from __future__ import annotations
import inspect
import io
import logging
import os
import threading
import uuid
from collections.abc import Iterable, Sequence
from enum import Enum
from typing import TYPE_CHECKING
import gamspy as gp
import gamspy._algebra.expression as expression
import gamspy._algebra.operation as operation
import gamspy._miro as miro
import gamspy._symbols.implicits as implicits
import gamspy._validation as validation
import gamspy.utils as utils
from gamspy._backend.backend import backend_factory
from gamspy._convert import GamsConverter, LatexConverter
from gamspy._model_instance import ModelInstance
from gamspy._options import (
EXECUTION_OPTIONS,
MODEL_ATTR_OPTION_MAP,
ModelInstanceOptions,
Options,
)
from gamspy.exceptions import GamspyException, ValidationError
if TYPE_CHECKING:
from typing import Literal
import pandas as pd
from gamspy import Container, Equation, Parameter, Variable
from gamspy._algebra.expression import Expression
from gamspy._algebra.operation import Operation
from gamspy._backend.engine import EngineClient
from gamspy._backend.neos import NeosClient
from gamspy._symbols.implicits import ImplicitParameter, ImplicitVariable
from gamspy._symbols.symbol import Symbol
IS_MIRO_INIT = os.getenv("MIRO", False)
logger = logging.getLogger("MODEL")
logger.setLevel(logging.INFO)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.INFO)
formatter = logging.Formatter("[%(name)s - %(levelname)s] %(message)s")
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
[docs]
class Problem(Enum):
"""An enumeration for problem all problem types"""
LP = "LP"
NLP = "NLP"
QCP = "QCP"
DNLP = "DNLP"
MIP = "MIP"
RMIP = "RMIP"
MINLP = "MINLP"
RMINLP = "RMINLP"
MIQCP = "MIQCP"
RMIQCP = "RMIQCP"
MCP = "MCP"
CNS = "CNS"
MPEC = "MPEC"
RMPEC = "RMPEC"
EMP = "EMP"
MPSGE = "MPSGE"
@classmethod
def values(cls):
"""Convenience function to return all values of enum"""
return list(cls._value2member_map_.keys())
def __str__(self) -> str:
return self.value
[docs]
class Sense(Enum):
"""An enumeration for sense types"""
MIN = "MIN"
MAX = "MAX"
FEASIBILITY = "FEASIBILITY"
@classmethod
def values(cls):
"""Convenience function to return all values of enum"""
return list(cls._value2member_map_.keys())
def __str__(self) -> str:
return self.value
[docs]
class ModelStatus(Enum):
"""An enumeration for model status types"""
OptimalGlobal = 1
OptimalLocal = 2
Unbounded = 3
InfeasibleGlobal = 4
InfeasibleLocal = 5
InfeasibleIntermed = 6
Feasible = 7
Integer = 8
NonIntegerIntermed = 9
IntegerInfeasible = 10
LicenseError = 11
ErrorUnknown = 12
ErrorNoSolution = 13
NoSolutionReturned = 14
SolvedUnique = 15
Solved = 16
SolvedSingular = 17
UnboundedNoSolution = 18
InfeasibleNoSolution = 19
[docs]
class SolveStatus(Enum):
"""An enumeration for solve status types"""
NormalCompletion = 1
IterationInterrupt = 2
ResourceInterrupt = 3
TerminatedBySolver = 4
EvaluationInterrupt = 5
CapabilityError = 6
LicenseError = 7
UserInterrupt = 8
SetupError = 9
SolverError = 10
InternalError = 11
Skipped = 12
SystemError = 13
INTERRUPT_STATUS = [
SolveStatus.IterationInterrupt,
SolveStatus.ResourceInterrupt,
SolveStatus.EvaluationInterrupt,
SolveStatus.UserInterrupt,
SolveStatus.TerminatedBySolver,
]
ERROR_STATUS = {
SolveStatus.CapabilityError: "The solver does not have the capability required by the model.",
SolveStatus.LicenseError: "The solver cannot find the appropriate license key needed to use a specific subsolver.",
SolveStatus.SetupError: "The solver encountered a fatal failure during problem set-up time.",
SolveStatus.SolverError: "The solver encountered a fatal error.",
SolveStatus.InternalError: "The solver encountered an internal fatal error.",
SolveStatus.SystemError: "This indicates a completely unknown or unexpected error condition.",
}
# GAMS name -> GAMSPy name
ATTRIBUTE_MAP = {
