private static IPredictor TrainKMeansAndLRCore()
{
string dataPath = s_dataPath;
using (var env = new TlcEnvironment(seed: 1))
{
// Pipeline
var loader = new TextLoader(env,
new TextLoader.Arguments()
{
HasHeader = true,
Separator = ",",
Column = new[] {
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 14, Max = 14
} },
Type = DataKind.R4
},
new TextLoader.Column()
{
Name = "CatFeatures",
Source = new [] {
new TextLoader.Range()
{
Min = 1, Max = 1
},
new TextLoader.Range()
{
Min = 3, Max = 3
},
new TextLoader.Range()
{
Min = 5, Max = 9
},
new TextLoader.Range()
{
Min = 13, Max = 13
}
},
Type = DataKind.TX
},
new TextLoader.Column()
{
Name = "NumFeatures",
Source = new [] {
new TextLoader.Range()
{
Min = 0, Max = 0
},
new TextLoader.Range()
{
Min = 2, Max = 2
},
new TextLoader.Range()
{
Min = 4, Max = 4
},
new TextLoader.Range()
{
Min = 10, Max = 12
}
},
Type = DataKind.R4
}
}
}, new MultiFileSource(dataPath));
IDataTransform trans = CategoricalTransform.Create(env, new CategoricalTransform.Arguments
{
Column = new[]
{
new CategoricalTransform.Column {
Name = "CatFeatures", Source = "CatFeatures"
}
}
}, loader);
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "NumFeatures");
trans = new ConcatTransform(env, trans, "Features", "NumFeatures", "CatFeatures");
trans = TrainAndScoreTransform.Create(env, new TrainAndScoreTransform.Arguments
{
Trainer = new SubComponent <ITrainer, SignatureTrainer>("KMeans", "k=100"),
FeatureColumn = "Features"
}, trans);
trans = new ConcatTransform(env, trans, "Features", "Features", "Score");
// Train
var trainer = new LogisticRegression(env, new LogisticRegression.Arguments()
{
EnforceNonNegativity = true, OptTol = 1e-3f
});
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
return(trainer.Train(trainRoles));
}
}
public ParameterMixingCalibratedPredictor TrainKMeansAndLR()
{
using (var env = new ConsoleEnvironment(seed: 1))
{
// Pipeline
var loader = TextLoader.ReadFile(env,
new TextLoader.Arguments()
{
HasHeader = true,
Separator = ",",
Column = new[] {
new TextLoader.Column("Label", DataKind.R4, 14),
new TextLoader.Column("CatFeatures", DataKind.TX,
new [] {
new TextLoader.Range()
{
Min = 1, Max = 1
},
new TextLoader.Range()
{
Min = 3, Max = 3
},
new TextLoader.Range()
{
Min = 5, Max = 9
},
new TextLoader.Range()
{
Min = 13, Max = 13
}
}),
new TextLoader.Column("NumFeatures", DataKind.R4,
new [] {
new TextLoader.Range()
{
Min = 0, Max = 0
},
new TextLoader.Range()
{
Min = 2, Max = 2
},
new TextLoader.Range()
{
Min = 4, Max = 4
},
new TextLoader.Range()
{
Min = 10, Max = 12
}
})
}
}, new MultiFileSource(_dataPath));
IDataView trans = CategoricalTransform.Create(env, loader, "CatFeatures");
trans = NormalizeTransform.CreateMinMaxNormalizer(env, trans, "NumFeatures");
trans = new ConcatTransform(env, "Features", "NumFeatures", "CatFeatures").Transform(trans);
trans = TrainAndScoreTransform.Create(env, new TrainAndScoreTransform.Arguments
{
Trainer = ComponentFactoryUtils.CreateFromFunction(host =>
new KMeansPlusPlusTrainer(host, new KMeansPlusPlusTrainer.Arguments()
{
K = 100
})),
FeatureColumn = "Features"
}, trans);
trans = new ConcatTransform(env, "Features", "Features", "Score").Transform(trans);
// Train
var trainer = new LogisticRegression(env, new LogisticRegression.Arguments()
{
EnforceNonNegativity = true, OptTol = 1e-3f
});
var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
return(trainer.Train(trainRoles));
}
}
