# GoldMajorityVote

crowdkit.aggregation.classification.gold_majority_vote.GoldMajorityVote | Source code

GoldMajorityVote(self)


Majority Vote when exist golden dataset (ground truth) for some tasks.

Calculates the probability of a correct label for each worker based on the golden set. Based on this, for each task, calculates the sum of the probabilities of each label. The correct label is the one where the sum of the probabilities is greater.

For Example: You have 10k tasks completed by 3k different workers. And you have 100 tasks where you already know ground truth labels. First you can call fit to calc percents of correct labels for each workers. And then call predict to calculate labels for you 10k tasks.

It's necessary that:

1. All workers must done at least one task from golden dataset.
2. All workers in dataset that send to predict, existed in answers dataset that was sent to fit.

## Parameters Description

Parameters Type Description
labels_ Optional[Series]

Tasks' labels. A pandas.Series indexed by task such that labels.loc[task] is the tasks's most likely true label.

skills_ Series

workers' skills. A pandas.Series index by workers and holding corresponding worker's skill

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

Examples:

import pandas as pd
from crowdkit.aggregation import GoldMajorityVote
df = pd.DataFrame(
[
['t1', 'p1', 0],
['t1', 'p2', 0],
['t1', 'p3', 1],
['t2', 'p1', 1],
['t2', 'p2', 0],
['t2', 'p3', 1],
],
)
true_labels = pd.Series({'t1': 0})
gold_mv = GoldMajorityVote()
result = gold_mv.fit_predict(df, true_labels)


## Methods Summary

Method Description
fit Estimate the workers' skills.
fit_predict Fit the model and return aggregated results.
fit_predict_proba Fit the model and return probability distributions on labels for each task.
predict Infer the true labels when the model is fitted.
predict_proba Return probability distributions on labels for each task when the model is fitted.