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Miscellaneous doc fixes #125

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Oct 29, 2024
Merged

Miscellaneous doc fixes #125

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1c1719b
fix: mace aggregator docs mathematical symbols
taharallouche Oct 29, 2024
1181098
fix: consistency metric docstring
taharallouche Oct 29, 2024
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6 changes: 3 additions & 3 deletions crowdkit/aggregation/classification/mace.py
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Expand Up @@ -87,7 +87,7 @@ class MACE(BaseClassificationAggregator):
The marginal data likelihood is maximized with the Expectation-Maximization algorithm:
1. **E-step**. Performs `n_restarts` random restarts, and keeps the model with the best marginal data likelihood.
2. **M-step**. Smooths parameters by adding a fixed value `smoothing` to the fractional counts before normalizing.
3. **Variational M-step**. Employs Variational-Bayes (VB) training with symmetric Beta priors on $\theta_j$ and symmetric Dirichlet priors on the strategy parameters $\xi_j.
3. **Variational M-step**. Employs Variational-Bayes (VB) training with symmetric Beta priors on $\theta_j$ and symmetric Dirichlet priors on the strategy parameters $\xi_j$.

D. Hovy, T. Berg-Kirkpatrick, A. Vaswani and E. Hovy. Learning Whom to Trust with MACE.
In *Proceedings of NAACL-HLT*, Atlanta, GA, USA (2013), 1120–1130.
Expand Down Expand Up @@ -122,10 +122,10 @@ class MACE(BaseClassificationAggregator):
"""The default noise parameter for the initialization."""

alpha: float = attr.ib(default=0.5)
"""The prior parameter for the Beta distribution on $\theta_j$."""
r"""The prior parameter for the Beta distribution on $\theta_j$."""

beta: float = attr.ib(default=0.5)
"""The prior parameter for the Beta distribution on $\theta_j$."""
r"""The prior parameter for the Beta distribution on $\theta_j$."""

random_state: int = attr.ib(default=0)
"""The state of the random number generator."""
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2 changes: 1 addition & 1 deletion crowdkit/metrics/data/_classification.py
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Expand Up @@ -48,7 +48,7 @@ def consistency(
) -> Union[float, "pd.Series[Any]"]:
"""
Consistency metric: posterior probability of aggregated label given workers skills
calculated using the standard Dawid-Skene model.
calculated using the specified aggregator.

Args:
answers (pandas.DataFrame): A data frame containing `task`, `worker` and `label` columns.
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