Update test fixture structure

assesspy/tests/conftest.py

@@ -27,10 +27,15 @@ def quintos_data() -> tuple:
     return sample.estimate, sample.sale_price
 
 
-@pt.fixture(scope="module", params=["1_1", "1_4", "d_1", "d_2"])
-def iaao_data(request) -> tuple:
-    sample = pd.read_csv(FIXTURE_DIR / f"iaao_table_{request.param}.csv")
-    return request.param, sample.estimate, sample.sale_price
+@pt.fixture(scope="session", params=["1_1", "1_4", "d_1", "d_2"])
+def iaao_data_name(request):
+    return request.param
+
+
+@pt.fixture(scope="session")
+def iaao_data(iaao_data_name) -> tuple:
+    sample = pd.read_csv(FIXTURE_DIR / f"iaao_table_{iaao_data_name}.csv")
+    return sample.estimate, sample.sale_price
 
 
 @pt.fixture(

assesspy/tests/test_metrics.py

@@ -24,12 +24,13 @@ def test_metric_value_is_correct_ccao(self, metric, metric_val):
         }
         assert pt.approx(metric_val, rel=0.01) == expected[metric]
 
-    def test_metric_value_is_correct_iaao(self, metric, iaao_data):
+    def test_metric_value_is_correct_iaao(
+        self, metric, iaao_data_name, iaao_data
+    ):
         if metric in ["mki", "ki"]:
             return None
         else:
-            table_name, estimate, sale_price = iaao_data
-            result = getattr(ap, metric)(estimate, sale_price)
+            result = getattr(ap, metric)(*iaao_data)
             expected = {
                 "1_1": {
                     "cod": 29.8,
@@ -52,7 +53,9 @@ def test_metric_value_is_correct_iaao(self, metric, iaao_data):
                     "prb": -0.011,
                 },
             }
-            assert pt.approx(result, rel=0.02) == expected[table_name][metric]
+            assert (
+                pt.approx(result, rel=0.02) == expected[iaao_data_name][metric]
+            )
 
     def test_metric_has_numeric_output(self, metric_val):
         assert type(metric_val) is float

assesspy/tests/test_sales_chasing.py

@@ -13,33 +13,37 @@ def sample_dist(self, ccao_data):
         return ratio
 
     @pt.fixture(params=["normal", "chased", "sample"])
-    def distribution(self, request, ccao_data, sample_dist):
-        return request.param, {
+    def distribution_name(self, request):
+        return request.param
+
+    @pt.fixture
+    def distribution(self, distribution_name, ccao_data, sample_dist):
+        return {
             "normal": np.random.normal(1, size=1000).tolist(),
             "chased": np.append(
                 np.random.normal(1, 0.15, 900), [1] * 100
             ).tolist(),
             "sample": sample_dist,
-        }[request.param]
+        }[distribution_name]
 
     @pt.fixture(params=["cdf", "dist", "both"])
     def method(self, request):
         return request.param
 
     def test_is_sales_chased_output_is_boolean(self, distribution, method):
-        dist_name, dist_data = distribution
-        assert isinstance(ap.is_sales_chased(dist_data, method), bool)
+        assert isinstance(ap.is_sales_chased(distribution, method), bool)
 
-    def test_is_sales_chased_has_expected_output(self, distribution, method):
-        dist_name, dist_data = distribution
+    def test_is_sales_chased_has_expected_output(
+        self, distribution_name, distribution, method
+    ):
         expected = {
             "normal": {"cdf": False, "dist": False, "both": False},
             "chased": {"cdf": True, "dist": True, "both": True},
             "sample": {"cdf": False, "dist": True, "both": False},
         }
         assert (
-            ap.is_sales_chased(dist_data, method)
-            == expected[dist_name][method]
+            ap.is_sales_chased(distribution, method)
+            == expected[distribution_name][method]
         )
 
     @pt.mark.parametrize(
@@ -61,13 +65,11 @@ def test_is_sales_chased_raises_on_invalid_values(
         self, input_data, distribution, method
     ):
         with pt.raises(Exception):
-            dist_name, dist_data = distribution
-            ap.is_outlier(input_data(dist_data), method)
+            ap.is_outlier(input_data(distribution), method)
 
     def test_is_sales_chased_raises_on_invalid_method(self, distribution):
         with pt.raises(Exception):
-            dist_name, dist_data = distribution
-            ap.is_sales_chased(dist_data, method="hug")
+            ap.is_sales_chased(distribution, method="hug")
 
     def test_is_sales_chased_warns_on_small_sample(self):
         with pt.warns(UserWarning):