ENH permutation importance

docs/model_card.rst

@@ -79,7 +79,10 @@ plots, save them on disk and then add them to the card by passing the path name
 to the :meth:`.Card.add_plot` method. For tables, you can pass either
 dictionaries with the key being the header and the values being list of row
 entries, or a pandas ``DataFrame``; use the :meth:`.Card.add_table` method for
-this.
+this. If you would like to add permutation importance results, you can pass
+your importances to :meth:`.Card.add_permutation_importances`. If you want to
+have multiple importance plots, you should pass a file name and a title for the
+plot. This will create a boxplot and write it to the model card for you.
 
 To add content to an existing subsection, or create a new subsection, use a
 ``"/"`` to indicate the subsection. E.g. let's assume you would like to add a

examples/plot_model_card.py

@@ -20,6 +20,7 @@
 from sklearn.datasets import load_breast_cancer
 from sklearn.ensemble import HistGradientBoostingClassifier
 from sklearn.experimental import enable_halving_search_cv  # noqa
+from sklearn.inspection import permutation_importance
 from sklearn.metrics import (
     ConfusionMatrixDisplay,
     accuracy_score,
@@ -153,6 +154,14 @@
     **{"Model description/Evaluation Results/Confusion Matrix": "confusion_matrix.png"}
 )
 
+importances = permutation_importance(model, X_test, y_test, n_repeats=10)
+model_card.add_permutation_importances(
+    importances,
+    X_test.columns,
+    plot_file=Path(local_repo) / "importance.png",
+    plot_name="Permutation Importance",
+)
+
 cv_results = model.cv_results_
 clf_report = classification_report(
     y_test, y_pred, output_dict=True, target_names=["malignant", "benign"]

skops/card/_model_card.py

@@ -17,6 +17,7 @@
 
 from skops.card._templates import CONTENT_PLACEHOLDER, SKOPS_TEMPLATE, Templates
 from skops.io import load
+from skops.utils.importutils import import_or_raise
 
 # Repr attributes can be used to control the behavior of repr
 aRepr = Repr()
@@ -206,7 +207,7 @@ def split_subsection_names(key: str) -> list[str]:
 
 
 def _getting_started_code(
-    file_name: str, model_format: Literal["pickle", "skops"], indent="    "
+    file_name: str, model_format: Literal["pickle", "skops"], indent: str = "    "
 ) -> list[str]:
     # get lines of code required to load the model
     lines = [
@@ -1085,11 +1086,64 @@ def add_metrics(
                     "You can find the details about evaluation process and "
                     "the evaluation results."
                 )
-
         self._metrics.update(kwargs)
         self._add_metrics(section, self._metrics, description=description)
         return self
 
+    def add_permutation_importances(
+        self,
+        permutation_importances,
+        columns: Sequence[str],
+        plot_file: str = "permutation_importances.png",
+        plot_name: str = "Permutation Importances",
+        overwrite: bool = False,
+    ) -> "Card":
+        """Plots permutation importance and saves it to model card.
+
+        Parameters
+        ----------
+        permutation_importances : sklearn.utils.Bunch
+            Output of :func:`sklearn.inspection.permutation_importance`.
+
+        columns : str, list or pandas.Index
+            Column names of the data used to generate importances.
+
+        plot_file : str
+            Filename for the plot.
+
+        plot_name : str
+            Name of the plot.
+
+        overwrite : bool (default=False)
+            Whether to overwrite the permutation importance plot file, if a plot by that
+            name already exists.
+
+        Returns
+        -------
+        self : object
+            Card object.
+        """
+        plt = import_or_raise("matplotlib.pyplot", "permutation importance")
+
+        if Path(plot_file).exists() and overwrite is False:
+            raise ValueError(
+                f"{str(plot_file)} already exists. Set `overwrite` to `True` or pass a"
+                " different filename for the plot."
+            )
+        sorted_importances_idx = permutation_importances.importances_mean.argsort()
+        _, ax = plt.subplots()
+        ax.boxplot(
+            x=permutation_importances.importances[sorted_importances_idx].T,
+            labels=columns[sorted_importances_idx],
+            vert=False,
+        )
+        ax.set_title(plot_name)
+        ax.set_xlabel("Decrease in Score")
+        plt.savefig(plot_file)
+        self.add_plot(**{plot_name: plot_file})
+
+        return self
+
     def _add_metrics(
         self,
         section: str,

