def __init__(self): self.featureCache = FeatureCache() self.beamSize = 3 self.bestWeights = [] self.centroids = [Counter(), Counter()] self.initializeWeights()
class KMeansExploreClassifier: def __init__(self): self.featureCache = FeatureCache() self.beamSize = 3 self.bestWeights = [] self.centroids = [Counter(), Counter()] self.initializeWeights() def weightedDistance(self, weights, point1, point2): result = 0 for key in point1: result += weights.get(key) * (point1.get(key, 0) - point2.get(key, 0))**2 return result def initializeWeights(self): exampleFeature = self.featureCache.getFeature(33387088) for i in range(self.beamSize): weights = Counter(); for key, value in exampleFeature.iteritems(): weights[key] = 1 self.bestWeights.append(weights) def initializeCentroids(self, songs): randomIndex1 = random.randint(0, len(songs)-1) randomIndex2 = random.randint(0, len(songs)-1) while randomIndex2 == randomIndex1: randomIndex2 = random.randint(0, len(songs)-1) self.centroids[0] = self.featureCache.getFeature(songs[randomIndex1]) self.centroids[1] = self.featureCache.getFeature(songs[randomIndex2]) def assignSongs(self, weights, songs): playlists = [[],[]] playlistLength = len(songs) / 2 for song in songs: dist1 = self.weightedDistance(weights, self.featureCache.getFeature(song), self.centroids[0]) dist2 = self.weightedDistance(weights, self.featureCache.getFeature(song), self.centroids[1]) if dist1 < dist2: if len(playlists[0]) < playlistLength: playlists[0].append((dist1, song)) elif dist1 <= max(playlists[0]): toSwap = max(playlists[0]) playlists[0].remove(toSwap) newDistance = self.weightedDistance(weights, self.featureCache.getFeature(toSwap[1]), self.centroids[1]) playlists[1].append((newDistance, toSwap[1])) playlists[0].append((dist1, song)) else: playlists[1].append((dist2, song)) else: if len(playlists[1]) < playlistLength: playlists[1].append((dist2, song)) elif dist2 <= max(playlists[1]): toSwap = max(playlists[1]) playlists[1].remove(toSwap) newDistance = self.weightedDistance(weights, self.featureCache.getFeature(toSwap[1]), self.centroids[0]) playlists[0].append((newDistance, toSwap[1])) playlists[1].append((dist2, song)) else: playlists[0].append((dist1, song)) for playlist in playlists: for index in xrange(len(playlist)): playlist[index] = playlist[index][1] if len(playlists[0]) != playlistLength or len(playlists[1]) != playlistLength: print "ERROR: NOT ALL SONGS ASSIGNED" return playlists def updateCentroids(self, playlists): for i in xrange(len(playlists)): self.centroids[i] = Counter() for song in playlists[i]: feature = self.featureCache.getFeature(song) for key in feature: toAdd = float(1)/len(playlists[i]) * feature[key] self.centroids[i][key] = self.centroids[i].get(key, 0) + toAdd #number of songs that aren't in test that should be def playlistDiff(self, test, correct): result = 0 for song in correct: if song not in test: result += 1 return result def loss(self, test, correct): return min(self.playlistDiff(test[0], correct[0]) + self.playlistDiff(test[1], correct[1]), \ self.playlistDiff(test[0], correct[1]) + self.playlistDiff(test[1], correct[0])) def generatePossibleDirections(self): possibleDirections = [] numNewDirections = 40 for i in xrange(numNewDirections): newDirection = Counter() for key in self.bestWeights[0]: newDirection[key] = random.uniform(-0.5, 0.5) possibleDirections.append(newDirection) return possibleDirections def cluster(self, weights, songs): numIters = 20 self.initializeCentroids(songs) for i in xrange(numIters): if self.centroids[0] == self.centroids[1]: print "ERROR: CENTROIDS ARE THE SAME" playlists = self.assignSongs(weights, songs) self.updateCentroids(playlists) return playlists def train(self, trainingPlaylistPairs): numIters = 10 eta = 0.1 bestLosses = [2] * self.beamSize bestWeights = [Counter()] * self.beamSize for _ in xrange(numIters): possibleDirections = self.generatePossibleDirections() for weights in copy.deepcopy(self.bestWeights): for i in xrange(len(possibleDirections)): totalLoss = 0 currentWeights = weights + possibleDirections[i] for trainPair in trainingPlaylistPairs: songs = trainPair[0] + trainPair[1] playlists = self.cluster(currentWeights, songs) loss = self.loss(playlists, trainPair) totalLoss += float(loss) / len(songs) averageLoss = totalLoss / len(trainingPlaylistPairs) #update best weights for index in range(self.beamSize): if averageLoss < bestLosses[index]: print "UPDATING WEIGHTS!" bestLosses.insert(index, averageLoss) bestLosses.pop(self.beamSize) newBestWeight = Counter() for key in weights: newBestWeight[key] = max(weights[key] + eta * possibleDirections[i][key], 0) bestWeights.insert(index, newBestWeight) bestWeights.pop(self.beamSize) break self.bestWeights = bestWeights print "Average Training Loss:", bestLosses[0] def test(self, testingPlaylistPairs): totalLoss = 0 count = 0 for testPair in testingPlaylistPairs: count += 1 songs = testPair[0] + testPair[1] playlists = self.cluster(self.bestWeights[0], songs) loss = self.loss(playlists, testPair) testLoss = float(loss) / len(songs) totalLoss += testLoss print "Test Loss:", testLoss print "Average Testing Loss:", totalLoss / len(testingPlaylistPairs) def validate(self, validationPlaylistPairs): totalLoss = 0 count = 0 for testPair in validationPlaylistPairs: count += 1 songs = testPair[0] + testPair[1] playlists = self.cluster(self.bestWeights[0], songs) loss = self.loss(playlists, testPair) validationLoss = float(loss) / len(songs) totalLoss += validationLoss print "Validation Loss:", validationLoss print "Average Validation Loss:", totalLoss / len(validationPlaylistPairs)
