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Diffstat (limited to 'comm/mailnews/extensions/bayesian-spam-filter/test/unit/test_junkAsTraits.js')
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1 files changed, 574 insertions, 0 deletions
diff --git a/comm/mailnews/extensions/bayesian-spam-filter/test/unit/test_junkAsTraits.js b/comm/mailnews/extensions/bayesian-spam-filter/test/unit/test_junkAsTraits.js new file mode 100644 index 0000000000..a1800b93e7 --- /dev/null +++ b/comm/mailnews/extensions/bayesian-spam-filter/test/unit/test_junkAsTraits.js @@ -0,0 +1,574 @@ +/* This Source Code Form is subject to the terms of the Mozilla Public + * License, v. 2.0. If a copy of the MPL was not distributed with this + * file, You can obtain one at http://mozilla.org/MPL/2.0/. */ + +// tests calls to the bayesian filter plugin to train, classify, and forget +// messages using both the older junk-oriented calls, as well as the newer +// trait-oriented calls. Only a single trait is tested. The main intent of +// these tests is to demonstrate that both the old junk-oriented calls and the +// new trait-oriented calls give the same results on junk processing. + +var { MailServices } = ChromeUtils.import( + "resource:///modules/MailServices.jsm" +); + +// local constants +var kUnclassified = MailServices.junk.UNCLASSIFIED; +var kJunk = MailServices.junk.JUNK; +var kGood = MailServices.junk.GOOD; +var kJunkTrait = MailServices.junk.JUNK_TRAIT; +var kGoodTrait = MailServices.junk.GOOD_TRAIT; +var kIsHamScore = MailServices.junk.IS_HAM_SCORE; +var kIsSpamScore = MailServices.junk.IS_SPAM_SCORE; + +// command functions for test data +var kTrainJ = 0; // train using junk method +var kTrainT = 1; // train using trait method +var kClassJ = 2; // classify using junk method +var kClassT = 3; // classify using trait method +var kForgetJ = 4; // forget training using junk method +var kForgetT = 5; // forget training using trait method +var kCounts = 6; // test token and message counts + +var gProArray = [], + gAntiArray = []; // traits arrays, pro is junk, anti is good +var gTest; // currently active test + +// The tests array defines the tests to attempt. Format of +// an element "test" of this array (except for kCounts): +// +// test.command: function to perform, see definitions above +// test.fileName: file containing message to test +// test.junkPercent: sets the classification (for Class or Forget commands) +// tests the classification (for Class commands) +// As a special case for the no-training tests, if +// junkPercent is negative, test its absolute value +// for percents, but reverse the junk/good classification +// test.traitListener: should we use the trait listener call? +// test.junkListener: should we use the junk listener call? + +var tests = [ + // test the trait-based calls. We mix trait listeners, junk listeners, + // and both + + { + // with no training, percents is 50 - but classifies as junk + command: kClassT, + fileName: "ham1.eml", + junkPercent: -50, // negative means classifies as junk + traitListener: false, + junkListener: true, + }, + { + // train 1 ham message + command: kTrainT, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + // with ham but no spam training, percents are 0 and classifies as ham + command: kClassT, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + // train 1 spam message + command: kTrainT, + fileName: "spam1.eml", + junkPercent: 100, + traitListener: true, + junkListener: false, + }, + { + // the trained messages will classify at 0 and 100 + command: kClassT, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kClassT, + fileName: "spam1.eml", + junkPercent: 100, + traitListener: true, + junkListener: false, + }, + { + // ham2, spam2, spam4 give partial percents, but still ham + command: kClassT, + fileName: "ham2.eml", + junkPercent: 8, + traitListener: true, + junkListener: true, + }, + { + command: kClassT, + fileName: "spam2.eml", + junkPercent: 81, + traitListener: false, + junkListener: true, + }, + { + command: kClassT, + fileName: "spam4.eml", + junkPercent: 81, + traitListener: true, + junkListener: false, + }, + { + // spam3 evaluates to spam + command: kClassT, + fileName: "spam3.eml", + junkPercent: 98, + traitListener: true, + junkListener: true, + }, + { + // train ham2, then test percents of 0 (clearly good) + command: kTrainT, + fileName: "ham2.eml", + junkPercent: 0, + traitListener: true, + junkListener: true, + }, + { + command: kClassT, + fileName: "ham2.eml", + junkPercent: 0, + traitListener: true, + junkListener: true, + }, + { + // forget ham2, percents should return to partial value + command: kForgetT, + fileName: "ham2.