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Add Tukey's Biweight and WMAPE implementations with comprehensive tests and documentation

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- Introduced Tukey's Biweight as a robust loss function, including mathematical foundation, usage patterns, and performance profile.
- Added WMAPE (Weighted Mean Absolute Percentage Error) implementation, emphasizing its advantages for intermittent demand forecasting.
- Created unit tests for WMAPE covering various scenarios including edge cases and batch calculations.
- Documented both Tukey's Biweight and WMAPE with detailed explanations, properties, and common use cases.
This commit is contained in:
Miha Kralj
2025-12-30 09:27:08 -08:00
parent bf611d319f
commit 6e24fea8b7
35 changed files with 8341 additions and 206 deletions
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lib/errors/logcosh/LogCosh.cs
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using System.Runtime.CompilerServices;
using System.Runtime.InteropServices;
namespace QuanTAlib;
/// <summary>
/// LogCosh: Log-Cosh Loss
/// </summary>
/// <remarks>
/// Log-Cosh is the logarithm of the hyperbolic cosine of the error. It is a
/// smooth approximation to the absolute error that is twice differentiable
/// everywhere, making it suitable for gradient-based optimization.
///
/// Formula:
/// LogCosh = (1/n) * Σ log(cosh(actual - predicted))
///
/// Key properties:
/// - Smooth and differentiable everywhere
/// - Approximates L1 loss for large errors
/// - Approximates L2 loss for small errors
/// - Less sensitive to outliers than MSE
/// - Numerically stable (uses stable computation for large values)
/// </remarks>
[SkipLocalsInit]
public sealed class LogCosh : AbstractBase
{
private readonly RingBuffer _logCoshBuffer;
[StructLayout(LayoutKind.Auto)]
private record struct State(double LogCoshSum, double LastValidActual, double LastValidPredicted, int TickCount);
private State _state;
private State _p_state;
private const int ResyncInterval = 1000;
public LogCosh(int period)
{
if (period <= 0)
throw new ArgumentException("Period must be greater than 0", nameof(period));
_logCoshBuffer = new RingBuffer(period);
Name = $"LogCosh({period})";
WarmupPeriod = period;
}
public override bool IsHot => _logCoshBuffer.IsFull;
/// <summary>
/// Computes log(cosh(x)) in a numerically stable way.
/// For large |x|, cosh(x) ≈ exp(|x|)/2, so log(cosh(x)) ≈ |x| - log(2)
/// </summary>
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private static double StableLogCosh(double x)
{
double absX = Math.Abs(x);
// For large values, use asymptotic approximation to avoid overflow
if (absX > 20.0)
return absX - 0.6931471805599453; // log(2)
return Math.Log(Math.Cosh(x));
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public TValue Update(TValue actual, TValue predicted, bool isNew = true)
{
double actualVal = actual.Value;
double predictedVal = predicted.Value;
if (!double.IsFinite(actualVal))
actualVal = double.IsFinite(_state.LastValidActual) ? _state.LastValidActual : 0.0;
else
_state.LastValidActual = actualVal;
if (!double.IsFinite(predictedVal))
predictedVal = double.IsFinite(_state.LastValidPredicted) ? _state.LastValidPredicted : 0.0;
else
_state.LastValidPredicted = predictedVal;
double error = actualVal - predictedVal;
double logCoshValue = StableLogCosh(error);
if (isNew)
{
_p_state = _state;
double removedLogCosh = _logCoshBuffer.Count == _logCoshBuffer.Capacity ? _logCoshBuffer.Oldest : 0.0;
_state.LogCoshSum = _state.LogCoshSum - removedLogCosh + logCoshValue;
_logCoshBuffer.Add(logCoshValue);
_state.TickCount++;
if (_logCoshBuffer.IsFull && _state.TickCount >= ResyncInterval)
{
_state.TickCount = 0;
_state.LogCoshSum = _logCoshBuffer.RecalculateSum();
}
}
else
{
_state = _p_state;
double removedLogCosh = _logCoshBuffer.Count == _logCoshBuffer.Capacity ? _logCoshBuffer.Oldest : 0.0;
