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kernelbuilder.py
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from __future__ import print_function
import os
import subprocess
import SPARQLWrapper
import json
import sys
import numpy as np
import pandas as pd
import networkx as nx
from networkx.drawing.nx_agraph import write_dot, graphviz_layout
import matplotlib.pyplot as plt
import seaborn as sns
from SPARQLWrapper import SPARQLWrapper, JSON, XML
import pandas as pd
from itertools import islice
import time
#function used to calculate the kernel matrix can be used with fractions of the relationship table, should be written in a .py
#file and imported in this module
def sparql_service_to_dataframe(service, query):
"""
Helper function to convert SPARQL results into a Pandas DataFrame.
Credit to Ted Lawless https://lawlesst.github.io/notebook/sparql-dataframe.html
"""
sparql = SPARQLWrapper(service)
sparql.setQuery(query)
sparql.setReturnFormat(JSON)
result = sparql.query()
processed_results = json.load(result.response)
cols = processed_results['head']['vars']
out = []
for row in processed_results['results']['bindings']:
item = []
for c in cols:
item.append(row.get(c, {}).get('value'))
out.append(item)
return pd.DataFrame(out, columns=cols)
def matrixfractionold(start, end, size):
path = '/fragments/'
df = pd.read_csv(path+'classessim.csv')
allrelations = pd.read_csv(path+'allrelations.csv')
axiomsimilaritymatrix = pd.DataFrame({"axiom1": [],"axiom1I": [], "axiom2": [],"axiom2I": [], "a1c1": [],
"a1c2": [],"a2c1": [],"a2c2": [],"sim1": [],"sim2": [],"overallsim": []})
for i, axiom1 in islice(allrelations.iterrows(),start,end,1):
for j, axiom2 in islice(allrelations.iterrows(),i, size, 1):
sim1 =float(df.loc[((df['class1']== axiom1['class1']) & (df['class2']== axiom2['class1']))
|((df['class1']== axiom2['class1']) & (df['class2']== axiom1['class1']))
, 'similarity'].values[0])
sim2= float(df.loc[((df['class1']== axiom1['class2']) & (df['class2']== axiom2['class2']))
|((df['class1']== axiom2['class2']) & (df['class2']== axiom1['class2']))
, 'similarity'].values[0])
axiomsimilaritymatrix = axiomsimilaritymatrix.append(
{"axiom1": i,"axiom1I": axiom1['I'], "axiom2": j,"axiom2I": axiom2['I'], "a1c1": axiom1['class1'],
"a1c2": axiom1['class2'],"a2c1": axiom2['class1'],"a2c2": axiom2['class2'],
"sim1": sim1,"sim2": sim2,"overallsim":min(sim1,sim2)}
,ignore_index=True)
axiomsimilaritymatrix.to_csv( path + str(start) + '.csv', sep=',', index=False)
return axiomsimilaritymatrix
def matrixfractionVerbose(start, end, size):
path = 'c:/Users/Ali/Desktop/fragments/'
df = pd.read_csv(path + 'classessim.csv')
allrelations = pd.read_csv(path + 'allrelations.csv')
rowlist = []
for i in range(start, end):
axiom1 = allrelations.iloc[i]
for j in range(i, size):
axiom2 = allrelations.iloc[j]
sim1 = df.loc[df['class2'] == axiom1['class1'], axiom2['class1']].values[0]
sim2 = df.loc[df['class2'] == axiom1['class2'], axiom2['class2']].values[0]
rowlist.append([i, axiom1['I'], j, axiom2['I'], axiom1['class1'],
axiom1['class2'], axiom2['class1'], axiom2['class2'], sim1, sim2, min(sim1, sim2)])
axiomsimilaritymatrix = pd.DataFrame(rowlist, columns=["axiom1", "axiom1I", "axiom2", "axiom2I", "a1c1",
"a1c2", "a2c1", "a2c2", "sim1", "sim2", "overallsim"])
axiomsimilaritymatrix.to_csv(path + str(start) + '.csv', sep=',', index=False)
return axiomsimilaritymatrix
def matrixfraction(start, end, size, df, allrelations):
rowlist = []
for i in range(start, end):
axiom1 = allrelations.iloc[i]
a1_c1 = axiom1['left']
a1_c2 = axiom1['right']
for j in range(i, size):
axiom2 = allrelations.iloc[j]
sim1 = df.loc[df['class2'] == a1_c1, axiom2['left']].values[0]
sim2 = df.loc[df['class2'] == a1_c2, axiom2['right']].values[0]
rowlist.append([i, j, min(sim1, sim2)])
axiomsimilaritymatrix = pd.DataFrame(rowlist, columns=["axiom1", "axiom2", "overallsim"])
return axiomsimilaritymatrix
def matrixfractionAverageSim(start, end, size, df, allrelations):#the column name for the first column is class2
rowlist = []
for i in range(start, end):
axiom1 = allrelations.iloc[i]
a1_c1 = axiom1['left']
a1_c2 = axiom1['right']
