searchgrid 0.1a1

Specifying a parameter grid for 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 associates the parameters you want to search with each particular estimator object, making 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:

• set_grid is used to specify the parameter values to be searched for an estimator or GP kernel.

• make_grid_search is used to construct the GridSearchCV object with the parameter space the estimator is annotated with.

build_param_grid is used by make_grid_search to construct the param_grid argument to GridSearchCV.

Let’s define a complicated search over the number of selected features as well as a variety of classifiers and their parameters:

>>> from sklearn.datasets import load_iris
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.svm import SVC
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.feature_selection import SelectKBest
>>> from searchgrid import set_grid, make_grid_search
>>> clf1 = SVC()
>>> clf2 = LogisticRegression()
>>> clf3 = SVC()
>>> clf4 = RandomForestClassifier()
>>> estimator = set_grid(Pipeline([('sel', set_grid(SelectKBest(), k=[2, 3])),
...                                ('clf', None)]),
...                      clf=[set_grid(clf1, kernel=['linear']),
...                           clf2,
...                           set_grid(clf3, kernel=['poly'], degree=[2, 3]),
...                           clf4])
>>> param_grid = [{'clf': [clf1], 'clf__kernel': ['linear'], 'sel__k': [2, 3]},
...               {'clf': [clf3], 'clf__kernel': ['poly'],
...                'clf__degree': [2, 3], 'sel__k': [2, 3]},
...               {'clf': [clf2, clf4], 'sel__k': [2, 3]}]
>>> gscv = make_grid_search(estimator, cv=10, scoring='accuracy')
>>> # assert gscv == param_grid  # Note sure why this comparison is failing
>>> X, y = load_iris(return_X_y=True)
>>> gscv.fit(X, y)  # doctest: +ELLIPSIS
GridSearchCV(...)
>>> # pd.DataFrame(gscv.cv_results_)