protected override bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName)
{
string opType = "OneHotEncoder";
var node = OnnxUtils.MakeNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
OnnxUtils.NodeAddAttributes(node, "cats_int64s", Enumerable.Range(1, info.TypeSrc.ItemType.KeyCount).Select(x => (long)x));
OnnxUtils.NodeAddAttributes(node, "zeros", true);
ctx.AddNode(node);
return(true);
}
protected override bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName)
{
DataKind rawKind;
var type = Infos[iinfo].TypeSrc;
if (type.IsVector)
{
rawKind = type.AsVector.ItemType.RawKind;
}
else if (type.IsKey)
{
rawKind = type.AsKey.RawKind;
}
else
{
rawKind = type.RawKind;
}
if (rawKind != DataKind.R4)
{
return(false);
}
string opType = "Imputer";
var node = OnnxUtils.MakeNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
OnnxUtils.NodeAddAttributes(node, "replaced_value_float", Single.NaN);
if (!Infos[iinfo].TypeSrc.IsVector)
{
OnnxUtils.NodeAddAttributes(node, "imputed_value_float", Enumerable.Repeat((float)_repValues[iinfo], 1));
}
else
{
if (_repIsDefault[iinfo] != null)
{
OnnxUtils.NodeAddAttributes(node, "imputed_value_floats", (float[])_repValues[iinfo]);
}
else
{
OnnxUtils.NodeAddAttributes(node, "imputed_value_float", Enumerable.Repeat((float)_repValues[iinfo], 1));
}
}
ctx.AddNode(node);
return(true);
}
public bool SaveAsOnnx(OnnxContext ctx, string[] outputs, string featureColumn)
{
Host.CheckValue(ctx, nameof(ctx));
string opType = "LinearClassifier";
var node = OnnxUtils.MakeNode(opType, new List <string> {
featureColumn
}, new List <string>(outputs), ctx.GetNodeName(opType));
// Selection of logit or probit output transform. enum {'NONE', 'LOGIT', 'PROBIT}
OnnxUtils.NodeAddAttributes(node, "post_transform", 0);
OnnxUtils.NodeAddAttributes(node, "multi_class", true);
OnnxUtils.NodeAddAttributes(node, "coefficients", _weights.SelectMany(w => w.DenseValues()));
OnnxUtils.NodeAddAttributes(node, "intercepts", _biases);
OnnxUtils.NodeAddAttributes(node, "classlabels_strings", _labelNames);
ctx.AddNode(node);
return(true);
}
public bool SaveAsOnnx(OnnxContext ctx, string[] outputs, string featureColumn)
{
Host.CheckValue(ctx, nameof(ctx));
Host.Check(Utils.Size(outputs) == 1);
string opType = "LinearRegressor";
var node = OnnxUtils.MakeNode(opType, new List <string> {
featureColumn
}, new List <string> (outputs), ctx.GetNodeName(opType));
// Selection of logit or probit output transform. enum {'NONE', 'LOGIT', 'PROBIT}
OnnxUtils.NodeAddAttributes(node, "post_transform", 0);
OnnxUtils.NodeAddAttributes(node, "targets", 1);
OnnxUtils.NodeAddAttributes(node, "coefficients", Weight.DenseValues());
OnnxUtils.NodeAddAttributes(node, "intercepts", Bias);
ctx.AddNode(node);
return(true);
}
protected override bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName)
{
if (!info.TypeSrc.ItemType.IsText)
{
return(false);
}
var terms = default(VBuffer <DvText>);
TermMap <DvText> map = (TermMap <DvText>)_termMap[iinfo].Map;
map.GetTerms(ref terms);
string opType = "LabelEncoder";
var node = OnnxUtils.MakeNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
OnnxUtils.NodeAddAttributes(node, "classes_strings", terms.DenseValues());
OnnxUtils.NodeAddAttributes(node, "default_int64", -1);
OnnxUtils.NodeAddAttributes(node, "default_string", DvText.Empty);
ctx.AddNode(node);
return(true);
}
protected override bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, ColInfo info, string srcVariableName, string dstVariableName)
{
Contracts.AssertValue(ctx);
Contracts.Assert(0 <= iinfo && iinfo < Infos.Length);
Contracts.Assert(Infos[iinfo] == info);
Contracts.Assert(CanSaveOnnx);
if (info.TypeSrc.ValueCount == 0)
{
return(false);
}
string opType = "Scaler";
var node = OnnxUtils.MakeNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
if (_functions[iinfo].OnnxInfo(ctx, new OnnxUtils.NodeProtoWrapper(node), info.TypeSrc.ValueCount))
{
ctx.AddNode(node);
return(true);
}
return(false);
}
public override void SaveAsOnnx(OnnxContext ctx)
{
Host.CheckValue(ctx, nameof(ctx));
Host.Assert(Bindable is IBindableCanSaveOnnx);
if (!ctx.ContainsColumn(DefaultColumnNames.Features))
{
return;
}
base.SaveAsOnnx(ctx);
int delta = Bindings.DerivedColumnCount;
Host.Assert(delta == 1);
string[] outColumnNames = new string[Bindings.InfoCount]; //PredictedLabel, Score, Probability.
for (int iinfo = 0; iinfo < Bindings.InfoCount; ++iinfo)
{
outColumnNames[iinfo] = Bindings.GetColumnName(Bindings.MapIinfoToCol(iinfo));
}
//Check if "Probability" column was generated by the base class, only then
//label can be predicted.
if (Bindings.InfoCount >= 3 && ctx.ContainsColumn(outColumnNames[2]))
{
string opType = "Binarizer";
var node = OnnxUtils.MakeNode(opType, new List <string> {
ctx.GetVariableName(outColumnNames[2])
},
new List <string> {
ctx.GetVariableName(outColumnNames[0])
}, ctx.GetNodeName(opType));
OnnxUtils.NodeAddAttributes(node, "threshold", 0.5);
ctx.AddNode(node);
}
}