s474139/Traktor
2
1
Fork 0
Code Issues Pull Requests 1 Packages Projects Releases Wiki Activity
4090c9f502
Traktor/myenv/Lib/site-packages/sklearn/tree/_tree.pyx

1983 lines
72 KiB
Cython
Raw Normal View History

dodanie neuralnetwork
2024-05-23 01:57:24 +02:00
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Lars Buitinck
# Arnaud Joly <arnaud.v.joly@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Fares Hedayati <fares.hedayati@gmail.com>
# Jacob Schreiber <jmschreiber91@gmail.com>
# Nelson Liu <nelson@nelsonliu.me>
#
# License: BSD 3 clause
from cpython cimport Py_INCREF, PyObject, PyTypeObject
from libc.stdlib cimport free
from libc.string cimport memcpy
from libc.string cimport memset
from libc.stdint cimport INTPTR_MAX
from libc.math cimport isnan
from libcpp.vector cimport vector
from libcpp.algorithm cimport pop_heap
from libcpp.algorithm cimport push_heap
from libcpp cimport bool
import struct
import numpy as np
cimport numpy as cnp
cnp.import_array()
from scipy.sparse import issparse
from scipy.sparse import csr_matrix
from ._utils cimport safe_realloc
from ._utils cimport sizet_ptr_to_ndarray
cdef extern from "numpy/arrayobject.h":
object PyArray_NewFromDescr(PyTypeObject* subtype, cnp.dtype descr,
int nd, cnp.npy_intp* dims,
cnp.npy_intp* strides,
void* data, int flags, object obj)
int PyArray_SetBaseObject(cnp.ndarray arr, PyObject* obj)
cdef extern from "<stack>" namespace "std" nogil:
cdef cppclass stack[T]:
ctypedef T value_type
stack() except +
bint empty()
void pop()
void push(T&) except + # Raise c++ exception for bad_alloc -> MemoryError
T& top()
# =============================================================================
# Types and constants
# =============================================================================
from numpy import float32 as DTYPE
from numpy import float64 as DOUBLE
cdef float64_t INFINITY = np.inf
cdef float64_t EPSILON = np.finfo('double').eps
# Some handy constants (BestFirstTreeBuilder)
cdef bint IS_FIRST = 1
cdef bint IS_NOT_FIRST = 0
cdef bint IS_LEFT = 1
cdef bint IS_NOT_LEFT = 0
TREE_LEAF = -1
TREE_UNDEFINED = -2
cdef intp_t _TREE_LEAF = TREE_LEAF
cdef intp_t _TREE_UNDEFINED = TREE_UNDEFINED
# Build the corresponding numpy dtype for Node.
# This works by casting `dummy` to an array of Node of length 1, which numpy
# can construct a `dtype`-object for. See https://stackoverflow.com/q/62448946
# for a more detailed explanation.
cdef Node dummy
NODE_DTYPE = np.asarray(<Node[:1]>(&dummy)).dtype
cdef inline void _init_parent_record(ParentInfo* record) noexcept nogil:
record.n_constant_features = 0
record.impurity = INFINITY
record.lower_bound = -INFINITY
record.upper_bound = INFINITY
# =============================================================================
# TreeBuilder
# =============================================================================
cdef class TreeBuilder:
"""Interface for different tree building strategies."""
cpdef build(
self,
Tree tree,
object X,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight=None,
const unsigned char[::1] missing_values_in_feature_mask=None,
):
"""Build a decision tree from the training set (X, y)."""
pass
cdef inline _check_input(
self,
object X,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight,
):
"""Check input dtype, layout and format"""
if issparse(X):
X = X.tocsc()
X.sort_indices()
if X.data.dtype != DTYPE:
X.data = np.ascontiguousarray(X.data, dtype=DTYPE)
if X.indices.dtype != np.int32 or X.indptr.dtype != np.int32:
raise ValueError("No support for np.int64 index based "
"sparse matrices")
elif X.dtype != DTYPE:
# since we have to copy we will make it fortran for efficiency
X = np.asfortranarray(X, dtype=DTYPE)
# TODO: This check for y seems to be redundant, as it is also
# present in the BaseDecisionTree's fit method, and therefore
# can be removed.
if y.base.dtype != DOUBLE or not y.base.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
if (
sample_weight is not None and
(
sample_weight.base.dtype != DOUBLE or
not sample_weight.base.flags.contiguous
)
):
sample_weight = np.asarray(sample_weight, dtype=DOUBLE, order="C")
return X, y, sample_weight
# Depth first builder ---------------------------------------------------------
# A record on the stack for depth-first tree growing
cdef struct StackRecord:
intp_t start
intp_t end
intp_t depth
intp_t parent
bint is_left
float64_t impurity
intp_t n_constant_features
float64_t lower_bound
float64_t upper_bound
cdef class DepthFirstTreeBuilder(TreeBuilder):
"""Build a decision tree in depth-first fashion."""