"domUsd": "_num_domain_violations",
"etAlg": "_algorithm_time",
"etSolve": "_total_solve_time",
"etSolver": "_total_solver_time",
"iterUsd": "_num_iterations",
"marginals": "_marginals",
"maxInfes": "_max_infeasibility",
"meanInfes": "_mean_infeasibility",
"modelStat": "_status",
"nodUsd": "_num_nodes_used",
"number": "_solve_number",
"numDepnd": "_num_dependencies",
"numDVar": "_num_discrete_variables",
"numEqu": "_num_equations",
"numInfes": "_num_infeasibilities",
"numNLIns": "_num_nonlinear_insts",
"numNLNZ": "_num_nonlinear_zeros",
"numNOpt": "_num_nonoptimalities",
"numNZ": "_num_nonzeros",
"numRedef": "_num_mcp_redefinitions",
"numVar": "_num_variables",
"numVarProj": "_num_bound_projections",
"objEst": "_objective_estimation",
"objVal": "_objective_value",
"procUsed": "_used_model_type",
"resGen": "_model_generation_time",
"resUsd": "_solve_model_time",
"solveStat": "_solve_status",
"sumInfes": "_sum_infeasibilities",
"sysVer": "_solver_version",
}
[docs]
class Model:
"""
Represents a list of equations to be solved.
Parameters
----------
container : Container
Container of the model.
name : str, optional
Name of the model. Name is autogenerated by default.
equations : Sequence[Equation]
Sequence of Equation objects.
problem : Problem | str, optional
'LP', 'NLP', 'QCP', 'DNLP', 'MIP', 'RMIP', 'MINLP', 'RMINLP', 'MIQCP', 'RMIQCP', 'MCP', 'CNS', 'MPEC', 'RMPEC', 'EMP', or 'MPSGE',
by default Problem.LP.
sense : Sense | str, optional
"MIN", "MAX", or "FEASIBILITY". By default, Sense.FEASIBILITY
objective : Variable | Expression, optional
Objective variable to minimize or maximize or objective itself.
matches : dict[Equation, Variable]
Equation - Variable matches for MCP models.
limited_variables : Sequence[ImplicitVariable], optional
Allows limiting the domain of variables used in a model.
external_module: str, optional
The name of the external module in which the external equations are implemented
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> v = gp.Variable(m, "v")
>>> e = gp.Equation(m, "e", definition= v == 5)
>>> my_model = gp.Model(m, "my_model", "LP", [e])
"""
# Prefix for auto-generated symbols
_generate_prefix = "autogenerated_"
_autogen_symbols: list[Variable | Equation] = []
def __init__(
self,
container: Container | None = None,
name: str | None = None,
problem: Problem | str = Problem.MIP,
equations: Sequence[Equation] = [],
sense: Sense | str = Sense.FEASIBILITY,
objective: Variable | Expression | None = None,
matches: dict[Equation, Variable] | None = None,
limited_variables: Sequence[ImplicitVariable] | None = None,
external_module: str | None = None,
):
self._auto_id = "m" + str(uuid.uuid4()).replace("-", "_")
if name is not None:
name = validation.validate_name(name)
self.name = validation.validate_model_name(name)
else:
self.name = self._auto_id
if container is not None:
self.container = container
else:
self.container = gp._ctx_managers[
(os.getpid(), threading.get_native_id())
]
assert self.container is not None
self._matches = matches
self.problem, self.sense = validation.validate_model(
equations, problem, sense
)
self.equations = list(equations)
self._objective = objective
self._objective_variable = self._set_objective_variable(objective)
if (
self._objective_variable is not None
and self._objective_variable.type.lower() != "free"
):
raise ValidationError(
f"Objective variable `{self._objective_variable}` must be a free variable"
)
if limited_variables is not None and not isinstance(
limited_variables, Iterable
):
raise ValidationError(
f"`limited_variables must be an Iterable of ImplicitVariable objects but found {limited_variables}`"
)
self._limited_variables: Sequence[ImplicitVariable] | None = (
limited_variables
)
if not self.equations and not self._matches:
raise ValidationError("Model requires at least one equation.")
