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// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using System;
using System.Collections.Generic;
using System.Collections.Immutable;
using System.Linq;
using System.Runtime.CompilerServices;
using Microsoft.ML.Data;
using Microsoft.ML.Internal.Utilities;
using Microsoft.ML.Model.OnnxConverter;
using Microsoft.ML.Model.Pfa;
using Microsoft.ML.Runtime;
using Newtonsoft.Json.Linq;
namespace Microsoft.ML.Transforms
{
// !!! WARNING !!!
// This file contains the Double version for normalizers and is almost identical with NormalizeColumnSng.cs
// When making changes to one, use BeyondCompare or a similar tool to view diffs and propagate
// appropriate changes to the other.
using TFloat = Double;
internal static partial class AffineNormSerializationUtils
{
public static void SaveModel(ModelSaveContext ctx,
int numFeatures, int[] indices, TFloat[] scales, TFloat[] offsets, bool saveText = false)
{
Contracts.AssertValue(ctx);
ctx.CheckAtModel();
Contracts.Check(numFeatures > 0);
Contracts.CheckValueOrNull(indices);
Contracts.CheckValue(scales, nameof(scales));
Contracts.CheckValueOrNull(offsets);
ctx.SetVersionInfo(GetVersionInfo());
// *** Binary format ***
// int: sizeof(TFloat)
// int: number of features (size)
// int: number of indices morphed (morph: -1 means that we assume all are, zero means none are)
// int[]: morphed indices (max(0, morph) of them)
// int: number of scales (if morph >= 0, this should be morph, otherwise, should be size)
// TFloat[]: scale values
// int: number of offsets (zero if they are all zero, otherwise, should be morph or size - same as scales)
// TFloat[]: offset values
ctx.Writer.Write(sizeof(TFloat));
ctx.Writer.Write(numFeatures);
Contracts.Assert(offsets == null || offsets.Length == scales.Length);
if (indices == null)
{
Contracts.Assert(scales.Length == numFeatures);
ctx.Writer.Write(-1);
}
else
{
Contracts.Assert(indices.Length < numFeatures);
Contracts.Assert(scales.Length == indices.Length);
ctx.Writer.WriteIntArray(indices);
}
ctx.Writer.WriteDoubleArray(scales);
ctx.Writer.WriteDoubleArray(offsets);
if (saveText)
{
ctx.SaveTextStream("AffineNormalizer.txt",
writer =>
{
writer.WriteLine("NumNormalizationFeatures={0}", numFeatures);
if (indices == null)
{
for (int i = 0; i < numFeatures; i++)
writer.WriteLine("{0}\t{1}\t{2}", i, offsets != null ? offsets[i] : 0, scales[i]);
}
else
{
for (int ii = 0; ii < indices.Length; ii++)
writer.WriteLine("{0}\t{1}\t{2}", indices[ii], offsets != null ? offsets[ii] : 0,
scales[ii]);
}
writer.WriteLine();
});
}
}
public static void LoadModel(ModelLoadContext ctx, ref List<int> indicesShift,
out int numFeatures, out TFloat[] scales, out TFloat[] offsets,
out int[] indicesMorph, out TFloat[] scalesSparse, out TFloat[] offsetsSparse)
{
Contracts.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// int: sizeof(TFloat)
// int: number of features (size)
// int: number of indices morphed (morph: -1 means that we assume all are, zero means none are)
// int[]: morphed indices (max(0, morph) of them)
// int: number of scales (if morph >= 0, this should be morph, otherwise, should be size)
// TFloat[]: scale values
// int: number of offsets (zero if they are all zero, otherwise, should be morph or size - same as scales)
// TFloat[]: offset values
int cbFloat = ctx.Reader.ReadInt32();
Contracts.CheckDecode(cbFloat == sizeof(TFloat));
int size = ctx.Reader.ReadInt32();
Contracts.CheckDecode(size > 0);
numFeatures = size;
int morphCount = ctx.Reader.ReadInt32();
Contracts.CheckDecode(-1 <= morphCount && morphCount < size);
if (indicesShift != null)
indicesShift.Clear();
if (morphCount == -1)
{
// Not using sparsity.
indicesMorph = null;
int scaleCount = ctx.Reader.ReadInt32();
Contracts.CheckDecode(scaleCount == size);
scalesSparse = ctx.Reader.ReadDoubleArray(scaleCount);
int offsetCount = ctx.Reader.ReadInt32();
Contracts.CheckDecode(offsetCount == 0 || offsetCount == size);
offsetsSparse = ctx.Reader.ReadDoubleArray(offsetCount);
scales = scalesSparse;
offsets = offsetsSparse;
for (int iv = 0; iv < scales.Length; iv++)
{
TFloat scale = scales[iv];
Contracts.CheckDecode(!TFloat.IsNaN(scale));
if (offsets == null)
continue;
if (scale == 0)
{
offsets[iv] = 0;
continue;
}
TFloat offset = offsets[iv];
Contracts.CheckDecode(!TFloat.IsNaN(offset));
if (!(offset == 0))
Utils.Add(ref indicesShift, iv);
}
}
else
{
// Using sparsity.
indicesMorph = ctx.Reader.ReadIntArray(morphCount) ?? new int[0];
int scaleCount = ctx.Reader.ReadInt32();
Contracts.CheckDecode(scaleCount == morphCount);
scalesSparse = ctx.Reader.ReadDoubleArray(scaleCount) ?? new TFloat[0];
int offsetCount = ctx.Reader.ReadInt32();
Contracts.CheckDecode(offsetCount == 0 || offsetCount == morphCount);
offsetsSparse = ctx.Reader.ReadDoubleArray(offsetCount);
// Construct the dense representations.
scales = Utils.CreateArray<TFloat>(numFeatures, 1);
offsets = offsetsSparse != null ? new TFloat[numFeatures] : null;
int ivPrev = -1;
for (int iiv = 0; iiv < indicesMorph.Length; iiv++)
{
int iv = indicesMorph[iiv];
Contracts.CheckDecode(ivPrev < iv && iv < numFeatures);
ivPrev = iv;
TFloat scale = scales[iv] = scalesSparse[iiv];
Contracts.CheckDecode(!TFloat.IsNaN(scale));
if (offsetsSparse == null)
continue;
if (scale == 0)
{
offsetsSparse[iiv] = 0;
continue;
}
TFloat offset = offsets[iv] = offsetsSparse[iiv];
Contracts.CheckDecode(!TFloat.IsNaN(offset));
if (!(offset == 0))
Utils.Add(ref indicesShift, iv);
}
}
Contracts.Assert(numFeatures > 0);
Contracts.Assert(scalesSparse != null);
Contracts.Assert(indicesMorph == null || indicesMorph.Length == scalesSparse.Length);
Contracts.Assert(offsetsSparse == null || offsetsSparse.Length == scalesSparse.Length);
Contracts.Assert((offsets == null) == (offsetsSparse == null));
}
}
internal static partial class BinNormSerializationUtils
{
public static void SaveModel(ModelSaveContext ctx, TFloat[][] binUpperBounds, bool saveText = false)
{
Contracts.AssertValue(ctx);
ctx.SetVersionInfo(GetVersionInfo());
// *** Binary format ***
// int: sizeof(TFloat)
// int: number of bin upper bounds arrays = number of features
// for each array:
// int: number of elements in bin upper bounds
// TFloat[]: bin upper bounds
ctx.Writer.Write(sizeof(TFloat));
ctx.Writer.Write(binUpperBounds.Length);
foreach (var featureUpperBounds in binUpperBounds)
ctx.Writer.WriteDoubleArray(featureUpperBounds);
if (saveText)
{
ctx.SaveTextStream("BinNormalizer.txt",
writer =>
{
writer.WriteLine("NumNormalizationFeatures={0}", binUpperBounds.Length);
for (int i = 0; i < binUpperBounds.Length; i++)
{
string pre = "";
for (int j = 0; j < binUpperBounds[i].Length - 1; j++)
{
writer.Write(pre);
pre = "\t";
writer.Write(binUpperBounds[i][j]);
}
writer.WriteLine();
}
});
}
}
public static void LoadModel(ModelLoadContext ctx, out TFloat[][] binUpperBounds)
{
Contracts.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// int: sizeof(TFloat)
// int: number of bin upper bounds arrays = number of features
// for each array:
// int: number of elements in bin upper bounds
// TFloat[]: bin upper bounds
int cbFloat = ctx.Reader.ReadInt32();
Contracts.CheckDecode(cbFloat == sizeof(TFloat));
// Core model
int numFeatures = ctx.Reader.ReadInt32();
Contracts.CheckDecode(numFeatures > 0);
binUpperBounds = new TFloat[numFeatures][];
for (int i = 0; i < numFeatures; i++)
{
TFloat[] curUpperBounds = ctx.Reader.ReadDoubleArray();
Contracts.CheckDecode(Utils.Size(curUpperBounds) > 0);
binUpperBounds[i] = curUpperBounds;
for (int j = 1; j < curUpperBounds.Length; j++)
Contracts.CheckDecode(curUpperBounds[j - 1] < curUpperBounds[j]);
Contracts.CheckDecode(curUpperBounds[curUpperBounds.Length - 1] == TFloat.PositiveInfinity);
}
}
}
internal static partial class CdfNormSerializationUtils
{
public static void SaveModel(ModelSaveContext ctx, bool useLog, TFloat[] mean, TFloat[] stddev)
{
// *** Binary format ***
// int: sizeof(TFloat)
// bool: useLog
// int: number of features (size)
// TFloat[]: mean values
// TFloat[]: stddev values
ctx.Writer.Write(sizeof(TFloat));
ctx.Writer.WriteBoolByte(useLog);
ctx.Writer.Write(mean.Length);
ctx.Writer.WriteDoublesNoCount(mean);
ctx.Writer.WriteDoublesNoCount(stddev.AsSpan(0, mean.Length));
ctx.SaveTextStream("CdfNormalizer.txt",
writer =>
{
writer.WriteLine("NumNormalizationFeatures={0}", mean.Length);
writer.WriteLine("Log={0}", useLog);
for (int i = 0; i < mean.Length; i++)
writer.WriteLine("{0}\t{1}", mean[i], stddev[i]);
});
}
public static void LoadModel(ModelLoadContext ctx, int cv, out bool useLog, out TFloat[] mean, out TFloat[] stddev)
{
// *** Binary format ***
// int: sizeof(TFloat)
// bool: useLog
// int: number of features (size)
// TFloat[]: mean values
// TFloat[]: stddev values
int cbFloat = ctx.Reader.ReadInt32();
Contracts.CheckDecode(cbFloat == sizeof(TFloat));
useLog = ctx.Reader.ReadBoolByte();
int size = ctx.Reader.ReadInt32();
Contracts.CheckDecode(size > 0);
if (size != cv)
throw Contracts.Except("Normalizer expected {0} slots, but the input data column has {1} slots.", size, cv);
mean = ctx.Reader.ReadDoubleArray(size);
stddev = ctx.Reader.ReadDoubleArray(size);
}
}
/// <summary>
/// Base class for tracking min and max values for a vector valued column.