def __cinit__(self, Splitter splitter, intp_t min_samples_split,
intp_t min_samples_leaf, float64_t min_weight_leaf,
intp_t max_depth, float64_t min_impurity_decrease):
self.splitter = splitter
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_leaf = min_weight_leaf
self.max_depth = max_depth
self.min_impurity_decrease = min_impurity_decrease
cpdef build(
self,
Tree tree,
object X,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight=None,
const unsigned char[::1] missing_values_in_feature_mask=None,
):
"""Build a decision tree from the training set (X, y)."""
# check input
X, y, sample_weight = self._check_input(X, y, sample_weight)
# Initial capacity
cdef intp_t init_capacity
if tree.max_depth <= 10:
init_capacity = <intp_t> (2 ** (tree.max_depth + 1)) - 1
else:
init_capacity = 2047
tree._resize(init_capacity)
# Parameters
cdef Splitter splitter = self.splitter
cdef intp_t max_depth = self.max_depth
cdef intp_t min_samples_leaf = self.min_samples_leaf
cdef float64_t min_weight_leaf = self.min_weight_leaf
cdef intp_t min_samples_split = self.min_samples_split
cdef float64_t min_impurity_decrease = self.min_impurity_decrease
# Recursive partition (without actual recursion)
splitter.init(X, y, sample_weight, missing_values_in_feature_mask)
cdef intp_t start
cdef intp_t end
cdef intp_t depth
cdef intp_t parent
cdef bint is_left
cdef intp_t n_node_samples = splitter.n_samples
cdef float64_t weighted_n_node_samples
cdef SplitRecord split
cdef intp_t node_id
cdef float64_t middle_value
cdef float64_t left_child_min
cdef float64_t left_child_max
cdef float64_t right_child_min
cdef float64_t right_child_max
cdef bint is_leaf
cdef bint first = 1
cdef intp_t max_depth_seen = -1
cdef int rc = 0
cdef stack[StackRecord] builder_stack
cdef StackRecord stack_record
cdef ParentInfo parent_record
_init_parent_record(&parent_record)
with nogil:
# push root node onto stack
builder_stack.push({
"start": 0,
"end": n_node_samples,
"depth": 0,
"parent": _TREE_UNDEFINED,
"is_left": 0,
"impurity": INFINITY,
"n_constant_features": 0,
"lower_bound": -INFINITY,
"upper_bound": INFINITY,
})
while not builder_stack.empty():
stack_record = builder_stack.top()
builder_stack.pop()
start = stack_record.start
end = stack_record.end
depth = stack_record.depth
parent = stack_record.parent
is_left = stack_record.is_left
parent_record.impurity = stack_record.impurity
parent_record.n_constant_features = stack_record.n_constant_features
parent_record.lower_bound = stack_record.lower_bound
parent_record.upper_bound = stack_record.upper_bound
n_node_samples = end - start
splitter.node_reset(start, end, &weighted_n_node_samples)
is_leaf = (depth >= max_depth or
n_node_samples < min_samples_split or
n_node_samples < 2 * min_samples_leaf or
weighted_n_node_samples < 2 * min_weight_leaf)
if first:
parent_record.impurity = splitter.node_impurity()
first = 0
# impurity == 0 with tolerance due to rounding errors
is_leaf = is_leaf or parent_record.impurity <= EPSILON
if not is_leaf:
splitter.node_split(
&parent_record,
&split,
)
# If EPSILON=0 in the below comparison, float precision
# issues stop splitting, producing trees that are
# dissimilar to v0.18
is_leaf = (is_leaf or split.pos >= end or
(split.improvement + EPSILON <
min_impurity_decrease))
node_id = tree._add_node(parent, is_left, is_leaf, split.feature,
split.threshold, parent_record.impurity,
n_node_samples, weighted_n_node_samples,
split.missing_go_to_left)
if node_id == INTPTR_MAX:
rc = -1
break
# Store value for all nodes, to facilitate tree/model
# inspection and interpretation
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, parent_record.lower_bound, parent_record.upper_bound)
if not is_leaf:
if (
not splitter.with_monotonic_cst or
splitter.monotonic_cst[split.feature] == 0
):
