private protected override void CheckDataValid(IChannel ch, RoleMappedData data)
{
Host.AssertValue(ch);
base.CheckDataValid(ch, data);
// Check label types.
var labelCol = data.Schema.Label.Value;
var labelType = labelCol.Type;
if (!(labelType is KeyType || labelType == NumberType.R4))
{
throw ch.ExceptParam(nameof(data),
$"Label column '{labelCol.Name}' is of type '{labelType}', but must be key or R4.");
}
// Check group types.
ch.CheckParam(data.Schema.Group.HasValue, nameof(data), "Need a group column.");
var groupCol = data.Schema.Group.Value;
var groupType = groupCol.Type;
if (!(groupType == NumberType.U4 || groupType is KeyType))
{
throw ch.ExceptParam(nameof(data),
$"Group column '{groupCol.Name}' is of type '{groupType}', but must be U4 or a Key.");
}
}
private protected override void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels)
{
// Only initialize one time.
if (_numClass < 0)
{
float minLabel = float.MaxValue;
float maxLabel = float.MinValue;
bool hasNaNLabel = false;
foreach (var labelColumn in labels)
{
if (float.IsNaN(labelColumn))
{
hasNaNLabel = true;
}
else
{
minLabel = Math.Min(minLabel, labelColumn);
maxLabel = Math.Max(maxLabel, labelColumn);
}
}
ch.CheckParam(minLabel >= 0, nameof(data), "Minimum value in label column cannot be negative");
if (maxLabel >= _maxNumClass)
{
throw ch.ExceptParam(nameof(data), $"Maximum value {maxLabel} in label column exceeds {_maxNumClass}");
}
if (data.Schema.Label.Value.Type is KeyDataViewType keyType)
{
if (hasNaNLabel)
{
_numClass = keyType.GetCountAsInt32(Host) + 1;
}
else
{
_numClass = keyType.GetCountAsInt32(Host);
}
_tlcNumClass = keyType.GetCountAsInt32(Host);
}
else
{
if (hasNaNLabel)
{
_numClass = (int)maxLabel + 2;
}
else
{
_numClass = (int)maxLabel + 1;
}
_tlcNumClass = (int)maxLabel + 1;
}
}
float defaultLabel = _numClass - 1;
for (int i = 0; i < labels.Length; ++i)
{
if (float.IsNaN(labels[i]))
{
labels[i] = defaultLabel;
}
}
}
protected virtual void CheckDataValid(IChannel ch, RoleMappedData data)
{
data.CheckFeatureFloatVector();
ch.CheckParam(data.Schema.Label != null, nameof(data), "Need a label column");
}
protected override void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels)
{
// Only initialize one time.
if (_numClass < 0)
{
float minLabel = float.MaxValue;
float maxLabel = float.MinValue;
bool hasNaNLabel = false;
foreach (var labelColumn in labels)
{
if (float.IsNaN(labelColumn))
{
hasNaNLabel = true;
}
else
{
minLabel = Math.Min(minLabel, labelColumn);
maxLabel = Math.Max(maxLabel, labelColumn);
}
}
ch.CheckParam(minLabel >= 0, nameof(data), "min labelColumn cannot be negative");
if (maxLabel >= _maxNumClass)
{
throw ch.ExceptParam(nameof(data), $"max labelColumn cannot exceed {_maxNumClass}");
}
if (data.Schema.Label.Type.IsKey)
{
ch.Check(data.Schema.Label.Type.AsKey.Contiguous, "labelColumn value should be contiguous");
if (hasNaNLabel)
{
_numClass = data.Schema.Label.Type.AsKey.Count + 1;
}
else
{
_numClass = data.Schema.Label.Type.AsKey.Count;
}
_tlcNumClass = data.Schema.Label.Type.AsKey.Count;
}
else
{
if (hasNaNLabel)
{
_numClass = (int)maxLabel + 2;
}
else
{
_numClass = (int)maxLabel + 1;
}
_tlcNumClass = (int)maxLabel + 1;
}
}
float defaultLabel = _numClass - 1;
for (int i = 0; i < labels.Length; ++i)
{
if (float.IsNaN(labels[i]))
{
labels[i] = defaultLabel;
}
}
}
private MatrixFactorizationPredictor TrainCore(IChannel ch, RoleMappedData data, RoleMappedData validData = null)
{
Host.AssertValue(ch);
ch.AssertValue(data);
ch.AssertValueOrNull(validData);
ch.CheckParam(data.Schema.Label.HasValue, nameof(data), "Input data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(data, out var matrixColumnIndexColInfo, out var matrixRowIndexColInfo, isDecode: false);
var labelCol = data.Schema.Label.Value;
if (labelCol.Type != NumberType.R4 && labelCol.Type != NumberType.R8)
{
throw ch.Except("Column '{0}' for label should be floating point, but is instead {1}", labelCol.Name, labelCol.Type);
}
MatrixFactorizationPredictor predictor;
if (validData != null)
{
ch.CheckValue(validData, nameof(validData));
ch.CheckParam(validData.Schema.Label.HasValue, nameof(validData), "Input validation data did not have a unique label");
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out var validMatrixColumnIndexColInfo, out var validMatrixRowIndexColInfo, isDecode: false);
var validLabelCol = validData.Schema.Label.Value;
