# 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

ParametersTypeDescription
n_iterint

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

## Methods Summary

MethodDescription
fitFit the model through the EM-algorithm.
Crowd-Kit
Overview
Reference
Aggregation
Datasets
Learning
Metrics
Postprocessing