/// <summary>
/// Generates a new Collaborative Filtering model.
/// </summary>
/// <param name="X">Training matrix values.</param>
/// <param name="y">Vector of entity identifiers.</param>
/// <returns></returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X.Copy());
// inputs are ratings from each user (X = entities x ratings), y = entity id.
// create rating range in case we don't have one already
if (this.Ratings == null)
this.Ratings = new Range(X.Min(), X.Max());
// indicator matrix of 1's where rating was provided otherwise 0's.
Matrix R = X.ToBinary(f => this.Ratings.Test(f));
// The mean needs to be values within rating range only.
Vector mean = X.GetRows().Select(s =>
s.Where(w => this.Ratings.Test(w)).Sum() /
s.Where(w => this.Ratings.Test(w)).Count()
).ToVector();
// update feature averages before preprocessing features.
this.FeatureProperties.Average = mean;
this.Preprocess(X);
// where references could be user ratings and entities are movies / books, etc.
int references = X.Cols, entities = X.Rows;
// initialize Theta parameters
Matrix ThetaX = Matrix.Rand(entities, this.CollaborativeFeatures, -1d);
Matrix ThetaY = Matrix.Rand(references, this.CollaborativeFeatures, -1d);
numl.Math.Functions.Cost.ICostFunction costFunction = new numl.Math.Functions.Cost.CofiCostFunction()
{
CollaborativeFeatures = this.CollaborativeFeatures,
Lambda = this.Lambda,
R = R,
Regularizer = null,
X = ThetaX,
Y = X.Unshape()
};
// we're optimising two params so combine them
Vector Theta = Vector.Combine(ThetaX.Unshape(), ThetaY.Unshape());
numl.Math.Optimization.Optimizer optimizer = new numl.Math.Optimization.Optimizer(Theta, this.MaxIterations, this.LearningRate)
{
CostFunction = costFunction
};
optimizer.Run();
// extract the optimised parameter Theta
ThetaX = optimizer.Properties.Theta.Slice(0, (ThetaX.Rows * ThetaX.Cols) - 1).Reshape(entities, VectorType.Row);
ThetaY = optimizer.Properties.Theta.Slice(ThetaX.Rows * ThetaX.Cols, Theta.Length - 1).Reshape(references, VectorType.Row);
// create reference mappings, each value is the original index.
this.ReferenceFeatureMap = (this.ReferenceFeatureMap == null ? Vector.Create(references, i => i) : this.ReferenceFeatureMap);
this.EntityFeatureMap = (this.EntityFeatureMap == null ? Vector.Create(entities, i => i) : this.EntityFeatureMap);
return new CofiRecommenderModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = this.NormalizeFeatures,
FeatureNormalizer = this.FeatureNormalizer,
FeatureProperties = this.FeatureProperties,
Ratings = this.Ratings,
ReferenceFeatureMap = this.ReferenceFeatureMap,
EntityFeatureMap = this.EntityFeatureMap,
Mu = mean,
Y = y,
Reference = X,
ThetaX = ThetaX,
ThetaY = ThetaY
};
}
/// <summary>
/// Generates a new Collaborative Filtering model.
/// </summary>
/// <param name="X">Training matrix values.</param>
/// <param name="y">Vector of entity identifiers.</param>
/// <returns></returns>
public override IModel Generate(Matrix X, Vector y)
{
this.Preprocess(X.Copy());
// inputs are ratings from each user (X = entities x ratings), y = entity id.
// create rating range in case we don't have one already
if (this.Ratings == null)
{
this.Ratings = new Range(X.Min(), X.Max());
}
// indicator matrix of 1's where rating was provided otherwise 0's.
Matrix R = X.ToBinary(f => this.Ratings.Test(f));
// The mean needs to be values within rating range only.
Vector mean = X.GetRows().Select(s =>
s.Where(w => this.Ratings.Test(w)).Sum() /
s.Where(w => this.Ratings.Test(w)).Count()
).ToVector();
// update feature averages before preprocessing features.
this.FeatureProperties.Average = mean;
this.Preprocess(X);
// where references could be user ratings and entities are movies / books, etc.
int references = X.Cols, entities = X.Rows;
// initialize Theta parameters
Matrix ThetaX = Matrix.Rand(entities, this.CollaborativeFeatures, -1d);
Matrix ThetaY = Matrix.Rand(references, this.CollaborativeFeatures, -1d);
numl.Math.Functions.Cost.ICostFunction costFunction = new numl.Math.Functions.Cost.CofiCostFunction()
{
CollaborativeFeatures = this.CollaborativeFeatures,
Lambda = this.Lambda,
R = R,
Regularizer = null,
X = ThetaX,
Y = X.Unshape()
};
// we're optimising two params so combine them
Vector Theta = Vector.Combine(ThetaX.Unshape(), ThetaY.Unshape());
numl.Math.Optimization.Optimizer optimizer = new numl.Math.Optimization.Optimizer(Theta, this.MaxIterations, this.LearningRate)
{
CostFunction = costFunction
};
optimizer.Run();
// extract the optimised parameter Theta
ThetaX = optimizer.Properties.Theta.Slice(0, (ThetaX.Rows * ThetaX.Cols) - 1).Reshape(entities, VectorType.Row);
ThetaY = optimizer.Properties.Theta.Slice(ThetaX.Rows * ThetaX.Cols, Theta.Length - 1).Reshape(references, VectorType.Row);
// create reference mappings, each value is the original index.
this.ReferenceFeatureMap = (this.ReferenceFeatureMap == null ? Vector.Create(references, i => i) : this.ReferenceFeatureMap);
this.EntityFeatureMap = (this.EntityFeatureMap == null ? Vector.Create(entities, i => i) : this.EntityFeatureMap);
return(new CofiRecommenderModel()
{
Descriptor = this.Descriptor,
NormalizeFeatures = this.NormalizeFeatures,
FeatureNormalizer = this.FeatureNormalizer,
FeatureProperties = this.FeatureProperties,
Ratings = this.Ratings,
ReferenceFeatureMap = this.ReferenceFeatureMap,
EntityFeatureMap = this.EntityFeatureMap,
Mu = mean,
Y = y,
Reference = X,
ThetaX = ThetaX,
ThetaY = ThetaY
});
}