if (validLabelCol.Type != NumberType.R4 && validLabelCol.Type != NumberType.R8)
{
throw ch.Except("Column '{0}' for validation label should be floating point, but is instead {1}", validLabelCol.Name, validLabelCol.Type);
}
if (!matrixColumnIndexColInfo.Type.Equals(validMatrixColumnIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-column types differed, {0} vs. {1}",
matrixColumnIndexColInfo.Type, validMatrixColumnIndexColInfo.Type);
}
if (!matrixRowIndexColInfo.Type.Equals(validMatrixRowIndexColInfo.Type))
{
throw ch.ExceptParam(nameof(validData), "Train and validation sets' matrix-row types differed, {0} vs. {1}",
matrixRowIndexColInfo.Type, validMatrixRowIndexColInfo.Type);
}
}
int colCount = matrixColumnIndexColInfo.Type.GetKeyCount();
int rowCount = matrixRowIndexColInfo.Type.GetKeyCount();
ch.Assert(rowCount > 0);
ch.Assert(colCount > 0);
// Checks for equality on the validation set ensure it is correct here.
using (var cursor = data.Data.GetRowCursor(c => c == matrixColumnIndexColInfo.Index || c == matrixRowIndexColInfo.Index || c == data.Schema.Label.Value.Index))
{
// LibMF works only over single precision floats, but we want to be able to consume either.
var labGetter = RowCursorUtils.GetGetterAs <float>(NumberType.R4, cursor, data.Schema.Label.Value.Index);
var matrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, cursor, matrixColumnIndexColInfo.Index);
var matrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, cursor, matrixRowIndexColInfo.Index);
if (validData == null)
{
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.Train(ch, rowCount, colCount, cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter);
predictor = new MatrixFactorizationPredictor(Host, buffer, (KeyType)matrixColumnIndexColInfo.Type, (KeyType)matrixRowIndexColInfo.Type);
}
}
else
{
RecommenderUtils.CheckAndGetMatrixIndexColumns(validData, out var validMatrixColumnIndexColInfo, out var validMatrixRowIndexColInfo, isDecode: false);
using (var validCursor = validData.Data.GetRowCursor(
c => c == validMatrixColumnIndexColInfo.Index || c == validMatrixRowIndexColInfo.Index || c == validData.Schema.Label.Value.Index))
{
ValueGetter <float> validLabelGetter = RowCursorUtils.GetGetterAs <float>(NumberType.R4, validCursor, validData.Schema.Label.Value.Index);
var validMatrixColumnIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, validCursor, validMatrixColumnIndexColInfo.Index);
var validMatrixRowIndexGetter = RowCursorUtils.GetGetterAs <uint>(NumberType.U4, validCursor, validMatrixRowIndexColInfo.Index);
// Have the trainer do its work.
using (var buffer = PrepareBuffer())
{
buffer.TrainWithValidation(ch, rowCount, colCount,
cursor, labGetter, matrixRowIndexGetter, matrixColumnIndexGetter,
validCursor, validLabelGetter, validMatrixRowIndexGetter, validMatrixColumnIndexGetter);
predictor = new MatrixFactorizationPredictor(Host, buffer, (KeyType)matrixColumnIndexColInfo.Type, (KeyType)matrixRowIndexColInfo.Type);
}
}
}
}
return(predictor);
}
GeneralFunctionAnalyzer <TIn, TDelegateInput, TOutShape>(
IHostEnvironment env,
IChannel ch,
TDelegateInput input,
ReaderReconciler <TIn> baseReconciler,
Func <TDelegateInput, TOutShape> mapper,
out IEstimator <ITransformer> estimator,
Func <PipelineColumn, string> inputNameFunction)
{
Contracts.CheckValue(mapper, nameof(mapper));
var method = mapper.Method;
var output = mapper(input);
KeyValuePair <string, PipelineColumn>[] outPairs = StaticPipeInternalUtils.GetNamesValues(output, method.ReturnParameter);
// Map where the key depends on the set of things in the value. The value contains the yet unresolved dependencies.
var keyDependsOn = new Dictionary <PipelineColumn, HashSet <PipelineColumn> >();
// Map where the set of things in the value depend on the key.
var dependsOnKey = new Dictionary <PipelineColumn, HashSet <PipelineColumn> >();
// The set of columns detected with zero dependencies.
var zeroDependencies = new List <PipelineColumn>();
// First we build up the two structures above, using a queue and visiting from the outputs up.
var toVisit = new Queue <PipelineColumn>(outPairs.Select(p => p.Value));
while (toVisit.Count > 0)
{
var col = toVisit.Dequeue();
ch.CheckParam(col != null, nameof(mapper), "The delegate seems to have null columns returned somewhere in the pipe.");
if (keyDependsOn.ContainsKey(col))
{
continue; // Already visited.
