Implement MemorizingLazyConstrComponent

miplearn/collectors/basic.py

@@ -62,9 +62,7 @@ class BasicCollector:
                         and model.fix_violations is not None
                     ):
                         model.fix_violations(model, model.violations_, "aot")
-                        h5.put_scalar(
-                            "mip_constr_violations", json.dumps(model.violations_)
-                        )
+                        h5.put_scalar("mip_constr_violations", repr(model.violations_))
 
                     # Save MPS file
                     model.write(mps_filename)

miplearn/collectors/lazy.py

@@ -1,117 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from io import StringIO
-from typing import Callable
-
-import gurobipy as gp
-import numpy as np
-from gurobipy import GRB, LinExpr
-
-from ..h5 import H5File
-from ..io import _RedirectOutput
-
-
-class LazyCollector:
-    def __init__(
-        self,
-        min_constrs: int = 100_000,
-        time_limit: float = 900,
-    ) -> None:
-        self.min_constrs = min_constrs
-        self.time_limit = time_limit
-
-    def collect(
-        self, data_filename: str, build_model: Callable, tol: float = 1e-6
-    ) -> None:
-        h5_filename = f"{data_filename}.h5"
-        with H5File(h5_filename, "r+") as h5:
-            streams = [StringIO()]
-            lazy = None
-            with _RedirectOutput(streams):
-                slacks = h5.get_array("mip_constr_slacks")
-                assert slacks is not None
-
-                # Check minimum problem size
-                if len(slacks) < self.min_constrs:
-                    print("Problem is too small. Skipping.")
-                    h5.put_array("mip_constr_lazy", np.zeros(len(slacks)))
-                    return
-
-                # Load model
-                print("Loading model...")
-                model = build_model(data_filename)
-                model.params.LazyConstraints = True
-                model.params.timeLimit = self.time_limit
-                gp_constrs = np.array(model.getConstrs())
-                gp_vars = np.array(model.getVars())
-
-                # Load constraints
-                lhs = h5.get_sparse("static_constr_lhs")
-                rhs = h5.get_array("static_constr_rhs")
-                sense = h5.get_array("static_constr_sense")
-                assert lhs is not None
-                assert rhs is not None
-                assert sense is not None
-                lhs_csr = lhs.tocsr()
-                lhs_csc = lhs.tocsc()
-                constr_idx = np.array(range(len(rhs)))
-                lazy = np.zeros(len(rhs))
-
-                # Drop loose constraints
-                selected = (slacks > 0) & ((sense == b"<") | (sense == b">"))
-                loose_constrs = gp_constrs[selected]
-                print(
-                    f"Removing {len(loose_constrs):,d} constraints (out of {len(rhs):,d})..."
-                )
-                model.remove(list(loose_constrs))
-
-                # Filter to constraints that were dropped
-                lhs_csr = lhs_csr[selected, :]
-                lhs_csc = lhs_csc[selected, :]
-                rhs = rhs[selected]
-                sense = sense[selected]
-                constr_idx = constr_idx[selected]
-                lazy[selected] = 1
-
-                # Load warm start
-                var_names = h5.get_array("static_var_names")
-                var_values = h5.get_array("mip_var_values")
-                assert var_values is not None
-                assert var_names is not None
-                for (var_idx, var_name) in enumerate(var_names):
-                    var = model.getVarByName(var_name.decode())
-                    var.start = var_values[var_idx]
-
-                print("Solving MIP with lazy constraints callback...")
-
-                def callback(model: gp.Model, where: int) -> None:
-                    assert rhs is not None
-                    assert lazy is not None
-                    assert sense is not None
-
-                    if where == GRB.Callback.MIPSOL:
-                        x_val = np.array(model.cbGetSolution(model.getVars()))
-                        slack = lhs_csc * x_val - rhs
-                        slack[sense == b">"] *= -1
-                        is_violated = slack > tol
-
-                        for (j, rhs_j) in enumerate(rhs):
-                            if is_violated[j]:
-                                lazy[constr_idx[j]] = 0
-                                expr = LinExpr(
-                                    lhs_csr[j, :].data, gp_vars[lhs_csr[j, :].indices]
-                                )
-                                if sense[j] == b"<":
-                                    model.cbLazy(expr <= rhs_j)
-                                elif sense[j] == b">":
-                                    model.cbLazy(expr >= rhs_j)
-                                else:
-                                    raise RuntimeError(f"Unknown sense: {sense[j]}")
-
-                model.optimize(callback)
-                print(f"Marking {lazy.sum():,.0f} constraints as lazy...")
-
-            h5.put_array("mip_constr_lazy", lazy)
-            h5.put_scalar("mip_constr_lazy_log", streams[0].getvalue())