test: Include all estimators (with coef_) in test_all_sklearn_estimators

skore/src/skore/sklearn/_estimator/feature_importance_accessor.py

@@ -203,10 +203,24 @@ def coefficients(self) -> pd.DataFrame:
             if isinstance(parent_estimator, Pipeline)
             else parent_estimator
         )
-        intercept = np.atleast_2d(estimator.intercept_)
-        coef = np.atleast_2d(estimator.coef_)
-
-        data = np.concatenate([intercept, coef.T])
+        try:
+            intercept = np.atleast_2d(estimator.intercept_)
+        except AttributeError:
+            # SGDOneClassSVM does not expose `intercept_`
+            intercept = None
+
+        try:
+            coef = np.atleast_2d(estimator.coef_)
+        except AttributeError:
+            # TransformedTargetRegressor() does not expose `coef_`
+            coef = np.atleast_2d(estimator.regressor_.coef_)
+
+        if intercept is None:
+            data = coef.T
+            index = list(feature_names)
+        else:
+            data = np.concatenate([intercept, coef.T])
+            index = ["Intercept"] + list(feature_names)
 
         if data.shape[1] == 1:
             columns = ["Coefficient"]
@@ -217,7 +231,7 @@ def coefficients(self) -> pd.DataFrame:
 
         df = pd.DataFrame(
             data=data,
-            index=["Intercept"] + list(feature_names),
+            index=index,
             columns=columns,
         )
 

skore/src/skore/utils/_accessor.py

@@ -59,6 +59,12 @@ def check(accessor: Any) -> bool:
         )
         if hasattr(estimator, "coef_"):
             return True
+        try:  # e.g. TransformedTargetRegressor()
+            if hasattr(estimator.regressor_, "coef_"):
+                return True
+        except AttributeError as msg:
+            if "object has no attribute 'regressor_'" not in str(msg):
+                raise
         raise AttributeError(
             f"Estimator {parent_estimator} is not a supported estimator by "
             "the function called."

skore/tests/unit/sklearn/estimator/feature_importance/conftest.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 from sklearn.datasets import make_classification, make_regression
 
@@ -7,6 +8,12 @@ def regression_data():
     return make_regression(n_features=5, random_state=42)
 
 
+@pytest.fixture
+def positive_regression_data():
+    X, y = make_regression(n_features=5, random_state=42)
+    return X, np.abs(y) + 0.1
+
+
 @pytest.fixture
 def classification_data():
     return make_classification(n_features=5, random_state=42)

skore/tests/unit/sklearn/estimator/feature_importance/test_coefficients.py

@@ -7,6 +7,7 @@
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import StandardScaler
 from skore import EstimatorReport
+from skore.externals._sklearn_compat import get_tags
 
