基于Sklearn机器学习实战---基于Sklearn模块的链路预测

摘要：简介自年发布以来，已经成为重要的机器学习库了。简称，支持包括分类回归降维和聚类四大机器学习算法。利用这几大模块的优势，可以大大提高机器学习的效率。已经封装了大量的机器学习算法，包括和。

自2007年发布以来，scikit-learn已经成为Python重要的机器学习库了。scikit-learn简称sklearn，支持包括分类、回归、降维和聚类四大机器学习算法。还包含了特征提取、数据处理和模型评估三大模块。

  sklearn是Scipy的扩展，建立在NumPy和matplotlib库的基础上。利用这几大模块的优势，可以大大提高机器学习的效率。
  sklearn拥有着完善的文档，上手容易，具有着丰富的API，在学术界颇受欢迎。sklearn已经封装了大量的机器学习算法，包括LIBSVM和LIBINEAR。同时sklearn内置了大量数据集，节省了获取和整理数据集的时间。

链路预测是通过历史连接信息预测未来可能产生的连接，即通过当前网络中的连边信息预测将来可能产生的连边信息。

from sklearn.model_selection import train_test_split # 分割数据模块
from sklearn.neighbors import KNeighborsClassifier # K最近邻(kNN，k-NearestNeighbor)分类算法
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from math import isnan

def Jaccavrd(MatrixAdjacency_Train):

Matrix_similarity = np.dot(MatrixAdjacency_Train,MatrixAdjacency_Train)

deg_row = sum(MatrixAdjacency_Train)
deg_row.shape = (deg_row.shape[0],1)
deg_row_T = deg_row.T
tempdeg = deg_row + deg_row_T
temp = tempdeg - Matrix_similarity

Matrix_similarity = Matrix_similarity / temp
return Matrix_similarity

def Salton_Cal(MatrixAdjacency_Train):

similarity = np.dot(MatrixAdjacency_Train,MatrixAdjacency_Train)

deg_row = sum(MatrixAdjacency_Train)
deg_row.shape = (deg_row.shape[0],1)
deg_row_T = deg_row.T
tempdeg = np.dot(deg_row,deg_row_T)
temp = np.sqrt(tempdeg)

np.seterr(divide="ignore", invalid="ignore")
Matrix_similarity = np.nan_to_num(similarity / temp)

return Matrix_similarity

def file2matrix(filepath):

f = open(filepath)
lines = f.readlines()
matrix = np.zeros((50, 50), dtype=float)
A_row = 0
for line in lines:
    list = line.strip("
").split(" ")
    matrix[A_row:] = list[0:50]
    A_row += 1
return matrix    

filepath = "3600/s0001.txt"
MatrixAdjacency = file2matrix(filepath)

similarity_matrix_Jaccavrd = Jaccavrd(MatrixAdjacency)
similarity_matrix_Salton = Salton_Cal(MatrixAdjacency)

filepath2 = "3600/s0002.txt"
MatrixAdjacency2 = file2matrix(filepath2)

similarity_matrix_Jaccavrd2 = Jaccavrd(MatrixAdjacency2)
similarity_matrix_Salton2 = Salton_Cal(MatrixAdjacency2)

filepath3 = "3600/s0003.txt"
MatrixAdjacency3 = file2matrix(filepath3)

similarity_matrix_Jaccavrd3 = Jaccavrd(MatrixAdjacency3)
similarity_matrix_Salton3 = Salton_Cal(MatrixAdjacency3)

Jaccard_Row = similarity_matrix_Jaccavrd.shape[0]
Jaccard_Column = similarity_matrix_Jaccavrd.shape[1]
Jaccard_List = []
for i in range(Jaccard_Row):

for j in range(Jaccard_Column):
    if i
获取Salton相似性矩阵的行数和列数
Salton_Row = similarity_matrix_Salton.shape[0]
Salton_Column = similarity_matrix_Salton.shape[1]
Salton_List = []
for i in range(Salton_Row):
for j in range(Salton_Column):
    if i
获取jaccard相似性矩阵的行数和列数
Jaccard_Row2 = similarity_matrix_Jaccavrd2.shape[0]
Jaccard_Column2 = similarity_matrix_Jaccavrd2.shape[1]
Jaccard_List2 = []
for i in range(Jaccard_Row2):
for j in range(Jaccard_Column2):
    if i
获取Salton相似性矩阵的行数和列数
Salton_Row2 = similarity_matrix_Salton2.shape[0]
Salton_Column2 = similarity_matrix_Salton2.shape[1]
Salton_List2 = []
for i in range(Salton_Row2):
for j in range(Salton_Column2):
    if i
获取jaccard相似性矩阵的行数和列数
Jaccard_Row3 = similarity_matrix_Jaccavrd3.shape[0]
Jaccard_Column3 = similarity_matrix_Jaccavrd3.shape[1]
Jaccard_List3 = []
for i in range(Jaccard_Row3):
for j in range(Jaccard_Column3):
    if i
获取Salton相似性矩阵的行数和列数
Salton_Row3 = similarity_matrix_Salton3.shape[0]
Salton_Column3 = similarity_matrix_Salton3.shape[1]
Salton_List3 = []
for i in range(Salton_Row3):
for j in range(Salton_Column3):
    if i
获取邻接矩阵的行数和列数
Adjacency_Row = MatrixAdjacency.shape[0]
Adjacency_Column = MatrixAdjacency.shape[1]
Adjacency = []
for i in range(Adjacency_Row):
for j in range(Adjacency_Column):
    if i
获取邻接矩阵的行数和列数
Adjacency_Row2 = MatrixAdjacency2.shape[0]
Adjacency_Column2 = MatrixAdjacency2.shape[1]
Adjacency2 = []
for i in range(Adjacency_Row2):
for j in range(Adjacency_Column2):
    if i
获取邻接矩阵的行数和列数
Adjacency_Row3 = MatrixAdjacency3.shape[0]
Adjacency_Column3 = MatrixAdjacency3.shape[1]
Adjacency3 = []
for i in range(Adjacency_Row3):
for j in range(Adjacency_Column3):
    if i
data = np.zeros((1225,3))
data2 = np.zeros((1225,3))
data3 = np.zeros((1225,3))  
for i in range(1225):
data[i][0] =  Jaccard_List[i]
data[i][1] = Salton_List[i]
data[i][2] = Adjacency[i]

for j in range(1225):
data2[j][0] =  Jaccard_List2[j]
data2[j][1] = Salton_List2[j]
data2[j][2] = Adjacency2[j]

for k in range(1225):
data3[k][0] =  Jaccard_List3[k]
data3[k][1] = Salton_List3[k]
data3[k][2] = Adjacency3[k]

data_train_X = data[:,0:2]
data_train_y = data[:,2]
data_test_X = data2[:,0:2]
data_test_y = data2[:,2]
data_target_X = data3[:,0:2]
data_target_y = data3[:,2]
knn = KNeighborsClassifier()
knn.fit(data_train_X,data_train_y)
print(knn.predict(data_test_X))
print(data_test_y)
clf = SVC()
clf.fit(data_train_X,data_test_y)
print(clf.score(data_test_X,data_target_y))
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