I am trying to fit the ExtraTreesClassifier() from sklearn.ensemble on a sample dataset, but it keeps throwing this error. I have implemented other sklearn models and they seem to run well. What am I missing here?
from sklearn.ensemble import ExtraTreesClassifier
model = ExtraTreesClassifier()
model.fit(X,y)
The error is thrown when I am calling the ExtraTreesClassifier function.
This is the full error. here best is just a dictionary that contains the parameters, and df is a dataframe that I using to store the outputs of different models I have made.
---> 97 df.loc[ind,'model']=ExtraTreesClassifier(**best)
98 df.loc[ind,'param']=str(best)
99 Start=time.time()
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in __setitem__(self, key, value)
669 key = com.apply_if_callable(key, self.obj)
670 indexer = self._get_setitem_indexer(key)
--> 671 self._setitem_with_indexer(indexer, value)
672
673 def _validate_key(self, key, axis: int):
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
848 indexer, self.obj.axes
849 )
--> 850 self._setitem_with_indexer(new_indexer, value)
851
852 return self.obj
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
1008 # we have an equal len list/ndarray
1009 elif _can_do_equal_len(
-> 1010 labels, value, plane_indexer, lplane_indexer, self.obj
1011 ):
1012 setter(labels[0], value)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py in _can_do_equal_len(labels, value, plane_indexer, lplane_indexer, obj)
2474 True if we have an equal len settable.
2475 """
-> 2476 if not len(labels) == 1 or not np.iterable(value) or is_scalar(plane_indexer[0]):
2477 return False
2478
/usr/local/lib/python3.6/dist-packages/numpy/lib/function_base.py in iterable(y)
281 """
282 try:
--> 283 iter(y)
284 except TypeError:
285 return False
/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_base.py in __iter__(self)
171 def __iter__(self):
172 """Return iterator over estimators in the ensemble."""
--> 173 return iter(self.estimators_)
174
175
AttributeError: 'ExtraTreesClassifier' object has no attribute 'estimators_'
When setting elements of a dataframe with .loc, pandas tries to unpack an iterable, thinking you're wanting to set more than one entry of the dataframe, one per element of the iterable. You can see in the traceback that pandas tests whether your ExtraTreesClassifier is an iterable:
282 try:
--> 283 iter(y)
284 except TypeError:
285 return False
And unfortunately, ExtraTreesClassifier is iterable, containing each of its trees, though that of course only works after it's fitted, and hence the error.
I'd suggest first that storing model objects in a dataframe is a little against the spirit of a dataframe, and that you instead save the model objects in another place. Maybe saving the model name and best params is enough in the frame?
Anyway, if you want to save the model object itself, it's down to "how do I set a dataframe entry to an iterable," which has been asked before, e.g. Create and set an element of a Pandas DataFrame to a list
and I personally like the answer "use at".