private static InferenceResult InferTextFileColumnTypesCore(IHostEnvironment env, IMultiStreamSource fileSource, Arguments args, IChannel ch)
{
Contracts.AssertValue(ch);
ch.AssertValue(env);
ch.AssertValue(fileSource);
ch.AssertValue(args);
if (args.ColumnCount == 0)
{
ch.Error("Too many empty columns for automatic inference.");
return(InferenceResult.Fail());
}
if (args.ColumnCount >= SmartColumnsLim)
{
ch.Error("Too many columns for automatic inference.");
return(InferenceResult.Fail());
}
// Read the file as the specified number of text columns.
var textLoaderArgs = new TextLoader.Arguments
{
Column = new[] { TextLoader.Column.Parse(string.Format("C:TX:0-{0}", args.ColumnCount - 1)) },
Separator = args.Separator,
AllowSparse = args.AllowSparse,
AllowQuoting = args.AllowQuote,
};
var textLoader = new TextLoader(env, textLoaderArgs, fileSource);
var idv = textLoader.Take(args.MaxRowsToRead);
// Read all the data into memory.
// List items are rows of the dataset.
var data = new List <DvText[]>();
using (var cursor = idv.GetRowCursor(col => true))
{
int columnIndex;
bool found = cursor.Schema.TryGetColumnIndex("C", out columnIndex);
Contracts.Assert(found);
var colType = cursor.Schema.GetColumnType(columnIndex);
Contracts.Assert(colType.ItemType.IsText);
ValueGetter <VBuffer <DvText> > vecGetter = null;
ValueGetter <DvText> oneGetter = null;
bool isVector = colType.IsVector;
if (isVector)
{
vecGetter = cursor.GetGetter <VBuffer <DvText> >(columnIndex);
}
else
{
Contracts.Assert(args.ColumnCount == 1);
oneGetter = cursor.GetGetter <DvText>(columnIndex);
}
VBuffer <DvText> line = default(VBuffer <DvText>);
DvText tsValue = default(DvText);
while (cursor.MoveNext())
{
if (isVector)
{
vecGetter(ref line);
Contracts.Assert(line.Length == args.ColumnCount);
var values = new DvText[args.ColumnCount];
line.CopyTo(values);
data.Add(values);
}
else
{
oneGetter(ref tsValue);
var values = new[] { tsValue };
data.Add(values);
}
}
}
if (data.Count < 2)
{
ch.Error("Too few rows ({0}) for automatic inference.", data.Count);
return(InferenceResult.Fail());
}
var cols = new IntermediateColumn[args.ColumnCount];
for (int i = 0; i < args.ColumnCount; i++)
{
cols[i] = new IntermediateColumn(data.Select(x => x[i]).ToArray(), i);
}
foreach (var expert in GetExperts())
{
expert.Apply(cols);
}
Contracts.Check(cols.All(x => x.SuggestedType != null), "Column type inference must be conclusive");
// Aggregating header signals.
int suspect = 0;
var usedNames = new HashSet <string>();
for (int i = 0; i < args.ColumnCount; i++)
{
if (cols[i].HasHeader == true)
{
if (usedNames.Add(cols[i].RawData[0].ToString()))
{
suspect++;
}
else
{
// duplicate value in the first column is a strong signal that this is not a header
suspect -= args.ColumnCount;
}
}
else if (cols[i].HasHeader == false)
{
suspect--;
}
}
// REVIEW: Why not use this for column names as well?
TextLoader.Arguments fileArgs;
bool hasHeader;
if (TextLoader.FileContainsValidSchema(env, fileSource, out fileArgs))
{
hasHeader = fileArgs.HasHeader;
}
else
{
hasHeader = suspect > 0;
}
// suggest names
var names = new List <string>();
usedNames.Clear();
foreach (var col in cols)
{
string name0;
string name;
name0 = name = SuggestName(col, hasHeader);
int i = 0;
while (!usedNames.Add(name))
{
name = string.Format("{0}_{1:00}", name0, i++);
}
names.Add(name);
}
var outCols =
cols.Select((x, i) => new Column(x.ColumnId, names[i], x.SuggestedType)).ToArray();
var numerics = outCols.Count(x => x.ItemType.IsNumber);
ch.Info("Detected {0} numeric and {1} text columns.", numerics, outCols.Length - numerics);
if (hasHeader)
{
ch.Info("Generated column names from the file header.");
}
return(InferenceResult.Success(outCols, hasHeader, cols.Select(col => col.RawData).ToArray()));
}
public ParameterSet[] ProposeSweeps(int maxSweeps, IEnumerable <IRunResult> previousRuns = null)
{
int numSweeps = Math.Min(maxSweeps, _dim + 1 - _simplexVertices.Count);
if (previousRuns == null)
{
return(_initSweeper.ProposeSweeps(numSweeps, previousRuns));
}
foreach (var run in previousRuns)
{
Contracts.Check(run != null);
}
foreach (var run in previousRuns)
{
if (_simplexVertices.Count == _dim + 1)
{
break;
}
if (!_simplexVertices.ContainsKey(run))
{
_simplexVertices.Add(run, ParameterSetAsFloatArray(run.ParameterSet));
}
if (_simplexVertices.Count == _dim + 1)
{
ComputeExtremes();
}
}
if (_simplexVertices.Count < _dim + 1)
{
numSweeps = Math.Min(maxSweeps, _dim + 1 - _simplexVertices.Count);
return(_initSweeper.ProposeSweeps(numSweeps, previousRuns));
}
switch (_stage)
{
case OptimizationStage.NeedReflectionPoint:
_pendingSweeps.Clear();
var nextPoint = GetNewPoint(_centroid, _worst.Value, _args.DeltaReflection);
if (OutOfBounds(nextPoint) && _args.ProjectInbounds)
{
// if the reflection point is out of bounds, get the inner contraction point.
