searchgrid 0.2.0

Quick Start

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

pip install searchgrid

and use in Python:

from search_grid 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)