def wss(e: trectools.TrecEval) -> float:
ret = e.get_retrieved_documents()
r = recall(e)
N = 30000000
wss = ((N - ret) / N) - (1.0 - r)
if wss < 0: return 0
return wss
def precision(e: trectools.TrecEval, per_query=False):
rel_ret = e.get_relevant_retrieved_documents(per_query=per_query)
rel = e.get_retrieved_documents(per_query=per_query)
if per_query:
return (rel_ret / rel).fillna(0)
else:
return rel_ret / rel
def to_trec_df(e: trectools.TrecEval, per_query=True) -> pd.Series:
return join_series(
pd.Series({
"P": precision(e, per_query=per_query),
"R": recall(e, per_query=per_query),
# "F$_{0.5}$": f_measure(e, 0.5, per_query=per_query),
# "F$_1$": f_measure(e, 1, per_query=per_query),
"NumRet": e.get_retrieved_documents(per_query=per_query)
# "F$_3$": f_measure(e, 3, per_query=per_query),
}),
eval_rank_df(e, per_query=per_query))
def wss(e: trectools.TrecEval, per_query=False):
ret = e.get_retrieved_documents(per_query=True)
r = recall(e, per_query=per_query)
N = 30000000
if per_query:
return pd.Series(
dict([(t, (((N - ret.T[t]) / N) - (1.0 - r.T[t])))
for t in ret.index]))
else:
wss = ((N - ret) / N) - (1.0 - r)
if wss < 0: return 0 # don't ask.
return wss
def precision(e: trectools.TrecEval) -> float:
return e.get_precision(depth=e.get_retrieved_documents(per_query=False), per_query=False)
