Add attribute builder accessors for fits

Author: altavir

CHANGELOG.md

@@ -3,6 +3,7 @@
 ## Unreleased
 
 ### Added
+- Fit accessors with Attribute
 
 ### Changed
 - Upgrade tensorflow version to 1.0.0

examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt

@@ -14,7 +14,10 @@ import space.kscience.kmath.expressions.autodiff
 import space.kscience.kmath.expressions.symbol
 import space.kscience.kmath.operations.asIterable
 import space.kscience.kmath.operations.toList
-import space.kscience.kmath.optimization.*
+import space.kscience.kmath.optimization.minimize
+import space.kscience.kmath.optimization.optimizeWith
+import space.kscience.kmath.optimization.result
+import space.kscience.kmath.optimization.resultValue
 import space.kscience.kmath.random.RandomGenerator
 import space.kscience.kmath.real.DoubleVector
 import space.kscience.kmath.real.map
@@ -79,9 +82,10 @@ suspend fun main() {
     val result = chi2.optimizeWith(
         CMOptimizer,
         mapOf(a to 1.5, b to 0.9, c to 1.0),
-    ) {
-        FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
-    }
+        attributesBuilder = {
+            minimize()
+        }
+    )
 
     //display a page with plot and numerical results
     val page = Plotly.page {

examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt

@@ -7,7 +7,6 @@ package space.kscience.kmath.fit
 
 import kotlinx.html.br
 import kotlinx.html.h3
-import space.kscience.attributes.Attributes
 import space.kscience.kmath.data.XYErrorColumnarData
 import space.kscience.kmath.distributions.NormalDistribution
 import space.kscience.kmath.expressions.Symbol
@@ -65,7 +64,9 @@ suspend fun main() {
         QowOptimizer,
         Double.autodiff,
         mapOf(a to 0.9, b to 1.2, c to 2.0, e to 1.0, d to 1.0, e to 0.0),
-        attributes = Attributes(OptimizationParameters, listOf(a, b, c, d))
+        attributesBuilder = {
+            freeParameters(a, b, c, d)
+        },
     ) { arg ->
         //bind variables to autodiff context
         val a by binding

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/FunctionOptimization.kt

@@ -22,6 +22,15 @@ public enum class OptimizationDirection {
 
 public object FunctionOptimizationTarget : OptimizationAttribute<OptimizationDirection>
 
+public fun AttributesBuilder<FunctionOptimization<*>>.maximize() {
+    FunctionOptimizationTarget(OptimizationDirection.MAXIMIZE)
+}
+
+public fun AttributesBuilder<FunctionOptimization<*>>.minimize() {
+    FunctionOptimizationTarget(OptimizationDirection.MINIMIZE)
+}
+
+
 public class FunctionOptimization<T>(
     public val expression: DifferentiableExpression<T>,
     override val attributes: Attributes,
@@ -74,11 +83,11 @@ public fun <T> FunctionOptimization<T>.withAttributes(
 public suspend fun <T> DifferentiableExpression<T>.optimizeWith(
     optimizer: Optimizer<T, FunctionOptimization<T>>,
     startingPoint: Map<Symbol, T>,
-    modifier: AttributesBuilder<FunctionOptimization<T>>.() -> Unit = {},
+    attributesBuilder: AttributesBuilder<FunctionOptimization<T>>.() -> Unit = {},
 ): FunctionOptimization<T> {
     val problem = FunctionOptimization(this) {
         startAt(startingPoint)
-        modifier()
+        attributesBuilder()
     }
     return optimizer.optimize(problem)
 }
@@ -93,11 +102,11 @@ public val <T> FunctionOptimization<T>.resultValue: T
 public suspend fun <T> DifferentiableExpression<T>.optimizeWith(
     optimizer: Optimizer<T, FunctionOptimization<T>>,
     vararg startingPoint: Pair<Symbol, T>,
-    builder: AttributesBuilder<FunctionOptimization<T>>.() -> Unit = {},
+    attributesBuilder: AttributesBuilder<FunctionOptimization<T>>.() -> Unit = {},
 ): FunctionOptimization<T> {
     val problem = FunctionOptimization<T>(this) {
         startAt(mapOf(*startingPoint))
-        builder()
+        attributesBuilder()
     }
     return optimizer.optimize(problem)
 }

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/OptimizationProblem.kt

@@ -58,9 +58,15 @@ public object OptimizationLog : OptimizationAttribute<Loggable>
  */
 public object OptimizationParameters : OptimizationAttribute<List<Symbol>>
 
+public fun AttributesBuilder<OptimizationProblem<*>>.freeParameters(vararg symbols: Symbol) {
+    OptimizationParameters(symbols.asList())
+}
+
 /**
  * Maximum allowed number of iterations
  */
 public object OptimizationIterations : OptimizationAttribute<Int>
 
-
+public fun AttributesBuilder<OptimizationProblem<*>>.iterations(iterations: Int) {
+    OptimizationIterations(iterations)
+}

kmath-optimization/src/commonMain/kotlin/space/kscience/kmath/optimization/XYFit.kt

@@ -137,6 +137,24 @@ public suspend fun XYColumnarData<Double, Double, Double>.fitWith(
     return optimizer.optimize(problem)
 }
 
+public suspend fun XYColumnarData<Double, Double, Double>.fitWith(
+    optimizer: Optimizer<Double, XYFit>,
+    modelExpression: DifferentiableExpression<Float64>,
+    startingPoint: Map<Symbol, Double>,
+    attributesBuilder: AttributesBuilder<XYFit>.() -> Unit,
+    xSymbol: Symbol = Symbol.x,
+    pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
+    pointWeight: PointWeight = PointWeight.byYSigma,
+): XYFit = fitWith(
+    optimizer = optimizer,
+    modelExpression = modelExpression,
+    startingPoint = startingPoint,
+    attributes = Attributes<XYFit>(attributesBuilder),
+    xSymbol = xSymbol,
+    pointToCurveDistance = pointToCurveDistance,
+    pointWeight = pointWeight
+)
+
 /**
  * Fit given data with a model provided as an expression
  */
@@ -166,6 +184,26 @@ public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
     )
 }
 
+public suspend fun <I : Any, A> XYColumnarData<Double, Double, Double>.fitWith(
+    optimizer: Optimizer<Double, XYFit>,
+    processor: AutoDiffProcessor<Double, I, A>,
+    startingPoint: Map<Symbol, Double>,
+    attributesBuilder: AttributesBuilder<XYFit>.() -> Unit,
+    xSymbol: Symbol = Symbol.x,
+    pointToCurveDistance: PointToCurveDistance = PointToCurveDistance.byY,
+    pointWeight: PointWeight = PointWeight.byYSigma,
+    model: A.(I) -> I,
+): XYFit where A : ExtendedField<I>, A : ExpressionAlgebra<Double, I> = fitWith(
+    optimizer = optimizer,
+    processor = processor,
+    startingPoint = startingPoint,
+    attributes = Attributes<XYFit>(attributesBuilder),
+    xSymbol = xSymbol,
+    pointToCurveDistance = pointToCurveDistance,
+    pointWeight = pointWeight,
+    model = model
+)
+
 /**
  * Compute chi squared value for completed fit. Return null for incomplete fit
  */