# Split on a feature with no monotonicity constraint
# Current bounds must always be propagated to both children.
# If a monotonic constraint is active, bounds are used in
# node value clipping.
left_child_min = right_child_min = parent_record.lower_bound
left_child_max = right_child_max = parent_record.upper_bound
elif splitter.monotonic_cst[split.feature] == 1:
# Split on a feature with monotonic increase constraint
left_child_min = parent_record.lower_bound
right_child_max = parent_record.upper_bound
# Lower bound for right child and upper bound for left child
# are set to the same value.
middle_value = splitter.criterion.middle_value()
right_child_min = middle_value
left_child_max = middle_value
else: # i.e. splitter.monotonic_cst[split.feature] == -1
# Split on a feature with monotonic decrease constraint
right_child_min = parent_record.lower_bound
left_child_max = parent_record.upper_bound
# Lower bound for left child and upper bound for right child
# are set to the same value.
middle_value = splitter.criterion.middle_value()
left_child_min = middle_value
right_child_max = middle_value
# Push right child on stack
builder_stack.push({
"start": split.pos,
"end": end,
"depth": depth + 1,
"parent": node_id,
"is_left": 0,
"impurity": split.impurity_right,
"n_constant_features": parent_record.n_constant_features,
"lower_bound": right_child_min,
"upper_bound": right_child_max,
})
# Push left child on stack
builder_stack.push({
"start": start,
"end": split.pos,
"depth": depth + 1,
"parent": node_id,
"is_left": 1,
"impurity": split.impurity_left,
"n_constant_features": parent_record.n_constant_features,
"lower_bound": left_child_min,
"upper_bound": left_child_max,
})
if depth > max_depth_seen:
max_depth_seen = depth
if rc >= 0:
rc = tree._resize_c(tree.node_count)
if rc >= 0:
tree.max_depth = max_depth_seen
if rc == -1:
raise MemoryError()
# Best first builder ----------------------------------------------------------
cdef struct FrontierRecord:
# Record of information of a Node, the frontier for a split. Those records are
# maintained in a heap to access the Node with the best improvement in impurity,
# allowing growing trees greedily on this improvement.
intp_t node_id
intp_t start
intp_t end
intp_t pos
intp_t depth
bint is_leaf
float64_t impurity
float64_t impurity_left
float64_t impurity_right
float64_t improvement
float64_t lower_bound
float64_t upper_bound
float64_t middle_value
cdef inline bool _compare_records(
const FrontierRecord& left,
const FrontierRecord& right,
):
return left.improvement < right.improvement
cdef inline void _add_to_frontier(
FrontierRecord rec,
vector[FrontierRecord]& frontier,
) noexcept nogil:
"""Adds record `rec` to the priority queue `frontier`."""
frontier.push_back(rec)
push_heap(frontier.begin(), frontier.end(), &_compare_records)
cdef class BestFirstTreeBuilder(TreeBuilder):
"""Build a decision tree in best-first fashion.
The best node to expand is given by the node at the frontier that has the
highest impurity improvement.
"""
cdef intp_t max_leaf_nodes
def __cinit__(self, Splitter splitter, intp_t min_samples_split,
intp_t min_samples_leaf, min_weight_leaf,
intp_t max_depth, intp_t max_leaf_nodes,
float64_t min_impurity_decrease):
self.splitter = splitter
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.min_weight_leaf = min_weight_leaf
self.max_depth = max_depth
self.max_leaf_nodes = max_leaf_nodes
self.min_impurity_decrease = min_impurity_decrease
cpdef build(
self,
Tree tree,
object X,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight=None,
const unsigned char[::1] missing_values_in_feature_mask=None,
):
"""Build a decision tree from the training set (X, y)."""
