/// <summary>
/// Copy from this buffer to the given destination, making sure to explicitly include the
/// first count indices in indicesInclude. Note that indicesInclude should be sorted
/// with each index less than this.Length. Note that this can make the destination be
/// dense even if "this" is sparse.
/// </summary>
public void CopyTo(ref VBuffer <T> dst, int[] indicesInclude, int count)
{
Contracts.CheckParam(count >= 0, nameof(count));
Contracts.CheckParam(Utils.Size(indicesInclude) >= count, nameof(indicesInclude));
Contracts.CheckParam(Utils.Size(indicesInclude) <= Length, nameof(indicesInclude));
// REVIEW: Ideally we should Check that indicesInclude is sorted and in range. Would that
// check be too expensive?
#if DEBUG
int prev = -1;
for (int i = 0; i < count; i++)
{
Contracts.Assert(prev < indicesInclude[i]);
prev = indicesInclude[i];
}
Contracts.Assert(prev < Length);
#endif
if (IsDense || count == 0)
{
CopyTo(ref dst);
return;
}
if (count >= Length / 2 || Count >= Length / 2)
{
CopyToDense(ref dst);
return;
}
var indices = dst.Indices;
var values = dst.Values;
if (Count == 0)
{
// No values in "this".
if (Utils.Size(indices) < count)
{
indices = new int[count];
}
Array.Copy(indicesInclude, indices, count);
if (Utils.Size(values) < count)
{
values = new T[count];
}
else
{
Array.Clear(values, 0, count);
}
dst = new VBuffer <T>(Length, count, values, indices);
return;
}
int size = 0;
int max = count + Count;
Contracts.Assert(max < Length);
int ii1;
int ii2;
if (max >= Length / 2 || Utils.Size(values) < max || Utils.Size(indices) < max)
{
// Compute the needed size.
ii1 = 0;
ii2 = 0;
for (; ;)
{
Contracts.Assert(ii1 < Count);
Contracts.Assert(ii2 < count);
size++;
int diff = Indices[ii1] - indicesInclude[ii2];
if (diff == 0)
{
ii1++;
ii2++;
if (ii1 >= Count)
{
size += count - ii2;
break;
}
if (ii2 >= count)
{
size += Count - ii1;
break;
}
}
else if (diff < 0)
{
if (++ii1 >= Count)
{
size += count - ii2;
break;
}
}
else
{
if (++ii2 >= count)
{
size += Count - ii1;
break;
}
}
}
Contracts.Assert(size >= count && size >= Count);
if (size == Count)
{
CopyTo(ref dst);
return;
}
if (size >= Length / 2)
{
CopyToDense(ref dst);
return;
}
if (Utils.Size(values) < size)
{
values = new T[size];
}
if (Utils.Size(indices) < size)
{
indices = new int[size];
}
max = size;
}
int ii = 0;
ii1 = 0;
ii2 = 0;
for (; ;)
{
Contracts.Assert(ii < max);
Contracts.Assert(ii1 < Count);
Contracts.Assert(ii2 < count);
int i1 = Indices[ii1];
int i2 = indicesInclude[ii2];
if (i1 <= i2)
{
indices[ii] = i1;
values[ii] = Values[ii1];
ii++;
if (i1 == i2)
{
ii2++;
}
if (++ii1 >= Count)
{
if (ii2 >= count)
{
break;
}
Array.Clear(values, ii, count - ii2);
Array.Copy(indicesInclude, ii2, indices, ii, count - ii2);
ii += count - ii2;
break;
}
if (ii2 >= count)
{
Array.Copy(Values, ii1, values, ii, Count - ii1);
Array.Copy(Indices, ii1, indices, ii, Count - ii1);
ii += Count - ii1;
break;
}
}
else
{
indices[ii] = i2;
values[ii] = default(T);
ii++;
if (++ii2 >= count)
{
Array.Copy(Values, ii1, values, ii, Count - ii1);
Array.Copy(Indices, ii1, indices, ii, Count - ii1);
ii += Count - ii1;
break;
}
}
}
Contracts.Assert(size == ii || size == 0);
dst = new VBuffer <T>(Length, ii, values, indices);
}
private void GetKeyNames(int col, ref VBuffer <DvText> dst)
{
Contracts.Assert(col == 0);
Contracts.AssertValue(_keyNamesType);
_keyNames.CopyTo(ref dst);
}
private void GetTerms(int iinfo, ref VBuffer <ReadOnlyMemory <char> > dst)
{
Host.Assert(0 <= iinfo && iinfo < _exes.Length);
Host.Assert(_slotNames[iinfo].Length > 0);
_slotNames[iinfo].CopyTo(ref dst);
}
protected override void CopyValue(ref VBuffer <TItem> src, ref VBuffer <TItem> dst)
{
src.CopyTo(ref dst);
}
private static void FillValues(IExceptionContext ectx, ref VBuffer <Float> src, ref VBuffer <Float> dst, Float divisor, Float scale, Float offset = 0)
{
int count = src.Count;
int length = src.Length;
ectx.Assert(Utils.Size(src.Values) >= count);
ectx.Assert(divisor >= 0);
if (count == 0)
{
dst = new VBuffer <Float>(length, 0, dst.Values, dst.Indices);
return;
}
ectx.Assert(count > 0);
ectx.Assert(length > 0);
Float normScale = scale;
if (divisor > 0)
{
normScale /= divisor;
}
// Don't normalize small values.
