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Search relevance evaluation metrics
##
## Python implementations of the search relevance evaluation metrics described at
## https://opensourceconnections.com/blog/2020/02/28/choosing-your-search-relevance-metric/
##
##
def precision(docs):
return sum(docs) / len(docs) if docs else 0
def avg_precision(docs):
vals_to_avg = [precision(docs[:i+1]) for (i, doc) in enumerate(docs) if doc == 1]
return sum(vals_to_avg) / len(vals_to_avg) if vals_to_avg else 0
def cumulative_gain(docs):
return sum(docs)
def discounted_cumulative_gain(docs):
from math import log2
scores_to_sum = [d / log2(i+2) for (i,d) in enumerate(docs)]
return sum(scores_to_sum)
def alternative_discounted_cumulative_gain(docs):
from math import log2
scores_to_sum = [(2**d - 1)/ log2(i+2) for (i, d) in enumerate(docs)]
return sum(scores_to_sum)
def normalized_discounted_cumulative_gain(docs):
topK = 5
real = discounted_cumulative_gain(docs[:topK])
ideal = discounted_cumulative_gain(sorted(docs, reverse=True)[:topK])
return real / ideal if ideal else 0
if __name__ == '__main__':
docs1 = (1,1,1,0,0)
docs2 = (0,0,1,1,1)
print("precision")
print(precision(docs1), precision(docs2), '\n')
print("avg_precision")
print(avg_precision(docs1), avg_precision(docs2), '\n')
# example used in https://trec.nist.gov/pubs/trec15/appendices/CE.MEASURES06.pdf
docs3 = (1, 1, 0, 1, 0, 0, 1)
print("avg_precision")
print(avg_precision(docs3), '\n')
print("cumulative gain")
docsG1 = (4,3,2,1,0)
docsG2 = tuple(reversed(docsG1))
print(cumulative_gain(docsG1), cumulative_gain(docsG2), '\n')
print("discounted_cumulative_gain")
print(discounted_cumulative_gain(docsG1), discounted_cumulative_gain(docsG2), '\n')
print("alternative_discounted_cumulative_gain")
print(alternative_discounted_cumulative_gain(docsG1), alternative_discounted_cumulative_gain(docsG2), '\n')
query1 = (4, 4, 3, 3, 3)
query2 = (2, 1, 1, 1, 0)
print("alternative_discounted_cumulative_gain")
print(alternative_discounted_cumulative_gain(query1), alternative_discounted_cumulative_gain(query2), '\n')
print("normalized_discounted_cumulative_gain")
print(normalized_discounted_cumulative_gain(query1), normalized_discounted_cumulative_gain(query2), '\n')
print("normalized_discounted_cumulative_gain")
query3 = (3,2,1,4,0)
print(normalized_discounted_cumulative_gain(query3), '\n')
print("normalized_discounted_cumulative_gain")
query4 = (0,1,2,3,4)
print(normalized_discounted_cumulative_gain(query4), '\n')
print("normalized_discounted_cumulative_gain")
docs_all = (4,3,2,1,1,0,3,4,0,0)
print(normalized_discounted_cumulative_gain(docs_all), '\n')
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