Create redundant_facet_removal_backup.py

dingo/backup/redundant_facet_removal_experimental.py

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+# dingo : a python library for metabolic networks sampling and analysis
+# dingo is part of GeomScale project
+
+# Copyright (c) 2021 Apostolos Chalkis
+
+# Licensed under GNU LGPL.3, see LICENCE file
+
+import sys
+import numpy as np
+import scipy.sparse as sp
+import gurobipy as gp
+from gurobipy import GRB
+import math
+
+
+
+
+
+
+
+
+
+
+
+
+def update_model(model, n, Aeq_sparse, beq, lb, ub, A_sparse, b, objective_function):
+    """A function to update a gurobi model that solves a linear program
+    Keyword arguments:
+    model -- gurobi model
+    n -- the dimension
+    Aeq_sparse -- a sparse matrix s.t. Aeq_sparse x = beq
+    beq -- a vector s.t. Aeq_sparse x = beq
+    lb -- lower bounds for the variables, i.e., a n-dimensional vector
+    ub -- upper bounds for the variables, i.e., a n-dimensional vector
+    A_sparse -- a sparse matrix s.t. A_sparse x <= b
+    b -- a vector matrix s.t. A_sparse x <= b
+    objective_function -- the objective function, i.e., a n-dimensional vector
+    """
+    model.remove(model.getVars())
+    model.update()
+    model.remove(model.getConstrs())
+    model.update()
+    x = model.addMVar(
+        shape=n,
+        vtype=GRB.CONTINUOUS,
+        name="x",
+        lb=lb,
+        ub=ub,
+    )
+    model.update()
+    model.addMConstr(Aeq_sparse, x, "=", beq, name="c")
+    model.update()
+    model.addMConstr(A_sparse, x, "<", b, name="d")
+    model.update()
+    model.setMObjective(None, objective_function, 0.0, None, None, x, GRB.MINIMIZE)
+    model.update()
+
+    return model
+
+
+
+def solve_lp_with_different_objectives(model, new_objective_coeffs):
+    """
+    Solve a linear program with a different objective function.
+
+    Parameters:
+        model (gurobipy.Model): The Gurobi model with the original constraints.
+        new_objective_coeffs (list): List of new objective coefficients for the variables.
+
+    Returns:
+        gurobipy.Model: The updated Gurobi model with the new objective function.
+    """
+    # Clear the existing objective function
+    model.setObjective(0, clear=True)
+
+    # Update the objective function with the new coefficients
+    for i, var in enumerate(model.getVars()):
+        var.setAttr(GRB.Attr.Obj, new_objective_coeffs[i])
+
+    # Optimize the updated model
+    model.optimize()
+
+    return model
+
+def fast_remove_redundant_facets(lb, ub, S, c, opt_percentage=100):
+    if lb.size != S.shape[1] or ub.size != S.shape[1]:
+        raise Exception(
+            "The number of reactions must be equal to the number of given flux bounds."
+        )
+
+    redundant_facet_tol = 1e-07
+    tol = 1e-06
+
+    m = S.shape[0]
+    n = S.shape[1]
+    beq = np.zeros(m)
+    Aeq_res = S
+
+    A = np.zeros((2 * n, n), dtype="float")
+    A[0:n] = np.eye(n)
+    A[n:] -= np.eye(n, n, dtype="float")
+
+    b = np.concatenate((ub, -lb), axis=0)
+    b = np.asarray(b, dtype="float")
+    b = np.ascontiguousarray(b, dtype="float")
+
+    max_biomass_flux_vector, max_biomass_objective = fast_fba(lb, ub, S, c)
+    val = -np.floor(max_biomass_objective / tol) * tol * opt_percentage / 100
+
+    b_res = []
+    A_res = np.empty((0, n), float)
+    beq_res = np.array(beq)
+
+    try:
+        with gp.Env(empty=True) as env:
+            env.setParam("OutputFlag", 0)
+            env.start()
+
+            with gp.Model(env=env) as model:
+                x = model.addMVar(
+                    shape=n,
+                    vtype=GRB.CONTINUOUS,
+                    name="x",
+                    lb=lb,
+                    ub=ub,
+                )
+
+                Aeq_sparse = sp.csr_matrix(S)
+                A_sparse = sp.csr_matrix(np.array(-c))
+                b_sparse = np.array(val)
+
+                b = np.array(b)
+                beq = np.array(beq)
+
+                model.addMConstr(Aeq_sparse, x, "=", beq, name="c")
+                model.update()
+                model.addMConstr(A_sparse, x, "<", [val], name="d")
+                model.update()
+
+                model_iter = model.copy()
+
+                indices_iter = range(n)
+                removed = 1
+                offset = 1
+                facet_left_removed = np.zeros((1, n), dtype=bool)
+                facet_right_removed = np.zeros((1, n), dtype=bool)
+
+                while removed > 0 or offset > 0:
+                    removed = 0
+                    offset = 0
+                    indices = indices_iter
+                    indices_iter = []
+
+                    Aeq_sparse = sp.csr_matrix(Aeq_res)
+                    beq = np.array(beq_res)
+
+                    b_res = []
+                    A_res = np.empty((0, n), float)
+                    for i in indices:
+                        objective_function = A[i]
+
+                        redundant_facet_right = True
+                        redundant_facet_left = True
+
+                        objective_function_max = np.asarray(
+                            [-x for x in objective_function]
+                        )
+
+                        model_iter = solve_lp_with_different_objectives(
+                            model_iter.copy(), objective_function_max
+                        )
+
+
+                        status = model_iter.status
+                        if status == GRB.OPTIMAL:
+                            max_objective = -model_iter.objVal
+                        else:
+                            max_objective = ub[i]
+
+                        if not facet_right_removed[0, i]:
+                            ub_iter = ub.copy()
+                            ub_iter[i] = ub_iter[i] + 1
+
+                            # Call solve_lp_with_different_objectives to solve LP
+                            model_iter = solve_lp_with_different_objectives(
+                                model_iter.copy(), objective_function
+                            )
+
+                            status = model_iter.status
+                            if status == GRB.OPTIMAL:
+                                max_objective2 = -model_iter.objVal
+                                if (
+                                    np.abs(max_objective2 - max_objective)
+                                    > redundant_facet_tol
+                                ):
+                                    redundant_facet_right = False
+                                else:
+                                    removed += 1
+                                    facet_right_removed[0, i] = True
+
+                        model_iter.reset()
+                        x = model_iter.getVars()
+                        for j in range(n):
+                            x[j].LB = lb[j]
+                            x[j].UB = ub[j]
+                            x[j].obj = objective_function[j]
+
+                        model_iter.optimize()
+
+                        status = model_iter.status
+                        if status == GRB.OPTIMAL:
+                            min_objective = model_iter.objVal
+                        else:
+                            min_objective = lb[i]
+
+                        if not facet_left_removed[0, i]:
+                            lb_iter = lb.copy()
+                            lb_iter[i] = lb_iter[i] - 1
+
+                            # Call solve_lp_with_different_objectives to solve LP
+                            model_iter = solve_lp_with_different_objectives(
+                                model_iter.copy(), objective_function
+                            )
+
+
+                            status = model_iter.status
+                            if status == GRB.OPTIMAL:
+                                min_objective2 = model_iter.objVal
+                                if (
+                                    np.abs(min_objective2 - min_objective)
+                                    > redundant_facet_tol
+                                ):
+                                    redundant_facet_left = False
+                                else:
+                                    removed += 1
+                                    facet_left_removed[0, i] = True
+
+                        if (not redundant_facet_left) or (not redundant_facet_right):
+                            width = abs(max_objective - min_objective)
+
+                            if width < redundant_facet_tol:
+                                offset += 1
+                                Aeq_res = np.vstack(
+                                    (
+                                        Aeq_res,
+                                        A[
+                                            i,
+                                        ],
+                                    )
+                                )
+                                beq_res = np.append(
+                                    beq_res, min(max_objective, min_objective)
+                                )
+                                ub[i] = sys.float_info.max
+                                lb[i] = -sys.float_info.max
+                            else:
+                                indices_iter.append(i)
+
+                                if not redundant_facet_left:
+                                    A_res = np.append(
+                                        A_res,
+                                        np.array(
+                                            [
+                                                A[
+                                                    n + i,
+                                                ]
+                                            ]
+                                        ),
+                                        axis=0,
+                                    )
+                                    b_res.append(b[n + i])
+                                else:
+                                    lb[i] = -sys.float_info.max
+
+                                if not redundant_facet_right:
+                                    A_res = np.append(
+                                        A_res,
+                                        np.array(
+                                            [
+                                                A[
+                                                    i,
+                                                ]
+                                            ]
+                                        ),
+                                        axis=0,
+                                    )
+                                    b_res.append(b[i])
+                                else:
+                                    ub[i] = sys.float_info.max
+                        else:
+                            ub[i] = sys.float_info.max
+                            lb[i] = -sys.float_info.max
+
+                b_res = np.asarray(b_res)
+                A_res = np.asarray(A_res, dtype="float")
+                A_res = np.ascontiguousarray(A_res, dtype="float")
+
+                return A_res, b_res, Aeq_res, beq_res
+
+    except gp.GurobiError as e:
+        print("Error code " + str(e.errno) + ": " + str(e))
+    except AttributeError:
+        print("Gurobi solver failed.")
+
+
+

