fix(skore): Follow up on FeatureImportanceCoefficientsDisplay

skore/src/skore/__init__.py

@@ -21,6 +21,9 @@
     train_test_split,
 )
 from skore._sklearn._plot.base import Display
+from skore._sklearn._plot.metrics.feature_importance_coefficients_display import (
+    FeatureImportanceCoefficientsDisplay,
+)
 from skore._utils._patch import setup_jupyter_display
 from skore._utils._show_versions import show_versions
 from skore.project import Project
@@ -41,6 +44,7 @@
     "get_config",
     "set_config",
     "TableReportDisplay",
+    "FeatureImportanceCoefficientsDisplay",
 ]
 
 logger = logging.getLogger(__name__)

skore/src/skore/_sklearn/_comparison/feature_importance_accessor.py

@@ -10,8 +10,8 @@
 from skore._sklearn._base import _BaseAccessor
 from skore._sklearn._cross_validation import CrossValidationReport
 from skore._sklearn._estimator import EstimatorReport
-from skore._sklearn._plot.metrics.feature_importance_display import (
-    FeatureImportanceDisplay,
+from skore._sklearn._plot.metrics.feature_importance_coefficients_display import (
+    FeatureImportanceCoefficientsDisplay,
 )
 from skore._utils._accessor import _check_comparison_report_sub_estimators_have_coef
 
@@ -29,20 +29,25 @@ def __init__(self, parent: ComparisonReport) -> None:
         super().__init__(parent)
 
     @available_if(_check_comparison_report_sub_estimators_have_coef())
-    def coefficients(self) -> FeatureImportanceDisplay:
+    def coefficients(self) -> FeatureImportanceCoefficientsDisplay:
         """Retrieve the coefficients for each report, including the intercepts.
 
-        If the compared reports are `EstimatorReport`s, the coefficients from each
-        report's estimator are returned as a single-column DataFrame.
+        If the compared reports are :class:`EstimatorReport` instances, the coefficients
+        from each report's estimator are returned as a single-column DataFrame.
 
-        If the compared reports are `CrossValidationReport`s, the coefficients
-        across all cross-validation splits are retained and the columns are prefixed
-        with the corresponding estimator name to distinguish them.
+        If the compared reports are :class:`CrossValidationReport` instances, the
+        coefficients across all cross-validation splits are retained and the columns are
+        prefixed with the corresponding estimator name to distinguish them.
 
         Comparison reports with the same features are put under one key and are plotted
-        together.
-        When some reports share the same features and others do not, those with the same
-        features are plotted together.
+        together. When some reports share the same features and others do not, those
+        with the same features are plotted together.
+
+        Returns
+        -------
+        :class:`FeatureImportanceCoefficientsDisplay`
+            The feature importance display containing model coefficients and
+            intercept.
         """
         similar_reports = defaultdict(list)
 
@@ -89,7 +94,12 @@ def coefficients(self) -> FeatureImportanceDisplay:
         else:
             raise TypeError(f"Unexpected report type: {self._parent._reports_type}")
 
-        return FeatureImportanceDisplay(self._parent, coef_frames)
+        return FeatureImportanceCoefficientsDisplay(
+            "comparison-estimator"
+            if self._parent._reports_type == "EstimatorReport"
+            else "comparison-cross-validation",
+            coef_frames,
+        )
 
     ####################################################################################
     # Methods related to the help tree

skore/src/skore/_sklearn/_comparison/report.py

@@ -103,6 +103,7 @@ class ComparisonReport(_BaseReport, DirNamesMixin):
 
     _ACCESSOR_CONFIG: dict[str, dict[str, str]] = {
         "metrics": {"name": "metrics"},
+        "feature_importance": {"name": "feature_importance"},
     }
     metrics: _MetricsAccessor
     feature_importance: _FeatureImportanceAccessor

skore/src/skore/_sklearn/_cross_validation/feature_importance_accessor.py

@@ -6,8 +6,8 @@
 from skore._externals._pandas_accessors import DirNamesMixin
 from skore._sklearn._base import _BaseAccessor
 from skore._sklearn._cross_validation.report import CrossValidationReport
-from skore._sklearn._plot.metrics.feature_importance_display import (
-    FeatureImportanceDisplay,
+from skore._sklearn._plot.metrics.feature_importance_coefficients_display import (
+    FeatureImportanceCoefficientsDisplay,
 )
 from skore._utils._accessor import _check_cross_validation_sub_estimator_has_coef
 
@@ -22,9 +22,15 @@ def __init__(self, parent: CrossValidationReport) -> None:
         super().__init__(parent)
 
     @available_if(_check_cross_validation_sub_estimator_has_coef())
-    def coefficients(self) -> FeatureImportanceDisplay:
+    def coefficients(self) -> FeatureImportanceCoefficientsDisplay:
         """Retrieve the coefficients across splits, including the intercept.
 
