http://scikit-learn.org/stable/modules/feature_extraction.html

带病在网吧里。

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写。求支持。

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1、首先澄清两个概念：特征提取和特征选择（ 

Feature extraction is very different from Feature selection

the former consists in transforming arbitrary data, such as text or images, into numerical features usable for machine learning. The latter is a machine learning technique applied on these features（从已经提取的特征中选择更好的特征）.

以下分为四大部分来讲。主要还是4、text feature extraction

2、loading features form dicts

class DictVectorizer。举个样例就好：

>>> measurements = [
...     {‘city‘: ‘Dubai‘, ‘temperature‘: 33.},
...     {‘city‘: ‘London‘, ‘temperature‘: 12.},
...     {‘city‘: ‘San Fransisco‘, ‘temperature‘: 18.},
... ]
>>> from sklearn.feature_extraction import DictVectorizer
>>> vec = DictVectorizer()
>>> vec.fit_transform(measurements).toarray()
array([[  1.,   0.,   0.,  33.],
       [  0.,   1.,   0.,  12.],
       [  0.,   0.,   1.,  18.]])
>>> vec.get_feature_names()
[‘city=Dubai‘, ‘city=London‘, ‘city=San Fransisco‘, ‘temperature‘]

>>> pos_window = [
...     {
...         ‘word-2‘: ‘the‘,
...         ‘pos-2‘: ‘DT‘,
...         ‘word-1‘: ‘cat‘,
...         ‘pos-1‘: ‘NN‘,
...         ‘word+1‘: ‘on‘,
...         ‘pos+1‘: ‘PP‘,
...     },
...     # in a real application one would extract many such dictionaries
... ]

上面的PoS特征就能够vectorized into a sparse two-dimensional matrix suitable for feeding into a classifier (maybe after being piped into a text.TfidfTransformer for normalization):

>>> vec = DictVectorizer()
>>> pos_vectorized = vec.fit_transform(pos_window)
>>> pos_vectorized                
<1x6 sparse matrix of type ‘<... ‘numpy.float64‘>‘
    with 6 stored elements in Compressed Sparse ... format>
>>> pos_vectorized.toarray()
array([[ 1.,  1.,  1.,  1.,  1.,  1.]])
>>> vec.get_feature_names()
[‘pos+1=PP‘, ‘pos-1=NN‘, ‘pos-2=DT‘, ‘word+1=on‘, ‘word-1=cat‘, ‘word-2=the‘]

3、feature hashing

The class FeatureHasher is a high-speed, low-memory vectorizer that uses a technique known as feature hashing, or the “hashing trick”. 

因为hash。所以仅仅保存feature的interger index。而不保存原来feature的string名字。所以没有inverse_transform方法。

FeatureHasher 接收dict对，即 (feature, value) 对，或者strings，由构造函数的參数input_type决定.结果是scipy.sparse matrix。假设是strings，则value默认取1，比如 [‘feat1‘, ‘feat2‘, ‘feat2‘] 被解释为[(‘feat1‘, 1), (‘feat2‘, 2)].

4、text feature extraction

由于内容太多，分开写了。參考着篇博客：http://blog.csdn.net/mmc2015/article/details/46997379

5、image feature extraction

提取部分图片（Patch extraction）：

The extract_patches_2d function从图片中提取小块，存储成two-dimensional array, or three-dimensional with color information along the third axis. 使用reconstruct_from_patches_2d. 可以将全部的小块重构成原图：

>>> import numpy as np
>>> from sklearn.feature_extraction import image

>>> one_image = np.arange(4 * 4 * 3).reshape((4, 4, 3))
>>> one_image[:, :, 0]  # R channel of a fake RGB picture
array([[ 0,  3,  6,  9],
       [12, 15, 18, 21],
       [24, 27, 30, 33],
       [36, 39, 42, 45]])

>>> patches = image.extract_patches_2d(one_image, (2, 2), max_patches=2,
...     random_state=0)
>>> patches.shape
(2, 2, 2, 3)
>>> patches[:, :, :, 0]
array([[[ 0,  3],
        [12, 15]],

       [[15, 18],
        [27, 30]]])
>>> patches = image.extract_patches_2d(one_image, (2, 2))
>>> patches.shape
(9, 2, 2, 3)
>>> patches[4, :, :, 0]
array([[15, 18],
       [27, 30]])

>>> reconstructed = image.reconstruct_from_patches_2d(patches, (4, 4, 3))
>>> np.testing.assert_array_equal(one_image, reconstructed)

The PatchExtractor class和 extract_patches_2d,一样，仅仅只是能够同一时候接受多个图片作为输入：

>>> five_images = np.arange(5 * 4 * 4 * 3).reshape(5, 4, 4, 3)
>>> patches = image.PatchExtractor((2, 2)).transform(five_images)
>>> patches.shape
(45, 2, 2, 3)

图片像素的连接（Connectivity graph of an image）：

主要是依据像素的区别来推断图片的每两个像素点是否连接。

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The function img_to_graph returns such a matrix from a 2D or 3D image. Similarly, grid_to_graph build a connectivity matrix for images given the shape of these image.

这有个直观的样例：http://scikit-learn.org/stable/auto_examples/cluster/plot_lena_ward_segmentation.html#example-cluster-plot-lena-ward-segmentation-py

头疼。。。。

碎觉。

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