Toolkit for bias estimation in unbiased learning to rank
Project description
Personal toolkit for bias estimation in unbiased learning to rank
Offline bias estimation methods
We implement multiple offline position bias estimation methods, including three intervention harvesting approaches:
from ultr_bias_toolkit.bias.naive import NaiveCtrEstimator
from ultr_bias_toolkit.bias.intervention_harvesting import PivotEstimator, AdjacentChainEstimator, AllPairsEstimator
estimators = {
"CTR Rate": NaiveCtrEstimator(),
"Pivot One": PivotEstimator(pivot_rank=1),
"Adjacent Chain": AdjacentChainEstimator(),
"Global All Pairs": AllPairsEstimator(),
}
examination_dfs = []
for name, estimator in estimators.items():
examination_df = estimator(df)
examination_df["estimator"] = name
examination_dfs.append(examination_df)
examination_df = pd.concat(examination_dfs)
examination_df.head()
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