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# encoding: utf-8
require File.dirname(__FILE__) + '/../test_helper'
require_relative '../data/test_data_loader'
class BayesianIntegrationTest < Minitest::Test
TRAINING_SIZE = 4000
TESTING_SIZE = 1000
def setup
begin
@memory_classifier = ClassifierReborn::Bayes.new 'Ham', 'Spam'
@redis_backend = ClassifierReborn::BayesRedisBackend.new
@redis_backend.instance_variable_get(:@redis).config(:set, "save", "")
@redis_classifier = ClassifierReborn::Bayes.new 'Ham', 'Spam', backend: @redis_backend
rescue Redis::CannotConnectError => e
skip(e)
end
data = TestDataLoader.sms_data
if data.length < TRAINING_SIZE + TESTING_SIZE
TestDataLoader.report_insufficient_data(data.length, TRAINING_SIZE + TESTING_SIZE)
skip(e)
end
@training_set = data[0, TRAINING_SIZE]
@testing_set = data[TRAINING_SIZE, TESTING_SIZE]
end
def teardown
@redis_backend.reset unless @redis_backend.nil?
end
def test_equality_of_backends
train_model @memory_classifier
train_model @redis_classifier
assert_equal classification_scores(@memory_classifier).hash, classification_scores(@redis_classifier).hash
untrain_model @memory_classifier, TRAINING_SIZE/2
untrain_model @redis_classifier, TRAINING_SIZE/2
assert_equal classification_scores(@memory_classifier).hash, classification_scores(@redis_classifier).hash
end
def train_model(classifier)
@training_set.each do |line|
parts = line.strip.split("\t")
classifier.train(parts.first, parts.last)
end
end
def untrain_model(classifier, limit=Float::INFINITY)
@training_set.each_with_index do |line, i|
break if i >= limit
parts = line.strip.split("\t")
classifier.untrain(parts.first, parts.last)
end
end
def classification_scores(classifier)
@testing_set.collect do |line|
parts = line.strip.split("\t")
result, score = classifier.classify_with_score(parts.last)
score.infinite? ? "irrelevant" : "#{result}:#{score}"
end
end
end
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