# check input
X, y, sample_weight = self._check_input(X, y, sample_weight)
# Parameters
cdef Splitter splitter = self.splitter
cdef intp_t max_leaf_nodes = self.max_leaf_nodes
# Recursive partition (without actual recursion)
splitter.init(X, y, sample_weight, missing_values_in_feature_mask)
cdef vector[FrontierRecord] frontier
cdef FrontierRecord record
cdef FrontierRecord split_node_left
cdef FrontierRecord split_node_right
cdef float64_t left_child_min
cdef float64_t left_child_max
cdef float64_t right_child_min
cdef float64_t right_child_max
cdef intp_t n_node_samples = splitter.n_samples
cdef intp_t max_split_nodes = max_leaf_nodes - 1
cdef bint is_leaf
cdef intp_t max_depth_seen = -1
cdef int rc = 0
cdef Node* node
cdef ParentInfo parent_record
_init_parent_record(&parent_record)
# Initial capacity
cdef intp_t init_capacity = max_split_nodes + max_leaf_nodes
tree._resize(init_capacity)
with nogil:
# add root to frontier
rc = self._add_split_node(
splitter=splitter,
tree=tree,
start=0,
end=n_node_samples,
is_first=IS_FIRST,
is_left=IS_LEFT,
parent=NULL,
depth=0,
parent_record=&parent_record,
res=&split_node_left,
)
if rc >= 0:
_add_to_frontier(split_node_left, frontier)
while not frontier.empty():
pop_heap(frontier.begin(), frontier.end(), &_compare_records)
record = frontier.back()
frontier.pop_back()
node = &tree.nodes[record.node_id]
is_leaf = (record.is_leaf or max_split_nodes <= 0)
if is_leaf:
# Node is not expandable; set node as leaf
node.left_child = _TREE_LEAF
node.right_child = _TREE_LEAF
node.feature = _TREE_UNDEFINED
node.threshold = _TREE_UNDEFINED
else:
# Node is expandable
if (
not splitter.with_monotonic_cst or
splitter.monotonic_cst[node.feature] == 0
):
# Split on a feature with no monotonicity constraint
# Current bounds must always be propagated to both children.
# If a monotonic constraint is active, bounds are used in
# node value clipping.
left_child_min = right_child_min = record.lower_bound
left_child_max = right_child_max = record.upper_bound
elif splitter.monotonic_cst[node.feature] == 1:
# Split on a feature with monotonic increase constraint
left_child_min = record.lower_bound
right_child_max = record.upper_bound
# Lower bound for right child and upper bound for left child
# are set to the same value.
right_child_min = record.middle_value
left_child_max = record.middle_value
else: # i.e. splitter.monotonic_cst[split.feature] == -1
# Split on a feature with monotonic decrease constraint
right_child_min = record.lower_bound
left_child_max = record.upper_bound
# Lower bound for left child and upper bound for right child
# are set to the same value.
left_child_min = record.middle_value
right_child_max = record.middle_value
# Decrement number of split nodes available
max_split_nodes -= 1
# Compute left split node
parent_record.lower_bound = left_child_min
parent_record.upper_bound = left_child_max
parent_record.impurity = record.impurity_left
rc = self._add_split_node(
splitter=splitter,
tree=tree,
start=record.start,
end=record.pos,
is_first=IS_NOT_FIRST,
is_left=IS_LEFT,
parent=node,
depth=record.depth + 1,
parent_record=&parent_record,
res=&split_node_left,
)
if rc == -1:
break
# tree.nodes may have changed
node = &tree.nodes[record.node_id]
# Compute right split node
parent_record.lower_bound = right_child_min
parent_record.upper_bound = right_child_max
parent_record.impurity = record.impurity_right
rc = self._add_split_node(
splitter=splitter,
tree=tree,
start=record.pos,
end=record.end,
is_first=IS_NOT_FIRST,
is_left=IS_NOT_LEFT,
parent=node,
depth=record.depth + 1,
parent_record=&parent_record,
res=&split_node_right,
)
if rc == -1:
break
# Add nodes to queue
_add_to_frontier(split_node_left, frontier)
_add_to_frontier(split_node_right, frontier)
if record.depth > max_depth_seen:
max_depth_seen = record.depth
if rc >= 0:
rc = tree._resize_c(tree.node_count)
if rc >= 0:
tree.max_depth = max_depth_seen
if rc == -1:
raise MemoryError()
cdef inline int _add_split_node(
self,
Splitter splitter,
Tree tree,
intp_t start,
intp_t end,
bint is_first,
bint is_left,
Node* parent,
intp_t depth,
ParentInfo* parent_record,
FrontierRecord* res
) except -1 nogil:
"""Adds node w/ partition ``[start, end)`` to the frontier. """
cdef SplitRecord split
cdef intp_t node_id
cdef intp_t n_node_samples
