/* 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.

Components.utils.import("resource:///modules/mailServices.js");

// 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: function(aMsgURI, aClassification, aJunkPercent)
  {
    if (!aMsgURI)
      return; // ignore end-of-batch signal
    //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
    do_check_eq(getSpec(gTest.fileName), aMsgURI);

    // checks of aClassification

    // forget returns unclassified
    if (command == kForgetJ || command == kForgetT)
      do_check_eq(aClassification, kUnclassified);
    // classification or train should return an actual classification
    else
    {
      // check junk classification set by default cutoff of 90
      var isGood = Math.abs(junkPercent) < 90;
      if (junkPercent < 0)
        isGood = !isGood;
      do_check_eq(aClassification, isGood ? kGood : kJunk);
    }

    // checks of aJunkPercent

    if (command == kClassJ || command == kClassT)
      // classify returns the actual junk percents
      do_check_eq(Math.abs(junkPercent), aJunkPercent);
    else if (command == kTrainJ || command == kTrainT)
      // train returns the ham and spam limits
      do_check_eq(aJunkPercent, junkPercent < 90 ? kIsHamScore : kIsSpamScore);
    else
      // forget always returns 0
      do_check_eq(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: function(aMsgURI, {}, aTraits, aPercents)
  {
    if (!aMsgURI)
      return; //ignore end-of-batch signal
    //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);

    do_check_eq(getSpec(gTest.fileName), aMsgURI);

    // checks of aPercents

    if (command == kForgetJ || command == kForgetT)
      // "forgets" with null newClassifications does not return a percent
      do_check_eq(aPercents.length, 0);
    else
    {
      var percent = aPercents[0];
      //print("Percent is " + percent);
      if (command == kClassJ || command == kClassT)
      // Classify returns actual percents
        do_check_eq(percent, junkPercent);
      else
      // Train simply returns 100
        do_check_eq(percent, 100);
    }

    // checks of aTraits

    if (command == kForgetJ || command == kForgetT)
      // "forgets" with null newClassifications does not return a
      // classification
      do_check_eq(aTraits.length, 0);
    else if (command == kClassJ || command == kClassT)
    {
      // classification just returns the tested "Pro" trait (junk)
      var trait = aTraits[0];
      do_check_eq(trait, kJunkTrait);
    }
    else
    {
      // training returns the actual trait trained
      var trait = aTraits[0];
      do_check_eq(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), // in string aMsgURI
        0,            // length of aOldTraits array
        null,         // in array aOldTraits
        gProArray.length, // length of array
        junkPercent == kIsSpamScore ? gProArray :
          gAntiArray, // in array aNewTraits
        tListener ? traitListener :
          null,       // in nsIMsgTraitClassificationListener aTraitListener
        null,         // in nsIMsgWindow aMsgWindow
        jListener ? junkListener :
          null);      // in nsIJunkMailClassificationListener aJunkListener
      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.length, // length of traits arrays
        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
        gProArray.length,  // length of aOldTraits array (1 in this test)
        junkPercent == kIsSpamScore ? gProArray :
          gAntiArray,  // in array aOldTraits
        0,           // length of aNewTraits array
        null,        // 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);
      do_check_eq(tokenCount, gTest.tokenCount);
      do_check_eq(junkCount, gTest.junkCount);
      do_check_eq(goodCount, gTest.goodCount);
      do_timeout(0, startCommand);
      break;
  }
}
