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TfidfTransformer.FitTransform

Learn the document frequencies and weight the same matrix.

public CsrMatrix FitTransform(CsrMatrix counts)

Parameters — counts is the count matrix, both learned from and weighted.

Returns — CsrMatrix, the same shape as counts, weighted and normalized by TfidfOptions.Norm.

Exceptions — ArgumentNullException when counts is null.

Example — the common case, where the counts and the corpus to weight are the same.

using Lodestar.Abstractions;
using Lodestar.Text.Vectorization;

string[] docs = ["the cat eats", "the dog eats", "the cat and the dog"];
CsrMatrix counts = new CountVectorizer().FitTransform(docs);

CsrMatrix weighted = new TfidfTransformer().FitTransform(counts);

int rows = weighted.RowCount;      // => 3
int columns = weighted.ColumnCount; // => 5

Remarks — the shape is unchanged: weighting replaces values, it does not add or remove columns. A count of zero stays absent, because a term the document does not hold has no weight however rare it is.

The returned matrix is a new one; counts is left as it was, unlike CsrMatrix.NormalizeRows, which works in place.

Applies to — net10.0, netstandard2.0.

See also — TfidfTransformer.Fit, TfidfVectorizer.FitTransform.