title: Scikit-learn's GridSearchCV with linear kernel svm takes too long
body: <p>I took sample code from sklearn website, which is</p>
  
  <pre><code>tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]},
          {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
  
  scores = [('f1', f1_score)]
  
  for score_name, score_func in scores:
      print "# Tuning hyper-parameters for %s" % score_name
      print
  
      clf = GridSearchCV( SVC(), tuned_parameters, score_func=score_func, n_jobs=-1, verbose=2 )
      clf.fit(X_train, Y_train)
  
      print "Best parameters set found on development set:"
      print
      print clf.best_estimator_
      print
      print "Grid scores on development set:"
  
      print
      for params, mean_score, scores in clf.grid_scores_:
          print "%0.3f (+/-%0.03f) for %r" % (
              mean_score, scores.std() / 2, params)
      print
  
      print "Detailed classification report:"
      print
      print "The model is trained on the full development set."
      print "The scores are computed on the full evaluation set."
      print
      y_true, y_pred = Y_test, clf.predict(X_test)
      print cross_validation.classification_report(y_true, y_pred)
      print
  </code></pre>
  
  <p>X_train is a pandas DataFrame with approx 70 rows.</p>
  
  <p>The output is </p>
  
  <pre><code>[GridSearchCV] kernel=rbf, C=1, gamma=0.001 ....................................
  [GridSearchCV] kernel=rbf, C=1, gamma=0.001 ....................................
  [GridSearchCV] kernel=rbf, C=1, gamma=0.001 ....................................
  [GridSearchCV] kernel=rbf, C=1, gamma=0.0001 ...................................
  [Parallel(n_jobs=-1)]: Done   1 jobs       | elapsed:    0.0s
  [GridSearchCV] ........................... kernel=rbf, C=1, gamma=0.001 -   0.0s
  [GridSearchCV] ........................... kernel=rbf, C=1, gamma=0.001 -   0.0s
  [GridSearchCV] ........................... kernel=rbf, C=1, gamma=0.001 -   0.0s
  [GridSearchCV] .......................... kernel=rbf, C=1, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=rbf, C=1, gamma=0.0001 ...................................
  [GridSearchCV] kernel=rbf, C=1, gamma=0.0001 ...................................
  [GridSearchCV] kernel=rbf, C=10, gamma=0.001 ...................................
  [GridSearchCV] kernel=rbf, C=10, gamma=0.001 ...................................
  [GridSearchCV] .......................... kernel=rbf, C=1, gamma=0.0001 -   0.0s
  [GridSearchCV] .......................... kernel=rbf, C=1, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=rbf, C=10, gamma=0.001 ...................................
  [GridSearchCV] .......................... kernel=rbf, C=10, gamma=0.001 -   0.0s
  [GridSearchCV] .......................... kernel=rbf, C=10, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=10, gamma=0.0001 ..................................
  [GridSearchCV] .......................... kernel=rbf, C=10, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=10, gamma=0.0001 ..................................
  [GridSearchCV] kernel=rbf, C=10, gamma=0.0001 ..................................
  [GridSearchCV] ......................... kernel=rbf, C=10, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=rbf, C=100, gamma=0.001 ..................................
  [GridSearchCV] ......................... kernel=rbf, C=10, gamma=0.0001 -   0.0s
  [GridSearchCV] ......................... kernel=rbf, C=10, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=rbf, C=100, gamma=0.001 ..................................
  [GridSearchCV] ......................... kernel=rbf, C=100, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=100, gamma=0.001 ..................................
  [GridSearchCV] kernel=rbf, C=100, gamma=0.0001 .................................
  [GridSearchCV] ......................... kernel=rbf, C=100, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=100, gamma=0.0001 .................................
  [GridSearchCV] ......................... kernel=rbf, C=100, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=100, gamma=0.0001 .................................
  [GridSearchCV] kernel=rbf, C=1000, gamma=0.001 .................................
  [GridSearchCV] ........................ kernel=rbf, C=100, gamma=0.0001 -   0.0s
  [GridSearchCV] ........................ kernel=rbf, C=100, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=rbf, C=1000, gamma=0.001 .................................
  [GridSearchCV] ........................ kernel=rbf, C=100, gamma=0.0001 -   0.0s
  [GridSearchCV] ........................ kernel=rbf, C=1000, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=1000, gamma=0.001 .................................
  [GridSearchCV] kernel=rbf, C=1000, gamma=0.0001 ................................
  [GridSearchCV] kernel=rbf, C=1000, gamma=0.0001 ................................
  [GridSearchCV] ........................ kernel=rbf, C=1000, gamma=0.001 -   0.0s
  [GridSearchCV] kernel=rbf, C=1000, gamma=0.0001 ................................
  [GridSearchCV] ........................ kernel=rbf, C=1000, gamma=0.001 -   0.0s
  [GridSearchCV] ....................... kernel=rbf, C=1000, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=linear, C=1 ..............................................
  [GridSearchCV] ....................... kernel=rbf, C=1000, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=linear, C=1 ..............................................
  [GridSearchCV] kernel=linear, C=1 ..............................................
  [GridSearchCV] ....................... kernel=rbf, C=1000, gamma=0.0001 -   0.0s
  [GridSearchCV] kernel=linear, C=10 .............................................
  </code></pre>
  
  <p>And then it never finishes. I run it on Mac Book Pro with Lion. What do I do wrong?</p>