KNearestNeighborsRegressor has no attribute 'estimator' when printing show_models()

[MrKevinDC]

I have tried to print the models composing the best ensemble with show_models(), but it fails if a k_nearest_neighbours_regressor is one of them. Is this due to this component not having an initialised self.estimator? I am making a custom component with that modification now, and will update this issue if said model comes up in the ensemble again (whether it fixes it or not).

automl.leaderboard()
rank ensemble_weight type cost duration
model_id
826 1 0.34 decision_tree 0.556544 3.839919
742 2 0.42 k_nearest_neighbors 0.563224 2.659213
1856 3 0.24 adaboost 0.570269 9.341588
automl.show_models()
Traceback (most recent call last):
File "/gpfs/home/xxx/automlBiscuits.py", line 40, in
pprint(automl.show_models(), indent=4)
File "/gpfs/home/xxx/miniconda3/lib/python3.9/site-packages/autosklearn/estimators.py", line 888, in show_models
return self.automl_.show_models()
File "/gpfs/home/xxx/miniconda3/lib/python3.9/site-packages/autosklearn/automl.py", line 2227, in show_models
] = autosklearn_wrapped_model.choice.estimator
AttributeError: 'KNearestNeighborsRegressor' object has no attribute 'estimator'

[eddiebergman]

Hi @MrKevinDC,

This seems like a bug, I checked the estimator and this does actually seem to be set if the KNN has been fit:

auto-sklearn/autosklearn/pipeline/components/regression/k_nearest_neighbors.py

Lines 21 to 40 in 66b782a

	def fit(self, X, y):
	import sklearn.neighbors
	self.n_neighbors = int(self.n_neighbors)
	self.p = int(self.p)
	self.estimator = sklearn.neighbors.KNeighborsRegressor(
	n_neighbors=self.n_neighbors, weights=self.weights, p=self.p
	)
	if y.ndim == 2 and y.shape[1] == 1:
	y = y.flatten()
	self.estimator.fit(X, y)
	return self
	def predict(self, X):
	if self.estimator is None:
	raise NotImplementedError()
	return self.estimator.predict(X)

I'll look into this while I can but thanks for reporting it!

[MrKevinDC]

This seems like a bug, I checked the estimator and this does actually seem to be set if the KNN has been fit:

Yes, there is no problem with fitting, predicting, fetching the run history, etc; it is just with show_models(). Thank you for your reply and congratulations on this awesome package by the way.

[MrKevinDC]

I think the issue can indeed be solved by simply initializing in init the self.estimator=None. I reran the algorithm with a custom KNearestNeighborsRegressor that only had this modification, and one of such type appeared in the show_models() print without causing an error.

The run history with details of this fitted custom regressor :

...
{'cost_2.64485719244043_ensembleWeight_0.16': Configuration(values={
'data_preprocessor:choice': 'feature_type',
'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent',
'data_preprocessor:feature_type:numerical_transformer:rescaling:choice': 'minmax',
'feature_preprocessor:CustomFeatureAgglomeration:affinity': 'cosine',
'feature_preprocessor:CustomFeatureAgglomeration:linkage': 'average',
'feature_preprocessor:CustomFeatureAgglomeration:n_clusters': 11,
'feature_preprocessor:CustomFeatureAgglomeration:pooling_func': 'max',
'feature_preprocessor:choice': 'CustomFeatureAgglomeration',
'regressor:CustomKNearestNeighborsRegressor:n_neighbors': 6,
'regressor:CustomKNearestNeighborsRegressor:p': 2,
'regressor:CustomKNearestNeighborsRegressor:weights': 'uniform',
'regressor:choice': 'CustomKNearestNeighborsRegressor',
})
...

The show_models() did not break:

automl.show_models()
{ 3: { 'cost': 2.6638271909003906,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x145653845e20>,
'ensemble_weight': 0.02,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x145653845d30>,
'model_id': 3,
'rank': 1,
'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x145653845a90>,
'sklearn_regressor': None},
...