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firestar5683
75 lines
3.5 KiB
Python
75 lines
3.5 KiB
Python
"""Numerical selection regressions; no fabricated hardware/plant admission."""
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from dataclasses import replace
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from types import SimpleNamespace
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import pytest
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from openpilot.starpilot.flm.live_tracking import HookSensitivity
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from openpilot.starpilot.flm.live_tracking_episodes import TrackingEpisodeBuilder
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from openpilot.starpilot.flm.live_tracking_probe import _physical_row
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from openpilot.starpilot.flm.live_trial_contract import MAX_STEP
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from openpilot.starpilot.flm.tests.test_tracking_source_context import owner, refresh
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from openpilot.starpilot.flm.tests.test_live_tracking import episode
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@pytest.mark.parametrize('minus,plus,low,high,negative,positive', [
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(-MAX_STEP, MAX_STEP, 0.1, 0.102, 0.0, 0.002),
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(-MAX_STEP, MAX_STEP, 0.098, 0.1, 0.002, 0.0),
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(-MAX_STEP, MAX_STEP, 0.098, 0.102, 0.002, 0.002),
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(0.0, MAX_STEP, 0.1, 0.102, 0.0, 0.002),
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])
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def test_directional_visibility_uses_actual_encoded_baseline(minus, plus, low, high, negative, positive):
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probe = _physical_row('ff_gain_right', 0.0, minus, plus, 0.1, low, high, 1.0, 'separated')
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points = [SimpleNamespace(control_ns=100 + i * 100, apply_ns=110 + i * 100, error=0.1) for i in range(4)]
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probes = {point.control_ns: SimpleNamespace(apply_ns=point.apply_ns, rows=(probe,)) for point in points}
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builder = object.__new__(TrackingEpisodeBuilder)
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feature = builder._sensitivities(points, probes)[0].validate()
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assert feature.native_gradient_complete
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count, effect = feature.native_effect(-MAX_STEP)
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assert count == (4 if negative else 0) and effect == pytest.approx(negative)
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count, effect = feature.native_effect(MAX_STEP)
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assert count == (4 if positive else 0) and effect == pytest.approx(positive)
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def feature(name='ff_gain_right', error=-0.05):
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return HookSensitivity(name, error, 1.0, 20, 20, 0.01, True, 0, 20, 0.0, 0.02).validate()
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def test_selected_direction_cannot_borrow_opposite_native_visibility():
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o = owner()
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for i in range(9):
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e = episode(o, i, 'ff_gain_right', kind='large')
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e = replace(e, sensitivity=(feature(),), normalization_accel=max(0.1, e.goal_rms))
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refresh(o, e.end_ns)
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o.observe_episode(e, now_ns=e.end_ns)
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assert o.offer_state is None and o.discovery is None
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assert not any(o.accepted.values())
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def test_visible_direction_still_gets_a_bounded_trial():
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o = owner()
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for i in range(20):
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e = episode(o, i, 'ff_gain_right', kind='large')
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e = replace(e, sensitivity=(feature(error=0.05),), normalization_accel=max(0.1, e.goal_rms))
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refresh(o, e.end_ns)
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o.observe_episode(e, now_ns=e.end_ns)
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if o.offer_state is not None:
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break
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assert o.offer_state is not None
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assert o.offer_state['target']['ff_gain_right'] == MAX_STEP
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assert not o.offer_state.get('accepted', False) and (not any(o.accepted.values()))
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def test_recovery_requires_visibility_in_the_already_selected_direction():
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o = owner()
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e = episode(o, 0, 'ff_gain_right', kind='large')
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e = replace(e, sensitivity=(feature(),))
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target = dict(o.accepted)
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target['ff_gain_right'] = -MAX_STEP
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assert o._fresh_selected_effect((e,), 'ff_gain_right', target) is None
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target['ff_gain_right'] = MAX_STEP
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assert o._fresh_selected_effect((e,), 'ff_gain_right', target) > 0
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@pytest.mark.parametrize('changes', [
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{'minus_native_separated': None},
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{'plus_max_step_physical_rms': float('nan')},
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{'minus_max_step_physical_rms': 0.01},
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{'plus_native_separated': 21},
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])
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def test_partial_or_impossible_directional_support_is_rejected(changes):
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with pytest.raises(ValueError):
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replace(feature(), **changes).validate()
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