if (normScale < MinScale)
{
normScale = 1;
}
if (offset == 0)
{
var dstValues = dst.Values;
if (Utils.Size(dstValues) < count)
{
dstValues = new Float[count];
}
var dstIndices = dst.Indices;
if (!src.IsDense)
{
if (Utils.Size(dstIndices) < count)
{
dstIndices = new int[count];
}
Array.Copy(src.Indices, dstIndices, count);
}
SseUtils.Scale(normScale, src.Values, dstValues, count);
dst = new VBuffer <Float>(length, count, dstValues, dstIndices);
return;
}
// Subtracting the mean requires a dense representation.
src.CopyToDense(ref dst);
if (normScale != 1)
{
SseUtils.ScaleAdd(normScale, -offset, dst.Values, length);
}
else
{
SseUtils.Add(-offset, dst.Values, length);
}
}
public override Delegate[] CreateGetters(IRow input, Func <int, bool> activeOutput, out Action disposer)
{
disposer = null;
var getters = new Delegate[3];
if (!activeOutput(ClusterIdCol) && !activeOutput(SortedClusterCol) && !activeOutput(SortedClusterScoreCol))
{
return(getters);
}
long cachedPosition = -1;
VBuffer <Single> scores = default(VBuffer <Single>);
var scoresArr = new Single[_numClusters];
int[] sortedIndices = new int[_numClusters];
var scoreGetter = input.GetGetter <VBuffer <Single> >(ScoreIndex);
Action updateCacheIfNeeded =
() =>
{
if (cachedPosition != input.Position)
{
scoreGetter(ref scores);
scores.CopyTo(scoresArr);
int j = 0;
foreach (var index in Enumerable.Range(0, scoresArr.Length).OrderBy(i => scoresArr[i]))
{
sortedIndices[j++] = index;
}
cachedPosition = input.Position;
}
};
if (activeOutput(ClusterIdCol))
{
ValueGetter <uint> assignedFn =
(ref uint dst) =>
{
updateCacheIfNeeded();
dst = (uint)sortedIndices[0] + 1;
};
getters[ClusterIdCol] = assignedFn;
}
if (activeOutput(SortedClusterScoreCol))
{
ValueGetter <VBuffer <Single> > topKScoresFn =
(ref VBuffer <Single> dst) =>
{
updateCacheIfNeeded();
var values = dst.Values;
if (Utils.Size(values) < _numClusters)
{
values = new Single[_numClusters];
}
for (int i = 0; i < _numClusters; i++)
{
values[i] = scores.GetItemOrDefault(sortedIndices[i]);
}
dst = new VBuffer <Single>(_numClusters, values);
};
getters[SortedClusterScoreCol] = topKScoresFn;
}
if (activeOutput(SortedClusterCol))
{
ValueGetter <VBuffer <uint> > topKClassesFn =
(ref VBuffer <uint> dst) =>
{
updateCacheIfNeeded();
var values = dst.Values;
if (Utils.Size(values) < _numClusters)
{
values = new uint[_numClusters];
}
for (int i = 0; i < _numClusters; i++)
{
values[i] = (uint)sortedIndices[i] + 1;
}
dst = new VBuffer <uint>(_numClusters, values);
};
getters[SortedClusterCol] = topKClassesFn;
}
return(getters);
}
protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer)
{
Host.AssertValueOrNull(ch);
Host.AssertValue(input);
Host.Assert(0 <= iinfo && iinfo < Infos.Length);
Host.Assert(Infos[iinfo].TypeSrc.IsVector);
Host.Assert(Infos[iinfo].TypeSrc.ItemType.IsKey);
disposer = null;
var getSrc = RowCursorUtils.GetVecGetterAs <uint>(NumberType.U4, input, Infos[iinfo].Source);
var src = default(VBuffer <uint>);
var bldr = new NgramBufferBuilder(_exes[iinfo].NgramLength, _exes[iinfo].SkipLength,
_ngramMaps[iinfo].Count, GetNgramIdFinder(iinfo));
var keyCount = (uint)Infos[iinfo].TypeSrc.ItemType.KeyCount;
if (keyCount == 0)
{
keyCount = uint.MaxValue;
}
ValueGetter <VBuffer <Float> > del;
switch (_exes[iinfo].Weighting)
{
case WeightingCriteria.TfIdf:
Host.AssertValue(_invDocFreqs[iinfo]);
del =
(ref VBuffer <Float> dst) =>
{
getSrc(ref src);
if (!bldr.IsEmpty)
{
bldr.Reset();
bldr.AddNgrams(ref src, 0, keyCount);
bldr.GetResult(ref dst);
VBufferUtils.Apply(ref dst, (int i, ref Float v) => v = (Float)(v * _invDocFreqs[iinfo][i]));
}
else
{
dst = new VBuffer <Float>(0, dst.Values, dst.Indices);
}
};
break;
case WeightingCriteria.Idf:
Host.AssertValue(_invDocFreqs[iinfo]);
del =
(ref VBuffer <Float> dst) =>
{
getSrc(ref src);
if (!bldr.IsEmpty)
{
bldr.Reset();
bldr.AddNgrams(ref src, 0, keyCount);
bldr.GetResult(ref dst);
VBufferUtils.Apply(ref dst, (int i, ref Float v) => v = v >= 1 ? (Float)_invDocFreqs[iinfo][i] : 0);
}
else
{
dst = new VBuffer <Float>(0, dst.Values, dst.Indices);
}
};
break;
case WeightingCriteria.Tf:
del =
(ref VBuffer <Float> dst) =>
{
getSrc(ref src);
if (!bldr.IsEmpty)
{
bldr.Reset();
bldr.AddNgrams(ref src, 0, keyCount);
bldr.GetResult(ref dst);
}
else
{
dst = new VBuffer <Float>(0, dst.Values, dst.Indices);
}
};
break;
default:
throw Host.Except("Unsupported weighting criteria");
}
return(del);
}
protected override void UpdateCore(Float label, ref VBuffer <Float> score, ref VBuffer <Double> loss, Float weight)
{
AddL1AndL2Loss(label, ref score, weight);
AddCustomLoss(weight, ref loss);
}
protected override void ApplyLossFunction(ref VBuffer <float> score, float label, ref VBuffer <Double> loss)
{
VBufferUtils.PairManipulator <Float, Double> lossFn =
(int slot, Float src, ref Double dst) => dst = LossFunction.Loss(src, label);
VBufferUtils.ApplyWith(ref score, ref loss, lossFn);
}
private static void Load(IChannel ch, ModelLoadContext ctx, CodecFactory factory, ref VBuffer <ReadOnlyMemory <char> > values)
{
Contracts.AssertValue(ch);
ch.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel(GetVersionInfo());
// *** Binary format ***
// Codec parameterization: A codec parameterization that should be a ReadOnlyMemory codec
// int: n, the number of bytes used to write the values
// byte[n]: As encoded using the codec
// Get the codec from the factory, and from the stream. We have to
// attempt to read the codec from the stream, since codecs can potentially
// be versioned based on their parameterization.
