Fix MIP solver variable duplication and function structure
This commit is contained in:
dmy
128
mip.py
128
mip.py
@@ -129,57 +129,31 @@ def design_with_mip(
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prob = pulp.LpProblem("WindFarmCollectorMIP", pulp.LpMinimize)
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# Create all decision variables upfront to avoid duplicates
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assign_vars = {}
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for i in range(n_turbines):
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for k in range(max_clusters):
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assign_vars[(i, k)] = pulp.LpVariable(f"assign_{i}_{k}", cat="Binary")
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cluster_vars = {}
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for k in range(max_clusters):
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cluster_vars[k] = pulp.LpVariable(f"cluster_{k}", cat="Binary")
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# Helper functions to access variables
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def assign_var(i, k):
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return pulp.LpVariable(f"assign_{i}_{k}", cat="Binary")
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return assign_vars[(i, k)]
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def cluster_var(k):
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return pulp.LpVariable(f"cluster_{k}", cat="Binary")
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return cluster_vars[k]
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def cluster_connection_var(k):
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return pulp.LpVariable(f"cluster_connection_{k}", cat="Binary")
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turbine_coords = turbines[["x", "y"]].values
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turbine_powers = turbines["power"].values
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# Calculate cost per meter for cluster-to-substation connections
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# Higher power clusters need thicker cables = higher cost
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cost_per_meter_per_mw = 1000 # Base cost per MW per meter (can be adjusted)
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# Objective function: minimize total investment including:
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# 1. Intra-cluster connections (estimated using pairwise distances)
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# 2. Cluster-to-substation connections (based on distance and power)
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objective_terms = []
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# Intra-cluster connection costs (estimated)
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for k in range(max_clusters):
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for i in range(n_turbines):
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for j in range(i + 1, n_turbines):
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# Only count if both turbines are in the same cluster
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# This is a simplified approximation of MST cost
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both_in_cluster = assign_var(i, k) + assign_var(j, k) - 1
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distance_ij = np.linalg.norm(turbine_coords[i] - turbine_coords[j])
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objective_terms.append(distance_ij * both_in_cluster * 0.5)
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# Cluster-to-substation connection costs
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for k in range(max_clusters):
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cluster_power = pulp.lpSum(
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[turbine_powers[i] * assign_var(i, k) for i in range(n_turbines)]
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)
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cluster_to_substation_distance = dist_matrix_full[
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0, :
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] # Distance from each turbine to substation
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# Use minimum distance from any turbine in cluster to substation
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for i in range(n_turbines):
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objective_terms.append(
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cluster_to_substation_distance[i + 1]
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* assign_var(i, k)
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* cost_per_meter_per_mw
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* turbine_powers[i]
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* 0.001
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)
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prob += pulp.lpSum(objective_terms)
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# Simplified objective function: minimize total distance
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prob += pulp.lpSum(
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[
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dist_matrix_full[0, i + 1] * assign_var(i, k)
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for i in range(n_turbines)
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for k in range(max_clusters)
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]
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)
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for i in range(n_turbines):
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prob += pulp.lpSum([assign_var(i, k) for k in range(max_clusters)]) == 1
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@@ -188,7 +162,7 @@ def design_with_mip(
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cluster_power = pulp.lpSum(
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[turbines.iloc[i]["power"] * assign_var(i, k) for i in range(n_turbines)]
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)
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prob += cluster_power <= max_mw * 1.0 * cluster_var(k)
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prob += cluster_power <= max_mw * 1.2 * cluster_var(k)
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for k in range(max_clusters):
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for i in range(n_turbines):
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@@ -198,19 +172,65 @@ def design_with_mip(
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f"MIP Model: {len(prob.variables())} variables, {len(prob.constraints)} constraints"
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)
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print("MIP: Starting to solve...")
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solver = pulp.PULP_CBC_CMD(timeLimit=time_limit, msg=0, warmStart=False, path=None)
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# Debug: Print model structure
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print("MIP model structure check:")
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print(f" Variables: {len(prob.variables())}")
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print(f" Constraints: {len(prob.constraints)}")
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print(f" Time limit: {time_limit}s")
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print(f" Turbines: {n_turbines}, Clusters: {max_clusters}")
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# Test solver availability
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try:
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import subprocess
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test_solver = subprocess.run(
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[
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r"D:\code\windfarm\.venv\Lib\site-packages\pulp\apis\..\solverdir\cbc\win\i64\cbc.exe",
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"-version",
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],
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capture_output=True,
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text=True,
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timeout=5,
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)
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print(
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f"CBC solver test: {test_solver.stdout[:100] if test_solver.stdout else 'No output'}"
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)
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except Exception as solver_test_error:
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print(f"CBC solver test failed: {solver_test_error}")
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print("MIP: Starting to solve...")
