I am trying to solve a MIP in pyomo with the Cplex solver (Interactive Optimizer 20.1.0.0). I want to turn off the presolve in pyomo, and I found out that I have to use:
opt = SolverFactory("cplex")
opt.options['preprocessing presolve'] = 0
, but I get the error:
CPLEX> New value could not be interpreted.
New value for presolve indicator ['y' or 'n']: New value could not be interpreted.
If I use:
opt = SolverFactory("cplex")
opt.options['preprocessing presolve', 'n']
I won't get any error, but I don't know that it worked or not. Since "Presolve time = 0.03 sec. (34.16 ticks)" in the output remains almost the same when I don't use the (opt.options['preprocessing presolve', 'n'])
In the cplex log you will see
CPXPARAM_Preprocessing_Presolve 0
And that means that presolve is off
Related
I have a question that confused me for a long time. As you know, when we use an if condition in Modelica, that means if the expression is true, then Modelica will do the corresponding equation.
But when i test the following code, I am confused:
model Model134
Real a(start = 0);
equation
if not sample(0, 2) then
a = 1;
else
a = 3;
end if;
end Model134;
I think a will be changed every 2s (start time=0), but when I simulate this model, it dose not change and a is equal to 1 all the time.
Dose anybody know the root cause?
a does change its value, but depending on your simulation tool you might not see it in the plot.
sample(0, 2) creates a time event every 2 seconds. The return value of sample() is only true during the event. So the value of a changes, but after the event it immediately changes back.
In this answer to a similar question, it is mentioned that Dymola stores the value before and after the event in result file. Intermediate values are skipped for efficiency reasons (there can be many for every event, which would bloat up your result file). Hence you can not plot this change in Dymola. For OpenModelica see the answer by
Akhil Nandan.
To proof that a really does change its value you can use this code for example:
model Model134
import Modelica.Utilities.Streams.print;
Real a;
equation
if sample(0, 2) then
a = 1;
else
a = 0;
end if;
when a > 0.5 then
print("a is " + String(a) + " at t=" + String(time) + "s");
end when;
annotation (experiment(StopTime=10));
end Model134;
You should see something like this in the simulation log:
a is 1 at t=2s
a is 1 at t=4s
a is 1 at t=6s
a is 1 at t=8s
a is 1 at t=10s
This is the plot simulated when trying your above code in OpenModelica with settings shown in the second figure.
A time event is triggered when sample(startTime,interval) evaluates true at every multiple of 2 seconds and based on your code logic this should activate else
block and assign value of variable a to be 3.
I have a vector of nominal values and I need to know the probability of occurring each of the nominal values. Basically, I need those to obtain the min, max, mean, std of the probability of observing the nominal values and to get the Class Entropy value.
For example, lets assume there is a data-set in which the target is predicting 0, 1, or 2. In the training data-set. We can count the number of records which their target is 1, and call it n_1 and similarly we can define n_0 and n_2. Then, the probability of observing class 1 in the training data-set is simply p_1=n_1/(n_0 + n_2). Once p_0, p_1, and p_2 are obtained, one can get min, max, mean, and std of the these probabilitis.
It is easy to get that in python by pandas, but want to avoid reading the data-set twice. I was wondering if there is any CAS-action in SAS that can provide it to me. Note that I use the Python API of SAS through swat and I need to have the API in python.
I found the following solution and it works fine. It uses s.dataPreprocess.highcardinality to get the number of classes and then uses s.dataPreprocess.binning to obtain the number of observations within each class. Then, there is just some straightforward calculation.
import swat
# create a CAS server
s = swat.CAS(server, port)
# load the table
tbl_name = 'hmeq'
s.upload("./data/hmeq.csv", casout=dict(name=tbl_name, replace=True))
# call to get the number of classes
cardinality_result = s.dataPreprocess.highcardinality(table=tbl_name, vars=[target_var])
cardinality_result_df = pd.DataFrame(cardinality_result["HighCardinalityDetails"])
number_of_classes = int(cardinality_result_df["CardinalityEstimate"])
# call dataPreprocess.binning action to get the probability of each class
s.loadactionset(actionset="dataPreprocess")
result_binning = s.dataPreprocess.binning(table=tbl_name, vars=[target_var], nBinsArray=[number_of_classes])
result_binning_df = pd.DataFrame(result_binning["BinDetails"])
probs = result_binning_df["NInBin"]/result_binning_df["NInBin"].sum()
prob_min = probs.min()
prob_max = probs.max()
prob_mean = probs.mean()
prob_std = probs.std()
entropy = -sum(probs*np.log2(probs))
I have a Pyomo model connected to a Django-created website.
My decision variable has 4 indices and I have a huge amount of constraints running on it.
