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build_lp.py
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build_lp.py
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#!/usr/bin/python
import sys
import timeit
from directed_hypergraph import *
def main(argv):
# Specify the name of the file containing the nodes and edges
title = argv[0]
print("...\n...\n...")
# print("...\n...\n...")
# print("...\n...\n...")
# dc = input("Use the D&C formulation? (y/n) ");
nodeFile = "../datasets/hypergraphs/"+title+"-nodes.txt"
edgeFile = "../datasets/hypergraphs/"+title+"-edges.txt"
out = title+".lp"
outDC = title+"_DC.lp"
outHypershrub = title+"_hypershrub.lp"
outHyperedge = title+"_hypershrub_hyperedges.txt"
H = DirectedHypergraph()
node_delimeter = ","
column_delimeter = ";"
# Read the edge and node files to create a weighted hypergraph.
H.read(edgeFile, node_delimeter, column_delimeter)
weight_nodes(H, nodeFile, node_delimeter, column_delimeter)
weightNodes(H,title)
# A = timeit.Timer(lambda: build_lp(H,out))
# B = timeit.Timer(lambda: build_lp_dc(H,outDC))
#
# listA = A.repeat(repeat=100,number=1)
#
# listB = B.repeat(repeat=100,number=1)
#
# print("Average run time of build_lp:")
# print(sum(listA)/len(listA))
#
# print("Average run time of build_lp_dc:")
# print(sum(listB)/len(listB))
#build_lp(H,out)
#build_lp_dc(H,outDC)
hypershrub(H,outHypershrub,outHyperedge)
def weight_nodes(Hypergraph, file_name, delim=',', sep='\t'):
"""Read a file of nodes and their associated prized and
penalties. Addes these two attributes to each node in the hypergraph
and adds the node if the node is not already part of the hypergraph.
Takes a file such as:
name;prize;penalty
A;7;10
B;14;32
C;21;1
To add the weights.
"""
in_file = open(file_name, 'r')
# Skip the header line
in_file.readline()
line_number = 2
for line in in_file.readlines():
line = line.strip()
# Skip empty lines
if not line:
continue
words = line.split(sep)
if not (len(words) == 3):
raise \
IOError("Line {} ".format(line_number) +
"of "+file_name+" contains {} ".format(len(words)) +
"columns -- must contain only 3.")
node_ID = str(words[0])
node_prize = float(words[1])
node_penalty = float(words[2])
attributes = {'prize': node_prize, 'penalty': node_penalty,}
Hypergraph.add_node(node_ID, attributes)
line_number += 1
def weightNodes(H,prefix):
name2prize = nameToPrize(prefix)
uniprot2name = uniprotToName(prefix)
reactome2uniprot = reactomeIdToUniprot(prefix)
count = 0
for node in H.node_iterator():
name = str(node)
if not name in reactome2uniprot:
continue
UP = reactome2uniprot[name]
if not UP in uniprot2name:
continue
up2n = uniprot2name[UP]
if not up2n in name2prize:
continue
prize = float(name2prize[up2n])
prize = prize * 10
prize = str(prize)
atts = H.get_node_attributes(node)
pen = atts["penalty"]
if name[0] == "P":
attributes = {"prize" : prize, "penalty" : pen}
H.add_node(name, attributes)
count += 1
elif name[0] == "C":
#stuff
proteins = proteinsInComplex(filename,name)
prizes = []
for prot in proteins:
up = reactome2uniprot[prot]
pz = float(name2prize[up])
pz = pz * 10
pz = str(pz)
prizes.append(pz)
minPrize = min(prizes)
attributes = {"prize" : minPrize, "penalty" : pen}
H.add_node(name, attributes)
count += 1
print("Nodes weighted:",count)
def nameToPrize(prefix):
D = {}
sep = ","
filename = "../datasets/aliases/"+prefix+"-name2prize.csv"
in_file = open(filename, 'r')
# Skip the header line
in_file.readline()
line_number = 2
for line in in_file.readlines():
line = line.strip()
# Skip empty lines
if not line:
continue
words = line.split(sep)
if not (len(words) == 3):
raise \
IOError("Line {} ".format(line_number) +
"contains {} ".format(len(words)) +
"columns -- must contain only 3.")