nextPoint = GetNewPoint(_centroid, _worst.Value, _args.DeltaInsideContraction);
_stage = OptimizationStage.WaitingForInnerContractionResult;
}
else
{
_stage = OptimizationStage.WaitingForReflectionResult;
}
_pendingSweeps.Add(new KeyValuePair <ParameterSet, Float[]>(FloatArrayAsParameterSet(nextPoint), nextPoint));
if (previousRuns.Any(runResult => runResult.ParameterSet.Equals(_pendingSweeps[0].Key)))
{
_stage = OptimizationStage.WaitingForReductionResult;
_pendingSweeps.Clear();
if (!TryGetReductionPoints(maxSweeps, previousRuns))
{
_stage = OptimizationStage.Done;
return(null);
}
return(_pendingSweeps.Select(kvp => kvp.Key).ToArray());
}
return(new ParameterSet[] { _pendingSweeps[0].Key });
case OptimizationStage.WaitingForReflectionResult:
Contracts.Assert(_pendingSweeps.Count == 1);
_lastReflectionResult = FindRunResult(previousRuns)[0];
if (_secondWorst.Key.CompareTo(_lastReflectionResult.Key) < 0 && _lastReflectionResult.Key.CompareTo(_best.Key) <= 0)
{
// the reflection result is better than the second worse, but not better than the best
UpdateSimplex(_lastReflectionResult.Key, _lastReflectionResult.Value);
goto case OptimizationStage.NeedReflectionPoint;
}
if (_lastReflectionResult.Key.CompareTo(_best.Key) > 0)
{
// the reflection result is the best so far
nextPoint = GetNewPoint(_centroid, _worst.Value, _args.DeltaExpansion);
if (OutOfBounds(nextPoint) && _args.ProjectInbounds)
{
// if the expansion point is out of bounds, get the inner contraction point.
nextPoint = GetNewPoint(_centroid, _worst.Value, _args.DeltaInsideContraction);
_stage = OptimizationStage.WaitingForInnerContractionResult;
}
else
{
_stage = OptimizationStage.WaitingForExpansionResult;
}
}
else if (_lastReflectionResult.Key.CompareTo(_worst.Key) > 0)
{
// other wise, get results for the outer contraction point.
nextPoint = GetNewPoint(_centroid, _worst.Value, _args.DeltaOutsideContraction);
_stage = OptimizationStage.WaitingForOuterContractionResult;
}
else
{
// other wise, reflection result is not better than worst, get results for the inner contraction point
nextPoint = GetNewPoint(_centroid, _worst.Value, _args.DeltaInsideContraction);
_stage = OptimizationStage.WaitingForInnerContractionResult;
}
_pendingSweeps.Clear();
_pendingSweeps.Add(new KeyValuePair <ParameterSet, Float[]>(FloatArrayAsParameterSet(nextPoint), nextPoint));
if (previousRuns.Any(runResult => runResult.ParameterSet.Equals(_pendingSweeps[0].Key)))
{
_stage = OptimizationStage.WaitingForReductionResult;
_pendingSweeps.Clear();
if (!TryGetReductionPoints(maxSweeps, previousRuns))
{
_stage = OptimizationStage.Done;
return(null);
}
return(_pendingSweeps.Select(kvp => kvp.Key).ToArray());
}
return(new ParameterSet[] { _pendingSweeps[0].Key });
case OptimizationStage.WaitingForExpansionResult:
Contracts.Assert(_pendingSweeps.Count == 1);
var expansionResult = FindRunResult(previousRuns)[0].Key;
if (expansionResult.CompareTo(_lastReflectionResult.Key) > 0)
{
// expansion point is better than reflection point
UpdateSimplex(expansionResult, _pendingSweeps[0].Value);
goto case OptimizationStage.NeedReflectionPoint;
}
// reflection point is better than expansion point
UpdateSimplex(_lastReflectionResult.Key, _lastReflectionResult.Value);
goto case OptimizationStage.NeedReflectionPoint;
case OptimizationStage.WaitingForOuterContractionResult:
Contracts.Assert(_pendingSweeps.Count == 1);
var outerContractionResult = FindRunResult(previousRuns)[0].Key;
if (outerContractionResult.CompareTo(_lastReflectionResult.Key) > 0)
{
// outer contraction point is better than reflection point
UpdateSimplex(outerContractionResult, _pendingSweeps[0].Value);