dingo/backup/update_gurobi_model_backup.py

@@ -0,0 +1,41 @@
+import gurobipy as gp
+import numpy as np
+import scipy.sparse as sp
+
+def update_model_constraints_and_bounds(model, Aeq_sparse=None, beq=None, A_sparse=None, b=None,
+                                       new_lower_bounds=None, new_upper_bounds=None):
+    if Aeq_sparse is not None and beq is not None:
+        # Remove the old equality constraints
+        model.remove("c")
+
+        # Add new equality constraints
+        model.addMConstr(sp.csr_matrix(Aeq_sparse), model.getVars(), "=", np.array(beq), name="c")
+
+    if A_sparse is not None and b is not None:
+        # Remove the old inequality constraints
+        model.remove("d")
+
+        # Add new inequality constraints
+        model.addMConstr(sp.csr_matrix(A_sparse), model.getVars(), "<", np.array(b), name="d")
+
+    if new_lower_bounds is not None or new_upper_bounds is not None:
+        # Retrieve the variable objects
+        x_vars = model.getVars()
+
+        # Update variable bounds if specified
+        if new_lower_bounds is not None:
+            for i, x_var in enumerate(x_vars):
+                x_var.lb = new_lower_bounds[i]
+
+        if new_upper_bounds is not None:
+            for i, x_var in enumerate(x_vars):
+                x_var.ub = new_upper_bounds[i]
+
+    # Update the model to apply the changes
+    model.update()
+
+# Usage example:
+# Assuming you have a Gurobi model 'model' and new constraint matrices/vectors and bounds
+# Call the function to update the model
+# update_model_constraints_and_bounds(model, Aeq_sparse_new, beq_new, A_sparse_new, b_new, new_lower_bounds, new_upper_bounds)
+