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https://github.com/samuelclay/NewsBlur.git
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97 lines
No EOL
4 KiB
Python
97 lines
No EOL
4 KiB
Python
from utils import json
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from django.test.client import Client
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from django.contrib.auth.models import User
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from apps.rss_feeds.models import Feed, Story
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from django.test import TestCase
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from django.core import management
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from pprint import pprint
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# from apps.analyzer.classifier import FisherClassifier
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from apps.analyzer.tokenizer import Tokenizer
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from utils.reverend.thomas import Bayes
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from apps.analyzer.phrase_filter import PhraseFilter
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class ClassifierTest(TestCase):
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fixtures = ['classifiers.json', 'brownstoner.json']
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def setUp(self):
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self.client = Client()
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#
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# def test_filter(self):
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# user = User.objects.all()
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# feed = Feed.objects.all()
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#
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# management.call_command('loaddata', 'brownstoner.json', verbosity=0)
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# response = self.client.get('/reader/refresh_feed', { "feed_id": 1, "force": True })
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# management.call_command('loaddata', 'brownstoner2.json', verbosity=0)
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# response = self.client.get('/reader/refresh_feed', { "feed_id": 1, "force": True })
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# management.call_command('loaddata', 'gothamist1.json', verbosity=0)
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# response = self.client.get('/reader/refresh_feed', { "feed_id": 4, "force": True })
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# management.call_command('loaddata', 'gothamist2.json', verbosity=0)
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# response = self.client.get('/reader/refresh_feed', { "feed_id": 4, "force": True })
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#
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# stories = Story.objects.filter(story_feed=feed[1]).order_by('-story_date')[:100]
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#
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# phrasefilter = PhraseFilter()
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# for story in stories:
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# # print story.story_title, story.id
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# phrasefilter.run(story.story_title, story.id)
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#
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# phrasefilter.pare_phrases()
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# phrasefilter.print_phrases()
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#
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def test_train(self):
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user = User.objects.all()
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feed = Feed.objects.all()
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management.call_command('loaddata', 'brownstoner.json', verbosity=0)
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response = self.client.get('/reader/refresh_feed', { "feed_id": 1, "force": True })
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management.call_command('loaddata', 'brownstoner2.json', verbosity=0)
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response = self.client.get('/reader/refresh_feed', { "feed_id": 1, "force": True })
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stories = Story.objects.filter(story_feed=1)[:53]
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phrasefilter = PhraseFilter()
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for story in stories:
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# print story.story_title, story.id
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phrasefilter.run(story.story_title, story.id)
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phrasefilter.pare_phrases()
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phrases = phrasefilter.get_phrases()
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print phrases
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tokenizer = Tokenizer(phrases)
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classifier = Bayes(tokenizer) # FisherClassifier(user[0], feed[0], phrases)
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classifier.train('good', 'House of the Day: 393 Pacific St.')
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classifier.train('good', 'House of the Day: 393 Pacific St.')
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classifier.train('good', 'Condo of the Day: 393 Pacific St.')
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classifier.train('good', 'Co-op of the Day: 393 Pacific St. #3')
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classifier.train('good', 'Co-op of the Day: 393 Pacific St. #3')
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classifier.train('good', 'Development Watch: 393 Pacific St. #3')
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classifier.train('bad', 'Development Watch: 393 Pacific St. #3')
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classifier.train('bad', 'Development Watch: 393 Pacific St. #3')
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# classifier.train('Development Watch: 393 Pacific St. #3', 'bad')
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# classifier.train('Streetlevel: 393 Pacific St. #3', 'good')
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c1 = classifier.guess('Co-op of the Day: 413 Atlantic')
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self.assertEquals(c1[0][0], "good")
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print c1
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c1 = classifier.guess('House of the Day: 413 Atlantic')
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self.assertEquals(c1[0][0], "good")
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print c1
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c2 = classifier.guess('Development Watch: Yatta')
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print c2
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self.assertEquals(c2[0][0], "bad")
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c2 = classifier.guess('Development Watch: 393 Pacific St.')
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print c2
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c3 = classifier.guess('Extra, Extra')
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print c3
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c4 = classifier.guess('Nothing doing: 393 Pacific St.')
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print c4
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