def sentiment_confidence_scores():
model = _models.SentimentConfidenceScores(positive=0.99,
neutral=0.05,
negative=0.02)
model_repr = "SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)"
assert repr(model) == model_repr
return model, model_repr
def target_assessment_confidence_score():
model = _models.SentimentConfidenceScores(
positive=0.5,
negative=0.5
)
model_repr = "SentimentConfidenceScores(positive=0.5, neutral=0.0, negative=0.5)"
assert repr(model) == model_repr
return model, model_repr
def aspect_opinion_confidence_score():
model = _models.SentimentConfidenceScores(
positive=0.5,
negative=0.5
)
model_repr = "SentimentConfidenceScores(positive=0.5, neutral=0.0, negative=0.5)"
assert repr(model) == model_repr
return model, model_repr
def test_repr(self):
detected_language = _models.DetectedLanguage(name="English", iso6391_name="en", score=1.0)
categorized_entity = _models.CategorizedEntity(text="Bill Gates", category="Person", subcategory="Age",
grapheme_offset=0, grapheme_length=8, score=0.899)
pii_entity = _models.PiiEntity(text="555-55-5555", category="SSN", subcategory=None, grapheme_offset=0,
grapheme_length=8, score=0.899)
text_document_statistics = _models.TextDocumentStatistics(grapheme_count=14, transaction_count=18)
recognize_entities_result = _models.RecognizeEntitiesResult(
id="1",
entities=[categorized_entity],
statistics=text_document_statistics,
is_error=False
)
recognize_pii_entities_result = _models.RecognizePiiEntitiesResult(
id="1",
entities=[pii_entity],
statistics=text_document_statistics,
is_error=False
)
detect_language_result = _models.DetectLanguageResult(
id="1",
primary_language=detected_language,
statistics=text_document_statistics,
is_error=False
)
text_analytics_error = _models.TextAnalyticsError(
code="invalidRequest",
message="The request is invalid",
target="request",
)
extract_key_phrases_result = \
_models.ExtractKeyPhrasesResult(
id="1", key_phrases=["dog", "cat", "bird"], statistics=text_document_statistics, is_error=False
)
linked_entity_match = _models.LinkedEntityMatch(score=0.999, text="Bill Gates", grapheme_offset=0,
grapheme_length=8)
linked_entity = _models.LinkedEntity(
name="Bill Gates",
matches=[linked_entity_match, linked_entity_match],
language="English",
data_source_entity_id="Bill Gates",
url="https://en.wikipedia.org/wiki/Bill_Gates",
data_source="wikipedia"
)
recognize_linked_entities_result = \
_models.RecognizeLinkedEntitiesResult(
id="1", entities=[linked_entity], statistics=text_document_statistics, is_error=False
)
sentiment_confidence_score_per_label = \
_models.SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)
sentence_sentiment = _models.SentenceSentiment(
sentiment="neutral",
confidence_scores=sentiment_confidence_score_per_label,
grapheme_offset=0,
grapheme_length=10,
warnings=["sentence was too short to find sentiment"]
)
analyze_sentiment_result = _models.AnalyzeSentimentResult(
id="1",
sentiment="positive",
statistics=text_document_statistics,
confidence_scores=sentiment_confidence_score_per_label,
sentences=[sentence_sentiment],
is_error=False
)
document_error = _models.DocumentError(id="1", error=text_analytics_error, is_error=True)
detect_language_input = _models.DetectLanguageInput(id="1", text="hello world", country_hint="US")
text_document_input = _models.TextDocumentInput(id="1", text="hello world", language="en")
text_document_batch_statistics = _models.TextDocumentBatchStatistics(
document_count=1,
valid_document_count=2,
erroneous_document_count=3,
transaction_count=4
)
self.assertEqual("DetectedLanguage(name=English, iso6391_name=en, score=1.0)", repr(detected_language))
self.assertEqual("CategorizedEntity(text=Bill Gates, category=Person, subcategory=Age, grapheme_offset=0, "
"grapheme_length=8, score=0.899)",
repr(categorized_entity))
self.assertEqual("PiiEntity(text=555-55-5555, category=SSN, subcategory=None, grapheme_offset=0, "
"grapheme_length=8, score=0.899)", repr(pii_entity))
self.assertEqual("TextDocumentStatistics(grapheme_count=14, transaction_count=18)",
repr(text_document_statistics))
self.assertEqual("RecognizeEntitiesResult(id=1, entities=[CategorizedEntity(text=Bill Gates, category=Person, "