self._external_module_file: str | None = None
self._external_module: str | None = None
if external_module is not None:
self.external_module = external_module
else:
# To avoid adding it twice
self.container._add_statement(self)
# allow freezing
self._is_frozen: bool = False
# Attributes
self._num_domain_violations: float | None = None
self._algorithm_time: float | None = None
self._total_solve_time: float | None = None
self._total_solver_time: float | None = None
self._num_iterations: float | None = None
self._marginals: float | None = None
self._max_infeasibility: float | None = None
self._mean_infeasibility: float | None = None
self._status: ModelStatus | None = None
self._num_nodes_used: float | None = None
self._solve_number: float | None = None
self._num_dependencies: float | None = None
self._num_discrete_variables: float | None = None
self._num_equations: float | None = None
self._num_infeasibilities: float | None = None
self._num_nonlinear_insts: float | None = None
self._num_nonlinear_zeros: float | None = None
self._num_nonoptimalities: float | None = None
self._num_nonzeros: float | None = None
self._num_mcp_redefinitions: float | None = None
self._num_variables: float | None = None
self._num_bound_projections: float | None = None
self._objective_estimation: float | None = None
self._objective_value: float | None = None
self._used_model_type: float | None = None
self._model_generation_time: float | None = None
self._solve_model_time: float | None = None
self._sum_infeasibilities: float | None = None
self._solve_status: SolveStatus | None = None
self._solver_version: float | None = None
self.container.models.update({self.name: self})
self.container._synch_with_gams()
def _serialize(self) -> dict:
info = {
"name": self.name,
"problem": str(self.problem),
"sense": str(self.sense),
}
# equations
equations = [equation.name for equation in self.equations]
info["equations"] = equations
# matches
if self._matches is not None:
matches = {
key.name: value.name for key, value in self._matches.items()
}
info["_matches"] = matches
# objective variable
if self._objective_variable is not None:
info["_objective_variable"] = self._objective_variable.name
# attributes
for attribute in ATTRIBUTE_MAP.values():
if attribute in ["_status", "_solve_status"]:
value = getattr(self, attribute, None)
if value is not None:
value = value.value
info[attribute] = value
else:
info[attribute] = getattr(self, attribute, None)
return info
def __repr__(self) -> str:
return f"Model(name='{self.name}', problem='{str(self.problem)}', equations={self.equations}, sense='{str(self.sense)}', objective={self._objective_variable}, matches={self._matches}, limited_variables={self._limited_variables}"
def __str__(self) -> str:
return (
f"Model {self.name}:\n Problem Type: {self.problem}\n Sense:"
f" {self.sense}\n Equations: {self.equations}"
)
@property
def num_domain_violations(self) -> float | None:
"""
Number of domain violations.
Returns
-------
float | None
"""
return self._num_domain_violations
@property
def algorithm_time(self) -> float | None:
"""
Solver dependent timing information. This attribute was intended to allow
solvers to return the elapsed time used by the solve algorithm without
including any model generation, communication, or setup time. However,
solvers are free to adapt this convention and return time-related information
(but not necessarily elapsed time) for executing the solve algorithm. Please
inspect your solver manual for the actual meaning of the value returned in
this attribute.
Returns
-------
float | None
"""
return self._algorithm_time
@property
def total_solve_time(self) -> float | None:
"""
Elapsed time it took to execute a solve statement in total. This model
attribute returns the elapsed time it took to execute a solve statement in total.
This time includes the model generation time, the time to read and write files,
the time to create the solution report and the time taken by the actual solve.
The time is expressed in seconds of wall-clock time.
Returns
-------
float | None
"""
return self._total_solve_time
@property
def total_solver_time(self) -> float | None:
"""
Elapsed time taken by the solver only. This model attribute returns the elapsed
time taken by the solver only. This does not include the GAMS model generation time
and the time taken to report and load the solution back into the GAMS database.
The time is expressed in seconds of wall-clock time.
Returns
-------
float | None
"""
return self._total_solver_time
@property
def num_iterations(self) -> float | None:
"""
Number of iterations used.
Returns
-------
float | None
"""
return self._num_iterations
@property
def marginals(self) -> float | None:
"""
Indicates whether there are marginals.
Returns
-------
float | None
"""
return self._marginals
@property
def max_infeasibility(self) -> float | None:
"""
Maximum of infeasibilities
Returns
-------
float | None
"""
return self._max_infeasibility
@property
def mean_infeasibility(self) -> float | None:
"""
Mean of infeasibilities
Returns
-------
float | None
"""
return self._mean_infeasibility
@property
def status(self) -> ModelStatus | None:
"""
Model status after solve.
