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[WIP] optimization refactor in process

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This commit is contained in:
Alexander Nozik 2021-04-26 15:02:19 +03:00
parent 257337f4fb
commit 7b6361e59d
8 changed files with 146 additions and 166 deletions
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96
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/CMOptimization.kt
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@ -11,10 +11,8 @@ import org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer
import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunction
import org.apache.commons.math3.optim.nonlinear.scalar.ObjectiveFunctionGradient
import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.AbstractSimplex
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer
import space.kscience.kmath.expressions.*
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.withSymbols
import space.kscience.kmath.misc.Symbol
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.optimization.*
@ -24,107 +22,73 @@ public operator fun PointValuePair.component1(): DoubleArray = point
public operator fun PointValuePair.component2(): Double = value
public class CMOptimizerFactory(public val optimizerBuilder: () -> MultivariateOptimizer) : OptimizationFeature
//public class CMOptimizerData(public val )
public class CMOptimizerData(public val data: List<OptimizationData>) : OptimizationFeature {
public constructor(vararg data: OptimizationData) : this(data.toList())
}
@OptIn(UnstableKMathAPI::class)
public class CMOptimization : Optimizer<FunctionOptimization<Double>> {
override suspend fun process(
problem: FunctionOptimization<Double>
): FunctionOptimization<Double> = withSymbols(problem.parameters){
val cmOptimizer: MultivariateOptimizer =
problem.getFeature<CMOptimizerFactory>()?.optimizerBuilder?.invoke() ?: SimplexOptimizer()
problem: FunctionOptimization<Double>,
): FunctionOptimization<Double> = withSymbols(problem.parameters) {
val convergenceChecker: ConvergenceChecker<PointValuePair> = SimpleValueChecker(
DEFAULT_RELATIVE_TOLERANCE,
DEFAULT_ABSOLUTE_TOLERANCE,
DEFAULT_MAX_ITER
)
val cmOptimizer: MultivariateOptimizer = problem.getFeature<CMOptimizerFactory>()?.optimizerBuilder?.invoke()
?: NonLinearConjugateGradientOptimizer(
NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
convergenceChecker
)
val optimizationData: HashMap<KClass<out OptimizationData>, OptimizationData> = HashMap()
fun addOptimizationData(data: OptimizationData) {
optimizationData[data::class] = data
}
addOptimizationData(MaxEval.unlimited())
addOptimizationData(InitialGuess(problem.initialGuess.toDoubleArray()))
fun exportOptimizationData(): List<OptimizationData> = optimizationData.values.toList()
/**
* Register no-deriv function instead of differentiable function
*/
/**
* Register no-deriv function instead of differentiable function
*/
fun noDerivFunction(expression: Expression<Double>): Unit {
val objectiveFunction = ObjectiveFunction {
val args = problem.initialGuess + it.toMap()
expression(args)
problem.expression(args)
}
addOptimizationData(objectiveFunction)
}
public override fun function(expression: DifferentiableExpression<Double, Expression<Double>>) {
noDerivFunction(expression)
val gradientFunction = ObjectiveFunctionGradient {
val args = startingPoint + it.toMap()
val args = problem.initialGuess + it.toMap()
DoubleArray(symbols.size) { index ->
expression.derivative(symbols[index])(args)
problem.expression.derivative(symbols[index])(args)
}
}
addOptimizationData(gradientFunction)
if (optimizerBuilder == null) {
optimizerBuilder = {
NonLinearConjugateGradientOptimizer(
NonLinearConjugateGradientOptimizer.Formula.FLETCHER_REEVES,
convergenceChecker
)
val logger = problem.getFeature<OptimizationLog>()
for (feature in problem.features) {
when (feature) {
is CMOptimizerData -> feature.data.forEach { addOptimizationData(it) }
is FunctionOptimizationTarget -> when(feature){
FunctionOptimizationTarget.MAXIMIZE -> addOptimizationData(GoalType.MAXIMIZE)
FunctionOptimizationTarget.MINIMIZE -> addOptimizationData(GoalType.MINIMIZE)
}
else -> logger?.log { "The feature $feature is unused in optimization" }
}
}
public fun simplex(simplex: AbstractSimplex) {
addOptimizationData(simplex)
//Set optimization builder to simplex if it is not present
if (optimizerBuilder == null) {
optimizerBuilder = { SimplexOptimizer(convergenceChecker) }
}
val (point, value) = cmOptimizer.optimize(*optimizationData.values.toTypedArray())
return problem.withFeatures(FunctionOptimizationResult(point.toMap(), value))
}
public fun simplexSteps(steps: Map<Symbol, Double>) {
simplex(NelderMeadSimplex(steps.toDoubleArray()))
}
public fun goal(goalType: GoalType) {
addOptimizationData(goalType)
}
public fun optimizer(block: () -> MultivariateOptimizer) {
optimizerBuilder = block
}
override fun update(result: OptimizationResult<Double>) {
initialGuess(result.point)
}
override suspend fun optimize(): OptimizationResult<Double> {
val optimizer = optimizerBuilder?.invoke() ?: error("Optimizer not defined")
val (point, value) = optimizer.optimize(*optimizationData.values.toTypedArray())
return OptimizationResult(point.toMap(), value)
}
return@withSymbols TODO()
}
public companion object : OptimizationProblemFactory<Double, CMOptimization> {
public companion object {
public const val DEFAULT_RELATIVE_TOLERANCE: Double = 1e-4
public const val DEFAULT_ABSOLUTE_TOLERANCE: Double = 1e-4
public const val DEFAULT_MAX_ITER: Int = 1000
override fun build(symbols: List<Symbol>): CMOptimization = CMOptimization(symbols)
}
}
public fun CMOptimization.initialGuess(vararg pairs: Pair<Symbol, Double>): Unit = initialGuess(pairs.toMap())
public fun CMOptimization.simplexSteps(vararg pairs: Pair<Symbol, Double>): Unit = simplexSteps(pairs.toMap())

