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[#22] Fixed bug that was introduced when migrating dictionaries to us…
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…e row

and column indices rather than names.   Added utility routines to remove empty
rows and columns from a model to provide more less cluttered explanation files.
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@EdKlotz
EdKlotz committed Aug 14, 2023
1 parent 3c57712 commit ade1511
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Showing 2 changed files with 186 additions and 33 deletions.
218 changes: 185 additions & 33 deletions model_analyzer/results_analyzer.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,8 @@
OFF = 0
MODERATE = 1
VERBOSE = 2
_debug = OFF # Change to MODERATE or VERBOSE as needed
_debugger = OFF
_debug = VERBOSE # Change to MODERATE or VERBOSE as needed
_debugger = MODERATE

SOLVELP = 0 # relobjtype choices
SOLVEQP = 1
Expand Down Expand Up @@ -106,7 +106,6 @@ def kappa_explain(
if _debug != OFF:
if _debugger != OFF:
import pdb

pdb.set_trace()

if expltype == "COLS":
Expand Down Expand Up @@ -160,7 +159,7 @@ def kappa_explain(
condthresh = math.pow(2, texp)

splitfreevars = method == LASSO
explmodel, splitvardict, RSinginfo, CSinginfo = extract_basis(
explmodel, splitvardict, RSinginfo, CSinginfo, Singflag = extract_basis(
model, modvars, modcons, expltype, method, condthresh
)
if explmodel == None: # Didn't find a basis for original model
Expand All @@ -169,6 +168,16 @@ def kappa_explain(
kappa_stats(model, data, KappaExact)
if _debug != OFF:
explmodel.write("explmodel.lp")
if Singflag:
#
# Detected an explanation based on row or column singletons
# with a huge ratio among two entries. Use a separate, simpler
# routine to create the explanation.
#
resmodel, maxmult, minmult = build_singleton_resmodel(explmodel, \
expltype)
illcond_report(resmodel, model.ModelName, expltype, maxmult, minmult)
return(resmodel)
#
# Minimize the violations of B'y = 0 constraints. Do not relax
# the e'y == 1 constraint, and the y variables are free, so they
Expand Down Expand Up @@ -266,6 +275,11 @@ def kappa_explain(
# The y variables were created before calling feasRelax. Extract
# them, as they are the certificate of ill conditioning.
#
# TODO: This subsequent section to build the resmodel that contains
# the explanation has grown large enough that it probably belongs in
# either one separate routine or two routines (one for building by
# rows and one for building by column.
#
yvaldict = {}
ynamedict = {}
yvars = None
Expand Down Expand Up @@ -496,24 +510,9 @@ def kappa_explain(
#
# Done with column specific part of explanation.
#
resmodel.setObjective(0)
if model.ModelName == "":
modelname = "model"
else:
modelname = model.ModelName
if expltype == BYROWS:
filename = modelname + "_kappaexplain.lp"
else:
filename = modelname + "_kappaexplain.mps"
resmodel.write(filename)
#
# Final info
#
print("--------------------------------------------------------")
print("Ill conditioning explanation written to file ", filename)
print("Maximum absolute multiplier value: ", str(max(yvaldict.values())))
print("Minimum absolute multiplier value: ", str(min(yvaldict.values())))
print("--------------------------------------------------------")

illcond_report(resmodel, model.ModelName, expltype, \
max(yvaldict.values()), min(yvaldict.values()))
if method == ANGLES:
return ([], [], None) # Compatibility with method=ANGLES
else:
Expand All @@ -532,6 +531,7 @@ def kappa_explain(
def extract_basis(
model, modvars, modcons, modeltype=BYROWS, method=DEFAULT, condthresh=1e10
):
Singflag = False
#
# Does the model have a factorized basis? If not, need to solve it
# first.
Expand Down Expand Up @@ -625,7 +625,8 @@ def extract_basis(
explmodel.dispose()
explmodel = gp.Model("rowsingleton_basismodel")
build_explmodel(
model, explmodel, [minvar, maxvar], [mincon, maxcon], None, None, BYROWS
model, explmodel, [minvar, maxvar], [mincon, maxcon], \
None, None, BYROWS
)
#
#
Expand All @@ -649,12 +650,13 @@ def extract_basis(
lhs = explmodel.getRow(con)
coeff = lhs.getCoeff(0)
var = lhs.getVar(0)
if coeff < maxcoeff:
if abs(coeff) < maxcoeff:
var.LB = maxmult
var.UB = maxmult
else:
var.LB = minmult
var.UB = minmult
Singflag = True
elif modeltype == BYCOLS and len(CSinginfo) > 1:
maxrat = 0
maxcoeff = 0
Expand All @@ -681,9 +683,10 @@ def extract_basis(
# for the explanation
#
explmodel.dispose()
explmodel = gp.Model("rowsingleton_basismodel")
explmodel = gp.Model("colsingleton_basismodel")
build_explmodel(
model, explmodel, [minvar, maxvar], [mincon, maxcon], None, None, BYROWS
model, explmodel, [minvar, maxvar], [mincon, maxcon], \
None, None, BYCOLS
)
#
# The 2x2 matrix is of the form a1 0 | A2(1,.)
Expand All @@ -704,13 +707,14 @@ def extract_basis(
for var in explmodel.getVars():
thiscol = explmodel.getCol(var)
coeff = thiscol.getCoeff(0)
if coeff < maxcoeff:
if abs(coeff) < maxcoeff:
var.LB = maxmult
var.UB = maxmult
else:
var.LB = minmult
var.UB = minmult