Returns
-------
ModelStatus | None
"""
return self._status
@property
def num_nodes_used(self) -> float | None:
"""
Number of nodes used by the MIP solver.
Returns
-------
float | None
"""
return self._num_nodes_used
@property
def solve_number(self) -> float | None:
"""
Number of the last solve.
Returns
-------
float | None
"""
return self._solve_number
@property
def num_dependencies(self) -> float | None:
"""
Number of dependencies in a CNS model.
Returns
-------
float | None
"""
return self._num_dependencies
@property
def num_discrete_variables(self) -> float | None:
"""
Number of discrete variables.
Returns
-------
float | None
"""
return self._num_discrete_variables
@property
def num_equations(self) -> float | None:
"""
Number of equations.
Returns
-------
float | None
"""
return self._num_equations
@property
def num_infeasibilities(self) -> float | None:
"""
Number of infeasibilities.
Returns
-------
float | None
"""
return self._num_infeasibilities
@property
def num_nonlinear_insts(self) -> float | None:
"""
Number of nonlinear instructions.
Returns
-------
float | None
"""
return self._num_nonlinear_insts
@property
def num_nonlinear_zeros(self) -> float | None:
"""
Number of nonlinear nonzeros.
Returns
-------
float | None
"""
return self._num_nonlinear_zeros
@property
def num_nonoptimalities(self) -> float | None:
"""
Number of nonoptimalities.
Returns
-------
float | None
"""
return self._num_nonoptimalities
@property
def num_nonzeros(self) -> float | None:
"""
Number of nonzero entries in the model coefficient matrix.
Returns
-------
float | None
"""
return self._num_nonzeros
@property
def num_mcp_redefinitions(self) -> float | None:
"""
Number of MCP redefinitions.
Returns
-------
float | None
"""
return self._num_mcp_redefinitions
@property
def num_variables(self) -> float | None:
"""
Number of variables.
Returns
-------
float | None
"""
return self._num_variables
@property
def num_bound_projections(self) -> float | None:
"""
Number of bound projections during model generation.
Returns
-------
float | None
"""
return self._num_bound_projections
@property
def objective_estimation(self) -> float | None:
"""
Estimate of the best possible solution for a mixed-integer model
Returns
-------
float | None
"""
return self._objective_estimation
@property
def objective_value(self) -> float | None:
"""
Objective function value
Returns
-------
float | None
"""
return self._objective_value
@property
def used_model_type(self) -> float | None:
"""
Integer number that indicates the used model type.
Returns
-------
float | None
"""
return self._used_model_type
@property
def model_generation_time(self) -> float | None:
"""
Time GAMS took to generate the model in wall-clock seconds.
Returns
-------
float | None
"""
return self._model_generation_time
@property
def solve_model_time(self) -> float | None:
"""
Time the solver used to solve the model in seconds
Returns
-------
float | None
"""
return self._solve_model_time
@property
def solve_status(self) -> SolveStatus | None:
"""
Indicates the solver termination condition.
Returns
-------
SolveStatus | None
"""
return self._solve_status
@property
def sum_infeasibilities(self) -> float | None:
"""
Sum of infeasibilities.
Returns
-------
float | None
"""
return self._sum_infeasibilities
@property
def solver_version(self) -> float | None:
"""
Solver version.
Returns
-------
float | None
"""
return self._solver_version
@property
def external_module(self) -> str | None:
"""
Name of the external module in which the external equations are implemented.
By default, this parameter is set to None. When provided, it triggers the
opening of the specified file using a File statement and incorporates the
file into the model by adding it as an external module.
This feature requires a solid understanding of programming, compilation,
and linking processes. For more information, please refer to the
https://gamspy.readthedocs.io/en/latest/user/advanced/external_equations.html .
Returns
-------
str | None
"""
return self._external_module
@external_module.setter
def external_module(self, value: str | None):
if self._external_module_file is not None:
self.container._add_statement(
f"putclose {self._external_module_file};"
)
write_model_statement = value != self._external_module
self._external_module = None
self._external_module_file = None
if value is not None:
filename = "f" + str(uuid.uuid4()).replace("-", "_")
self._external_module_file = filename
self._external_module = value
self.container._add_statement(f"File {filename} / '{value}' /;")
if write_model_statement:
self.container._add_statement(self)
def _generate_obj_var_and_equation(self) -> tuple[Variable, Equation]:
variable = gp.Variable._constructor_bypass(
self.container,
f"{self.name}_objective_variable",
domain=[],
)
equation = gp.Equation._constructor_bypass(
self.container,
f"{self.name}_objective",
domain=[],
)
self._autogen_symbols.append(variable.name)
self._autogen_symbols.append(equation.name)
return variable, equation
def _set_objective_variable(
self,
assignment: None | Variable | Operation | Expression = None,
) -> Variable | None:
"""
Returns objective variable. If the assignment is an Expression
or an Operation (Sum, Product etc.), it automatically generates
an objective variable and a equation.
