sklearn.neighbors_Nearest Neighbors
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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
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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
sklearn.neighbors
: Nearest Neighbors
The sklearn.neighbors
module implements the k-nearest neighbors algorithm.
User guide: See the Nearest Neighbors section for further details.
neighbors.NearestNeighbors
([n_neighbors, …])Unsupervised learner for implementing neighbor searches.neighbors.KNeighborsClassifier
([…])Classifier implementing the k-nearest neighbors vote.neighbors.RadiusNeighborsClassifier
([…])Classifier implementing a vote among neighbors within a given radiusneighbors.KNeighborsRegressor
([n_neighbors, …])Regression based on k-nearest neighbors.neighbors.RadiusNeighborsRegressor
([radius, …])Regression based on neighbors within a fixed radius.neighbors.NearestCentroid
([metric, …])Nearest centroid classifier.neighbors.BallTree
BallTree for fast generalized N-point problemsneighbors.KDTree
KDTree for fast generalized N-point problemsneighbors.LSHForest
([n_estimators, radius, …])Performs approximate nearest neighbor search using LSH forest.neighbors.DistanceMetric
DistanceMetric classneighbors.KernelDensity
([bandwidth, …])Kernel Density Estimationneighbors.kneighbors_graph
(X, n_neighbors[, …])Computes the (weighted) graph of k-Neighbors for points in Xneighbors.radius_neighbors_graph
(X, radius)Computes the (weighted) graph of Neighbors for points in X首先看一个简单的小例子:
Finding the Nearest Neighbors
sklearn.neighbors.NearestNeighbors具体说明查看:URL 在这只是将用到的加以注释
- #coding:utf-8
- ””’
- Created on 2016/4/24
- @author: Gamer Think
- ”’
- #导入NearestNeighbor包 和 numpy
- from sklearn.neighbors import NearestNeighbors
- import numpy as np
- #定义一个数组
- X = np.array([[-1,-1],
- [-2,-1],
- [-3,-2],
- [1,1],
- [2,1],
- [3,2]
- ])
- ”“”
- NearestNeighbors用到的参数解释
- n_neighbors=5,默认值为5,表示查询k个最近邻的数目
- 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
- fit(X)表示用X来训练算法
- ”“”
- nbrs = NearestNeighbors(n_neighbors=3, algorithm=“ball_tree”).fit(X)
- #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
- distances, indices = nbrs.kneighbors(X)
- print indices
- print distances
#coding:utf-8#导入NearestNeighbor包 和 numpyfrom sklearn.neighbors import NearestNeighborsimport numpy as np#定义一个数组X = np.array([[-1,-1], [-2,-1], [-3,-2], [1,1], [2,1], [3,2] ])"""NearestNeighbors用到的参数解释n_neighbors=5,默认值为5,表示查询k个最近邻的数目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fit(X)表示用X来训练算法"""nbrs = NearestNeighbors(n_neighbors=3, algorithm="ball_tree").fit(X)#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distances, indices = nbrs.kneighbors(X)print indicesprint distances输出结果为:
”’
Created on 2016/4/24
@author: Gamer Think
”’
执行
- #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
- print nbrs.kneighbors_graph(X).toarray()
#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print nbrs.kneighbors_graph(X).toarray()
KDTree and BallTree Classes- #测试 KDTree
- ””’
- leaf_size:切换到蛮力的点数。改变leaf_size不会影响查询结果,
- 但能显著影响查询和存储所需的存储构造树的速度。
- 需要存储树的规模约n_samples / leaf_size内存量。
- 为指定的leaf_size,叶节点是保证满足leaf_size <= n_points < = 2 * leaf_size,
- 除了在的情况下,n_samples < leaf_size。
-
- metric:用于树的距离度量。默认’minkowski与P = 2(即欧氏度量)。
- 看到一个可用的度量的距离度量类的文档。
- kd_tree.valid_metrics列举这是有效的基础指标。
- ”’
- from sklearn.neighbors import KDTree
- import numpy as np
- X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
- kdt = KDTree(X,leaf_size=30,metric=“euclidean”)
- print kdt.query(X, k=3, return_distance=False)
-
-
- #测试 BallTree
- from sklearn.neighbors import BallTree
- import numpy as np
