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# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import pytest
from azure.ai.textanalytics._models import (
AnalyzeSentimentResult,
AspectSentiment,
OpinionSentiment,
SentenceSentiment,
_get_indices,
)
from azure.ai.textanalytics._response_handlers import sentiment_result
from azure.ai.textanalytics._generated.v3_1_preview_2 import models as _generated_models
@pytest.fixture
def generated_aspect_opinion_confidence_scores():
return _generated_models.AspectConfidenceScoreLabel(
positive=1.0,
neutral=0.0,
negative=0.0,
)
@pytest.fixture
def generated_sentiment_confidence_score():
return _generated_models.SentimentConfidenceScorePerLabel(
positive=1.0,
neutral=0.0,
negative=0.0,
)
@pytest.fixture
def generated_aspect_relation():
return _generated_models.AspectRelation(
relation_type="opinion",
ref="#/documents/0/sentences/1/opinions/0"
)
@pytest.fixture
def generated_aspect(generated_aspect_opinion_confidence_scores, generated_aspect_relation):
return _generated_models.SentenceAspect(
text="aspect",
sentiment="positive",
confidence_scores=generated_aspect_opinion_confidence_scores,
offset=0,
length=6,
relations=[generated_aspect_relation],
)
@pytest.fixture
def generated_opinion(generated_aspect_opinion_confidence_scores):
return _generated_models.SentenceOpinion(
text="good",
sentiment="positive",
confidence_scores=generated_aspect_opinion_confidence_scores,
offset=0,
length=4,
is_negated=False,
)
def generated_sentence_sentiment(generated_sentiment_confidence_score, index, aspects=[], opinions=[]):
return _generated_models.SentenceSentiment(
text="not relevant",
sentiment="positive",
confidence_scores=generated_sentiment_confidence_score,
offset=0,
length=12,
aspects=aspects,
opinions=opinions,
)
@pytest.fixture
def generated_document_sentiment(generated_aspect, generated_opinion, generated_sentiment_confidence_score):
aspect_sentence = generated_sentence_sentiment(generated_sentiment_confidence_score, index=0, aspects=[generated_aspect])
opinion_sentence = generated_sentence_sentiment(generated_sentiment_confidence_score, index=1, opinions=[generated_opinion])
return _generated_models.DocumentSentiment(
id=1,
sentiment="positive",
confidence_scores=generated_sentiment_confidence_score,
sentences=[aspect_sentence, opinion_sentence],
warnings=[],
)
@pytest.fixture
def generated_sentiment_response(generated_document_sentiment):
return _generated_models.SentimentResponse(
documents=[generated_document_sentiment],
errors=[],
model_version="0000-00-00",
)
class TestJsonPointer():
def test_json_pointer_parsing(self):
assert [1, 0, 15] == _get_indices("#/documents/1/sentences/0/opinions/15")
def test_opinion_different_sentence_aspect(self, generated_sentiment_response):
# the first sentence has the aspect, and the second sentence has the opinion
# the desired behavior is the first wrapped sentence object has an aspect, and it's opinion
# is in the second sentence.
# the second sentence will have no mined opinions, since we define that as an aspect and opinion duo
wrapped_sentiment = sentiment_result("not relevant", generated_sentiment_response, {})[0]
assert wrapped_sentiment.sentences[0].mined_opinions[0].opinions[0].text == "good"
assert not wrapped_sentiment.sentences[1].mined_opinions
|
