I am using the Yellowbrick package to plot an elbow curve for a dataset to find the most optimal number of clusters for the dataset using KMeans as a model.
I am using Scikit-learn's KMeans and Yellowbrick's kelbow_visualizer functions.
The elbow curve is generated and I am able to read the elbow value however the following error is thrown afterwards:
AttributeError: 'KMeans' object has no attribute 'k'
The way I am generating the curve is as follows:
from sklearn.cluster import KMeans
from yellowbrick.cluster.elbow import kelbow_visualizer
def elbow_method(X, max_range_for_elbow, rseed = RSEED):
return kelbow_visualizer(KMeans(random_state=rseed), X, k=(1, max_range_for_elbow))
elbow_method(data_standardized,10)
Where data_standardized is my dataset and RSEED is a constant with value 2.
I'm not sure what the issue is since the elbow curve is generated before the error is thrown.
This is the full error message:
AttributeError: 'KMeans' object has no attribute 'k'
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj, include, exclude)
968
969 if method is not None:
--> 970 return method(include=include, exclude=exclude)
971 return None
972 else:
~\anaconda3\lib\site-packages\sklearn\base.py in _repr_mimebundle_(self, **kwargs)
462 def _repr_mimebundle_(self, **kwargs):
463 """Mime bundle used by jupyter kernels to display estimator"""
--> 464 output = {"text/plain": repr(self)}
465 if get_config()["display"] == 'diagram':
466 output["text/html"] = estimator_html_repr(self)
~\anaconda3\lib\site-packages\sklearn\base.py in __repr__(self, N_CHAR_MAX)
258 n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW)
259
--> 260 repr_ = pp.pformat(self)
261
262 # Use bruteforce ellipsis when there are a lot of non-blank characters
~\anaconda3\lib\pprint.py in pformat(self, object)
151 def pformat(self, object):
152 sio = _StringIO()
--> 153 self._format(object, sio, 0, 0, {}, 0)
154 return sio.getvalue()
155
~\anaconda3\lib\pprint.py in _format(self, object, stream, indent, allowance, context, level)
168 self._readable = False
169 return
--> 170 rep = self._repr(object, context, level)
171 max_width = self._width - indent - allowance
172 if len(rep) > max_width:
~\anaconda3\lib\pprint.py in _repr(self, object, context, level)
402
403 def _repr(self, object, context, level):
--> 404 repr, readable, recursive = self.format(object, context.copy(),
405 self._depth, level)
406 if not readable:
~\anaconda3\lib\site-packages\sklearn\utils\_pprint.py in format(self, object, context, maxlevels, level)
178
179 def format(self, object, context, maxlevels, level):
--> 180 return _safe_repr(object, context, maxlevels, level,
181 changed_only=self._changed_only)
182
~\anaconda3\lib\site-packages\sklearn\utils\_pprint.py in _safe_repr(object, context, maxlevels, level, changed_only)
423 recursive = False
424 if changed_only:
--> 425 params = _changed_params(object)
426 else:
427 params = object.get_params(deep=False)
~\anaconda3\lib\site-packages\sklearn\utils\_pprint.py in _changed_params(estimator)
89 estimator with non-default values."""
90
---> 91 params = estimator.get_params(deep=False)
92 init_func = getattr(estimator.__init__, 'deprecated_original',
93 estimator.__init__)
~\anaconda3\lib\site-packages\yellowbrick\base.py in get_params(self, deep)
340 the estimator params.
341 """
--> 342 params = super(ModelVisualizer, self).get_params(deep=deep)
343 for param in list(params.keys()):
344 if param.startswith("estimator__"):
~\anaconda3\lib\site-packages\sklearn\base.py in get_params(self, deep)
193 out = dict()
194 for key in self._get_param_names():
--> 195 value = getattr(self, key)
196 if deep and hasattr(value, 'get_params'):
197 deep_items = value.get_params().items()
~\anaconda3\lib\site-packages\yellowbrick\utils\wrapper.py in __getattr__(self, attr)
40 def __getattr__(self, attr):
41 # proxy to the wrapped object
---> 42 return getattr(self._wrapped, attr)