+        Returns
+        -------
+        :class:`FeatureImportanceCoefficientsDisplay`
+            The feature importance display containing model coefficients and
+            intercept.
+
         Examples
         --------
         >>> from sklearn.datasets import make_regression
@@ -34,15 +40,16 @@ def coefficients(self) -> FeatureImportanceDisplay:
         >>> report = CrossValidationReport(
         >>>     estimator=Ridge(), X=X, y=y, splitter=5, n_jobs=4
         >>> )
-        >>> report.feature_importance.coefficients().frame()
+        >>> display = report.feature_importance.coefficients()
+        >>> display.frame()
                     Intercept	Feature #0	Feature #1	Feature #2
         Split index
         0       	0.064837	74.100966	27.309656	17.367865
         1       	0.030257	74.276481	27.571421	17.392395
         2       	0.000084	74.107126	27.614821	17.277730
         3       	0.145613	74.207645	27.523667	17.391055
         4       	0.033695	74.259575	27.599610	17.390481
-        >>> report.feature_importance.coefficients().plot() # shows plot
+        >>> display.plot() # shows plot
         """
         combined = pd.concat(
             {
@@ -58,7 +65,7 @@ def coefficients(self) -> FeatureImportanceDisplay:
         ).T
         combined.index.name = "Split index"
 
-        return FeatureImportanceDisplay(self._parent, combined)
+        return FeatureImportanceCoefficientsDisplay("cross-validation", combined)
 
     ####################################################################################
     # Methods related to the help tree

skore/src/skore/_sklearn/_cross_validation/report.py

@@ -143,6 +143,7 @@ class CrossValidationReport(_BaseReport, DirNamesMixin):
 
     _ACCESSOR_CONFIG: dict[str, dict[str, str]] = {
         "metrics": {"name": "metrics"},
+        "feature_importance": {"name": "feature_importance"},
     }
     metrics: _MetricsAccessor
     feature_importance: _FeatureImportanceAccessor

skore/src/skore/_sklearn/_estimator/feature_importance_accessor.py

@@ -17,8 +17,8 @@
 from skore._externals._pandas_accessors import DirNamesMixin
 from skore._sklearn._base import _BaseAccessor
 from skore._sklearn._estimator.report import EstimatorReport
-from skore._sklearn._plot.metrics.feature_importance_display import (
-    FeatureImportanceDisplay,
+from skore._sklearn._plot.metrics.feature_importance_coefficients_display import (
+    FeatureImportanceCoefficientsDisplay,
 )
 from skore._sklearn.types import Aggregate
 from skore._utils._accessor import (
@@ -158,9 +158,15 @@ def __init__(self, parent: EstimatorReport) -> None:
         super().__init__(parent)
 
     @available_if(_check_estimator_has_coef())
-    def coefficients(self) -> FeatureImportanceDisplay:
+    def coefficients(self) -> FeatureImportanceCoefficientsDisplay:
         """Retrieve the coefficients of a linear model, including the intercept.
 
+        Returns
+        -------
+        :class:`FeatureImportanceCoefficientsDisplay`
+            The feature importance display containing model coefficients and
+            intercept.
+
         Examples
         --------
         >>> from sklearn.datasets import load_diabetes
@@ -171,7 +177,8 @@ def coefficients(self) -> FeatureImportanceDisplay:
         >>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
         >>> regressor = Ridge()
         >>> report = EstimatorReport(regressor, **split_data)
-        >>> report.feature_importance.coefficients().frame()
+        >>> display = report.feature_importance.coefficients()
+        >>> display.frame()
                    Coefficient
         Intercept     152.4...
         Feature #0     21.2...
@@ -184,7 +191,7 @@ def coefficients(self) -> FeatureImportanceDisplay:
         Feature #7    112.6...
         Feature #8    250.5...
         Feature #9     99.5...
-        >>> report.feature_importance.coefficients().plot() # shows plot
+        >>> display.plot() # shows plot
         """
         parent_estimator = self._parent.estimator_
 
@@ -239,7 +246,7 @@ def coefficients(self) -> FeatureImportanceDisplay:
             columns=columns,
         )
 
-        return FeatureImportanceDisplay(self._parent, df)
+        return FeatureImportanceCoefficientsDisplay("estimator", df)
 
     @available_if(_check_has_feature_importances())
     def mean_decrease_impurity(self):

skore/src/skore/_sklearn/_plot/metrics/feature_importance_coefficients_display.py

