internal EnsemblePredictor(IHostEnvironment env, PredictionKind kind,
FeatureSubsetModel <TScalarPredictor>[] models, IOutputCombiner <Single> combiner, Single[] weights = null)
: base(env, LoaderSignature, models, combiner, weights)
{
PredictionKind = kind;
InputType = InitializeMappers(out _mappers);
}
private EnsembleDistributionPredictor(IHostEnvironment env, ModelLoadContext ctx)
: base(env, RegistrationName, ctx)
{
PredictionKind = (PredictionKind)ctx.Reader.ReadInt32();
_probabilityCombiner = new Median(env);
InputType = InitializeMappers(out _mappers);
ComputeAveragedWeights(out _averagedWeights);
}
internal EnsembleDistributionPredictor(IHostEnvironment env, PredictionKind kind,
FeatureSubsetModel <TDistPredictor>[] models, IOutputCombiner <Single> combiner, Single[] weights = null)
: base(env, RegistrationName, models, combiner, weights)
{
PredictionKind = kind;
_probabilityCombiner = new Median(env);
InputType = InitializeMappers(out _mappers);
ComputeAveragedWeights(out _averagedWeights);
}
protected XGBoostTrainerBase(IHostEnvironment env, XGBoostArguments args, PredictionKind predictionKind, string name)
{
Contracts.CheckValue(env, nameof(env));
env.CheckNonWhiteSpace(name, nameof(name));
_host = env.Register(name);
_host.CheckValue(args, nameof(args));
_args = args;
_predictionKind = predictionKind;
}
public TreeEnsembleCombiner(IHostEnvironment env, PredictionKind kind)
{
_host = env.Register("TreeEnsembleCombiner");
switch (kind)
{
case PredictionKind.BinaryClassification:
case PredictionKind.Regression:
case PredictionKind.Ranking:
_kind = kind;
break;
default:
throw _host.ExceptUserArg(nameof(kind), $"Tree ensembles can be either of type {nameof(PredictionKind.BinaryClassification)}, " +
$"{nameof(PredictionKind.Regression)} or {nameof(PredictionKind.Ranking)}");
}
}
protected LightGbmTrainerBase(IHostEnvironment env, LightGbmArguments args, PredictionKind predictionKind, string name)
{
Contracts.CheckValue(env, nameof(env));
env.CheckNonWhiteSpace(name, nameof(name));
Host = env.Register(name);
Host.CheckValue(args, nameof(args));
Args = args;
Options = Args.ToDictionary(Host);
_predictionKind = predictionKind;
_env = env;
ParallelTraining = Args.ParallelTrainer != null?Args.ParallelTrainer.CreateComponent(env) : new SingleTrainer();
InitParallelTraining();
}
/// <summary>
/// Suggests a default schema for a predictor
/// </summary>
/// <param name="kind">DataKind</param>
/// <param name="dim">dimension</param>
/// <returns>ISchema</returns>
public static ISchema PredictionDefaultSchema(PredictionKind kind, int dim = 0)
{
switch (kind)
{
case PredictionKind.BinaryClassification:
return(new ExtendedSchema((ISchema)null, new[] { "Score" }, new[] { NumberDataViewType.Single }));
case PredictionKind.Regression:
return(new ExtendedSchema((ISchema)null, new[] { "Prediction" }, new[] { NumberDataViewType.Single }));
case PredictionKind.MulticlassClassification:
return(new ExtendedSchema((ISchema)null, new[] { "Scores" }, new[] { new VectorDataViewType(NumberDataViewType.Single, dim) }));
case PredictionKind.MultiOutputRegression:
return(new ExtendedSchema((ISchema)null, new[] { "Predictions" }, new[] { new VectorDataViewType(NumberDataViewType.Single, dim) }));
default:
throw Contracts.Except("Unable to build the schema for kind {0}", kind);
}
}
private MulticlassDataPartitionEnsembleTrainer(IHostEnvironment env, Arguments args, PredictionKind predictionKind)
: this(env, args)
{
Host.CheckParam(predictionKind == PredictionKind.MulticlassClassification, nameof(PredictionKind));
}
private EnsembleTrainer(IHostEnvironment env, Arguments args, PredictionKind predictionKind)
: this(env, args)
{
Host.CheckParam(predictionKind == PredictionKind.BinaryClassification, nameof(PredictionKind));
}
/// <summary>
/// Finalizes the test on a predictor, calls the predictor with a scorer,
/// saves the data, saves the models, loads it back, saves the data again,
/// checks the output is the same.