D[words[0]] = words[2]
line_number += 1
in_file.close()
return D
def proteinsInComplex(prefix, nodeName):
proteins = []
sep = "\t"
delim = ";"
filename = "../datasets/reactome/parsed/"+prefix+"-complexes.txt"
in_file = open(filename, 'r')
# Skip the header line
in_file.readline()
line_number = 2
for line in in_file.readlines():
line = line.strip()
# Skip empty lines
if not line:
continue
words = line.split(sep)
prots = words[3].split(delim)
if words[0] == nodeName:
proteins = prots
line_number += 1
in_file.close()
return proteins
def reactomeIdToUniprot(prefix):
D = {}
filename = "../datasets/reactome/parsed/"+prefix+"-elements.txt"
sep = "\t"
delim = ";"
delim2 = ":"
in_file = open(filename, 'r')
# Skip the header line
in_file.readline()
line_number = 2
for line in in_file.readlines():
line = line.strip()
# Skip empty lines
if not line:
continue
words = line.split(sep)
if words[0][0] == "P":
aliases = words[3].split(delim)
#print(aliases)
if len(aliases) > 1:
UP = aliases[1].split(delim2)
#print(UP)
D[words[0]] = UP[1]
line_number += 1
in_file.close()
return D
def uniprotToName(prefix):
D = {}
sep = "\t"
filename = "../datasets/aliases/"+prefix+"-conversion.txt"
in_file = open(filename, 'r')
# Skip the header line
in_file.readline()
line_number = 2
for line in in_file.readlines():
line = line.strip('\n')
# Skip empty lines
if not line:
continue
words = line.split(sep)
D[words[1]] = words[0]
line_number += 1
in_file.close()
#print("up2n: ",D)
return D
def build_lp(Hypergraph,outputFile):
""" Defines a function that will build a .lp file after being Given
a hypergraph. The hypergraph must be weighted with
- Edge weights (Added during H.read())
- Node prized (Added during H.weight_nodes())
- Node penalties (Added during H.weight_nodes())
"""
# Open a new text file (default 'text.lp').
# Edit this name to make a new file based on which hypergraph
# is being used for the example.
lp_file = open("output/"+outputFile, "w")
# Write the first line of the .lp file.
# This can be "Maximize\n" or "Minimize\n".
lp_file.write("Maximize\n")
# Begin the line for the objective function.
lp_file.write(" obj: ")
# Create three lists of the variables present in the linear program.
xVariables = []
# xVarVals = []
dVariables = []
# dVarVals = []
eVariables = []
# eVarVals = []
# First, write sum_{v \in V}{g_{v}'a_{v}}
isFirst = True
for n in Hypergraph.node_iterator():
if isFirst == True:
# print(Hypergraph.get_node_attribute(n,"prize"))
lp_file.write(str(Hypergraph.get_node_attribute(n,"prize")))
lp_file.write(" ")
lp_file.write(str(n)+"x")
isFirst = False
else:
lp_file.write(" + ")
# print(Hypergraph.get_node_attribute(n,"prize"))
lp_file.write(str(Hypergraph.get_node_attribute(n,"prize")))
lp_file.write(" ")
lp_file.write(str(n)+"x")
xVariables.append(str(n)+"x")
# Next, write sum_{v \in V}{h_{v}'d_{v}}
for n in Hypergraph.node_iterator():
lp_file.write(" - ")
if str(Hypergraph.get_node_attribute(n,"penalty")) == "inf":
lp_file.write(str(0.0))
else:
lp_file.write(str(Hypergraph.get_node_attribute(n,"penalty")))
lp_file.write(" ")
lp_file.write(str(n)+"d")
dVariables.append(str(n)+"d")
# Last, write sum_{e \in E}{c_{e}'a_{e}}
for e in Hypergraph.hyperedge_id_iterator():
lp_file.write(" - ")
lp_file.write(str(Hypergraph.get_hyperedge_weight(e)))
lp_file.write(" ")
lp_file.write(str(e))
eVariables.append(str(e))
xVariables.sort()
dVariables.sort()
eVariables.sort()
# print("xVariables:",xVariables)
# print("dVariables:",dVariables)
# print("eVariables:",eVariables)
# Done writing the objective function
lp_file.write("\n")
print("Objective function written successfully.")
print("...\n...\n...")
