mirror of
https://github.com/deepmodeling/Uni-Lab-OS
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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>
262 lines
10 KiB
Python
262 lines
10 KiB
Python
"""End-to-end pipeline test: intents → interpret → optimize → verify.
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Tests each stage boundary independently so failures are easy to localize.
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Uses real PCR workflow devices with footprints from the catalog.
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"""
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import math
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import pytest
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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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# -- Scene: 5 PCR devices the user has already placed in the scene --
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PCR_DEVICES = [
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{"id": "thermo_orbitor_rs2_hotel", "name": "Plate Hotel", "device_type": "static"},
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{"id": "arm_slider", "name": "Robot Arm", "device_type": "articulation"},
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{"id": "opentrons_liquid_handler", "name": "Liquid Handler", "device_type": "static"},
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{"id": "agilent_plateloc", "name": "Plate Sealer", "device_type": "static"},
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{"id": "inheco_odtc_96xl", "name": "Thermal Cycler", "device_type": "static"},
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]
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PCR_LAB = {"width": 6.0, "depth": 4.0}
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# -- Stage 1: simulated LLM output (what the LLM would produce from NL) --
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# User said: "take plate from hotel, prepare sample in opentrons,
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# seal plate then pcr cycle, arm_slider handles transfers"
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LLM_INTENTS = [
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{
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"intent": "reachable_by",
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"params": {
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"arm": "arm_slider",
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"targets": [
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"thermo_orbitor_rs2_hotel",
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"opentrons_liquid_handler",
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"agilent_plateloc",
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"inheco_odtc_96xl",
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],
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},
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"description": "arm_slider must reach all workflow devices",
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},
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{
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"intent": "workflow_hint",
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"params": {
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"workflow": "pcr",
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"devices": [
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"thermo_orbitor_rs2_hotel",
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"opentrons_liquid_handler",
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"agilent_plateloc",
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"inheco_odtc_96xl",
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],
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},
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"description": "PCR order: hotel → liquid handler → sealer → thermal cycler",
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},
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{
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"intent": "close_together",
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"params": {
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"devices": ["opentrons_liquid_handler", "agilent_plateloc"],
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"priority": "high",
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},
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"description": "Seal immediately after sample prep",
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},
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{
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"intent": "min_spacing",
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"params": {"min_gap": 0.15},
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"description": "Minimum 15cm gap for accessibility",
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},
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]
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class TestStage1Interpret:
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"""Stage 1: /interpret translates intents → constraints."""
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def test_interpret_returns_correct_constraint_count(self):
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resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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assert resp.status_code == 200
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data = resp.json()
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# 4 reachability + 3 workflow minimize + 1 close minimize + 1 min_spacing = 9
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assert len(data["constraints"]) == 9
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assert len(data["errors"]) == 0
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def test_interpret_has_translations_for_each_intent(self):
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resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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data = resp.json()
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assert len(data["translations"]) == len(LLM_INTENTS)
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# 每个 translation 都有 explanation
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for t in data["translations"]:
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assert t["explanation"] != ""
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def test_interpret_extracts_workflow_edges(self):
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resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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data = resp.json()
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assert len(data["workflow_edges"]) == 3
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assert ["thermo_orbitor_rs2_hotel", "opentrons_liquid_handler"] in data["workflow_edges"]
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assert ["opentrons_liquid_handler", "agilent_plateloc"] in data["workflow_edges"]
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assert ["agilent_plateloc", "inheco_odtc_96xl"] in data["workflow_edges"]
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def test_interpret_constraint_types_correct(self):
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resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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data = resp.json()
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constraints = data["constraints"]
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by_rule = {}
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for c in constraints:
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by_rule.setdefault(c["rule_name"], []).append(c)
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assert len(by_rule["reachability"]) == 4
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assert all(c["type"] == "hard" for c in by_rule["reachability"])
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assert len(by_rule["minimize_distance"]) == 4 # 3 workflow + 1 close
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assert all(c["type"] == "soft" for c in by_rule["minimize_distance"])
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assert len(by_rule["min_spacing"]) == 1
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assert by_rule["min_spacing"][0]["type"] == "hard"
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class TestStage2Optimize:
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"""Stage 2: pipe /interpret output into /optimize → placements."""
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@pytest.fixture()
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def interpret_result(self):
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resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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return resp.json()
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def test_optimize_accepts_interpret_output(self, interpret_result):
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"""Constraints + workflow_edges from /interpret are valid /optimize input."""
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resp = client.post("/optimize", json={
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"devices": PCR_DEVICES,
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"lab": PCR_LAB,
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"constraints": interpret_result["constraints"],
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"workflow_edges": interpret_result["workflow_edges"],
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"run_de": False, # seeder only — fast
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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 len(data["placements"]) == 5
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assert data["success"] is True
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def test_optimize_with_de(self, interpret_result):
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"""Full DE optimization completes without error."""