Singflag = True
#
# B'y = 0 or By = 0 constraints are done. All variables are now created, so
# add the e'y = 1 constraint and we are done. This is the same,
Expand All @@ -727,19 +731,20 @@ def extract_basis(
minusvars.append(minusvar)
# explmodel.addSOS(GRB.SOS_TYPE1, [v, minusvar])

explmodel.addConstr(gp.quicksum(plusvars) - gp.quicksum(minusvars) == 1)
explmodel.addConstr(gp.quicksum(plusvars) - gp.quicksum(minusvars) \
== 1, name="GRBNormalize")
explmodel.setObjective(gp.quicksum(plusvars) + gp.quicksum(minusvars))
else:
explvars = explmodel.getVars()
explmodel.addConstr(gp.quicksum(explvars) == 1)
explmodel.addConstr(gp.quicksum(explvars) == 1, name="GRBNormalize")
if method == RLS:
numvars = len(explvars)
sumsquares = gp.QuadExpr()
sumsquares.addTerms(numvars * [1], explvars, explvars)
explmodel.setObjective(sumsquares)

explmodel.update()
return explmodel, splitvardict, RSinginfo, CSinginfo
return explmodel, splitvardict, RSinginfo, CSinginfo, Singflag


#
Expand Down Expand Up @@ -973,7 +978,99 @@ def build_explmodel(
if _debug != OFF:
endtime = time.time()
print("Time build_explmodel = ", endtime - starttime)

#
# Handle the special case where the explanation consists of just
# two row singletons or column singletons with a large ratio.
# No need to map back to the original model or run feasrelax
# on the explainer model, which already has an optimal solution
# provided since the variables are fixed.
#
def build_singleton_resmodel(explmodel, expltype):
vars = explmodel.getVars()
cons = explmodel.getConstrs()
#
# No need to optimize the explmodel since all its variables are fixed.
#
if vars[0].LB > vars[1].LB:
maxmult = vars[0].LB
minmult = vars[1].LB
maxvar = vars[0]
minvar = vars[1]
else:
maxmult = vars[1].LB
minmult = vars[0].LB
maxvar = vars[0]
minvar = vars[1]

if expltype == BYROWS:
#
# Transpose the explanation model into the results model.
# For example, this explanation model (from neos-799716.mps)
#
# cons(6,1): - 0.000576 c2931 = 0
# beginFamily(0,3): - 1e+08 c14960 = 0
# R2: c2931 + c14960 = 1
# Bounds
# c2931 = 1
# c14960 = 5.76e-12
#
# would result in
# (mult=0.9999999999942399)c2931: - 0.000576 cons(6,1) = 0
# (mult=5.759999999966822e-12)c14960: - 1e+08 beginFamily(0,3) = 0
#
resmodel = gp.Model()
maxconname = "(mult=" + str(maxmult) + ")" + maxvar.VarName
col = explmodel.getCol(maxvar)
con0 = col.getConstr(0)
con1 = col.getConstr(1)
if con1.ConstrName == "GRBNormalize":
maxvarname = con0.ConstrName
maxcoeff = col.getCoeff(0)
else:
maxvarname = con1.ConstrName
maxcoeff = col.getCoeff(1)
thisvar = resmodel.addVar(name = maxvarname)
resmodel.addConstr(maxcoeff*thisvar == 0, name = maxconname)
minconname = "(mult=" + str(minmult) + ")" + minvar.VarName
col = explmodel.getCol(minvar)
con0 = col.getConstr(0)
con1 = col.getConstr(1)
if con1.ConstrName == "GRBNormalize":
minvarname = con0.ConstrName
mincoeff = col.getCoeff(0)
else:
minvarname = con1.ConstrName
mincoeff = col.getCoeff(1)
thisvar = resmodel.addVar(name = minvarname)
resmodel.addConstr(mincoeff*thisvar == 0, name = minconname)
else:
#
# Column based explanation is easier, as no tranposition of the
# model is needed. Just modeify a copy of the explmodel.
# For example, this explanation model will simply have its
# names modified.
#
# c1311: 1e+11 x3259 = 0
# c1312: x3260 = 0
# GRBNormalize: x3260 + x3259 = 1
#Bounds
# x3260 = 1
# x3259 = 1e-11
#
# would result in
# At this point we no longer need the explmodel, so just modify
# it in place.
#
maxvar.VarName = "(mult=" + str(maxmult) + ")" + maxvar.VarName
minvar.VarName = "(mult=" + str(minmult) + ")" + maxvar.VarName
cons = explmodel.getConstrs()
for c in cons:
if c.ConstrName == "GRBNormalize":
explmodel.remove(c)
explmodel.update()
resmodel = explmodel
resmodel.ModelName = explmodel.ModelName
return resmodel, maxmult, minmult

