Merge branch 'main' into rename

doc/whats_new/v0.8.rst

@@ -40,4 +40,4 @@ Thanks to everyone who has contributed to the maintenance and improvement of
 the project since version inception, including:
 
 * `Adam Li`_
-
+* `Sambit Panda`_

doc/whats_new/v0.9.rst

@@ -10,7 +10,7 @@ Version 0.9
 
 **In Development**
 
-This release include a rename of the package to from ``scikit-learn`` to ``treeple``
+This release include a rename of the package to from ``scikit-tree`` to ``treeple``
 The users can replace the previous usage as follows:
 ``import sktree`` to ``import treeple``
 ``from sktree import tree`` to ``from treeple import tree``
@@ -21,7 +21,10 @@ Changelog
 ---------
 
 - |API| Rename the package to ``treeple``. By `SUKI-O`_ (:pr:`#292`)
-
+- |Fix| Fixed a bug in the predict_proba function of the :class:`treeple.HonestForestClassifier` where posteriors
+    estimated on empty leaf with ``ignore`` prior would result in ``np.nan``
+    values for all trees on that sample.
+    By `Haoyin Xu`_ (:pr:`#291`)
 
 Code and Documentation Contributors
 -----------------------------------
@@ -31,3 +34,4 @@ the project since version inception, including:
 
 * `Adam Li`_
 * `SUKI-O`_
+* `Haoyin Xu`_

treeple/ensemble/_honest_forest.py

@@ -259,7 +259,7 @@ class HonestForestClassifier(ForestClassifier, ForestClassifierMixin):
         - If int, then draw `max_samples` samples.
         - If float, then draw `max_samples * X.shape[0]` samples.
 
-    honest_prior : {"ignore", "uniform", "empirical"}, default="empirical"
+    honest_prior : {"ignore", "uniform", "empirical"}, default="ignore"
         Method for dealing with empty leaves during evaluation of a test
         sample. If "ignore", the tree is ignored. If "uniform", the prior tree
         posterior is 1/(number of classes). If "empirical", the prior tree
@@ -444,7 +444,7 @@ def __init__(
         class_weight=None,
         ccp_alpha=0.0,
         max_samples=None,
-        honest_prior="empirical",
+        honest_prior="ignore",
         honest_fraction=0.5,
         tree_estimator=None,
         stratify=False,
@@ -648,7 +648,7 @@ def predict_proba(self, X):
         """
         return self._predict_proba(X)
 
-    def _predict_proba(self, X, indices=None, impute_missing=None):
+    def _predict_proba(self, X, indices=None, impute_missing=np.nan):
         """predict_proba helper class"""
         check_is_fitted(self)
         X = self._validate_X_predict(X)
@@ -672,10 +672,7 @@ def _predict_proba(self, X, indices=None, impute_missing=None):
         zero_mask = posteriors.sum(2) == 0
         posteriors[~zero_mask] /= posteriors[~zero_mask].sum(1, keepdims=True)
 
-        if impute_missing is None:
-            pass
-        else:
-            posteriors[zero_mask] = impute_missing
+        posteriors[zero_mask] = impute_missing
 
         # preserve shape of multi-outputs
         if self.n_outputs_ > 1:
@@ -823,7 +820,7 @@ def _accumulate_prediction(predict, X, out, lock, indices=None):
 
     with lock:
         if len(out) == 1:
-            out[0][indices] += proba
+            out[0][indices] = np.nansum([out[0][indices], proba], axis=0)
         else:
             for i in range(len(out)):
-                out[i][indices] += proba[i]
+                out[i][indices] = np.nansum([out[i][indices], proba[i]], axis=0)

treeple/stats/tests/test_forestht.py

@@ -119,11 +119,10 @@ def test_small_dataset_independent(seed):
 @flaky(max_runs=3)
 @pytest.mark.parametrize("seed", [10, 0])
 def test_small_dataset_dependent(seed):
-    n_samples = 50
+    n_samples = 100
     n_features = 5
     rng = np.random.default_rng(seed)
 
-    X = rng.uniform(size=(n_samples, n_features))
     X = rng.uniform(size=(n_samples // 2, n_features))
     X2 = X + 3
     X = np.vstack([X, X2])
@@ -157,12 +156,15 @@ def test_small_dataset_dependent(seed):
         n_repeats=1000,
         metric="mi",
         return_posteriors=False,
+        seed=seed,
     )
     assert ~np.isnan(result.pvalue)
     assert ~np.isnan(result.observe_test_stat)
     assert result.pvalue <= 0.05
 
-    result = build_coleman_forest(clf, perm_clf, X, y, metric="mi", return_posteriors=False)
+    result = build_coleman_forest(
+        clf, perm_clf, X, y, metric="mi", return_posteriors=False, seed=seed
+    )
     assert result.pvalue <= 0.05
 
 

treeple/tests/test_extensions.py

@@ -32,7 +32,10 @@ def test_predict_proba_per_tree(Forest, n_classes):
     )
 
     # Call the method being tested
-    est = Forest(n_estimators=10, bootstrap=True, random_state=0)
+    if Forest == HonestForestClassifier:
+        est = Forest(n_estimators=10, bootstrap=True, random_state=0, honest_prior="empirical")
+    else:
+        est = Forest(n_estimators=10, bootstrap=True, random_state=0)
     est.fit(X, y)
     proba_per_tree = est.predict_proba_per_tree(X)
 

treeple/tests/test_honest_forest.py

@@ -263,6 +263,23 @@ def test_impute_posteriors(honest_prior, val):
         ), f"Failed with {honest_prior}, prior {clf.estimators_[0].empirical_prior_}"
 
 
+def test_honestforest_predict_proba_with_honest_prior():
+    X = rng.normal(0, 1, (100, 2))
+    y = [0] * 75 + [1] * 25
+    honest_prior = "ignore"
+    clf = HonestForestClassifier(
+        honest_fraction=0.5, random_state=0, honest_prior=honest_prior, n_estimators=100
+    )
+    clf = clf.fit(X, y)
+
+    y_proba = clf.predict_proba(X)
+
+    # With enough trees no nan values should exist
+    assert (
+        len(np.where(np.isnan(y_proba[:, 0]))[0]) == 0
+    ), f"Failed with {honest_prior}, prior {clf.estimators_[0].empirical_prior_}"
+
+
 @pytest.mark.parametrize(
     "honest_fraction, val",
     [