/// </summary>
/// <param name="env">environment</param>
/// <param name="outModelFilePath">output filename</param>
/// <param name="predictor">predictor</param>
/// <param name="roles">label, feature, ...</param>
/// <param name="outData">first output data</param>
/// <param name="outData2">second output data</param>
/// <param name="kind">prediction kind</param>
/// <param name="checkError">checks errors</param>
/// <param name="ratio">check the error is below that threshold (if checkError is true)</param>
/// <param name="ratioReadSave">check the predictions difference after reloading the model are below this threshold</param>
public static void FinalizeSerializationTest(IHostEnvironment env,
string outModelFilePath, IPredictor predictor,
RoleMappedData roles, string outData, string outData2,
PredictionKind kind, bool checkError = true,
float ratio = 0.8f, float ratioReadSave = 0.06f,
bool checkType = true)
{
string labelColumn = kind != PredictionKind.Clustering ? roles.Schema.Label.Value.Name : null;
#region save, reading, running
// Saves model.
using (var ch = env.Start("Save"))
using (var fs = File.Create(outModelFilePath))
TrainUtils.SaveModel(env, ch, fs, predictor, roles);
if (!File.Exists(outModelFilePath))
{
throw new FileNotFoundException(outModelFilePath);
}
// Loads the model back.
using (var fs = File.OpenRead(outModelFilePath))
{
var pred_local = ModelFileUtils.LoadPredictorOrNull(env, fs);
if (pred_local == null)
{
throw new Exception(string.Format("Unable to load '{0}'", outModelFilePath));
}
if (checkType && predictor.GetType() != pred_local.GetType())
{
throw new Exception(string.Format("Type mismatch {0} != {1}", predictor.GetType(), pred_local.GetType()));
}
}
// Checks the outputs.
var sch1 = SchemaHelper.ToString(roles.Schema.Schema);
var scorer = PredictorHelper.CreateDefaultScorer(env, roles, predictor);
var sch2 = SchemaHelper.ToString(scorer.Schema);
if (string.IsNullOrEmpty(sch1) || string.IsNullOrEmpty(sch2))
{
throw new Exception("Empty schemas");
}
var saver = env.CreateSaver("Text");
var columns = new int[scorer.Schema.Count];
for (int i = 0; i < columns.Length; ++i)
{
columns[i] = saver.IsColumnSavable(scorer.Schema[i].Type) ? i : -1;
}
columns = columns.Where(c => c >= 0).ToArray();
using (var fs2 = File.Create(outData))
saver.SaveData(fs2, scorer, columns);
if (!File.Exists(outModelFilePath))
{
throw new FileNotFoundException(outData);
}
// Check we have the same output.
using (var fs = File.OpenRead(outModelFilePath))
{
var model = ModelFileUtils.LoadPredictorOrNull(env, fs);
scorer = PredictorHelper.CreateDefaultScorer(env, roles, model);
saver = env.CreateSaver("Text");
using (var fs2 = File.Create(outData2))
saver.SaveData(fs2, scorer, columns);
}
var t1 = File.ReadAllLines(outData);
var t2 = File.ReadAllLines(outData2);
if (t1.Length != t2.Length)
{
throw new Exception(string.Format("Not the same number of lines: {0} != {1}", t1.Length, t2.Length));
}
var linesN = new List <int>();
for (int i = 0; i < t1.Length; ++i)
{
if (t1[i] != t2[i])
{
linesN.Add(i);
}
}
if (linesN.Count > (int)(t1.Length * ratioReadSave))
{
var rows = linesN.Select(i => string.Format("1-Mismatch on line {0}/{3}:\n{1}\n{2}", i, t1[i], t2[i], t1.Length)).ToList();
rows.Add($"Number of differences: {linesN.Count}/{t1.Length}");
throw new Exception(string.Join("\n", rows));
}
#endregion
#region clustering
if (kind == PredictionKind.Clustering)
{
// Nothing to do here.
return;
}
#endregion
#region supervized
string expectedOuput = kind == PredictionKind.Regression ? "Score" : "PredictedLabel";
// Get label and basic checking about performance.
using (var cursor = scorer.GetRowCursor(scorer.Schema))
{
int ilabel, ipred;
ilabel = SchemaHelper.GetColumnIndex(cursor.Schema, labelColumn);
ipred = SchemaHelper.GetColumnIndex(cursor.Schema, expectedOuput);
var ty1 = cursor.Schema[ilabel].Type;
var ty2 = cursor.Schema[ipred].Type;
var dist1 = new Dictionary <int, int>();
var dist2 = new Dictionary <int, int>();
var conf = new Dictionary <Tuple <int, int>, long>();
if (kind == PredictionKind.MulticlassClassification)
{
#region Multiclass
if (!ty2.IsKey())
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
if (ty1.RawKind() == DataKind.Single)
{
var lgetter = cursor.GetGetter <float>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ipred, cursor));
float ans = 0;
uint pre = 0;
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
// The scorer +1 to the argmax.
