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Change use of deprecated method. Update links
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@torressa
torressa committed Nov 11, 2024
1 parent 9100642 commit f692e95
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2 changes: 1 addition & 1 deletion CODE_OF_CONDUCT.md
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ representative at an online or offline event.

Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement
[here](https://gurobi-optimization-gurobi-modelanalyzer.readthedocs-hosted.com/en/latest/contactus.html).
[here](https://gurobi-modelanalyzer.readthedocs.io/en/latest/contactus.html).
All complaints will be reviewed and investigated promptly and fairly.

All community leaders are obligated to respect the privacy and security of the
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2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@ interactive = [
]

[project.urls]
Documentation = "https://gurobi-optimization-gurobi-modelanalyzer.readthedocs-hosted.com/en/latest"
Documentation = "https://gurobi-modelanalyzer.readthedocs.io/en/latest/"
Issues = "https://github.com/Gurobi/gurobi-modelanalyzer/issues"
Source = "https://github.com/Gurobi/gurobi-modelanalyzer"

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142 changes: 78 additions & 64 deletions src/gurobi_modelanalyzer/results_analyzer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,6 +106,7 @@ def kappa_explain(
if _debug != OFF:
if _debugger != OFF:
import pdb

pdb.set_trace()

if expltype == "COLS":
Expand Down Expand Up @@ -174,10 +175,9 @@ def kappa_explain(
# with a huge ratio among two entries. Use a separate, simpler
# routine to create the explanation.
#
resmodel, maxmult, minmult = build_singleton_resmodel(explmodel, \
expltype)
resmodel, maxmult, minmult = build_singleton_resmodel(explmodel, expltype)
illcond_report(resmodel, model.ModelName, expltype, maxmult, minmult)
return(resmodel)
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 @@ -520,8 +520,13 @@ def kappa_explain(
# Done with column specific part of explanation.
#

illcond_report(resmodel, model.ModelName, expltype, \
max(yvaldict.values()), min(yvaldict.values()))
illcond_report(
resmodel,
model.ModelName,
expltype,
max(yvaldict.values()),
min(yvaldict.values()),
)
if method == ANGLES:
return ([], [], None) # Compatibility with method=ANGLES
else:
Expand Down Expand Up @@ -634,8 +639,7 @@ 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 Down Expand Up @@ -694,8 +698,7 @@ def extract_basis(
explmodel.dispose()
explmodel = gp.Model("colsingleton_basismodel")
build_explmodel(
model, explmodel, [minvar, maxvar], [mincon, maxcon], \
None, None, BYCOLS
model, explmodel, [minvar, maxvar], [mincon, maxcon], None, None, BYCOLS
)
#
# The 2x2 matrix is of the form a1 0 | A2(1,.)
Expand Down Expand Up @@ -740,8 +743,9 @@ def extract_basis(
minusvars.append(minusvar)
# explmodel.addSOS(GRB.SOS_TYPE1, [v, minusvar])

explmodel.addConstr(gp.quicksum(plusvars) - gp.quicksum(minusvars) \
== 1, name="GRBNormalize")
explmodel.addConstr(
gp.quicksum(plusvars) - gp.quicksum(minusvars) == 1, name="GRBNormalize"
)
explmodel.setObjective(gp.quicksum(plusvars) + gp.quicksum(minusvars))
else:
explvars = explmodel.getVars()
Expand Down Expand Up @@ -896,7 +900,7 @@ def build_explmodel(
# with constraints not in modcons after the next loop.
#
conindexdict[con.index] = con
explcondict[con.index] = explmodel.addConstr(
explcondict[con.index] = explmodel.addLConstr(
0, GRB.EQUAL, 0, name=con.ConstrName
)

Expand Down Expand Up @@ -987,30 +991,32 @@ 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.
# provided since the variables are fixed.
#
def build_singleton_resmodel(explmodel, expltype):
vars = explmodel.getVars()
cons = explmodel.getConstrs()
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:
if vars[0].LB > vars[1].LB:
maxmult = vars[0].LB
minmult = vars[1].LB
maxvar = vars[0]
minvar = vars[1]
maxvar = vars[0]
minvar = vars[1]
else:
maxmult = vars[1].LB
minmult = vars[0].LB
maxvar = vars[0]
minvar = vars[1]
maxvar = vars[0]
minvar = vars[1]

if expltype == BYROWS:
#
# Transpose the explanation model into the results model.
Expand All @@ -1029,29 +1035,29 @@ def build_singleton_resmodel(explmodel, expltype):
#
resmodel = gp.Model()
maxconname = "(mult=" + str(maxmult) + ")" + maxvar.VarName
col = explmodel.getCol(maxvar)
con0 = col.getConstr(0)
con1 = col.getConstr(1)
col = explmodel.getCol(maxvar)
con0 = col.getConstr(0)
con1 = col.getConstr(1)
if con1.ConstrName == "GRBNormalize":
maxvarname = con0.ConstrName
maxcoeff = col.getCoeff(0)
maxcoeff = col.getCoeff(0)
else:
maxvarname = con1.ConstrName
maxcoeff = col.getCoeff(1)
thisvar = resmodel.addVar(name = maxvarname)
resmodel.addConstr(maxcoeff*thisvar == 0, name = maxconname)
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)
col = explmodel.getCol(minvar)
con0 = col.getConstr(0)
con1 = col.getConstr(1)
if con1.ConstrName == "GRBNormalize":
minvarname = con0.ConstrName
mincoeff = col.getCoeff(0)
mincoeff = col.getCoeff(0)
else:
minvarname = con1.ConstrName
mincoeff = col.getCoeff(1)
thisvar = resmodel.addVar(name = minvarname)
resmodel.addConstr(mincoeff*thisvar == 0, name = minconname)
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
Expand All @@ -1062,7 +1068,7 @@ def build_singleton_resmodel(explmodel, expltype):
# c1311: 1e+11 x3259 = 0
# c1312: x3260 = 0
# GRBNormalize: x3260 + x3259 = 1
#Bounds
# Bounds
# x3260 = 1
# x3259 = 1e-11
#
Expand All @@ -1081,6 +1087,7 @@ def build_singleton_resmodel(explmodel, expltype):
resmodel.ModelName = explmodel.ModelName
return resmodel, maxmult, minmult


