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Sets up an evaluation which includes a set of scorers and a dataset. Calling evaluation.evaluate(model) will pass in rows form a dataset into a model matching the names of the columns of the dataset to the argument names in model.predict. Then it will call all of the scorers and save the results in weave.

Example

Extends

Type Parameters

extends DatasetRow
extends DatasetRow

Constructor

new Evaluation<R, E, M>(parameters): Evaluation<R, E, M>

Parameters

object
required

Returns

Evaluation<R, E, M>

Overrides

WeaveObject.constructor

Properties

__savedRef

optional __savedRef: ObjectRef | Promise<ObjectRef>

Inherited from

WeaveObject.__savedRef

Accessors

description

Get Signature

get description(): string | undefined
Returns
string | undefined

Inherited from

WeaveObject.description

name

Get Signature

get name(): string
Returns
string

Inherited from

WeaveObject.name

Methods

evaluate()

evaluate(__namedParameters): Promise<Record<string, any>>

Parameters

object
required

Returns

Promise<Record<string, any>>

predictAndScore()

predictAndScore(__namedParameters): Promise<{ model_latency: number; model_output: any; model_success: boolean; scores: {[key: string]: any; }; }>

Parameters

object
required

Returns

Promise<{ model_latency: number; model_output: any; model_success: boolean; scores: {[key: string]: any; }; }>

saveAttrs()

saveAttrs(): object

Returns

object

Inherited from

WeaveObject.saveAttrs