IValueCodec codec;
// This *could* happen if we have an old version attempt to read a new version.
// Enabling this sort of binary classification is why we also need to write the
// codec specification.
if (!factory.TryReadCodec(ctx.Reader.BaseStream, out codec))
{
throw ch.ExceptDecode();
}
ch.AssertValue(codec);
ch.CheckDecode(codec.Type.IsVector);
ch.CheckDecode(codec.Type.ItemType.IsText);
var textCodec = (IValueCodec <VBuffer <ReadOnlyMemory <char> > >)codec;
var bufferLen = ctx.Reader.ReadInt32();
ch.CheckDecode(bufferLen >= 0);
using (var stream = new SubsetStream(ctx.Reader.BaseStream, bufferLen))
{
using (var reader = textCodec.OpenReader(stream, 1))
{
reader.MoveNext();
values = default(VBuffer <ReadOnlyMemory <char> >);
reader.Get(ref values);
}
ch.CheckDecode(stream.ReadByte() == -1);
}
}
private static void Save(IChannel ch, ModelSaveContext ctx, CodecFactory factory, ref VBuffer <ReadOnlyMemory <char> > values)
{
Contracts.AssertValue(ch);
ch.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel();
ctx.SetVersionInfo(GetVersionInfo());
// *** Binary format ***
// Codec parameterization: A codec parameterization that should be a ReadOnlyMemory codec
// int: n, the number of bytes used to write the values
// byte[n]: As encoded using the codec
// Get the codec from the factory
IValueCodec codec;
var result = factory.TryGetCodec(new VectorType(TextType.Instance), out codec);
ch.Assert(result);
ch.Assert(codec.Type.IsVector);
ch.Assert(codec.Type.VectorSize == 0);
ch.Assert(codec.Type.ItemType.RawType == typeof(ReadOnlyMemory <char>));
IValueCodec <VBuffer <ReadOnlyMemory <char> > > textCodec = (IValueCodec <VBuffer <ReadOnlyMemory <char> > >)codec;
factory.WriteCodec(ctx.Writer.BaseStream, codec);
using (var mem = new MemoryStream())
{
using (var writer = textCodec.OpenWriter(mem))
{
writer.Write(ref values);
writer.Commit();
}
ctx.Writer.WriteByteArray(mem.ToArray());
}
// Make this resemble, more or less, the auxiliary output from the TermTransform.
// It will differ somewhat due to the vector being possibly sparse. To distinguish
// between missing and empty, empties are not written at all, while missings are.
var v = values;
char[] buffer = null;
ctx.SaveTextStream("Terms.txt",
writer =>
{
writer.WriteLine("# Number of terms = {0} of length {1}", v.Count, v.Length);
foreach (var pair in v.Items())
{
var text = pair.Value;
if (text.IsEmpty)
{
continue;
}
writer.Write("{0}\t", pair.Key);
// REVIEW: What about escaping this, *especially* for linebreaks?
