def evaluate( self, test_rankings, top_values = [10] ):
scores = {}
for top in top_values:
trunc_classes = rankings.truncate_term_rankings(self.class_rankings,top)
trunc_test = rankings.truncate_term_rankings(test_rankings,top)
sim = self.agreement_measure.similarity( trunc_classes, trunc_test )
scores[ "terms-%03d" % (top) ] = sim
return scores
def evaluate(self, test_rankings, top_values=[10]):
scores = {}
for top in top_values:
trunc_classes = rankings.truncate_term_rankings(
self.class_rankings, top)
trunc_test = rankings.truncate_term_rankings(test_rankings, top)
sim = self.agreement_measure.similarity(trunc_classes, trunc_test)
scores["terms-%03d" % (top)] = sim
return scores
def evaluate( self, test_rankings, top_values = [10] ):
scores = {}
k = len(test_rankings)
for top in top_values:
trunc_rankings = rankings.truncate_term_rankings( test_rankings, top )
pairs = 0
diversity = 0.0
for ranking_index1 in range(k):
for ranking_index2 in range(ranking_index1 + 1, k):
pair_dissimilarity = 1.0 - self.metric.similarity( trunc_rankings[ranking_index1], trunc_rankings[ranking_index2] )
diversity += pair_dissimilarity
pairs += 1
scores[ "div-%03d" % (top) ] = diversity / pairs
return scores
def evaluate(self, test_rankings, top_values=[10]):
scores = {}
k = len(test_rankings)
for top in top_values:
trunc_rankings = rankings.truncate_term_rankings(
test_rankings, top)
pairs = 0
diversity = 0.0
for ranking_index1 in range(k):
for ranking_index2 in range(ranking_index1 + 1, k):
pair_dissimilarity = 1.0 - self.metric.similarity(
trunc_rankings[ranking_index1],
trunc_rankings[ranking_index2])
diversity += pair_dissimilarity
pairs += 1
scores["div-%03d" % (top)] = diversity / pairs
return scores
