def __init__(self, clfs, voting, weights=None, threshold=None):
self.clfs = clfs
self.named_clfs = {key:value for key,value in _name_estimators(clfs)}
self.voting=voting
if voting is 'weighted':
self.combiner=WeightedVote(weights=weights, threshold=threshold)
elif voting is 'majority':
self.combiner=MajorityVote()
else:
raise AttributeError('Unrecognized voting method')
class EnsembleSKLearn(BaseEstimator, ClassifierMixin, TransformerMixin):
def __init__(self, clfs, voting, weights=None, threshold=None):
self.clfs = clfs
self.named_clfs = {key:value for key,value in _name_estimators(clfs)}
self.voting=voting
if voting is 'weighted':
self.combiner=WeightedVote(weights=weights, threshold=threshold)
elif voting is 'majority':
self.combiner=MajorityVote()
else:
raise AttributeError('Unrecognized voting method')
def fit(self, X, y):
self.clfs_ = []
for clf in self.clfs:
fitted_clf = clone(clf).fit(X, y)
self.clfs_.append(fitted_clf)
return self
def predict(self, X):
return self._predict(X)
def _predict(self, X):
""" Collect results from clf.predict calls. """
predictions = np.asarray([clf.predict(X) for clf in self.clfs_]).T
predicted_labels = self.combiner.combine(predictions)
return predicted_labels
def get_params(self, deep=True):
""" Return estimator parameter names for GridSearch support"""
if not deep:
return super(EnsembleSKLearn, self).get_params(deep=False)
else:
out = self.named_clfs.copy()
for name, step in six.iteritems(self.named_clfs):
for key, value in six.iteritems(step.get_params(deep=True)):
out['%s__%s' % (name, key)] = value
return out