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kClassT, + fileName: "ham2.eml", + junkPercent: 8, + traitListener: true, + junkListener: true, + }, + { + // train, classify, forget, reclassify spam4 + command: kTrainT, + fileName: "spam4.eml", + junkPercent: 100, + traitListener: true, + junkListener: true, + }, + { + command: kClassT, + fileName: "spam4.eml", + junkPercent: 100, + traitListener: true, + junkListener: true, + }, + { + command: kCounts, + tokenCount: 66, // count of tokens in the corpus + junkCount: 2, // count of junk messages in the corpus + goodCount: 1, // count of good messages in the corpus + }, + { + command: kForgetT, + fileName: "spam4.eml", + junkPercent: 100, + traitListener: true, + junkListener: false, + }, + { + command: kClassT, + fileName: "spam4.eml", + junkPercent: 81, + traitListener: true, + junkListener: true, + }, + { + // forget ham1 and spam1 to empty training + command: kForgetT, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: true, + junkListener: true, + }, + { + command: kForgetT, + fileName: "spam1.eml", + junkPercent: 100, + traitListener: true, + junkListener: true, + }, + // repeat the whole sequence using the junk calls + { + // train 1 ham and 1 spam message + command: kTrainJ, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kTrainJ, + fileName: "spam1.eml", + junkPercent: 100, + traitListener: false, + junkListener: true, + }, + { + // the trained messages will classify at 0 and 100 + command: kClassJ, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "spam1.eml", + junkPercent: 100, + traitListener: false, + junkListener: true, + }, + { + // ham2, spam2, spam4 give partial percents, but still ham + command: kClassJ, + fileName: "ham2.eml", + junkPercent: 8, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "spam2.eml", + junkPercent: 81, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "spam4.eml", + junkPercent: 81, + traitListener: false, + junkListener: true, + }, + { + // spam3 evaluates to spam + command: kClassJ, + fileName: "spam3.eml", + junkPercent: 98, + traitListener: false, + junkListener: true, + }, + { + // train ham2, then test percents of 0 (clearly good) + command: kTrainJ, + fileName: "ham2.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "ham2.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + // forget ham2, percents should return to partial value + command: kForgetJ, + fileName: "ham2.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "ham2.eml", + junkPercent: 8, + traitListener: false, + junkListener: true, + }, + { + // train, classify, forget, reclassify spam4 + command: kTrainJ, + fileName: "spam4.eml", + junkPercent: 100, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "spam4.eml", + junkPercent: 100, + traitListener: false, + junkListener: true, + }, + { + command: kForgetJ, + fileName: "spam4.eml", + junkPercent: 100, + traitListener: false, + junkListener: true, + }, + { + command: kClassJ, + fileName: "spam4.eml", + junkPercent: 81, + traitListener: false, + junkListener: true, + }, + { + // forget ham1 and spam1 to be empty + command: kForgetJ, + fileName: "ham1.eml", + junkPercent: 0, + traitListener: false, + junkListener: true, + }, + { + command: kForgetJ, + fileName: "spam1.eml", + junkPercent: 100, + traitListener: false, + junkListener: true, + }, +]; + +// main test +function run_test() { + localAccountUtils.loadLocalMailAccount(); + do_test_pending(); + + // setup pro/anti arrays as junk/good + gProArray.push(kJunkTrait); + gAntiArray.push(kGoodTrait); + + startCommand(); +} + +var junkListener = { + // nsIJunkMailClassificationListener implementation + onMessageClassified(aMsgURI, aClassification, aJunkPercent) { + if (!aMsgURI) { + // Ignore end-of-batch signal. + return; + } + // print("Message URI is " + aMsgURI); + // print("Junk percent is " + aJunkPercent); + // print("Classification is " + aClassification); + var command = gTest.command; + var junkPercent = gTest.junkPercent; + // file returned correctly + Assert.equal(getSpec(gTest.fileName), aMsgURI); + + // checks of aClassification + + // forget returns unclassified + if (command == kForgetJ || command == kForgetT) { + Assert.equal(aClassification, kUnclassified); + } else { + // classification or train should return an actual classification + // check junk classification set by default cutoff of 90 + var isGood = Math.abs(junkPercent) < 90; + if (junkPercent < 0) { + isGood = !isGood; + } + Assert.equal(aClassification, isGood ? kGood : kJunk); + } + + // checks of aJunkPercent + + if (command == kClassJ || command == kClassT) { + // classify returns the actual junk percents + Assert.equal(Math.abs(junkPercent), aJunkPercent); + } else if (command == kTrainJ || command == kTrainT) { + // train returns the ham and spam limits + Assert.equal(aJunkPercent, junkPercent < 90 ? kIsHamScore : kIsSpamScore); + } else { + // Forget always returns 0. + Assert.equal(aJunkPercent, 0); + } + + // if the current test includes a trait listener, it will + // run next, so we defer to it for starting the next command + if (gTest.traitListener) { + return; + } + startCommand(); + }, +}; + +var traitListener = { + // nsIMsgTraitClassificationListener implementation + onMessageTraitsClassified(aMsgURI, aTraits, aPercents) { + if (!aMsgURI) { + // Ignore end-of-batch signal. + return; + } + // print("(Trait Listener)Message URI is " + aMsgURI); + // print("(Trait Listener)Junk percent is " + aPercents); + var command = gTest.command; + var junkPercent = gTest.junkPercent; + // print("command, junkPercent is " + command + " , " + junkPercent); + + Assert.equal(getSpec(gTest.fileName), aMsgURI); + + // checks of aPercents + + if (command == kForgetJ || command == kForgetT) { + // "forgets" with null newClassifications does not return a percent + Assert.equal(aPercents.length, 0); + } else { + var percent = aPercents[0]; + // print("Percent is " + percent); + if (command == kClassJ || command == kClassT) { + // Classify returns actual percents + Assert.equal(percent, junkPercent); + } else { + // Train simply returns 100. + Assert.equal(percent, 100); + } + } + + // checks of aTraits + + if (command == kForgetJ || command == kForgetT) { + // "forgets" with null newClassifications does not return a + // classification + Assert.equal(aTraits.length, 0); + } else if (command == kClassJ || command == kClassT) { + // classification just returns the tested "Pro" trait (junk) + let trait = aTraits[0]; + Assert.equal(trait, kJunkTrait); + } else { + // training returns the actual trait trained + let trait = aTraits[0]; + Assert.equal(trait, junkPercent < 90 ? kGoodTrait : kJunkTrait); + } + + // All done, start the next test + startCommand(); + }, +}; + +// start the next test command +function startCommand() { + if (!tests.length) { + // Do we have more commands? + // no, all done + do_test_finished(); + return; + } + + gTest = tests.shift(); + print( + "StartCommand command = " + + gTest.command + + ", remaining tests " + + tests.length + ); + var command = gTest.command; + var junkPercent = gTest.junkPercent; + var fileName = gTest.fileName; + var tListener = gTest.traitListener; + var jListener = gTest.junkListener; + switch (command) { + case kTrainJ: + // train message using junk call + MailServices.junk.setMessageClassification( + getSpec(fileName), // in string aMsgURI + null, // in nsMsgJunkStatus aOldUserClassification + junkPercent == kIsHamScore ? kGood : kJunk, // in nsMsgJunkStatus aNewClassification + null, // in nsIMsgWindow aMsgWindow + junkListener + ); // in nsIJunkMailClassificationListener aListener); + break; + + case kTrainT: + // train message using trait call + MailServices.junk.setMsgTraitClassification( + getSpec(fileName), // aMsgURI + [], // aOldTraits + junkPercent == kIsSpamScore ? gProArray : gAntiArray, // aNewTraits + tListener ? traitListener : null, // aTraitListener + null, // aMsgWindow + jListener ? junkListener : null + ); + break; + + case kClassJ: + // classify message using junk call + MailServices.junk.classifyMessage( + getSpec(fileName), // in string aMsgURI + null, // in nsIMsgWindow aMsgWindow + junkListener + ); // in nsIJunkMailClassificationListener aListener + break; + + case kClassT: + // classify message using trait call + MailServices.junk.classifyTraitsInMessage( + getSpec(fileName), // in string aMsgURI + gProArray, // in array aProTraits, + gAntiArray, // in array aAntiTraits + tListener ? traitListener : null, // in nsIMsgTraitClassificationListener aTraitListener + null, // in nsIMsgWindow aMsgWindow + jListener ? junkListener : null + ); // in nsIJunkMailClassificationListener aJunkListener + break; + + case kForgetJ: + // forget message using junk call + MailServices.junk.setMessageClassification( + getSpec(fileName), // in string aMsgURI + junkPercent == kIsHamScore ? kGood : kJunk, // in nsMsgJunkStatus aOldUserClassification + null, // in nsMsgJunkStatus aNewClassification, + null, // in nsIMsgWindow aMsgWindow, + junkListener + ); // in nsIJunkMailClassificationListener aListener + break; + + case kForgetT: + // forget message using trait call + MailServices.junk.setMsgTraitClassification( + getSpec(fileName), // in string aMsgURI + junkPercent == kIsSpamScore ? gProArray : gAntiArray, // in array aOldTraits + [], // in array aNewTraits + tListener ? traitListener : null, // in nsIMsgTraitClassificationListener aTraitListener + null, // in nsIMsgWindow aMsgWindow + jListener ? junkListener : null + ); // in nsIJunkMailClassificationListener aJunkListener + break; + + case kCounts: + // test counts + let msgCount = {}; + let nsIMsgCorpus = MailServices.junk.QueryInterface(Ci.nsIMsgCorpus); + let tokenCount = nsIMsgCorpus.corpusCounts(null, {}); + nsIMsgCorpus.corpusCounts(kJunkTrait, msgCount); + let junkCount = msgCount.value; + nsIMsgCorpus.corpusCounts(kGoodTrait, msgCount); + let goodCount = msgCount.value; + print( + "tokenCount, junkCount, goodCount is " + tokenCount, + junkCount, + goodCount + ); + Assert.equal(tokenCount, gTest.tokenCount); + Assert.equal(junkCount, gTest.junkCount); + Assert.equal(goodCount, gTest.goodCount); + do_timeout(0, startCommand); + break; + } +} |