}
var dependsOn = new HashSet <PipelineColumn>();
foreach (var dep in col.Dependencies ?? Enumerable.Empty <PipelineColumn>())
{
dependsOn.Add(dep);
if (!dependsOnKey.TryGetValue(dep, out var dependsOnDep))
{
dependsOnKey[dep] = dependsOnDep = new HashSet <PipelineColumn>();
toVisit.Enqueue(dep);
}
dependsOnDep.Add(col);
}
keyDependsOn[col] = dependsOn;
if (dependsOn.Count == 0)
{
zeroDependencies.Add(col);
}
}
// Get the base input columns.
var baseInputs = keyDependsOn.Select(p => p.Key).Where(col => col.ReconcilerObj == baseReconciler).ToArray();
// The columns that utilize the base reconciler should have no dependencies. This could only happen if
// the caller of this function has introduced a situation whereby they are claiming they can reconcile
// to a data-reader object but still have input data dependencies, which does not make sense and
// indicates that there is a bug in that component code. Unfortunately we can only detect that condition,
// not determine exactly how it arose, but we can still do so to indicate to the user that there is a
// problem somewhere in the stack.
ch.CheckParam(baseInputs.All(col => keyDependsOn[col].Count == 0),
nameof(input), "Bug detected where column producing object was yielding columns with dependencies.");
// This holds the mappings of columns to names and back. Note that while the same column could be used on
// the *output*, for example, you could hypothetically have `(a: r.Foo, b: r.Foo)`, we treat that as the last thing
// that is done.
var nameMap = new BidirectionalDictionary <string, PipelineColumn>();
// Check to see if we have any set of initial names. This is important in the case where we are mapping
// in an input data view.
foreach (var col in baseInputs)
{
string inputName = inputNameFunction(col);
if (inputName != null)
{
ch.Assert(!nameMap.ContainsKey(col));
ch.Assert(!nameMap.ContainsKey(inputName));
nameMap[col] = inputName;
ch.Trace($"Using input with name {inputName}.");
}
}
estimator = null;
var toCopy = new List <(string src, string dst)>();
int tempNum = 0;
// For all outputs, get potential name collisions with used inputs. Resolve by assigning the input a temporary name.
foreach (var p in outPairs)
{
// If the name for the output is already used by one of the inputs, and this output column does not
// happen to have the same name, then we need to rename that input to keep it available.
if (nameMap.TryGetValue(p.Key, out var inputCol) && p.Value != inputCol)
{
ch.Assert(baseInputs.Contains(inputCol));
string tempName = $"#Temp_{tempNum++}";
ch.Trace($"Input/output name collision: Renaming '{p.Key}' to '{tempName}'.");
toCopy.Add((p.Key, tempName));
nameMap[tempName] = nameMap[p.Key];
ch.Assert(!nameMap.ContainsKey(p.Key));
}
// If we already have a name for this output column, maybe it is used elsewhere. (This can happen when
// the only thing done with an input is we rename it, or output it twice, or something like this.) In
// this case it is most appropriate to delay renaming till after all other processing has been done in
// that case. But otherwise we may as well just take the name.
if (!nameMap.ContainsKey(p.Value))
{
nameMap[p.Key] = p.Value;
}
}
// If any renamings were necessary, create the CopyColumns estimator.
if (toCopy.Count > 0)
{
estimator = new ColumnCopyingEstimator(env, toCopy.ToArray());
}
// First clear the inputs from zero-dependencies yet to be resolved.
foreach (var col in baseInputs)
{
ch.Assert(zeroDependencies.Contains(col));
ch.Assert(col.ReconcilerObj == baseReconciler);
zeroDependencies.Remove(col); // Make more efficient...
if (!dependsOnKey.TryGetValue(col, out var depends))
{
continue;
}
// If any of these base inputs do not have names because, for example, they do not directly appear
// in the outputs and otherwise do not have names, assign them a name.
