Text tfidfoptions

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TfidfOptions

The four switches that decide how a count becomes a weight.

public sealed record TfidfOptions

Properties — UseIdf (default true) multiplies each term frequency by the inverse document frequency; turn it off and the result is term frequencies with the normalization still applied. SmoothIdf (default true) adds one to every document count, as if a document containing every term had been seen, which is what keeps a term appearing in every document from dividing by zero. SublinearTf (default false) replaces the count tf with 1 + log(tf), so a term appearing twenty times counts as a little more than one appearing ten, rather than twice as much. Norm (default SparseNorm.L2) is the norm each row is divided by afterwards, and is nullable: null leaves the rows unnormalized, which is scikit-learn's norm=None. Measured, a row whose normalized length would be 1 comes out at 1.9938235052555415 with Norm = null.

Example — the defaults, and the row they produce.

using Lodestar.Text.Vectorization;

string[] docs = ["the cat eats", "the dog eats", "the cat and the dog"];

CsrMatrix weighted = new TfidfVectorizer().FitTransform(docs);
double rowLength = weighted.RowL2Norm(0);  // => 1

Remarks — every default here is scikit-learn's, and the four names answer to use_idf, smooth_idf, sublinear_tf and norm. The one worth understanding rather than accepting is SmoothIdf: with it on, the IDF of a term is log((1 + n) / (1 + df)) + 1, and the trailing + 1 means a term appearing in every document still has a non-zero weight. That surprises readers who expect ubiquitous terms to vanish, and it is deliberate on both sides — the normalization that follows is what actually demotes them.

SublinearTf is the switch to reach for when documents vary a lot in length and a long document should not dominate merely by repeating itself. It is off by default because scikit-learn has it off.

Applies to — net10.0, netstandard2.0.

See also — TfidfTransformer, TfidfVectorizer, SparseNorm, the Python equivalence table.