// Do C# and .NET really have no equivalent to Python's "repr"? :(
if (text.IsEmpty)
{
writer.WriteLine();
continue;
}
Utils.EnsureSize(ref buffer, text.Length);
var span = text.Span;
for (int i = 0; i < text.Length; i++)
{
buffer[i] = span[i];
}
writer.WriteLine(buffer, 0, text.Length);
}
});
}
private void DropNAs <TDst>(ref VBuffer <TDst> src, ref VBuffer <TDst> dst, InPredicate <TDst> isNA)
{
Host.AssertValue(isNA);
int newCount = 0;
for (int i = 0; i < src.Count; i++)
{
if (!isNA(in src.Values[i]))
{
newCount++;
}
}
Host.Assert(newCount <= src.Count);
if (newCount == 0)
{
dst = new VBuffer <TDst>(src.Length - src.Count, 0, dst.Values, dst.Indices);
return;
}
if (newCount == src.Count)
{
Utils.Swap(ref src, ref dst);
return;
}
var values = dst.Values;
if (Utils.Size(values) < newCount)
{
values = new TDst[newCount];
}
int iDst = 0;
if (src.IsDense)
{
for (int i = 0; i < src.Count; i++)
{
if (!isNA(in src.Values[i]))
{
values[iDst] = src.Values[i];
iDst++;
}
}
Host.Assert(iDst == newCount);
dst = new VBuffer <TDst>(newCount, values, dst.Indices);
}
else
{
var indices = dst.Indices;
if (Utils.Size(indices) < newCount)
{
indices = new int[newCount];
}
int offset = 0;
for (int i = 0; i < src.Count; i++)
{
if (!isNA(in src.Values[i]))
{
values[iDst] = src.Values[i];
indices[iDst] = src.Indices[i] - offset;
iDst++;
}
public void Conv(ref byte[] src, ref VBuffer <Byte> dst) => dst = src != null ? new VBuffer <byte>(src.Length, src) : new VBuffer <byte>(0, new byte[0]);
public static void Copy(ref VBuffer <T> src, ref VBuffer <T> dst)
{
src.CopyTo(ref dst);
}
private static Float[] Train(IHost host, ColInfo[] infos, Arguments args, IDataView trainingData)
{
Contracts.AssertValue(host, "host");
host.AssertNonEmpty(infos);
var avgDistances = new Float[infos.Length];
const int reservoirSize = 5000;
bool[] activeColumns = new bool[trainingData.Schema.ColumnCount];
for (int i = 0; i < infos.Length; i++)
{
activeColumns[infos[i].Source] = true;
}
var reservoirSamplers = new ReservoirSamplerWithReplacement <VBuffer <Float> > [infos.Length];
using (var cursor = trainingData.GetRowCursor(col => activeColumns[col]))
{
var rng = args.Seed.HasValue ? RandomUtils.Create(args.Seed) : host.Rand;
for (int i = 0; i < infos.Length; i++)
{
if (infos[i].TypeSrc.IsVector)
{
var get = cursor.GetGetter <VBuffer <Float> >(infos[i].Source);
reservoirSamplers[i] = new ReservoirSamplerWithReplacement <VBuffer <Float> >(rng, reservoirSize, get);
}
else
{
var getOne = cursor.GetGetter <Float>(infos[i].Source);
Float val = 0;
ValueGetter <VBuffer <Float> > get =
(ref VBuffer <Float> dst) =>
{
getOne(ref val);
dst = new VBuffer <float>(1, new[] { val });
};
reservoirSamplers[i] = new ReservoirSamplerWithReplacement <VBuffer <Float> >(rng, reservoirSize, get);
}
}
while (cursor.MoveNext())
{
for (int i = 0; i < infos.Length; i++)
{
reservoirSamplers[i].Sample();
}
}
for (int i = 0; i < infos.Length; i++)
{
reservoirSamplers[i].Lock();
}
}
for (int iinfo = 0; iinfo < infos.Length; iinfo++)
{
var instanceCount = reservoirSamplers[iinfo].NumSampled;
// If the number of pairs is at most the maximum reservoir size / 2, we go over all the pairs,
// so we get all the examples. Otherwise, get a sample with replacement.
VBuffer <Float>[] res;
int resLength;
if (instanceCount < reservoirSize && instanceCount * (instanceCount - 1) <= reservoirSize)
{
res = reservoirSamplers[iinfo].GetCache();
resLength = reservoirSamplers[iinfo].Size;
Contracts.Assert(resLength == instanceCount);
}
else
{
res = reservoirSamplers[iinfo].GetSample().ToArray();
resLength = res.Length;
}
// If the dataset contains only one valid Instance, then we can't learn anything anyway, so just return 1.
if (instanceCount <= 1)
{
avgDistances[iinfo] = 1;
}
else
{
Float[] distances;
var sub = args.Column[iinfo].MatrixGenerator;
if (sub == null)
{
sub = args.MatrixGenerator;
}
// create a dummy generator in order to get its type.
// REVIEW this should be refactored. See https://github.com/dotnet/machinelearning/issues/699
var matrixGenerator = sub.CreateComponent(host, 1);
bool gaussian = matrixGenerator is GaussianFourierSampler;
// If the number of pairs is at most the maximum reservoir size / 2, go over all the pairs.
if (resLength < reservoirSize)
{
distances = new Float[instanceCount * (instanceCount - 1) / 2];
int count = 0;
for (int i = 0; i < instanceCount; i++)
{
for (int j = i + 1; j < instanceCount; j++)
{
distances[count++] = gaussian ? VectorUtils.L2DistSquared(ref res[i], ref res[j])
: VectorUtils.L1Distance(ref res[i], ref res[j]);
}
}
host.Assert(count == distances.Length);
}
else
{
distances = new Float[reservoirSize / 2];
for (int i = 0; i < reservoirSize - 1; i += 2)
{
// For Gaussian kernels, we scale by the L2 distance squared, since the kernel function is exp(-gamma ||x-y||^2).
// For Laplacian kernels, we scale by the L1 distance, since the kernel function is exp(-gamma ||x-y||_1).
distances[i / 2] = gaussian ? VectorUtils.L2DistSquared(ref res[i], ref res[i + 1]) :
VectorUtils.L1Distance(ref res[i], ref res[i + 1]);
}
}
// If by chance, in the random permutation all the pairs are the same instance we return 1.