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resp = client.post("/optimize", json={
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"devices": PCR_DEVICES,
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"lab": PCR_LAB,
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"constraints": interpret_result["constraints"],
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"workflow_edges": interpret_result["workflow_edges"],
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"run_de": True,
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"maxiter": 50, # reduced for test speed
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"seed": 42,
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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 len(data["placements"]) == 5
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assert data["de_ran"] is True
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class TestStage3VerifyPlacements:
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"""Stage 3: verify optimized placements satisfy constraint intent."""
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@pytest.fixture()
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def placements(self):
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# Full pipeline: interpret → optimize (with DE), all intents including reachability
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# MockReachabilityChecker uses large fallback reach for unknown arms like arm_slider
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interpret_resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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interpret_data = interpret_resp.json()
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optimize_resp = client.post("/optimize", json={
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"devices": PCR_DEVICES,
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"lab": PCR_LAB,
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"constraints": interpret_data["constraints"],
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"workflow_edges": interpret_data["workflow_edges"],
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"run_de": True,
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"maxiter": 50,
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"seed": 42,
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})
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return {p["device_id"]: p for p in optimize_resp.json()["placements"]}
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def test_all_devices_placed(self, placements):
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expected_ids = {d["id"] for d in PCR_DEVICES}
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assert set(placements.keys()) == expected_ids
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def test_all_within_lab_bounds(self, placements):
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for dev_id, p in placements.items():
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assert 0 <= p["position"]["x"] <= PCR_LAB["width"], f"{dev_id} x out of bounds"
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assert 0 <= p["position"]["y"] <= PCR_LAB["depth"], f"{dev_id} y out of bounds"
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def test_no_hard_constraint_violation(self):
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"""Full pipeline with all intents including reachability converges cleanly.
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MockReachabilityChecker now includes arm_slider in the default reach table
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(1.07m). Binary final evaluation checks all hard constraints including
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user-defined reachability.
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"""
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interpret_data = client.post("/interpret", json={"intents": LLM_INTENTS}).json()
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optimize_resp = client.post("/optimize", json={
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"devices": PCR_DEVICES,
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"lab": PCR_LAB,
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"constraints": interpret_data["constraints"],
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"workflow_edges": interpret_data["workflow_edges"],
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"run_de": True,
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"maxiter": 100,
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"seed": 42,
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})
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data = optimize_resp.json()
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assert data["success"] is True
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assert not math.isinf(data["cost"])
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def test_workflow_neighbors_closer_than_diagonal(self, placements):
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"""Workflow-adjacent devices should be closer than lab diagonal (basic sanity)."""
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max_diagonal = math.sqrt(PCR_LAB["width"] ** 2 + PCR_LAB["depth"] ** 2)
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workflow_pairs = [
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("thermo_orbitor_rs2_hotel", "opentrons_liquid_handler"),
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("opentrons_liquid_handler", "agilent_plateloc"),
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("agilent_plateloc", "inheco_odtc_96xl"),
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]
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for a_id, b_id in workflow_pairs:
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a, b = placements[a_id], placements[b_id]
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dist = math.sqrt(
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(a["position"]["x"] - b["position"]["x"]) ** 2
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+ (a["position"]["y"] - b["position"]["y"]) ** 2
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)
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# 应该远小于对角线（workflow minimize_distance 约束）
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assert dist < max_diagonal * 0.8, (
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f"Workflow pair {a_id}↔{b_id} distance {dist:.2f}m "
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f"exceeds 80% of diagonal {max_diagonal:.2f}m"
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)
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class TestPipelineStageIsolation:
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"""Verify each stage's output format is valid input for the next stage."""
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def test_interpret_output_schema_matches_optimize_input(self):
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"""constraints from /interpret have all fields /optimize expects."""
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resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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data = resp.json()
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for c in data["constraints"]:
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assert "type" in c
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assert "rule_name" in c
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assert "params" in c
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assert "weight" in c
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assert c["type"] in ("hard", "soft")
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for edge in data["workflow_edges"]:
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assert isinstance(edge, list)
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assert len(edge) == 2
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def test_round_trip_no_data_loss(self):
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"""Interpret → optimize → check that all device IDs survive the pipeline."""
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interpret_resp = client.post("/interpret", json={"intents": LLM_INTENTS})
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interpret_data = interpret_resp.json()
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optimize_resp = client.post("/optimize", json={
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"devices": PCR_DEVICES,
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"lab": PCR_LAB,
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"constraints": interpret_data["constraints"],
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"workflow_edges": interpret_data["workflow_edges"],
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"run_de": False,
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})
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result_ids = {p["device_id"] for p in optimize_resp.json()["placements"]}
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input_ids = {d["id"] for d in PCR_DEVICES}
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assert result_ids == input_ids
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