[API 2]: CFI, PFI, LOCO

examples/plot_conditional_vs_marginal_xor_data.py

@@ -117,7 +117,7 @@
         random_state=0,
     )
     vim.fit(X_train, y_train)
-    importances.append(vim.importance(X_test, y_test)["importance"])
+    importances.append(vim.importance(X_test, y_test))
 
 importances = np.array(importances).T
 

examples/plot_diabetes_variable_importance_example.py

@@ -184,14 +184,14 @@ def compute_pval(vim):
 # -------------------
 
 
-cfi_vim_arr = np.array([x["importance"] for x in cfi_importance_list]) / 2
+cfi_vim_arr = np.array(cfi_importance_list) / 2
 cfi_pval = compute_pval(cfi_vim_arr)
 
 vim = [
     pd.DataFrame(
         {
             "var": np.arange(cfi_vim_arr.shape[1]),
-            "importance": x["importance"],
+            "importance": x,
             "fold": i,
             "pval": cfi_pval,
             "method": "CFI",
@@ -200,14 +200,14 @@ def compute_pval(vim):
     for x in cfi_importance_list
 ]
 
-loco_vim_arr = np.array([x["importance"] for x in loco_importance_list])
+loco_vim_arr = np.array(loco_importance_list)
 loco_pval = compute_pval(loco_vim_arr)
 
 vim += [
     pd.DataFrame(
         {
             "var": np.arange(loco_vim_arr.shape[1]),
-            "importance": x["importance"],
+            "importance": x,
             "fold": i,
             "pval": loco_pval,
             "method": "LOCO",
@@ -216,14 +216,14 @@ def compute_pval(vim):
     for x in loco_importance_list
 ]
 
-pfi_vim_arr = np.array([x["importance"] for x in pfi_importance_list])
+pfi_vim_arr = np.array(pfi_importance_list)
 pfi_pval = compute_pval(pfi_vim_arr)
 
 vim += [
     pd.DataFrame(
         {
             "var": np.arange(pfi_vim_arr.shape[1]),
-            "importance": x["importance"],
+            "importance": x,
             "fold": i,
             "pval": pfi_pval,
             "method": "PFI",

examples/plot_importance_classification_iris.py

@@ -93,7 +93,7 @@ def run_one_fold(X, y, model, train_index, test_index, vim_name="CFI", groups=No
         )
 
     vim.fit(X[train_index], y[train_index], groups=groups)
-    importance = vim.importance(X[test_index], y[test_index])["importance"]
+    importance = vim.importance(X[test_index], y[test_index])
 
     return pd.DataFrame(
         {

examples/plot_model_agnostic_importance.py

@@ -108,10 +108,8 @@
     vim_linear.fit(X[train], y[train])
     vim_non_linear.fit(X[train], y[train])
 
-    importances_linear.append(vim_linear.importance(X[test], y[test])["importance"])
-    importances_non_linear.append(
-        vim_non_linear.importance(X[test], y[test])["importance"]
-    )
+    importances_linear.append(vim_linear.importance(X[test], y[test]))
+    importances_non_linear.append(vim_non_linear.importance(X[test], y[test]))
 
 
 ################################################################################

examples/plot_pitfalls_permutation_importance.py

@@ -132,7 +132,7 @@
     )
     pfi.fit(X_test, y_test)
 
-    permutation_importances.append(pfi.importance(X_test, y_test)["importance"])
+    permutation_importances.append(pfi.importance(X_test, y_test))
 permutation_importances = np.stack(permutation_importances)
 pval_pfi = ttest_1samp(
     permutation_importances, 0.0, axis=0, alternative="greater"
@@ -200,7 +200,7 @@
     )
     cfi.fit(X_test, y_test)
 
-    conditional_importances.append(cfi.importance(X_test, y_test)["importance"])
+    conditional_importances.append(cfi.importance(X_test, y_test))
 
 
 cfi_pval = ttest_1samp(

src/hidimstat/__init__.py

@@ -14,16 +14,16 @@
     desparsified_group_lasso_pvalue,
 )
 from .distilled_conditional_randomization_test import d0crt, D0CRT
-from .conditional_feature_importance import CFI
+from .conditional_feature_importance import cfi, CFI
 from .knockoffs import (
     model_x_knockoff,
     model_x_knockoff_pvalue,
     model_x_knockoff_bootstrap_quantile,
     model_x_knockoff_bootstrap_e_value,
 )
-from .leave_one_covariate_out import LOCO
+from .leave_one_covariate_out import loco, LOCO
 from .noise_std import reid
-from .permutation_feature_importance import PFI
+from .permutation_feature_importance import pfi, PFI
 
 from .statistical_tools.aggregation import quantile_aggregation
 
@@ -49,6 +49,9 @@
     "model_x_knockoff_bootstrap_quantile",
     "model_x_knockoff_bootstrap_e_value",
     "CFI",
+    "cfi",
     "LOCO",
+    "loco",
     "PFI",
+    "pfi",
 ]

src/hidimstat/_utils/utils.py

@@ -25,3 +25,34 @@ def _check_vim_predict_method(method):
             "The method {} is not a valid method "
             "for variable importance measure prediction".format(method)
         )
+
+
+def get_generated_attributes(cls):
+    """
+    Get all attributes from a class that end with a single underscore
+    and doesn't start with one underscore.
+
+    Parameters
+    ----------
+    cls : class
+        The class to inspect for attributes.
+
+    Returns
+    -------
+    list
+        A list of attribute names that end with a single underscore but not double underscore.
+    """
+    # Get all attributes and methods of the class
+    all_attributes = dir(cls)
+
+    # Filter out attributes that start with an underscore
+    filtered_attributes = [attr for attr in all_attributes if not attr.startswith("_")]
+
+    # Filter out attributes that do not end with a single underscore
+    result = [
+        attr
+        for attr in filtered_attributes
+        if attr.endswith("_") and not attr.endswith("__")
+    ]
+
+    return result