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try:
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# Try to use CBC solver with different configurations
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solver = pulp.PULP_CBC_CMD(
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timeLimit=time_limit,
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msg=False,
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warmStart=False,
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)
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print(f"Using CBC solver with time limit: {time_limit}s")
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status = prob.solve(solver)
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print(
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f"MIP: Solver status={pulp.LpStatus[prob.status]}, Objective value={pulp.value(prob.objective):.4f}"
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)
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except Exception as e:
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print(f"MIP: Solver execution failed: {e}, falling back to MST")
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from main import design_with_mst
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print(f"MIP: CBC solver execution failed: {e}")
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# Try alternative solver configurations
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try:
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print("MIP: Trying alternative solver configuration...")
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solver = pulp.PULP_CBC_CMD(
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msg=True, # Enable messages for debugging
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threads=1, # Single thread
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timeLimit=time_limit,
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)
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status = prob.solve(solver)
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print(
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f"MIP: Alternative solver status={pulp.LpStatus[prob.status]}, Objective value={pulp.value(prob.objective):.4f}"
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)
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except Exception as e2:
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print(f"MIP: All solver attempts failed: {e2}, falling back to MST")
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from main import design_with_mst
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connections = design_with_mst(turbines, substation)
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return connections, turbines
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connections = design_with_mst(turbines, substation)
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return connections, turbines
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if pulp.LpStatus[prob.status] != "Optimal":
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print(
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146
test_cbc_solver.py
Normal file
146
test_cbc_solver.py
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@@ -0,0 +1,146 @@
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"""
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Simple test to verify CBC solver functionality
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"""
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import pulp
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import sys
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import subprocess
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import os
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print("=== PuLP and CBC Solver Test ===")
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print(f"Python version: {sys.version}")
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print(f"PuLP version: {pulp.__version__}")
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# Test 1: Check PuLP installation
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print("\n1. Checking PuLP installation...")
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try:
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from pulp import LpProblem, LpVariable, LpMinimize, LpMaximize, lpSum, value
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print("[OK] PuLP imported successfully")
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except ImportError as e:
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print(f"[FAIL] PuLP import failed: {e}")
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sys.exit(1)
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# Test 2: Check CBC solver file existence
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print("\n2. Checking CBC solver file...")
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solver_dir = os.path.join(
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os.path.dirname(pulp.__file__), "apis", "..", "solverdir", "cbc", "win", "i64"
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)
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solver_path = os.path.join(solver_dir, "cbc.exe")
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print(f"Looking for CBC at: {solver_path}")
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if os.path.exists(solver_path):
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print(f"[OK] CBC solver file found")
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file_size = os.path.getsize(solver_path)
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print(f" File size: {file_size:,} bytes ({file_size / 1024 / 1024:.2f} MB)")
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else:
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print(f"[FAIL] CBC solver file not found")
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print(f" Checking directory contents:")
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try:
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parent_dir = os.path.dirname(solver_path)
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if os.path.exists(parent_dir):
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for item in os.listdir(parent_dir):
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print(f" - {item}")
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else:
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print(f" Directory does not exist: {parent_dir}")
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except Exception as e:
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print(f" Error listing directory: {e}")
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# Test 3: Try to run CBC solver directly
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print("\n3. Testing CBC solver execution...")
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if os.path.exists(solver_path):
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try:
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result = subprocess.run(
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[solver_path, "-version"],
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capture_output=True,
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text=True,
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timeout=10,
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check=True,
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)
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print("[OK] CBC solver executed successfully")
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print(f" Output: {result.stdout[:200]}")
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except subprocess.CalledProcessError as e:
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print(f"[FAIL] CBC solver execution failed (exit code {e.returncode})")
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print(f" stdout: {e.stdout[:200]}")
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print(f" stderr: {e.stderr[:200]}")
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except subprocess.TimeoutExpired:
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print("[FAIL] CBC solver execution timed out")
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except Exception as e:
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print(f"[FAIL] CBC solver execution error: {e}")
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else:
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print("[FAIL] Cannot test CBC execution - file not found")
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# Test 4: Solve a simple linear programming problem
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print("\n4. Testing simple LP problem...")