skops/card/tests/test_card.py

@@ -5,13 +5,14 @@
 import textwrap
 from pathlib import Path
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pytest
 import sklearn
 from huggingface_hub import CardData, metadata_load
 from sklearn.datasets import load_iris
+from sklearn.inspection import permutation_importance
 from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.metrics import f1_score, make_scorer
 from sklearn.neighbors import KNeighborsClassifier
 
 from skops import hub_utils
@@ -403,6 +404,96 @@ def test_add_twice(self, model_card):
         assert text1 == text2
 
 
+def test_permutation_importances(
+    iris_estimator, iris_data, model_card, destination_path
+):
+    X, y = iris_data
+    result = permutation_importance(
+        iris_estimator, X, y, n_repeats=10, random_state=42, n_jobs=2
+    )
+
+    model_card.add_permutation_importances(
+        result,
+        X.columns,
+        Path(destination_path) / "importance.png",
+        "Permutation Importance",
+    )
+    temp_path = Path(destination_path) / "importance.png"
+    assert f"![Permutation Importance]({temp_path}" in model_card.render()
+
+
+def test_multiple_permutation_importances(
+    iris_estimator, iris_data, model_card, destination_path
+):
+    X, y = iris_data
+    result = permutation_importance(
+        iris_estimator, X, y, n_repeats=10, random_state=42, n_jobs=2
+    )
+    model_card.add_permutation_importances(
+        result, X.columns, plot_file=Path(destination_path) / "importance.png"
+    )
+    f1 = make_scorer(f1_score, average="micro")
+    result = permutation_importance(
+        iris_estimator, X, y, scoring=f1, n_repeats=10, random_state=42, n_jobs=2
+    )
+    model_card.add_permutation_importances(
+        result,
+        X.columns,
+        plot_file=Path(destination_path) / "f1_importance.png",
+        plot_name="Permutation Importance on f1",
+    )
+    # check for default one
+    temp_path = Path(destination_path) / "importance.png"
+    assert f"![Permutation Importances]({temp_path}" in model_card.render()
+    # check for F1
+    temp_path_f1 = Path(destination_path) / "f1_importance.png"
+    assert f"![Permutation Importance on f1]({temp_path_f1}" in model_card.render()
+
+
+def test_duplicate_permutation_importances(
+    iris_estimator, iris_data, model_card, destination_path
+):
+    X, y = iris_data
+    result = permutation_importance(
+        iris_estimator, X, y, n_repeats=10, random_state=42, n_jobs=2
+    )
+    plot_path = os.path.join(destination_path, "importance.png")
+    model_card.add_permutation_importances(result, X.columns, plot_file=plot_path)
+    with pytest.raises(
+        ValueError,
+        match=(
+            "already exists. Set `overwrite` to `True` or pass a"
+            " different filename for the plot."
+        ),
+    ):
+        model_card.add_permutation_importances(
+            result,
+            X.columns,
+            plot_file=plot_path,
+            plot_name="Permutation Importance on f1",
+        )
+
+
+def test_duplicate_permutation_importances_overwrite(
+    iris_estimator, iris_data, model_card, destination_path
+):
+    X, y = iris_data
+    result = permutation_importance(
+        iris_estimator, X, y, n_repeats=10, random_state=42, n_jobs=2
+    )
+    plot_path = os.path.join(destination_path, "importance.png")
+    model_card.add_permutation_importances(result, X.columns, plot_file=plot_path)
+
+    model_card.add_permutation_importances(
+        result,
+        X.columns,
+        plot_file=plot_path,
+        plot_name="Permutation Importance on f1",
+        overwrite=True,
+    )
+    assert f"![Permutation Importance on f1]({plot_path}" in model_card.render()
+
+
 class TestAddGetStartedCode:
     """Tests for getting started code"""
 