cdef float64_t min_impurity_decrease = self.min_impurity_decrease
cdef float64_t weighted_n_node_samples
cdef bint is_leaf
splitter.node_reset(start, end, &weighted_n_node_samples)
# reset n_constant_features for this specific split before beginning split search
parent_record.n_constant_features = 0
if is_first:
parent_record.impurity = splitter.node_impurity()
n_node_samples = end - start
is_leaf = (depth >= self.max_depth or
n_node_samples < self.min_samples_split or
n_node_samples < 2 * self.min_samples_leaf or
weighted_n_node_samples < 2 * self.min_weight_leaf or
parent_record.impurity <= EPSILON # impurity == 0 with tolerance
)
if not is_leaf:
splitter.node_split(
parent_record,
&split,
)
# If EPSILON=0 in the below comparison, float precision issues stop
# splitting early, producing trees that are dissimilar to v0.18
is_leaf = (is_leaf or split.pos >= end or
split.improvement + EPSILON < min_impurity_decrease)
node_id = tree._add_node(parent - tree.nodes
if parent != NULL
else _TREE_UNDEFINED,
is_left, is_leaf,
split.feature, split.threshold, parent_record.impurity,
n_node_samples, weighted_n_node_samples,
split.missing_go_to_left)
if node_id == INTPTR_MAX:
return -1
# 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, parent_record.lower_bound, parent_record.upper_bound)
res.node_id = node_id
res.start = start
res.end = end
res.depth = depth
res.impurity = parent_record.impurity
res.lower_bound = parent_record.lower_bound
res.upper_bound = parent_record.upper_bound
res.middle_value = splitter.criterion.middle_value()
if not is_leaf:
# is split node
res.pos = split.pos
res.is_leaf = 0
res.improvement = split.improvement
res.impurity_left = split.impurity_left
res.impurity_right = split.impurity_right
else:
# is leaf => 0 improvement
res.pos = end
res.is_leaf = 1
res.improvement = 0.0
res.impurity_left = parent_record.impurity
res.impurity_right = parent_record.impurity
return 0
# =============================================================================
# Tree
# =============================================================================
cdef class Tree:
"""Array-based representation of a binary decision tree.
The binary tree is represented as a number of parallel arrays. The i-th
element of each array holds information about the node `i`. Node 0 is the
tree's root. You can find a detailed description of all arrays in
`_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split
nodes, resp. In this case the values of nodes of the other type are
arbitrary!
Attributes
----------
node_count : intp_t
The number of nodes (internal nodes + leaves) in the tree.
capacity : intp_t
The current capacity (i.e., size) of the arrays, which is at least as
great as `node_count`.
max_depth : intp_t
The depth of the tree, i.e. the maximum depth of its leaves.
children_left : array of intp_t, shape [node_count]
children_left[i] holds the node id of the left child of node i.
For leaves, children_left[i] == TREE_LEAF. Otherwise,
children_left[i] > i. This child handles the case where
X[:, feature[i]] <= threshold[i].
children_right : array of intp_t, shape [node_count]
children_right[i] holds the node id of the right child of node i.
For leaves, children_right[i] == TREE_LEAF. Otherwise,
children_right[i] > i. This child handles the case where
X[:, feature[i]] > threshold[i].
n_leaves : intp_t
Number of leaves in the tree.
feature : array of intp_t, shape [node_count]
feature[i] holds the feature to split on, for the internal node i.
threshold : array of float64_t, shape [node_count]
threshold[i] holds the threshold for the internal node i.
value : array of float64_t, shape [node_count, n_outputs, max_n_classes]
Contains the constant prediction value of each node.
impurity : array of float64_t, shape [node_count]
impurity[i] holds the impurity (i.e., the value of the splitting
criterion) at node i.
n_node_samples : array of intp_t, shape [node_count]
n_node_samples[i] holds the number of training samples reaching node i.
weighted_n_node_samples : array of float64_t, shape [node_count]
weighted_n_node_samples[i] holds the weighted number of training samples
reaching node i.
missing_go_to_left : array of bool, shape [node_count]
missing_go_to_left[i] holds a bool indicating whether or not there were
missing values at node i.