goto case OptimizationStage.NeedReflectionPoint;
}
// get the reduction points
_stage = OptimizationStage.WaitingForReductionResult;
_pendingSweeps.Clear();
if (!TryGetReductionPoints(maxSweeps, previousRuns))
{
_stage = OptimizationStage.Done;
return(null);
}
return(_pendingSweeps.Select(kvp => kvp.Key).ToArray());
case OptimizationStage.WaitingForInnerContractionResult:
Contracts.Assert(_pendingSweeps.Count == 1);
var innerContractionResult = FindRunResult(previousRuns)[0].Key;
if (innerContractionResult.CompareTo(_worst.Key) > 0)
{
// inner contraction point is better than worst point
UpdateSimplex(innerContractionResult, _pendingSweeps[0].Value);
goto case OptimizationStage.NeedReflectionPoint;
}
// get the reduction points
_stage = OptimizationStage.WaitingForReductionResult;
_pendingSweeps.Clear();
if (!TryGetReductionPoints(maxSweeps, previousRuns))
{
_stage = OptimizationStage.Done;
return(null);
}
return(_pendingSweeps.Select(kvp => kvp.Key).ToArray());
case OptimizationStage.WaitingForReductionResult:
Contracts.Assert(_pendingSweeps.Count + _pendingSweepsNotSubmitted.Count == _dim);
if (_pendingSweeps.Count < _dim)
{
return(SubmitMoreReductionPoints(maxSweeps));
}
ReplaceSimplexVertices(previousRuns);
// if the diameter of the new simplex has become too small, stop sweeping.
if (SimplexDiameter() < _args.StoppingSimplexDiameter)
{
return(null);
}
goto case OptimizationStage.NeedReflectionPoint;
case OptimizationStage.Done:
default:
return(null);
}
}
private static bool TryParseFile(IChannel ch, TextLoader.Arguments args, IMultiStreamSource source, bool skipStrictValidation, out ColumnSplitResult result)
{
result = default(ColumnSplitResult);
try
{
// No need to provide information from unsuccessful loader, so we create temporary environment and get information from it in case of success
using (var loaderEnv = new ConsoleEnvironment(0, verbose: true))
{
var messages = new ConcurrentBag <ChannelMessage>();
loaderEnv.AddListener <ChannelMessage>(
(src, msg) =>
{
messages.Add(msg);
});
var idv = TextLoader.ReadFile(loaderEnv, args, source).Take(1000);
var columnCounts = new List <int>();
int columnIndex;
bool found = idv.Schema.TryGetColumnIndex("C", out columnIndex);
ch.Assert(found);
using (var cursor = idv.GetRowCursor(x => x == columnIndex))
{
var getter = cursor.GetGetter <VBuffer <ReadOnlyMemory <char> > >(columnIndex);
VBuffer <ReadOnlyMemory <char> > line = default;
while (cursor.MoveNext())
{
getter(ref line);
columnCounts.Add(line.Length);
}
}
Contracts.Check(columnCounts.Count > 0);
var mostCommon = columnCounts.GroupBy(x => x).OrderByDescending(x => x.Count()).First();
if (!skipStrictValidation && mostCommon.Count() < UniformColumnCountThreshold * columnCounts.Count)
{
return(false);
}
// If user explicitly specified separator we're allowing "single" column case;
// Otherwise user will see message informing that we were not able to detect any columns.
if (!skipStrictValidation && mostCommon.Key <= 1)
{
return(false);
}
result = new ColumnSplitResult(true, args.Separator, args.AllowQuoting, args.AllowSparse, mostCommon.Key);
ch.Trace("Discovered {0} columns using separator '{1}'", mostCommon.Key, args.Separator);
foreach (var msg in messages)
{
ch.Send(msg);
}
return(true);
}
}
catch (Exception ex)
{
if (!ex.IsMarked())
{
throw;
}
// For known exceptions, we just continue to the next separator candidate.
}
return(false);
}
public void GetValue(ref VBuffer <T> dst)
{
Contracts.Check(Cursor.IsGood);
Src.CopyTo(ref dst);
}
/// <summary>
/// This function converts <paramref name="kind"/> to <see cref="DataKind"/>.