@@ -0,0 +1,171 @@
+import matplotlib.pyplot as plt
+
+from skore._sklearn._plot.base import DisplayMixin
+
+
+class FeatureImportanceCoefficientsDisplay(DisplayMixin):
+    """Feature importance display.
+
+    Each report type produces its own output frame and plot.
+
+    Parameters
+    ----------
+    report_type : {"estimator", "cross-validation", "comparison-estimator", \
+            "comparison-cross-validation"}
+        Report type from which the display is created.
+
+    coefficients : DataFrame | list[DataFrame]
+        The coefficients data to display.
+
+    Attributes
+    ----------
+    ax_ : matplotlib Axes
+        Axes with the different matplotlib axis.
+
+    figure_ : matplotlib Figure
+        Figure containing the plot.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_diabetes
+    >>> from sklearn.linear_model import LinearRegression
+    >>> from skore import train_test_split
+    >>> from skore import EstimatorReport
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> split_data = train_test_split(
+    >>>     X=X, y=y, random_state=0, as_dict=True, shuffle=False
+    >>> )
+    >>> report = EstimatorReport(LinearRegression(), **split_data)
+    >>> display = report.feature_importance.coefficients()
+    >>> display.plot()
+    >>> display.frame()
+                Coefficient
+    Intercept	151.487952
+    Feature #0	-11.861904
+    Feature #1	-238.445509
+    Feature #2	505.395493
+    Feature #3	298.977119
+    ...         ...
+    """
+
+    def __init__(self, report_type, coefficients):
+        self.report_type = report_type
+        self.coefficients = coefficients
+
+    def frame(self):
+        """Return coefficients as a DataFrame.
+
+        Returns
+        -------
+        pd.DataFrame
+            The structure of the returned frame depends on the underlying report type:
+
+            - If an :class:`EstimatorReport`, a single column "Coefficient", with the
+              index being the feature names.
+
+            - If a :class:`CrossValidationReport`, the columns are the feature names,
+              and the index is the respective split number.
+
+            - If a :class:`ComparisonReport`, the columns are the models passed in the
+              report, with the index being the feature names.
+        """
+        if self.report_type == "estimator":
+            return self._frame_estimator_report()
+        elif self.report_type == "cross-validation":
+            return self._frame_cross_validation_report()
+        else:
+            return self._frame_comparison_report()
+
+    def _frame_estimator_report(self):
+        return self.coefficients
+
+    def _frame_cross_validation_report(self):
+        return self.coefficients
+
+    def _frame_comparison_report(self):
+        import pandas as pd
+
+        return pd.concat(self.coefficients, axis=1)
+
+    @DisplayMixin.style_plot
+    def plot(self, **kwargs) -> None:
+        """Plot the coefficients of linear models.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Additional keyword arguments to be passed to the plot method.
+        """
+        return self._plot(**kwargs)
+
+    def _style_plot_matplotlib(self, ax, title=None, legend=True):
+        if title:
+            ax.set_title(title)
+        if legend:
+            ax.legend(loc="best", bbox_to_anchor=(1, 1), borderaxespad=1)
+        ax.grid(False)
+        for spine in ["top", "right", "left"]:
+            ax.spines[spine].set_visible(False)
+        ax.tick_params(axis="y", length=0)
+
+    def _plot_matplotlib(self, **kwargs):
+        if self.report_type == "estimator":
+            return self._plot_estimator_report()
+        elif self.report_type == "cross-validation":
+            return self._plot_cross_validation_report()
+        elif self.report_type == "comparison-estimator":
+            return self._plot_comparison_report_estimator()
+        elif self.report_type == "comparison-cross-validation":
+            return self._plot_comparison_report_cross_validation()
+        else:
+            raise TypeError(f"Unexpected report type: {self.report_type!r}")
+
+    def _plot_estimator_report(self):
+        self.figure_, self.ax_ = plt.subplots()
+        self.coefficients.plot.barh(ax=self.ax_)
+        self._style_plot_matplotlib(self.ax_, title="Coefficients")
+        self.figure_.tight_layout()
+        plt.show()
+
+    def _plot_cross_validation_report(self):
+        self.figure_, self.ax_ = plt.subplots()
+        self.coefficients.boxplot(ax=self.ax_, vert=False)
+        self._style_plot_matplotlib(
+            self.ax_, title="Coefficient variance across CV splits", legend=None
+        )
+        self.figure_.tight_layout()
+        plt.show()
+
+    def _plot_comparison_report_estimator(self):
+        self.figure_, self.ax_ = plt.subplots(
+            nrows=1,
+            ncols=len(self.coefficients),
+            figsize=(5 * len(self.coefficients), 6),
+            squeeze=False,
+        )
+        self.ax_ = self.ax_.flatten()
+        self.figure_.suptitle("Coefficients")
+        for ax, coef_frame in zip(self.ax_, self.coefficients, strict=False):
+            coef_frame.plot.barh(ax=ax)
+            self._style_plot_matplotlib(ax, title=None)
+        self.figure_.tight_layout()
+        plt.show()
+
+    def _plot_comparison_report_cross_validation(self):
+        self.figure_, self.ax_ = plt.subplots(
+            nrows=1,
+            ncols=len(self.coefficients),
+            figsize=(5 * len(self.coefficients), 6),
+            squeeze=False,
+        )
+        self.ax_ = self.ax_.flatten()
+        for ax, coef_frame in zip(self.ax_, self.coefficients, strict=False):
+            coef_frame.boxplot(ax=ax, vert=False)
+            model_name = coef_frame.columns[0].split("__")[0]
+            self._style_plot_matplotlib(
+                ax,
+                title=f"{model_name} Coefficients across splits",
+                legend=None,
+            )
+        self.figure_.tight_layout()
+        plt.show()