Float median = MathUtils.GetMedianInPlace(distances, distances.Length);
avgDistances[iinfo] = median == 0 ? 1 : median;
}
}
return(avgDistances);
}
protected override bool IsNaN(ref VBuffer <Float> score)
{
return(VBufferUtils.HasNaNs(ref score));
}
private static void TransformFeatures(IExceptionContext ectx, ref VBuffer <Float> src, ref VBuffer <Float> dst, TransformInfo transformInfo)
{
ectx.Check(src.Length == transformInfo.Dimension);
var values = dst.Values;
if (Utils.Size(values) < transformInfo.Rank)
{
values = new Float[transformInfo.Rank];
}
for (int i = 0; i < transformInfo.Rank; i++)
{
values[i] = VectorUtils.DotProductWithOffset(transformInfo.Eigenvectors[i], 0, ref src) -
(transformInfo.MeanProjected == null ? 0 : transformInfo.MeanProjected[i]);
}
dst = new VBuffer <Float>(transformInfo.Rank, values, dst.Indices);
}
public override Delegate[] CreateGetters(IRow input, Func <int, bool> activeCols, out Action disposer)
{
Host.Assert(LabelIndex >= 0);
Host.Assert(ScoreIndex >= 0);
disposer = null;
long cachedPosition = -1;
Float label = 0;
var score = default(VBuffer <Float>);
var l1 = VBufferUtils.CreateDense <Double>(_scoreSize);
ValueGetter <Float> nanGetter = (ref Float value) => value = Single.NaN;
var labelGetter = activeCols(L1Col) || activeCols(L2Col) ? RowCursorUtils.GetLabelGetter(input, LabelIndex) : nanGetter;
ValueGetter <VBuffer <Float> > scoreGetter;
if (activeCols(L1Col) || activeCols(L2Col))
{
scoreGetter = input.GetGetter <VBuffer <Float> >(ScoreIndex);
}
else
{
scoreGetter = (ref VBuffer <Float> dst) => dst = default(VBuffer <Float>);
}
Action updateCacheIfNeeded =
() =>
{
if (cachedPosition != input.Position)
{
labelGetter(ref label);
scoreGetter(ref score);
var lab = (Double)label;
foreach (var s in score.Items(all: true))
{
l1.Values[s.Key] = Math.Abs(lab - s.Value);
}
cachedPosition = input.Position;
}
};
var getters = new Delegate[2];
if (activeCols(L1Col))
{
ValueGetter <VBuffer <Double> > l1Fn =
(ref VBuffer <Double> dst) =>
{
updateCacheIfNeeded();
l1.CopyTo(ref dst);
};
getters[L1Col] = l1Fn;
}
if (activeCols(L2Col))
{
VBufferUtils.PairManipulator <Double, Double> sqr =
(int slot, Double x, ref Double y) => y = x * x;
ValueGetter <VBuffer <Double> > l2Fn =
(ref VBuffer <Double> dst) =>
{
updateCacheIfNeeded();
dst = new VBuffer <Double>(_scoreSize, 0, dst.Values, dst.Indices);
VBufferUtils.ApplyWith(ref l1, ref dst, sqr);
};
getters[L2Col] = l2Fn;
}
return(getters);
}
private SequencePool[] Train(Arguments args, IDataView trainingData, out double[][] invDocFreqs)
{
// Contains the maximum number of grams to store in the dictionary, for each level of ngrams,
// from 1 (in position 0) up to ngramLength (in position ngramLength-1)
var lims = new int[Infos.Length][];
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
var all = args.Column[iinfo].AllLengths ?? args.AllLengths;
var ngramLength = _exes[iinfo].NgramLength;
var maxNumTerms = Utils.Size(args.Column[iinfo].MaxNumTerms) > 0 ? args.Column[iinfo].MaxNumTerms : args.MaxNumTerms;
if (!all)
{
Host.CheckUserArg(Utils.Size(maxNumTerms) == 0 ||
Utils.Size(maxNumTerms) == 1 && maxNumTerms[0] > 0, nameof(args.MaxNumTerms));
lims[iinfo] = new int[ngramLength];
lims[iinfo][ngramLength - 1] = Utils.Size(maxNumTerms) == 0 ? Arguments.DefaultMaxTerms : maxNumTerms[0];
}
else
{
Host.CheckUserArg(Utils.Size(maxNumTerms) <= ngramLength, nameof(args.MaxNumTerms));
Host.CheckUserArg(Utils.Size(maxNumTerms) == 0 || maxNumTerms.All(i => i >= 0) && maxNumTerms[maxNumTerms.Length - 1] > 0, nameof(args.MaxNumTerms));
var extend = Utils.Size(maxNumTerms) == 0 ? Arguments.DefaultMaxTerms : maxNumTerms[maxNumTerms.Length - 1];
lims[iinfo] = Utils.BuildArray(ngramLength,
i => i < Utils.Size(maxNumTerms) ? maxNumTerms[i] : extend);
}
}
var helpers = new NgramBufferBuilder[Infos.Length];
var getters = new ValueGetter <VBuffer <uint> > [Infos.Length];
var src = new VBuffer <uint> [Infos.Length];
// Keep track of how many grams are in the pool for each value of n. Position
// i in _counts counts how many (i+1)-grams are in the pool for column iinfo.