# Begin the linear constraints.
lp_file.write("Subject To\n")
# 4.3 and 4.4
for e in Hypergraph.hyperedge_id_iterator():
lp_file.write(" c44_"+str(e)+": ")
isFirst = True
for tail in Hypergraph.get_hyperedge_tail(e):
if isFirst == True:
lp_file.write(str(tail)+"x")
isFirst = False
else:
lp_file.write(" + ")
lp_file.write(str(tail)+"x")
lp_file.write(" - "+str(len(Hypergraph.get_hyperedge_tail(e)))
+" "+e+" >= 0\n")
lp_file.write(" c43_"+str(e)+": ")
isFirst = True
for head in Hypergraph.get_hyperedge_head(e):
if isFirst == True:
lp_file.write(str(head)+"x")
isFirst = False
else:
lp_file.write(" + ")
lp_file.write(str(head)+"x")
lp_file.write(" - "+str(len(Hypergraph.get_hyperedge_head(e)))
+" "+e+" >= 0\n")
# 4.5
for i in range(len(xVariables)):
lp_file.write(" c45_"+xVariables[i][0]+": ")
lp_file.write(str(dVariables[i])+" - "+str(xVariables[i])+" <= 0\n")
# 4.6
for n in Hypergraph.node_iterator():
lp_file.write(" c46_"+str(n)+": ")
lp_file.write(str(n)+"d - "+str(n)+"x")
for edge in Hypergraph.get_backward_star(n):
lp_file.write(" + "+str(edge))
lp_file.write(" >= 0\n")
# Done writing the linear constraints.
print("Linear constraints written successfully.")
print("...\n...\n...")
# Begin writing bounds.
lp_file.write("Bounds\n")
for n in Hypergraph.node_iterator():
if str(Hypergraph.get_node_attribute(n,"penalty")) == "inf":
lp_file.write(" "+str(n)+"x = 1\n")
lp_file.write(" "+str(n)+"d = 0\n")
#Done writing the bounds.
print("Bounds written successfully.")
print("...\n...\n...")
# Write which binary variables are being optimized.
lp_file.write("Binary\n")
for x in xVariables:
lp_file.write(" "+str(x)+"\n")
for d in dVariables:
lp_file.write(" "+str(d)+"\n")
for e in eVariables:
lp_file.write(" "+str(e)+"\n")
# Done adding binary variables.
# End the .lp file
lp_file.write("End\n")
print("Done.")
lp_file.close()
def goesTo(Hypergraph,node,L,D,firstNode):
for e in Hypergraph.get_forward_star(node):
for h in Hypergraph.get_hyperedge_head(e):
if h not in L:
#L.append(h)
L[h]=True
name = str(firstNode)+'To'+str(h)
D[name] = True
goesTo(Hypergraph,h,L,D,firstNode)
def connectivityFinder(Hypergraph):
# Make a dictionary that contains whether or not any 2 nodes are connected
D = {}
connected = set()
count = 0
for node in Hypergraph.node_iterator():
for otherNode in Hypergraph.node_iterator():
if count % 100000 == 0:
print("count:",count)
name = str(node)+'To'+str(otherNode)
if node == otherNode:
D[name] = True
else:
D[name] = False
count += 1
for n in Hypergraph.node_iterator():
#connected = []
connected = {}
goesTo(Hypergraph,n,connected,D,n)
return D
def build_lp_dc(Hypergraph,outputFile):
""" Defines a function that will build a .lp file after being Given
a hypergraph. The hypergraph must be weighted with
- Edge weights (Added during H.read())
- Node prized (Added during H.weight_nodes())
- Node penalties (Added during H.weight_nodes())
"""
D = connectivityFinder(Hypergraph)
# print(D)
ts = 0
for key in D:
if D[key] == True:
ts += 1
#print("ts: ",ts)
# Open a new text file (default 'text.lp').
# Edit this name to make a new file based on which hypergraph
# is being used for the example.
lp_file = open("output/"+outputFile, "w")
