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ip.py
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ip.py
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import argparse
import time
import numpy as np
import pulp
from src.util import load_knapsack_problem, save_results
def solve_knapsack_with_ip(values, weights, capacities, time_limit=3600):
item_count = len(values)
knapsack_count = len(capacities)
# Create the problem variable:
prob = pulp.LpProblem("MultiKnapsack", pulp.LpMaximize)
# Create decision variables
x = pulp.LpVariable.dicts("item", [(i, k) for i in range(item_count) for k in range(knapsack_count)], cat=pulp.LpBinary)
# Objective function: Maximize the total value of all items in all knapsacks
prob += pulp.lpSum([values[i] * x[(i, k)] for i in range(item_count) for k in range(knapsack_count)])
# Constraint: Do not exceed the capacity of any knapsack
for k in range(knapsack_count):
prob += pulp.lpSum([weights[i] * x[(i, k)] for i in range(item_count)]) <= capacities[k], f"Capacity_{k}"
# Constraint: An item can be in at most one knapsack
for i in range(item_count):
prob += pulp.lpSum([x[(i, k)] for k in range(knapsack_count)]) <= 1, f"OneKnapsack_{i}"
# Solver settings
pulp_solver = pulp.PULP_CBC_CMD(timeLimit=time_limit, msg=True)
# Solve the problem
prob.solve(pulp_solver)
# Check if the solution is optimal
is_optimal = pulp.LpStatus[prob.status] == 'Optimal'
# Extract the results
result = {
"Status": pulp.LpStatus[prob.status],
"Total Value": pulp.value(prob.objective),
"Items in Knapsacks": {(i, k): pulp.value(x[(i, k)]) for i in range(item_count) for k in range(knapsack_count) if pulp.value(x[(i, k)]) == 1},
"Is Optimal": is_optimal
}
return result
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("problem_name", type=str)
parser.add_argument("--timelimit", type=int, default=300, help="Time limit in seconds")
args = parser.parse_args()
problem_name = args.problem_name
time_limit = args.timelimit
knapsack_df, item_df = load_knapsack_problem(problem_name)
capacities = knapsack_df['capacity'].values
values = item_df['value'].values
weights = item_df['weight'].values
print("Knapsack Problem:")
print("Values:", values)
print("Weights:", weights)
print("Capacities:", capacities)
print("\nSolving with Integer Programming...")
start_time = time.time()
ip_result = solve_knapsack_with_ip(values, weights, capacities, time_limit)
end_time = time.time()
train_time = end_time - start_time
total_value = ip_result["Total Value"]
status = ip_result["Status"]
is_optimal = ip_result["Is Optimal"]
policy = [0]*len(values)
for key in ip_result["Items in Knapsacks"]:
policy[key[0]] = key[1] + 1
result_df = save_results(
problem_name=problem_name,
method="Integer Programming",
total_value=total_value,
train_time=train_time,
inference_time=train_time,
optimal=is_optimal
)
print("Inference results (the last one is the current result):")
print(result_df)