/// Because <see cref="DataKind"/> is a subset of <see cref="InternalDataKind"/>, we should check if <paramref name="kind"/>
/// can be found in <see cref="DataKind"/>.
/// </summary>
public static DataKind ToDataKind(this InternalDataKind kind)
{
Contracts.Check(kind != InternalDataKind.UG);
return((DataKind)kind);
}
public override void Combine(ref ReadOnlyMemory <char> dst, ReadOnlyMemory <char> src)
{
Contracts.Check(IsDefault(dst));
dst = src;
}
public override ValueGetter <T> GetGetter()
{
Contracts.Check(IsActive, "column is not active");
return(_getter);
}
public static ScikitSubComponent Create(Type type)
{
Contracts.Check(type != null && typeof(ScikitSubComponent).IsAssignableFrom(type));
return((ScikitSubComponent)Activator.CreateInstance(type));
}
public GetterInfoPrimitive(string kind, ColumnType type, TValue value)
: base(kind, type)
{
Contracts.Check(type.RawType == typeof(TValue), "Incompatible types");
Value = value;
}
public DiscreteValueGenerator(DiscreteParamArguments args)
{
Contracts.Check(args.Values.Length > 0);
_args = args;
}
public DiscreteValueGenerator(DiscreteParamOptions options)
{
Contracts.Check(options.Values.Length > 0);
_options = options;
}
public void AllocateDataMemory(int docNum, long corpusSize)
{
Contracts.Check(docNum >= 0);
Contracts.Check(corpusSize >= 0);
LdaInterface.AllocateDataMemory(_engine, docNum, corpusSize);
}
public string GetPathOrNull(int index)
{
Contracts.Check(index == 0);
return(null);
}
/// <summary>
/// Predict with data.
/// This function uses a modified API which does not use caches.
/// </summary>
/// <param name="vbuf">one row</param>
/// <param name="predictedValues">Results of the prediction</param>
/// <param name="internalBuffer">buffers allocated by Microsoft.ML and given to XGBoost to avoid XGBoost to allocated caches on its own</param>
/// <param name="outputMargin">Whether to output the raw untransformed margin value.</param>
/// <param name="ntreeLimit">Limit number of trees in the prediction; defaults to 0 (use all trees).</param>
public void PredictOneOff(ref VBuffer <Float> vbuf, ref VBuffer <Float> predictedValues,
ref XGBoostTreeBuffer internalBuffer, bool outputMargin = true, int ntreeLimit = 0)
{
// REVIEW xadupre: XGBoost can produce an output per tree (pred_leaf=true)
// When this option is on, the output will be a matrix of (nsample, ntrees)
// with each record indicating the predicted leaf index of each sample in each tree.
// Note that the leaf index of a tree is unique per tree, so you may find leaf 1
// in both tree 1 and tree 0.
// if (pred_leaf)
// option_mask |= 0x02;
// This might be an interesting feature to implement.
int optionMask = 0x00;
if (outputMargin)
{
optionMask |= 0x01;
}
Contracts.Check(internalBuffer != null);
uint length = 0;
uint lengthBuffer = 0;
uint nb = (uint)vbuf.Count;
// This function relies on a modified API. Instead of letting XGBoost handle its own caches,
// the function calls XGBoosterPredictOutputSize to know what cache size is required.
// Microsoft.ML allocated the caches and gives them to XGBoost.
// First, we allocated the cache for the features. Only then XGBoost
// will be able to known the required cache size.
#if (XGB_EXTENDED)
internalBuffer.ResizeEntries(nb, vbuf.Length);
#else
internalBuffer.ResizeEntries(nb);
#endif
unsafe
{
fixed(float *p = vbuf.Values)
fixed(int *i = vbuf.Indices)
fixed(byte *entries = internalBuffer.XGBoostEntries)
{
WrappedXGBoostInterface.XGBoosterCopyEntries((IntPtr)entries, ref nb, p, vbuf.IsDense ? null : i, float.NaN);
WrappedXGBoostInterface.XGBoosterPredictOutputSize(_handle,
(IntPtr)entries, nb, optionMask, (uint)ntreeLimit, ref length, ref lengthBuffer);
}
}
// Then we allocated the cache for the prediction.
