added alias for estimator type

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v2.3.0
+    rev: v4.4.0
     hooks:
     -   id: check-yaml
     -   id: end-of-file-fixer
@@ -9,7 +9,7 @@ repos:
     -   id: check-merge-conflict
     -   id: check-added-large-files
 -   repo: https://github.com/psf/black
-    rev: stable
+    rev: 23.9.1
     hooks:
     -   id: black
         files: sklego

sklego/meta/decay_estimator.py

@@ -31,6 +31,11 @@ def _is_classifier(self):
             ["ClassifierMixin" in p.__name__ for p in type(self.model).__bases__]
         )
 
+    @property
+    def _estimator_type(self):
+        """Estimator type is computed dynamically from the given model."""
+        return self.model._estimator_type
+
     def fit(self, X, y):
         """
         Fit the data after adapting the same weight.

tests/test_meta/test_decay_estimator.py

@@ -3,6 +3,7 @@
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.linear_model import LinearRegression, Ridge, LogisticRegression
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.base import is_regressor, is_classifier
 
 
 from sklego.common import flatten
@@ -14,17 +15,13 @@
 )
 
 
-@pytest.mark.parametrize(
-    "test_fn", flatten([general_checks, regressor_checks])
-)
+@pytest.mark.parametrize("test_fn", flatten([general_checks, regressor_checks]))
 def test_estimator_checks_regression(test_fn):
     trf = DecayEstimator(LinearRegression())
     test_fn(DecayEstimator.__name__, trf)
 
 
-@pytest.mark.parametrize(
-    "test_fn", flatten([general_checks, classifier_checks])
-)
+@pytest.mark.parametrize("test_fn", flatten([general_checks, classifier_checks]))
 def test_estimator_checks_classification(test_fn):
     trf = DecayEstimator(LogisticRegression(solver="lbfgs"))
     test_fn(DecayEstimator.__name__, trf)
@@ -36,7 +33,7 @@ def test_estimator_checks_classification(test_fn):
 def test_decay_weight_regr(mod):
     X, y = np.random.normal(0, 1, (100, 100)), np.random.normal(0, 1, (100,))
     mod = DecayEstimator(mod, decay=0.95).fit(X, y)
-    assert mod.weights_[0] == pytest.approx(0.95 ** 100, abs=0.001)
+    assert mod.weights_[0] == pytest.approx(0.95**100, abs=0.001)
 
 
 @pytest.mark.parametrize(
@@ -48,7 +45,7 @@ def test_decay_weight_clf(mod):
         (np.random.normal(0, 1, (100,)) < 0).astype(int),
     )
     mod = DecayEstimator(mod, decay=0.95).fit(X, y)
-    assert mod.weights_[0] == pytest.approx(0.95 ** 100, abs=0.001)
+    assert mod.weights_[0] == pytest.approx(0.95**100, abs=0.001)
 
 
 @pytest.mark.parametrize("mod", flatten([KNeighborsClassifier()]))
@@ -58,3 +55,20 @@ def test_throw_warning(mod):
         DecayEstimator(mod, decay=0.95).fit(X, y)
         assert "sample_weight" in str(e)
         assert type(mod).__name__ in str(e)
+
+
+@pytest.mark.parametrize(
+    "mod, is_regr",
+    [
+        (LinearRegression(), True),
+        (Ridge(), True),
+        (DecisionTreeRegressor(), True),
+        (LogisticRegression(), False),
+        (DecisionTreeClassifier(), False),
+    ],
+)
+def test_estimator_type_regressor(mod, is_regr):
+    mod = DecayEstimator(mod)
+    assert mod._estimator_type == mod.model._estimator_type
+    assert is_regressor(mod) == is_regr
+    assert is_classifier(mod) == (not is_regr)