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转载: scikit-learn学习之K最近邻算法(KNN)

时间:2016-09-08 14:25:15      阅读:930      评论:0      收藏:0      [点我收藏+]

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本系列博客主要参考 Scikit-Learn 官方网站上的每一个算法进行,并进行部分翻译,如有错误,请大家指正   

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决策树的算法分析与Python代码实现请参考之前的一篇博客:K最近邻Python实现    接下来我主要演示怎么使用Scikit-Learn完成决策树算法的调用


Scikit-Learn中 sklearn.neighbors的函数包括(点击查看来源URL

 

The sklearn.neighbors module implements the k-nearest neighbors algorithm.

 

User guide: See the Nearest Neighbors section for further details.

neighbors.NearestNeighbors([n_neighbors, ...]) Classifier implementing the k-nearest neighbors vote. Classifier implementing a vote among neighbors within a given radius neighbors.KNeighborsRegressor([n_neighbors, ...]) neighbors.RadiusNeighborsRegressor([radius, ...]) neighbors.NearestCentroid([metric, ...]) BallTree for fast generalized N-point problems KDTree for fast generalized N-point problems neighbors.LSHForest([n_estimators, radius, ...]) DistanceMetric class neighbors.KernelDensity([bandwidth, ...])

Unsupervised learner for implementing neighbor searches.
Regression based on k-nearest neighbors.
Regression based on neighbors within a fixed radius.
Nearest centroid classifier.
Performs approximate nearest neighbor search using LSH forest.
Kernel Density Estimation

neighbors.kneighbors_graph(X, n_neighbors[, ...])neighbors.radius_neighbors_graph(X, radius)

Computes the (weighted) graph of k-Neighbors for points in X
Computes the (weighted) graph of Neighbors for points in X


首先看一个简单的小例子:

 

sklearn.neighbors.NearestNeighbors具体说明查看:URL  在这只是将用到的加以注释

 

 

[python] view plain copy 技术分享技术分享
  1. #coding:utf-8  
  2. ‘‘‘‘‘ 
  3. Created on 2016/4/24 
  4. @author: Gamer Think 
  5. ‘‘‘  
  6. #导入NearestNeighbor包 和 numpy  
  7. from sklearn.neighbors import NearestNeighbors  
  8. import numpy as np  
  9.   
  10. #定义一个数组  
  11. X = np.array([[-1,-1],  
  12.               [-2,-1],  
  13.               [-3,-2],  
  14.               [1,1],  
  15.               [2,1],  
  16.               [3,2]  
  17.               ])  
  18. """ 
  19. NearestNeighbors用到的参数解释 
  20. n_neighbors=5,默认值为5,表示查询k个最近邻的数目 
  21. algorithm=‘auto‘,指定用于计算最近邻的算法,auto表示试图采用最适合的算法计算最近邻 
  22. fit(X)表示用X来训练算法 
  23. """  
  24. nbrs = NearestNeighbors(n_neighbors=3, algorithm="ball_tree").fit(X)  
  25. #返回距离每个点k个最近的点和距离指数,indices可以理解为表示点的下标,distances为距离  
  26. distances, indices = nbrs.kneighbors(X)  
  27. print indices  
  28. print distances  

输出结果为:

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执行

[python] view plain copy 技术分享技术分享
  1. #输出的是求解n个最近邻点后的矩阵图,1表示是最近点,0表示不是最近点  
  2. print nbrs.kneighbors_graph(X).toarray()  


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[python] view plain copy 技术分享技术分享
  1. #测试 KDTree  
  2. ‘‘‘‘‘ 
  3. leaf_size:切换到蛮力的点数。改变leaf_size不会影响查询结果, 
  4.                           但能显著影响查询和存储所需的存储构造树的速度。 
  5.                         需要存储树的规模约n_samples / leaf_size内存量。 
  6.                         为指定的leaf_size,叶节点是保证满足leaf_size <= n_points < = 2 * leaf_size, 
  7.                         除了在的情况下,n_samples < leaf_size。 
  8.                          
  9. metric:用于树的距离度量。默认‘minkowski与P = 2(即欧氏度量)。 
  10.                   看到一个可用的度量的距离度量类的文档。 
  11.        kd_tree.valid_metrics列举这是有效的基础指标。 
  12. ‘‘‘  
  13. from sklearn.neighbors import KDTree  
  14. import numpy as np  
  15. X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])  
  16. kdt = KDTree(X,leaf_size=30,metric="euclidean")  
  17. print kdt.query(X, k=3, return_distance=False)  
  18.   
  19.   
  20. #测试 BallTree  
  21. from sklearn.neighbors import BallTree  
  22. import numpy as np  
  23. X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])  
  24. bt = BallTree(X,leaf_size=30,metric="euclidean")  
  25. print bt.query(X, k=3, return_distance=False)  


其输出结果均为:

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这是在小数据集的情况下并不能看到他们的差别,当数据集变大时,这种差别便显而易见了