@@ -856,13 +947,17 @@ def test_delete_empty_key_subsection_raises(self, model_card):
 
 class TestAddPlot:
     def test_add_plot(self, destination_path, model_card):
+        import matplotlib.pyplot as plt
+
         plt.plot([4, 5, 6, 7])
         plt.savefig(Path(destination_path) / "fig1.png")
         model_card = model_card.add_plot(fig1="fig1.png")
         plot_content = model_card.select("fig1").content.format()
         assert plot_content == "![fig1](fig1.png)"
 
     def test_add_plot_to_existing_section(self, destination_path, model_card):
+        import matplotlib.pyplot as plt
+
         plt.plot([4, 5, 6, 7])
         plt.savefig(Path(destination_path) / "fig1.png")
         model_card = model_card.add_plot(**{"Model description/Figure 1": "fig1.png"})

skops/card/tests/test_parser.py

@@ -90,7 +90,6 @@ def test_example_model_cards(tmp_path, file_name):
     path = Path(os.getcwd()) / "skops" / "card" / "tests" / "examples"
     file0 = path / file_name
     diff = (path / file_name).with_suffix(".md.diff")
-
     parsed_card = parse_modelcard(file0)
     file1 = tmp_path / "readme-parsed.md"
     parsed_card.save(file1)

skops/conftest.py

@@ -1,3 +1,4 @@
+import builtins
 from unittest.mock import patch
 
 import pytest
@@ -7,7 +8,8 @@
 def pandas_not_installed():
     # patch import so that it raises an ImportError when trying to import
     # pandas. This works because pandas is only imported lazily.
-    orig_import = __import__
+
+    orig_import = builtins.__import__
 
     def mock_import(name, *args, **kwargs):
         if name == "pandas":
@@ -16,3 +18,28 @@ def mock_import(name, *args, **kwargs):
 
     with patch("builtins.__import__", side_effect=mock_import):
         yield
+
+
+@pytest.fixture
+def matplotlib_not_installed():
+    # patch import so that it raises an ImportError when trying to import
+    # matplotlib. This works because matplotlib is only imported lazily.
+
+    # ugly way of removing matplotlib from cached imports
+    import sys
+
+    for key in list(sys.modules.keys()):
+        if key.startswith("matplotlib"):
+            del sys.modules[key]
+
+    orig_import = builtins.__import__
+
+    def mock_import(name, *args, **kwargs):
+        if name == "matplotlib":
+            raise ImportError
+        return orig_import(name, *args, **kwargs)
+
+    with patch("builtins.__import__", side_effect=mock_import):
+        yield
+
+    import matplotlib  # noqa

skops/hub_utils/_hf_hub.py

@@ -206,7 +206,7 @@ def _create_config(
         "text-regression",
     ],
     data,
-    model_format: Literal[  # type: ignore
+    model_format: Literal[
         "skops",
         "pickle",
         "auto",
@@ -337,7 +337,7 @@ def init(
         "text-regression",
     ],
     data,
-    model_format: Literal[  # type: ignore
+    model_format: Literal[
         "skops",
         "pickle",
         "auto",

skops/utils/importutils.py

@@ -0,0 +1,29 @@
+from importlib import import_module
+
+
+def import_or_raise(module, feature_name):
+    """Raise error if a given library is not present in the environment.
+
+    Parameters
+    ----------
+    module: str
+        Name of the module.
+
+    feature_name: str
+        Name of the feature module is required for.
+
+    Raises
+    ------
+    ModuleNotFoundError
+        Is raised if a given module is not present in the environment
+    """
+    try:
+        module = import_module(module)
+    except ImportError as e:
+        package = module.split(".")[0]
+        raise ModuleNotFoundError(
+            f"{feature_name.capitalize()} requires {package} to be installed. In order"
+            f" to use {feature_name}, you need to install the package in your current"
+            " python environment."
+        ) from e
+    return module

skops/utils/tests/test_importutils.py

@@ -0,0 +1,16 @@
+import pytest
+
+from skops.utils.importutils import import_or_raise
+
+
+@pytest.mark.usefixtures("matplotlib_not_installed")
+def test_import_or_raise():
+    with pytest.raises(
+        ModuleNotFoundError,
+        match=(
+            "Permutation importance requires matplotlib to be installed. In order"
+            " to use permutation importance, you need to install the package in"
+            " your current python environment."
+        ),
+    ):
+        import_or_raise("matplotlib", "permutation importance")