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ad474c9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 | """Tests for the idle / learning / readahead mode.
No kernel, no encoder, no network: a fake assistant supplies exactly the surface
``AutonomousMode`` touches (``d``, ``threshold``, ``intent_vectors``,
``classify``, ``chat``, ``kernel``), so what is under test is the autonomy logic
and not the machine underneath it.
"""
import math
import unittest
from hdc.autonomy import ACT_FLOOR, SAFE_COMMANDS, AutonomousMode
from hdc.hypervec import hvc
#: One lap of the behaviour the mode has to learn: a four-command cycle.
CYCLE = ("status", "registers", "disassemble", "help")
#: Utterance -> intent, deterministic on purpose.
UTTERANCES = {
"show the statistics": "status",
"show the registers": "registers",
"show the instruction set": "disassemble",
"what can you do": "help",
"run the program": "run",
"boot the machine": "boot",
}
class FakeKernel:
"""Records what was executed, so "did it act?" is a list, not a flag."""
def __init__(self):
self.calls = []
def execute(self, command):
self.calls.append(command)
return "output of " + command
class FakeAssistant:
"""The slice of ``Assistant`` that ``AutonomousMode`` depends on."""
def __init__(self, d=2000):
self.d = d
self.threshold = 0.42
self.kernel = FakeKernel()
# every intent the mode may predict, including the unsafe ones it must
# refuse to execute
self.intent_vectors = {
name: hvc("intent/" + name, d)
for name in ("status", "registers", "disassemble", "help", "run", "boot")
}
self.history = []
def vector(self, intent):
return self.intent_vectors[intent]
def classify(self, text):
intent = UTTERANCES.get(text, "")
return intent, (0.9 if intent else 0.0)
def chat(self, text, execute=None):
intent, confidence = self.classify(text)
command = {"registers": "regs", "disassemble": "disasm", "status": "stat",
"help": "help", "run": "run", "boot": "boot"}.get(intent, "")
output = ""
if command:
output = (execute or self.kernel.execute)(command)
record = {"input": text, "intent": intent, "confidence": confidence,
"executed": command, "output": output}
self.history.append(record)
return record
def mode_with(**kwargs):
return AutonomousMode(FakeAssistant(d=kwargs.pop("d", 2000)), **kwargs)
def lap(mode, learn=True, cycles=1):
"""Observe whole laps of CYCLE, closing the loop back to 'status'."""
for _ in range(cycles):
for name in CYCLE + ("status",):
mode.observe(name, learn=learn)
return mode
def prediction(intent="registers", command="regs", confidence=0.5, depth=1, cached=False):
return {"intent": intent, "command": command, "confidence": confidence,
"path": confidence, "depth": depth, "cached": cached}
class TestObserveAndLearn(unittest.TestCase):
def test_observe_records_the_intent_and_returns_a_verdict(self):
mode = mode_with()
verdict = mode.observe("status")
self.assertEqual(mode.sequence, ["status"])
self.assertEqual(verdict["actual"], "status")
self.assertIsNone(verdict["predicted"]) # nothing to grade yet
self.assertFalse(verdict["hit"])
self.assertEqual(mode.graded, 0) # an ungraded first turn is not a miss
def test_cycle_is_predicted_after_one_lap(self):
mode = lap(mode_with())
self.assertEqual(mode.readahead[0]["intent"], "registers")
def test_learning_is_what_makes_the_prediction_exist(self):
learned = lap(mode_with())
unlearned = lap(mode_with(), learn=False)
self.assertGreater(learned.model.transitions, 0)
self.assertEqual(unlearned.model.transitions, 0)
self.assertEqual(learned.readahead[0]["intent"], "registers")
self.assertEqual(unlearned.readahead, [])
def test_hits_and_graded_track_the_predictions(self):
mode = lap(mode_with())
# the exact key 'status' now has a learned successor, so this turn is
# predicted before the answer is known
verdict = mode.observe("registers")
self.assertTrue(verdict["hit"])
self.assertEqual(verdict["predicted"], "registers")
self.assertGreaterEqual(mode.graded, 1)
self.assertGreaterEqual(mode.hits, 1)
self.assertLessEqual(mode.hits, mode.graded)
self.assertEqual(mode.stats()["accuracy"], round(mode.hits / mode.graded, 3))
class TestReadahead(unittest.TestCase):
