
'emailSubject': 'word1',
'emailBody': 'word2 word3',
'minTextLength': '0',
'labelProbabilityThreshold': '0',
'wordThreshold': '0',
'topWordsLimit': '10',
'returnError': 'true'
}
mocker.patch.object(demisto, 'args', return_value=args)
mocker.patch.object(demisto,
'incidents',
return_value=[{
'isPlayground': True
}])
mocker.patch.object(demisto, 'executeCommand', side_effect=executeCommand)
mocker.patch('demisto_ml.phishing_model_loads_handler',
return_value=phishing_mock,
create=True)
mocker.patch.object(phishing_mock,
'filter_model_words',
return_value=("text", 2),
create=True)
mocker.patch.object(phishing_mock,
'explain_model_words',
return_value=d,
create=True)
TOKENIZATION_RESULT = {
'originalText': '%s %s' % (args['emailSubject'], args['emailBody']),
'tokenizedText': '%s %s' % (args['emailSubject'], args['emailBody']),
'originalWordsToTokens': {
'word1': ['word1'],
'word2': ['word2'],
'word3': ['word3']
},
}
res = main()
correct_res = {
'OriginalText':
concatenate_subject_body(args['emailSubject'], args['emailBody']),
'Probability':
0.7,
'NegativeWords': ['word2'],
'TextTokensHighlighted':
concatenate_subject_body(bold(args['emailSubject']),
args['emailBody']),
'PositiveWords': ['word1'],
'Label':
'Valid'
}
assert res['Contents'] == correct_res
args['emailBodyHTML'] = args.pop('emailBody')
TOKENIZATION_RESULT = {
'originalText':
concatenate_subject_body(args['emailSubject'], args['emailBodyHTML']),
'tokenizedText':
concatenate_subject_body(args['emailSubject'], args['emailBodyHTML']),
'originalWordsToTokens': {
'word1': ['word1'],
'word2': ['word2'],
'word3': ['word3']
},
}
main()
assert res['Contents'] == correct_res