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238 lines
8.6 KiB
238 lines
8.6 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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from copy import deepcopy
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from typing import Optional, List
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from p_tqdm import p_map
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from .. import (ObjectiveValueComponent,
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PrimalSolutionComponent,
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DynamicLazyConstraintsComponent,
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UserCutsComponent)
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from .pyomo.cplex import CplexPyomoSolver
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from .pyomo.gurobi import GurobiPyomoSolver
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logger = logging.getLogger(__name__)
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# Global memory for multiprocessing
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SOLVER = [None] # type: List[Optional[LearningSolver]]
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INSTANCES = [None] # type: List[Optional[dict]]
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def _parallel_solve(instance_idx):
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solver = deepcopy(SOLVER[0])
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instance = INSTANCES[0][instance_idx]
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results = solver.solve(instance)
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return {
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"Results": results,
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"Solution": instance.solution,
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"LP solution": instance.lp_solution,
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"Violated lazy constraints": instance.found_violated_lazy_constraints,
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"Violated user cuts": instance.found_violated_user_cuts,
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}
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class LearningSolver:
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"""
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Mixed-Integer Linear Programming (MIP) solver that extracts information
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from previous runs, using Machine Learning methods, to accelerate the
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solution of new (yet unseen) instances.
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Parameters
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----------
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solve_lp_first: bool
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If true, solve LP relaxation first, then solve original MILP. This
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option should be activated if the LP relaxation is not very
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expensive to solve and if it provides good hints for the integer
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solution.
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"""
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def __init__(self,
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components=None,
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gap_tolerance=None,
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mode="exact",
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solver="gurobi",
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threads=None,
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time_limit=None,
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node_limit=None,
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solve_lp_first=True):
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self.components = {}
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self.mode = mode
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self.internal_solver = None
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self.internal_solver_factory = solver
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self.threads = threads
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self.time_limit = time_limit
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self.gap_tolerance = gap_tolerance
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self.tee = False
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self.node_limit = node_limit
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self.solve_lp_first = solve_lp_first
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if components is not None:
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for comp in components:
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self.add(comp)
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else:
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self.add(ObjectiveValueComponent())
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self.add(PrimalSolutionComponent())
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self.add(DynamicLazyConstraintsComponent())
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self.add(UserCutsComponent())
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assert self.mode in ["exact", "heuristic"]
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for component in self.components.values():
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component.mode = self.mode
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def _create_internal_solver(self):
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logger.debug("Initializing %s" % self.internal_solver_factory)
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if self.internal_solver_factory == "cplex":
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solver = CplexPyomoSolver()
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elif self.internal_solver_factory == "gurobi":
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solver = GurobiPyomoSolver()
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elif callable(self.internal_solver_factory):
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solver = self.internal_solver_factory()
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else:
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solver = self.internal_solver_factory
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if self.threads is not None:
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logger.info("Setting threads to %d" % self.threads)
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solver.set_threads(self.threads)
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if self.time_limit is not None:
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logger.info("Setting time limit to %f" % self.time_limit)
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solver.set_time_limit(self.time_limit)
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if self.gap_tolerance is not None:
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logger.info("Setting gap tolerance to %f" % self.gap_tolerance)
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solver.set_gap_tolerance(self.gap_tolerance)
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if self.node_limit is not None:
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logger.info("Setting node limit to %d" % self.node_limit)
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solver.set_node_limit(self.node_limit)
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return solver
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def solve(self,
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instance,
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model=None,
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tee=False):
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"""
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Solves the given instance. If trained machine-learning models are
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available, they will be used to accelerate the solution process.
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This method modifies the instance object. Specifically, the following
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properties are set:
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- instance.lp_solution
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- instance.lp_value
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- instance.lower_bound
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- instance.upper_bound
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- instance.solution
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- instance.found_violated_lazy_constraints
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- instance.solver_log
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Additional solver components may set additional properties. Please
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see their documentation for more details.
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If `solve_lp_first` is False, the properties lp_solution and lp_value
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will be set to dummy values.
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Parameters
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----------
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instance: miplearn.Instance
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The instance to be solved
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model: pyomo.core.ConcreteModel
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The corresponding Pyomo model. If not provided, it will be created.
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tee: bool
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If true, prints solver log to screen.
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Returns
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-------
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dict
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A dictionary of solver statistics containing at least the following
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keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes",
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"Sense", "Log", "Warm start value" and "LP value".
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Additional components may generate additional keys. For example,
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ObjectiveValueComponent adds the keys "Predicted LB" and
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"Predicted UB". See the documentation of each component for more
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details.
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"""
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if model is None:
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model = instance.to_model()
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self.tee = tee
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self.internal_solver = self._create_internal_solver()
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self.internal_solver.set_instance(instance, model)
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if self.solve_lp_first:
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logger.info("Solving LP relaxation...")
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results = self.internal_solver.solve_lp(tee=tee)
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instance.lp_solution = self.internal_solver.get_solution()
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instance.lp_value = results["Optimal value"]
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else:
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instance.lp_solution = self.internal_solver.get_empty_solution()
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instance.lp_value = 0.0
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logger.debug("Running before_solve callbacks...")
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for component in self.components.values():
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component.before_solve(self, instance, model)
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def iteration_cb():
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should_repeat = False
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for component in self.components.values():
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if component.after_iteration(self, instance, model):
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should_repeat = True
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return should_repeat
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logger.info("Solving MILP...")
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results = self.internal_solver.solve(tee=tee, iteration_cb=iteration_cb)
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results["LP value"] = instance.lp_value
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# Read MIP solution and bounds
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instance.lower_bound = results["Lower bound"]
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instance.upper_bound = results["Upper bound"]
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instance.solver_log = results["Log"]
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instance.solution = self.internal_solver.get_solution()
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logger.debug("Calling after_solve callbacks...")
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for component in self.components.values():
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component.after_solve(self, instance, model, results)
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return results
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def parallel_solve(self,
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instances,
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n_jobs=4,
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label="Solve"):
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self.internal_solver = None
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SOLVER[0] = self
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INSTANCES[0] = instances
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p_map_results = p_map(_parallel_solve,
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list(range(len(instances))),
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num_cpus=n_jobs,
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desc=label)
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results = [p["Results"] for p in p_map_results]
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for (idx, r) in enumerate(p_map_results):
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instances[idx].solution = r["Solution"]
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instances[idx].lp_solution = r["LP solution"]
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instances[idx].lp_value = r["Results"]["LP value"]
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instances[idx].lower_bound = r["Results"]["Lower bound"]
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instances[idx].upper_bound = r["Results"]["Upper bound"]
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instances[idx].found_violated_lazy_constraints = r["Violated lazy constraints"]
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instances[idx].found_violated_user_cuts = r["Violated user cuts"]
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instances[idx].solver_log = r["Results"]["Log"]
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return results
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def fit(self, training_instances):
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if len(training_instances) == 0:
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return
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for component in self.components.values():
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component.fit(training_instances)
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def add(self, component):
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name = component.__class__.__name__
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self.components[name] = component
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def __getstate__(self):
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self.internal_solver = None
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return self.__dict__
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