FeatureStacker.py

# class FeatureStacker(BaseEstimator):
#     """Stacks several transformer objects to yield concatenated features.
#     Similar to pipeline, a list of tuples ``(name, estimator)`` is passed
#     to the constructor.
#     """
#     def __init__(self, transformer_list):
#         self.transformer_list = transformer_list
# 
#     def get_feature_names(self):
#         pass
# 
#     def fit(self, X, y=None):
#         for name, trans in self.transformer_list:
#             trans.fit(X, y)
#         return self
# 
#     def transform(self, X):
#         features = []
#         for name, trans in self.transformer_list:
#             features.append(trans.transform(X))
#         issparse = [sparse.issparse(f) for f in features]
#         if np.any(issparse):
#             features = sparse.hstack(features).tocsr()
#         else:
#             features = np.hstack(features)
#         return features
# 
#     def get_params(self, deep=True):
#         if not deep:
#             return super(FeatureStacker, self).get_params(deep=False)
#         else:
#             out = dict(self.transformer_list)
#             for name, trans in self.transformer_list:
#                 for key, value in trans.get_params(deep=True).iteritems():
#                     out['%s__%s' % (name, key)] = value
#             return out