/// It tracks min, max, number of non-sparse values (vCount) and number of ProcessValue() calls (trainCount).
/// NaNs are ignored when updating min and max.
/// </summary>
internal sealed class MinMaxDblAggregator : IColumnAggregator<VBuffer<TFloat>>
{
private readonly TFloat[] _min;
private readonly TFloat[] _max;
private readonly long[] _vCount;
private long _trainCount;
public MinMaxDblAggregator(int size)
{
Contracts.Check(size > 0);
_min = new TFloat[size];
_max = new TFloat[size];
_vCount = new long[size];
for (int i = 0; i < size; i++)
{
_min[i] = TFloat.PositiveInfinity;
_max[i] = TFloat.NegativeInfinity;
}
}
public TFloat[] Min
{
get { return _min; }
}
public TFloat[] Max
{
get { return _max; }
}
public long[] Count
{
get { return _vCount; }
}
public void ProcessValue(in VBuffer<TFloat> value)
{
var size = _min.Length;
Contracts.Check(value.Length == size);
_trainCount++;
var values = value.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
if (values.Length == 0)
return;
if (values.Length == size)
{
for (int j = 0; j < values.Length; j++)
{
var val = values[j];
_vCount[j]++;
Update(j, val);
}
}
else
{
var indices = value.GetIndices();
for (int k = 0; k < values.Length; k++)
{
var val = values[k];
var j = indices[k];
_vCount[j]++;
Update(j, val);
}
}
}
public void Finish()
{
var size = _min.Length;
for (int i = 0; i < size; i++)
{
if (_vCount[i] < _trainCount)
Update(i, 0);
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private void Update(int j, TFloat val)
{
if (_max[j] < val)
_max[j] = val;
if (_min[j] > val)
_min[j] = val;
}
}
/// <summary>
/// Class for computing the mean and variance for a vector valued column.
/// It tracks the current mean and the M2 (sum of squared diffs of the values from the mean),
/// the number of NaNs and the number of non-zero elements.
/// Uses the algorithm described here: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm
/// </summary>
internal sealed class MeanVarDblAggregator
{
private readonly bool _useLog;
private readonly Double[] _mean;
private readonly Double[] _m2;
private readonly long[] _cnan;
private readonly long[] _cnz;
private long _trainCount;
public MeanVarDblAggregator(int size, bool useLog)
{
_useLog = useLog;
_mean = new Double[size];
_m2 = new Double[size];
if (!_useLog)
_cnan = new long[size];
_cnz = new long[size];
}
public long[] Counts
{
get { return _cnz; }
}
public Double[] Mean
{
get { return _mean; }
}
public Double[] StdDevPopulation
{
get { return _m2.Select((m2, i) => Math.Sqrt(m2 / _cnz[i])).ToArray(); }
}
public Double[] StdDevSample
{
get { return _m2.Select((m2, i) => Math.Sqrt(m2 / Math.Max(0, _cnz[i] - 1))).ToArray(); }
}
public Double[] MeanSquareError
{
get { return _m2.Select((m2, i) => m2 / _cnz[i]).ToArray(); }
}
public Double[] SampleVariance
{
get { return _m2.Select((m2, i) => m2 / Math.Max(0, _cnz[i] - 1)).ToArray(); }
}
public Double[] M2
{
get { return _m2; }
}
public void ProcessValue(in VBuffer<TFloat> value)
{
_trainCount++;
var size = _mean.Length;
var values = value.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
if (values.Length == 0)
return;
if (values.Length == size)
{
for (int j = 0; j < values.Length; j++)
{
var origVal = values[j];
Update(j, origVal);
}
}
else
{
var indices = value.GetIndices();
for (int k = 0; k < values.Length; k++)
{
var origVal = values[k];
var j = indices[k];
Update(j, origVal);
}
}
}
public void Finish()
{
if (!_useLog)
{
for (int i = 0; i < _mean.Length; i++)
{
Contracts.Assert(_trainCount >= _cnan[i] + _cnz[i]);
MeanVarUtils.AdjustForZeros(ref _mean[i], ref _m2[i], ref _cnz[i], _trainCount - _cnan[i] - _cnz[i]);
}
}
}
[MethodImpl(MethodImplOptions.AggressiveInlining)]
private void Update(int j, TFloat origVal)
{
if (origVal == 0)
return;
var val = _useLog ? (TFloat)Math.Log(origVal) : origVal;
if (!FloatUtils.IsFinite(val))
{
if (!_useLog)
_cnan[j]++;
return;
}
_cnz[j]++;
var delta = val - _mean[j];
_mean[j] += delta / _cnz[j];
var dm2 = delta * (val - _mean[j]);
Contracts.Assert(dm2 >= 0);
_m2[j] += dm2;
Contracts.Assert(_m2[j] >= 0);
}
}
[BestFriend]
internal static partial class MedianAggregatorUtils
{
/// <summary>
/// Based on the algorithm on GeeksForGeeks https://www.geeksforgeeks.org/median-of-stream-of-integers-running-integers/.
/// </summary>
/// <param name="num">The new number to account for in our median calculation.</param>
/// <param name="median">The current median.</param>
/// <param name="belowMedianHeap">The MaxHeap that has all the numbers below the median.</param>
/// <param name="aboveMedianHeap">The MinHeap that has all the numbers above the median.</param>
[BestFriend]
internal static void GetMedianSoFar(in double num, ref double median, ref MaxHeap<double> belowMedianHeap, ref MinHeap<double> aboveMedianHeap)
{
int comparison = belowMedianHeap.Count().CompareTo(aboveMedianHeap.Count());
if (comparison < 0)
{ // More elements in aboveMedianHeap than belowMedianHeap.
if (num < median)
{ // Current element belongs in the belowMedianHeap.
// Insert new number into belowMedianHeap
belowMedianHeap.Add(num);
}
else
{ // Current element belongs in aboveMedianHeap.
// Need to move one to belowMedianHeap to keep heeps balanced.
belowMedianHeap.Add(aboveMedianHeap.Pop());
aboveMedianHeap.Add(num);
}
// Both heaps are balanced so median is the average of the 2 heaps.
median = (aboveMedianHeap.Peek() + belowMedianHeap.Peek()) / 2;
}
else if (comparison == 0)
{ // Both heaps have the same number of elements. Simple put the number where it belongs.
if (num < median)
{ // Current element belongs in the belowMedianHeap.
belowMedianHeap.Add(num);
// Now we have an odd number of items, median is the new root of the belowMedianHeap
median = belowMedianHeap.Peek();
}
else
{ // Current element belongs in above median heap.
aboveMedianHeap.Add(num);
// Now we have an odd number of items, median is the new root of the aboveMedianHeap
median = aboveMedianHeap.Peek();
}
}
else
{ // More elements in belowMedianHeap than aboveMedianHeap.
if (num < median)
{ // Current element belongs in the belowMedianHeap.
// Need to move one to aboveMedianHeap to keep heeps balanced.
aboveMedianHeap.Add(belowMedianHeap.Pop());
// Insert new number into belowMedianHeap
belowMedianHeap.Add(num);
}
else
{ // Current element belongs in aboveMedianHeap.
aboveMedianHeap.Add(num);
}
// Both heaps are balanced so median is the average of the 2 heaps.
median = (aboveMedianHeap.Peek() + belowMedianHeap.Peek()) / 2;
}
}
}
/// <summary>
/// Base class for tracking median values for a single valued column.