"""
# Wrap for outside world.
# WARNING: these reference the current `nodes` and `value` buffers, which
# must not be freed by a subsequent memory allocation.
# (i.e. through `_resize` or `__setstate__`)
@property
def n_classes(self):
return sizet_ptr_to_ndarray(self.n_classes, self.n_outputs)
@property
def children_left(self):
return self._get_node_ndarray()['left_child'][:self.node_count]
@property
def children_right(self):
return self._get_node_ndarray()['right_child'][:self.node_count]
@property
def n_leaves(self):
return np.sum(np.logical_and(
self.children_left == -1,
self.children_right == -1))
@property
def feature(self):
return self._get_node_ndarray()['feature'][:self.node_count]
@property
def threshold(self):
return self._get_node_ndarray()['threshold'][:self.node_count]
@property
def impurity(self):
return self._get_node_ndarray()['impurity'][:self.node_count]
@property
def n_node_samples(self):
return self._get_node_ndarray()['n_node_samples'][:self.node_count]
@property
def weighted_n_node_samples(self):
return self._get_node_ndarray()['weighted_n_node_samples'][:self.node_count]
@property
def missing_go_to_left(self):
return self._get_node_ndarray()['missing_go_to_left'][:self.node_count]
@property
def value(self):
return self._get_value_ndarray()[:self.node_count]
# TODO: Convert n_classes to cython.integral memory view once
# https://github.com/cython/cython/issues/5243 is fixed
def __cinit__(self, intp_t n_features, cnp.ndarray n_classes, intp_t n_outputs):
"""Constructor."""
cdef intp_t dummy = 0
size_t_dtype = np.array(dummy).dtype
n_classes = _check_n_classes(n_classes, size_t_dtype)
# Input/Output layout
self.n_features = n_features
self.n_outputs = n_outputs
self.n_classes = NULL
safe_realloc(&self.n_classes, n_outputs)
self.max_n_classes = np.max(n_classes)
self.value_stride = n_outputs * self.max_n_classes
cdef intp_t k
for k in range(n_outputs):
self.n_classes[k] = n_classes[k]
# Inner structures
self.max_depth = 0
self.node_count = 0
self.capacity = 0
self.value = NULL
self.nodes = NULL
def __dealloc__(self):
"""Destructor."""
# Free all inner structures
free(self.n_classes)
free(self.value)
free(self.nodes)
def __reduce__(self):
"""Reduce re-implementation, for pickling."""
return (Tree, (self.n_features,
sizet_ptr_to_ndarray(self.n_classes, self.n_outputs),
self.n_outputs), self.__getstate__())
def __getstate__(self):
"""Getstate re-implementation, for pickling."""
d = {}
# capacity is inferred during the __setstate__ using nodes
d["max_depth"] = self.max_depth
d["node_count"] = self.node_count
d["nodes"] = self._get_node_ndarray()
d["values"] = self._get_value_ndarray()
return d
def __setstate__(self, d):
"""Setstate re-implementation, for unpickling."""
self.max_depth = d["max_depth"]
self.node_count = d["node_count"]
if 'nodes' not in d:
raise ValueError('You have loaded Tree version which '
'cannot be imported')
node_ndarray = d['nodes']
value_ndarray = d['values']
value_shape = (node_ndarray.shape[0], self.n_outputs,
self.max_n_classes)
node_ndarray = _check_node_ndarray(node_ndarray, expected_dtype=NODE_DTYPE)
value_ndarray = _check_value_ndarray(
value_ndarray,
expected_dtype=np.dtype(np.float64),
expected_shape=value_shape
)
self.capacity = node_ndarray.shape[0]
if self._resize_c(self.capacity) != 0:
raise MemoryError("resizing tree to %d" % self.capacity)
memcpy(self.nodes, cnp.PyArray_DATA(node_ndarray),
self.capacity * sizeof(Node))
memcpy(self.value, cnp.PyArray_DATA(value_ndarray),
self.capacity * self.value_stride * sizeof(float64_t))
cdef int _resize(self, intp_t capacity) except -1 nogil:
"""Resize all inner arrays to `capacity`, if `capacity` == -1, then
double the size of the inner arrays.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
if self._resize_c(capacity) != 0:
# Acquire gil only if we need to raise
with gil:
raise MemoryError()
cdef int _resize_c(self, intp_t capacity=INTPTR_MAX) except -1 nogil:
"""Guts of _resize
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
if capacity == self.capacity and self.nodes != NULL:
return 0
if capacity == INTPTR_MAX:
if self.capacity == 0:
capacity = 3 # default initial value
else:
capacity = 2 * self.capacity
safe_realloc(&self.nodes, capacity)
safe_realloc(&self.value, capacity * self.value_stride)
if capacity > self.capacity:
# value memory is initialised to 0 to enable classifier argmax
memset(<void*>(self.value + self.capacity * self.value_stride), 0,
(capacity - self.capacity) * self.value_stride *
sizeof(float64_t))
# node memory is initialised to 0 to ensure deterministic pickle (padding in Node struct)
memset(<void*>(self.nodes + self.capacity), 0, (capacity - self.capacity) * sizeof(Node))
# if capacity smaller than node_count, adjust the counter
if capacity < self.node_count:
self.node_count = capacity
self.capacity = capacity
return 0
cdef intp_t _add_node(self, intp_t parent, bint is_left, bint is_leaf,
intp_t feature, float64_t threshold, float64_t impurity,
intp_t n_node_samples,
float64_t weighted_n_node_samples,
unsigned char missing_go_to_left) except -1 nogil:
"""Add a node to the tree.
