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cplex_final.py
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cplex_final.py
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# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
import numpy as np
from math import floor, fabs
import sys
import cplex
from cplex.exceptions import CplexError
import matplotlib.pyplot as plt
import pandas as pd
# example: python cplex_final.py x.csv y.csv result.csv
# X = np.loadtxt(str(sys.argv[1]))
# Y = np.loadtxt(str(sys.argv[2]))
# result = []
class cplex_CPS:
def __init__(self, X, Y,filepath,filename):
self.X = X
self.Y = Y
self.result = []
self.filepath = filepath
self.filename = filename
def run_cplex(self,loc_X,loc_Y,m=30,M=50):
N = loc_X.shape[0]
C = []
for i in range(N):
c = []
for j in range(N):
c.append(np.sqrt((loc_X[i]-loc_X[j])**2+(loc_Y[i]-loc_Y[j])**2))
C.append(c)
print('Number of people for assignment')
print(len(loc_Y))
print('Number of potential social center')
print(len(loc_X))
num_facilities = len(loc_X)
num_clients = len(loc_Y)
model = cplex.Cplex()
model.parameters.mip.tolerances.mipgap.set(0.2)
model.set_log_stream(None)
model.set_error_stream(None)
model.set_warning_stream(None)
model.set_results_stream(None)
# Create one binary variable for each individual. The variables model
# whether this individual is social center or not
model.variables.add(obj = np.zeros(N),
lb=[0] * num_facilities,
ub=[1] * num_facilities,
types=["B"] * num_facilities)
center = []
# for c in range(num_clients):
# supply.append([])
for f in range(num_facilities):
center.append(f)
# Create one binary variable for each facility/client pair. The variables
# model whether a client is served by a facility.
for c in range(num_clients):
model.variables.add(obj=C[c],
lb=[0] * num_facilities,
ub=[1] * num_facilities,
types=["B"] * num_facilities)
# Create corresponding indices for later use
supply = []
for c in range(num_clients):
supply.append([])
for f in range(num_facilities):
supply[c].append((c + 1) * (num_facilities) + f)
# Each client must be assigned to exactly one location
for c in range(num_clients):
assignment_constraint = cplex.SparsePair(ind=[supply[c][f] for f in
range(num_facilities)],
val=[1.0] * num_facilities)
model.linear_constraints.add(lin_expr=[assignment_constraint],
senses=["E"],
rhs=[1])
# The number of clients assigned to a facility must be less than the
# capacity of the facility, and clients must be assigned to an open
# facility
for f in range(num_facilities):
index = [f]
value = [-M*center[f]]
for c in range(num_clients):
index.append(supply[c][f])
value.append(1.0*center[f])
capacity_constraint = cplex.SparsePair(ind=index, val=value)
model.linear_constraints.add(lin_expr=[capacity_constraint],
senses=["L"],
rhs=[0])
for f in range(num_facilities):
index = [f]
value = [-m*center[f]]
for c in range(num_clients):
index.append(supply[c][f])
value.append(1.0*center[f])
capacity_constraint = cplex.SparsePair(ind=index, val=value)
model.linear_constraints.add(lin_expr=[capacity_constraint],
senses=["G"],
rhs=[0])
# Our objective is to minimize cost. Fixed and variable costs
# have been set when variables were created.
model.objective.set_sense(model.objective.sense.minimize)
model.solve()
solution = model.solution
C = solution.get_values()[0:300]
# print(sum(C))
Z = solution.get_values()[300:90300]
# print(sum(Z))
Z = np.array(Z)
Z = np.reshape(Z, (300,300))
return C,Z
def run_parallel(self):
for i in xrange(len(self.X)/300):
if i< len(self.X)/300-1:
print('............')
# print(i*300)
# print((i+1)*300)
loc_X = self.X[i*300:(i+1)*300]
loc_Y = self.Y[i*300:(i+1)*300]
C,Z = self.run_cplex(loc_X,loc_Y)
self.result.append([loc_X,loc_Y,C,Z])
else:
print('............')
loc_X = self.X[i*300:len(self.X)]
loc_Y = self.Y[i*300:len(self.X)]
C,Z = self.run_cplex(loc_X,loc_Y)
self.result.append([loc_X,loc_Y,C,Z])
def create_DF(self):
C = np.zeros(len(self.X))
total_commmunities = 0
for i in xrange(len(self.X)/300):
c = self.result[i][2]
z = self.result[i][3]
count = 0
for j in range(len(c)):
if int(c[j]!=0):
c_index = total_commmunities + count + 1
count +=1
for k in range(len(z[:,j])):
if z[k,j]!=0:
C[300*i+k] = int(c_index)
total_commmunities += int(sum(c))
self.DF = pd.DataFrame({'X':self.X,'Y':self.Y,"C":C})
def run(self):
self.run_parallel()
self.create_DF()
self.DF.to_csv(self.filepath +'/'+ self.filename,index=False)