/// It tracks median values of non-sparse values (vCount).
/// NaNs are ignored when updating min and max.
/// </summary>
[BestFriend]
internal sealed class MedianDblAggregator : IColumnAggregator<double>
{
private MedianAggregatorUtils.MaxHeap<double> _belowMedianHeap;
private MedianAggregatorUtils.MinHeap<double> _aboveMedianHeap;
private double _median;
public MedianDblAggregator(int contatinerStartingSize = 1000)
{
Contracts.Check(contatinerStartingSize > 0);
_belowMedianHeap = new MedianAggregatorUtils.MaxHeap<double>(contatinerStartingSize);
_aboveMedianHeap = new MedianAggregatorUtils.MinHeap<double>(contatinerStartingSize);
_median = default;
}
public double Median
{
get { return _median; }
}
public void ProcessValue(in double value)
{
MedianAggregatorUtils.GetMedianSoFar(value, ref _median, ref _belowMedianHeap, ref _aboveMedianHeap);
}
public void Finish()
{
// Finish is a no-op because we are updating the median continually as we go
}
}
internal sealed partial class NormalizeTransform
{
internal abstract partial class AffineColumnFunction
{
public static IColumnFunction Create(IHost host, TFloat scale, TFloat offset)
{
return new Dbl.ImplOne(host, scale, offset);
}
public static IColumnFunction Create(IHost host, TFloat[] scale, TFloat[] offset, int[] indicesNonZeroOffset)
{
return new Dbl.ImplVec(host, scale, offset, indicesNonZeroOffset);
}
private static class Dbl
{
// REVIEW: Should we have separate classes for offset==0 and/or scale==1?
public sealed class ImplOne : ImplOne<TFloat>
{
public ImplOne(IHost host, TFloat scale, TFloat offset)
: base(host, scale, offset)
{
}
public static new ImplOne Create(ModelLoadContext ctx, IHost host, DataViewType typeSrc)
{
host.Check(typeSrc.RawType == typeof(TFloat), "The column type must be R8.");
List<int> nz = null;
int cfeat;
TFloat[] scales;
TFloat[] offsets;
int[] indices;
TFloat[] scalesSparse;
TFloat[] offsetsSparse;
AffineNormSerializationUtils.LoadModel(ctx, ref nz, out cfeat, out scales, out offsets,
out indices, out scalesSparse, out offsetsSparse);
host.Assert(scales.Length == cfeat);
host.Assert(offsets == null || offsets.Length == cfeat);
host.Assert(Utils.Size(nz) == 0 || offsets != null);
if (cfeat != 1)
throw host.Except("Normalizer expected {0} slots, but the input data column has 1 slot.", cfeat);
return new ImplOne(host, scales[0], (offsets != null) ? offsets[0] : 0);
}
private void GetResult(ref TFloat input, ref TFloat value)
{
value = (input - Offset) * Scale;
}
private protected override void SaveModel(ModelSaveContext ctx)
{
AffineNormSerializationUtils.SaveModel(ctx, 1, null, new[] { Scale }, new[] { Offset }, saveText: true);
}
public override JToken PfaInfo(BoundPfaContext ctx, JToken srcToken)
=> PfaUtils.Call("*", PfaUtils.Call("-", srcToken, Offset), Scale);
public override bool OnnxInfo(OnnxContext ctx, OnnxNode nodeProtoWrapper, int featureCount)
{
nodeProtoWrapper.AddAttribute("offset", Enumerable.Repeat(Offset, featureCount));
nodeProtoWrapper.AddAttribute("scale", Enumerable.Repeat(Scale, featureCount));
return true;
}
public override Delegate GetGetter(DataViewRow input, int icol)
{
var getSrc = input.GetGetter<TFloat>(input.Schema[icol]);
ValueGetter<TFloat> del =
(ref TFloat dst) =>
{
getSrc(ref dst);
GetResult(ref dst, ref dst);
};
return del;
}
}
// REVIEW: Does it make sense to have 3 separate classes for the 3 cases in GetResult?
public sealed class ImplVec : ImplVec<TFloat>
{
public ImplVec(IHost host, TFloat[] scale, TFloat[] offset, int[] indicesNonZeroOffset)
: base(host, scale, offset, indicesNonZeroOffset)
{
}
public static ImplVec Create(ModelLoadContext ctx, IHost host, VectorDataViewType typeSrc)
{
host.Check(typeSrc.ItemType.RawType == typeof(TFloat), "The column type must be vector of Double.");
int cv = Math.Max(1, typeSrc.Size);
List<int> nz = null;
int cfeat;
TFloat[] scales;
TFloat[] offsets;
int[] indices;
TFloat[] scalesSparse;
TFloat[] offsetsSparse;
AffineNormSerializationUtils.LoadModel(ctx, ref nz, out cfeat, out scales, out offsets,
out indices, out scalesSparse, out offsetsSparse);
host.Assert(scales.Length == cfeat);
host.Assert(offsets == null || offsets.Length == cfeat);
host.Assert(Utils.Size(nz) == 0 || offsets != null);
if (cfeat != cv)
throw host.Except("Normalizer expected {0} slots, but the input data column has {1} slots.", cfeat, cv);
return new ImplVec(host, scales, offsets, (offsets != null && nz.Count < cv / 2) ? nz.ToArray() : null);
}
private protected override void SaveModel(ModelSaveContext ctx)
{
AffineNormSerializationUtils.SaveModel(ctx, Scale.Length, null, Scale, Offset, saveText: true);
}
public override JToken PfaInfo(BoundPfaContext ctx, JToken srcToken)
{
var itemType = PfaUtils.Type.Double;
var arrType = PfaUtils.Type.Array(itemType);
var cellName = ctx.DeclareCell("AffNormScale", arrType, new JArray(Scale));
var scaleCell = PfaUtils.Cell(cellName);
if (Offset != null)
{
cellName = ctx.DeclareCell("AffNormOffset", arrType, new JArray(Offset));
var offsetCell = PfaUtils.Cell(cellName);
srcToken = PfaUtils.Call("a.zipmap", srcToken, offsetCell, PfaUtils.FuncRef(ctx.Pfa.EnsureSub(itemType)));
}
return PfaUtils.Call("a.zipmap", srcToken, scaleCell, PfaUtils.FuncRef(ctx.Pfa.EnsureMul(itemType)));
}
public override bool OnnxInfo(OnnxContext ctx, OnnxNode node, int featureCount)
{
if (Offset != null)
node.AddAttribute("offset", Offset);
else
node.AddAttribute("offset", Enumerable.Repeat<TFloat>(0, featureCount));
node.AddAttribute("scale", Scale);
return true;
}
public override Delegate GetGetter(DataViewRow input, int icol)
{
var getSrc = input.GetGetter<VBuffer<TFloat>>(input.Schema[icol]);
var bldr = new BufferBuilder<TFloat>(R8Adder.Instance);
ValueGetter<VBuffer<TFloat>> del;
if (Offset == null)
{
del = (ref VBuffer<TFloat> dst) =>
{
getSrc(ref dst);
Contracts.Check(dst.Length == Scale.Length);
FillValues(in dst, bldr, Scale);
bldr.GetResult(ref dst);
};
}
else if (IndicesNonZeroOffset == null)
{
del = (ref VBuffer<TFloat> dst) =>
{
getSrc(ref dst);
Contracts.Check(dst.Length == Scale.Length);
FillValues(in dst, bldr, Scale, Offset);
bldr.GetResult(ref dst);
};
}
else
{
del = (ref VBuffer<TFloat> dst) =>
{
getSrc(ref dst);
Contracts.Check(dst.Length == Scale.Length);
FillValues(in dst, bldr, Scale, Offset, IndicesNonZeroOffset);
bldr.GetResult(ref dst);
};
}
return del;
}
// REVIEW: Change to normalize in place. when there are no offsets.
private static void FillValues(in VBuffer<TFloat> input, BufferBuilder<TFloat> bldr, TFloat[] scale)
{
Contracts.Assert(input.Length == scale.Length);
int size = scale.Length;
var values = input.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
// We always start with sparse, since we may make things sparser than the source.
bldr.Reset(size, dense: false);
if (values.Length == 0)
return;
if (values.Length >= size)
{
for (int i = 0; i < size; i++)
bldr.AddFeature(i, values[i] * scale[i]);
return;
}
// The input is sparse.
var indices = input.GetIndices();
for (int ii = 0; ii < values.Length; ii++)
{
int i = indices[ii];
Contracts.Assert(0 <= i && i < size);
bldr.AddFeature(i, values[ii] * scale[i]);
}
}
private static void FillValues(in VBuffer<TFloat> input, BufferBuilder<TFloat> bldr, TFloat[] scale,
TFloat[] offset)
{
Contracts.Assert(input.Length == scale.Length);
int size = scale.Length;
var values = input.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
// We always start with sparse, since we may make things sparser than the source.
bldr.Reset(size, dense: false);
if (values.Length == 0)
{
for (int i = 0; i < size; i++)
bldr.AddFeature(i, -offset[i] * scale[i]);
return;
}
if (values.Length >= size)
{
for (int i = 0; i < size; i++)
bldr.AddFeature(i, (values[i] - offset[i]) * scale[i]);
return;
}
// The input is sparse.
