FIX correct node splitting order & remove class weight (#56)

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neurodata/treeple#107
#### What does this implement/fix? Explain your changes.


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---------

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

Author: PSSF23

sklearn/ensemble/_forest.py

@@ -1252,7 +1252,11 @@ def partial_fit(self, X, y, sample_weight=None, classes=None):
 
         self.n_outputs_ = y.shape[1]
 
-        y, expanded_class_weight = self._validate_y_class_weight(y)
+        classes = self.classes_
+        if self.n_outputs_ == 1:
+            classes = [classes]
+
+        y, expanded_class_weight = self._validate_y_class_weight(y, classes)
 
         if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
             y = np.ascontiguousarray(y, dtype=DOUBLE)
@@ -1341,7 +1345,7 @@ def partial_fit(self, X, y, sample_weight=None, classes=None):
                 verbose=self.verbose,
                 class_weight=self.class_weight,
                 n_samples_bootstrap=n_samples_bootstrap,
-                classes=classes,
+                classes=classes[0],
             )
             for i, t in enumerate(self.estimators_)
         )

sklearn/tree/_splitter.pxd

@@ -126,7 +126,7 @@ cdef class Splitter(BaseSplitter):
     # Methods that allow modifications to stopping conditions
     cdef bint check_presplit_conditions(
         self,
-        SplitRecord current_split,
+        SplitRecord* current_split,
         SIZE_t n_missing,
         bint missing_go_to_left,
     ) noexcept nogil

sklearn/tree/_splitter.pyx

@@ -327,9 +327,9 @@ cdef class Splitter(BaseSplitter):
 
         return self.criterion.node_impurity()
 
-    cdef bint check_presplit_conditions(
+    cdef inline bint check_presplit_conditions(
         self,
-        SplitRecord current_split,
+        SplitRecord* current_split,
         SIZE_t n_missing,
         bint missing_go_to_left,
     ) noexcept nogil:
@@ -356,7 +356,7 @@ cdef class Splitter(BaseSplitter):
 
         return 0
 
-    cdef bint check_postsplit_conditions(
+    cdef inline bint check_postsplit_conditions(
         self
     ) noexcept nogil:
         """Check stopping conditions after evaluating the split.
@@ -571,7 +571,7 @@ cdef inline int node_split_best(
                 else:
                     n_left = current_split.pos - splitter.start
                     n_right = end_non_missing - current_split.pos + n_missing
-                if splitter.check_presplit_conditions(current_split, n_missing, missing_go_to_left) == 1:
+                if splitter.check_presplit_conditions(&current_split, n_missing, missing_go_to_left) == 1:
                     continue
 
                 criterion.update(current_split.pos)
@@ -914,7 +914,7 @@ cdef inline int node_split_random(
         current_split.pos = partitioner.partition_samples(current_split.threshold)
 
         # Reject if min_samples_leaf is not guaranteed
-        if splitter.check_presplit_conditions(current_split, 0, 0) == 1:
+        if splitter.check_presplit_conditions(&current_split, 0, 0) == 1:
             continue
 
         # Evaluate split

sklearn/tree/_tree.pyx

@@ -268,16 +268,6 @@ cdef class DepthFirstTreeBuilder(TreeBuilder):
         # check input
         X, y, sample_weight = self._check_input(X, y, sample_weight)
 
-        # Initial capacity
-        cdef int init_capacity
-
-        if tree.max_depth <= 10:
-            init_capacity = <int> (2 ** (tree.max_depth + 1)) - 1
-        else:
-            init_capacity = 2047
-
-        tree._resize(init_capacity)
-
         # Parameters
         cdef Splitter splitter = self.splitter
         cdef SIZE_t max_depth = self.max_depth
@@ -286,10 +276,19 @@ cdef class DepthFirstTreeBuilder(TreeBuilder):
         cdef SIZE_t min_samples_split = self.min_samples_split
         cdef double min_impurity_decrease = self.min_impurity_decrease
 
+        # Initial capacity
+        cdef int init_capacity
         cdef bint first = 0
         if self.initial_roots is None:
             # Recursive partition (without actual recursion)
             splitter.init(X, y, sample_weight, missing_values_in_feature_mask)
+
+            if tree.max_depth <= 10:
+                init_capacity = <int> (2 ** (tree.max_depth + 1)) - 1
+            else:
+                init_capacity = 2047
+
+            tree._resize(init_capacity)
             first = 1
 
         cdef SIZE_t start = 0
@@ -1148,7 +1147,7 @@ cdef class BaseTree:
 
         return node_id
 
-    cdef SIZE_t _update_node(
+    cdef inline SIZE_t _update_node(
         self,
         SIZE_t parent,
         bint is_left,