crowdkit.aggregation.pairwise.noisy_bt.NoisyBradleyTerry | Source code
NoisyBradleyTerry( self, n_iter: int = 100, tol: float = 1e-05, regularization_ratio: float = 1e-05, random_state: int = 0)Bradley-Terry model for pairwise comparisons with additional parameters.
This model is a modification of the Bradley-Terry model with parameters for workers' skills (reliability) and biases.
| Parameters | Type | Description |
|---|---|---|
scores_ | Series | 'Labels' scores. A pandas.Series index by labels and holding corresponding label's scores |
skills_ | Series | workers' skills. A pandas.Series index by workers and holding corresponding worker's skill |
biases_ | Series | Predicted biases for each worker. Indicates the probability of a worker to choose the left item.. A series of workers' biases indexed by workers |
Examples:
The following example shows how to aggregate results of comparisons grouped by some column.
In the example the two questions q1 and q2 are used to group the labeled data.
Temporary data structure is created and the model is applied to it.
The results are splitted in two arrays, and each array contains scores for one of the initial groups.
import pandas as pdfrom crowdkit.aggregation import NoisyBradleyTerrydata = pd.DataFrame( [ ['q1', 'w1', 'a', 'b', 'a'], ['q1', 'w2', 'a', 'b', 'b'], ['q1', 'w3', 'a', 'b', 'a'], ['q2', 'w1', 'a', 'b', 'b'], ['q2', 'w2', 'a', 'b', 'a'], ['q2', 'w3', 'a', 'b', 'b'], ], columns=['question', 'worker', 'left', 'right', 'label'])# Append question to other columns. After that the data looks like:# question worker left right label# 0 q1 w1 (q1, a) (q1, b) (q1, a)for col in 'left', 'right', 'label': data[col] = list(zip(data['question'], data[col]))result = NoisyBradleyTerry(n_iter=10).fit_predict(data)# Separate resultsresult.index = pd.MultiIndex.from_tuples(result.index, names=['question', 'label'])print(result['q1']) # Scores for all items in the q1 questionprint(result['q2']['b']) # Score for the item b in the q2 question| Method | Description |
|---|---|
| fit | None |
| fit_predict | None |