"subcategory=Age, grapheme_offset=0, grapheme_length=8, score=0.899)], "
"statistics=TextDocumentStatistics(grapheme_count=14, transaction_count=18), "
"is_error=False)", repr(recognize_entities_result))
self.assertEqual("RecognizePiiEntitiesResult(id=1, entities=[PiiEntity(text=555-55-5555, category=SSN, "
"subcategory=None, grapheme_offset=0, grapheme_length=8, score=0.899)], "
"statistics=TextDocumentStatistics(grapheme_count=14, transaction_count=18), "
"is_error=False)", repr(recognize_pii_entities_result))
self.assertEqual("DetectLanguageResult(id=1, primary_language=DetectedLanguage(name=English, "
"iso6391_name=en, score=1.0), statistics=TextDocumentStatistics(grapheme_count=14, "
"transaction_count=18), is_error=False)", repr(detect_language_result))
self.assertEqual("TextAnalyticsError(code=invalidRequest, message=The request is invalid, target=request)",
repr(text_analytics_error))
self.assertEqual("ExtractKeyPhrasesResult(id=1, key_phrases=['dog', 'cat', 'bird'], statistics="
"TextDocumentStatistics(grapheme_count=14, transaction_count=18), is_error=False)",
repr(extract_key_phrases_result))
self.assertEqual("LinkedEntityMatch(score=0.999, text=Bill Gates, grapheme_offset=0, grapheme_length=8)",
repr(linked_entity_match))
self.assertEqual("LinkedEntity(name=Bill Gates, matches=[LinkedEntityMatch(score=0.999, text=Bill Gates, "
"grapheme_offset=0, grapheme_length=8), LinkedEntityMatch(score=0.999, text=Bill Gates, "
"grapheme_offset=0, grapheme_length=8)], language=English, data_source_entity_id=Bill Gates, "
"url=https://en.wikipedia.org/wiki/Bill_Gates, data_source=wikipedia)", repr(linked_entity))
self.assertEqual("RecognizeLinkedEntitiesResult(id=1, entities=[LinkedEntity(name=Bill Gates, "
"matches=[LinkedEntityMatch(score=0.999, text=Bill Gates, grapheme_offset=0, "
"grapheme_length=8), LinkedEntityMatch(score=0.999, text=Bill Gates, grapheme_offset=0, "
"grapheme_length=8)], language=English, data_source_entity_id=Bill Gates, "
"url=https://en.wikipedia.org/wiki/Bill_Gates, data_source=wikipedia)], "
"statistics=TextDocumentStatistics(grapheme_count=14, "
"transaction_count=18), is_error=False)", repr(recognize_linked_entities_result))
self.assertEqual("SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)",
repr(sentiment_confidence_score_per_label))
self.assertEqual("SentenceSentiment(sentiment=neutral, confidence_scores=SentimentConfidenceScores("
"positive=0.99, neutral=0.05, negative=0.02), grapheme_offset=0, grapheme_length=10, warnings="
"['sentence was too short to find sentiment'])", repr(sentence_sentiment))
self.assertEqual("AnalyzeSentimentResult(id=1, sentiment=positive, statistics=TextDocumentStatistics("
"grapheme_count=14, transaction_count=18), confidence_scores=SentimentConfidenceScores"
"(positive=0.99, neutral=0.05, negative=0.02), "
"sentences=[SentenceSentiment(sentiment=neutral, confidence_scores="
"SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02), "
"grapheme_offset=0, grapheme_length=10, "
"warnings=['sentence was too short to find sentiment'])], is_error=False)",
repr(analyze_sentiment_result))
self.assertEqual("DocumentError(id=1, error=TextAnalyticsError(code=invalidRequest, "
"message=The request is invalid, target=request), is_error=True)", repr(document_error))
self.assertEqual("DetectLanguageInput(id=1, text=hello world, country_hint=US)", repr(detect_language_input))
self.assertEqual("TextDocumentInput(id=1, text=hello world, language=en)", repr(text_document_input))
self.assertEqual("TextDocumentBatchStatistics(document_count=1, valid_document_count=2, "
"erroneous_document_count=3, transaction_count=4)", repr(text_document_batch_statistics))
def test_repr(self):
detected_language = _models.DetectedLanguage(name="English",
iso6391_name="en",
confidence_score=1.0)
categorized_entity = _models.CategorizedEntity(text="Bill Gates",
category="Person",
subcategory="Age",
confidence_score=0.899)
text_document_statistics = _models.TextDocumentStatistics(
character_count=14, transaction_count=18)
warnings = [
_models.TextAnalyticsWarning(
code="LongWordsInDocument",
message=
"The document contains very long words (longer than 64 characters). These words will be truncated and may result in unreliable model predictions."