var indices = input.GetIndices();
int ii = 0;
int ivSrc = indices[ii];
Contracts.Assert(ivSrc < size);
for (int ivDst = 0; ivDst < size; ivDst++)
{
Contracts.Assert(ivDst <= ivSrc && ivSrc <= size);
if (ivDst == ivSrc)
{
bldr.AddFeature(ivDst, (values[ii] - offset[ivDst]) * scale[ivDst]);
ivSrc = ++ii < values.Length ? indices[ii] : size;
Contracts.Assert(ii == values.Length || ivSrc < size);
}
else
bldr.AddFeature(ivDst, -offset[ivDst] * scale[ivDst]);
}
}
private static void FillValues(in VBuffer<TFloat> input, BufferBuilder<TFloat> bldr, TFloat[] scale,
TFloat[] offset, int[] nz)
{
Contracts.Assert(input.Length == scale.Length);
int size = scale.Length;
var values = input.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
// We always start with sparse, since we may make things sparser than the source.
bldr.Reset(size, dense: false);
if (values.Length == 0)
{
foreach (int i in nz)
bldr.AddFeature(i, -offset[i] * scale[i]);
return;
}
if (values.Length >= size)
{
for (int i = 0; i < size; i++)
bldr.AddFeature(i, (values[i] - offset[i]) * scale[i]);
return;
}
// The input is sparse.
var indices = input.GetIndices();
int ii = 0;
int ivSrc = indices[ii];
int inz = 0;
int ivDst = nz[inz];
for (; ; )
{
Contracts.Assert(0 <= ivDst && ivDst <= size);
Contracts.Assert(0 <= ivSrc && ivSrc <= size);
Contracts.Assert(ii < values.Length && ivSrc == indices[ii] || ii == values.Length && ivSrc == size);
Contracts.Assert(inz < nz.Length && ivDst == nz[inz] || inz == nz.Length && ivDst == size);
int diff = ivSrc - ivDst;
if (diff > 0)
{
// Offset but no value
bldr.AddFeature(ivDst, -offset[ivDst] * scale[ivDst]);
ivDst = ++inz < nz.Length ? nz[inz] : size;
}
else if (diff < 0)
{
// Value but no offset
bldr.AddFeature(ivSrc, values[ii] * scale[ivSrc]);
ivSrc = ++ii < values.Length ? indices[ii] : size;
Contracts.Assert((ii == values.Length) == (ivSrc >= size));
}
else
{
Contracts.Assert(ivSrc == ivDst);
if (ivDst >= size)
break;
bldr.AddFeature(ivDst, (values[ii] - offset[ivDst]) * scale[ivDst]);
ivSrc = ++ii < values.Length ? indices[ii] : size;
Contracts.Assert((ii == values.Length) == (ivSrc >= size));
ivDst = ++inz < nz.Length ? nz[inz] : size;
Contracts.Assert((inz == nz.Length) == (ivDst >= size));
}
}
Contracts.Assert(ii == values.Length);
Contracts.Assert(inz == nz.Length);
}
}
}
}
internal abstract partial class CdfColumnFunction
{
public static IColumnFunction Create(IHost host, TFloat mean, TFloat stddev, bool useLog)
{
return new Dbl.ImplOne(host, mean, stddev, useLog);
}
public static IColumnFunction Create(IHost host, TFloat[] mean, TFloat[] stddev, bool useLog)
{
return new Dbl.ImplVec(host, mean, stddev, useLog);
}
private static class Dbl
{
public sealed class ImplOne : ImplOne<TFloat>
{
public ImplOne(IHost host, TFloat mean, TFloat stddev, bool useLog)
: base(host, mean, stddev, useLog)
{
}
public static new ImplOne Create(ModelLoadContext ctx, IHost host, DataViewType typeSrc)
{
host.Check(typeSrc.RawType == typeof(TFloat), "The column type must be Double.");
host.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
bool useLog;
TFloat[] mean;
TFloat[] stddev;
CdfNormSerializationUtils.LoadModel(ctx, 1, out useLog, out mean, out stddev);
return new ImplOne(host, mean[0], stddev[0], useLog);
}
private void GetResult(ref TFloat input, ref TFloat value)
{
var val = UseLog ? (TFloat)Math.Log(input) : input;
if (!FloatUtils.IsFinite(val))
{
value = 0;
return;
}
value = CdfUtils.Cdf(val, Mean, Stddev);
}
private protected override void SaveModel(ModelSaveContext ctx)
{
Contracts.AssertValue(ctx);
ctx.CheckAtModel();
ctx.SetVersionInfo(GetVersionInfo());
CdfNormSerializationUtils.SaveModel(ctx, UseLog, new[] { Mean }, new[] { Stddev });
}
public override Delegate GetGetter(DataViewRow input, int icol)
{
if (Stddev <= TFloat.Epsilon)
{
ValueGetter<TFloat> trivial =
(ref TFloat dst) =>
{
dst = 0;
};
return trivial;
}
var getSrc = input.GetGetter<TFloat>(input.Schema[icol]);
ValueGetter<TFloat> del =
(ref TFloat dst) =>
{
getSrc(ref dst);
GetResult(ref dst, ref dst);
};
return del;
}
}
public sealed class ImplVec : ImplVec<TFloat>
{
public ImplVec(IHost host, TFloat[] mean, TFloat[] stddev, bool useLog)
: base(host, mean, stddev, useLog)
{
}
public static ImplVec Create(ModelLoadContext ctx, IHost host, VectorDataViewType typeSrc)
{
host.Check(typeSrc.ItemType.RawType == typeof(TFloat), "The column type must be vector of Double.");
int cv = Math.Max(1, typeSrc.Size);
host.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
bool useLog;
TFloat[] mean;
TFloat[] stddev;
CdfNormSerializationUtils.LoadModel(ctx, cv, out useLog, out mean, out stddev);
return new ImplVec(host, mean, stddev, useLog);
}
private protected override void SaveModel(ModelSaveContext ctx)
{
Contracts.AssertValue(ctx);
ctx.CheckAtModel();
ctx.SetVersionInfo(GetVersionInfo());
CdfNormSerializationUtils.SaveModel(ctx, UseLog, Mean, Stddev);
}
public override Delegate GetGetter(DataViewRow input, int icol)
{
var getSrc = input.GetGetter<VBuffer<TFloat>>(input.Schema[icol]);
var bldr = new BufferBuilder<TFloat>(R8Adder.Instance);
ValueGetter<VBuffer<TFloat>> del;
del = (ref VBuffer<TFloat> dst) =>
{
getSrc(ref dst);
Host.Check(dst.Length == Mean.Length);
FillValues(in dst, bldr, Mean, Stddev, UseLog);
bldr.GetResult(ref dst);
};
return del;
}
private static void FillValues(in VBuffer<TFloat> input, BufferBuilder<TFloat> bldr, TFloat[] mean,
TFloat[] stddev, bool useLog)
{
Contracts.Assert(input.Length == mean.Length);
int size = mean.Length;
var values = input.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
// We always start with sparse, since we may make things sparser than the source.
bldr.Reset(size, dense: false);
if (values.Length == 0)
return;
if (values.Length >= size)
{
for (int i = 0; i < size; i++)
{
var sigma = stddev[i];
if (sigma > TFloat.Epsilon)
{
var val = useLog ? (TFloat)Math.Log(values[i]) : values[i];
if (FloatUtils.IsFinite(val))
bldr.AddFeature(i, CdfUtils.Cdf(val, mean[i], sigma));
}
}
return;
}
// The input is sparse.
