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| import random | |
| import pytest | |
| from store import OrderRequest, PriceUpdate, StoreAction, StoreEnv | |
| from store.models import dump_model | |
| from store.server.store_environment import StoreEnvironment | |
| def test_reset_returns_full_plan_like_observation() -> None: | |
| env = StoreEnvironment() | |
| observation = env.reset() | |
| assert observation.store.day == 1 | |
| assert len(observation.stock) == 30 | |
| assert len(observation.market) == 100 | |
| assert len(observation.marketing_actions) == 15 | |
| assert len(observation.seller_profiles) == 4 | |
| assert observation.pending_orders == [] | |
| assert observation.available_action.pass_action == StoreAction() | |
| assert observation.available_action.example_action.action_ids == [7] | |
| def test_pass_step_advances_three_days_but_reward_uses_six_day_forecast() -> None: | |
| env = StoreEnvironment() | |
| env.reset() | |
| observation = env.step(StoreAction()) | |
| assert observation.store.day == 4 | |
| assert observation.metadata["reward_breakdown"]["commit_window_days"] == 3 | |
| assert observation.metadata["reward_breakdown"]["reward_horizon_days"] == 6 | |
| def test_eta_sampling_is_deterministic_for_a_seed() -> None: | |
| env_a = StoreEnvironment() | |
| env_b = StoreEnvironment() | |
| env_a.reset() | |
| env_b.reset() | |
| env_a._rng = random.Random(7) | |
| env_b._rng = random.Random(7) | |
| assert env_a._sample_actual_eta("Seller C", 5) == env_b._sample_actual_eta("Seller C", 5) | |
| def test_commit_state_only_keeps_three_days_of_progress() -> None: | |
| env = StoreEnvironment() | |
| env.reset() | |
| env._sample_actual_eta = lambda seller, quoted: 4 # type: ignore[method-assign] | |
| observation = env.step( | |
| StoreAction( | |
| orders=[OrderRequest(product="Rice", seller="Seller B", quantity=10)], | |
| ) | |
| ) | |
| assert observation.store.day == 4 | |
| assert len(observation.pending_orders) == 1 | |
| assert observation.pending_orders[0].days_remaining == 1 | |
| def test_price_update_changes_customer_satisfaction() -> None: | |
| env = StoreEnvironment() | |
| reset_observation = env.reset() | |
| milk_row = next(row for row in reset_observation.stock if row.product_name == "Milk") | |
| observation = env.step( | |
| StoreAction( | |
| prices=[ | |
| PriceUpdate( | |
| product="Milk", | |
| price=max(1, int(milk_row.selling_price_inr) - 5), | |
| ) | |
| ] | |
| ) | |
| ) | |
| assert observation.store.customer_satisfaction > reset_observation.store.customer_satisfaction | |
| def test_marketing_actions_are_durable_and_cannot_be_reactivated() -> None: | |
| env = StoreEnvironment() | |
| env.reset() | |
| observation = env.step(StoreAction(action_ids=[7])) | |
| assert observation.store.active_action_ids == [7] | |
| assert observation.store.active_recurring_cost_inr > 0 | |
| assert any( | |
| action.action_id == 7 and action.is_active for action in observation.marketing_actions | |
| ) | |
| with pytest.raises(ValueError): | |
| env.step(StoreAction(action_ids=[7])) | |
| def test_recurring_costs_are_deducted_each_simulated_day() -> None: | |
| env = StoreEnvironment() | |
| env.reset() | |
| runtime = env._build_initial_runtime() | |
| runtime["store"]["bank_balance_inr"] = 10_000.0 | |
| runtime["store"]["cumulative_profit_inr"] = 0.0 | |
| runtime["store"]["current_daily_expense_inr"] = 0.0 | |
| runtime["store"]["customer_satisfaction"] = 0.0 | |
| runtime["active_action_ids"] = {7} | |
| metrics = env._simulate_days(runtime, 3) | |
| assert metrics["recurring_costs_paid_inr"] == 1_800.0 | |
| assert runtime["store"]["bank_balance_inr"] == 8_200.0 | |
| assert runtime["store"]["cumulative_profit_inr"] == -1_800.0 | |
| def test_large_capital_loss_reduces_reward_and_ends_episode() -> None: | |
| baseline = StoreEnvironment() | |
| baseline.reset() | |
| baseline_reward = baseline.step(StoreAction()).reward | |
| env = StoreEnvironment() | |
| env.reset() | |
| observation = env.step(StoreAction(action_ids=[11])) | |
| assert observation.reward < baseline_reward | |
| assert observation.done is True | |
| assert observation.metadata["done_reason"] == "bank_balance_below_zero" | |
| def test_episode_ends_after_tenth_decision_window() -> None: | |
| env = StoreEnvironment() | |
| env.reset() | |
| observation = None | |
| for _ in range(9): | |
| observation = env.step(StoreAction()) | |
| assert observation.done is False | |
| observation = env.step(StoreAction()) | |
| assert observation is not None | |
| assert observation.done is True | |
| assert observation.store.day == 31 | |
| assert observation.metadata["done_reason"] == "episode_horizon_reached" | |
| def test_client_payload_and_result_parsing_support_nested_models() -> None: | |
| client = StoreEnv(base_url="http://localhost:8000") | |
| action = StoreAction( | |
| orders=[OrderRequest(product="Rice", seller="Seller A", quantity=5)], | |
| prices=[PriceUpdate(product="Milk", price=33)], | |
| action_ids=[7], | |
| ) | |
| payload = client._step_payload(action) | |
| assert payload == { | |
| "orders": [{"product": "Rice", "seller": "Seller A", "quantity": 5}], | |
| "prices": [{"product": "Milk", "price": 33}], | |
| "action_ids": [7], | |
| } | |
| env = StoreEnvironment() | |
| observation = env.reset() | |
| result = client._parse_result( | |
| { | |
| "observation": dump_model(observation, exclude_none=True), | |
| "reward": 0.0, | |
| "done": False, | |
| } | |
| ) | |
| assert result.observation.store.day == 1 | |
| assert len(result.observation.market) == 100 | |