test option dic checks

tests/test_options_tests.py

@@ -1,11 +1,133 @@
-from dcegm.pre_processing.setup_model import setup_model
+import numpy as np
+import pytest
 
+from dcegm.pre_processing.check_options import check_options_and_set_defaults
 
-def test_n_periods():
 
-    test_model = setup_model(
-        options={"state_space": {"n_periods": 1}},
-        utility_functions={},
-        utility_functions_final_period={},
-        budget_constraint=lambda x: x,
+@pytest.fixture
+def valid_options():
+    """Fixture providing a valid options dictionary."""
+    return {
+        "state_space": {
+            "n_periods": 5,
+            "choices": [1, 2, 3],
+            "endogenous_states": {
+                "education": np.arange(2, dtype=int),
+            },
+            "continuous_states": {
+                "wealth": np.linspace(0, 10, 11),
+                "experience": np.linspace(0, 5, 6),
+            },
+            "exogenous_processes": {
+                "health": {
+                    "transition": lambda x: x,
+                    "states": np.arange(3, dtype=int),
+                },
+            },
+        },
+        "model_params": {},
+        "tuning_params": {},
+    }
+
+
+def test_invalid_options_type():
+    with pytest.raises(ValueError, match="Options must be a dictionary."):
+        check_options_and_set_defaults([])
+
+
+def test_missing_state_space(valid_options):
+    del valid_options["state_space"]
+    with pytest.raises(
+        ValueError, match="Options must contain a state space dictionary."
+    ):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_invalid_state_space_type():
+    with pytest.raises(ValueError, match="State space must be a dictionary."):
+        check_options_and_set_defaults({"state_space": "not_a_dict"})
+
+
+def test_missing_n_periods(valid_options):
+    del valid_options["state_space"]["n_periods"]
+    with pytest.raises(
+        ValueError, match="State space must contain the number of periods."
+    ):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_invalid_n_periods_type(valid_options):
+    valid_options["state_space"]["n_periods"] = "not_an_int"
+    with pytest.raises(ValueError, match="Number of periods must be an integer."):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_invalid_n_periods_value(valid_options):
+    valid_options["state_space"]["n_periods"] = 1
+    with pytest.raises(ValueError, match="Number of periods must be greater than 1."):
+        check_options_and_set_defaults(valid_options)
+
+
+@pytest.mark.parametrize(
+    "choices, expected_array",
+    [
+        ([1, 2, 3], np.array([1, 2, 3], dtype=np.uint8)),
+        (5, np.array([5], dtype=np.uint8)),
+        (np.array([1, 2, 3]), np.array([1, 2, 3], dtype=np.uint8)),
+    ],
+)
+def test_valid_choices_conversion(valid_options, choices, expected_array):
+    valid_options["state_space"]["choices"] = choices
+    options = check_options_and_set_defaults(valid_options)
+    np.testing.assert_array_equal(options["state_space"]["choices"], expected_array)
+
+
+def test_invalid_choices_type(valid_options):
+    valid_options["state_space"]["choices"] = "not_a_valid_type"
+    with pytest.raises(ValueError, match="Choices must be a list or an integer."):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_missing_model_params(valid_options):
+    del valid_options["model_params"]
+    with pytest.raises(
+        ValueError, match="Options must contain a model parameters dictionary."
+    ):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_invalid_model_params_type(valid_options):
+    valid_options["model_params"] = "not_a_dict"
+    with pytest.raises(ValueError, match="Model parameters must be a dictionary."):
+        check_options_and_set_defaults(valid_options)
+
+
+# maybe also check this in the check_options_and_set_defaults function
+def test_missing_continuous_states(valid_options):
+    del valid_options["state_space"]["continuous_states"]
+    with pytest.raises(KeyError):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_tuning_params_defaults(valid_options):
+    del valid_options["tuning_params"]
+    options = check_options_and_set_defaults(valid_options)
+    assert options["tuning_params"]["extra_wealth_grid_factor"] == 0.2
+    assert options["tuning_params"]["n_constrained_points_to_add"] == 1
+
+
+def test_tuning_params_invalid_grid_factors(valid_options):
+    valid_options["tuning_params"]["extra_wealth_grid_factor"] = 0.01
+    valid_options["tuning_params"]["n_constrained_points_to_add"] = 100
+    with pytest.raises(
+        ValueError, match="The extra wealth grid factor .* is too small"
+    ):
+        check_options_and_set_defaults(valid_options)
+
+
+def test_second_continuous_state_handling(valid_options):
+    options = check_options_and_set_defaults(valid_options)
+    assert options["second_continuous_state_name"] == "experience"
+    assert options["tuning_params"]["n_second_continuous_grid"] == len(
+        valid_options["state_space"]["continuous_states"]["experience"]
     )