var counts = new int[Infos.Length][];
var ngramMaps = new SequencePool[Infos.Length];
bool[] activeInput = new bool[trainingData.Schema.ColumnCount];
foreach (var info in Infos)
{
activeInput[info.Source] = true;
}
using (var cursor = trainingData.GetRowCursor(col => activeInput[col]))
using (var pch = Host.StartProgressChannel("Building n-gram dictionary"))
{
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
Host.Assert(Infos[iinfo].TypeSrc.IsVector && Infos[iinfo].TypeSrc.ItemType.IsKey);
var ngramLength = _exes[iinfo].NgramLength;
var skipLength = _exes[iinfo].SkipLength;
getters[iinfo] = RowCursorUtils.GetVecGetterAs <uint>(NumberType.U4, cursor, Infos[iinfo].Source);
src[iinfo] = default(VBuffer <uint>);
counts[iinfo] = new int[ngramLength];
ngramMaps[iinfo] = new SequencePool();
// Note: GetNgramIdFinderAdd will control how many ngrams of a specific length will
// be added (using lims[iinfo]), therefore we set slotLim to the maximum
helpers[iinfo] = new NgramBufferBuilder(ngramLength, skipLength, Utils.ArrayMaxSize,
GetNgramIdFinderAdd(counts[iinfo], lims[iinfo], ngramMaps[iinfo], _exes[iinfo].RequireIdf(), Host));
}
int cInfoFull = 0;
bool[] infoFull = new bool[Infos.Length];
invDocFreqs = new double[Infos.Length][];
long totalDocs = 0;
Double rowCount = trainingData.GetRowCount(true) ?? Double.NaN;
var buffers = new VBuffer <float> [Infos.Length];
pch.SetHeader(new ProgressHeader(new[] { "Total n-grams" }, new[] { "documents" }),
e => e.SetProgress(0, totalDocs, rowCount));
while (cInfoFull < Infos.Length && cursor.MoveNext())
{
totalDocs++;
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
getters[iinfo](ref src[iinfo]);
var keyCount = (uint)Infos[iinfo].TypeSrc.ItemType.KeyCount;
if (keyCount == 0)
{
keyCount = uint.MaxValue;
}
if (!infoFull[iinfo])
{
if (_exes[iinfo].RequireIdf())
{
helpers[iinfo].Reset();
}
helpers[iinfo].AddNgrams(ref src[iinfo], 0, keyCount);
if (_exes[iinfo].RequireIdf())
{
int totalNgrams = counts[iinfo].Sum();
Utils.EnsureSize(ref invDocFreqs[iinfo], totalNgrams);
helpers[iinfo].GetResult(ref buffers[iinfo]);
foreach (var pair in buffers[iinfo].Items())
{
if (pair.Value >= 1)
{
invDocFreqs[iinfo][pair.Key] += 1;
}
}
}
}
AssertValid(counts[iinfo], lims[iinfo], ngramMaps[iinfo]);
}
}
pch.Checkpoint(counts.Sum(c => c.Sum()), totalDocs);
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
for (int i = 0; i < Utils.Size(invDocFreqs[iinfo]); i++)
{
if (invDocFreqs[iinfo][i] != 0)
{
invDocFreqs[iinfo][i] = Math.Log(totalDocs / invDocFreqs[iinfo][i]);
}
}
}
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
AssertValid(counts[iinfo], lims[iinfo], ngramMaps[iinfo]);
int ngramLength = _exes[iinfo].NgramLength;
for (int i = 0; i < ngramLength; i++)
{
_exes[iinfo].NonEmptyLevels[i] = counts[iinfo][i] > 0;
}
}
return(ngramMaps);
}
}
public static void GetSlotNames(RoleMappedSchema schema, RoleMappedSchema.ColumnRole role, int vectorSize, ref VBuffer <DvText> slotNames)
{
Contracts.CheckValueOrNull(schema);
Contracts.CheckValue(role.Value, nameof(role));
Contracts.CheckParam(vectorSize >= 0, nameof(vectorSize));
IReadOnlyList <ColumnInfo> list;
if ((list = schema?.GetColumns(role)) == null || list.Count != 1 || !schema.Schema.HasSlotNames(list[0].Index, vectorSize))
{
slotNames = new VBuffer <DvText>(vectorSize, 0, slotNames.Values, slotNames.Indices);
}
else
{
schema.Schema.GetMetadata(Kinds.SlotNames, list[0].Index, ref slotNames);
}
}
protected override void GetMissing(ref VBuffer <TItem> dst)
{
dst = new VBuffer <TItem>(Type.VectorSize, 0, dst.Values, dst.Indices);
}
/// <summary>
/// Getter generator for inputs of type <typeparamref name="TSrc"/>, where output type is a vector of hashes
/// </summary>
/// <typeparam name="TSrc">Input type. Must be a vector</typeparam>
/// <param name="input">Row input</param>
/// <param name="iinfo">Index of the getter</param>
private ValueGetter <VBuffer <uint> > ComposeGetterVecToVec <TSrc>(IRow input, int iinfo)
{
Host.AssertValue(input);
Host.Assert(Infos[iinfo].TypeSrc.IsVector);
var getSrc = GetSrcGetter <VBuffer <TSrc> >(input, iinfo);
var hashFunction = ComposeHashDelegate <TSrc>();
var src = default(VBuffer <TSrc>);
int n = _exes[iinfo].OutputValueCount;
int expectedSrcLength = Infos[iinfo].TypeSrc.VectorSize;
int[][] slotMap = _exes[iinfo].SlotMap;
// REVIEW: consider adding a fix-zero functionality (subtract emptyTextHash from all hashes)
var mask = (1U << _exes[iinfo].HashBits) - 1;
var hashSeed = _exes[iinfo].HashSeed;
bool ordered = _exes[iinfo].Ordered;
TSrc[] denseValues = null;
return
((ref VBuffer <uint> dst) =>
{
getSrc(ref src);
Host.Check(src.Length == expectedSrcLength);
TSrc[] values;
// force-densify the input
// REVIEW: this performs poorly if only a fraction of sparse vector is used for hashing.
// This scenario was unlikely at the time of writing. Regardless of performance, the hash value
// needs to be consistent across equivalent representations - sparse vs dense.
if (src.IsDense)
{
values = src.Values;
}
else
{
if (denseValues == null)
{
denseValues = new TSrc[expectedSrcLength];
}
values = denseValues;
src.CopyTo(values);
}
var hashes = dst.Values;
if (Utils.Size(hashes) < n)
{
hashes = new uint[n];
}
for (int i = 0; i < n; i++)
{
uint hash = hashSeed;
foreach (var srcSlot in slotMap[i])
{
// REVIEW: some legacy code hashes 0 for srcSlot in ord- case, do we need to preserve this behavior?
