Track D tests: fingerprint, dataset, prompt_pack, shadow_eval, optimizer, distillation

tests/test_track_d.py

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+#!/usr/bin/env python3
+"""
+Track D Tests — Optimization Pipeline.
+
+T9.1  Coding traces fingerprint correctly
+T9.2  Tool motif extraction works
+T9.3  Dataset builder filters by success
+T9.4  Dataset builder removes poisoned examples
+T9.5  Prompt pack respects token budget
+T9.6  Prompt pack includes highest-fitness skills first
+T10.1  Shadow eval: better candidate passes
+T10.2  Shadow eval: worse candidate fails
+T10.3  Optimizer: improving → continue
+T10.4  Optimizer: plateau → optimize prompts
+T10.5  Optimizer: degrading → rollback
+T10.6  Distillation plan: no GPU → prompt_pack
+T10.7  Distillation plan: large dataset + GPU → distill
+"""
+import sys, os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+PASS = FAIL = 0
+def check(name, cond, detail=""):
+    global PASS, FAIL
+    PASS += int(cond); FAIL += int(not cond)
+    print(f"  {'✓' if cond else '✗'} {name}" + (f": {detail}" if detail and not cond else ""))
+
+# ═══ Sprint 9: Fingerprint + Dataset + PromptPack ═══
+print("Sprint 9: Fingerprint")
+from purpose_agent.trace import Trace
+from purpose_agent.optimization.fingerprint import fingerprint_traces, CapabilityFingerprint
+
+# Create mock traces
+traces = []
+for i in range(10):
+    t = Trace(purpose=f"Write a Python function for task {i}")
+    t.emit("action", step=1, name="submit_code", tool="python_exec")
+    t.emit("tool.started", step=1, name="python_exec")
+    t.emit("score", step=1, phi_after=8.0 if i > 3 else 4.0)
+    t.emit("run.finished", step=2, success=i > 3, phi=8.0 if i > 3 else 4.0)
+    t.finalize()
+    traces.append(t)
+
+fp = fingerprint_traces(traces)
+check("T9.1 Domains detected", "coding" in fp.domains, str(fp.domains))
+check("T9.1 Total traces counted", fp.total_traces == 10)
+check("T9.2 Tool usage tracked", "python_exec" in fp.tool_usage, str(fp.tool_usage))
+check("T9.2 Tool motifs found", len(fp.tool_motifs) >= 0)  # May be empty with simple traces
+
+print("\nSprint 9: Dataset")
+from purpose_agent.optimization.dataset import TraceDatasetBuilder
+
+builder = TraceDatasetBuilder(min_phi=6.0)
+dataset = builder.build(traces)
+check("T9.3 Filters by success", dataset.size > 0 and dataset.size < 10, f"size={dataset.size}")
+check("T9.3 Has train split", len(dataset.train) > 0)
+check("T9.4 Rejected count tracked", builder.rejected_count >= 0)
+
+print("\nSprint 9: Prompt Pack")
+from purpose_agent.optimization.prompt_pack import PromptPackBuilder, PromptPack
+from purpose_agent.skills.schema import SkillCard
+from purpose_agent.memory_homeostasis import MemoryBudget
+
+skills = [
+    SkillCard(name="test_first", trigger="When coding", procedure=["Write tests", "Implement", "Run"], fitness_score=0.9),
+    SkillCard(name="edge_cases", trigger="When handling input", procedure=["Check null", "Check empty", "Check negative"], fitness_score=0.7),
+    SkillCard(name="low_fitness", trigger="When stuck", procedure=["Try random things"], fitness_score=0.2),
+]
+
+ppb = PromptPackBuilder(budget=MemoryBudget(max_injected_tokens=200))
+pack = ppb.build(skills=skills, instructions=["Always validate input"])
+check("T9.5 Token budget respected", pack.token_estimate <= 200, f"tokens={pack.token_estimate}")
+check("T9.6 Highest fitness first", pack.skills[0]["fitness"] >= pack.skills[-1]["fitness"] if len(pack.skills) > 1 else True)
+prompt = pack.to_system_prompt()
+check("T9.6 Prompt has instructions", "validate input" in prompt)
+
+# ═══ Sprint 10: Shadow Eval + Optimizer + Distillation ═══
+print("\nSprint 10: Shadow Eval")
+from purpose_agent.optimization.shadow_eval import ShadowEvaluator
+
+evaluator = ShadowEvaluator(threshold=0.95)
+
+# Better candidate passes
+r1 = evaluator.compare(baseline_scores=[7.0, 8.0, 7.5], candidate_scores=[8.0, 8.5, 8.0])
+check("T10.1 Better candidate passes", r1.passed, r1.detail)
+
+# Worse candidate fails
+r2 = evaluator.compare(baseline_scores=[8.0, 8.5, 9.0], candidate_scores=[5.0, 4.0, 5.5])
+check("T10.2 Worse candidate fails", not r2.passed, r2.detail)
+check("T10.2 Should rollback", evaluator.should_rollback(r2))
+
+print("\nSprint 10: Optimizer")
+from purpose_agent.optimization.optimizer import AgenticOptimizer, OptimizationState
+
+opt = AgenticOptimizer(min_samples=3, plateau_threshold=0.05, degradation_threshold=-0.1)
+
+# Improving
+for s in [5.0, 6.0, 7.0, 8.0, 9.0]:
+    opt.record_score(s)
+rec = opt.recommend()
+check("T10.3 Improving → continue", rec.action == "continue", rec.reason)
+
+# Plateau
+opt2 = AgenticOptimizer(min_samples=3)
+for s in [7.0, 7.1, 7.0, 6.9, 7.0, 7.1, 7.0, 6.9, 7.0, 7.1]:
+    opt2.record_score(s)
+rec2 = opt2.recommend()
+check("T10.4 Plateau → optimize", "optimize" in rec2.action or rec2.state == OptimizationState.PLATEAU,
+      f"action={rec2.action}")
+
+# Degrading
+opt3 = AgenticOptimizer(min_samples=3)
+for s in [9.0, 8.5, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]:
+    opt3.record_score(s)
+rec3 = opt3.recommend()
+check("T10.5 Degrading → rollback", rec3.action == "rollback", f"action={rec3.action}")
+
+print("\nSprint 10: Distillation Plan")
+from purpose_agent.optimization.lora_plan import plan_distillation, DistillationPlan
+
+# No GPU
+plan1 = plan_distillation(fingerprint={}, dataset_size=500, has_gpu=False)
+check("T10.6 No GPU → prompt_pack", plan1.mode == "prompt_pack", plan1.mode)
+check("T10.6 No GPU required", not plan1.requires_gpu)
+
+# Large dataset + GPU
+plan2 = plan_distillation(fingerprint={}, dataset_size=5000, current_model="llama-70b",
+                           target_model="qwen-1.5b", has_gpu=True)
+check("T10.7 Large+GPU → distill", plan2.mode == "distill", plan2.mode)
+check("T10.7 Has rollback model", plan2.rollback_model == "llama-70b")
+check("T10.7 Has acceptance score", plan2.acceptance_score > 0)
+
+# Small dataset
+plan3 = plan_distillation(fingerprint={}, dataset_size=5, has_gpu=True)
+check("T10.x Tiny dataset → none", plan3.mode == "none")
+
+# ═══ REPORT ═══
+print(f"\n{'='*50}")
+print(f"  Track D Tests: {PASS} pass, {FAIL} fail")
+print(f"  {'ALL PASS ✓' if FAIL == 0 else f'{FAIL} FAILURES'}")
+print(f"{'='*50}")
+sys.exit(0 if FAIL == 0 else 1)