def rank(self):
"""
Perform the ranking with the given parameters
"""
values = deepcopy(self.values)
# Get the current scoring
changes = {
'lock' : Lock()
}
scoringThread = CoordinateDescentRankingThread(values, self.keywords, changes, 'original', self.relevantResults,
CoordinateDescentRanking.getIndexLocation() + '-original')
scoringThread.start()
scoringThread.join()
results = scoringThread.results
return results
def learn(self):
"""
Run the learning algorithm, and return the learned feature weights
"""
# Get a copy of the values to tweak
values = deepcopy(self.values)
# Get the current scoring
changes = {
'lock' : Lock()
}
print "about to start learning"
scoringThread = CoordinateDescentRankingThread(values, self.keywords, changes, 'original',
self.relevantResults, CoordinateDescentRanking.getIndexLocation() + '-original')
scoringThread.start()
scoringThread.join()
currentWeightingScoring = changes['original']
print "Original scoring: %1.5f" % currentWeightingScoring
# The set of features to still tweak
features = self.values.keys()
# Keep looping until no further change is necessary
complete = False
iterations = 0
while not complete:
complete = True
iterations += 1
# Create data structures for managing threads
changes = {
'lock' : Lock()
}
threads = []
# Launch threads to test changes to each feature weight
print "Preparing threads..."
for feature in features:
# Form the path to the index to use for this test
featureIndexLocation = CoordinateDescentRanking.getIndexLocation() + '-' + feature
# Launch test for increasing weight of this feature
values = deepcopy(self.values)
values[feature] += self.stepSizes[feature]
scoringThread = CoordinateDescentRankingThread(values, self.keywords, changes, feature + '+',
self.relevantResults, featureIndexLocation)
threads.append(scoringThread)
# Launch test for decreasing weight of this feature
values = deepcopy(self.values)
values[feature] -= self.stepSizes[feature]
scoringThread = CoordinateDescentRankingThread(values, self.keywords, changes, feature + '-',
self.relevantResults, featureIndexLocation)
threads.append(scoringThread)
print "Launching threads..."
for thread in threads:
thread.start()
# Wait for all threads to finish
print "Waiting for threads to finish..."
for thread in threads:
thread.join()
# Update feature weights accordingly
print "Analyzing ranking results"
for feature in deepcopy(features):
increaseFeatureWeightResultScoring = changes[feature+'+']
decreaseFeatureWeightResultScoring = changes[feature+'-']
# If one of these improved...
if increaseFeatureWeightResultScoring > currentWeightingScoring or decreaseFeatureWeightResultScoring > currentWeightingScoring:
if increaseFeatureWeightResultScoring > decreaseFeatureWeightResultScoring:
self.values[feature] += self.stepSizes[feature]
elif decreaseFeatureWeightResultScoring >= increaseFeatureWeightResultScoring:
self.values[feature] -= self.stepSizes[feature]
complete = False
else:
del features[feature]
print "No improvement found for tweaking %s!" % feature
print "Finished learning iteration %d, new values:" % iterations
pprint(self.values)