技术分享技术分享

  1. <span style="font-size:18px;">#coding:utf-8  
  2. ‘‘‘‘‘ 
  3. Created on 2016年4月24日 
  4.  
  5. @author: Gamer Think 
  6. ‘‘‘  
  7. from sklearn.datasets import load_iris  
  8. from sklearn import neighbors  
  9. import sklearn  
  10.   
  11. #查看iris数据集  
  12. iris = load_iris()  
  13. print iris  
  14.   
  15. knn = neighbors.KNeighborsClassifier()  
  16. #训练数据集  
  17. knn.fit(iris.data, iris.target)  
  18. #预测  
  19. predict = knn.predict([[0.1,0.2,0.3,0.4]])  
  20. print predict  
  21. print iris.target_names[predict]</span>  

预测结果为:

[0]    #第0类
[‘setosa‘]    #第0类对应花的名字

技术分享技术分享

  1. <span style="font-size:18px;"> #-*- coding: UTF-8 -*-   
  2. ‘‘‘‘‘ 
  3. Created on 2016/4/24 
  4.  
  5. @author: Administrator 
  6. ‘‘‘  
  7. import csv     #用于处理csv文件  
  8. import random    #用于随机数  
  9. import math           
  10. import operator  #  
  11. from sklearn import neighbors  
  12.   
  13. #加载数据集  
  14. def loadDataset(filename,split,trainingSet=[],testSet = []):  
  15.     with open(filename,"rb") as csvfile:  
  16.         lines = csv.reader(csvfile)  
  17.         dataset = list(lines)  
  18.         for x in range(len(dataset)-1):  
  19.             for y in range(4):  
  20.                 dataset[x][y] = float(dataset[x][y])  
  21.             if random.random()<split:  
  22.                 trainingSet.append(dataset[x])  
  23.             else:  
  24.                 testSet.append(dataset[y])  
  25.   
  26. #计算距离  
  27. def euclideanDistance(instance1,instance2,length):  
  28.     distance = 0  
  29.     for x in range(length):  
  30.         distance = pow((instance1[x] - instance2[x]),2)  
  31.     return math.sqrt(distance)  
  32.   
  33. #返回K个最近邻  
  34. def getNeighbors(trainingSet,testInstance,k):  
  35.     distances = []  
  36.     length = len(testInstance) -1  
  37.     #计算每一个测试实例到训练集实例的距离  
  38.     for x in range(len(trainingSet)):  
  39.         dist = euclideanDistance(testInstance, trainingSet[x], length)  
  40.         distances.append((trainingSet[x],dist))  
  41.     #对所有的距离进行排序  
  42.     distances.sort(key=operator.itemgetter(1))  
  43.     neighbors = []  
  44.     #返回k个最近邻  
  45.     for x in range(k):  
  46.         neighbors.append(distances[x][0])  
  47.     return neighbors  
  48.   
  49. #对k个近邻进行合并,返回value最大的key  
  50. def getResponse(neighbors):  
  51.     classVotes = {}  
  52.     for x in range(len(neighbors)):  
  53.         response = neighbors[x][-1]  
  54.         if response in classVotes:  
  55.             classVotes[response]+=1  
  56.         else:  
  57.             classVotes[response] = 1  
  58.     #排序  
  59.     sortedVotes = sorted(classVotes.iteritems(),key = operator.itemgetter(1),reverse =True)  
  60.     return sortedVotes[0][0]  
  61.   
  62. #计算准确率  
  63. def getAccuracy(testSet,predictions):  
  64.     correct = 0  
  65.     for x in range(len(testSet)):  
  66.         if testSet[x][-1] == predictions[x]:  
  67.             correct+=1  
  68.     return (correct/float(len(testSet))) * 100.0  
  69.   
  70. def main():  
  71.     trainingSet = []  #训练数据集  
  72.     testSet = []      #测试数据集  
  73.     split = 0.67      #分割的比例  
  74.     loadDataset(r"iris.txt", split, trainingSet, testSet)   
  75.     print "Train set :" + repr(len(trainingSet))  
  76.     print "Test set :" + repr(len(testSet))                  
  77.       
  78.     predictions = []  
  79.     k = 3  
  80.     for x in range(len(testSet)):  
  81.         neighbors = getNeighbors(trainingSet, testSet[x], k)  
  82.         result = getResponse(neighbors)  
  83.         predictions.append(result)  
  84.         print ">predicted = " + repr(result) + ",actual = " + repr(testSet[x][-1])  
  85.     accuracy = getAccuracy(testSet, predictions)  
  86.     print "Accuracy:" + repr(accuracy) + "%"  
  87.   
  88. if __name__ =="__main__":  
  89.     main()  </span>  


附iris.txt文件的内容 5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor?
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica

转载: scikit-learn学习之K最近邻算法(KNN)

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原文地址:http://www.cnblogs.com/harvey888/p/5852741.html

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