internalBuffer.ResizeOutputs(length, lengthBuffer, ref predictedValues);
unsafe
{
fixed(byte *entries = internalBuffer.XGBoostEntries)
fixed(float *ppreds = predictedValues.Values)
fixed(float *ppredBuffer = internalBuffer.PredBuffer)
fixed(uint *ppredCounter = internalBuffer.PredCounter)
{
WrappedXGBoostInterface.XGBoosterPredictNoInsideCache(_handle,
(IntPtr)entries, nb, optionMask, (uint)ntreeLimit, length, lengthBuffer, ppreds, ppredBuffer, ppredCounter
#if (XGB_EXTENDED)
, internalBuffer.RegTreeFVec
#endif
);
}
}
}
public TreeEnsemble GetModel(int[] categoricalFeatureBoudaries)
{
TreeEnsemble res = new TreeEnsemble();
string modelString = GetModelString();
string[] lines = modelString.Split('\n');
int i = 0;
for (; i < lines.Length;)
{
if (lines[i].StartsWith("Tree="))
{
Dictionary <string, string> kvPairs = new Dictionary <string, string>();
++i;
while (!lines[i].StartsWith("Tree=") && lines[i].Trim().Length != 0)
{
string[] kv = lines[i].Split('=');
Contracts.Check(kv.Length == 2);
kvPairs[kv[0].Trim()] = kv[1].Trim();
++i;
}
int numLeaves = int.Parse(kvPairs["num_leaves"]);
int numCat = int.Parse(kvPairs["num_cat"]);
if (numLeaves > 1)
{
var leftChild = Str2IntArray(kvPairs["left_child"], ' ');
var rightChild = Str2IntArray(kvPairs["right_child"], ' ');
var splitFeature = Str2IntArray(kvPairs["split_feature"], ' ');
var threshold = Str2DoubleArray(kvPairs["threshold"], ' ');
var splitGain = Str2DoubleArray(kvPairs["split_gain"], ' ');
var leafOutput = Str2DoubleArray(kvPairs["leaf_value"], ' ');
var decisionType = Str2UIntArray(kvPairs["decision_type"], ' ');
var defaultValue = GetDefalutValue(threshold, decisionType);
var categoricalSplitFeatures = new int[numLeaves - 1][];
var categoricalSplit = new bool[numLeaves - 1];
if (categoricalFeatureBoudaries != null)
{
// Add offsets to split features.
for (int node = 0; node < numLeaves - 1; ++node)
{
splitFeature[node] = categoricalFeatureBoudaries[splitFeature[node]];
}
}
if (numCat > 0)
{
var catBoundaries = Str2IntArray(kvPairs["cat_boundaries"], ' ');
var catThreshold = Str2UIntArray(kvPairs["cat_threshold"], ' ');
for (int node = 0; node < numLeaves - 1; ++node)
{
if (GetIsCategoricalSplit(decisionType[node]))
{
int catIdx = (int)threshold[node];
var cats = GetCatThresholds(catThreshold, catBoundaries[catIdx], catBoundaries[catIdx + 1]);
categoricalSplitFeatures[node] = new int[cats.Length];
// Convert Cat thresholds to feature indices.
for (int j = 0; j < cats.Length; ++j)
{
categoricalSplitFeatures[node][j] = splitFeature[node] + cats[j] - 1;
}
splitFeature[node] = -1;
categoricalSplit[node] = true;
// Swap left and right child.
int t = leftChild[node];
leftChild[node] = rightChild[node];
rightChild[node] = t;
}
else
{
categoricalSplit[node] = false;
}
}
}
RegressionTree tree = RegressionTree.Create(numLeaves, splitFeature, splitGain,
threshold.Select(x => (float)(x)).ToArray(), defaultValue.Select(x => (float)(x)).ToArray(), leftChild, rightChild, leafOutput,
categoricalSplitFeatures, categoricalSplit);
res.AddTree(tree);
}
else
{
RegressionTree tree = new RegressionTree(2);
var leafOutput = Str2DoubleArray(kvPairs["leaf_value"], ' ');
if (leafOutput[0] != 0)
{
// Convert Constant tree to Two-leaf tree, avoid being filter by TLC.
