title: Unable to solve an error while running Gridsearch
body: <p>I am new to the realm of machine learning, and I started competing in Kaggle competitions to get some practical experience. I am competing in the knowledge competition CIFAR 10- Object Recognition in Images, where you have to classify thousands of images in 10 classes,all the data I use can be found there. I tried to implement Gridsearch to optimize the parameters of my machine learning algorithm, but whenever I try to fit my classifier with my training data I get an error. I have found the function that raised the error, and it has something to do with my labels not having the right type, but I have no clue on how to solve it. The Labels I use are strings, and I preprocess them so that I can feed them to the algorithm. Am I doing something wrong there? Or perhaps something goes wrong when I split the dataset for the grid search? Frankly, I lack the experience and knowledge to solve this problem, and I could definitely use your help.</p>
  
  <p><strong>The code involved:</strong></p>
  
  <pre><code>import glob
  import os
  from sklearn.svm import SVC
  from sklearn import preprocessing
  import pandas as pd
  from sklearn import cross_validation 
  from sklearn import metrics
  from sklearn.grid_search import GridSearchCV
  
  def label_preprocessing(Labels):
      Labels = np.array(Labels)[:,1]
      le = preprocessing.LabelEncoder()
      le.fit_transform(Labels)
      return Labels
  def model_selection(train,Labels):
      parameters = {"C":[0.1,1,10,100],"gamma":[0.0001,0.001,0.01,0.1]}
      X_train, X_test, y_train, y_test = cross_validation.train_test_split(train, Labels, test_size = 0.2, random_state = 0)
      svm = SVC()
      clf  = GridSearchCV(svm,parameters)
      clf  = clf.fit(X_train,y_train)
      print ("20 fold cv score: ",np.mean(cross_validation.cross_val_score(clf,X_test,y_test,cv = 10,scoring = "roc_auc")))
      return clf
  
  if  __name__ == "__main__":
      train_images = np.array(file_open(image_dir1,"*.png"))[:100]
      test_images = np.array(file_open(image_dir2,"*.png"))[:100]
      Labels = label_preprocessing(pd.read_csv(image_dir3)[:100])
      train_set = [matrix_image(image) for image in train_images]
      test_set = [matrix_image(image) for image in test_images]
      train_set = np.array(train_set)
      test_set = np.array(test_set)
  
      print("selecting best model and evaluating it")
      svm = model_selection(train_set,Labels)
      print("predicting stuff")
      result = svm.predict(test_set)
  
      np.savetxt("submission.csv", result, fmt = "%s", delimiter = ",")
  </code></pre>
  
  <p><b> full traceback:</b> </p>
  
  <pre><code>Traceback (most recent call last):
    File "C:\Users\Abdc\workspace\final_submission\src\SVM.py", line 49, in &lt;module&gt;
      svm = model_selection(train_set,Labels)
    File "C:\Users\Abdc\workspace\final_submission\src\SVM.py", line 35, in model_selection
      clf  = clf.fit(X_train,y_train)
    File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 707, in fit
      return self._fit(X, y, ParameterGrid(self.param_grid))
    File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 493, in _fit
      for parameters in parameter_iterable
    File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 517, in __call__
      self.dispatch(function, args, kwargs)
    File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 312, in dispatch
      job = ImmediateApply(func, args, kwargs)
    File "C:\Python27\lib\site-packages\sklearn\externals\joblib\parallel.py", line 136, in __init__
      self.results = func(*args, **kwargs)
    File "C:\Python27\lib\site-packages\sklearn\grid_search.py", line 311, in fit_grid_point
      this_score = clf.score(X_test, y_test)
    File "C:\Python27\lib\site-packages\sklearn\base.py", line 294, in score
      return accuracy_score(y, self.predict(X))
    File "C:\Python27\lib\site-packages\sklearn\metrics\metrics.py", line 1064, in accuracy_score
      y_type, y_true, y_pred = _check_clf_targets(y_true, y_pred)
    File "C:\Python27\lib\site-packages\sklearn\metrics\metrics.py", line 123, in _check_clf_targets
      raise ValueError("{0} is not supported".format(y_type))
  ValueError: unknown is not supported
  </code></pre>
  
  <p><b>This is the function that raised the error. It can be found in the sklearn.metrics module:</b></p>
  
  <pre><code>def _check_clf_targets(y_true, y_pred):
      """Check that y_true and y_pred belong to the same classification task
  
      This converts multiclass or binary types to a common shape, and raises a
      ValueError for a mix of multilabel and multiclass targets, a mix of
      multilabel formats, for the presence of continuous-valued or multioutput
      targets, or for targets of different lengths.
  
      Column vectors are squeezed to 1d.
  
      Parameters
      ----------
      y_true : array-like,
  
      y_pred : array-like
  
      Returns
      -------
      type_true : one of {'multilabel-indicator', 'multilabel-sequences', \
                          'multiclass', 'binary'}
          The type of the true target data, as output by
          ``utils.multiclass.type_of_target``
  
      y_true : array or indicator matrix or sequence of sequences
  
      y_pred : array or indicator matrix or sequence of sequences
      """
      y_true, y_pred = check_arrays(y_true, y_pred, allow_lists=True)
      type_true = type_of_target(y_true)
      type_pred = type_of_target(y_pred)
  
      y_type = set([type_true, type_pred])
      if y_type == set(["binary", "multiclass"]):
          y_type = set(["multiclass"])
  
      if len(y_type) &gt; 1:
          raise ValueError("Can't handle mix of {0} and {1}"
                           "".format(type_true, type_pred))
  
      # We can't have more than one value on y_type =&gt; The set is no more needed
      y_type = y_type.pop()
  
      # No metrics support "multiclass-multioutput" format
      if (y_type not in ["binary", "multiclass", "multilabel-indicator",
                         "multilabel-sequences"]):
          raise ValueError("{0} is not supported".format(y_type))
  
      if y_type in ["binary", "multiclass"]:
          y_true = column_or_1d(y_true)
          y_pred = column_or_1d(y_pred)
  
      return y_type, y_true, y_pred
  </code></pre>
  
  <p><b> Extra information about the Labels: </b></p>
  
  <p><b> Content of labels and dtype:</b></p>
  
  <pre><code>In [21]:
  Labels = np.array(Labels)[:,1]
  Labels
  
  
  Out[21]:
  array(['frog', 'truck', 'truck', ..., 'truck', 'automobile', 'automobile'], dtype=object)
  </code></pre>
  
  <p><b> Content of labels after preprocessing</b></p>
  
  <pre><code>In [25]:
  
  Labels = np.array(Labels)[:,1]
  Labels
  le = preprocessing.LabelEncoder()
  Labels = le.fit_transform(Labels)
  Labels
  
  
  
  Out[25]:
  array([6, 9, 9, ..., 9, 1, 1])
  </code></pre>
  
  <p><b>Shape of labels after preprocessing:</b></p>
  
  <pre><code>In [18]:
      Labels = np.array(Labels)[:,1]
      Labels.shape
      le = preprocessing.LabelEncoder()
      Labels = le.fit_transform(Labels)
      Labels.shape
  
  Out[18]:
  (50000L,)
  </code></pre>
  
  <p>The original content can be found here: <a href="https://www.kaggle.com/c/cifar-10/data" rel="nofollow">https://www.kaggle.com/c/cifar-10/data</a>. Which contains an ID for a datapoint and its class Label. So it is a nx2 matrix.</p>