mirror of
https://github.com/deepmodeling/Uni-Lab-OS
synced 2026-05-23 21:09:56 +00:00
9ef24b7768
Replace scipy differential_evolution with custom DE loop for per-device crossover, circular θ wrapping, and configurable mutation strategy (currenttobest1bin default, best1bin as turbo mode). Key improvements: - Graduate ALL hard constraints during DE (proportional penalty instead of flat inf), giving DE smooth gradient for reachability, min_spacing, etc. Binary inf preserved for final pass/fail reporting. - 2-axis sweep-and-prune AABB broad phase for collision pair pruning - Multi-seed injection from multiple seeder presets + Gaussian variants - snap_theta_safe: collision-check after angle snapping, revert on violation - Weight normalization (100 distance / 60 angle / 5× hard multiplier) - Constraint priority field (critical/high/normal/low → weight multiplier) with LLM intent interpreter setting priority per constraint type - Final success field now checks user hard constraints in binary mode - arm_slider added to mock checker reach table (1.07m) Tests: 202 passed, 24 new tests added (optimizer 7, constraints 6, broad_phase 11) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
442 lines
15 KiB
Python
442 lines
15 KiB
Python
"""差分进化优化器端到端测试。"""
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import math
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from ..mock_checkers import MockCollisionChecker
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from ..models import Device, Lab, Placement
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import numpy as np
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import pytest
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from ..optimizer import _run_de, optimize, snap_theta
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def test_optimize_three_devices_no_collision():
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"""3 台设备在 5m×5m 实验室中优化，结果应无碰撞且在边界内。"""
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devices = [
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Device(id="a", name="A", bbox=(0.8, 0.6)),
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Device(id="b", name="B", bbox=(0.6, 0.5)),
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Device(id="c", name="C", bbox=(0.5, 0.5)),
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]
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lab = Lab(width=5.0, depth=5.0)
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placements = optimize(devices, lab, seed=42, maxiter=100, popsize=10)
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assert len(placements) == 3
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# 验证无碰撞
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checker = MockCollisionChecker()
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checker_placements = [
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{"id": p.device_id, "bbox": next(d.bbox for d in devices if d.id == p.device_id),
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"pos": (p.x, p.y, p.theta)}
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for p in placements
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]
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collisions = checker.check(checker_placements)
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assert collisions == [], f"Unexpected collisions: {collisions}"
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# 验证在边界内
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oob = checker.check_bounds(checker_placements, lab.width, lab.depth)
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assert oob == [], f"Devices out of bounds: {oob}"
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def test_optimize_single_device():
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"""单个设备应直接放置成功。"""
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devices = [Device(id="solo", name="Solo", bbox=(0.5, 0.5))]
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lab = Lab(width=3.0, depth=3.0)
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placements = optimize(devices, lab, seed=42, maxiter=50)
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assert len(placements) == 1
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p = placements[0]
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assert 0.25 <= p.x <= 2.75
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assert 0.25 <= p.y <= 2.75
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def test_optimize_tight_space():
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"""紧凑空间：2 台设备在刚好够大的实验室中。"""
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devices = [
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Device(id="x", name="X", bbox=(1.0, 1.0)),
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Device(id="y", name="Y", bbox=(1.0, 1.0)),
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]
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# 2.5m 宽足够放 2 个 1m 宽设备（加间距）
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lab = Lab(width=2.5, depth=2.0)
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placements = optimize(devices, lab, seed=42, maxiter=100)
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assert len(placements) == 2
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checker = MockCollisionChecker()
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checker_placements = [
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{"id": p.device_id, "bbox": next(d.bbox for d in devices if d.id == p.device_id),
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"pos": (p.x, p.y, p.theta)}
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for p in placements
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]
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collisions = checker.check(checker_placements)
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assert collisions == []
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def test_optimize_returns_valid_placement_ids():
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"""验证返回的 placement device_id 与输入设备一致。"""
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devices = [
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Device(id="dev_1", name="D1", bbox=(0.5, 0.5)),
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Device(id="dev_2", name="D2", bbox=(0.5, 0.5)),
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]
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lab = Lab(width=5.0, depth=5.0)
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placements = optimize(devices, lab, seed=42, maxiter=50)
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result_ids = {p.device_id for p in placements}
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expected_ids = {d.id for d in devices}
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assert result_ids == expected_ids
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def test_snap_theta_near_cardinal():
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"""Theta within 15° of 90° snaps to 90°."""