var indices = input.GetIndices();
for (int ii = 0; ii < values.Length; ii++)
{
var ivDst = indices[ii];
var sigma = stddev[ivDst];
if (sigma > TFloat.Epsilon)
{
var val = useLog ? (TFloat)Math.Log(values[ii]) : values[ii];
if (FloatUtils.IsFinite(val))
bldr.AddFeature(ivDst, CdfUtils.Cdf(val, mean[ivDst], sigma));
}
}
}
}
}
}
internal abstract partial class BinColumnFunction
{
public static IColumnFunction Create(IHost host, TFloat[] binUpperBounds, bool fixZero)
{
return new Dbl.ImplOne(host, binUpperBounds, fixZero);
}
public static IColumnFunction Create(IHost host, TFloat[][] binUpperBounds, bool fixZero)
{
return new Dbl.ImplVec(host, binUpperBounds, fixZero);
}
private static class Dbl
{
public sealed class ImplOne : BinColumnFunction
{
private readonly TFloat[] _binUpperBounds;
private readonly TFloat _den;
private readonly TFloat _offset;
public ImplOne(IHost host, TFloat[] binUpperBounds, bool fixZero)
: base(host)
{
_binUpperBounds = binUpperBounds;
_den = Math.Max(1, _binUpperBounds.Length - 1);
if (fixZero)
_offset = _binUpperBounds.FindIndexSorted(0) / _den;
Host.Assert(0 <= _offset && _offset <= 1);
}
public static new ImplOne Create(ModelLoadContext ctx, IHost host, DataViewType typeSrc)
{
host.Check(typeSrc.RawType == typeof(TFloat), "The column type must be Double.");
host.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// Byte: fixZero bool
bool fixZero = ctx.Reader.ReadBoolByte();
TFloat[][] binUpperBounds = null;
if (!ctx.TryProcessSubModel("BinNormalizer",
c => BinNormSerializationUtils.LoadModel(c, out binUpperBounds)))
{
throw host.ExceptDecode();
}
if (binUpperBounds.Length != 1)
throw host.Except("Normalizer expected {0} slots, but the input data column has 1 slot.", binUpperBounds.Length);
return new ImplOne(host, binUpperBounds[0], fixZero);
}
private protected override void SaveModel(ModelSaveContext ctx)
{
Contracts.AssertValue(ctx);
ctx.CheckAtModel();
ctx.SetVersionInfo(GetVersionInfo());
// *** Binary format ***
// Byte: fixZero bool
ctx.Writer.WriteBoolByte(_offset != 0);
ctx.SaveSubModel("BinNormalizer",
c => BinNormSerializationUtils.SaveModel(c, new[] { _binUpperBounds }, saveText: true));
}
public override Delegate GetGetter(DataViewRow input, int icol)
{
var getSrc = input.GetGetter<TFloat>(input.Schema[icol]);
ValueGetter<TFloat> del =
(ref TFloat dst) =>
{
getSrc(ref dst);
GetResult(ref dst, ref dst);
};
return del;
}
private void GetResult(ref TFloat input, ref TFloat value)
{
value = BinUtils.GetValue(in input, _binUpperBounds, _den, _offset);
}
public override NormalizingTransformer.NormalizerModelParametersBase GetNormalizerModelParams()
=> new NormalizingTransformer.BinNormalizerModelParameters<TFloat>(ImmutableArray.Create(_binUpperBounds), _den, _offset);
}
public sealed class ImplVec : BinColumnFunction
{
private readonly TFloat[][] _binUpperBounds;
private readonly TFloat[] _den;
private readonly TFloat[] _offset;
public ImplVec(IHost host, TFloat[][] binUpperBounds, bool fixZero)
: base(host)
{
_binUpperBounds = binUpperBounds;
_den = new TFloat[_binUpperBounds.Length];
for (int i = 0; i < _binUpperBounds.Length; i++)
_den[i] = Math.Max(1, _binUpperBounds[i].Length - 1);
if (fixZero)
{
_offset = new TFloat[_binUpperBounds.Length];
bool any = false;
for (int i = 0; i < _binUpperBounds.Length; i++)
{
_offset[i] = _binUpperBounds[i].FindIndexSorted(0) / _den[i];
Host.Assert(0 <= _offset[i] && _offset[i] <= 1);
any |= _offset[i] != 0;
}
if (!any)
_offset = null;
}
}
public static ImplVec Create(ModelLoadContext ctx, IHost host, VectorDataViewType typeSrc)
{
host.Check(typeSrc.ItemType.RawType == typeof(TFloat), "The column type must be vector of Double.");
int cv = Math.Max(1, typeSrc.Size);
host.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// Byte: fixZero bool
bool fixZero = ctx.Reader.ReadBoolByte();
TFloat[][] binUpperBounds = null;
if (!ctx.TryProcessSubModel("BinNormalizer",
c => BinNormSerializationUtils.LoadModel(c, out binUpperBounds)))
{
throw host.ExceptDecode();
}
if (binUpperBounds.Length != cv)
throw host.Except("Normalizer expected {0} slots, but the input data column has {1} slots.", binUpperBounds.Length, cv);
return new ImplVec(host, binUpperBounds, fixZero);
}
private protected override void SaveModel(ModelSaveContext ctx)
{
Contracts.AssertValue(ctx);
ctx.CheckAtModel();
ctx.SetVersionInfo(GetVersionInfo());
// *** Binary format ***
// Byte: fixZero bool
ctx.Writer.WriteBoolByte(_offset != null);
ctx.SaveSubModel("BinNormalizer", c => BinNormSerializationUtils.SaveModel(c, _binUpperBounds, saveText: true));
}
public override Delegate GetGetter(DataViewRow input, int icol)
{
var getSrc = input.GetGetter<VBuffer<TFloat>>(input.Schema[icol]);
var bldr = new BufferBuilder<TFloat>(R8Adder.Instance);
ValueGetter<VBuffer<TFloat>> del =
(ref VBuffer<TFloat> dst) =>
{
getSrc(ref dst);
Host.Check(dst.Length == _binUpperBounds.Length);
GetResult(in dst, ref dst, bldr);
};
return del;
}
private void GetResult(in VBuffer<TFloat> input, ref VBuffer<TFloat> value, BufferBuilder<TFloat> bldr)
{
Contracts.Assert(input.Length == _binUpperBounds.Length);
int size = _binUpperBounds.Length;
var values = input.GetValues();
Contracts.Assert(0 <= values.Length && values.Length <= size);
// We always start with sparse, since we may make things sparser than the source.
bldr.Reset(size, dense: false);
if (values.Length == 0)
{
bldr.GetResult(ref value);
return;
}
if (values.Length >= size)
{
if (_offset != null)
{
for (int i = 0; i < size; i++)
bldr.AddFeature(i, BinUtils.GetValue(in values[i], _binUpperBounds[i], _den[i], _offset[i]));
}
else
{
for (int i = 0; i < size; i++)
bldr.AddFeature(i, BinUtils.GetValue(in values[i], _binUpperBounds[i], _den[i]));
}
bldr.GetResult(ref value);
return;
}
// The input is sparse.
if (_offset != null)
{
var indices = input.GetIndices();
int ii = 0;
int ivSrc = indices[ii];
Contracts.Assert(ivSrc < size);
TFloat zero = 0;
for (int ivDst = 0; ivDst < size; ivDst++)
{
Contracts.Assert(ivDst <= ivSrc && ivSrc <= size);
if (ivDst == ivSrc)
{
bldr.AddFeature(ivDst,
BinUtils.GetValue(in values[ii], _binUpperBounds[ivDst], _den[ivDst], _offset[ivDst]));
ivSrc = ++ii < values.Length ? indices[ii] : size;
Contracts.Assert(ii == values.Length || ivSrc < size);
}
else
bldr.AddFeature(ivDst,
BinUtils.GetValue(in zero, _binUpperBounds[ivDst], _den[ivDst], _offset[ivDst]));
}
}
else
{
var indices = input.GetIndices();
for (int ii = 0; ii < values.Length; ii++)
{
int i = indices[ii];
Contracts.Assert(0 <= i && i < size);
bldr.AddFeature(i, BinUtils.GetValue(in values[ii], _binUpperBounds[i], _den[i]));
}
}
bldr.GetResult(ref value);
}
public override NormalizingTransformer.NormalizerModelParametersBase GetNormalizerModelParams()
=> new NormalizingTransformer.BinNormalizerModelParameters<ImmutableArray<TFloat>>(_binUpperBounds.Select(b => ImmutableArray.Create(b)).ToImmutableArray(),
ImmutableArray.Create(_den),
ImmutableArray.Create(_offset));
}
}
}
internal static partial class MinMaxUtils
{
public static void ComputeScaleAndOffset(bool fixZero, TFloat max, TFloat min, out TFloat scale, out TFloat offset)
{
if (fixZero)
ComputeScaleAndOffsetFixZero(max, min, out scale, out offset);
else
ComputeScaleAndOffset(max, min, out scale, out offset);
}
private static void ComputeScaleAndOffset(TFloat max, TFloat min, out TFloat scale, out TFloat offset)
{
Contracts.Assert(!TFloat.IsNaN(min));
Contracts.Assert(!TFloat.IsNaN(max));
// If the column has only NaNs, or has no rows at all, then min==infinity and max==-infinity. In all
// other cases, min<=max.
Contracts.Assert(min <= max || (TFloat.IsPositiveInfinity(min) && TFloat.IsNegativeInfinity(max)));
// In the case where max <= min, the slot contains no useful information (since it is either constant, or
// is all NaNs, or has no rows), so we force it to zero.
// Note that setting scale to zero effectively maps finite values to zero,
// but infinities and NaN to NaN.
// REVIEW: If min <= 0 and max >= 0, then why not fix zero for this slot and simply scale by 1 / max(abs(..))?
// We could even be more aggressive about it, and fix zero if 0 < min < max <= 2 * min.
// Then the common case where features are in the range [1, N] (and integer valued) wouldn't subtract 1 every time....
if (!(max > min))
scale = offset = 0;
else if ((scale = 1 / (max - min)) == 0)
offset = 0;
else
offset = min;
Contracts.Assert(0 <= scale && scale < TFloat.PositiveInfinity);
}
private static void ComputeScaleAndOffsetFixZero(TFloat max, TFloat min, out TFloat scale, out TFloat offset)
{
Contracts.Assert(!TFloat.IsNaN(min));
Contracts.Assert(!TFloat.IsNaN(max));
// If the column has only NaNs, or has no rows at all, then min==infinity and max==-infinity. In all
// other cases, min<=max.
Contracts.Assert(min <= max || (TFloat.IsPositiveInfinity(min) && TFloat.IsNegativeInfinity(max)));
// In the case where max <= min, the slot contains no useful information (since it is either constant, or
// is all NaNs, or has no rows), so we force it to zero.