if (ordered)
{
hash = Hashing.MurmurRound(hash, (uint)srcSlot);
}
hash = hashFunction(ref values[srcSlot], hash);
}
hashes[i] = (Hashing.MixHash(hash) & mask) + 1; // +1 to offset from zero, which has special meaning for KeyType
}
dst = new VBuffer <uint>(n, hashes, dst.Indices);
});
}
private ValueGetter <ReadOnlyMemory <char> > GetTextValueGetter <TSrc>(Row input, int colSrc, VBuffer <ReadOnlyMemory <char> > slotNames)
{
Contracts.AssertValue(input);
Contracts.AssertValue(Predictor);
var featureGetter = input.GetGetter <TSrc>(colSrc);
var map = Predictor.GetFeatureContributionMapper <TSrc, VBuffer <float> >(_topContributionsCount, _bottomContributionsCount, _normalize);
var features = default(TSrc);
var contributions = default(VBuffer <float>);
return
((ref ReadOnlyMemory <char> dst) =>
{
featureGetter(ref features);
map(in features, ref contributions);
var indices = new Span <int>();
var values = new Span <float>();
if (contributions.IsDense)
{
Utils.GetIdentityPermutation(contributions.Length).AsSpan().CopyTo(indices);
}
else
{
contributions.GetIndices().CopyTo(indices);
}
contributions.GetValues().CopyTo(values);
var count = values.Length;
var sb = new StringBuilder();
GenericSpanSortHelper <int> .Sort(indices, values, 0, count);
for (var i = 0; i < count; i++)
{
var val = values[i];
var ind = indices[i];
var name = GetSlotName(ind, slotNames);
sb.AppendFormat("{0}: {1}, ", name, val);
}
if (sb.Length > 0)
{
_env.Assert(sb.Length >= 2);
sb.Remove(sb.Length - 2, 2);
}
dst = new ReadOnlyMemory <char>(sb.ToString().ToCharArray());
});
}
private static DropSlotsTransform.Column CreateDropSlotsColumn(Arguments args, ref VBuffer <Single> scores, out int selectedCount)
{
// Not checking the scores.Length, because:
// 1. If it's the same as the features column length, we should be constructing the right DropSlots arguments.
// 2. If it's less, we assume that the rest of the scores are zero and we drop the slots.
// 3. If it's greater, the drop slots ignores the ranges that are outside the valid range of indices for the column.
Contracts.Assert(args.Threshold.HasValue != args.NumSlotsToKeep.HasValue);
var col = new DropSlotsTransform.Column();
col.Source = args.FeatureColumn;
selectedCount = 0;
// Degenerate case, dropping all slots.
if (scores.Count == 0)
{
var range = new DropSlotsTransform.Range();
col.Slots = new DropSlotsTransform.Range[] { range };
return(col);
}
int tiedScoresToKeep;
float threshold;
if (args.Threshold.HasValue)
{
threshold = args.Threshold.Value;
tiedScoresToKeep = threshold > 0 ? int.MaxValue : 0;
}
else
{
Contracts.Assert(args.NumSlotsToKeep.HasValue);
threshold = ComputeThreshold(scores.Values, scores.Count, args.NumSlotsToKeep.Value, out tiedScoresToKeep);
}
var slots = new List <DropSlotsTransform.Range>();
for (int i = 0; i < scores.Count; i++)
{
var score = Math.Abs(scores.Values[i]);
if (score > threshold)
{
selectedCount++;
continue;
}
if (score == threshold && tiedScoresToKeep > 0)
{
tiedScoresToKeep--;
selectedCount++;
continue;
}
var range = new DropSlotsTransform.Range();
range.Min = i;
while (++i < scores.Count)
{
score = Math.Abs(scores.Values[i]);
if (score > threshold)
{
selectedCount++;
break;
}
if (score == threshold && tiedScoresToKeep > 0)
{
tiedScoresToKeep--;
selectedCount++;
break;
}
}
range.Max = i - 1;
slots.Add(range);
}
if (!scores.IsDense)
{
int ii = 0;
var count = slots.Count;
for (int i = 0; i < count; i++)
{
var range = slots[i];
Contracts.Assert(range.Max != null);
var min = range.Min;
var max = range.Max.Value;
Contracts.Assert(min <= max);
Contracts.Assert(max < scores.Count);
range.Min = min == 0 ? 0 : scores.Indices[min - 1] + 1;
range.Max = max == scores.Count - 1 ? scores.Length - 1 : scores.Indices[max + 1] - 1;
// Add the gaps before this range.
for (; ii < min; ii++)
{
var gapMin = ii == 0 ? 0 : scores.Indices[ii - 1] + 1;
var gapMax = scores.Indices[ii] - 1;
if (gapMin <= gapMax)
{
var gap = new DropSlotsTransform.Range();
gap.Min = gapMin;
gap.Max = gapMax;
slots.Add(gap);
}
}
ii = max;
}
// Add the gaps after the last range.
for (; ii <= scores.Count; ii++)
{
var gapMin = ii == 0 ? 0 : scores.Indices[ii - 1] + 1;
var gapMax = ii == scores.Count ? scores.Length - 1 : scores.Indices[ii] - 1;
if (gapMin <= gapMax)
{
var gap = new DropSlotsTransform.Range();
gap.Min = gapMin;
gap.Max = gapMax;
slots.Add(gap);
}
}
// Remove all slots past scores.Length.
var lastRange = new DropSlotsTransform.Range();
lastRange.Min = scores.Length;
slots.Add(lastRange);
}
if (slots.Count > 0)
{
col.Slots = slots.ToArray();
return(col);
}
return(null);
}
public static void GetSlotNames(RoleMappedSchema schema, RoleMappedSchema.ColumnRole role, int vectorSize, ref VBuffer <ReadOnlyMemory <char> > slotNames)
{
Contracts.CheckValueOrNull(schema);
Contracts.CheckParam(vectorSize >= 0, nameof(vectorSize));
IReadOnlyList <ColumnInfo> list;
if ((list = schema?.GetColumns(role)) == null || list.Count != 1 || !schema.Schema.HasSlotNames(list[0].Index, vectorSize))
{
VBufferUtils.Resize(ref slotNames, vectorSize, 0);
}
else
{
schema.Schema.GetMetadata(Kinds.SlotNames, list[0].Index, ref slotNames);
}
}
/// <summary>
/// Returns a score for each slot of the features column.