var categoricalSplitFeatures = new int[1][];
var categoricalSplit = new bool[1];
tree = RegressionTree.Create(2, new int[] { 0 }, new double[] { 0 },
new float[] { 0 }, new float[] { 0 }, new int[] { -1 }, new int[] { -2 }, new double[] { leafOutput[0], leafOutput[0] },
categoricalSplitFeatures, categoricalSplit);
}
res.AddTree(tree);
}
}
else
{
++i;
}
}
return(res);
}
//Project the covariance matrix A on to Omega: Y <- A * Omega
//A = X' * X / n, where X = data - mean
//Note that the covariance matrix is not computed explicitly
private void Project(IDataView trainingData, Float[][] mean, Float[][][] omega, Float[][][] y, TransformInfo[] transformInfos)
{
Host.Assert(mean.Length == omega.Length && omega.Length == y.Length && y.Length == Infos.Length);
for (int i = 0; i < omega.Length; i++)
{
Contracts.Assert(omega[i].Length == y[i].Length);
}
// set y to be all zeros
for (int iinfo = 0; iinfo < y.Length; iinfo++)
{
for (int i = 0; i < y[iinfo].Length; i++)
{
Array.Clear(y[iinfo][i], 0, y[iinfo][i].Length);
}
}
bool[] center = Enumerable.Range(0, mean.Length).Select(i => mean[i] != null).ToArray();
Double[] totalColWeight = new Double[Infos.Length];
bool[] activeColumns = new bool[Source.Schema.ColumnCount];
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
activeColumns[Infos[iinfo].Source] = true;
if (_weightColumnIndex[iinfo] >= 0)
{
activeColumns[_weightColumnIndex[iinfo]] = true;
}
}
using (var cursor = trainingData.GetRowCursor(col => activeColumns[col]))
{
var weightGetters = new ValueGetter <Float> [Infos.Length];
var columnGetters = new ValueGetter <VBuffer <Float> > [Infos.Length];
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
if (_weightColumnIndex[iinfo] >= 0)
{
weightGetters[iinfo] = cursor.GetGetter <Float>(_weightColumnIndex[iinfo]);
}
columnGetters[iinfo] = cursor.GetGetter <VBuffer <Float> >(Infos[iinfo].Source);
}
var features = default(VBuffer <Float>);
while (cursor.MoveNext())
{
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
Contracts.Check(Infos[iinfo].TypeSrc.IsVector && Infos[iinfo].TypeSrc.ItemType.IsNumber,
"PCA transform can only be performed on numeric columns of dimension > 1");
Float weight = 1;
if (weightGetters[iinfo] != null)
{
weightGetters[iinfo](ref weight);
}
columnGetters[iinfo](ref features);
if (FloatUtils.IsFinite(weight) && weight >= 0 && (features.Count == 0 || FloatUtils.IsFinite(features.Values, features.Count)))
{
totalColWeight[iinfo] += weight;
if (center[iinfo])
{
VectorUtils.AddMult(ref features, mean[iinfo], weight);
}
for (int i = 0; i < omega[iinfo].Length; i++)
{
VectorUtils.AddMult(ref features, y[iinfo][i], weight * VectorUtils.DotProductWithOffset(omega[iinfo][i], 0, ref features));
}
}
}
}
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
if (totalColWeight[iinfo] <= 0)
{
throw Host.Except("Empty data in column '{0}'", Source.Schema.GetColumnName(Infos[iinfo].Source));
}
}
for (int iinfo = 0; iinfo < Infos.Length; iinfo++)
{
var invn = (Float)(1 / totalColWeight[iinfo]);
for (var i = 0; i < omega[iinfo].Length; ++i)
{
VectorUtils.ScaleBy(y[iinfo][i], invn);
}
if (center[iinfo])
{
VectorUtils.ScaleBy(mean[iinfo], invn);
for (int i = 0; i < omega[iinfo].Length; i++)
{
VectorUtils.AddMult(mean[iinfo], y[iinfo][i], -VectorUtils.DotProduct(omega[iinfo][i], mean[iinfo]));
}
}
}
}
}
private static Session LoadTFSession(IHostEnvironment env, string exportDirSavedModel)
{
Contracts.Check(env != null, nameof(env));
env.CheckValue(exportDirSavedModel, nameof(exportDirSavedModel));
return(Session.LoadFromSavedModel(exportDirSavedModel));
}
public uint Hash(uint seed)
{
Contracts.Check(!IsNA);
return(Hashing.MurmurHash(seed, _outerBuffer, _ichMin, IchLim));
}
/// <summary>
/// Returns whether the given column is active in this row.
/// </summary>
public override bool IsColumnActive(DataViewSchema.Column column)
{
Contracts.Check(column.Index < _getters.Length);
return(_getters[column.Index] != null);
}
// REVIEW: Add method to NormStr.Pool that deal with DvText instead of the other way around.
public NormStr AddToPool(NormStr.Pool pool)
{
Contracts.Check(!IsNA);
Contracts.CheckValue(pool, nameof(pool));
return(pool.Add(_outerBuffer, _ichMin, IchLim));
}
public void GetValue(ref T dst)
{
Contracts.Check(Cursor.IsGood);
dst = Src;
}
/// <summary>
/// Set the progress value for the index <paramref name="index"/> to <paramref name="value"/>,
/// and the limit value for the progress becomes 'unknown'.
/// </summary>
public void SetProgress(int index, Double value)
{
Contracts.Check(0 <= index && index < Progress.Length);
Progress[index] = value;
ProgressLim[index] = null;
}
/// <summary>
/// Maps from an index into an array of size KindCount to the corresponding DataKind
/// </summary>
public static InternalDataKind FromIndex(int index)
{
Contracts.Check(0 <= index && index < KindCount);
return((InternalDataKind)(index + (int)KindMin));
}
/// <summary>
/// Sets the metric with index <paramref name="index"/> to <paramref name="value"/>.