++ans;
var key = new Tuple <int, int>((int)pre, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey((int)pre))
{
dist2[(int)pre] = 1;
}
else
{
++dist2[(int)pre];
}
}
}
else if (ty1.RawKind() == DataKind.UInt32 && ty1.IsKey())
{
var lgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ipred, cursor));
uint ans = 0;
uint pre = 0;
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
var key = new Tuple <int, int>((int)pre, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey((int)pre))
{
dist2[(int)pre] = 1;
}
else
{
++dist2[(int)pre];
}
}
}
else
{
throw new NotImplementedException(string.Format("Not implemented for type {0}", ty1.ToString()));
}
#endregion
}
else if (kind == PredictionKind.BinaryClassification)
{
#region binary classification
if (ty2.RawKind() != DataKind.Boolean)
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
if (ty1.RawKind() == DataKind.Single)
{
var lgetter = cursor.GetGetter <float>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ipred, cursor));
float ans = 0;
bool pre = default(bool);
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
if (ans != 0 && ans != 1)
{
throw Contracts.Except("The problem is not binary, expected answer is {0}", ans);
}
var key = new Tuple <int, int>(pre ? 1 : 0, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey(pre ? 1 : 0))
{
dist2[pre ? 1 : 0] = 1;
}
else
{
++dist2[pre ? 1 : 0];
}
}
}
else if (ty1.RawKind() == DataKind.UInt32)
{
var lgetter = cursor.GetGetter <uint>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ipred, cursor));
uint ans = 0;
bool pre = default(bool);
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
if (ty1.IsKey())
{
--ans;
}
if (ans != 0 && ans != 1)
{
throw Contracts.Except("The problem is not binary, expected answer is {0}", ans);
}
var key = new Tuple <int, int>(pre ? 1 : 0, (int)ans);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey(pre ? 1 : 0))
{
dist2[pre ? 1 : 0] = 1;
}
else
{
++dist2[pre ? 1 : 0];
}
}
}
else if (ty1.RawKind() == DataKind.Boolean)
{
var lgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <bool>(SchemaHelper._dc(ipred, cursor));
bool ans = default(bool);
bool pre = default(bool);
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
var key = new Tuple <int, int>(pre ? 1 : 0, ans ? 1 : 0);
if (!conf.ContainsKey(key))
{
conf[key] = 1;
}
else
{
++conf[key];
}
if (!dist1.ContainsKey(ans ? 1 : 0))
{
dist1[ans ? 1 : 0] = 1;
}
else
{
++dist1[ans ? 1 : 0];
}
if (!dist2.ContainsKey(pre ? 1 : 0))
{
dist2[pre ? 1 : 0] = 1;
}
else
{
++dist2[pre ? 1 : 0];
}
}
}
else
{
throw new NotImplementedException(string.Format("Not implemented for type {0}", ty1));
}
#endregion
}
else if (kind == PredictionKind.Regression)
{
#region regression
if (ty1.RawKind() != DataKind.Single)
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
if (ty2.RawKind() != DataKind.Single)
{
throw new Exception(string.Format("Label='{0}' Predicted={1}'\nSchema: {2}", ty1, ty2, SchemaHelper.ToString(cursor.Schema)));
}
var lgetter = cursor.GetGetter <float>(SchemaHelper._dc(ilabel, cursor));
var pgetter = cursor.GetGetter <float>(SchemaHelper._dc(ipred, cursor));
float ans = 0;
float pre = 0f;
float error = 0f;
while (cursor.MoveNext())
{
lgetter(ref ans);
pgetter(ref pre);
error += (ans - pre) * (ans - pre);
if (!dist1.ContainsKey((int)ans))
{
dist1[(int)ans] = 1;
}
else
{
++dist1[(int)ans];
}
if (!dist2.ContainsKey((int)pre))
{
dist2[(int)pre] = 1;
}
else
{
++dist2[(int)pre];
}
}
if (float.IsNaN(error) || float.IsInfinity(error))
{
throw new Exception("Regression wen wrong. Error is infinite.");
}
#endregion
}
else
{
throw new NotImplementedException(string.Format("Not implemented for kind {0}", kind));
