UPdate and address permute forest fraction

Signed-off-by: Adam Li <adam2392@gmail.com>

sktree/stats/forestht.py

@@ -114,19 +114,19 @@ def __init__(
         random_state=None,
         verbose=0,
         test_size=0.2,
-        permute_per_tree=False,
-        sample_dataset_per_tree=False,
         stratify=False,
+        sample_dataset_per_tree=False,
         permute_forest_fraction=None,
     ):
         self.estimator = estimator
         self.random_state = random_state
         self.verbose = verbose
         self.test_size = test_size
-        self.permute_per_tree = permute_per_tree
+        self.stratify = stratify
+
+        # XXX: possibly removing these parameters
         self.sample_dataset_per_tree = sample_dataset_per_tree
         self.permute_forest_fraction = permute_forest_fraction
-        self.stratify = stratify
 
         self.n_samples_test_ = None
         self._n_samples_ = None
@@ -166,15 +166,33 @@ def _get_estimators_indices(self, stratifier=None, sample_separate=False):
         # Get drawn indices along both sample and feature axes
         rng = np.random.default_rng(self.estimator_.random_state)
 
-        if self.sample_dataset_per_tree:
+        if self.permute_forest_fraction is None:
+            permute_forest_fraction = 0.0
+        else:
+            permute_forest_fraction = self.permute_forest_fraction
+
+        # TODO: consolidate how we "sample/permute" per subset of the forest
+        if self.sample_dataset_per_tree or permute_forest_fraction > 0.0:
+            # sample random seeds
             if self._seeds is None:
                 self._seeds = []
+                self._n_permutations = 0
 
-                for tree in self.estimator_.estimators_:
-                    if tree.random_state is None:
-                        self._seeds.append(rng.integers(low=0, high=np.iinfo(np.int32).max))
-                    else:
-                        self._seeds.append(tree.random_state)
+                num_trees_per_seed = max(
+                    int(permute_forest_fraction * len(self.estimator_.estimators_)), 1
+                )
+                for tree_idx, tree in enumerate(self.estimator_.estimators_):
+                    if tree_idx == 0 or tree_idx % num_trees_per_seed == 0:
+                        if tree.random_state is None:
+                            seed = rng.integers(low=0, high=np.iinfo(np.int32).max)
+                        else:
+                            seed = tree.random_state
+
+                        self._n_permutations += 1
+                    self._seeds.append(seed)
+
+            # now that we have the random seeds, we can sample the train/test indices
+            # deterministically
             seeds = self._seeds
 
             # if sample_separate:
@@ -632,7 +650,7 @@ def __init__(
         random_state=None,
         verbose=0,
         test_size=0.2,
-        permute_per_tree=False,
+        # permute_per_tree=False,
         sample_dataset_per_tree=False,
         permute_forest_fraction=None,
     ):
@@ -641,7 +659,7 @@ def __init__(
             random_state=random_state,
             verbose=verbose,
             test_size=test_size,
-            permute_per_tree=permute_per_tree,
+            # permute_per_tree=permute_per_tree,
             sample_dataset_per_tree=sample_dataset_per_tree,
             permute_forest_fraction=permute_forest_fraction,
         )
@@ -721,7 +739,7 @@ def _statistic(
 
         # both sampling dataset per tree or permuting per tree requires us to bypass the
         # sklearn API to fit each tree individually
-        if self.sample_dataset_per_tree or self.permute_per_tree:
+        if self.sample_dataset_per_tree or self.permute_forest_fraction:
             # Parallel(n_jobs=estimator.n_jobs, verbose=self.verbose, prefer="threads")(
             #     delayed(_parallel_build_trees_and_compute_posteriors)(
             #         estimator,
@@ -741,7 +759,7 @@ def _statistic(
             #     )
             # )
 
-            if self.permute_per_tree and covariate_index is not None:
+            if self.permute_forest_fraction and covariate_index is not None:
                 random_states = [tree.random_state for tree in estimator.estimators_]
             else:
                 random_states = [estimator.random_state] * len(estimator.estimators_)
@@ -799,9 +817,7 @@ def _statistic(
             delayed(_parallel_predict_proba)(estimator.estimators_[idx].predict, X, indices_test)
             for idx, (_, indices_test) in enumerate(self.train_test_samples_)
         )
-        for itree, (proba, est_indices) in enumerate(
-            zip(all_proba, self.train_test_samples_)
-        ):
+        for itree, (proba, est_indices) in enumerate(zip(all_proba, self.train_test_samples_)):
             _, indices_test = est_indices
             posterior_arr[itree, indices_test, ...] = proba.reshape(-1, estimator.n_outputs_)
 
@@ -923,7 +939,7 @@ def __init__(
         random_state=None,
         verbose=0,
         test_size=0.2,
-        permute_per_tree=False,
+        # permute_per_tree=False,
         sample_dataset_per_tree=False,
         stratify=True,
         permute_forest_fraction=None,
@@ -933,7 +949,7 @@ def __init__(
             random_state=random_state,
             verbose=verbose,
             test_size=test_size,
-            permute_per_tree=permute_per_tree,
+            # permute_per_tree=permute_per_tree,
             sample_dataset_per_tree=sample_dataset_per_tree,
             stratify=stratify,
             permute_forest_fraction=permute_forest_fraction,
@@ -982,7 +998,7 @@ def _statistic(
 
         # both sampling dataset per tree or permuting per tree requires us to bypass the
         # sklearn API to fit each tree individually
-        if self.sample_dataset_per_tree or self.permute_per_tree:
+        if self.sample_dataset_per_tree or self.permute_forest_fraction:
             # Parallel(n_jobs=estimator.n_jobs, verbose=self.verbose, prefer="threads")(
             #     delayed(_parallel_build_trees_and_compute_posteriors)(
             #         estimator,
@@ -1001,7 +1017,7 @@ def _statistic(
             #         self._get_estimators_indices(sample_separate=True)
             #     )
             # )
-            if self.permute_per_tree and covariate_index is not None:
+            if self.permute_forest_fraction and covariate_index is not None:
                 random_states = [tree.random_state for tree in estimator.estimators_]
             else:
                 random_states = [estimator.random_state] * len(estimator.estimators_)
@@ -1074,9 +1090,7 @@ def _statistic(
                 )
                 for idx, (_, indices_test) in enumerate(self.train_test_samples_)
             )
-        for itree, (proba, est_indices) in enumerate(
-            zip(all_proba, self.train_test_samples_)
-        ):
+        for itree, (proba, est_indices) in enumerate(zip(all_proba, self.train_test_samples_)):
             _, indices_test = est_indices
 
             if predict_posteriors: