Understanding DictVectorizer in scikit-learn?

问题

I'm exploring the different feature extracccion classes that scikit-learn provide. Reading the documentation i did not understand very well for what DictVectorizer can be used?. Other questions come to mind for example how can DictVectorizer can be used for text classification?, i.e. how does this class could help handle labeled textual data?. Could anybody provide some little example apart from the example that i all ready read at the documentation web page?

回答1:

say your feature space is length, width and height and you have had 3 observations; i.e. you measure length, width & height of 3 objects:

       length  width  height
obs.1       1      0       2
obs.2       0      1       1
obs.3       3      2       1

another way to show this is to use a list of dictionaries:

[{'height': 1, 'length': 0, 'width': 1},   # obs.2
 {'height': 2, 'length': 1, 'width': 0},   # obs.1
 {'height': 1, 'length': 3, 'width': 2}]   # obs.3

DictVectorizer goes the other way around; i.e given the list of dictionaries builds the top frame:

>>> from sklearn.feature_extraction import DictVectorizer
>>> v = DictVectorizer(sparse=False)
>>> d = [{'height': 1, 'length': 0, 'width': 1},
...      {'height': 2, 'length': 1, 'width': 0},
...      {'height': 1, 'length': 3, 'width': 2}]
>>> v.fit_transform(d)
array([[ 1.,  0.,  1.],   # obs.2
       [ 2.,  1.,  0.],   # obs.1
       [ 1.,  3.,  2.]])  # obs.3
   # height, len., width   

来源：https://stackoverflow.com/questions/27473957/understanding-dictvectorizer-in-scikit-learn