149 lines
4.7 KiB
Ruby
149 lines
4.7 KiB
Ruby
# frozen_string_literal: true
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require_relative "../../../support/sentiment_inference_stubs"
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RSpec.describe DiscourseAi::Sentiment::EntryPoint do
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fab!(:user) { Fabricate(:user, refresh_auto_groups: true) }
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describe "registering event callbacks" do
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context "when creating a post" do
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let(:creator) do
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PostCreator.new(
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user,
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raw: "this is the new content for my topic",
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title: "this is my new topic title",
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)
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end
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it "queues a job on create if sentiment analysis is enabled" do
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SiteSetting.ai_sentiment_enabled = true
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expect { creator.create }.to change(Jobs::PostSentimentAnalysis.jobs, :size).by(1)
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end
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it "does nothing if sentiment analysis is disabled" do
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SiteSetting.ai_sentiment_enabled = false
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expect { creator.create }.not_to change(Jobs::PostSentimentAnalysis.jobs, :size)
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end
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end
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context "when editing a post" do
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fab!(:post) { Fabricate(:post, user: user) }
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let(:revisor) { PostRevisor.new(post) }
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it "queues a job on update if sentiment analysis is enabled" do
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SiteSetting.ai_sentiment_enabled = true
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expect { revisor.revise!(user, raw: "This is my new test") }.to change(
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Jobs::PostSentimentAnalysis.jobs,
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:size,
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).by(1)
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end
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it "does nothing if sentiment analysis is disabled" do
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SiteSetting.ai_sentiment_enabled = false
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expect { revisor.revise!(user, raw: "This is my new test") }.not_to change(
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Jobs::PostSentimentAnalysis.jobs,
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:size,
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)
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end
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end
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end
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describe "custom reports" do
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before do
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SiteSetting.ai_sentiment_model_configs =
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"[{\"model_name\":\"SamLowe/roberta-base-go_emotions\",\"endpoint\":\"http://samlowe-emotion.com\",\"api_key\":\"123\"},{\"model_name\":\"j-hartmann/emotion-english-distilroberta-base\",\"endpoint\":\"http://jhartmann-emotion.com\",\"api_key\":\"123\"},{\"model_name\":\"cardiffnlp/twitter-roberta-base-sentiment-latest\",\"endpoint\":\"http://cardiffnlp-sentiment.com\",\"api_key\":\"123\"}]"
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end
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fab!(:pm) { Fabricate(:private_message_post) }
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fab!(:post_1) { Fabricate(:post) }
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fab!(:post_2) { Fabricate(:post) }
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describe "overall_sentiment report" do
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let(:positive_classification) { { negative: 0.2, neutral: 0.3, positive: 0.7 } }
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let(:negative_classification) { { negative: 0.65, neutral: 0.2, positive: 0.1 } }
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def sentiment_classification(post, classification)
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Fabricate(:sentiment_classification, target: post, classification: classification)
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end
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it "calculate averages using only public posts" do
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sentiment_classification(post_1, positive_classification)
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sentiment_classification(post_2, negative_classification)
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sentiment_classification(pm, positive_classification)
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report = Report.find("overall_sentiment")
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positive_data_point = report.data[0][:data].first[:y].to_i
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negative_data_point = report.data[1][:data].first[:y].to_i
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expect(positive_data_point).to eq(1)
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expect(negative_data_point).to eq(-1)
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end
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end
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describe "post_emotion report" do
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let(:emotion_1) do
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{
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sadness: 0.49,
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surprise: 0.23,
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neutral: 0.6,
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fear: 0.34,
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anger: 0.87,
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joy: 0.22,
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disgust: 0.70,
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}
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end
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let(:emotion_2) do
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{
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sadness: 0.19,
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surprise: 0.63,
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neutral: 0.45,
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fear: 0.44,
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anger: 0.27,
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joy: 0.62,
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disgust: 0.30,
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}
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end
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let(:model_used) { "j-hartmann/emotion-english-distilroberta-base" }
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def emotion_classification(post, classification)
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Fabricate(
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:sentiment_classification,
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target: post,
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model_used: model_used,
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classification: classification,
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)
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end
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def strip_emoji_and_downcase(str)
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stripped_str = str.gsub(/[^\p{L}\p{N}]+/, "") # remove any non-alphanumeric characters
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stripped_str.downcase
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end
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it "calculate averages using only public posts" do
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threshold = 0.30
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emotion_classification(post_1, emotion_1)
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emotion_classification(post_2, emotion_2)
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emotion_classification(pm, emotion_2)
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report = Report.find("post_emotion")
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data_point = report.data
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data_point.each do |point|
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emotion = strip_emoji_and_downcase(point[:label])
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expected =
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(emotion_1[emotion.to_sym] > threshold ? 1 : 0) +
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(emotion_2[emotion.to_sym] > threshold ? 1 : 0)
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expect(point[:data][0][:y]).to eq(expected)
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end
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end
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end
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end
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end
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