Can anyone please tell me what I'm doing wrong?
The example here (
http://scikit-learn.org/dev/modules/generated/sklearn.grid_search.GridSearchCV.html
)
>>> from sklearn import svm, grid_search, datasets>>> iris =
>>> datasets.load_iris()>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1,
>>> 10]}>>> svr = svm.SVC()>>> clf = grid_search.GridSearchCV(svr,
>>> parameters)>>> clf.fit(iris.data, iris.target)...
>>> GridSearchCV(cv=None, estimator=SVC(C=1.0, cache_size=..., coef0=...,
>>> degree=..., gamma=..., kernel='rbf', max_iter=-1, probability=False,
>>> shrinking=True, tol=...), fit_params={}, iid=True,
>>> loss_func=None, n_jobs=1, param_grid=..., ...)
gives .98
when i change svr and the parameters:
from sklearn import svm, grid_search, datasets,linear_model
iris = datasets.load_iris()
parameters = {'alpha':[.0001,.001]}
sgd = linear_model.SGDRegressor()
clf = grid_search.GridSearchCV(sgd, parameters)
clf.fit(iris.data, iris.target)
print clf.best_score_
suddenly I get
Traceback (most recent call last):
File "GridSearch_SGDRegressor.py", line 6, in <module>
clf.fit(iris.data, iris.target)
File "/usr/local/lib/python2.7/dist-packages/sklearn/grid_search.py",
line 415, in fit
best_estimator.fit(X, y, **self.fit_params)
AttributeError: 'NoneType' object has no attribute 'fit'
this seems like an error since I'm running 0.13 and
http://scikit-learn.org/dev/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor.fit
says that it _does_ have fit, and fit works independently for me.
It seems the interface between them is broken somehow?
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