searchgrid documentation

Helps building parameter grids for scikit-learn grid search <scikit-learn:grid_search>.

Specifying a parameter grid for sklearn.model_selection.GridSearchCV <GridSearchCV> in Scikit-Learn can be annoying, particularly when:

• you change your code to wrap some estimator in, say, a Pipeline and then need to prefix all the parameters in the grid using lots of __s
• you are searching over multiple grids (i.e. your param_grid is a list) and you want to make a change to all of those grids

searchgrid allows you to define (and change) the grid together with the esimator, reducing effort and sometimes code. It stores the parameters you want to search on each particular estimator object. This makes it much more straightforward to specify complex parameter grids, and means you don't need to update your grid when you change the structure of your composite estimator.

It provides two main functions:

• searchgrid.set_grid is used to specify the parameter values to be searched for an estimator or GP kernel.
• searchgrid.make_grid_search is used to construct the GridSearchCV object using the parameter space the estimator is annotated with.

Other utilities for constructing search spaces include:

• searchgrid.build_param_grid
• searchgrid.make_pipeline
• searchgrid.make_union

Quick Start

If scikit-learn is installed, then, in a terminal:

pip install searchgrid

and use in Python:

from searchgrid import set_grid, make_grid_search
estimator = set_grid(MyEstimator(), param=[value1, value2, value3])
search = make_grid_search(estimator, cv=..., scoring=...)
search.fit(X, y)

Or search for the best among multiple distinct estimators/pipelines:

search = make_grid_search([estimator1, estimator2], cv=..., scoring=...)
search.fit(X, y)

Motivating examples

Let's look over some of the messy change cases. We'll get some imports out of the way.:

>>> from sklearn.pipeline import Pipeline
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.decomposition import PCA
>>> from searchgrid import set_grid, make_grid_search
>>> from sklearn.model_selection import GridSearchCV

Wrapping an estimator in a pipeline.

You had code which searched over parameters for a classifier. Now you want to search for that classifier in a Pipeline. With plain old scikit-learn, you have to insert __s and change:

>>> gs = GridSearchCV(LogisticRegression(), {'C': [.1, 1, 10]})

to:

>>> gs = GridSearchCV(Pipeline([('reduce', SelectKBest()),
...                             ('clf', LogisticRegression())]),
...                   {'clf__C': [.1, 1, 10]})

With searchgrid we only have to wrap our classifier in a Pipeline, and do not have to change the parameter grid, adding the clf__ prefix. From:

>>> lr = set_grid(LogisticRegression(), C=[.1, 1, 10])
>>> gs = make_grid_search(lr)

to:

>>> lr = set_grid(LogisticRegression(), C=[.1, 1, 10])
>>> gs = make_grid_search(Pipeline([('reduce', SelectKBest()),
...                                 ('clf', lr)]))

You want to change the estimator being searched in a pipeline.

With scikit-learn, to use PCA instead of SelectKBest, you change:

>>> pipe = Pipeline([('reduce', SelectKBest()),
...                  ('clf', LogisticRegression())])
>>> gs = GridSearchCV(pipe,
...                   {'reduce__k': [5, 10, 20],
...                    'clf__C': [.1, 1, 10]})

to:

>>> pipe = Pipeline([('reduce', PCA()),
...                  ('clf', LogisticRegression())])
>>> gs = GridSearchCV(pipe,
...                   {'reduce__n_components': [5, 10, 20],
...                    'clf__C': [.1, 1, 10]})

Note that reduce__k became reduce__n_components.

With searchgrid it's easier because you change the estimator and the parameters in the same place:

>>> reduce = set_grid(SelectKBest(), k=[5, 10, 20])
>>> lr = set_grid(LogisticRegression(), C=[.1, 1, 10])
>>> pipe = Pipeline([('reduce', reduce),
...                  ('clf', lr)])
>>> gs = make_grid_search(pipe)

becomes:

>>> reduce = set_grid(PCA(), n_components=[5, 10, 20])
>>> lr = set_grid(LogisticRegression(), C=[.1, 1, 10])
>>> pipe = Pipeline([('reduce', reduce),
...                  ('clf', lr)])
>>> gs = make_grid_search(pipe)

Searching over multiple grids.

You want to take the code from the previous example, but instead search over feature selection and PCA reduction in the same search.

Without searchgrid:

>>> pipe = Pipeline([('reduce', None),
...                  ('clf', LogisticRegression())])
>>> gs = GridSearchCV(pipe, [{'reduce': [SelectKBest()],
...                           'reduce__k': [5, 10, 20],
...                           'clf__C': [.1, 1, 10]},
...                          {'reduce': [PCA()],
...                           'reduce__n_components': [5, 10, 20],
...                           'clf__C': [.1, 1, 10]}])

With searchgrid:

>>> kbest = set_grid(SelectKBest(), k=[5, 10, 20])
>>> pca = set_grid(PCA(), n_components=[5, 10, 20])
>>> lr = set_grid(LogisticRegression(), C=[.1, 1, 10])
>>> pipe = set_grid(Pipeline([('reduce', None),
...                           ('clf', lr)]),
...                 reduce=[kbest, pca])
>>> gs = make_grid_search(pipe)

And since you no longer care about step names, use searchgrid.make_pipeline to express alternative steps even more simply:

>>> from searchgrid import make_pipeline
>>> kbest = set_grid(SelectKBest(), k=[5, 10, 20])
>>> pca = set_grid(PCA(), n_components=[5, 10, 20])
>>> lr = set_grid(LogisticRegression(), C=[.1, 1, 10])
>>> pipe = make_pipeline([kbest, pca], lr)
>>> gs = make_grid_search(pipe)