8
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/optimization/cmFit.kt
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@ -17,22 +17,22 @@ import space.kscience.kmath.structures.asBuffer
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
*/
public fun FunctionOptimization.Companion.chiSquared(
public fun FunctionOptimization.Companion.chiSquaredExpression(
x: Buffer<Double>,
y: Buffer<Double>,
yErr: Buffer<Double>,
model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
): DifferentiableExpression<Double, Expression<Double>> = chiSquared(DerivativeStructureField, x, y, yErr, model)
): DifferentiableExpression<Double, Expression<Double>> = chiSquaredExpression(DerivativeStructureField, x, y, yErr, model)
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
*/
public fun FunctionOptimization.Companion.chiSquared(
public fun FunctionOptimization.Companion.chiSquaredExpression(
x: Iterable<Double>,
y: Iterable<Double>,
yErr: Iterable<Double>,
model: DerivativeStructureField.(x: DerivativeStructure) -> DerivativeStructure,
): DifferentiableExpression<Double, Expression<Double>> = chiSquared(
): DifferentiableExpression<Double, Expression<Double>> = chiSquaredExpression(
DerivativeStructureField,
x.toList().asBuffer(),
y.toList().asBuffer(),

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/Featured.kt
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@ -17,7 +17,7 @@ public interface Featured<F : Any> {
/**
* A container for a set of features
*/
public class FeatureSet<F : Any> private constructor(public val features: Map<KClass<out F>, Any>) : Featured<F> {
public class FeatureSet<F : Any> private constructor(public val features: Map<KClass<out F>, F>) : Featured<F> {
@Suppress("UNCHECKED_CAST")
override fun <T : F> getFeature(type: KClass<out T>): T? = features[type] as? T
@ -31,6 +31,8 @@ public class FeatureSet<F : Any> private constructor(public val features: Map<KC
public fun with(vararg otherFeatures: F): FeatureSet<F> =
FeatureSet(features + otherFeatures.associateBy { it::class })
public operator fun iterator(): Iterator<F> = features.values.iterator()
public companion object {
public fun <F : Any> of(vararg features: F): FeatureSet<F> = FeatureSet(features.associateBy { it::class })
}

14
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/logging.kt Normal file
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@ -0,0 +1,14 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.misc
public interface Loggable {
public fun log(tag: String = INFO, block: () -> String)
public companion object {
public const val INFO: String = "INFO"
}
}

