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from __future__ import annotations
import pytest
from ml_training_debugger.models import MLTrainingAction
from server.environment import MLTrainingEnvironment
@pytest.fixture
def env():
return MLTrainingEnvironment()
class TestReset:
def test_reset_returns_valid_observation(self, env):
obs = env.reset(seed=42, episode_id="test", task_id="task_001")
assert obs.run_id == "test"
assert obs.framework == "pytorch"
assert len(obs.training_loss_history) == 20
assert len(obs.val_accuracy_history) == 20
assert obs.done is False
def test_reset_initial_state(self, env):
obs = env.reset(seed=42, episode_id="test", task_id="task_001")
assert obs.episode_state.step_count == 0
assert not obs.episode_state.gradients_inspected
assert not obs.episode_state.diagnosis_submitted
def test_reset_progressive_reveal(self, env):
obs = env.reset(seed=42, episode_id="test", task_id="task_001")
assert obs.gradient_stats == []
assert obs.model_weight_stats is None
assert obs.data_batch_stats is None
assert obs.model_mode_info is None
assert obs.code_snippet is None
def test_reset_available_actions(self, env):
obs = env.reset(seed=42, episode_id="test", task_id="task_001")
assert "inspect_gradients" in obs.available_actions
assert "mark_diagnosed" in obs.available_actions
assert "fix_code" not in obs.available_actions
assert "restart_run" not in obs.available_actions
class TestStepInspections:
def test_inspect_gradients_populates_stats(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert len(obs.gradient_stats) > 0
assert obs.episode_state.gradients_inspected
def test_inspect_gradients_gives_investigation_bonus(self, env):
"""First-time inspection must give +0.05 bonus (total +0.04 with step penalty)."""
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert obs.reward == pytest.approx(0.04)
def test_inspect_data_batch_gives_investigation_bonus(self, env):
"""First-time data inspection must give +0.05 bonus."""
env.reset(seed=42, episode_id="test", task_id="task_003")
obs = env.step(MLTrainingAction(action_type="inspect_data_batch"))
assert obs.reward == pytest.approx(0.04)
def test_inspect_model_modes_gives_investigation_bonus(self, env):
"""First-time model modes inspection must give +0.05 bonus."""
env.reset(seed=42, episode_id="test", task_id="task_005")
obs = env.step(MLTrainingAction(action_type="inspect_model_modes"))
assert obs.reward == pytest.approx(0.04)
def test_repeat_inspection_no_bonus(self, env):
"""Second inspection of same type must NOT give bonus."""
env.reset(seed=42, episode_id="test", task_id="task_001")
env.step(MLTrainingAction(action_type="inspect_gradients"))
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert obs.reward == pytest.approx(-0.01)
def test_inspect_data_batch(self, env):
env.reset(seed=42, episode_id="test", task_id="task_003")
obs = env.step(MLTrainingAction(action_type="inspect_data_batch"))
assert obs.data_batch_stats is not None
assert obs.episode_state.data_inspected
def test_inspect_model_modes(self, env):
env.reset(seed=42, episode_id="test", task_id="task_005")
obs = env.step(MLTrainingAction(action_type="inspect_model_modes"))
assert obs.model_mode_info is not None
assert obs.episode_state.model_modes_inspected
def test_inspect_model_weights(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(MLTrainingAction(action_type="inspect_model_weights"))
assert obs.model_weight_stats is not None
assert obs.episode_state.model_weights_inspected
class TestStepFixActions:
def test_modify_config(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(
MLTrainingAction(
action_type="modify_config",
target="learning_rate",
value=0.001,
)
)
assert obs.episode_state.fix_action_taken
assert "restart_run" in obs.available_actions
def test_restart_run_after_fix(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
env.step(
MLTrainingAction(
action_type="modify_config",
target="learning_rate",
value=0.001,
)
)
obs = env.step(MLTrainingAction(action_type="restart_run"))
assert obs.episode_state.restart_after_fix
class TestStepDiagnosis:
def test_mark_diagnosed_ends_episode(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="lr_too_high",
)
)
assert obs.done is True
assert obs.episode_state.diagnosis_submitted
def test_step_after_done(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="lr_too_high",
)
)
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert obs.done is True
assert obs.reward == 0.0
class TestErrorHandling:
def test_invalid_action_type(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(MLTrainingAction(action_type="nonexistent_action"))
assert obs.reward == pytest.approx(-0.01 + -0.05)
assert obs.error_log is not None
def test_action_not_in_available(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
# fix_code requires code_inspected=True
obs = env.step(
MLTrainingAction(
action_type="fix_code",
line=1,
replacement="pass",
)
)
assert obs.reward < 0
def test_modify_config_missing_target(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(MLTrainingAction(action_type="modify_config"))
assert "target" in obs.error_log.lower() or "value" in obs.error_log.lower()
def test_mark_diagnosed_missing_diagnosis(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(MLTrainingAction(action_type="mark_diagnosed"))
assert "diagnosis" in obs.error_log.lower()
def test_mark_diagnosed_invalid_diagnosis(self, env):
env.reset(seed=42, episode_id="test", task_id="task_001")
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="not_a_real_diagnosis",
)
)
assert "invalid" in obs.error_log.lower()
def test_step_before_reset(self, env):
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert obs.done is True
class TestFullEpisodeFlow:
def test_task_001_full_flow(self, env):
"""Full optimal flow for Task 1."""
obs = env.reset(seed=42, episode_id="test", task_id="task_001")
assert not obs.done
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert obs.episode_state.gradients_inspected
assert any(g.is_exploding for g in obs.gradient_stats)
obs = env.step(
MLTrainingAction(
action_type="modify_config",
target="learning_rate",
value=0.001,
)
)
assert obs.episode_state.fix_action_taken
obs = env.step(MLTrainingAction(action_type="restart_run"))
assert obs.episode_state.restart_after_fix
obs = env.step(
MLTrainingAction(
action_type="mark_diagnosed",
diagnosis="lr_too_high",
)
)
assert obs.done
assert obs.reward > 0
def test_task_005_context_gated_penalty(self, env):
"""Task 5: inspect gradients (normal) → add_callback → penalty fires."""
obs = env.reset(seed=42, episode_id="test", task_id="task_005")
obs = env.step(MLTrainingAction(action_type="inspect_gradients"))
assert obs.episode_state.gradients_inspected
assert obs.episode_state.gradients_were_normal
# All layers is_exploding=False
for g in obs.gradient_stats:
assert not g.is_exploding
# Now add_callback should trigger context-gated penalty
obs = env.step(MLTrainingAction(action_type="add_callback"))
assert obs.reward == pytest.approx(-0.01 + -0.20)
def test_task_003_data_leakage(self, env):
"""Task 3: data inspection reveals leakage."""
obs = env.reset(seed=42, episode_id="test", task_id="task_003")
obs = env.step(MLTrainingAction(action_type="inspect_data_batch"))
assert obs.data_batch_stats is not None
assert obs.data_batch_stats.class_overlap_score > 0.5
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