 
 @pytest.mark.parametrize(
@@ -119,29 +120,25 @@ def test_estimator_report_coefficients_pandas_dataframe(estimator):
     [
         pytest.param(sklearn.svm.NuSVC(kernel="linear"), id="NuSVC"),
         pytest.param(sklearn.svm.NuSVR(kernel="linear"), id="NuSVR"),
-        # pytest.param(sklearn.svm.OneClassSVM(), id="OneClassSVM"),
         pytest.param(sklearn.svm.SVC(kernel="linear"), id="SVC"),
         pytest.param(sklearn.svm.SVR(kernel="linear"), id="SVR"),
         pytest.param(sklearn.svm.LinearSVC(), id="LinearSVC"),
         pytest.param(sklearn.svm.LinearSVR(), id="LinearSVR"),
-        # pytest.param(sklearn.cross_decomposition.CCA(), id="CCA"),
-        # pytest.param(sklearn.cross_decomposition.PLSCanonical(), id="PLSCanonical"),
         pytest.param(sklearn.cross_decomposition.PLSRegression(), id="PLSRegression"),
         pytest.param(
             sklearn.discriminant_analysis.LinearDiscriminantAnalysis(),
             id="LinearDiscriminantAnalysis",
         ),
-        # pytest.param(
-        #     sklearn.compose.TransformedTargetRegressor(),
-        #     id="TransformedTargetRegressor",
-        # ),
+        pytest.param(
+            sklearn.compose.TransformedTargetRegressor(),
+            id="TransformedTargetRegressor",
+        ),
         pytest.param(sklearn.linear_model.ElasticNet(), id="ElasticNet"),
-        pytest.param(sklearn.linear_model.Lasso(), id="Lasso"),
         pytest.param(sklearn.linear_model.ARDRegression(), id="ARDRegression"),
         pytest.param(sklearn.linear_model.BayesianRidge(), id="BayesianRidge"),
         pytest.param(sklearn.linear_model.ElasticNet(), id="ElasticNet"),
         pytest.param(sklearn.linear_model.ElasticNetCV(), id="ElasticNetCV"),
-        # pytest.param(sklearn.linear_model.GammaRegressor(), id="GammaRegressor"),
+        pytest.param(sklearn.linear_model.GammaRegressor(), id="GammaRegressor"),
         pytest.param(sklearn.linear_model.HuberRegressor(), id="HuberRegressor"),
         pytest.param(sklearn.linear_model.Lars(), id="Lars"),
         pytest.param(sklearn.linear_model.LarsCV(), id="LarsCV"),
@@ -157,14 +154,6 @@ def test_estimator_report_coefficients_pandas_dataframe(estimator):
         pytest.param(
             sklearn.linear_model.LogisticRegressionCV(), id="LogisticRegressionCV"
         ),
-        # pytest.param(
-        #     sklearn.linear_model.MultiTaskElasticNet(), id="MultiTaskElasticNet"
-        # ),
-        # pytest.param(
-        #     sklearn.linear_model.MultiTaskElasticNetCV(), id="MultiTaskElasticNetCV"
-        # ),
-        # pytest.param(sklearn.linear_model.MultiTaskLasso(), id="MultiTaskLasso"),
-        # pytest.param(sklearn.linear_model.MultiTaskLassoCV(), id="MultiTaskLassoCV"),
         pytest.param(
             sklearn.linear_model.OrthogonalMatchingPursuit(),
             id="OrthogonalMatchingPursuit",
@@ -182,44 +171,73 @@ def test_estimator_report_coefficients_pandas_dataframe(estimator):
             id="PassiveAggressiveRegressor",
         ),
         pytest.param(sklearn.linear_model.Perceptron(), id="Perceptron"),
-        # pytest.param(sklearn.linear_model.PoissonRegressor(), id="PoissonRegressor"),
+        pytest.param(sklearn.linear_model.PoissonRegressor(), id="PoissonRegressor"),
         pytest.param(sklearn.linear_model.QuantileRegressor(), id="QuantileRegressor"),
         pytest.param(sklearn.linear_model.Ridge(), id="Ridge"),
         pytest.param(sklearn.linear_model.RidgeClassifier(), id="RidgeClassifier"),
         pytest.param(sklearn.linear_model.RidgeClassifierCV(), id="RidgeClassifierCV"),
         pytest.param(sklearn.linear_model.RidgeCV(), id="RidgeCV"),
         pytest.param(sklearn.linear_model.SGDClassifier(), id="SGDClassifier"),
-        # pytest.param(sklearn.linear_model.SGDOneClassSVM(), id="SGDOneClassSVM"),
         pytest.param(sklearn.linear_model.SGDRegressor(), id="SGDRegressor"),
         pytest.param(sklearn.linear_model.TheilSenRegressor(), id="TheilSenRegressor"),
         pytest.param(sklearn.linear_model.TweedieRegressor(), id="TweedieRegressor"),
+        # The following models would be tested in the future when the `EstimatorReport`
+        # will have metrics specific to these models:
+        #
+        # 1. multi-task
+        # pytest.param(
+        #     sklearn.linear_model.MultiTaskElasticNet(), id="MultiTaskElasticNet"
+        # ),
+        # pytest.param(
+        #     sklearn.linear_model.MultiTaskElasticNetCV(), id="MultiTaskElasticNetCV"
+        # ),
+        # pytest.param(sklearn.linear_model.MultiTaskLasso(), id="MultiTaskLasso"),
+        # pytest.param(sklearn.linear_model.MultiTaskLassoCV(), id="MultiTaskLassoCV"),
+        # 2. cross_decomposition
+        # pytest.param(sklearn.cross_decomposition.CCA(), id="CCA"),
+        # pytest.param(sklearn.cross_decomposition.PLSCanonical(), id="PLSCanonical"),
+        # 3. outlier detectors
+        # pytest.param(sklearn.linear_model.SGDOneClassSVM(), id="SGDOneClassSVM"),
+        # pytest.param(sklearn.svm.OneClassSVM(kernel="linear"), id="OneClassSVM"),
     ],
 )
 def test_all_sklearn_estimators(
-    request, estimator, regression_data, classification_data
+    request,
+    estimator,
+    regression_data,
+    positive_regression_data,
+    classification_data,
 ):
     """Check that `coefficients` is supported for every sklearn estimator."""
     if is_classifier(estimator):
         X, y = classification_data
     elif is_regressor(estimator):
-        X, y = regression_data
+        if get_tags(estimator).target_tags.positive_only:
+            X, y = positive_regression_data
+        else:
+            X, y = regression_data
     else:
-        raise Exception("Estimator is neither a classifier nor a regressor")
+        raise Exception("Estimator not in ['classifier', 'regressor']")
 
     estimator.fit(X, y)
 
     report = EstimatorReport(estimator)
     result = report.feature_importance.coefficients()
 
-    assert result.shape == (6, 1)
-    assert result.index.tolist() == [
+    rows = [
         "Intercept",
         "Feature #0",
         "Feature #1",
         "Feature #2",
         "Feature #3",
         "Feature #4",
     ]
+    if result.shape == (5, 1):  # for TransformedTargetRegressor()
+        assert rows[1:] == result.index.tolist()
+    else:
+        assert result.shape == (6, 1)
+        assert rows == result.index.tolist()
+
     assert result.columns.tolist() == ["Coefficient"]
 
 

skore/tests/unit/utils/test_accessors.py

@@ -82,6 +82,10 @@ class Estimator:
         def __init__(self):
             self.coef_ = 0
 
+    class MetaEstimator:
+        def __init__(self):
+            self.regressor_ = Estimator()
+
     parent = MockParent(Estimator())
     accessor = MockAccessor(parent)
 
@@ -92,6 +96,11 @@ def __init__(self):
 
     assert _check_has_coef()(accessor)
 
+    parent = MockParent(MetaEstimator())
+    accessor = MockAccessor(parent)
+
+    assert _check_has_coef()(accessor)
+
     parent = MockParent(estimator="hello")
     accessor = MockAccessor(parent)