Returns
-------
Variable | None
Raises
------
TypeError
In case assignment is not a Variable, Expression or an Operation.
"""
if assignment is not None and not isinstance(
assignment,
(gp.Variable, expression.Expression, operation.Operation),
):
raise TypeError(
"Objective must be a Variable or an Expression but"
f" {type(assignment)} given"
)
if self.sense == gp.Sense.FEASIBILITY:
if assignment is not None:
raise ValidationError(
"Cannot set an objective when the sense is FEASIBILITY!"
)
if self.problem in [Problem.CNS, Problem.MCP, Problem.EMP]:
return None
variable, equation = self._generate_obj_var_and_equation()
statement = expression.Expression(
implicits.ImplicitEquation(
equation,
name=equation.name,
type=equation.type,
domain=[],
),
"..",
variable == 0,
)
self.container._add_statement(statement)
equation._definition = statement
equation.modified = False
variable.modified = False
if equation.name not in [symbol.name for symbol in self.equations]:
self.equations.append(equation)
return variable
if isinstance(
assignment, (expression.Expression, operation.Operation)
):
variable, equation = self._generate_obj_var_and_equation()
# Sum((i,j),c[i,j]*x[i,j])->Sum((i,j),c[i,j]*x[i,j]) =e= var
assignment = assignment == variable
# equation .. Sum((i,j),c[i,j]*x[i,j]) =e= var
statement = expression.Expression(
implicits.ImplicitEquation(
equation,
name=equation.name,
type=equation.type,
domain=[],
),
"..",
assignment,
)
self.container._add_statement(statement)
equation._definition = statement
equation.modified = False
variable.modified = False
if equation.name not in [symbol.name for symbol in self.equations]:
self.equations.append(equation)
return variable
return assignment
def _generate_solve_string(self) -> str:
solve_string = f"solve {self.name} using {self.problem}"
if self.sense == gp.Sense.FEASIBILITY:
# Set sense as min or max for feasibility
self.sense = gp.Sense("MIN")
if self.problem not in [Problem.MCP, Problem.CNS, Problem.EMP]:
solve_string += f" {self.sense}"
if self._objective_variable is not None:
solve_string += f" {self._objective_variable.gamsRepr()}"
return solve_string + ";"
def _add_runtime_options(
self, options: Options, backend: str = "local"
) -> None:
for key, value in options.model_dump(exclude_none=True).items():
if key in MODEL_ATTR_OPTION_MAP:
if isinstance(value, bool):
value = int(value)
elif isinstance(value, str):
value = f"'{value}'"
self.container._add_statement(
f"{self.name}.{MODEL_ATTR_OPTION_MAP[key]} = {value};\n"
)
elif key in EXECUTION_OPTIONS:
if backend == "engine" and key == "loadpoint":
value = os.path.relpath(
value, self.container.working_directory
)
self.container._add_statement(
f"{EXECUTION_OPTIONS[key]} '{value}';\n"
)
def _append_solve_string(self) -> None:
solve_string = self._generate_solve_string()
self.container._add_statement(solve_string + "\n")
def _create_model_attributes(self) -> None:
self.container._add_statement("$offListing")
for attr_name in ATTRIBUTE_MAP:
symbol_name = f"{self._generate_prefix}{attr_name}_{self._auto_id}"
_ = gp.Parameter._constructor_bypass(self.container, symbol_name)
self.container._add_statement(
f"{symbol_name} = {self.name}.{attr_name};"
)
self.container._add_statement("$onListing")
def _update_model_attributes(self) -> None:
container = self.container._temp_container
gdx_handle = utils._open_gdx_file(
self.container.system_directory, self.container._gdx_out
)
for gams_attr, python_attr in ATTRIBUTE_MAP.items():
symbol_name = f"{self._generate_prefix}{gams_attr}_{self._auto_id}"
data = utils._get_scalar_data(
container._gams2np, gdx_handle, symbol_name
)
if python_attr == "_status":
setattr(self, python_attr, ModelStatus(data))
elif python_attr == "_solve_status":
status = SolveStatus(data)
setattr(self, python_attr, status)
if status in INTERRUPT_STATUS:
logger.warning(
f"The solve was interrupted! Solve status: {status.name}. "
"For further information, see https://gamspy.readthedocs.io/en/latest/reference/gamspy._model.html#gamspy.SolveStatus."