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placements = [Placement(device_id="a", x=1, y=1, theta=math.radians(85))]
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result = snap_theta(placements, threshold_deg=15)
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assert result[0].theta == pytest.approx(math.pi / 2)
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def test_snap_theta_far_from_cardinal():
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"""Theta 30° away from nearest cardinal: no snap."""
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placements = [Placement(device_id="a", x=1, y=1, theta=math.radians(60))]
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result = snap_theta(placements, threshold_deg=15)
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assert result[0].theta == pytest.approx(math.radians(60))
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def test_snap_theta_at_cardinal():
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"""Already at cardinal: unchanged."""
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placements = [Placement(device_id="a", x=1, y=1, theta=math.pi)]
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result = snap_theta(placements, threshold_deg=15)
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assert result[0].theta == pytest.approx(math.pi)
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def test_snap_theta_near_360():
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"""Theta near 360° (=0°) snaps correctly."""
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placements = [Placement(device_id="a", x=1, y=1, theta=math.radians(355))]
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result = snap_theta(placements, threshold_deg=15)
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snapped = result[0].theta % (2 * math.pi)
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assert snapped == pytest.approx(0.0, abs=0.01) or snapped == pytest.approx(2 * math.pi, abs=0.01)
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def test_optimize_endpoint_accepts_seeder_field():
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"""POST /optimize should accept seeder and run_de fields."""
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from fastapi.testclient import TestClient
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from ..server import app
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client = TestClient(app)
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resp = client.post("/optimize", json={
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"devices": [{"id": "test_device", "name": "Test"}],
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"lab": {"width": 5, "depth": 4},
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"seeder": "compact_outward",
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"run_de": False,
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})
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assert resp.status_code == 200
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data = resp.json()
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assert data["seeder_used"] == "compact_outward"
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assert data["de_ran"] is False
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assert len(data["placements"]) == 1
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def test_optimize_endpoint_unknown_seeder_returns_400():
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"""Unknown seeder preset should return 400."""
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from fastapi.testclient import TestClient
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from ..server import app
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client = TestClient(app)
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resp = client.post("/optimize", json={
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"devices": [{"id": "test_device", "name": "Test"}],
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"lab": {"width": 5, "depth": 4},
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"seeder": "nonexistent_preset",
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"run_de": False,
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})
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assert resp.status_code == 400
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def test_optimize_endpoint_backward_compatible():
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"""Existing calls without seeder/run_de fields still work."""
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from fastapi.testclient import TestClient
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from ..server import app
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client = TestClient(app)
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resp = client.post("/optimize", json={
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"devices": [{"id": "test_device", "name": "Test"}],
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"lab": {"width": 5, "depth": 4},
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})
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assert resp.status_code == 200
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data = resp.json()
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assert "seeder_used" in data
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assert "de_ran" in data
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def test_full_pipeline_seed_only():
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"""Full pipeline: seeder → snap_theta → correct count and bounds.
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compact_outward is tested for collision-free (devices clustered, not at walls).
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spread_inward pushes to walls where rotated AABB bounds may flag — tested separately.
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"""
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from ..seeders import seed_layout, PRESETS
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from ..constraints import evaluate_default_hard_constraints
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devices = [
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Device(id=f"dev{i}", name=f"Device {i}", bbox=(0.6, 0.4))
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for i in range(6)
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]
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lab = Lab(width=6.0, depth=5.0)
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# compact_outward: devices cluster toward center, should be collision-free
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placements = seed_layout(devices, lab, PRESETS["compact_outward"])
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placements = snap_theta(placements)
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assert len(placements) == len(devices)
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(devices, placements, lab, checker)
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assert cost < 1e17, f"compact_outward: hard constraint violation (cost={cost})"
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# spread_inward: verify correct count + all positions within lab canvas
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placements = seed_layout(devices, lab, PRESETS["spread_inward"])
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placements = snap_theta(placements)
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assert len(placements) == len(devices)
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for p in placements:
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assert 0 <= p.x <= lab.width, f"spread_inward: x={p.x} out of bounds"
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assert 0 <= p.y <= lab.depth, f"spread_inward: y={p.y} out of bounds"
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def test_full_pipeline_with_de():
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"""Full pipeline: seeder → DE → snap_theta."""