- X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
- bt = BallTree(X,leaf_size=30,metric=“euclidean”)
- print bt.query(X, k=3, return_distance=False)
#测试 KDTree'''leaf_size:切换到蛮力的点数。改变leaf_size不会影响查询结果, 但能显著影响查询和存储所需的存储构造树的速度。 需要存储树的规模约n_samples / leaf_size内存量。 为指定的leaf_size,叶节点是保证满足leaf_size <= n_points < = 2 * leaf_size, 除了在的情况下,n_samples < leaf_size。metric:用于树的距离度量。默认'minkowski与P = 2(即欧氏度量)。 看到一个可用的度量的距离度量类的文档。 kd_tree.valid_metrics列举这是有效的基础指标。'''from sklearn.neighbors import KDTreeimport numpy as npX = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])kdt = KDTree(X,leaf_size=30,metric="euclidean")print kdt.query(X, k=3, return_distance=False)
#测试 BallTreefrom sklearn.neighbors import BallTreeimport numpy as npX = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])bt = BallTree(X,leaf_size=30,metric="euclidean")print bt.query(X, k=3, return_distance=False)- #测试 KDTree
- ””’
- leaf_size:切换到蛮力的点数。改变leaf_size不会影响查询结果,
- 但能显著影响查询和存储所需的存储构造树的速度。
- 需要存储树的规模约n_samples / leaf_size内存量。
- 为指定的leaf_size,叶节点是保证满足leaf_size <= n_points < = 2 * leaf_size,
- 除了在的情况下,n_samples < leaf_size。
- metric:用于树的距离度量。默认’minkowski与P = 2(即欧氏度量)。
- 看到一个可用的度量的距离度量类的文档。
- kd_tree.valid_metrics列举这是有效的基础指标。
- ”’
- from sklearn.neighbors import KDTree
- import numpy as np
- X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
- kdt = KDTree(X,leaf_size=30,metric=“euclidean”)
- print kdt.query(X, k=3, return_distance=False)
- #测试 BallTree
- from sklearn.neighbors import BallTree
- import numpy as np
- X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
- bt = BallTree(X,leaf_size=30,metric=“euclidean”)
- print bt.query(X, k=3, return_distance=False)
其输出结果均为:
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
使用scikit-learn的KNN算法进行分类的一个实例,使用数据集依旧是iris(鸢尾花)数据集
- <span style=“font-size:18px;”>#coding:utf-8
- ””’
- Created on 2016年4月24日
- @author: Gamer Think
- ”’
- from sklearn.datasets import load_iris
- from sklearn import neighbors
- import sklearn
- #查看iris数据集
- iris = load_iris()
- print iris
- knn = neighbors.KNeighborsClassifier()
- #训练数据集
- knn.fit(iris.data, iris.target)
- #预测
- predict = knn.predict([[0.1,0.2,0.3,0.4]])
- print predict
- print iris.target_names[predict]</span>
<span style="font-size:18px;">#coding:utf-8'''Created on 2016年4月24日@author: Gamer Think'''from sklearn.datasets import load_irisfrom sklearn import neighborsimport sklearn#查看iris数据集iris = load_iris()print irisknn = neighbors.KNeighborsClassifier()#训练数据集knn.fit(iris.data, iris.target)#预测predict = knn.predict([[0.1,0.2,0.3,0.4]])print predictprint iris.target_names[predict]</span>预测结果为:
[0] #第0类
[‘setosa’] #第0类对应花的名字
使用python实现的KNN算法进行分类的一个实例,使用数据集依旧是iris(鸢尾花)数据集,只不过将其保存在iris.txt文件中
- <span style=“font-size:18px;”> #-*- coding: UTF-8 -*-
- ””’
- Created on 2016/4/24
- @author: Administrator
- ”’
- import csv #用于处理csv文件
- import random #用于随机数
- import math
- import operator #
- from sklearn import neighbors
- #加载数据集
- def loadDataset(filename,split,trainingSet=[],testSet = []):
- with open(filename,”rb”) as csvfile:
- lines = csv.reader(csvfile)
- dataset = list(lines)
- for x in range(len(dataset)-1):
- for y in range(4):
- dataset[x][y] = float(dataset[x][y])
- if random.random()<split:
- trainingSet.append(dataset[x])
- else:
- testSet.append(dataset[y])
- #计算距离
- def euclideanDistance(instance1,instance2,length):
- distance = 0
- for x in range(length):
- distance = pow((instance1[x] - instance2[x]),2)
- return math.sqrt(distance)
- #返回K个最近邻
- def getNeighbors(trainingSet,testInstance,k):
- distances = []
- length = len(testInstance) -1
- #计算每一个测试实例到训练集实例的距离
- for x in range(len(trainingSet)):
- dist = euclideanDistance(testInstance, trainingSet[x], length)