if (!nameMap.ContainsKey(col))
{
nameMap[col] = $"Temp_{tempNum++}";
}
foreach (var depender in depends)
{
var dependencies = keyDependsOn[depender];
ch.Assert(dependencies.Contains(col));
dependencies.Remove(col);
if (dependencies.Count == 0)
{
zeroDependencies.Add(depender);
}
}
dependsOnKey.Remove(col);
}
// Call the reconciler to get the base reader estimator.
var readerEstimator = baseReconciler.Reconcile(env, baseInputs, nameMap.AsOther(baseInputs));
ch.AssertValueOrNull(readerEstimator);
// Next we iteratively find those columns with zero dependencies, "create" them, and if anything depends on
// these add them to the collection of zero dependencies, etc. etc.
while (zeroDependencies.Count > 0)
{
// All columns with the same reconciler can be transformed together.
// Note that the following policy of just taking the first group is not optimal. So for example, we
// could have three columns, (a, b, c). If we had the output (a.X(), b.X() c.Y().X()), then maybe we'd
// reconcile a.X() and b.X() together, then reconcile c.Y(), then reconcile c.Y().X() alone. Whereas, we
// could have reconciled c.Y() first, then reconciled a.X(), b.X(), and c.Y().X() together.
var group = zeroDependencies.GroupBy(p => p.ReconcilerObj).First();
// Beyond that first group that *might* be a data reader reconciler, all subsequent operations will
// be on where the data is already loaded and so accept data as an input, that is, they should produce
// an estimator. If this is not the case something seriously wonky is going on, most probably that the
// user tried to use a column from another source. If this is detected we can produce a sensible error
// message to tell them not to do this.
if (!(group.Key is EstimatorReconciler rec))
{
throw ch.Except("Columns from multiple sources were detected. " +
"Did the caller use a " + nameof(PipelineColumn) + " from another delegate?");
}
PipelineColumn[] cols = group.ToArray();
// All dependencies should, by this time, have names.
ch.Assert(cols.SelectMany(c => c.Dependencies).All(dep => nameMap.ContainsKey(dep)));
foreach (var newCol in cols)
{
if (!nameMap.ContainsKey(newCol))
{
nameMap[newCol] = $"#Temp_{tempNum++}";
}
}
var localInputNames = nameMap.AsOther(cols.SelectMany(c => c.Dependencies ?? Enumerable.Empty <PipelineColumn>()));
var localOutputNames = nameMap.AsOther(cols);
var usedNames = new HashSet <string>(nameMap.Keys1.Except(localOutputNames.Values));
var localEstimator = rec.Reconcile(env, cols, localInputNames, localOutputNames, usedNames);
readerEstimator = readerEstimator?.Append(localEstimator);
estimator = estimator?.Append(localEstimator) ?? localEstimator;
foreach (var newCol in cols)
{
zeroDependencies.Remove(newCol); // Make more efficient!!
// Finally, we find all columns that depend on this one. If this happened to be the last pending
// dependency, then we add it to the list.
if (dependsOnKey.TryGetValue(newCol, out var depends))
{
foreach (var depender in depends)
{
var dependencies = keyDependsOn[depender];
Contracts.Assert(dependencies.Contains(newCol));
dependencies.Remove(newCol);
if (dependencies.Count == 0)
{
zeroDependencies.Add(depender);
}
}
dependsOnKey.Remove(newCol);
}
}
}
if (keyDependsOn.Any(p => p.Value.Count > 0))
{
// This might happen if the user does something incredibly strange, like, say, take some prior
// lambda, assign a column to a local variable, then re-use it downstream in a different lambdas.
// The user would have to go to some extraorindary effort to do that, but nonetheless we want to
// fail with a semi-sensible error message.
throw ch.Except("There were some leftover columns with unresolved dependencies. " +
"Did the caller use a " + nameof(PipelineColumn) + " from another delegate?");
}
// Now do the final renaming, if any is necessary.
toCopy.Clear();
foreach (var p in outPairs)
{
// TODO: Right now we just write stuff out. Once the copy-columns estimator is in place
// we ought to do this for real.
Contracts.Assert(nameMap.ContainsKey(p.Value));
string currentName = nameMap[p.Value];
if (currentName != p.Key)
{
ch.Trace($"Will copy '{currentName}' to '{p.Key}'");
toCopy.Add((currentName, p.Key));
}
}
// If any final renamings were necessary, insert the appropriate CopyColumns transform.
if (toCopy.Count > 0)
{
var copyEstimator = new ColumnCopyingEstimator(env, toCopy.ToArray());
if (estimator == null)
{
estimator = copyEstimator;
}
else
{
estimator = estimator.Append(copyEstimator);
}
}
ch.Trace($"Exiting {nameof(ReaderEstimatorAnalyzerHelper)}");
return(readerEstimator);
}