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from ..seeders import seed_layout, PRESETS
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devices = [
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Device(id=f"dev{i}", name=f"Device {i}", bbox=(0.6, 0.4))
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for i in range(4)
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]
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lab = Lab(width=5.0, depth=4.0)
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checker = MockCollisionChecker()
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seed = seed_layout(devices, lab, PRESETS["compact_outward"])
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result = optimize(devices, lab, seed_placements=seed, collision_checker=checker,
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maxiter=50, seed=42)
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result = snap_theta(result)
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assert len(result) == len(devices)
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for p in result:
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assert 0 <= p.x <= lab.width
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assert 0 <= p.y <= lab.depth
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# ──────────────────────────────────────────────────────────────
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# DE V2 新增测试
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# ──────────────────────────────────────────────────────────────
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def test_theta_wrapping_convergence():
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"""θ 在 0/2π 边界附近应正确收敛，不发散。
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构造一个简单的 cost function，最优解 θ≈0（即 2π 附近也可以）。
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验证自定义 DE 在 θ wrapping 下能收敛。
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"""
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n_devices = 2
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def cost_fn(x: np.ndarray) -> float:
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# 最优：两设备分开放置，θ 接近 0
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cost = 0.0
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for d in range(n_devices):
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theta = x[3 * d + 2]
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# θ penalty: 偏离 0 的圆周距离
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cost += (1 - math.cos(theta)) * 10.0
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# 两设备距离 penalty
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dx = x[0] - x[3]
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dy = x[1] - x[4]
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dist = math.sqrt(dx * dx + dy * dy)
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if dist < 1.0:
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cost += (1.0 - dist) * 100.0
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return cost
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bounds = np.array([
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[0.5, 4.5], [0.5, 4.5], [0.0, 2 * math.pi], # 设备 0
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[0.5, 4.5], [0.5, 4.5], [0.0, 2 * math.pi], # 设备 1
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])
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rng = np.random.default_rng(42)
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pop_size = 30
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init_pop = rng.uniform(bounds[:, 0], bounds[:, 1], size=(pop_size, 6))
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# 故意注入 θ 接近 2π 的个体，测试 wrapping
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init_pop[0, 2] = 2 * math.pi - 0.01
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init_pop[0, 5] = 2 * math.pi - 0.05
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best_vec, best_cost, n_gen = _run_de(
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cost_fn=cost_fn,
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bounds=bounds,
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init_pop=init_pop,
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maxiter=100,
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tol=1e-6,
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atol=1e-3,
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mutation=(0.5, 1.0),
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recombination=0.7,
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seed=42,
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n_devices=n_devices,
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)
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# θ 应在 [0, 2π) 范围内
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for d in range(n_devices):
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theta = best_vec[3 * d + 2]
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assert 0 <= theta < 2 * math.pi, f"θ 超出 [0, 2π): {theta}"
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# cost 应显著下降（不发散）
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assert best_cost < 50.0, f"θ wrapping 未正确收敛，cost={best_cost}"
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def test_per_device_crossover_atomicity():
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"""验证 per-device crossover 的原子性：同一设备的 (x, y, θ) 来自同一来源。
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构造 2 设备场景，跟踪 crossover 后每个设备三元组是否完整来自 mutant 或 parent。
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"""
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rng = np.random.default_rng(123)
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n_devices = 3
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ndim = 3 * n_devices
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# 构造明显不同的 parent 和 mutant
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parent = np.zeros(ndim)
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mutant = np.ones(ndim) * 10.0
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# 模拟多次 per-device crossover，检查原子性
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violations = 0
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n_trials = 200
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for _ in range(n_trials):
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trial = parent.copy()
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j_rand = rng.integers(0, n_devices)
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for d in range(n_devices):
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if rng.random() < 0.7 or d == j_rand:
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trial[3 * d: 3 * d + 3] = mutant[3 * d: 3 * d + 3]
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# 检查每个设备的三元组要么全是 0（parent），要么全是 10（mutant）
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for d in range(n_devices):
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triple = trial[3 * d: 3 * d + 3]
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all_parent = np.allclose(triple, 0.0)
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all_mutant = np.allclose(triple, 10.0)
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if not (all_parent or all_mutant):
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violations += 1
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assert violations == 0, f"Per-device crossover 原子性违反 {violations}/{n_trials * n_devices} 次"
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def test_strategy_currenttobest1bin_converges():
