add tests for sign

tests/test_metrics/test_ignore_error_scorer.py

@@ -3,8 +3,10 @@
 import unittest
 
 import numpy as np
+from sklearn import linear_model
 
 from molpipeline.metrics import ignored_value_scorer
+from sklearn.metrics import get_scorer
 
 
 class IgnoreErrorScorerTest(unittest.TestCase):
@@ -45,3 +47,39 @@ def test_filter_none_with_nan(self) -> None:
             ba_score._score_func(y_true, y_pred),  # pylint: disable=protected-access
             1.0,
         )
+
+    def test_correct_init_mse(self) -> None:
+        """Test that initialization is correct as we access via protected vars."""
+        x_train = np.array([0.1,0.2, 0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]).reshape(-1, 1)
+        y_train = np.array([0.1,0.3, 0.3,0.4,0.5,0.5,0.7,0.88,0.9,1])
+        regr = linear_model.LinearRegression()
+        regr.fit(x_train, y_train)
+        cix_scorer = ignored_value_scorer("neg_mean_squared_error", None)
+        scikit_scorer = get_scorer("neg_mean_squared_error")
+        self.assertEqual(
+            cix_scorer(regr, x_train,y_train), scikit_scorer(regr, x_train,y_train)
+        )
+
+    def test_correct_init_rmse(self) -> None:
+        """Test that initialization is correct as we access via protected vars."""
+        x_train = np.array([0.1,0.2, 0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]).reshape(-1, 1)
+        y_train = np.array([0.1,0.3, 0.3,0.4,0.5,0.5,0.7,0.88,0.9,1])
+        regr = linear_model.LinearRegression()
+        regr.fit(x_train, y_train)
+        cix_scorer = ignored_value_scorer("neg_root_mean_squared_error", None)
+        scikit_scorer = get_scorer("neg_root_mean_squared_error")
+        self.assertEqual(
+            cix_scorer(regr, x_train,y_train), scikit_scorer(regr, x_train,y_train)
+        )
+
+    def test_correct_init_inheritance(self) -> None:
+        """Test that initialization is correct if we pass an initialized scorer."""
+        x_train = np.array([0.1,0.2, 0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]).reshape(-1, 1)
+        y_train = np.array([0.1,0.3, 0.3,0.4,0.5,0.5,0.7,0.88,0.9,1])
+        regr = linear_model.LinearRegression()
+        regr.fit(x_train, y_train)
+        scikit_scorer = get_scorer("neg_root_mean_squared_error")
+        cix_scorer = ignored_value_scorer(get_scorer("neg_root_mean_squared_error"), None)
+        self.assertEqual(
+            cix_scorer(regr, x_train,y_train), scikit_scorer(regr, x_train,y_train)
+        )