# Write the first line of the .lp file.
# This can be "Maximize\n" or "Minimize\n".
lp_file.write("Maximize\n")
# Begin the line for the objective function.
lp_file.write(" obj: ")
# Create three lists of the variables present in the linear program.
xVariables = []
# xVarVals = []
dVariables = []
# dVarVals = []
eVariables = []
# eVarVals = []
cVariables = []
# First, write sum_{v \in V}{g_{v}'a_{v}}
isFirst = True
for n in Hypergraph.node_iterator():
if isFirst == True:
lp_file.write(str(Hypergraph.get_node_attribute(n,"prize")))
lp_file.write(" ")
lp_file.write(str(n)+"x")
isFirst = False
else:
lp_file.write(" + ")
lp_file.write(str(Hypergraph.get_node_attribute(n,"prize")))
lp_file.write(" ")
lp_file.write(str(n)+"x")
xVariables.append(str(n)+"x")
# Next, write sum_{v \in V}{h_{v}'d_{v}}
for n in Hypergraph.node_iterator():
lp_file.write(" - ")
if str(Hypergraph.get_node_attribute(n,"penalty")) == "inf":
lp_file.write(str(0.0))
else:
lp_file.write(str(Hypergraph.get_node_attribute(n,"penalty")))
lp_file.write(" ")
lp_file.write(str(n)+"d")
dVariables.append(str(n)+"d")
# Dummy variables for chads
for n in Hypergraph.node_iterator():
lp_file.write(" + ")
lp_file.write(str(0.0))
lp_file.write(str(n)+"c")
cVariables.append(str(n)+"c")
# Dummy variables for connections
for key in D:
lp_file.write(" + 0.0 "+key)
# Last, write sum_{e \in E}{c_{e}'a_{e}}
for e in Hypergraph.hyperedge_id_iterator():
lp_file.write(" - ")
lp_file.write(str(Hypergraph.get_hyperedge_weight(e)))
lp_file.write(" ")
lp_file.write(str(e))
eVariables.append(str(e))
xVariables.sort()
dVariables.sort()
eVariables.sort()
cVariables.sort()
#print("proof that something is happening: ",cVariables)
# print("xVariables:",xVariables)
# print("dVariables:",dVariables)
# print("eVariables:",eVariables)
# Done writing the objective function
lp_file.write("\n")
print("Objective function written successfully.")
print("...\n...\n...")
# Begin the linear constraints.
lp_file.write("Subject To\n")
# 4.3 and 4.4
for e in Hypergraph.hyperedge_id_iterator():
lp_file.write(" c44_"+str(e)+": ")
isFirst = True
for tail in Hypergraph.get_hyperedge_tail(e):
if isFirst == True:
lp_file.write(str(tail)+"x")
isFirst = False
else:
lp_file.write(" + ")
lp_file.write(str(tail)+"x")
lp_file.write(" - "+str(len(Hypergraph.get_hyperedge_tail(e)))
+" "+e+" >= 0\n")
lp_file.write(" c43_"+str(e)+": ")
isFirst = True
for head in Hypergraph.get_hyperedge_head(e):
if isFirst == True:
lp_file.write(str(head)+"x")
isFirst = False
else:
lp_file.write(" + ")
lp_file.write(str(head)+"x")
lp_file.write(" - "+str(len(Hypergraph.get_hyperedge_head(e)))
+" "+e+" >= 0\n")
# 4.5
for i in range(len(xVariables)):
lp_file.write(" c45_"+xVariables[i][0]+": ")
lp_file.write(str(dVariables[i])+" - "+str(xVariables[i])+" <= 0\n")
# 4.6
for n in Hypergraph.node_iterator():
lp_file.write(" c46_"+str(n)+": ")
lp_file.write(str(n)+"d - "+str(n)+"x")
for edge in Hypergraph.get_backward_star(n):
lp_file.write(" + "+str(edge))
lp_file.write(" >= 0\n")
# Danglers and Chads issue
# D&C1
for i in range(len(xVariables)):
lp_file.write(" dc1_"+xVariables[i][0]+": ")
lp_file.write(str(cVariables[i])+" - "+str(xVariables[i])+" <= 0\n")
# D&C2
for n in Hypergraph.node_iterator():
lp_file.write(" dc2_"+str(n)+": ")
lp_file.write(str(n)+"c - "+str(n)+"x")
for edge in Hypergraph.get_forward_star(n):
lp_file.write(" + "+str(edge))
lp_file.write(" >= 0\n")
# D&C3
for n in Hypergraph.node_iterator():
for m in Hypergraph.node_iterator():
name = str(n)+'To'+str(m)
lp_file.write(" dc3_"+name+": ")
lp_file.write(str(n)+"c + "+str(m)+"d + "+name+" <= "+str(2)+"\n")
# Done writing the linear constraints.
print("Linear constraints written successfully.")
print("...\n...\n...")