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"""currenttobest1bin 策略在简单 2 设备问题上应能收敛。"""
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devices = [
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Device(id="a", name="A", bbox=(0.6, 0.4)),
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Device(id="b", name="B", bbox=(0.6, 0.4)),
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]
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lab = Lab(width=5.0, depth=5.0)
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placements = optimize(
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devices, lab, seed=42, maxiter=80, popsize=10,
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strategy="currenttobest1bin",
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)
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assert len(placements) == 2
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checker = MockCollisionChecker()
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checker_placements = [
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{"id": p.device_id, "bbox": next(d.bbox for d in devices if d.id == p.device_id),
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"pos": (p.x, p.y, p.theta)}
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for p in placements
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]
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collisions = checker.check(checker_placements)
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assert collisions == [], f"currenttobest1bin 策略产生碰撞: {collisions}"
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def test_strategy_best1bin_converges():
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"""best1bin 策略在简单 2 设备问题上应能收敛。"""
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devices = [
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Device(id="a", name="A", bbox=(0.6, 0.4)),
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Device(id="b", name="B", bbox=(0.6, 0.4)),
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]
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lab = Lab(width=5.0, depth=5.0)
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placements = optimize(
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devices, lab, seed=42, maxiter=80, popsize=10,
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strategy="best1bin",
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)
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assert len(placements) == 2
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checker = MockCollisionChecker()
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checker_placements = [
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{"id": p.device_id, "bbox": next(d.bbox for d in devices if d.id == p.device_id),
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"pos": (p.x, p.y, p.theta)}
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for p in placements
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]
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collisions = checker.check(checker_placements)
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assert collisions == [], f"best1bin 策略产生碰撞: {collisions}"
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def test_convergence_quality_two_devices():
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"""2 设备无碰撞放置，cost 应低于阈值，验证优化质量。"""
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devices = [
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Device(id="d1", name="D1", bbox=(0.8, 0.6)),
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Device(id="d2", name="D2", bbox=(0.6, 0.5)),
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]
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lab = Lab(width=5.0, depth=5.0)
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placements = optimize(devices, lab, seed=42, maxiter=100, popsize=10)
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# 验证结果质量：cost 应很低（无碰撞、在边界内）
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from ..constraints import evaluate_default_hard_constraints
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checker = MockCollisionChecker()
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cost = evaluate_default_hard_constraints(devices, placements, lab, checker)
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assert cost < 1.0, f"优化质量不佳，cost={cost}"
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def test_run_de_early_stopping():
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"""验证 _run_de 的 early stopping 机制：简单 cost function 应提前终止。"""
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def trivial_cost(x: np.ndarray) -> float:
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# 简单二次函数，最优解在原点附近
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return float(np.sum(x ** 2))
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ndim = 6
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n_devices = 2
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bounds = np.array([[-5.0, 5.0]] * ndim)
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rng = np.random.default_rng(42)
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pop_size = 20
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init_pop = rng.uniform(-5, 5, size=(pop_size, ndim))
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_, _, n_gen = _run_de(
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cost_fn=trivial_cost,
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bounds=bounds,
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init_pop=init_pop,
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maxiter=500,
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tol=1e-6,
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atol=1e-3,
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mutation=(0.5, 1.0),
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recombination=0.7,
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seed=42,
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n_devices=n_devices,
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)
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# 简单问题应在远少于 maxiter=500 代内收敛
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assert n_gen < 500, f"Early stopping 未生效，运行了 {n_gen} 代"
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def test_run_de_returns_correct_tuple():
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"""验证 _run_de 返回值格式正确。"""
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def const_cost(x: np.ndarray) -> float:
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return 42.0
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bounds = np.array([[0.0, 1.0]] * 3)
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init_pop = np.random.default_rng(0).uniform(0, 1, size=(5, 3))
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result = _run_de(
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cost_fn=const_cost,
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bounds=bounds,
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init_pop=init_pop,
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maxiter=10,
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tol=1e-6,
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atol=1e-3,
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mutation=(0.5, 1.0),
|
||
recombination=0.7,
|
||
seed=0,
|
||
n_devices=1,
|
||
)
|
||
|
||
assert isinstance(result, tuple) and len(result) == 3
|
||
best_vec, best_cost, n_gen = result
|
||
assert isinstance(best_vec, np.ndarray)
|
||
assert best_vec.shape == (3,)
|
||
assert best_cost == pytest.approx(42.0)
|
||
assert isinstance(n_gen, int) and n_gen >= 1
|