- distances.append((trainingSet[x],dist))
- #对所有的距离进行排序
- distances.sort(key=operator.itemgetter(1))
- neighbors = []
- #返回k个最近邻
- for x in range(k):
- neighbors.append(distances[x][0])
- return neighbors
- #对k个近邻进行合并,返回value最大的key
- def getResponse(neighbors):
- classVotes = {}
- for x in range(len(neighbors)):
- response = neighbors[x][-1]
- if response in classVotes:
- classVotes[response]+=1
- else:
- classVotes[response] = 1
- #排序
- sortedVotes = sorted(classVotes.iteritems(),key = operator.itemgetter(1),reverse =True)
- return sortedVotes[0][0]
- #计算准确率
- def getAccuracy(testSet,predictions):
- correct = 0
- for x in range(len(testSet)):
- if testSet[x][-1] == predictions[x]:
- correct+=1
- return (correct/float(len(testSet))) * 100.0
- def main():
- trainingSet = [] #训练数据集
- testSet = [] #测试数据集
- split = 0.67 #分割的比例
- loadDataset(r”iris.txt”, split, trainingSet, testSet)
- print “Train set :” + repr(len(trainingSet))
- print “Test set :” + repr(len(testSet))
- predictions = []
- k = 3
- for x in range(len(testSet)):
- neighbors = getNeighbors(trainingSet, testSet[x], k)
- result = getResponse(neighbors)
- predictions.append(result)
- print “>predicted = ” + repr(result) + “,actual = ” + repr(testSet[x][-1])
- accuracy = getAccuracy(testSet, predictions)
- print “Accuracy:” + repr(accuracy) + “%”
- if __name__ ==“__main__”:
- main() </span>
<span style="font-size:18px;"> #-*- coding: UTF-8 -*- '''Created on 2016/4/24@author: Administrator'''import csv #用于处理csv文件import random #用于随机数import math import operator #from sklearn import neighbors#加载数据集def loadDataset(filename,split,trainingSet=[],testSet = []): with open(filename,"rb") as csvfile: lines = csv.reader(csvfile) dataset = list(lines) for x in range(len(dataset)-1): for y in range(4): dataset[x][y] = float(dataset[x][y]) if random.random()<split: trainingSet.append(dataset[x]) else: testSet.append(dataset[y])#计算距离def euclideanDistance(instance1,instance2,length): distance = 0 for x in range(length): distance = pow((instance1[x] - instance2[x]),2) return math.sqrt(distance)#返回K个最近邻def getNeighbors(trainingSet,testInstance,k): distances = [] length = len(testInstance) -1 #计算每一个测试实例到训练集实例的距离 for x in range(len(trainingSet)): dist = euclideanDistance(testInstance, trainingSet[x], length) distances.append((trainingSet[x],dist)) #对所有的距离进行排序 distances.sort(key=operator.itemgetter(1)) neighbors = [] #返回k个最近邻 for x in range(k): neighbors.append(distances[x][0]) return neighbors#对k个近邻进行合并,返回value最大的keydef getResponse(neighbors): classVotes = {} for x in range(len(neighbors)): response = neighbors[x][-1] if response in classVotes: classVotes[response]+=1 else: classVotes[response] = 1 #排序 sortedVotes = sorted(classVotes.iteritems(),key = operator.itemgetter(1),reverse =True) return sortedVotes[0][0]#计算准确率def getAccuracy(testSet,predictions): correct = 0 for x in range(len(testSet)): if testSet[x][-1] == predictions[x]: correct+=1 return (correct/float(len(testSet))) * 100.0def main(): trainingSet = [] #训练数据集 testSet = [] #测试数据集 split = 0.67 #分割的比例 loadDataset(r"iris.txt", split, trainingSet, testSet) print "Train set :" + repr(len(trainingSet)) print "Test set :" + repr(len(testSet)) predictions = [] k = 3 for x in range(len(testSet)): neighbors = getNeighbors(trainingSet, testSet[x], k) result = getResponse(neighbors) predictions.append(result) print ">predicted = " + repr(result) + ",actual = " + repr(testSet[x][-1]) accuracy = getAccuracy(testSet, predictions) print "Accuracy:" + repr(accuracy) + "%"if __name__ =="__main__": 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
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