Toloka documentation

aggregate_solutions_by_pool

toloka.client.TolokaClient.aggregate_solutions_by_pool

Starts aggregation of solutions in the pool

Responses to all completed tasks will be aggregated. The method only starts the aggregation and returns the operation for further tracking.

Note

In all aggregation purposes we are strongly recommending using our crowd-kit library, that have more aggregation methods and can perform on your computers.

Parameters Description

Parameters Type Description
type Union[AggregatedSolutionType, str, None]

Aggregation type. WEIGHTED_DYNAMIC_OVERLAP - Aggregation of responses in a pool with dynamic overlap. DAWID_SKENE - Dawid-Skene aggregation model. A. Philip Dawid and Allan M. Skene. 1979. Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 28, 1 (1979), 20–28. https://doi.org/10.2307/2346806

pool_id Optional[str]

In which pool to aggregate the results.

answer_weight_skill_id Optional[str]

A skill that determines the weight of the performer's response.

fields Optional[List[PoolAggregatedSolutionRequest.Field]]

Output data fields to use for aggregating responses. For best results, each of these fields must have a limited number of response options.

Examples:

How to start aggregating solutions by pool.

aggregation_operation = toloka_client.aggregate_solutions_by_pool(
        type=toloka.aggregation.AggregatedSolutionType.WEIGHTED_DYNAMIC_OVERLAP,
        pool_id=some_existing_pool_id,   # Aggregate in this pool
        answer_weight_skill_id=some_skill_id,   # Aggregate by this skill
        fields=[toloka.aggregation.PoolAggregatedSolutionRequest.Field(name='result')]  # Aggregate this field
    )
aggregation_operation = toloka_client.wait_operation(aggregation_operation)