130
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt
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@ -15,91 +15,57 @@ import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.indices
public class FunctionOptimizationResult<T>(point: Map<Symbol, T>, public val value: T) : OptimizationResult<T>(point)
public class FunctionOptimization<T : Any>(
public enum class FunctionOptimizationTarget : OptimizationFeature {
MAXIMIZE,
MINIMIZE
}
public class FunctionOptimization<T>(
override val features: FeatureSet<OptimizationFeature>,
public val expression: DifferentiableExpression<T, Expression<T>>,
public val initialGuess: Map<Symbol, T>,
public val parameters: Collection<Symbol>,
public val maximize: Boolean,
) : OptimizationProblem
) : OptimizationProblem{
public companion object{
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
*/
public fun <T : Any, I : Any, A> chiSquaredExpression(
autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
x: Buffer<T>,
y: Buffer<T>,
yErr: Buffer<T>,
model: A.(I) -> I,
): DifferentiableExpression<T, Expression<T>> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
require(x.size == y.size) { "X and y buffers should be of the same size" }
require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
return autoDiff.process {
var sum = zero
x.indices.forEach {
val xValue = const(x[it])
val yValue = const(y[it])
val yErrValue = const(yErr[it])
val modelValue = model(xValue)
sum += ((yValue - modelValue) / yErrValue).pow(2)
}
sum
}
}
}
}
public fun <T> FunctionOptimization<T>.withFeatures(
vararg newFeature: OptimizationFeature,
): FunctionOptimization<T> = FunctionOptimization(
features.with(*newFeature),
expression,
initialGuess,
parameters
)
//
///**
// * A likelihood function optimization problem with provided derivatives
// */
//public interface FunctionOptimizationBuilder<T : Any> {
// /**
// * The optimization direction. If true search for function maximum, if false, search for the minimum
// */
// public var maximize: Boolean
//
// /**
// * Define the initial guess for the optimization problem
// */
// public fun initialGuess(map: Map<Symbol, T>)
//
// /**
// * Set a differentiable expression as objective function as function and gradient provider
// */
// public fun function(expression: DifferentiableExpression<T, Expression<T>>)
//
// public companion object {
// /**
// * Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic differentiation
// */
// public fun <T : Any, I : Any, A> chiSquared(
// autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
// x: Buffer<T>,
// y: Buffer<T>,
// yErr: Buffer<T>,
// model: A.(I) -> I,
// ): DifferentiableExpression<T, Expression<T>> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
// require(x.size == y.size) { "X and y buffers should be of the same size" }
// require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
//
// return autoDiff.process {
// var sum = zero
//
// x.indices.forEach {
// val xValue = const(x[it])
// val yValue = const(y[it])
// val yErrValue = const(yErr[it])
// val modelValue = model(xValue)
// sum += ((yValue - modelValue) / yErrValue).pow(2)
// }
//
// sum
// }
// }
// }
//}
//
///**
// * Define a chi-squared-based objective function
// */
//public fun <T : Any, I : Any, A> FunctionOptimization<T>.chiSquared(
// autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
// x: Buffer<T>,
// y: Buffer<T>,
// yErr: Buffer<T>,
// model: A.(I) -> I,
//) where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
// val chiSquared = FunctionOptimization.chiSquared(autoDiff, x, y, yErr, model)
// function(chiSquared)
// maximize = false
//}
//
///**
// * Optimize differentiable expression using specific [OptimizationProblemFactory]
// */
//public suspend fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
// factory: OptimizationProblemFactory<T, F>,
// vararg symbols: Symbol,
// configuration: F.() -> Unit,
//): OptimizationResult<T> {
// require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
// val problem = factory(symbols.toList(), configuration)
// problem.function(this)
// return problem.optimize()
//}

6
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt
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@ -7,6 +7,8 @@ package space.kscience.kmath.optimization
import space.kscience.kmath.misc.FeatureSet
import space.kscience.kmath.misc.Featured
import space.kscience.kmath.misc.Loggable
import space.kscience.kmath.misc.Symbol
import kotlin.reflect.KClass
public interface OptimizationFeature
@ -18,6 +20,10 @@ public interface OptimizationProblem : Featured<OptimizationFeature> {
public inline fun <reified T : OptimizationFeature> OptimizationProblem.getFeature(): T? = getFeature(T::class)
public open class OptimizationResult<T>(public val point: Map<Symbol, T>) : OptimizationFeature
public class OptimizationLog(private val loggable: Loggable) : Loggable by loggable, OptimizationFeature
//public class OptimizationResult<T>(
// public val point: Map<Symbol, T>,
// public val value: T,

34
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt
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@ -14,9 +14,11 @@ import space.kscience.kmath.misc.Symbol
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.Field
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.indices
@UnstableKMathAPI
public interface XYFit<T> : OptimizationProblem<T> {
public interface XYFit<T> : OptimizationProblem {
public val algebra: Field<T>
@ -43,3 +45,33 @@ public interface XYFit<T> : OptimizationProblem<T> {
autoDiff.process { modelFunction(const(arg)) }
}
}
//
///**
// * Define a chi-squared-based objective function
// */
//public fun <T : Any, I : Any, A> FunctionOptimization<T>.chiSquared(
// autoDiff: AutoDiffProcessor<T, I, A, Expression<T>>,
// x: Buffer<T>,
// y: Buffer<T>,
// yErr: Buffer<T>,
// model: A.(I) -> I,
//) where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
// val chiSquared = FunctionOptimization.chiSquared(autoDiff, x, y, yErr, model)
// function(chiSquared)
// maximize = false
//}
/**
* Optimize differentiable expression using specific [OptimizationProblemFactory]
*/
public suspend fun <T : Any, F : FunctionOptimization<T>> DifferentiableExpression<T, Expression<T>>.optimizeWith(
factory: OptimizationProblemFactory<T, F>,
vararg symbols: Symbol,
configuration: F.() -> Unit,
): OptimizationResult<T> {
require(symbols.isNotEmpty()) { "Must provide a list of symbols for optimization" }
val problem = factory(symbols.toList(), configuration)
problem.function(this)
return problem.optimize()
}

4
kmath-stat/src/commonMain/kotlin/space/kscience/kmath/optimization/qow/QowFit.kt
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@ -27,10 +27,6 @@ import kotlin.math.pow
private typealias ParamSet = Map<Symbol, Double>
public fun interface FitLogger {
public fun log(block: () -> String)
}
@OptIn(UnstableKMathAPI::class)
public class QowFit(
override val symbols: List<Symbol>,