OneCoinDawidSkene

crowdkit.aggregation.classification.dawid_skene.OneCoinDawidSkene | Source code

OneCoinDawidSkene(
    self,
    n_iter: int = 100,
    tol: float = 1e-05
)

One-coin Dawid-Skene aggregation model.

This model works exactly like original Dawid-Skene model based on EM Algorithm except for workers' error calculation on M-step of the algorithm.

First the workers' skills are calculated as their accuracy in accordance with labels probability.

Let $e^w$ be a worker's confusion (error) matrix of size $K \times K$ in case of $K$ class classification,

p

be a vector of prior classes probabilities,

z_j

be a true task's label, and

y^w_j

be a worker's answer for the task

j

. Let

s_{w}

be a worker's skill (accuracy).

Then the error

e^w_{j,z_j} = \begin{cases} s_{w} & y^w_j = z_j \\ \frac{1 - s_{w}}{K - 1} & y^w_j \neq z_j\end{cases}

Parameters Description

Parameters	Type	Description
`n_iter`	int	The number of EM iterations.
`labels_`	Optional[Series]	Tasks' labels. A pandas.Series indexed by `task` such that `labels.loc[task]` is the tasks's most likely true label.
`probas_`	DataFrame	Tasks' label probability distributions. A pandas.DataFrame indexed by `task` such that `result.loc[task, label]` is the probability of `task`'s true label to be equal to `label`. Each probability is between 0 and 1, all task's probabilities should sum up to 1
`priors_`	Series	A prior label distribution. A pandas.Series indexed by labels and holding corresponding label's probability of occurrence. Each probability is between 0 and 1, all probabilities should sum up to 1
`errors_`	DataFrame	Workers' error matrices. A pandas.DataFrame indexed by `worker` and `label` with a column for every label_id found in `data` such that `result.loc[worker, observed_label, true_label]` is the probability of `worker` producing an `observed_label` given that a task's true label is `true_label`
`skills_`	Series	workers' skills. A pandas.Series index by workers and holding corresponding worker's skill

Examples:

from crowdkit.aggregation import OneCoinDawidSkene
from crowdkit.datasets import load_dataset
df, gt = load_dataset('relevance-2')
hds = OneCoinDawidSkene(100)
result = hds.fit_predict(df)

Methods Summary

Method	Description
fit	Fit the model through the EM-algorithm.

Crowd-Kit

Overview

Reference

Aggregation

Datasets

Learning

Metrics

Postprocessing