_state.LogCoshSum = _state.LogCoshSum - removedLogCosh + logCoshValue;
_logCoshBuffer.UpdateNewest(logCoshValue);
_state.LogCoshSum = _logCoshBuffer.RecalculateSum();
}
// LogCosh = (1/n) * Σ log(cosh(error))
double result = _logCoshBuffer.Count > 0 ? _state.LogCoshSum / _logCoshBuffer.Count : 0.0;
Last = new TValue(actual.Time, result);
PubEvent(Last, isNew);
return Last;
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public TValue Update(double actual, double predicted, bool isNew = true)
{
return Update(new TValue(DateTime.UtcNow, actual), new TValue(DateTime.UtcNow, predicted), isNew);
}
public override TValue Update(TValue input, bool isNew = true)
{
throw new NotSupportedException("LogCosh requires two inputs. Use Update(actual, predicted).");
}
public override TSeries Update(TSeries source)
{
throw new NotSupportedException("LogCosh requires two inputs. Use Calculate(actualSeries, predictedSeries, period).");
}
public override void Prime(ReadOnlySpan<double> source, TimeSpan? step = null)
{
throw new NotSupportedException("LogCosh requires two inputs.");
}
public override void Reset()
{
_logCoshBuffer.Clear();
_state = default;
_p_state = default;
Last = default;
}
public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
{
if (actual.Count != predicted.Count)
throw new ArgumentException("Actual and predicted series must have the same length", nameof(predicted));
int len = actual.Count;
var t = new List<long>(len);
var v = new List<double>(len);
CollectionsMarshal.SetCount(t, len);
CollectionsMarshal.SetCount(v, len);
var tSpan = CollectionsMarshal.AsSpan(t);
var vSpan = CollectionsMarshal.AsSpan(v);
Batch(actual.Values, predicted.Values, vSpan, period);
actual.Times.CopyTo(tSpan);
return new TSeries(t, v);
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
public static void Batch(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output, int period)
{
if (actual.Length != predicted.Length || actual.Length != output.Length)
throw new ArgumentException("All spans must have the same length", nameof(output));
if (period <= 0)
throw new ArgumentException("Period must be greater than 0", nameof(period));
int len = actual.Length;
if (len == 0) return;
const int StackAllocThreshold = 256;
Span<double> logCoshBuffer = period <= StackAllocThreshold
? stackalloc double[period]
: new double[period];
double logCoshSum = 0;
double lastValidActual = 0;
double lastValidPredicted = 0;
for (int k = 0; k < len; k++)
{
if (double.IsFinite(actual[k])) { lastValidActual = actual[k]; break; }
}
for (int k = 0; k < len; k++)
{
if (double.IsFinite(predicted[k])) { lastValidPredicted = predicted[k]; break; }
}
int bufferIndex = 0;
int i = 0;
int warmupEnd = Math.Min(period, len);
for (; i < warmupEnd; i++)
{
double act = actual[i];
double pred = predicted[i];
if (double.IsFinite(act)) lastValidActual = act; else act = lastValidActual;
if (double.IsFinite(pred)) lastValidPredicted = pred; else pred = lastValidPredicted;
double error = act - pred;
double logCoshValue = StableLogCosh(error);
logCoshSum += logCoshValue;
logCoshBuffer[i] = logCoshValue;
output[i] = logCoshSum / (i + 1);
}
int tickCount = 0;
for (; i < len; i++)
{
double act = actual[i];
double pred = predicted[i];
if (double.IsFinite(act)) lastValidActual = act; else act = lastValidActual;
if (double.IsFinite(pred)) lastValidPredicted = pred; else pred = lastValidPredicted;
double error = act - pred;
double logCoshValue = StableLogCosh(error);
logCoshSum = logCoshSum - logCoshBuffer[bufferIndex] + logCoshValue;
logCoshBuffer[bufferIndex] = logCoshValue;
bufferIndex++;
if (bufferIndex >= period) bufferIndex = 0;
output[i] = logCoshSum / period;
tickCount++;
if (tickCount >= ResyncInterval)
{
tickCount = 0;
double recalcSum = 0;
for (int k = 0; k < period; k++)
recalcSum += logCoshBuffer[k];
logCoshSum = recalcSum;
}
}
}
}