Get Column Distribution
Calculates density distribution / histogram for a numeric column.
Args: project_id run_id split inference_name (bool, default ""): Optional inference name. Required if split is inference current_user (User, optional): The current user making the HTTP request. Defaults to Depends(authentication_service.current_user).
distribution_request (DistributionRequest): In request body: col: Required. The column to calculate distribution on nbins: Number of bins for the distribution. Default 20 limit_min: Minimum value for the limit of the dist. Default to min of column limit_max: Maximum value for the limit of the dist. Default to min of column Inherits from ContentRequest.
- Optional filters for the request
Returns: Distribution
Authorizations
Path Parameters
training, validation, test, inference Query Parameters
Body
A distribution histogram for a run/filter for a particular numerical column.
bins are boundaries for the x axis and counts are the y axis. bins will have 1 more value than counts because the count at index i is for the range [bin[i], bin[i+1]), with the lower boundary inclusive and the upper boundary exclusive
training, validation, test, inference Response
A distribution histogram for a run/filter for a particular numerical column.
bins are boundaries for the x axis and counts are the y axis. bins will have 1 more value than counts because the count at index i is for the range [bin[i], bin[i+1]), with the lower boundary inclusive and the upper boundary exclusive
Metrics for a cohort returned in columnar format.
Each field in the class is of equal length (or len 0), mapping to the y-axis of each chart. The labels field contains the x-axis labels which apply to all y-axes for all charts created for this data.
ex: labels: ["apple", "banana", "orange"] precision: [0.95, 0.55, 0.83] recall: [0.93, 0.25, 0.88] ...
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