)
]
recognize_entities_result = _models.RecognizeEntitiesResult(
id="1",
entities=[categorized_entity],
warnings=warnings,
statistics=text_document_statistics,
is_error=False)
detect_language_result = _models.DetectLanguageResult(
id="1",
primary_language=detected_language,
warnings=warnings,
statistics=text_document_statistics,
is_error=False)
text_analytics_error = _models.TextAnalyticsError(
code="invalidRequest",
message="The request is invalid",
target="request",
)
extract_key_phrases_result = \
_models.ExtractKeyPhrasesResult(
id="1", key_phrases=["dog", "cat", "bird"], warnings=warnings, statistics=text_document_statistics, is_error=False
)
linked_entity_match = _models.LinkedEntityMatch(confidence_score=0.999,
text="Bill Gates")
linked_entity = _models.LinkedEntity(
name="Bill Gates",
matches=[linked_entity_match, linked_entity_match],
language="English",
data_source_entity_id="Bill Gates",
url="https://en.wikipedia.org/wiki/Bill_Gates",
data_source="wikipedia")
recognize_linked_entities_result = \
_models.RecognizeLinkedEntitiesResult(
id="1", entities=[linked_entity], warnings=warnings, statistics=text_document_statistics, is_error=False
)
sentiment_confidence_score_per_label = \
_models.SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)
sentence_sentiment = _models.SentenceSentiment(
text="This is a sentence.",
sentiment="neutral",
confidence_scores=sentiment_confidence_score_per_label)
analyze_sentiment_result = _models.AnalyzeSentimentResult(
id="1",
sentiment="positive",
warnings=warnings,
statistics=text_document_statistics,
confidence_scores=sentiment_confidence_score_per_label,
sentences=[sentence_sentiment],
is_error=False)
document_error = _models.DocumentError(id="1",
error=text_analytics_error,
is_error=True)
detect_language_input = _models.DetectLanguageInput(id="1",
text="hello world",
country_hint="US")
text_document_input = _models.TextDocumentInput(id="1",
text="hello world",
language="en")
text_document_batch_statistics = _models.TextDocumentBatchStatistics(
document_count=1,
valid_document_count=2,
erroneous_document_count=3,
transaction_count=4)
self.assertEqual(
"DetectedLanguage(name=English, iso6391_name=en, confidence_score=1.0)",
repr(detected_language))
self.assertEqual(
"CategorizedEntity(text=Bill Gates, category=Person, subcategory=Age, confidence_score=0.899)",
repr(categorized_entity))
self.assertEqual(
"TextDocumentStatistics(character_count=14, transaction_count=18)",
repr(text_document_statistics))
self.assertEqual(
"RecognizeEntitiesResult(id=1, entities=[CategorizedEntity(text=Bill Gates, category=Person, "
"subcategory=Age, confidence_score=0.899)], "
"warnings=[TextAnalyticsWarning(code=LongWordsInDocument, message=The document contains very long words (longer than 64 characters). "
"These words will be truncated and may result in unreliable model predictions.)], "
"statistics=TextDocumentStatistics(character_count=14, transaction_count=18), "
"is_error=False)", repr(recognize_entities_result))
self.assertEqual(
"DetectLanguageResult(id=1, primary_language=DetectedLanguage(name=English, "
"iso6391_name=en, confidence_score=1.0), "
"warnings=[TextAnalyticsWarning(code=LongWordsInDocument, message=The document contains very long words (longer than 64 characters). "
"These words will be truncated and may result in unreliable model predictions.)], "
"statistics=TextDocumentStatistics(character_count=14, "
