base-tests

sklego/preprocessing/monotonicspline.py

@@ -61,6 +61,7 @@ def fit(self, X, y=None):
             )
             for col in range(X.shape[1])
         }
+        self.n_features_in_ = X.shape[1]
         return self
 
     def transform(self, X):
@@ -87,6 +88,9 @@ def transform(self, X):
             dtype=FLOAT_DTYPES,
             estimator=self,
         )
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError("Number of features going into .transform() do not match number going into .fit().")
+
         out = []
         for col in range(X.shape[1]):
             out.append(

tests/test_preprocessing/test_monospline.py

@@ -1,18 +1,22 @@
 import numpy as np
 import pytest
 from sklearn.preprocessing import SplineTransformer
+from sklearn.utils.estimator_checks import parametrize_with_checks
 
 from sklego.preprocessing import MonotonicSplineTransformer
 
 
+@parametrize_with_checks([MonotonicSplineTransformer()])
+def test_sklearn_compatible_estimator(estimator, check):
+    check(estimator)
+
+
 @pytest.mark.parametrize("n_knots", [3, 5])
 @pytest.mark.parametrize("degree", [3, 5])
 @pytest.mark.parametrize("knots", ["uniform", "quantile"])
 def test_monotonic_spline_transformer(n_knots, degree, knots):
     X = np.random.uniform(size=(100, 10))
-    transformer = MonotonicSplineTransformer(
-        n_knots=n_knots, degree=degree, knots=knots
-    )
+    transformer = MonotonicSplineTransformer(n_knots=n_knots, degree=degree, knots=knots)
     transformer_sk = SplineTransformer(n_knots=n_knots, degree=degree, knots=knots)
     transformer.fit(X)
     transformer_sk.fit(X)
@@ -33,13 +37,8 @@ def test_monotonic_spline_transformer(n_knots, degree, knots):
     # The features should be monotonically increasing
     for i in range(X.shape[1]):
         feature = X[:, i]
-        sorted_out = out[
-            np.argsort(feature),
-            i * n_splines_per_feature : (i + 1) * n_splines_per_feature
-        ]
+        sorted_out = out[np.argsort(feature), i * n_splines_per_feature : (i + 1) * n_splines_per_feature]
         differences = np.diff(sorted_out, axis=0)
 
         # All differences should be greater or equal to zero upto floating point errors
-        assert np.logical_or(
-            np.greater_equal(differences, 0), np.isclose(differences, 0)
-        ).all()
+        assert np.logical_or(np.greater_equal(differences, 0), np.isclose(differences, 0)).all()