How to transform items using sklearn Pipeline?
The reason why the results are different (and why calling transform even workds) is that LinearSVC also has a transform (now deprecated) that does feature selection
If you want to transform using just the first step, pipeline.named_steps['tfidf'].transform([item]) is the right thing to do.
If you would like to transform using all but the last step, olologin's answer provides the code.
By default, all steps of the pipeline are executed, so also the transform on the last step, which is the feature selection performed by the LinearSVC.
I have a simple scikit-learn Pipeline of two steps: a TfIdfVectorizer followed by a LinearSVC.
I have fit the pipeline using my data. All good.
Now I want to transform (not predict!) an item, using my fitted pipeline.
I tried pipeline.transform([item]), but it gives a different result compared to pipeline.named_steps['tfidf'].transform([item]). Even the shape and type of the result is different: the first is a 1x3000 CSR matrix, the second a 1x15000 CSC matrix. Which one is correct? Why do they differ?
How do I transform items, i.e. get an item's vector representation before the final estimator, when using scikit-learn's Pipeline?