The new node registers itself as the child of its parent.
Returns (size_t)(-1) on error.
"""
cdef intp_t node_id = self.node_count
if node_id >= self.capacity:
if self._resize_c() != 0:
return INTPTR_MAX
cdef Node* node = &self.nodes[node_id]
node.impurity = impurity
node.n_node_samples = n_node_samples
node.weighted_n_node_samples = weighted_n_node_samples
if parent != _TREE_UNDEFINED:
if is_left:
self.nodes[parent].left_child = node_id
else:
self.nodes[parent].right_child = node_id
if is_leaf:
node.left_child = _TREE_LEAF
node.right_child = _TREE_LEAF
node.feature = _TREE_UNDEFINED
node.threshold = _TREE_UNDEFINED
else:
# left_child and right_child will be set later
node.feature = feature
node.threshold = threshold
node.missing_go_to_left = missing_go_to_left
self.node_count += 1
return node_id
cpdef cnp.ndarray predict(self, object X):
"""Predict target for X."""
out = self._get_value_ndarray().take(self.apply(X), axis=0,
mode='clip')
if self.n_outputs == 1:
out = out.reshape(X.shape[0], self.max_n_classes)
return out
cpdef cnp.ndarray apply(self, object X):
"""Finds the terminal region (=leaf node) for each sample in X."""
if issparse(X):
return self._apply_sparse_csr(X)
else:
return self._apply_dense(X)
cdef inline cnp.ndarray _apply_dense(self, object X):
"""Finds the terminal region (=leaf node) for each sample in X."""
# Check input
if not isinstance(X, np.ndarray):
raise ValueError("X should be in np.ndarray format, got %s"
% type(X))
if X.dtype != DTYPE:
raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
# Extract input
cdef const float32_t[:, :] X_ndarray = X
cdef intp_t n_samples = X.shape[0]
cdef float32_t X_i_node_feature
# Initialize output
cdef intp_t[:] out = np.zeros(n_samples, dtype=np.intp)
# Initialize auxiliary data-structure
cdef Node* node = NULL
cdef intp_t i = 0
with nogil:
for i in range(n_samples):
node = self.nodes
# While node not a leaf
while node.left_child != _TREE_LEAF:
X_i_node_feature = X_ndarray[i, node.feature]
# ... and node.right_child != _TREE_LEAF:
if isnan(X_i_node_feature):
if node.missing_go_to_left:
node = &self.nodes[node.left_child]
else:
node = &self.nodes[node.right_child]
elif X_i_node_feature <= node.threshold:
node = &self.nodes[node.left_child]
else:
node = &self.nodes[node.right_child]
out[i] = <intp_t>(node - self.nodes) # node offset
return np.asarray(out)
cdef inline cnp.ndarray _apply_sparse_csr(self, object X):
"""Finds the terminal region (=leaf node) for each sample in sparse X.
