python - What should do to fix my scikit-learn program? -


a snippet of code involving randomforestclassifier using python machine learning library scikit-learn.

i trying give weight different classes using class_weight opition in scikit's randomforestclassifier.below code snippet , error getting 

print 'training...' forest = randomforestclassifier(n_estimators=500,class_weight= {0:1,1:1,2:1,3:1,4:1,5:1,6:1,7:4}) forest = forest.fit( train_data[0::,1::], train_data[0::,0] )  print 'predicting...' output = forest.predict(test_data).astype(int)   predictions_file = open("myfirstforest.csv", "wb") open_file_object = csv.writer(predictions_file) open_file_object.writerow(["passengerid","survived"]) open_file_object.writerows(zip(ids, output)) predictions_file.close() print 'done.'

and getting following error:

training...  indexerror                                traceback (most recent call last) <ipython-input-20-122f2e5a0d3b> in <module>()  84 print 'training...'  85 forest = randomforestclassifier(n_estimators=500,class_weight={0:1,1:1,2:1,3:1,4:1,5:1,6:1,7:4}) ---> 86 forest = forest.fit( train_data[0::,1::], train_data[0::,0] )  87   88 print 'predicting...'  /home/rpota/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc in fit(self, x, y, sample_weight) 216         self.n_outputs_ = y.shape[1] 217  --> 218         y, expanded_class_weight = self._validate_y_class_weight(y) 219  220         if getattr(y, "dtype", none) != double or not y.flags.contiguous:  /home/rpota/anaconda/lib/python2.7/site-packages/sklearn/ensemble/forest.pyc in _validate_y_class_weight(self, y) 433                     class_weight = self.class_weight 434                 expanded_class_weight = compute_sample_weight(class_weight, --> 435                                                               y_original) 436  437         return y, expanded_class_weight  /home/rpota/anaconda/lib/python2.7/site-packages/sklearn/utils/class_weight.pyc in compute_sample_weight(class_weight, y, indices) 150             weight_k = compute_class_weight(class_weight_k, 151                                             classes_full, --> 152                                             y_full) 153  154         weight_k = weight_k[np.searchsorted(classes_full, y_full)]  /home/rpota/anaconda/lib/python2.7/site-packages/sklearn/utils/class_weight.pyc in compute_class_weight(class_weight, classes, y)  58         c in class_weight:  59             = np.searchsorted(classes, c) ---> 60             if classes[i] != c:  61                 raise valueerror("class label %d not present." % c)  62             else:  indexerror: index 2 out of bounds axis 0 size 2

please help!.


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