for j in range(i, size):
axiom2 = allrelations.iloc[j]
sim1 = df.loc[df['class2'] == a1_c1, axiom2['left']].values[0]
sim2 = df.loc[df['class2'] == a1_c2, axiom2['right']].values[0]
rowlist.append([i, j, (sim1+sim2)/2])
axiomsimilaritymatrix = pd.DataFrame(rowlist, columns=["axiom1", "axiom2", "overallsim"])
return axiomsimilaritymatrix
def matrixfractionAverageSimdis(start, end, size, df, allrelations):#the column name for the first column is class2
rowlist = []
for i in range(start, end):
axiom1 = allrelations.iloc[i]
a1_l = axiom1['left']
a1_r = axiom1['right']
for j in range(i, size):
axiom2 = allrelations.iloc[j]
#because in disjointness left and right dont make a difference, we compare as dis(A B) dis(B A) and dis(B A) dis(B A)
sim1 = df.loc[df['class2'] == a1_l, axiom2['left']].values[0]
sim2 = df.loc[df['class2'] == a1_r, axiom2['right']].values[0]
sim3 = df.loc[df['class2'] == a1_l, axiom2['right']].values[0]
sim4 = df.loc[df['class2'] == a1_r, axiom2['left']].values[0]
if (sim1+sim2)/2 > (sim3+sim4)/2:
sim = (sim1+sim2)/2
else:
sim = (sim3+sim4)/2
rowlist.append([i, j, sim])
axiomsimilaritymatrix = pd.DataFrame(rowlist, columns=["axiom1", "axiom2", "overallsim"])
return axiomsimilaritymatrix
def matrixfractionl(start, end, size):
wds_Corese = 'http://localhost:8080/sparql'
df = pd.read_csv('classessim.csv')
allrelations = pd.read_csv('allrelations.csv')
rowlist = []
for i in range(start, end):
axiom1 = allrelations.iloc[i]
for j in range(i, size):
axiom2 = allrelations.iloc[j]
sim1 = df.loc[df['class2'] == axiom1['class1'], axiom2['class1']].values[0]
sim2 = df.loc[df['class2'] == axiom1['class2'], axiom2['class2']].values[0]
rowlist.append([i, axiom1['I'], j, axiom2['I'], axiom1['class1'],
axiom1['class2'], axiom2['class1'], axiom2['class2'], sim1, sim2, min(sim1, sim2)])
#path = 'c:/Users/Ali/OneDrive/Desktop/corese/fragments/'
#axiomsimilaritymatrix.to_csv(path + str(start) + '.csv', sep=',', index=False)
return rowlist
def matrixfractionq(start, end, size):
wds_Corese = 'http://localhost:8080/sparql'
df = pd.read_csv('classessim.csv')
allrelations = pd.read_csv('allrelations.csv')
axiomsimilaritymatrix = pd.DataFrame({"axiom1": [],"axiom1I": [], "axiom2": [],"axiom2I": [], "a1c1": [],
"a1c2": [],"a2c1": [],"a2c2": [],"sim1": [],"sim2": [],"overallsim": []})
for i in range(start, end):
axiom1 = allrelations.iloc[i]
for j in range(i, size):
axiom2 = allrelations.iloc[j]
query1 = '''
select (kg:similarity(?class1, ?class2) as ?similarity) where {
?class1 a owl:Class
?class2 a owl:Class
filter regex(?class1, ''' + '\'' + axiom1['class1'] + '\'' +''')
filter regex(?class2, ''' + '\'' + axiom2['class1'] + '\'' +''')
}
ORDER BY DESC (?similarity)
'''
query2 = '''
select (kg:similarity(?class1, ?class2) as ?similarity) where {
?class1 a owl:Class
?class2 a owl:Class
filter regex(?class1, ''' + '\'' + axiom1['class2'] + '\'' +''')
filter regex(?class2, ''' + '\'' + axiom2['class2'] + '\'' +''')
}
ORDER BY DESC (?similarity)
'''
pd.set_option('display.max_colwidth',None) # if your Pandas version is < 1.0 then use -1 as second parameter, None otherwise
pd.set_option('display.precision', 5)
pd.set_option('display.max_rows', 9999999999)
sim1 = float(sparql_service_to_dataframe(wds_Corese, query1).iloc[0][0])
sim2 = float(sparql_service_to_dataframe(wds_Corese, query2).iloc[0][0])
#sim1 =float(df.loc[((df['class1']== axiom1['class1']) & (df['class2']== axiom2['class1']))
# |((df['class1']== axiom2['class1']) & (df['class2']== axiom1['class1'])), 'similarity'].values[0])
#sim2= float(df.loc[((df['class1']== axiom1['class2']) & (df['class2']== axiom2['class2']))
# |((df['class1']== axiom2['class2']) & (df['class2']== axiom1['class2'])), 'similarity'].values[0])
axiomsimilaritymatrix = axiomsimilaritymatrix.append(
{"axiom1": i,"axiom1I": axiom1['I'], "axiom2": j,"axiom2I": axiom2['I'], "a1c1": axiom1['class1'],
"a1c2": axiom1['class2'],"a2c1": axiom2['class1'],"a2c2": axiom2['class2'],
"sim1": sim1,"sim2": sim2,"overallsim":min(sim1,sim2)}
,ignore_index=True)
path = '/fragments/'
axiomsimilaritymatrix.to_csv( path + str(start) + '.csv', sep=',', index=False)
return axiomsimilaritymatrix