def kappa_stats(model, data, KappaExact):
kappa = model.Kappa
kappaexact = -1
Expand All @@ -1098,6 +1105,8 @@ def kappa_stats(model, data, KappaExact):
if data != None:
data["Kappa"] = kappa
data["KappaExact"] = kappaexact


#
# Write out the final explanation, print summary info.
#
Expand All @@ -1118,7 +1127,7 @@ def illcond_report(resmodel, modelname, expltype, maxabsmult, minabsmult):
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 @@ -1505,6 +1514,8 @@ def angle_explain(model, howmany=1, partol=1e-6):
print("Time for main column based loop = ", endtime - starttime)

return almostparrowlist, almostparcollist, explmodel


#
# Utility routine to facilitate examination of explainer output.
# Provides a bitmap of the explanation matrix. Requires matplotlib
Expand All @@ -1515,14 +1526,14 @@ def matrix_bitmap(model):
# Help function info
#
"""Utility routine to facilitate examination of explainer output by
printing a bitmap of the nonzero structure. Can also be used to
obtain bitmaps for matrices of arbitrary models rather than the
printing a bitmap of the nonzero structure. Can also be used to
obtain bitmaps for matrices of arbitrary models rather than the
explainer model.
Arguments:
model (required) The model whose constraint matrix will generate
the bitmap. Note that the routine may remove
the bitmap. Note that the routine may remove
empty rows and columns from the constraint matrix
of the model, so make a copy of the model before
calling the routine if necessary.
Expand All @@ -1533,23 +1544,23 @@ def matrix_bitmap(model):
except ImportError as e:
print("Need matplotlib.pyplot installed to display bitmap.")
return
#
#
#
# We only want the bitmap for variables that intersect at least one
# constraint.
#
#
#
#
# We only want the bitmap for variables that intersect at least one
# constraint.
#
varstodelete = []
for var in model.getVars():
col = model.getCol(var)
if col.size() == 0:
varstodelete.append(var)
model.remove(varstodelete)
model.update()
#
# Similarly, we only want constraints that intersect at least one
# variable.
#
#
# Similarly, we only want constraints that intersect at least one
# variable.
#
constodelete = []
for con in model.getConstrs():
row = model.getRow(con)
Expand All @@ -1564,27 +1575,28 @@ def matrix_bitmap(model):
modelname = model.ModelName
if _debug != OFF:
model.printStats()
#
# Now do the plot.
#
#
# Now do the plot.
#
A = model.getA()
plt.spy(A, markersize=1)
plt.title(modelname)
plt.show()


#
# Utility function that takes a list of decimal values and
# Utility function that takes a list of decimal values and
# uses the Fraction package to provide a rational approximation.
#
from fractions import Fraction


def converttofractions(vals):
#
# Help function info
#
"""Utility routine to convert decimal values to fractions.
Arguments:
vals (required) Array of decimal values to convert
Returns: Nothing; prints results to screen."""
Expand All @@ -1594,7 +1606,7 @@ def converttofractions(vals):
continue
frac = Fraction(v).limit_denominator()
print(f"The approx fraction of {v} is {frac}.")


#
# Improve the readability of the output
Expand Down Expand Up @@ -1639,7 +1651,9 @@ def refine_output(model, absmultdict, expltype, submatrix):
for conname in sorteddict.keys():
contoadd = condict[conname]
lhs = model.getRow(contoadd)
model.addConstr(lhs, contoadd.sense, contoadd.rhs, name=contoadd.ConstrName)
model.addLConstr(
lhs, contoadd.sense, contoadd.rhs, name=contoadd.ConstrName
)
model.remove(cons[0:concnt])
model.update()
elif expltype == BYCOLS:
Expand Down Expand Up @@ -1938,21 +1952,21 @@ 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()
cons = model.getConstrs()
delcons = []
for c in cons:
row = model.getRow(c)
if row.size() == 0:
delcons.append(c)
model.remove(delcons)
model.update()
model.update()


#
# Delete variables that have no nonzero coefficients in the
Expand All @@ -1961,15 +1975,15 @@ def removeemptyrows(model):
# preserve the original model.
#
def removeemptycolumns(model):
vars = model.getVars()
vars = model.getVars()
delvars = []
for v in vars:
col = model.getCol(v)
if col.size() == 0:
delvars.append(v)
model.remove(delvars)
model.update()
model.update()


#
# L1 norm of a specific constraint in a model.
Expand Down

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