sklearn中Pipeline与make_pipeline的区别

转载自：https://stackoverflow.com/questions/40708077/what-is-the-difference-between-pipeline-and-make-pipeline-in-scikit

The only difference is that make_pipeline generates names for steps automatically.

Step names are needed e.g. if you want to use a pipeline with model selection utilities (e.g. GridSearchCV). With grid search you need to specify parameters for various steps of a pipeline:

pipe = Pipeline([('vec', CountVectorizer()), ('clf', LogisticRegression()])
param_grid = [{'clf__C': [1, 10, 100, 1000]}
gs = GridSearchCV(pipe, param_grid)
gs.fit(X, y)

compare it with make_pipeline:

pipe = make_pipeline(CountVectorizer(), LogisticRegression())     
param_grid = [{'logisticregression__C': [1, 10, 100, 1000]}
gs = GridSearchCV(pipe, param_grid)
gs.fit(X, y)

So, with Pipeline:

• names are explicit, you don’t have to figure them out if you need them;
• name doesn’t change if you change estimator/transformer used in a step, e.g. if you replace LogisticRegression() with LinearSVC() you can still use clf__C.

make_pipeline:

• shorter and arguably more readable notation;
• names are auto-generated using a straightforward rule (lowercase name of an estimator).

When to use them is up to you ? I prefer make_pipeline for quick experiments and Pipeline for more stable code; a rule of thumb: IPython Notebook -> make_pipeline; Python module in a larger project -> Pipeline. But it is certainly not a big deal to use make_pipeline in a module or Pipeline in a short script or a notebook.