/// </summary>
public void SetMetric(int index, Double value)
{
Contracts.Check(0 <= index && index < Metrics.Length);
Metrics[index] = value;
}
public static ModelArgs GetModelArgs(DataViewType type, string colName,
List <long> dims = null, List <bool> dimsParams = null)
{
Contracts.CheckValue(type, nameof(type));
Contracts.CheckNonEmpty(colName, nameof(colName));
TensorProto.Types.DataType dataType = TensorProto.Types.DataType.Undefined;
Type rawType;
if (type is VectorDataViewType vectorType)
{
rawType = vectorType.ItemType.RawType;
}
else
{
rawType = type.RawType;
}
if (rawType == typeof(bool))
{
dataType = TensorProto.Types.DataType.Float;
}
else if (rawType == typeof(ReadOnlyMemory <char>))
{
dataType = TensorProto.Types.DataType.String;
}
else if (rawType == typeof(sbyte))
{
dataType = TensorProto.Types.DataType.Int8;
}
else if (rawType == typeof(byte))
{
dataType = TensorProto.Types.DataType.Uint8;
}
else if (rawType == typeof(short))
{
dataType = TensorProto.Types.DataType.Int16;
}
else if (rawType == typeof(ushort))
{
dataType = TensorProto.Types.DataType.Uint16;
}
else if (rawType == typeof(int))
{
dataType = TensorProto.Types.DataType.Int32;
}
else if (rawType == typeof(uint))
{
dataType = TensorProto.Types.DataType.Int64;
}
else if (rawType == typeof(long))
{
dataType = TensorProto.Types.DataType.Int64;
}
else if (rawType == typeof(ulong))
{
dataType = TensorProto.Types.DataType.Uint64;
}
else if (rawType == typeof(float))
{
dataType = TensorProto.Types.DataType.Float;
}
else if (rawType == typeof(double))
{
dataType = TensorProto.Types.DataType.Double;
}
else
{
string msg = "Unsupported type: " + type.ToString();
Contracts.Check(false, msg);
}
string name = colName;
List <long> dimsLocal = null;
List <bool> dimsParamLocal = null;
if (dims != null)
{
dimsLocal = dims;
dimsParamLocal = dimsParams;
}
else
{
dimsLocal = new List <long>();
int valueCount = type.GetValueCount();
if (valueCount == 0) //Unknown size.
{
dimsLocal.Add(1);
dimsParamLocal = new List <bool>()
{
false, true
}; //false for batch size, true for dims.
}
else if (valueCount == 1)
{
dimsLocal.Add(1);
}
else if (valueCount > 1)
{
var vec = (VectorDataViewType)type;
for (int i = 0; i < vec.Dimensions.Length; i++)
{
dimsLocal.Add(vec.Dimensions[i]);
}
}
}
//batch size.
dimsLocal?.Insert(0, 1);
return(new ModelArgs(name, dataType, dimsLocal, dimsParamLocal));
}
public JObject GetJsonObject(object instance, Dictionary <string, List <ParameterBinding> > inputBindingMap, Dictionary <ParameterBinding, VariableBinding> inputMap)
{
Contracts.CheckValue(instance, nameof(instance));
Contracts.Check(instance.GetType() == _type);
var result = new JObject();
var defaults = Activator.CreateInstance(_type);
for (int i = 0; i < _fields.Length; i++)
{
var field = _fields[i];
var attr = _attrs[i];
var instanceVal = field.GetValue(instance);
var defaultsVal = field.GetValue(defaults);
if (inputBindingMap.TryGetValue(field.Name, out List <ParameterBinding> bindings))
{
// Handle variables.
Contracts.Assert(bindings.Count > 0);
VariableBinding varBinding;
var paramBinding = bindings[0];
if (paramBinding is SimpleParameterBinding)
{
Contracts.Assert(bindings.Count == 1);
bool success = inputMap.TryGetValue(paramBinding, out varBinding);
Contracts.Assert(success);
Contracts.AssertValue(varBinding);
result.Add(field.Name, new JValue(varBinding.ToJson()));
}
else if (paramBinding is ArrayIndexParameterBinding)
{
// Array parameter bindings.
var array = new JArray();
foreach (var parameterBinding in bindings)
{
Contracts.Assert(parameterBinding is ArrayIndexParameterBinding);
bool success = inputMap.TryGetValue(parameterBinding, out varBinding);
Contracts.Assert(success);
Contracts.AssertValue(varBinding);
array.Add(new JValue(varBinding.ToJson()));
}
result.Add(field.Name, array);
}
else
{
// Dictionary parameter bindings. Not supported yet.
