Python_sklearn机器学习库学习笔记（一）_Feature Extraction and Preprocessing(特征提取与预处理）
# Extracting features from categorical variables
#Extracting features from categorical variables 独热编码 from sklearn.feature_extraction import DictVectorizer onehot_encoder=DictVectorizer() instance=[{'city':'New York'},{'city':'San Francisco'}, {'city':'Chapel Hill'}] print onehot_encoder.fit_transform(instance).toarray()
输出结果：
# Extracting features from text文字特征提取
## The bag-of-words representation
#bag-of-words model.词库模型 corpus = [ 'UNC played Duke in basketball', 'Duke lost the basketball game' ]
#CountVectorizer类通过正则表达式用空格分割句子，然后抽取长度大于等于2的字母序列。scikit-learn实现代码如下： from sklearn.feature_extraction.text import CountVectorizer corpus = [ 'UNC played Duke in basketball', 'Duke lost the basketball game' ] vectorizer=CountVectorizer() print vectorizer.fit_transform(corpus).todense()#todense将稀疏矩阵转化为完整特征矩阵 print vectorizer.vocabulary_
输出结果：
[[1 1 0 1 0 1 0 1]
[1 1 1 0 1 0 1 0]]
{u'duke': 1, u'basketball': 0, u'lost': 4, u'played': 5, u'game': 2, u'unc': 7, u'in': 3, u'the': 6}
corpus = [ 'UNC played Duke in basketball', 'Duke lost the basketball game', 'I ate a sandwich' ] vectorizer = CountVectorizer() print(vectorizer.fit_transform(corpus).todense()) print(vectorizer.vocabulary_)
输出结果：
[[0 1 1 0 1 0 1 0 0 1]
[0 1 1 1 0 1 0 0 1 0]
[1 0 0 0 0 0 0 1 0 0]]
{u'duke': 2, u'basketball': 1, u'lost': 5, u'played': 6, u'in': 4, u'game': 3, u'sandwich': 7, u'unc': 9, u'ate': 0, u'the': 8}
scikit-learn里面的euclidean_distances函数可以计算若干向量的距离，表示两个语义最相似的
文档其向量在空间中也是最接近的。
from sklearn.metrics.pairwise import euclidean_distances count=[[0, 1, 1, 0, 0, 1, 0, 1], [0, 1, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 1, 0]] print 'Distance between 1st and 2nd documents:',euclidean_distances(count[0],count[1])
输出结果：Distance between 1st and 2nd documents: [[ 2.]]
#format方法 for x,y in[[0,1],[0,2],[1,2]]: count=[[0, 1, 1, 0, 0, 1, 0, 1], [0, 1, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 1, 0]] dist=euclidean_distances(count[x],count[y]) print '文档{}文档{}文档{}'.format(x,y,dist)
输出结果：
文档0文档1文档[[ 2.]] 文档0文档2文档[[ 2.44948974]] 文档1文档2文档[[ 2.44948974]]
## Stop-word filtering 停用词过滤
CountVectorizer类可以通过设置stop_words参数过滤停用词，默认是英语常用的停用词。
from sklearn.feature_extraction.text import CountVectorizer corpus = [ 'UNC played Duke in basketball', 'Duke lost the basketball game', 'I ate a sandwich' ] vectorizer=CountVectorizer(stop_words='english') print vectorizer.fit_transform(corpus).todense() print vectorizer.vocabulary_
输出结果：
# Stemming and lemmatization 词根还原和词形还原
from sklearn.feature_extraction.text import CountVectorizer corpus = ['He ate the sandwiches', 'Every sandwich was eaten by him'] vectorizer=CountVectorizer(binary=True,stop_words='english') print vectorizer.fit_transform(corpus).todense() print vectorizer.vocabulary_
输出结果：
[[1 0 0 1]
[0 1 1 0]]
{u'sandwich': 2, u'ate': 0, u'sandwiches': 3, u'eaten': 1}
### 让我们分析一下单词gathering的词形还原：
corpus = [ 'I am gathering ingredients for the sandwich.', 'There were many wizards at the gathering.' ] import nltk nltk.download() from nltk.stem.wordnet import WordNetLemmatizer from nltk import word_tokenize from nltk.stem import PorterStemmer from nltk.stem.wordnet import WordNetLemmatizer from nltk import pos_tag wordnet_tags = ['n', 'v'] corpus = [ 'He ate the sandwiches', 'Every sandwich was eaten by him' ] stemmer = PorterStemmer() print('Stemmed:', [[stemmer.stem(token) for token in word_tokenize(document)] for document in corpus])
输出结果：
('Stemmed:', [[u'He', u'ate', u'the', u'sandwich'], [u'Everi', u'sandwich', u'wa', u'eaten', u'by', u'him']])