// Note that setting scale to zero effectively maps finite values to zero,
// but infinities and NaN to NaN.
offset = 0;
if (!(max > min))
scale = 0;
else
scale = 1 / Math.Max(Math.Abs(max), Math.Abs(min));
Contracts.Assert(0 <= scale && scale < TFloat.PositiveInfinity);
}
}
internal static partial class MeanVarUtils
{
public static void ComputeScaleAndOffset(Double mean, Double stddev, out TFloat scale, out TFloat offset)
{
Contracts.Assert(!Double.IsNaN(mean));
Contracts.Assert(stddev >= 0);
// In the case where stdev==0, the slot contains no useful information (since it is constant),
// so we force it to zero. Note that setting scale to zero effectively maps finite values to zero,
// but infinities and NaN to NaN.
if (stddev == 0)
scale = offset = 0;
else if ((scale = 1 / (TFloat)stddev) == 0)
offset = 0;
else
offset = (TFloat)mean;
Contracts.Assert(0 <= scale && scale < TFloat.PositiveInfinity);
}
public static void ComputeScaleAndOffsetFixZero(Double mean, Double meanSquaredError, out TFloat scale, out TFloat offset)
{
Contracts.Assert(!Double.IsNaN(mean));
Contracts.Assert(meanSquaredError >= 0);
// In the case where stdev==0, the slot contains no useful information (since it is constant),
// so we force it to zero. Note that setting scale to zero effectively maps finite values to zero,
// but infinities and NaN to NaN.
offset = 0;
if (meanSquaredError == 0)
scale = 0;
else
scale = 1 / (TFloat)Math.Sqrt(meanSquaredError + mean * mean);
Contracts.Assert(0 <= scale && scale < TFloat.PositiveInfinity);
}
}
private static partial class CdfUtils
{
public static TFloat Cdf(TFloat input, TFloat mean, TFloat stddev)
{
// REVIEW: This should be changed to call the AML stats library.
// Temporarily, it does the following:
// Using CDF(x) = 0.5 ( 1 + erf( ( x - mu ) / ( sigma * sqrt(2) ) ) )
// Also using an approximation for erf(x) from https://en.wikipedia.org/wiki/Error_function#Approximation_with_elementary_functions
var x = (input - mean) / stddev;
var x2 = x * x / 2;
const TFloat a = (TFloat)0.147;
var ax2 = a * x2;
return (TFloat)(0.5 + 0.5 * Math.Sign(x) * Math.Sqrt(1 - Math.Exp(-x2 * (4 / Math.PI + ax2) / (1 + ax2))));
}
}
internal static partial class BinUtils
{
public static TFloat GetValue(in TFloat input, TFloat[] binUpperBounds, TFloat den, TFloat offset)
{
if (TFloat.IsNaN(input))
return input;
int binIdx = binUpperBounds.FindIndexSorted(0, binUpperBounds.Length - 1, input);
Contracts.Check(binIdx < binUpperBounds.Length);
var value = binIdx / den - offset;
Contracts.Assert(-1 <= value && value <= 1);
return value;
}
public static TFloat GetValue(in TFloat input, TFloat[] binUpperBounds, TFloat den)
{
if (TFloat.IsNaN(input))
return input;
int binIdx = binUpperBounds.FindIndexSorted(0, binUpperBounds.Length - 1, input);
Contracts.Check(binIdx < binUpperBounds.Length);
var value = binIdx / den;
Contracts.Assert(0 <= value & value <= 1);
return value;
}
}
private static class Dbl
{
public abstract class MinMaxOneColumnFunctionBuilderBase : OneColumnFunctionBuilderBase<TFloat>
{
protected readonly bool Fix;
protected readonly MinMaxDblAggregator Aggregator;
private VBuffer<TFloat> _buffer;
protected MinMaxOneColumnFunctionBuilderBase(IHost host, long lim, bool fix, ValueGetter<TFloat> getSrc)
: base(host, lim, getSrc)
{
Fix = fix;
Aggregator = new MinMaxDblAggregator(1);
_buffer = new VBuffer<TFloat>(1, new TFloat[1]);
}
protected override bool ProcessValue(in TFloat val)
{
if (!base.ProcessValue(in val))
return false;
VBufferEditor.CreateFromBuffer(ref _buffer).Values[0] = val;
Aggregator.ProcessValue(in _buffer);
return true;
}
}
public sealed class MinMaxOneColumnFunctionBuilder : MinMaxOneColumnFunctionBuilderBase
{
private MinMaxOneColumnFunctionBuilder(IHost host, long lim, bool fix, ValueGetter<TFloat> getSrc)
: base(host, lim, fix, getSrc)
{
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.MinMaxColumnOptions column, IHost host, DataViewType srcType,
ValueGetter<TFloat> getter)
{
host.CheckUserArg(column.MaximumExampleCount > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
return new MinMaxOneColumnFunctionBuilder(host, column.MaximumExampleCount, column.EnsureZeroUntouched, getter);
}
public override IColumnFunction CreateColumnFunction()
{
Aggregator.Finish();
TFloat scale;
TFloat offset;
MinMaxUtils.ComputeScaleAndOffset(Fix, Aggregator.Max[0], Aggregator.Min[0], out scale, out offset);
return AffineColumnFunction.Create(Host, scale, offset);
}
}
public abstract class MinMaxVecColumnFunctionBuilderBase : VecColumnFunctionBuilderBase<TFloat>
{
protected readonly MinMaxDblAggregator Aggregator;
protected readonly bool Fix;
protected MinMaxVecColumnFunctionBuilderBase(IHost host, int cv, long lim, bool fix, ValueGetter<VBuffer<TFloat>> getSrc)
: base(host, lim, getSrc)
{
Fix = fix;
Aggregator = new MinMaxDblAggregator(cv);
}
protected override bool ProcessValue(in VBuffer<TFloat> buffer)
{
if (!base.ProcessValue(in buffer))
return false;
var size = Aggregator.Min.Length;
if (buffer.Length != size)
throw Host.Except("Normalizer expected {0} slots but got {1}", size, buffer.Length);
Aggregator.ProcessValue(in buffer);
return true;
}
}
public sealed class MinMaxVecColumnFunctionBuilder : MinMaxVecColumnFunctionBuilderBase
{
private MinMaxVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix,
ValueGetter<VBuffer<TFloat>> getSrc)
: base(host, cv, lim, fix, getSrc)
{
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.MinMaxColumnOptions column, IHost host, VectorDataViewType srcType,
ValueGetter<VBuffer<TFloat>> getter)
{
host.CheckUserArg(column.MaximumExampleCount > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
var cv = srcType.Size;
return new MinMaxVecColumnFunctionBuilder(host, cv, column.MaximumExampleCount, column.EnsureZeroUntouched, getter);
}
public override IColumnFunction CreateColumnFunction()
{
Aggregator.Finish();
var cv = Aggregator.Min.Length;
// These are ignored if fix is true.
int lim = cv / 2;
var nz = new List<int>();
for (int i = 0; i < cv; i++)
{
MinMaxUtils.ComputeScaleAndOffset(Fix, Aggregator.Max[i], Aggregator.Min[i], out Aggregator.Max[i], out Aggregator.Min[i]);
if (Aggregator.Min[i] != 0 && nz.Count < lim)
nz.Add(i);
}
var min = Aggregator.Min;
// Note: There is a special case when cv == 1. In this case lim == 0, so nz will be empty regardless
// of whether the offset is non-zero.
