private LinearBinaryPredictor(IHostEnvironment env, ModelLoadContext ctx)
: base(env, RegistrationName, ctx)
{
// For model version earlier than 0x00020001, there is no model statisitcs.
if (ctx.Header.ModelVerWritten <= 0x00020001)
{
return;
}
// *** Binary format ***
// (Base class)
// LinearModelStatistics: model statistics (optional, in a separate stream)
string statsDir = Path.Combine(ctx.Directory ?? "", ModelStatsSubModelFilename);
using (var statsEntry = ctx.Repository.OpenEntryOrNull(statsDir, ModelLoadContext.ModelStreamName))
{
if (statsEntry == null)
{
_stats = null;
}
else
{
using (var statsCtx = new ModelLoadContext(ctx.Repository, statsEntry, statsDir))
_stats = LinearModelStatistics.Create(Host, statsCtx);
}
}
}
private MulticlassLogisticRegressionPredictor(IHostEnvironment env, ModelLoadContext ctx)
: base(env, RegistrationName, ctx)
{
// *** Binary format ***
// int: number of features
// int: number of classes = number of biases
// float[]: biases
// (weight matrix, in CSR if sparse)
// (see https://netlib.org/linalg/html_templates/node91.html#SECTION00931100000000000000)
// int: number of row start indices (_numClasses + 1 if sparse, 0 if dense)
// int[]: row start indices
// int: total number of column indices (0 if dense)
// int[]: column index of each non-zero weight
// int: total number of non-zero weights (same as number of column indices if sparse, num of classes * num of features if dense)
// float[]: non-zero weights
// int[]: Id of label names (optional, in a separate stream)
// LinearModelStatistics: model statistics (optional, in a separate stream)
_numFeatures = ctx.Reader.ReadInt32();
Host.CheckDecode(_numFeatures >= 1);
_numClasses = ctx.Reader.ReadInt32();
Host.CheckDecode(_numClasses >= 1);
_biases = ctx.Reader.ReadFloatArray(_numClasses);
int numStarts = ctx.Reader.ReadInt32();
if (numStarts == 0)
{
// The weights are entirely dense.
int numIndices = ctx.Reader.ReadInt32();
Host.CheckDecode(numIndices == 0);
int numWeights = ctx.Reader.ReadInt32();
Host.CheckDecode(numWeights == _numClasses * _numFeatures);
_weights = new VBuffer <float> [_numClasses];
for (int i = 0; i < _weights.Length; i++)
{
var w = ctx.Reader.ReadFloatArray(_numFeatures);
_weights[i] = new VBuffer <float>(_numFeatures, w);
}
_weightsDense = _weights;
}
else
{
// Read weight matrix as CSR.
Host.CheckDecode(numStarts == _numClasses + 1);
int[] starts = ctx.Reader.ReadIntArray(numStarts);
Host.CheckDecode(starts[0] == 0);
Host.CheckDecode(Utils.IsSorted(starts));
int numIndices = ctx.Reader.ReadInt32();
Host.CheckDecode(numIndices == starts[starts.Length - 1]);
var indices = new int[_numClasses][];
for (int i = 0; i < indices.Length; i++)
{
indices[i] = ctx.Reader.ReadIntArray(starts[i + 1] - starts[i]);
Host.CheckDecode(Utils.IsIncreasing(0, indices[i], _numFeatures));
}
int numValues = ctx.Reader.ReadInt32();
Host.CheckDecode(numValues == numIndices);
_weights = new VBuffer <float> [_numClasses];
for (int i = 0; i < _weights.Length; i++)
{
float[] values = ctx.Reader.ReadFloatArray(starts[i + 1] - starts[i]);
_weights[i] = new VBuffer <float>(_numFeatures, Utils.Size(values), values, indices[i]);
}
}
WarnOnOldNormalizer(ctx, GetType(), Host);
InputType = new VectorType(NumberType.R4, _numFeatures);
OutputType = new VectorType(NumberType.R4, _numClasses);
// REVIEW: Should not save the label names duplicately with the predictor again.
// Get it from the label column schema metadata instead.
string[] labelNames = null;
if (ctx.TryLoadBinaryStream(LabelNamesSubModelFilename, r => labelNames = LoadLabelNames(ctx, r)))
{
_labelNames = labelNames;
}
string statsDir = Path.Combine(ctx.Directory ?? "", ModelStatsSubModelFilename);
using (var statsEntry = ctx.Repository.OpenEntryOrNull(statsDir, ModelLoadContext.ModelStreamName))
{
if (statsEntry == null)
{
_stats = null;
}
else
{
using (var statsCtx = new ModelLoadContext(ctx.Repository, statsEntry, statsDir))
_stats = LinearModelStatistics.Create(Host, statsCtx);
}
}
}