/// </summary>
public static void Train(IHostEnvironment env, IDataView input, Arguments args, ref VBuffer <Single> scores)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register(RegistrationName);
host.CheckValue(args, nameof(args));
host.CheckValue(input, nameof(input));
args.Check(host);
TrainCore(host, input, args, ref scores);
}
// The multi-output regression evaluator prints only the per-label metrics for each fold.
protected override void PrintFoldResultsCore(IChannel ch, Dictionary <string, IDataView> metrics)
{
IDataView fold;
if (!metrics.TryGetValue(MetricKinds.OverallMetrics, out fold))
{
throw ch.Except("No overall metrics found");
}
int isWeightedCol;
bool needWeighted = fold.Schema.TryGetColumnIndex(MetricKinds.ColumnNames.IsWeighted, out isWeightedCol);
int stratCol;
bool hasStrats = fold.Schema.TryGetColumnIndex(MetricKinds.ColumnNames.StratCol, out stratCol);
int stratVal;
bool hasStratVals = fold.Schema.TryGetColumnIndex(MetricKinds.ColumnNames.StratVal, out stratVal);
ch.Assert(hasStrats == hasStratVals);
var colCount = fold.Schema.ColumnCount;
var vBufferGetters = new ValueGetter <VBuffer <double> > [colCount];
using (var cursor = fold.GetRowCursor(col => true))
{
bool isWeighted = false;
ValueGetter <bool> isWeightedGetter;
if (needWeighted)
{
isWeightedGetter = cursor.GetGetter <bool>(isWeightedCol);
}
else
{
isWeightedGetter = (ref bool dst) => dst = false;
}
ValueGetter <uint> stratGetter;
if (hasStrats)
{
var type = cursor.Schema.GetColumnType(stratCol);
stratGetter = RowCursorUtils.GetGetterAs <uint>(type, cursor, stratCol);
}
else
{
stratGetter = (ref uint dst) => dst = 0;
}
int labelCount = 0;
for (int i = 0; i < fold.Schema.ColumnCount; i++)
{
if (fold.Schema.IsHidden(i) || (needWeighted && i == isWeightedCol) ||
(hasStrats && (i == stratCol || i == stratVal)))
{
continue;
}
var type = fold.Schema.GetColumnType(i);
if (type.IsKnownSizeVector && type.ItemType == NumberType.R8)
{
vBufferGetters[i] = cursor.GetGetter <VBuffer <double> >(i);
if (labelCount == 0)
{
labelCount = type.VectorSize;
}
else
{
ch.Check(labelCount == type.VectorSize, "All vector metrics should contain the same number of slots");
}
}
}
var labelNames = new ReadOnlyMemory <char> [labelCount];
for (int j = 0; j < labelCount; j++)
{
labelNames[j] = string.Format("Label_{0}", j).AsMemory();
}
var sb = new StringBuilder();
sb.AppendLine("Per-label metrics:");
sb.AppendFormat("{0,12} ", " ");
for (int i = 0; i < labelCount; i++)
{
sb.AppendFormat(" {0,20}", labelNames[i]);
}
sb.AppendLine();
VBuffer <Double> metricVals = default(VBuffer <Double>);
bool foundWeighted = !needWeighted;
bool foundUnweighted = false;
uint strat = 0;
while (cursor.MoveNext())
{
isWeightedGetter(ref isWeighted);
if (foundWeighted && isWeighted || foundUnweighted && !isWeighted)
{
throw ch.Except("Multiple {0} rows found in overall metrics data view",
isWeighted ? "weighted" : "unweighted");
}
if (isWeighted)
{
foundWeighted = true;
}
else
{
foundUnweighted = true;
}
stratGetter(ref strat);
if (strat > 0)
{
continue;
}
for (int i = 0; i < colCount; i++)
{
if (vBufferGetters[i] != null)
{
vBufferGetters[i](ref metricVals);
ch.Assert(metricVals.Length == labelCount);
sb.AppendFormat("{0}{1,12}:", isWeighted ? "Weighted " : "", fold.Schema.GetColumnName(i));
foreach (var metric in metricVals.Items(all: true))
{
sb.AppendFormat(" {0,20:G20}", metric.Value);
}
sb.AppendLine();
}
}
if (foundUnweighted && foundWeighted)
{
break;
}
}
ch.Assert(foundUnweighted && foundWeighted);
ch.Info(sb.ToString());
}
}
private static void EnsureCachedResultValueMapper(ValueMapper <VBuffer <Float>, Float, Float> mapper,
ref long cachedPosition, ValueGetter <VBuffer <Float> > featureGetter, ref VBuffer <Float> features,
ref Float score, ref Float prob, Row input)
{
Contracts.AssertValue(mapper);
if (cachedPosition != input.Position)
{
if (featureGetter != null)
{
featureGetter(ref features);
}
mapper(in features, ref score, ref prob);
cachedPosition = input.Position;
}
}
protected override void Copy(ref VBuffer <T> src, ref VBuffer <T> dst)
{
src.CopyTo(ref dst);
}
protected abstract bool AcceptColumnValue(ref VBuffer <TFloat> buffer);
