lale.lib.rasl.scores module

class lale.lib.rasl.scores.FClassif(*args, **kwargs)[source]

Bases: ScoreMonoidFactory[FOnewayData]

Compute the ANOVA F-value for the provided sample.

from_monoid(monoid: FOnewayData)[source]

Given the monoid instance, return the appropriate type of output. This method may also modify self based on the monoid instance.

to_monoid(batch: Tuple[Any, Any]) → FOnewayData[source]

Create a monoid instance representing the input data

class lale.lib.rasl.scores.FOnewayData(*, classes, n_samples_per_class, n_samples, ss_alldata, sums_samples, sums_alldata)[source]

Bases: Monoid

Parameters

  • classes (list) – The list of classes.

  • n_samples_per_class (dictionary) – The number of samples in each class.

  • n_samples (number) – The total number of samples.

  • ss_alldata (array) – The sum of square of each feature.

  • sums_samples (dictionary) – The sum of each feaure per class.

  • sums_alldata (array) – The sum of each feaure.

combine(other: FOnewayData)[source]

Combines this monoid instance with another, producing a result. This operation must be observationally associative, satisfying x.from_monoid(a.combine(b.combine(c))) == x.from_monoid(a.combine(b).combine(c))) where x is the instance of :class:MonoidFactory that created these instances.

class lale.lib.rasl.scores.ScoreMonoidFactory(*args, **kwargs)[source]

Bases: MonoidFactory[Tuple[Any, Any], Tuple[float, float], _M], Protocol

score(X, y) → Tuple[float, float][source]