Update submodule

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

treeple/ensemble/_honest_forest.py

@@ -720,8 +720,12 @@ def oob_samples_(self):
             oob_samples.append(_oob_samples)
         return oob_samples
 
-    def _more_tags(self):
-        return {"multioutput": False}
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in HRF
+        tags = super().__sklearn_tags__()
+        tags.classifier_tags.multi_output = False
+        tags.input_tags.allow_nan = False
+        return tags
 
     def decision_path(self, X):
         """

treeple/ensemble/_unsupervised_forest.py

@@ -21,7 +21,12 @@
 )
 from sklearn.metrics import calinski_harabasz_score
 from sklearn.utils.parallel import Parallel, delayed
-from sklearn.utils.validation import _check_sample_weight, check_is_fitted, check_random_state
+from sklearn.utils.validation import (
+    _check_sample_weight,
+    check_is_fitted,
+    check_random_state,
+    validate_data,
+)
 
 from .._lib.sklearn.ensemble._forest import BaseForest
 from .._lib.sklearn.tree._tree import DTYPE
@@ -88,7 +93,8 @@ def fit(self, X, y=None, sample_weight=None):
         self._validate_params()
 
         # Validate or convert input data
-        X = self._validate_data(
+        X = validate_data(
+            self,
             X,
             dtype=DTYPE,  # accept_sparse="csc",
         )

treeple/tree/_classes.py

@@ -378,6 +378,13 @@ def _assign_labels(self, affinity_matrix):
         predict_labels = cluster.fit_predict(affinity_matrix)
         return predict_labels
 
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
+        # However, for MORF it is not supported
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = False
+        return tags
+
 
 class UnsupervisedObliqueDecisionTree(UnsupervisedDecisionTree):
     """Unsupervised oblique decision tree.
@@ -577,6 +584,13 @@ def _build_tree(
         builder.build(self.tree_, X, sample_weight)
         return self
 
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
+        # However, for MORF it is not supported
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = False
+        return tags
+
 
 class ObliqueDecisionTreeClassifier(SimMatrixMixin, DecisionTreeClassifier):
     """An oblique decision tree classifier.
@@ -1070,6 +1084,13 @@ def _update_tree(self, X, y, sample_weight):
         self._prune_tree()
         return self
 
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
+        # However, for MORF it is not supported
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = False
+        return tags
+
 
 class ObliqueDecisionTreeRegressor(SimMatrixMixin, DecisionTreeRegressor):
     """An oblique decision tree Regressor.
@@ -1450,6 +1471,13 @@ def _build_tree(
         builder.build(self.tree_, X, y, sample_weight, None)
         return self
 
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
+        # However, for MORF it is not supported
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = False
+        return tags
+
 
 class PatchObliqueDecisionTreeClassifier(SimMatrixMixin, DecisionTreeClassifier):
     """A oblique decision tree classifier that operates over patches of data.
@@ -1927,11 +1955,13 @@ def _build_tree(
 
         return self
 
-    def _more_tags(self):
+    def __sklearn_tags__(self):
         # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
         # However, for MORF it is not supported
-        allow_nan = False
-        return {"multilabel": True, "allow_nan": allow_nan}
+        tags = super().__sklearn_tags__()
+        tags.classifier_tags.multi_label = True
+        tags.input_tags.allow_nan = False
+        return tags
 
     @property
     def _inheritable_fitted_attribute(self):
@@ -2407,11 +2437,13 @@ def _build_tree(
 
         return self
 
-    def _more_tags(self):
+    def __sklearn_tags__(self):
         # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
         # However, for MORF it is not supported
-        allow_nan = False
-        return {"multilabel": True, "allow_nan": allow_nan}
+        tags = super().__sklearn_tags__()
+        tags.regressor_tags.multi_label = True
+        tags.input_tags.allow_nan = False
+        return tags
 
 
 class ExtraObliqueDecisionTreeClassifier(SimMatrixMixin, DecisionTreeClassifier):
@@ -2846,6 +2878,13 @@ def _inheritable_fitted_attribute(self):
             "feature_combinations_",
         ]
 
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
+        # However, for MORF it is not supported
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = False
+        return tags
+
 
 class ExtraObliqueDecisionTreeRegressor(SimMatrixMixin, DecisionTreeRegressor):
     """An oblique decision tree Regressor.
@@ -3237,3 +3276,10 @@ def _build_tree(
         builder.build(self.tree_, X, y, sample_weight)
 
         return self
+
+    def __sklearn_tags__(self):
+        # XXX: nans should be supportable in SPORF by just using RF-like splits on missing values
+        # However, for MORF it is not supported
+        tags = super().__sklearn_tags__()
+        tags.input_tags.allow_nan = False
+        return tags

treeple/tree/_neighbors.py

@@ -64,7 +64,3 @@ def compute_similarity_matrix(self, X):
             The similarity matrix among the samples.
         """
         return compute_forest_similarity_matrix(self, X)
-
-    def _more_tags(self):
-        # XXX: no treeple estimators support NaNs as of now
-        return {"allow_nan": False}