# Begin writing bounds.
lp_file.write("Bounds\n")
for n in Hypergraph.node_iterator():
if str(Hypergraph.get_node_attribute(n,"penalty")) == "inf":
lp_file.write(" "+str(n)+"x = 1\n")
lp_file.write(" "+str(n)+"d = 0\n")
# C&D bounds
for key in D:
if D[key] == True:
lp_file.write(" "+key+" = 1\n")
else:
lp_file.write(" "+key+" = 0\n")
#Done writing the bounds.
print("Bounds written successfully.")
print("...\n...\n...")
# Write which binary variables are being optimized.
lp_file.write("Binary\n")
for x in xVariables:
lp_file.write(" "+str(x)+"\n")
for d in dVariables:
lp_file.write(" "+str(d)+"\n")
for e in eVariables:
lp_file.write(" "+str(e)+"\n")
for key in D:
lp_file.write(" "+key+"\n")
# Done adding binary variables.
# End the .lp file
lp_file.write("End\n")
print("Done.")
lp_file.close()
def hypershrub(Hypergraph,outputFile,hyperedgeFile):
""" Defines a function that will build a .lp file after being Given
a hypergraph. The hypergraph must be weighted with
- Edge weights (Added during H.read())
- Node prized (Added during H.weight_nodes())
- Node penalties (Added during H.weight_nodes())
"""
# Open a new text file (default 'text.lp').
# Edit this name to make a new file based on which hypergraph
# is being used for the example.
lp_file = open("output/"+outputFile, "w")
################################################################
# Objective Function #
######################
################################
# Write the first line of the .lp file.
# This can be "Maximize\n" or "Minimize\n".
lp_file.write("Maximize\n")
# Begin the line for the objective function.
lp_file.write(" obj: ")
# Create three lists of the variables present in the linear program.
xVariables = [] # \alpha_{nodename}
dVariables = [] # \rho_{nodename}
eVariables = [] # \alpha_{edgename}
################################
# First, write sum_{v \in V}{p_{v}\alpha_{v}}
isFirst = True
for n in Hypergraph.node_iterator():
if isFirst == True:
lp_file.write(str(Hypergraph.get_node_attribute(n,"prize")))
lp_file.write(" ")
lp_file.write("alpha_"+str(n))
isFirst = False
else:
lp_file.write(" + ")
lp_file.write(str(Hypergraph.get_node_attribute(n,"prize")))
lp_file.write(" ")
lp_file.write("alpha_"+str(n))
xVariables.append("alpha_"+str(n))
################################
# Next, write sum_{v \in V}{r_{v}\rho_{v}}
''' TODO
Check if we need zeroes in obj
'''
for n in Hypergraph.node_iterator():
lp_file.write(" - ")
if str(Hypergraph.get_node_attribute(n,"penalty")) == "inf":
lp_file.write(str(0.0))
else:
lp_file.write(str(Hypergraph.get_node_attribute(n,"penalty")))
lp_file.write(" ")
lp_file.write("rho_"+str(n))
dVariables.append("rho_"+str(n))
################################
# Write sum_{e \in E}{c_{e}\alpha_{e}}
for e in Hypergraph.hyperedge_id_iterator():
lp_file.write(" - ")
lp_file.write(str(Hypergraph.get_hyperedge_weight(e)))
lp_file.write(" ")
lp_file.write("alpha_"+str(e))
eVariables.append("alpha_"+str(e))
################################
# Dummys #
##########
################################
# Dummy variable for sigma:
sigmas = []
for n in Hypergraph.node_iterator():
lp_file.write(" + 0.0 sigma_"+str(n))
sigmas.append("sigma_"+str(n))
################################
# Dummy variable for pi:
pis = []
for n in Hypergraph.node_iterator():
for e in Hypergraph.get_backward_star(n):
lp_file.write(" + 0.0 pi_"+str(e)+"~"+str(n))
pis.append("pi_"+str(e)+"~"+str(n))
'''
Figure out why pi is always zero.
