how to use gridSearch CV with scipy?

Question

i have been trying to tune my SVM using Gridsearchcv but it is throwing errors.

my code is :

train = pd.read_csv('train_set.csv')
label = pd.read.csv('lebel.csv')

params = { 'C' : [ 0.01 , 0.1 , 1 , 10]
clf = GridSearchCV(SVC() , params , n_jobs = -1)
clf.fit(train , label)

throws the error as : 'too much indices for array'

but when i simply do this :

clf = svc()
clf.fit(train.data , label.data)

the code works fine

Answer 1

I suspect the problem lies with your data structure train.data / label.data. I have tested both versions of your code and they work:

import sklearn.svm as sksvm
import sklearn.grid_search as skgs

params = { 'C' : [ 0.01 , 0.1 , 1 , 10]}
X = np.random.rand(1000, 10)  # (1000 x 10) matrix, 1000 points with 10 features
Y = np.random.randint(0, 2, 1000)  # 1000 array, binary labels

mod = sksvm.SVC()
mod.fit(X, Y)

Output:

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False)

and

import sklearn.svm as sksvm
import sklearn.grid_search as skgs

params = { 'C' : [ 0.01 , 0.1 , 1 , 10]}
X = np.random.rand(1000, 10)  # (1000 x 10) matrix, 1000 points with 10 features
Y = np.random.randint(0, 2, 1000)  # 1000 array, binary labels

mod = skgs.GridSearchCV(sksvm.SVC(), params, n_jobs=-1)
mod.fit(X, Y)

Output:

GridSearchCV(cv=None, error_score='raise',
       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
  kernel='rbf', max_iter=-1, probability=False, random_state=None,
  shrinking=True, tol=0.001, verbose=False),
       fit_params={}, iid=True, loss_func=None, n_jobs=-1,
       param_grid={'C': [0.01, 0.1, 1, 10]}, pre_dispatch='2*n_jobs',
       refit=True, score_func=None, scoring=None, verbose=0)

If your data is in dataframe and series the code still works, you can try it by adding:

X = pd.DataFrame(X)
Y = pd.Series(Y)

after you generate X and Y.

Difficult to say without a reproducible piece of code though. Also you probably should add the label sklearn to the question.