def init_weight_kendall(self, feature_name, samples, order): iw = {} for f in feature_name: print "Feature: %s" % f k = evaluate_kendall(sorted(samples.keys(), key = lambda m: samples[m].feature[f], reverse = True), order) iw[f] = k print "Init weight by Kendall: %s" % iw return iw
def init_weight_kendall(self, feature_name, samples, order): iw = {} for f in feature_name: print "Feature: %s" % f k = evaluate_kendall( sorted(samples.keys(), key=lambda m: samples[m].feature[f], reverse=True), order) iw[f] = k print "Init weight by Kendall: %s" % iw return iw
def evaluate(self): ranked = sorted(self.samples.values(), key=lambda m: self._weight_feature(m), reverse=True) ret = evaluate_kendall([m.msg_id for m in ranked], self.order) return ret
def evaluate(self): ranked = sorted(self.samples.values(), key = lambda m: self._weight_feature(m), reverse = True) ret = evaluate_kendall([m.msg_id for m in ranked], self.order) return ret