Host.Assert((lim == 0) == (cv == 1));
int[] indicesNonZeroOffset = null;
if (Fix)
min = null;
else if (cv == 1)
{
if (min[0] == 0)
min = null;
}
else if (nz.Count == 0)
min = null;
else if (nz.Count < lim)
indicesNonZeroOffset = nz.ToArray();
return AffineColumnFunction.Create(Host, Aggregator.Max, min, indicesNonZeroOffset);
}
}
public sealed class MeanVarOneColumnFunctionBuilder : OneColumnFunctionBuilderBase<TFloat>
{
private readonly bool _useLog;
private readonly bool _useCdf;
private readonly bool _fix;
private readonly bool _useSampleVariance;
private readonly MeanVarDblAggregator _aggregator;
private VBuffer<TFloat> _buffer;
private MeanVarOneColumnFunctionBuilder(IHost host, long lim, bool fix, ValueGetter<TFloat> getSrc, bool useLog, bool useCdf, bool useSampleVariance)
: base(host, lim, getSrc)
{
_useLog = useLog;
_useCdf = useCdf;
_fix = fix;
_useSampleVariance = useSampleVariance;
_aggregator = new MeanVarDblAggregator(1, useLog);
_buffer = new VBuffer<TFloat>(1, new TFloat[1]);
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, DataViewType srcType,
ValueGetter<TFloat> getter)
{
host.CheckUserArg(column.MaximumExampleCount > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
return new MeanVarOneColumnFunctionBuilder(host, column.MaximumExampleCount, column.EnsureZeroUntouched, getter, false, column.UseCdf, column.UseSampleVariance);
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, DataViewType srcType,
ValueGetter<TFloat> getter)
{
var lim = column.MaximumExampleCount;
host.CheckUserArg(lim > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
return new MeanVarOneColumnFunctionBuilder(host, lim, false, getter, true, column.UseCdf, column.UseSampleVariance);
}
protected override bool ProcessValue(in TFloat origVal)
{
if (!base.ProcessValue(in origVal))
return false;
VBufferEditor.CreateFromBuffer(ref _buffer).Values[0] = origVal;
_aggregator.ProcessValue(in _buffer);
return true;
}
public override IColumnFunction CreateColumnFunction()
{
_aggregator.Finish();
if (_useCdf)
return CreateCdfColumnFunction();
return CreateAffineColumnFunction();
}
private IColumnFunction CreateAffineColumnFunction()
{
Contracts.Assert(_aggregator.M2[0] >= 0);
if (_aggregator.M2[0] == 0)
return AffineColumnFunction.Create(Host, (TFloat)0, (TFloat)0);
TFloat scale;
TFloat offset;
var stdDev = _useSampleVariance ? _aggregator.StdDevSample[0] : _aggregator.StdDevPopulation[0];
var variance = _useSampleVariance ? _aggregator.SampleVariance[0] : _aggregator.MeanSquareError[0];
if (_fix)
MeanVarUtils.ComputeScaleAndOffsetFixZero(_aggregator.Mean[0], variance, out scale, out offset);
else
MeanVarUtils.ComputeScaleAndOffset(_aggregator.Mean[0], stdDev, out scale, out offset);
return AffineColumnFunction.Create(Host, scale, offset);
}
private IColumnFunction CreateCdfColumnFunction()
{
Contracts.Assert(_aggregator.M2[0] >= 0);
if (_aggregator.M2[0] == 0 || _aggregator.Counts[0] == 0)
return CdfColumnFunction.Create(Host, (TFloat)0, (TFloat)0, _useLog);
var stdDev = _useSampleVariance ? _aggregator.StdDevSample[0] : _aggregator.StdDevPopulation[0];
return CdfColumnFunction.Create(Host, (TFloat)_aggregator.Mean[0], (TFloat)stdDev, _useLog);
}
}
public sealed class MeanVarVecColumnFunctionBuilder : VecColumnFunctionBuilderBase<TFloat>
{
private readonly bool _fix;
private readonly bool _useLog;
private readonly bool _useCdf;
private readonly bool _useSampleVariance;
private readonly MeanVarDblAggregator _aggregator;
private MeanVarVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix,
ValueGetter<VBuffer<TFloat>> getSrc, bool useLog, bool useCdf, bool useSampleVariance)
: base(host, lim, getSrc)
{
_aggregator = new MeanVarDblAggregator(cv, useLog);
_fix = fix;
_useLog = useLog;
_useCdf = useCdf;
_useSampleVariance = useSampleVariance;
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.MeanVarianceColumnOptions column, IHost host, VectorDataViewType srcType,
ValueGetter<VBuffer<TFloat>> getter)
{
host.CheckUserArg(column.MaximumExampleCount > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
var cv = srcType.Size;
return new MeanVarVecColumnFunctionBuilder(host, cv, column.MaximumExampleCount, column.EnsureZeroUntouched, getter, false, column.UseCdf, column.UseSampleVariance);
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.LogMeanVarianceColumnOptions column, IHost host, VectorDataViewType srcType,
ValueGetter<VBuffer<TFloat>> getter)
{
var lim = column.MaximumExampleCount;
host.CheckUserArg(lim > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
var cv = srcType.Size;
return new MeanVarVecColumnFunctionBuilder(host, cv, lim, false, getter, true, column.UseCdf, column.UseSampleVariance);
}
protected override bool ProcessValue(in VBuffer<TFloat> buffer)
{
if (!base.ProcessValue(in buffer))
return false;
_aggregator.ProcessValue(in buffer);
return true;
}
public override IColumnFunction CreateColumnFunction()
{
_aggregator.Finish();
if (_useCdf)
return CreateCdfColumnFunction();
return CreateAffineColumnFunction();
}
private IColumnFunction CreateAffineColumnFunction()
{
int cv = _aggregator.Mean.Length;
// These are ignored if fix is true.
int lim = cv / 2;
var nz = new List<int>();
var scale = new TFloat[cv];
var offset = new TFloat[cv];
for (int i = 0; i < cv; i++)
{
Contracts.Assert(_aggregator.M2[i] >= 0);
if (_aggregator.M2[i] == 0)
{
scale[i] = offset[i] = 0;
continue;
}
var stdDev = _useSampleVariance ? _aggregator.StdDevSample[i] : _aggregator.StdDevPopulation[i];
var variance = _useSampleVariance ? _aggregator.SampleVariance[i] : _aggregator.MeanSquareError[i];
if (_fix)
MeanVarUtils.ComputeScaleAndOffsetFixZero(_aggregator.Mean[i], variance, out scale[i], out offset[i]);
else
MeanVarUtils.ComputeScaleAndOffset(_aggregator.Mean[i], stdDev, out scale[i], out offset[i]);
if (offset[i] != 0 && nz.Count < lim)
nz.Add(i);
}
// Note: There is a special case when cv == 1. In this case lim == 0, so nz will be empty regardless
// of whether the offset is non-zero.
Host.Assert((lim == 0) == (cv == 1));
int[] indicesNonZeroOffset = null;
if (_fix)
offset = null;
else if (cv == 1)
{
if (offset[0] == 0)
offset = null;
}
else if (nz.Count == 0)
offset = null;
else if (nz.Count < lim)
indicesNonZeroOffset = nz.ToArray();
return AffineColumnFunction.Create(Host, scale, offset, indicesNonZeroOffset);
}
private IColumnFunction CreateCdfColumnFunction()
{
int cv = _aggregator.Mean.Length;
var mean = new TFloat[cv];
var stddev = new TFloat[cv];
for (int i = 0; i < cv; i++)
{
Contracts.Assert(_aggregator.M2[i] >= 0);
if (_aggregator.M2[i] == 0 || _aggregator.Counts[i] == 0)
{
mean[i] = stddev[i] = 0;
continue;
}
mean[i] = (TFloat)_aggregator.Mean[i];
stddev[i] = (TFloat)(_useSampleVariance ? _aggregator.StdDevSample[i] : _aggregator.StdDevPopulation[i]);
}
return CdfColumnFunction.Create(Host, mean, stddev, _useLog);
}
}
public sealed class BinOneColumnFunctionBuilder : OneColumnFunctionBuilderBase<TFloat>
{
private readonly bool _fix;
private readonly int _numBins;
private readonly List<TFloat> _values;
private BinOneColumnFunctionBuilder(IHost host, long lim, bool fix, int numBins, ValueGetter<TFloat> getSrc)
: base(host, lim, getSrc)
{
_fix = fix;
_numBins = numBins;
_values = new List<TFloat>();
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.BinningColumnOptions column, IHost host, DataViewType srcType,
ValueGetter<TFloat> getter)
{
var lim = column.MaximumExampleCount;
host.CheckUserArg(lim > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
bool fix = column.EnsureZeroUntouched;
var numBins = column.MaximumBinCount;
host.CheckUserArg(numBins > 1, nameof(column.MaximumBinCount), "Must be greater than 1");
return new BinOneColumnFunctionBuilder(host, lim, fix, numBins, getter);
}
protected override bool ProcessValue(in TFloat val)
{
if (!base.ProcessValue(in val))
return false;
if (val != 0)
_values.Add(val);
return true;
}
public override IColumnFunction CreateColumnFunction()
{
var binFinder = new GreedyBinFinder();
var numZeroes = checked((int)(Lim - Rem - _values.Count));
_values.RemoveAll(TFloat.IsNaN);
var binUpperBounds = binFinder.FindBins(_numBins, _values, numZeroes);
return BinColumnFunction.Create(Host, binUpperBounds, _fix);
}
}
public sealed class BinVecColumnFunctionBuilder : VecColumnFunctionBuilderBase<TFloat>
{
private readonly bool _fix;
private readonly int _numBins;
private readonly List<TFloat>[] _values;
private BinVecColumnFunctionBuilder(IHost host, int cv, long lim, bool fix, int numBins,
ValueGetter<VBuffer<TFloat>> getSrc)
: base(host, lim, getSrc)
{
_fix = fix;
_numBins = numBins;
_values = new List<TFloat>[cv];
for (int i = 0; i < cv; i++)
{
_values[i] = new List<TFloat>();
}
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.BinningColumnOptions column, IHost host, VectorDataViewType srcType,
ValueGetter<VBuffer<TFloat>> getter)
{
var lim = column.MaximumExampleCount;
host.CheckUserArg(lim > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
bool fix = column.EnsureZeroUntouched;
var numBins = column.MaximumBinCount;
host.CheckUserArg(numBins > 1, nameof(column.MaximumBinCount), "Must be greater than 1");
