Created
June 22, 2011 20:15
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"Mahout in Action" Grouplens evaluator sample from section 2.5 ported to jython
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import sys, os, glob | |
from datetime import datetime | |
sys.path.append(os.environ.get("MAHOUT_CORE")) | |
for jar in glob.glob(os.environ.get("MAHOUT_JAR_DIR") + "/*.jar"): | |
sys.path.append(jar) | |
from org.apache.mahout.common import RandomUtils | |
from org.apache.mahout.cf.taste.common import TasteException | |
from org.apache.mahout.cf.taste.eval import * | |
from org.apache.mahout.cf.taste.impl.eval import * | |
from org.apache.mahout.cf.taste.impl.model.file import * | |
from org.apache.mahout.cf.taste.impl.neighborhood import * | |
from org.apache.mahout.cf.taste.impl.recommender import GenericUserBasedRecommender | |
from org.apache.mahout.cf.taste.impl.recommender.slopeone import SlopeOneRecommender | |
from org.apache.mahout.cf.taste.impl.similarity import * | |
from org.apache.mahout.cf.taste.model import * | |
from org.apache.mahout.cf.taste.neighborhood import * | |
from org.apache.mahout.cf.taste.recommender import * | |
from org.apache.mahout.cf.taste.similarity import * | |
from java.io import * | |
from java.util import * | |
class GenericRecommenderBuilder(RecommenderBuilder): | |
def __init__(self): | |
pass | |
def buildRecommender(self, model): | |
similarity = PearsonCorrelationSimilarity(model) | |
neighborhood = NearestNUserNeighborhood(2, similarity, model) | |
return GenericUserBasedRecommender(model, neighborhood, similarity) | |
class SlopeOneRecommenderBuilder(RecommenderBuilder): | |
def __init__(self): | |
pass | |
def buildRecommender(self, model): | |
similarity = PearsonCorrelationSimilarity(model) | |
neighborhood = NearestNUserNeighborhood(2, similarity, model) | |
return SlopeOneRecommender(model) | |
def main(): | |
RandomUtils.useTestSeed() | |
model = FileDataModel(File(sys.argv[1])) | |
builder = GenericRecommenderBuilder() | |
print 'Starting run at %s' % datetime.now() | |
for builder in [GenericRecommenderBuilder(), SlopeOneRecommenderBuilder()]: | |
print 'Starting evaluations of recommender created using %s at %s...' % (builder, datetime.now()) | |
for evaluator in [AverageAbsoluteDifferenceRecommenderEvaluator(), RMSRecommenderEvaluator()]: | |
print 'Evaluating recommender using %s at %s...' % (evaluator, datetime.now()) | |
score = evaluator.evaluate(builder, None, model, 0.7, 1.0) | |
print 'Score evaluated by %s=%.2f' % (evaluator, score) | |
print 'Ending run at %s' % datetime.now() | |
if __name__ == '__main__': | |
main() |
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