"transaction_count=18), is_error=False)",
repr(detect_language_result))
self.assertEqual(
"TextAnalyticsError(code=invalidRequest, message=The request is invalid, target=request)",
repr(text_analytics_error))
self.assertEqual(
"ExtractKeyPhrasesResult(id=1, key_phrases=['dog', 'cat', 'bird'], "
"warnings=[TextAnalyticsWarning(code=LongWordsInDocument, message=The document contains very long words (longer than 64 characters). "
"These words will be truncated and may result in unreliable model predictions.)], "
"statistics=TextDocumentStatistics(character_count=14, transaction_count=18), is_error=False)",
repr(extract_key_phrases_result))
self.assertEqual(
"LinkedEntityMatch(confidence_score=0.999, text=Bill Gates)",
repr(linked_entity_match))
self.assertEqual(
"LinkedEntity(name=Bill Gates, matches=[LinkedEntityMatch(confidence_score=0.999, text=Bill Gates), "
"LinkedEntityMatch(confidence_score=0.999, text=Bill Gates)], "
"language=English, data_source_entity_id=Bill Gates, "
"url=https://en.wikipedia.org/wiki/Bill_Gates, data_source=wikipedia)",
repr(linked_entity))
self.assertEqual(
"RecognizeLinkedEntitiesResult(id=1, entities=[LinkedEntity(name=Bill Gates, "
"matches=[LinkedEntityMatch(confidence_score=0.999, text=Bill Gates), "
"LinkedEntityMatch(confidence_score=0.999, text=Bill Gates)], language=English, data_source_entity_id=Bill Gates, "
"url=https://en.wikipedia.org/wiki/Bill_Gates, data_source=wikipedia)], "
"warnings=[TextAnalyticsWarning(code=LongWordsInDocument, message=The document contains very long words (longer than 64 characters). "
"These words will be truncated and may result in unreliable model predictions.)], "
"statistics=TextDocumentStatistics(character_count=14, "
"transaction_count=18), is_error=False)",
repr(recognize_linked_entities_result))
self.assertEqual(
"SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02)",
repr(sentiment_confidence_score_per_label))
self.assertEqual(
"SentenceSentiment(text=This is a sentence., sentiment=neutral, confidence_scores=SentimentConfidenceScores("
"positive=0.99, neutral=0.05, negative=0.02))",
repr(sentence_sentiment))
self.assertEqual(
"AnalyzeSentimentResult(id=1, sentiment=positive, "
"warnings=[TextAnalyticsWarning(code=LongWordsInDocument, message=The document contains very long words (longer than 64 characters). "
"These words will be truncated and may result in unreliable model predictions.)], "
"statistics=TextDocumentStatistics("
"character_count=14, transaction_count=18), confidence_scores=SentimentConfidenceScores"
"(positive=0.99, neutral=0.05, negative=0.02), "
"sentences=[SentenceSentiment(text=This is a sentence., sentiment=neutral, confidence_scores="
"SentimentConfidenceScores(positive=0.99, neutral=0.05, negative=0.02))], is_error=False)",
repr(analyze_sentiment_result))
self.assertEqual(
"DocumentError(id=1, error=TextAnalyticsError(code=invalidRequest, "
"message=The request is invalid, target=request), is_error=True)",
repr(document_error))
self.assertEqual(
"DetectLanguageInput(id=1, text=hello world, country_hint=US)",
repr(detect_language_input))
self.assertEqual(
"TextDocumentInput(id=1, text=hello world, language=en)",
repr(text_document_input))
self.assertEqual(
"TextDocumentBatchStatistics(document_count=1, valid_document_count=2, "
"erroneous_document_count=3, transaction_count=4)",
repr(text_document_batch_statistics))