var cv = srcType.Size;
return new BinVecColumnFunctionBuilder(host, cv, lim, fix, numBins, getter);
}
protected override bool ProcessValue(in VBuffer<TFloat> buffer)
{
if (!base.ProcessValue(in buffer))
return false;
int size = _values.Length;
Host.Check(buffer.Length == size);
var values = buffer.GetValues();
Host.Assert(0 <= values.Length && values.Length <= size);
if (values.Length == 0)
return true;
if (values.Length == size)
{
for (int j = 0; j < values.Length; j++)
_values[j].Add(values[j]);
}
else
{
var indices = buffer.GetIndices();
for (int k = 0; k < values.Length; k++)
{
var val = values[k];
var j = indices[k];
_values[j].Add(val);
}
}
return true;
}
public override IColumnFunction CreateColumnFunction()
{
var binFinder = new GreedyBinFinder();
var count = _values.Length;
var binUpperBounds = new TFloat[count][];
for (int i = 0; i < count; i++)
{
var numZeroes = checked((int)(Lim - Rem - _values[i].Count));
_values[i].RemoveAll(TFloat.IsNaN);
binUpperBounds[i] = binFinder.FindBins(_numBins, _values[i], numZeroes);
}
return BinColumnFunction.Create(Host, binUpperBounds, _fix);
}
}
public sealed class SupervisedBinOneColumnFunctionBuilder : OneColumnSupervisedBinFunctionBuilderBase<TFloat>
{
private readonly bool _fix;
private readonly int _numBins;
private readonly int _minBinSize;
private SupervisedBinOneColumnFunctionBuilder(IHost host, long lim, bool fix, int numBins, int minBinSize, int valueColumnId, int labelColumnId, DataViewRow dataRow)
: base(host, lim, valueColumnId, labelColumnId, dataRow)
{
_fix = fix;
_numBins = numBins;
_minBinSize = minBinSize;
}
protected override bool AcceptColumnValue(in TFloat colValue)
{
return !TFloat.IsNaN(colValue);
}
public override IColumnFunction CreateColumnFunction()
{
var binFinder = new SupervisedBinFinder();
var binUpperBounds = binFinder.FindBins(_numBins, _minBinSize, LabelCardinality, ColValues, Labels);
return BinColumnFunction.Create(Host, binUpperBounds, _fix);
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, int valueColumnId, int labelColumnId, DataViewRow dataRow)
{
var lim = column.MaximumExampleCount;
host.CheckUserArg(lim > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
bool fix = column.EnsureZeroUntouched;
var numBins = column.MaximumBinCount;
host.CheckUserArg(numBins > 1, nameof(column.MaximumBinCount), "Must be greater than 1");
host.CheckUserArg(column.MininimumBinSize > 0, nameof(column.MininimumBinSize), "Must be positive");
return new SupervisedBinOneColumnFunctionBuilder(host, lim, fix, numBins, column.MininimumBinSize, valueColumnId, labelColumnId, dataRow);
}
}
public sealed class SupervisedBinVecColumnFunctionBuilder : VecColumnSupervisedBinFunctionBuilderBase<TFloat>
{
private readonly bool _fix;
private readonly int _numBins;
private readonly int _minBinSize;
private SupervisedBinVecColumnFunctionBuilder(IHost host, long lim, bool fix, int numBins, int minBinSize, int valueColumnId, int labelColumnId, DataViewRow dataRow)
: base(host, lim, valueColumnId, labelColumnId, dataRow)
{
_fix = fix;
_numBins = numBins;
_minBinSize = minBinSize;
}
protected override bool AcceptColumnValue(in VBuffer<TFloat> colValuesBuffer)
{
return !VBufferUtils.HasNaNs(in colValuesBuffer);
}
public override IColumnFunction CreateColumnFunction()
{
var binFinder = new SupervisedBinFinder();
TFloat[][] binUpperBounds = new TFloat[ColumnSlotCount][];
for (int i = 0; i < ColumnSlotCount; i++)
binUpperBounds[i] = binFinder.FindBins(_numBins, _minBinSize, LabelCardinality, ColValues[i], Labels);
return BinColumnFunction.Create(Host, binUpperBounds, _fix);
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.SupervisedBinningColumOptions column, IHost host, int valueColumnId, int labelColumnId, DataViewRow dataRow)
{
var lim = column.MaximumExampleCount;
host.CheckUserArg(lim > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
bool fix = column.EnsureZeroUntouched;
var numBins = column.MaximumBinCount;
host.CheckUserArg(numBins > 1, nameof(column.MaximumBinCount), "Must be greater than 1");
host.CheckUserArg(column.MininimumBinSize > 0, nameof(column.MininimumBinSize), "Must be positive");
return new SupervisedBinVecColumnFunctionBuilder(host, lim, fix, numBins, column.MininimumBinSize, valueColumnId, labelColumnId, dataRow);
}
}
public sealed class RobustScalerOneColumnFunctionBuilder : OneColumnFunctionBuilderBase<double>
{
private readonly MinMaxDblAggregator _minMaxAggregator;
private readonly MedianDblAggregator _medianAggregator;
private readonly bool _centerData;
private readonly uint _quantileMin;
private readonly uint _quantileMax;
private VBuffer<double> _buffer;
private RobustScalerOneColumnFunctionBuilder(IHost host, long lim, bool centerData, uint quantileMin, uint quantileMax, ValueGetter<double> getSrc)
: base(host, lim, getSrc)
{
// Using the MinMax aggregator since that is what needs to be found here as well.
// The difference is how the min/max are used.
_minMaxAggregator = new MinMaxDblAggregator(1);
_medianAggregator = new MedianDblAggregator();
_buffer = new VBuffer<double>(1, new double[1]);
_centerData = centerData;
_quantileMin = quantileMin;
_quantileMax = quantileMax;
}
protected override bool ProcessValue(in double val)
{
if (!base.ProcessValue(in val))
return false;
VBufferEditor.CreateFromBuffer(ref _buffer).Values[0] = val;
_minMaxAggregator.ProcessValue(in _buffer);
_medianAggregator.ProcessValue(in val);
return true;
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.RobustScalingColumnOptions column, IHost host, DataViewType srcType,
bool centerData, uint quantileMin, uint quantileMax, ValueGetter<double> getter)
{
host.CheckUserArg(column.MaximumExampleCount > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
return new RobustScalerOneColumnFunctionBuilder(host, column.MaximumExampleCount, centerData, quantileMin, quantileMax, getter);
}
public override IColumnFunction CreateColumnFunction()
{
_minMaxAggregator.Finish();
_medianAggregator.Finish();
double median = _medianAggregator.Median;
double range = _minMaxAggregator.Max[0] - _minMaxAggregator.Min[0];
// Divide the range by 100 because we need to make the number, i.e. 75, into a decimal, .75
double quantileRange = (_quantileMax - _quantileMin) / 100f;
double scale = 1 / (range * quantileRange);
if (_centerData)
return AffineColumnFunction.Create(Host, scale, median);
else
return AffineColumnFunction.Create(Host, scale, 0);
}
}
public sealed class RobustScalerVecFunctionBuilder : OneColumnFunctionBuilderBase<VBuffer<double>>
{
private readonly MinMaxDblAggregator _minMaxAggregator;
private readonly MedianDblAggregator[] _medianAggregators;
private readonly bool _centerData;
private readonly uint _quantileMin;
private readonly uint _quantileMax;
private RobustScalerVecFunctionBuilder(IHost host, long lim, int vectorSize, bool centerData, uint quantileMin, uint quantileMax, ValueGetter<VBuffer<double>> getSrc)
: base(host, lim, getSrc)
{
// Using the MinMax aggregator since that is what needs to be found here as well.
// The difference is how the min/max are used.
_minMaxAggregator = new MinMaxDblAggregator(vectorSize);
// If we aren't centering data don't need the median.
_medianAggregators = new MedianDblAggregator[vectorSize];
for (int i = 0; i < vectorSize; i++)
{
_medianAggregators[i] = new MedianDblAggregator();
}
_centerData = centerData;
_quantileMin = quantileMin;
_quantileMax = quantileMax;
}
protected override bool ProcessValue(in VBuffer<double> val)
{
if (!base.ProcessValue(in val))
return false;
_minMaxAggregator.ProcessValue(in val);
// Have to calculate the median per slot
var span = val.GetValues();
for (int i = 0; i < _medianAggregators.Length; i++)
{
_medianAggregators[i].ProcessValue(span[i]);
}
return true;
}
public static IColumnFunctionBuilder Create(NormalizingEstimator.RobustScalingColumnOptions column, IHost host, VectorDataViewType srcType,
bool centerData, uint quantileMin, uint quantileMax, ValueGetter<VBuffer<double>> getter)
{
host.CheckUserArg(column.MaximumExampleCount > 1, nameof(column.MaximumExampleCount), "Must be greater than 1");
var vectorSize = srcType.Size;
return new RobustScalerVecFunctionBuilder(host, column.MaximumExampleCount, vectorSize, centerData, quantileMin, quantileMax, getter);
}
public override IColumnFunction CreateColumnFunction()
{
_minMaxAggregator.Finish();
double[] scale = new double[_medianAggregators.Length];
double[] median = new double[_medianAggregators.Length];
// Have to calculate the median per slot
for (int i = 0; i < _medianAggregators.Length; i++)
{
_medianAggregators[i].Finish();
median[i] = _medianAggregators[i].Median;
double range = _minMaxAggregator.Max[i] - _minMaxAggregator.Min[i];
// Divide the range by 100 because we need to make the number, i.e. 75, into a decimal, .75
double quantileRange = (_quantileMax - _quantileMin) / 100f;
scale[i] = 1 / (range * quantileRange);
}
if (_centerData)
return AffineColumnFunction.Create(Host, scale, median, null);
else
return AffineColumnFunction.Create(Host, scale, null, null);
}
}
}
}
}
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