def kappa_stats(model, data, KappaExact):
kappa = model.Kappa
Expand All @@ -992,7 +1089,27 @@ def kappa_stats(model, data, KappaExact):
if data != None:
data["Kappa"] = kappa
data["KappaExact"] = kappaexact

#
# Write out the final explanation, print summary info.
#
def illcond_report(resmodel, modelname, expltype, maxabsmult, minabsmult):
resmodel.setObjective(0)
if modelname == "":
modelname = "model"
if expltype == BYROWS:
filename = modelname + "_kappaexplain.lp"
else:
filename = modelname + "_kappaexplain.mps"
resmodel.write(filename)
#
# Final info
#
print("--------------------------------------------------------")
print("Ill conditioning explanation written to file ", filename)
print("Maximum absolute multiplier value: ", maxabsmult)
print("Minimum absolute multiplier value: ", minabsmult)
print("--------------------------------------------------------")


#
# Split free variables into difference of two nonnegative variables.
Expand Down Expand Up @@ -1149,7 +1266,7 @@ def angle_explain(model, howmany=1, partol=1e-6):
pdb.set_trace()
modcons = model.getConstrs()
modvars = model.getVars()
explmodel, splitvardict, junk1, junk2 = extract_basis(
explmodel, splitvardict, junk1, junk2, junk3 = extract_basis(
model, modvars, modcons, None, False
)
if explmodel == None: # No basis for original model found.
Expand Down Expand Up @@ -1486,6 +1603,7 @@ def refine_output(model, absmultdict, expltype, submatrix):
# import pdb; pdb.set_trace()
quit = False
if expltype == BYROWS:
removeemptycolumns(model)
condict = {}
#
# Skip the combined constraint (the last one); it has no multiplier
Expand Down Expand Up @@ -1516,6 +1634,7 @@ def refine_output(model, absmultdict, expltype, submatrix):
model.remove(cons[0:concnt])
model.update()
elif expltype == BYCOLS:
removeemptyrows(model)
vardict = {}
#
# Skip the combined column (the last one); it has no multiplier
Expand Down Expand Up @@ -1810,6 +1929,39 @@ def refine_col_output(model, abscolmultdict):
#



#
# Delete linear constraints that have no nonzero coefficients
# Changes the model, so pass in a copy of you want to
# preserve the original model.
#
def removeemptyrows(model):
cons = model.getConstrs()
delcons = []
for c in cons:
row = model.getRow(c)
if row.size() == 0:
delcons.append(c)
model.remove(delcons)
model.update()

#
# Delete variables that have no nonzero coefficients in the
# associated column of the linear constraint matrix.
# Changes the model, so pass in a copy of you want to
# preserve the original model.
#
def removeemptycolumns(model):
vars = model.getVars()
delvars = []
for v in vars:
col = model.getCol(v)
if col.size() == 0:
delvars.append(v)
model.remove(delvars)
model.update()


#
# L1 norm of a specific constraint in a model.
#
Expand Down
1 change: 1 addition & 0 deletions tests/test_explainer.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -90,3 +90,4 @@ def test_submatrix(self):
def test_angles(self):
list1, list2, model = kappa_explain(self.model, method="ANGLES")
assert list1 != None and list2 != None and model != None
print("Angle test completed.")

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