def lemmatize(token, tag): if tag[0].lower() in ['n', 'v']: return lemmatizer.lemmatize(token, tag[0].lower()) return token lemmatizer = WordNetLemmatizer() tagged_corpus = [pos_tag(word_tokenize(document)) for document in corpus] print('Lemmatized:', [[lemmatize(token, tag) for token, tag in document] for document in tagged_corpus])
输出结果：
('Lemmatized:', [['He', u'eat', 'the', u'sandwich'], ['Every', 'sandwich', u'be', u'eat', 'by', 'him']])
## 带TF-IDF权重的扩展词库
from sklearn.feature_extraction.text import CountVectorizer corpus=['The dog ate a sandwich, the wizard transfigured a sandwich, and I ate a sandwich'] vectorizer=CountVectorizer(stop_words='english') print vectorizer.fit_transform(corpus).todense() print vectorizer.vocabulary_
输出结果：
[[2 1 3 1 1]]
{u'sandwich': 2, u'wizard': 4, u'dog': 1, u'transfigured': 3, u'ate': 0}
#tf-idf from sklearn.feature_extraction.text import TfidfVectorizer corpus = ['The dog ate a sandwich and I ate a sandwich','The wizard transfigured a sandwich'] vectorizer=TfidfVectorizer(stop_words='english') print vectorizer.fit_transform(corpus).todense() print vectorizer.vocabulary_
输出结果：
[[ 0.75458397 0.37729199 0.53689271 0. 0. ]
[ 0. 0. 0.44943642 0.6316672 0.6316672 ]]
{u'sandwich': 2, u'wizard': 4, u'dog': 1, u'transfigured': 3, u'ate': 0}
## 通过哈希技巧实现特征向量
from sklearn.feature_extraction.text import HashingVectorizer corpus = ['the', 'ate', 'bacon', 'cat'] vectorizer = HashingVectorizer(n_features=6) print(vectorizer.transform(corpus).todense())
输出结果：
[[-1. 0. 0. 0. 0. 0.] [ 0. 0. 0. 1. 0. 0.] [ 0. 0. 0. 0. -1. 0.] [ 0. 1. 0. 0. 0. 0.]]
设置成6是为了演示。另外，注意有些单词频率是负数。由于Hash碰撞可能发生，所以HashingVectorizer用有符号哈希函数（signed hash function）。特征值和它的词块的哈希值带
同样符号，如果cats出现过两次，被哈希成-3，文档特征向量的第四个元素要减去2。如果dogs出现过两次，被哈希成3，文档特征向量的第四个元素要加上2。
## 图片特征提取
#通过像素值提取特征
scikit-learn的digits数字集包括至少1700种0-9的手写数字图像。每个图像都有8x8像像素构成。每
个像素的值是0-16，白色是0，黑色是16。如下图所示：
%matplotlib inline from sklearn import datasets import matplotlib.pyplot as plt digits=datasets.load_digits() print 'Digit:',digits.target[0] print digits.images[0] plt.imshow(digits.images[0], cmap=plt.cm.gray_r, interpolation='nearest') plt.show()
输出结果：
Digit: 0
[[ 0. 0. 5. 13. 9. 1. 0. 0.]
[ 0. 0. 13. 15. 10. 15. 5. 0.]
[ 0. 3. 15. 2. 0. 11. 8. 0.]
[ 0. 4. 12. 0. 0. 8. 8. 0.]
[ 0. 5. 8. 0. 0. 9. 8. 0.]
[ 0. 4. 11. 0. 1. 12. 7. 0.]
[ 0. 2. 14. 5. 10. 12. 0. 0.]
[ 0. 0. 6. 13. 10. 0. 0. 0.]]
digits=datasets.load_digits() print('Feature vector:\n',digits.images[0].reshape(-1,64))
输出结果：
('Feature vector:\n', array([[ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13.,
15., 10., 15., 5., 0., 0., 3., 15., 2., 0., 11.,
8., 0., 0., 4., 12., 0., 0., 8., 8., 0., 0.,
5., 8., 0., 0., 9., 8., 0., 0., 4., 11., 0.,
1., 12., 7., 0., 0., 2., 14., 5., 10., 12., 0.,
0., 0., 0., 6., 13., 10., 0., 0., 0.]]))
%matplotlib inline import numpy as np from skimage.feature import corner_harris,corner_peaks from skimage.color import rgb2gray import matplotlib.pyplot as plt import skimage.io as io from skimage.exposure import equalize_hist def show_corners(corners,image): fig=plt.figure() plt.gray() plt.imshow(image) y_corner,x_corner=zip(*corners) plt.plot(x_corner,y_corner,'or') plt.xlim(0,image.shape[1]) plt.ylim(image.shape[0],0) fig.set_size_inches(np.array(fig.get_size_inches())*1.5) plt.show()
mandrill=io.imread('1.jpg') mandrill=equalize_hist(rgb2gray(mandrill)) corners=corner_peaks(corner_harris(mandrill),min_distance=2) show_corners(corners,mandrill)
### SIFT和SURF
import mahotas as mh from mahotas.features import surf image = mh.imread('2.jpg', as_grey=True) print('第一个SURF描述符：\n{}\n'.format(surf.surf(image)[0])) print('抽取了%s个SURF描述符' % len(surf.surf(image)))
输出结果：
