MMSR

crowdkit.aggregation.classification.m_msr.MMSR

MMSR(
    self,
    n_iter: int = 10000,
    eps: float = ...,
    random_state: Optional[int] = 0,
    observation_matrix: ndarray = ...,
    covariation_matrix: ndarray = ...,
    n_common_tasks: ndarray = ...,
    n_performers: int = 0,
    n_tasks: int = 0,
    n_labels: int = 0,
    labels_mapping: dict = ...,
    performers_mapping: dict = ...,
    tasks_mapping: dict = ...
)

Matrix Mean-Subsequence-Reduced Algorithm

Qianqian Ma and Alex Olshevsky. 2020. Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion 34th Conference on Neural Information Processing Systems (NeurIPS 2020) https://arxiv.org/abs/2010.12181

Parameters Description

ParametersTypeDescription
labels_Optional[Series]

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

skills_Optional[Series]

Performers skills A pandas.Series index by performers and holding corresponding performers skill

scores_Optional[DataFrame]

Tasks label scores A pandas.DataFrame indexed by task such that result.loc[task, label] is the score of label for task.

ParametersTypeDescription
labels_Optional[Series]

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

skills_Optional[Series]

Performers skills A pandas.Series index by performers and holding corresponding performers skill

scores_Optional[DataFrame]

Tasks label scores A pandas.DataFrame indexed by task such that result.loc[task, label] is the score of label for task.