)
elif status in ERROR_STATUS:
raise GamspyException(
f"Solve status: {status.name}. {ERROR_STATUS[status]}",
status.value,
)
else:
setattr(self, python_attr, data)
utils._close_gdx_handle(gdx_handle)
self.container._temp_container.data = {}
[docs]
def computeInfeasibilities(self) -> dict[str, pd.DataFrame]:
"""
Computes infeasabilities for all equations of the model
Returns
-------
dict[str, pd.DataFrame]
Dictionary of infeasibilities where equation names are keys and
infeasibilities are values
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> i = gp.Set(m, name="i", records=["i1", "i2"])
>>> j = gp.Set(m, name="j", records=["j1", "j2", "j3"])
>>> a = gp.Parameter(m, name="a", domain=i, records=[("i1", 350), ("i2", 600)])
>>> b = gp.Parameter(m, name="b", domain=j, records=[("j1", 400), ("j2", 450), ("j3", 420)])
>>> x = gp.Variable(m, name="x", domain=[i,j], type="Positive")
>>> s = gp.Equation(m, name="s", domain=i)
>>> d = gp.Equation(m, name="d", domain=j)
>>> s[i] = gp.Sum(j, x[i, j]) <= a[i]
>>> d[j] = gp.Sum(i, x[i, j]) >= b[j]
>>> my_model = gp.Model(m, name="my_model", equations=m.getEquations(), problem="LP", sense="min", objective=gp.Sum((i, j), x[i, j]))
>>> summary = my_model.solve()
>>> infeasibilities = my_model.computeInfeasibilities()
>>> infeasibilities["s"].infeasibility.item()
320.0
"""
infeas_dict = {}
for equation in self.equations:
if equation.records is None:
continue
infeas_rows = utils._calculate_infeasibilities(equation)
infeas_dict[equation.name] = infeas_rows
if equation._definition is not None:
names = equation._definition._find_all_symbols()
names = [
name
for name in names
if name in self.container.data
and isinstance(self.container[name], gp.Variable)
]
for name in names:
variable = self.container[name]
infeas_rows = utils._calculate_infeasibilities(variable)
infeas_dict[variable.name] = infeas_rows
return infeas_dict
[docs]
def getEquationListing(
self,
infeasibility_threshold: float | None = None,
) -> str:
"""
Returns the generated equations.
Parameters
----------
infeasibility_threshold: float, optional
Filters out equations with infeasibilities that are above this value.
Returns
-------
str
"""
listings = []
for equation in self.equations:
listing = equation.getEquationListing(
infeasibility_threshold=infeasibility_threshold
)
if listing:
listings.append(listing)
return "\n".join(listings)
[docs]
def getVariableListing(self) -> str:
"""
Returns the variable listing.
Returns
-------
str
"""
if not hasattr(self, "_variables"):
raise ValidationError(
"The model must be solved with `variable_listing_limit` option for this functionality to work."
)
listings = []
for variable in self._variables:
listings.append(variable.getVariableListing())
return "\n".join(listings)
[docs]
def interrupt(self) -> None:
"""Sends interrupt signal to the running job."""
self.container._interrupt()
[docs]
def freeze(
self,
modifiables: list[Parameter | ImplicitParameter],
options: Options | None = None,
) -> None:
self._is_frozen = True
if options is None:
options = Options()
self.instance = ModelInstance(
self.container, self, modifiables, options
)
[docs]
def unfreeze(self) -> None:
"""Unfreezes the model"""
self._is_frozen = False
self.instance.close_license_session()
[docs]
def solve(
self,
solver: str | None = None,
options: Options | None = None,
solver_options: dict | None = None,
model_instance_options: ModelInstanceOptions | None = None,
output: io.TextIOWrapper | None = None,
backend: Literal["local", "engine", "neos"] = "local",
client: EngineClient | NeosClient | None = None,
load_symbols: list[Symbol] | None = None,
) -> pd.DataFrame | None:
"""
Solves the model with given options.