Contracts.Assert(paramBinding is DictionaryKeyParameterBinding);
throw Contracts.ExceptNotImpl("Dictionary of variables not yet implemented.");
}
}
else if (instanceVal == null && defaultsVal != null)
{
// Handle null values.
result.Add(field.Name, new JValue(instanceVal));
}
else if (instanceVal != null && (attr.Input.IsRequired || !instanceVal.Equals(defaultsVal)))
{
// A required field will be serialized regardless of whether or not its value is identical to the default.
var type = instanceVal.GetType();
if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional <>))
{
var isExplicit = ExtractOptional(ref instanceVal, ref type);
if (!isExplicit)
{
continue;
}
}
if (type == typeof(JArray))
{
result.Add(field.Name, (JArray)instanceVal);
}
else if (type.IsGenericType &&
((type.GetGenericTypeDefinition() == typeof(Var <>)) ||
type.GetGenericTypeDefinition() == typeof(ArrayVar <>) ||
type.GetGenericTypeDefinition() == typeof(DictionaryVar <>)))
{
result.Add(field.Name, new JValue($"${((IVarSerializationHelper)instanceVal).VarName}"));
}
else if (type == typeof(bool) ||
type == typeof(string) ||
type == typeof(char) ||
type == typeof(double) ||
type == typeof(float) ||
type == typeof(int) ||
type == typeof(long) ||
type == typeof(uint) ||
type == typeof(ulong))
{
// Handle simple types.
result.Add(field.Name, new JValue(instanceVal));
}
else if (type.IsEnum)
{
// Handle enums.
result.Add(field.Name, new JValue(instanceVal.ToString()));
}
else if (type.IsArray)
{
// Handle arrays.
var array = (Array)instanceVal;
var jarray = new JArray();
var elementType = type.GetElementType();
if (elementType == typeof(bool) ||
elementType == typeof(string) ||
elementType == typeof(char) ||
elementType == typeof(double) ||
elementType == typeof(float) ||
elementType == typeof(int) ||
elementType == typeof(long) ||
elementType == typeof(uint) ||
elementType == typeof(ulong))
{
foreach (object item in array)
{
jarray.Add(new JValue(item));
}
}
else
{
var builder = new InputBuilder(_ectx, elementType, _catalog);
foreach (object item in array)
{
jarray.Add(builder.GetJsonObject(item, inputBindingMap, inputMap));
}
}
result.Add(field.Name, jarray);
}
else if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Dictionary <,>) &&
type.GetGenericArguments()[0] == typeof(string))
{
// Handle dictionaries.
// REVIEW: Needs to be implemented when we will have entry point arguments that contain dictionaries.
}
else if (typeof(IComponentFactory).IsAssignableFrom(type))
{
// Handle component factories.
bool success = _catalog.TryFindComponent(type, out ModuleCatalog.ComponentInfo instanceInfo);
Contracts.Assert(success);
var builder = new InputBuilder(_ectx, type, _catalog);
var instSettings = builder.GetJsonObject(instanceVal, inputBindingMap, inputMap);
ModuleCatalog.ComponentInfo defaultInfo = null;
JObject defSettings = new JObject();
if (defaultsVal != null)
{
var deftype = defaultsVal.GetType();
if (deftype.IsGenericType && deftype.GetGenericTypeDefinition() == typeof(Optional <>))
{
ExtractOptional(ref defaultsVal, ref deftype);
}
success = _catalog.TryFindComponent(deftype, out defaultInfo);
Contracts.Assert(success);
builder = new InputBuilder(_ectx, deftype, _catalog);
defSettings = builder.GetJsonObject(defaultsVal, inputBindingMap, inputMap);
}
if (instanceInfo.Name != defaultInfo?.Name || instSettings.ToString() != defSettings.ToString())
{
var jcomponent = new JObject
{
{ FieldNames.Name, new JValue(instanceInfo.Name) }
};
if (instSettings.ToString() != defSettings.ToString())
{
jcomponent.Add(FieldNames.Settings, instSettings);
}
result.Add(field.Name, jcomponent);
}
}
else
{
// REVIEW: pass in the bindings once we support variables in inner fields.
// Handle structs.
var builder = new InputBuilder(_ectx, type, _catalog);
result.Add(field.Name, builder.GetJsonObject(instanceVal, new Dictionary <string, List <ParameterBinding> >(),
new Dictionary <ParameterBinding, VariableBinding>()));
}
}
}
return(result);
}
public override bool IsColumnActive(int col)
{
Contracts.Check(0 <= col && col < _getters.Length);
return(_getters[col] != null);
}
public static float DotProduct(float[] a, float[] b)
{
Contracts.Check(Utils.Size(a) == Utils.Size(b), "Arrays must have the same length");
Contracts.Check(Utils.Size(a) > 0);
return(CpuMathUtils.DotProductDense(a, b, a.Length));
}
protected override DataViewType GetColumnTypeCore(int iinfo)
{
Contracts.Check(iinfo == 0);
return(_outputColumnType);
}
public Stream Open(int index)
{
Contracts.Check(index == 0, "Index must be 0");
return(new MemoryStream(_buffer));
}