'''
################################
# Dummy variable for beta:
betas = []
for n in Hypergraph.node_iterator():
lp_file.write(" + 0.0 beta_"+str(n))
betas.append("beta_"+str(n))
################################
# Sorting #
###########
xVariables.sort()
dVariables.sort()
eVariables.sort()
sigmas.sort()
pis.sort()
betas.sort()
################################
# Done writing the objective function
lp_file.write("\n")
print("Objective function written successfully.")
print("...\n...\n...")
################################################################
# Linear Constraints #
######################
# Begin the linear constraints.
lp_file.write("Subject To\n")
################################
# (12)
for v in Hypergraph.node_iterator():
lp_file.write(" c12_"+str(v)+": ")
lp_file.write(" sigma_"+str(v)+" ")
for e in Hypergraph.get_backward_star(v):
lp_file.write(" - pi_"+str(e)+"~"+str(v))
lp_file.write(" = 0\n")
################################
# (13)
#
# In "bounds"
#
################################
# (14)
for v in Hypergraph.node_iterator():
lp_file.write(" c14_"+str(v)+": ")
lp_file.write("rho_"+str(v)+" - alpha_"+str(v)+" <= 0\n")
################################
# (15)
# for v in Hypergraph.node_iterator():
# lp_file.write(" c15_"+str(v)+": ")
# lp_file.write("sigma_"+str(v)+"+ rho_"+str(v)+" <= 1\n")
for v in Hypergraph.node_iterator():
lp_file.write(" c15_5_"+str(v)+": ")
lp_file.write("sigma_"+str(v)+" + rho_"+str(v)+" - alpha_"+str(v)+" = 0\n")
################################
# (16)
for v in Hypergraph.node_iterator():
lp_file.write(" c16_"+str(v)+": ")
lp_file.write("sigma_"+str(v)+" - alpha_"+str(v)+" <= 0\n")
################################
# (17)
for e in Hypergraph.hyperedge_id_iterator():
for v in Hypergraph.get_hyperedge_tail(e):
lp_file.write(" c17_"+str(e)+"~"+str(v)+": ")
lp_file.write("alpha_"+str(e)+" - alpha_"+str(v)+" <= 0\n")
################################
# (18)
for e in Hypergraph.hyperedge_id_iterator():
lp_file.write(" c18_"+str(e)+": ")
lp_file.write("alpha_"+str(e)+" - ")
first = True
for v in Hypergraph.get_hyperedge_head(e):
if first == True:
lp_file.write("pi_"+str(e)+"~"+str(v))
first = False
else:
lp_file.write(" - pi_"+str(e)+"~"+str(v))
lp_file.write(" <= 0\n")
################################
# (19)
'''
Triple check with JF
'''
for e in Hypergraph.hyperedge_id_iterator():
for v in Hypergraph.get_hyperedge_head(e):
lp_file.write(" c19_"+str(e)+"~"+str(v)+": ")
lp_file.write("alpha_"+str(e)+" - pi_"+str(e)+"~"+str(v)+" >= 0\n")
################################
# (20)
#
# Doesn't require a constraint.
#
################################
# (21)
#
# In "bounds"
#
################################
# (22)
epsilon = 1/len(xVariables)
eprime = 1 - epsilon
for e in Hypergraph.hyperedge_id_iterator():
for u in Hypergraph.get_hyperedge_tail(e):
for v in Hypergraph.get_hyperedge_head(e):
lp_file.write(" c22_"+str(e)+"~"+str(u)+"~"+str(v)+": ")
lp_file.write("beta_"+str(u)+" - beta_"+str(v)+" + pi_"+str(e)+"~"+str(v)+" <= "+str(eprime)+"\n")
################################
# Done writing the linear constraints.
print("Linear constraints written successfully.")
print("...\n...\n...")
################################################################