Parameters
----------
solver : str, optional
Solver name
options : Options, optional
GAMS options
solver_options : dict, optional
Solver options
model_instance_options : ModelInstanceOptions, optional
Model instance options
output : TextIOWrapper, optional
Output redirection target
backend : str, optional
Backend to run on
client : EngineClient, NeosClient, optional
EngineClient to communicate with GAMS Engine or NEOS Client to communicate with NEOS Server
load_symbols : list[Symbol], optional
Specifies the symbols that need to be loaded. If not given, all symbols are loaded after solve.
Returns
-------
DataFrame, optional
Summary of the solve
Raises
------
ValidationError
In case engine_config is not provided for `engine` backend or
neos_client is not provided for `neos` backend.
ValueError
In case problem is not in possible problem types
ValueError
In case sense is different than "MIN" or "MAX"
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> v = gp.Variable(m, "v")
>>> e = gp.Equation(m, "e", definition= v == 5)
>>> my_model = gp.Model(m, "my_model", "LP", [e], "max", v)
>>> solved = my_model.solve()
"""
if solver is None:
solver = utils.getDefaultSolvers()[str(self.problem).upper()]
validation.validate_solver_args(
self.container.system_directory,
backend,
solver,
self.problem,
options,
output,
load_symbols,
)
validation.validate_equations(self)
if options is None:
options = self.container._options
# Only for local until GAMS Engine and NEOS Server backends adopt the new GP_SolveLine option.
if solver == "local":
frame = inspect.currentframe().f_back
options._frame = frame
if self._is_frozen:
if model_instance_options is None:
model_instance_options = ModelInstanceOptions()
summary = self.instance.solve(
solver, model_instance_options, solver_options, output
)
return summary
self.container._add_statement(self.getDeclaration())
self._add_runtime_options(options, backend)
self._append_solve_string()
self._create_model_attributes()
options._set_solver_options(
working_directory=self.container.working_directory,
solver=solver,
problem=self.problem,
solver_options=solver_options,
)
runner = backend_factory(
self.container,
options,
solver,
solver_options,
output,
backend,
client,
self,
load_symbols,
)
summary = runner.run(relaxed_domain_mapping=False, gams_to_gamspy=True)
if IS_MIRO_INIT:
miro._write_default_gdx_miro(self.container)
return summary
[docs]
def getDeclaration(self) -> str:
"""
Declaration of the Model in GAMS
Returns
-------
str
Examples
--------
>>> import gamspy as gp
>>> m = gp.Container()
>>> v = gp.Variable(m, "v")
>>> e = gp.Equation(m, "e", definition= v == 5)
>>> my_model = gp.Model(m, "my_model", "LP", [e])
>>> my_model.getDeclaration()
'Model my_model / e,my_model_objective /;'
"""
equations = []
for equation in self.equations:
if self._matches:
if equation not in self._matches:
equations.append(equation.name)
else:
equations.append(equation.name)
equations_str = ",".join(equations)
if self._matches:
matches_str = ",".join(
[
f"{equation.name}.{variable.name}"
for equation, variable in self._matches.items()
]
)
equations_str = (
",".join([equations_str, matches_str])
if equations
else matches_str
)
if self._external_module_file:
equations_str = ",".join(
[equations_str, self._external_module_file]
)
if self._limited_variables:
limited_variables_str = ",".join(
[variable.gamsRepr() for variable in self._limited_variables]
)
equations_str += "," + limited_variables_str
model_str = f"Model {self.name}"
if equations_str != "":
model_str += f" / {equations_str} /"
model_str += ";"
return model_str
[docs]
def toGams(
self,
path: str,
options: Options | None = None,
dump_gams_state: bool = False,
) -> None:
"""
Generates GAMS model under path/<model_name>.gms
Parameters
----------
path : str
Path to the directory which will contain the GAMS model.
"""
if options is not None and not isinstance(options, gp.Options):
raise ValidationError(
f"`options` must be of type gp.Options of found {type(options)}"
)
converter = GamsConverter(self, path, options, dump_gams_state)
converter.convert()
[docs]
def toLatex(self, path: str, generate_pdf: bool = False) -> None:
"""
Generates a latex file that contains the model definition under path/<model_name>.tex
Parameters
----------
path : str
Path to the directory which will contain the .tex file.
"""
converter = LatexConverter(self, path)
converter.convert()
if generate_pdf:
converter.to_pdf()