}
var nbError = conf.Where(c => c.Key.Item1 != c.Key.Item2).Select(c => c.Value).Sum();
var nbTotal = conf.Select(c => c.Value).Sum();
if (checkError && (nbError * 1.0 > nbTotal * ratio || dist2.Count <= 1))
{
var sconf = string.Join("\n", conf.OrderBy(c => c.Key)
.Select(c => string.Format("pred={0} exp={1} count={2}", c.Key.Item1, c.Key.Item2, c.Value)));
var sdist2 = string.Join("\n", dist1.OrderBy(c => c.Key)
.Select(c => string.Format("label={0} count={1}", c.Key, c.Value)));
var sdist1 = string.Join("\n", dist2.OrderBy(c => c.Key).Take(20)
.Select(c => string.Format("label={0} count={1}", c.Key, c.Value)));
throw new Exception(string.Format("Too many errors {0}/{1}={7}\n###########\nConfusion:\n{2}\n########\nDIST1\n{3}\n###########\nDIST2\n{4}\nOutput:\n{5}\n...\n{6}",
nbError, nbTotal,
sconf, sdist1, sdist2,
string.Join("\n", t1.Take(Math.Min(30, t1.Length))),
string.Join("\n", t1.Skip(Math.Max(0, t1.Length - 30))),
nbError * 1.0 / nbTotal));
}
}
#endregion
}
private EnsemblePredictor(IHostEnvironment env, ModelLoadContext ctx)
: base(env, RegistrationName, ctx)
{
PredictionKind = (PredictionKind)ctx.Reader.ReadInt32();
InputType = InitializeMappers(out _mappers);
}
private RegressionEnsembleTrainer(IHostEnvironment env, Arguments args, PredictionKind predictionKind)
: this(env, args)
{
Host.CheckParam(predictionKind == PredictionKind.Regression, nameof(PredictionKind));
}
public int ComputeNbClass(PredictionKind kind)
{
_host.AssertValue(_labelWeights);
KeyValuePair <long, Tuple <TLabel, float> > first = new KeyValuePair <long, Tuple <TLabel, float> >(0, new Tuple <TLabel, float>(default(TLabel), 0));
foreach (var pair in _labelWeights)
{
first = pair;
break;
}
TLabel mini = first.Value.Item1, maxi = first.Value.Item1;
foreach (var pair in _labelWeights)
{
if (mini.CompareTo(pair.Value.Item1) > 0)
{
mini = pair.Value.Item1;
}
if (maxi.CompareTo(pair.Value.Item1) < 0)
{
maxi = pair.Value.Item1;
}
}
var conv = new TypedConverters <TLabel>(DataKind.Int32);
int imini = 0, imaxi = 0;
var convMapper = conv.GetMapper <int>();
if (convMapper == null)
{
throw _host.Except("No conversion from {0} to {1}", typeof(TLabel), typeof(int));
}
switch (kind)
{
case PredictionKind.BinaryClassification:
if (mini.CompareTo(maxi) == 0)
{
throw _host.Except("Only one class, two are expected.");
}
convMapper(in mini, ref imini);
convMapper(in maxi, ref imaxi);
if (imaxi - imini != 1)
{
throw _host.Except("More than two classes: min(labels)={0} max(labels)={1}", imini, imaxi);
}
return(2);
case PredictionKind.MulticlassClassification:
if (mini.CompareTo(maxi) == 0)
{
throw _host.Except("Only one class, more are expected.");
}
convMapper(in mini, ref imini);
convMapper(in maxi, ref imaxi);
return(imini == 0 ? imaxi + 1 : imini);
default:
throw _host.ExceptNotSupp("Not suported for predictor {0}", kind);
}
}
public ValueMapper <TIn, TOut> GetMapper <TIn, TOut>(NearestNeighborsTrees trees, int k,
NearestNeighborsAlgorithm algo, NearestNeighborsWeights weight, PredictionKind kind)
{
_host.Check(typeof(TIn) == typeof(VBuffer <float>));
_host.CheckValue(_labelWeights, "_labelWeights");
_host.Check(algo == NearestNeighborsAlgorithm.kdtree, "algo");
if (weight == NearestNeighborsWeights.uniform)
{
switch (kind)
{
case PredictionKind.BinaryClassification:
return(GetMapperBinaryPrediction <TIn, TOut>(trees, k, algo, weight));
case PredictionKind.MulticlassClassification:
return(GetMapperMulticlassPrediction <TIn, TOut>(trees, k, algo, weight));
default:
throw _host.ExceptNotImpl("Not implemented yet for kind={0}", kind);
}
}
else
{
throw _host.ExceptNotImpl("Not implemented yet for wieght={0}", weight);
}
}
