kumulant/com.eignex.kumulant.stat.regression.tree/RandomForestClassifierStat

RandomForestClassifierStat

class RandomForestClassifierStat(val featureSize: Int, val numClasses: Int, val splitCandidates: List<Split>, val nbrTrees: Int = 10, config: ClassificationTreeConfig = ClassificationTreeConfig(), val bagging: Boolean = true, val concurrency: Concurrency = Concurrency.None, leafArmFactory: () -> SeriesStat<ClassCountsResult> = { ClassCountsStat(numClasses, concurrency) }, randomSeed: Int = 0) : RegressionStat<ForestClassificationResult> (source)

Online random-forest classifier; the classification counterpart of RandomForestRegressionStat. Same diversity tricks (Oza & Russell bagging, per-leaf mtry), but per-tree leaves are ClassCountsResult and ensemble predictions average per-class probabilities across trees.

Constructors

constructor(featureSize: Int, numClasses: Int, splitCandidates: List<Split>, nbrTrees: Int = 10, config: ClassificationTreeConfig = ClassificationTreeConfig(), bagging: Boolean = true, concurrency: Concurrency = Concurrency.None, leafArmFactory: () -> SeriesStat<ClassCountsResult> = { ClassCountsStat(numClasses, concurrency) }, randomSeed: Int = 0)

Properties

bagging

val bagging: Boolean

Oza & Russell online bagging: per-tree Poisson(1) reweighting at update time.

concurrency

open override val concurrency: Concurrency

The thread-safety contract this stat was constructed with. Each stat picks the cell-encoding and lock strategy that honours this contract for its mathematical structure:

config

val config: ClassificationTreeConfig

ClassificationTreeConfig with ClassificationTreeConfig.mtry defaulted to ceil(sqrt(p)) when null.

featureSize

open override val featureSize: Int

Number of features expected in x on each update. Mismatched lengths throw.

nbrTrees

val nbrTrees: Int

Trees in the forest.

numClasses

val numClasses: Int

Number of classes; y must round to [0, numClasses).

splitCandidates

val splitCandidates: List<Split>

Candidate split pool. Used by every tree; the per-leaf mtry filter draws from here.

Functions

create

open override fun create(concurrency: Concurrency? = null): RandomForestClassifierStat

Spawn a fresh accumulator with the same configuration. Optionally override the Concurrency; useful for materialising a wire spec at a different concurrency level than the source.

merge

open override fun merge(values: ForestClassificationResult)

Fold another accumulator's snapshot into this one. The unit of merge is the immutable Result; not a live Stat; which is what lets the merge cross a process boundary. Many workers track slices of the same stream, call read periodically, ship snapshots to a coordinator, and the coordinator merges them in.

read

open override fun read(timestampNanos: Long = currentTimeNanos()): ForestClassificationResult

Materialise the current state as an immutable Result. Reads never mutate, so the caller can read as often as it likes without affecting the stream.

reset

open override fun reset()

Reset the stat to its prior-seeded baseline. Equivalent to constructing a fresh stat with the same configuration, but in place; keeps the same Concurrency and any per-stat tunables.

trees

fun trees(): List<ClassificationTree>

Live underlying classification trees. Use for inspection.

update

open fun update(x: VectorView, y: Double, weight: Double = 1.0)

Record an (x, y) observation with the given weight at the current time.

open fun update(x: DoubleArray, y: Double, weight: Double = 1.0)

Convenience overload that wraps x as a DenseVector.

open fun update(x: DoubleArray, y: Double, timestampNanos: Long, weight: Double = 1.0)

Timestamped convenience overload that wraps x as a DenseVector.

open override fun update(x: VectorView, y: Double, timestampNanos: Long, weight: Double = 1.0)

Record an (x, y) observation at timestampNanos with the given weight.

RandomForestClassifierStat

bagging

val bagging: Boolean(source)

Oza & Russell online bagging: per-tree Poisson(1) reweighting at update time.

concurrency

open override val concurrency: Concurrency(source)

The thread-safety contract this stat was constructed with. Each stat picks the cell-encoding and lock strategy that honours this contract for its mathematical structure:

Concurrency.None: single-threaded; no synchronisation. Cheapest path.
Concurrency.Relaxed: lock-free best-effort. Multi-cell stats (Welford-style MeanStat, VarianceStat, MomentsStat) may drift under contention but never throw.
Concurrency.Strict: serialised when needed for full correctness across coupled cells. Sketches always self-serialise; Welford stats lock per update.
Concurrency.HighWrite: optimised for many concurrent writers; JVM uses striped adders for naively additive stats.

Picked at construction; immutable after.

config

val config: ClassificationTreeConfig(source)

ClassificationTreeConfig with ClassificationTreeConfig.mtry defaulted to ceil(sqrt(p)) when null.

create

open override fun create(concurrency: Concurrency? = null): RandomForestClassifierStat(source)

Spawn a fresh accumulator with the same configuration. Optionally override the Concurrency; useful for materialising a wire spec at a different concurrency level than the source.

The returned stat is independent: its state starts at the configured baseline, not at the source's current state. Each modality subtype narrows the return type so chaining doesn't lose the modality.

featureSize

open override val featureSize: Int(source)

Number of features expected in x on each update. Mismatched lengths throw.

merge

open override fun merge(values: ForestClassificationResult)(source)

Most stat families implement merge exactly (Chan-style parallel formulas for Welford, cell-wise additions for histograms, cell-wise max for HLL). SGD-based regressors merge approximately; they have no second-moment information for the principled combine. Each stat's KDoc documents its merge semantics.

nbrTrees

val nbrTrees: Int(source)

Trees in the forest.

numClasses

val numClasses: Int(source)

Number of classes; y must round to [0, numClasses).

read

open override fun read(timestampNanos: Long = currentTimeNanos()): ForestClassificationResult(source)

Materialise the current state as an immutable Result. Reads never mutate, so the caller can read as often as it likes without affecting the stream.

Snapshot consistency depends on the configured Concurrency. Under Concurrency.Strict / Concurrency.HighWrite a read locks against writers so coupled cells stay consistent. Under Concurrency.Relaxed the cells race and the snapshot may drift by ULPs of the workload under heavy contention; the drift is bounded and the read never throws.

timestampNanos is the read timestamp. Stats that don't care about time silently drop it; stats that do (rates, decay families, recency, windowed wrappers) use it as the ordering signal.