Since Pyomo takes a ton of time to read in the constraints with so many variables, I want to sparse out the index set to only contain variables that actually could be 1 (i have some conditions on that)
I saw this post
Create a variable with sparse index in pyomo
and tried a for loop for all my conditions. I created a set "AllowedVariables" to later put this inside my constraints.
But Django's server takes so long to create this set while performing the system check, it never comes out.
Currently i have this model:
model = AbstractModel()
model.x = Var(model.K, model.L, model.F, model.Z, domain=Boolean)
def ObjRule(model):
# some rule, sense maximize
model.Obj = pyomo.environ.Objective(rule=ObjRule, sense=maximize)
def ARule(model,l):
maxA = sum(model.x[k,l,f,z] * for k in model.K for f in model.F
for z in model.Z and (k,l,f,z) in model.AllowedVariables)
return maxA <= 1
model.maxA = Constraint(model.L, rule=ARule)
The constraint is exemplary, I have 15 more similar ones. I currently create "AllowedVariables" this way:
AllowedVariables = []
for k in model.K:
for l in model.L:
..... check all sorts of conditions, break if not valid
AllowedVaraibles.append((k,l,f,z))
model.AllowedVariables = Set(initialize=AllowedVariables)
Using this, the Django server starts checking....and never stops
performing system checks...
Sadly, I somehow need some restriction on the variables or else the reading for the solver will take way to long since the constraints contain so many unnecessary variables that have to be 0 anyways.
Any ideas on how I can sparse my variable set?
I am new to Pyomo. I want to add an if..then.. type constraint to my linear programming problem. I have an abstract model and this is an example what I'd like to do:
if node j1 is receiving less than half of its water demand, the minimum flow in the link between j2 and j1 must be set to demand value in j1 (A and B are model variables, d is a known parameter).
if A(j1)<0.5 then B(j2,j1)>=d(j1)
I tried the following when I define model constraints. But since the model has not yet created the instance from its data file, it doesn't recognize j1 and j2.
def rule_(model):
term1=floor(model.A[j1]/0.5)
return (term1*model.B[j1,j2]>term1*mdoel.demand[j1])
model.rule=Constraint(rule=rule_)
If I take these lines after instantiating the model using data file, I think the constraint will not be implemented at all.
Can anyone help with this, please? Thanks.
"If/then" expressions and floor() are not linear, so they can't be inserted directly into a linear program. However, you can get the same effect by setting a binary flag and using that to activate and deactivate the constraint. Note that binary variables are also not linear, but they are commonly handled by mixed-integer solvers.
model.flag = Var(within=Binary)
def set_flag_rule(model):
# force the flag to be set if A[j1] < 0.5
return ((1 - model.flag) * 0.5 <= model.A[j1])
model.set_flag = Constraint(rule=set_flag_rule)
def rule(model):
# force B[j1, j2] to meet demand if the flag is set
return (model.B[j1,j2] >= model.flag * model.demand[j1])
model.rule=Constraint(rule=rule)
I have endeavored this problem which was an easy solution in GAMS file but I cannot do it with PYOMO.
My problem is that I would like to put a limit in production as a daily basis as a constraint for a certain generation.
The generation is variable with 8760 hourly set and summation of this generation per day should be lower than certain limit.
In GAMS, I can easily solve it with following code;
set t hours in a year /1*8760/ ;
set d day /1*365/;
I make parameter day(t) for spliting hours in a year
parameter day(t) ;
day(t)$(ord(t) <= 24)=1;
day(t)$(ord(t)>24 and mod(ord(t),24) ne 0)=round(ord(t)/24-mod(ord(t),24)/24)+1;
day(t)$(ord(t)>24 and mod(ord(t),24) eq 0)= ord(t)/24;
with the sets and paramter day(t) I can make following equation as constraint
hydro_day(d)..sum(t$(day(t)=ord(d)),hydro_el(t))=l=6*spec('Hydro', 'cap');
In Pyomo, I have tried as follow but it doesn't work for now
def dcap_rule(model) :
dailyLimit = {}
for k in range(365) :
dailyLimit[k] = sum(model.discharge[i] for i in sequence((24*(k-1)+1), 24*k))
return dailyLimit[k]<= model.capa['pumped']*5
model.dcap_limit = Constraint( rule=dcap_rule)
This code only applied to the first day(1-24hours) then after the first day, there seems to be no constraint .
Could you help me solve this issue?
Thanks in advance
You are declaring a scalar constraint, so only one constraint is being generated. You want to change your code to generate an indexed constraint:
def dcap_rule(model, k):
return sum(model.discharge[i] for i in sequence((24*(k-1)+1), 24*k)) \
<= model.capa['pumped']*5
model.dcap_limit = Constraint(range(365), rule=dcap_rule)