com.eharmony.aloha.models.multilabel.json
MultilabelModelReader
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package json
semantics used to generate features and labels of interest.
the auditor used to translate the MultilabelModel output values to B instances.
the possible plugins to which MultilabelModel can delegate to produce predictions.
Plugins are responsible for creating the predictorProducer passed to the
MultilabelModel constructor.
reflection information about the label type.
a JSON format capable of parsing the label type.
evidence that the label type is Serializable.
Either of Non-empty Seq (Like poor man's version of ValidationNel from scalaz)
Either of Non-empty Seq (Like poor man's version of ValidationNel from scalaz)
AST for multi-label models.
AST for multi-label models.
The sequence of all labels encountered in training. It is important that this is sequence (with the same order as the labels in the training set). This is because some algorithms may require indices based on the training data.
a string representing a function that will be used to extract labels.
the underlying model that will be produced by a
the auditor used to translate the MultilabelModel output values to B instances.
Translate the feature specification into features.
Translate the feature specification into features. This is done in a short circuiting way so that it stops when the any feature cannot be produced.
model input type
a map of feature name to feature specification
a semantics with which feature specifications should be interpretted.
a mapping from feature name to feature function. Note that the indices matter and that's why we don't want to use a map.
Like l.map(f).sequence[({type L[+A] = Either[Seq[String], A]})#L, C ] in scalaz except that it short circuits if it finds an error.
Like l.map(f).sequence[({type L[+A] = Either[Seq[String], A]})#L, C ] in scalaz except that it short circuits if it finds an error. (There must be some better way to do this w/ scalaz).
If we put a println("folding") at the top of the inner function h, we would get the following:
scala> mapSeq(Left(Seq("1")) +: (2 to 3).map(Right(_)))(identity) // Only 1 "folding" instead of 3. folding res0: ENS[Seq[Int]] = Left(List(0)) scala> mapSeq((1 to 3).map(Right(_)))(identity) folding folding folding res1: ENS[Seq[Int]] = Right(List(1, 2, 3))
type of values in the input sequence in the first parameter list.
type of values in the output sequence if successful.
list of values to which f should be applied.
function to map over l
the possible plugins to which MultilabelModel can delegate to produce predictions.
the possible plugins to which MultilabelModel can delegate to produce predictions.
Plugins are responsible for creating the predictorProducer passed to the
MultilabelModel constructor.
semantics used to generate features and labels of interest.
A JSON reader capable of turning JSON to a MultilabelModel. Created by ryan.deak on 9/7/17.
upper bound on model output type
Btype of label or class
input type of the model
output type of the model.
semantics used to generate features and labels of interest.
the auditor used to translate the MultilabelModel output values to
Binstances.the possible plugins to which MultilabelModel can delegate to produce predictions. Plugins are responsible for creating the
predictorProducerpassed to the MultilabelModel constructor.reflection information about the label type.
a JSON format capable of parsing the label type.
evidence that the label type is
Serializable.