adding all svm-perf-based quantifiers with timeout
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@ -12,6 +12,7 @@ import argparse
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parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
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parser = argparse.ArgumentParser(description='Run experiments for Tweeter Sentiment Quantification')
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parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
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parser.add_argument('results', metavar='RESULT_PATH', type=str, help='path to the directory where to store the results')
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parser.add_argument('svmperfpath', metavar='SVMPERF_PATH', type=str, help='path to the directory with svmperf')
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args = parser.parse_args()
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args = parser.parse_args()
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@ -26,11 +27,11 @@ def quantification_models():
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yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
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yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
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yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
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yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
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yield 'sld', qp.method.aggregative.EMQ(newLR()), lr_params
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yield 'sld', qp.method.aggregative.EMQ(newLR()), lr_params
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#yield 'svmq', OneVsAll(qp.method.aggregative.SVMQ(settings.SVMPERF_HOME)), svmperf_params
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yield 'svmq', OneVsAll(qp.method.aggregative.SVMQ(args.svmperfpath)), svmperf_params
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#yield 'svmkld', OneVsAll(qp.method.aggregative.SVMKLD(settings.SVMPERF_HOME)), svmperf_params
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yield 'svmkld', OneVsAll(qp.method.aggregative.SVMKLD(args.svmperfpath)), svmperf_params
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#yield 'svmnkld', OneVsAll(qp.method.aggregative.SVMNKLD(settings.SVMPERF_HOME)), svmperf_params
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yield 'svmnkld', OneVsAll(qp.method.aggregative.SVMNKLD(args.svmperfpath)), svmperf_params
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yield 'svmmae', OneVsAll(qp.method.aggregative.SVMAE(settings.SVMPERF_HOME)), svmperf_params
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yield 'svmmae', OneVsAll(qp.method.aggregative.SVMAE(args.svmperfpath)), svmperf_params
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yield 'svmmrae', OneVsAll(qp.method.aggregative.SVMRAE(settings.SVMPERF_HOME)), svmperf_params
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yield 'svmmrae', OneVsAll(qp.method.aggregative.SVMRAE(args.svmperfpath)), svmperf_params
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# 'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(),
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# 'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(),
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@ -1,5 +1,3 @@
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import multiprocessing
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import multiprocessing
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N_JOBS = -2 #multiprocessing.cpu_count()
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N_JOBS = -2 #multiprocessing.cpu_count()
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SVMPERF_HOME = '../svm_perf_quantification'
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