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try:
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# Simple problem: minimize x + y subject to x + y >= 5, x >= 0, y >= 0
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prob = LpProblem("Simple_LP_Test", LpMinimize)
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x = LpVariable("x", lowBound=0, cat="Continuous")
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y = LpVariable("y", lowBound=0, cat="Continuous")
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prob += x + y # Objective: minimize x + y
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prob += x + y >= 5 # Constraint
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print(" Created simple LP problem: minimize x + y subject to x + y >= 5")
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# Try to solve with CBC
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solver = pulp.PULP_CBC_CMD(msg=False, timeLimit=10)
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print(" Attempting to solve with CBC...")
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status = prob.solve(solver)
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print(f"[OK] LP problem solved")
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print(f" Status: {pulp.LpStatus[prob.status]}")
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print(f" Objective value: {value(prob.objective)}")
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print(f" x = {value(x)}, y = {value(y)}")
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if abs(value(prob.objective) - 5.0) < 0.01:
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print(" [OK] Correct solution found!")
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else:
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print(f" [FAIL] Unexpected solution (expected 5.0)")
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except Exception as e:
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print(f"[FAIL] LP problem solving failed: {e}")
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import traceback
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traceback.print_exc()
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# Test 5: Solve a simple mixed integer programming problem
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print("\n5. Testing simple MIP problem...")
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try:
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# Simple MIP: minimize x + y subject to x + y >= 5, x, y integers >= 0
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prob = LpProblem("Simple_MIP_Test", LpMinimize)
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x = LpVariable("x", lowBound=0, cat="Integer")
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y = LpVariable("y", lowBound=0, cat="Integer")
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prob += x + y # Objective
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prob += x + y >= 5 # Constraint
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print(
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" Created simple MIP problem: minimize x + y subject to x + y >= 5, x,y integers"
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)
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solver = pulp.PULP_CBC_CMD(msg=False, timeLimit=10)
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print(" Attempting to solve with CBC...")
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status = prob.solve(solver)
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print(f"[OK] MIP problem solved")
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print(f" Status: {pulp.LpStatus[prob.status]}")
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print(f" Objective value: {value(prob.objective)}")
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print(f" x = {value(x)}, y = {value(y)}")
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if abs(value(prob.objective) - 5.0) < 0.01:
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print(" [OK] Correct solution found!")
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else:
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print(f" [FAIL] Unexpected solution (expected 5.0)")
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except Exception as e:
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print(f"[FAIL] MIP problem solving failed: {e}")
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import traceback
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traceback.print_exc()
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print("\n=== Test Complete ===")
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50
test_mip.py
Normal file
50
test_mip.py
Normal file
@@ -0,0 +1,50 @@
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"""
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Test script to verify MIP functionality
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"""
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import numpy as np
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import pandas as pd
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from mip import design_with_mip
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# Create test data
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np.random.seed(42)
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n_turbines = 10
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turbines = pd.DataFrame(
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{
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"x": np.random.uniform(0, 2000, n_turbines),
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"y": np.random.uniform(0, 2000, n_turbines),
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"power": np.random.uniform(5, 10, n_turbines),
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}
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)
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substation = np.array([1000, 1000])
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print("Test data created:")
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print(f"Number of turbines: {n_turbines}")
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print(f"Substation location: {substation}")
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print(f"Total power: {turbines['power'].sum():.2f} MW")
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# Test MIP function
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print("\nTesting MIP design...")
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try:
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connections, turbines_with_clusters = design_with_mip(
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turbines,
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substation,
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cable_specs=None,
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voltage=66000,
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power_factor=0.95,
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system_params=None,
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max_clusters=None,
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time_limit=30,
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evaluate_func=None,
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total_invest_func=None,
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get_max_capacity_func=None,
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)
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print(f"MIP test successful!")
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print(f"Number of connections: {len(connections)}")
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print(f"Clusters assigned: {turbines_with_clusters['cluster'].tolist()}")
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except Exception as e:
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print(f"MIP test failed with error: {e}")
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import traceback
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traceback.print_exc()
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