def test_chain_follows_the_learned_cycle(self):
mode = lap(mode_with(), cycles=2)
lookahead = mode.predict(horizon=3)
self.assertEqual([item["intent"] for item in lookahead],
["registers", "disassemble", "help"])
self.assertEqual([item["depth"] for item in lookahead], [1, 2, 3])
self.assertEqual([item["command"] for item in lookahead],
["regs", "disasm", "help"])
for item in lookahead:
for key in ("intent", "command", "confidence", "path", "depth", "cached"):
self.assertIn(key, item)
def test_path_is_the_running_confidence_product(self):
mode = lap(mode_with(), cycles=2)
lookahead = mode.predict(horizon=3)
expected = 1.0
for item in lookahead:
expected *= item["confidence"]
# `path` multiplies the unrounded confidences, so compare at a
# precision that cannot hinge on the last printed digit
self.assertAlmostEqual(item["path"], expected, places=2)
paths = [item["path"] for item in lookahead]
self.assertEqual(paths, sorted(paths, reverse=True))
def test_empty_lookahead_before_anything_is_observed(self):
self.assertEqual(mode_with().predict(), [])
class TestAutonomy(unittest.TestCase):
def test_safe_prediction_is_executed_once_and_cached(self):
mode = mode_with(enabled=True)
mode.readahead = [prediction()]
self.assertEqual(len(mode._act()), 1)
self.assertEqual(mode.assistant.kernel.calls, ["regs"])
self.assertEqual(mode.stats()["cached_commands"], ["regs"])
# a second idle cycle must not run the same command again
mode.readahead = [prediction()]
self.assertEqual(mode._act(), [])
self.assertEqual(mode.assistant.kernel.calls, ["regs"])
self.assertEqual(mode.stats()["autonomous_actions"], 1)
def test_unsafe_commands_are_never_executed(self):
mode = mode_with(enabled=True)
mode.readahead = [prediction("run", "run", 0.9), prediction("boot", "boot", 0.9)]
self.assertEqual(mode._act(), [])
self.assertEqual(mode.assistant.kernel.calls, [])
for command in ("run", "boot", "asm", "clear", "step"):
self.assertNotIn(command, SAFE_COMMANDS)
def test_budget_caps_the_actions(self):
mode = mode_with(enabled=True, budget=2)
mode.readahead = [
prediction("help", "help", 0.5),
prediction("status", "stat", 0.5),
prediction("registers", "regs", 0.5),
prediction("disassemble", "disasm", 0.5),
]
acted = mode._act()
self.assertEqual(len(acted), 2)
self.assertEqual(len(mode.assistant.kernel.calls), 2)
def test_prediction_below_the_floor_is_skipped(self):
mode = mode_with(enabled=True)
mode.readahead = [prediction(confidence=ACT_FLOOR - 0.05)]
self.assertEqual(mode._act(), [])
self.assertEqual(mode.assistant.kernel.calls, [])
self.assertEqual(mode.at_floor_skips, 1)
def test_idle_does_nothing_when_disabled(self):
mode = lap(mode_with(enabled=False), cycles=2)
entry = mode.tick()
self.assertEqual(entry["acted"], [])
self.assertEqual(mode.assistant.kernel.calls, [])
self.assertEqual(mode.ticks, 1)
class TestChatWithReadahead(unittest.TestCase):
def test_warm_cache_serves_the_answer_and_records_the_saving(self):
mode = mode_with()
mode.cache["regs"] = {"output": "cached regs", "ms": 12.5, "confidence": 0.6}
record = mode.chat("show the registers")
self.assertTrue(record["cached"])
self.assertEqual(record["output"], "cached regs")
self.assertEqual(record["saved_ms"], 12.5)
self.assertEqual(mode.served, 1)
self.assertEqual(mode.ms_saved, 12.5)
self.assertEqual(mode.stats()["served_from_readahead"], 1)
# the turn is still observed, so it is still graded and learned from
self.assertEqual(mode.sequence, ["registers"])
self.assertIsNotNone(record["graded"])
class TestReport(unittest.TestCase):
def test_report_quotes_accuracy_and_chance(self):
mode = lap(mode_with(), cycles=2)
text = mode.report()
self.assertIn("next-intent accuracy", text)
self.assertIn("chance", text)
self.assertIn("autonomous actions", text)
def test_stats_expose_the_floor_and_the_chance_level(self):
mode = mode_with()
stats = mode.stats()
expected = round(1.0 / math.sqrt(len(mode.intents)), 3)
self.assertEqual(stats["chance"], expected)
self.assertEqual(stats["act_floor"], ACT_FLOOR)
self.assertEqual(stats["budget"], mode.budget)
self.assertIn("readahead", stats)
if __name__ == "__main__":
unittest.main()
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