WIP for sklearn monotonicity

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

sktree/tree/_oblique_splitter.pxd

@@ -83,7 +83,9 @@ cdef class BaseObliqueSplitter(Splitter):
         self,
         double impurity,   # Impurity of the node
         SplitRecord* split,
-        SIZE_t* n_constant_features
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
     ) except -1 nogil
 
 

sktree/tree/_oblique_splitter.pyx

@@ -169,7 +169,9 @@ cdef class BaseObliqueSplitter(Splitter):
         self,
         double impurity,
         SplitRecord* split,
-        SIZE_t* n_constant_features
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
     ) except -1 nogil:
         """Find the best_split split on node samples[start:end]
 
@@ -456,7 +458,9 @@ cdef class BestObliqueSplitter(ObliqueSplitter):
         self,
         double impurity,
         SplitRecord* split,
-        SIZE_t* n_constant_features
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
     ) except -1 nogil:
         """Find the best_split split on node samples[start:end]
 

sktree/tree/unsupervised/_unsup_oblique_splitter.pxd

@@ -55,10 +55,14 @@ cdef class UnsupervisedObliqueSplitter(UnsupervisedSplitter):
     cdef int node_reset(self, SIZE_t start, SIZE_t end,
                         double* weighted_n_node_samples) except -1 nogil
 
-    cdef int node_split(self,
-                        double impurity,   # Impurity of the node
-                        SplitRecord* split,
-                        SIZE_t* n_constant_features) except -1 nogil
+    cdef int node_split(
+        self,
+        double impurity,   # Impurity of the node
+        SplitRecord* split,
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
+    ) except -1 nogil
     cdef int init(
         self,
         const DTYPE_t[:, :] X,

sktree/tree/unsupervised/_unsup_oblique_splitter.pyx

@@ -201,8 +201,14 @@ cdef class BestObliqueUnsupervisedSplitter(UnsupervisedObliqueSplitter):
             proj_mat_indices[proj_i].push_back(feat_i)  # Store index of nonzero
             proj_mat_weights[proj_i].push_back(weight)  # Store weight of nonzero
 
-    cdef int node_split(self, double impurity, SplitRecord* split,
-                        SIZE_t* n_constant_features) except -1 nogil:
+    cdef int node_split(
+        self,
+        double impurity,
+        SplitRecord* split,
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
+    ) except -1 nogil:
         """Find the best_split split on node samples[start:end]
 
         Returns -1 in case of failure to allocate memory (and raise MemoryError)

sktree/tree/unsupervised/_unsup_splitter.pxd

@@ -40,7 +40,9 @@ cdef class UnsupervisedSplitter(BaseSplitter):
         self,
         double impurity,   # Impurity of the node
         SplitRecord* split,
-        SIZE_t* n_constant_features
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
     ) except -1 nogil
     cdef void node_value(self, double* dest) noexcept nogil
     cdef double node_impurity(self) noexcept nogil

sktree/tree/unsupervised/_unsup_splitter.pyx

@@ -34,9 +34,15 @@ cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) noexcept nogil
 cdef class UnsupervisedSplitter(BaseSplitter):
     """Base class for unsupervised splitters."""
 
-    def __cinit__(self, UnsupervisedCriterion criterion, SIZE_t max_features,
-                  SIZE_t min_samples_leaf, double min_weight_leaf,
-                  object random_state, *argv):
+    def __cinit__(
+        self,
+        UnsupervisedCriterion criterion,
+        SIZE_t max_features,
+        SIZE_t min_samples_leaf,
+        double min_weight_leaf,
+        object random_state,
+        *argv
+    ):
         """
         Parameters
         ----------
@@ -174,7 +180,9 @@ cdef class BestUnsupervisedSplitter(UnsupervisedSplitter):
         self,
         double impurity,
         SplitRecord* split,
-        SIZE_t* n_constant_features
+        SIZE_t* n_constant_features,
+        double lower_bound,
+        double upper_bound
     ) except -1 nogil:
         """Find the best_split split on node samples[start:end].
 

sktree/tree/unsupervised/_unsup_tree.pyx

@@ -295,6 +295,8 @@ cdef class UnsupervisedBestFirstTreeBuilder(UnsupervisedTreeBuilder):
         bint is_left,
         Node* parent,
         SIZE_t depth,
+        double lower_bound,
+        double upper_bound,
         FrontierRecord* res
     ) except -1 nogil:
         """Adds node w/ partition ``[start, end)`` to the frontier. """
@@ -325,7 +327,13 @@ cdef class UnsupervisedBestFirstTreeBuilder(UnsupervisedTreeBuilder):
                    )
 
         if not is_leaf:
-            splitter.node_split(impurity, split_ptr, &n_constant_features)
+            splitter.node_split(
+                impurity,
+                split_ptr,
+                &n_constant_features,
+                lower_bound,
+                upper_bound
+            )
 
             # assign local copy of SplitRecord to assign
             # pos, improvement, and impurity scores
@@ -348,12 +356,17 @@ cdef class UnsupervisedBestFirstTreeBuilder(UnsupervisedTreeBuilder):
 
         # compute values also for split nodes (might become leafs later).
         splitter.node_value(tree.value + node_id * tree.value_stride)
+        if splitter.with_monotonic_cst:
+            splitter.clip_node_value(tree.value + node_id * tree.value_stride, lower_bound, upper_bound)
 
         res.node_id = node_id
         res.start = start
         res.end = end
         res.depth = depth
         res.impurity = impurity
+        res.lower_bound = lower_bound
+        res.upper_bound = upper_bound
+        res.middle_value = splitter.criterion.middle_value()
 
         if not is_leaf:
             # is split node