Text countvectorizer transform

CountVectorizer.Transform

Count a corpus against the vocabulary already learned.

public CsrMatrix Transform(IEnumerable<string> documents)

Parameters — documents is the corpus to count. Its terms are looked up in the vocabulary learned by Fit; terms absent from it are dropped.

Returns — CsrMatrix, one row per document and one column per learned term, so its width is the fit's width whatever this corpus holds.

Exceptions — InvalidOperationException when nothing has been fitted yet. ArgumentNullException when documents is null.

Example — a document holding an unseen term, and one holding none of the vocabulary.

using Lodestar.Text.Vectorization;

var cv = new CountVectorizer();
cv.Fit(["the cat eats", "the dog eats"]);

CsrMatrix counts = cv.Transform(["the cat sleeps", "nothing here matches"]);

int width = counts.ColumnCount;   // => 4
int stored = counts.NonZeroCount; // => 2

Remarks — two stored cells: the and cat from the first document, nothing at all from the second. A document that shares no term with the vocabulary produces an empty row rather than an error — it is a legitimate answer, and one worth checking for downstream, because an empty row has no norm and normalizing leaves it alone.

Transforming before fitting throws rather than fitting implicitly, because the alternative is a vocabulary learned from whatever corpus happened to arrive first.

Applies to — net10.0, netstandard2.0.

See also — CountVectorizer.Fit, CountVectorizer.FitTransform.