[WIP] KMeans clustering #29
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Thanks, @novoselrok! Will take a look at it as soon as I find the time. |
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Looking at scikit-learn implementations only a subset of clustering algorithms are able to predict clusters on new data. Our base class should take that into account, we could have something like this: Clusterer extends Estimator and then another subclass for PredictiveClusterer extends Clusterer? What are your thoughts on this? If KMeans is the only implementation that requires a seed value to be passed to fit, should we find another way to get it there?
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| import com.picnicml.doddlemodel.data.{Features, Target} | ||
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| abstract class Clusterer[A <: Clusterer[A]] extends Estimator { |
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Clusterer currently only has a single publicly exposed function: def predict(x: Features): Target. Additionally, I don't think all clustering algorithms will expose predict, e.g. DBSCAN doesn't. On the other hand, all of them probably need fit exposed?
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| import scala.util.Random | ||
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| trait RandomizableClusterer[A <: RandomizableClusterer[A]] extends Clusterer[A] { |
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As already mentioned, fit here shouldn't be protected. On the other hand that introduces another problem: API (public functions defined in abstract classes and traits) should be in the base package. If you take a look at the linear.LinearModel and other traits there, none of them define any public interface, they only encapsulate functionality that is common to all linear models. We should identify what is common to all clustering estimators and expose that in the form of a base class(es).
Pull request for KMeans clustering (#22).