"""
# Check input
if not (issparse(X) and X.format == 'csr'):
raise ValueError("X should be in csr_matrix format, got %s"
% type(X))
if X.dtype != DTYPE:
raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
# Extract input
cdef const float32_t[:] X_data = X.data
cdef const int32_t[:] X_indices = X.indices
cdef const int32_t[:] X_indptr = X.indptr
cdef intp_t n_samples = X.shape[0]
cdef intp_t n_features = X.shape[1]
# Initialize output
cdef intp_t[:] out = np.zeros(n_samples, dtype=np.intp)
# Initialize auxiliary data-structure
cdef float32_t feature_value = 0.
cdef Node* node = NULL
cdef float32_t* X_sample = NULL
cdef intp_t i = 0
cdef int32_t k = 0
# feature_to_sample as a data structure records the last seen sample
# for each feature; functionally, it is an efficient way to identify
# which features are nonzero in the present sample.
cdef intp_t* feature_to_sample = NULL
safe_realloc(&X_sample, n_features)
safe_realloc(&feature_to_sample, n_features)
with nogil:
memset(feature_to_sample, -1, n_features * sizeof(intp_t))
for i in range(n_samples):
node = self.nodes
for k in range(X_indptr[i], X_indptr[i + 1]):
feature_to_sample[X_indices[k]] = i
X_sample[X_indices[k]] = X_data[k]
# While node not a leaf
while node.left_child != _TREE_LEAF:
# ... and node.right_child != _TREE_LEAF:
if feature_to_sample[node.feature] == i:
feature_value = X_sample[node.feature]
else:
feature_value = 0.
if feature_value <= node.threshold:
node = &self.nodes[node.left_child]
else:
node = &self.nodes[node.right_child]
out[i] = <intp_t>(node - self.nodes) # node offset
# Free auxiliary arrays
free(X_sample)
free(feature_to_sample)
return np.asarray(out)
cpdef object decision_path(self, object X):
"""Finds the decision path (=node) for each sample in X."""
if issparse(X):
return self._decision_path_sparse_csr(X)
else:
return self._decision_path_dense(X)
cdef inline object _decision_path_dense(self, object X):
"""Finds the decision path (=node) for each sample in X."""
# Check input
if not isinstance(X, np.ndarray):
raise ValueError("X should be in np.ndarray format, got %s"
% type(X))
if X.dtype != DTYPE:
raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
# Extract input
cdef const float32_t[:, :] X_ndarray = X
cdef intp_t n_samples = X.shape[0]
# Initialize output
cdef intp_t[:] indptr = np.zeros(n_samples + 1, dtype=np.intp)
cdef intp_t[:] indices = np.zeros(
n_samples * (1 + self.max_depth), dtype=np.intp
)
# Initialize auxiliary data-structure
cdef Node* node = NULL
cdef intp_t i = 0
with nogil:
for i in range(n_samples):
node = self.nodes
indptr[i + 1] = indptr[i]
# Add all external nodes
while node.left_child != _TREE_LEAF:
# ... and node.right_child != _TREE_LEAF:
indices[indptr[i + 1]] = <intp_t>(node - self.nodes)
indptr[i + 1] += 1
if X_ndarray[i, node.feature] <= node.threshold:
node = &self.nodes[node.left_child]
else:
node = &self.nodes[node.right_child]
# Add the leave node
indices[indptr[i + 1]] = <intp_t>(node - self.nodes)
indptr[i + 1] += 1
indices = indices[:indptr[n_samples]]
cdef intp_t[:] data = np.ones(shape=len(indices), dtype=np.intp)
out = csr_matrix((data, indices, indptr),
shape=(n_samples, self.node_count))
return out
cdef inline object _decision_path_sparse_csr(self, object X):
"""Finds the decision path (=node) for each sample in X."""
# Check input
if not (issparse(X) and X.format == "csr"):
raise ValueError("X should be in csr_matrix format, got %s"
% type(X))
if X.dtype != DTYPE:
raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)
# Extract input
cdef const float32_t[:] X_data = X.data
cdef const int32_t[:] X_indices = X.indices
cdef const int32_t[:] X_indptr = X.indptr
cdef intp_t n_samples = X.shape[0]
cdef intp_t n_features = X.shape[1]
# Initialize output
cdef intp_t[:] indptr = np.zeros(n_samples + 1, dtype=np.intp)
cdef intp_t[:] indices = np.zeros(
n_samples * (1 + self.max_depth), dtype=np.intp
)
# Initialize auxiliary data-structure
cdef float32_t feature_value = 0.
cdef Node* node = NULL
cdef float32_t* X_sample = NULL
cdef intp_t i = 0
cdef int32_t k = 0
# feature_to_sample as a data structure records the last seen sample
# for each feature; functionally, it is an efficient way to identify
# which features are nonzero in the present sample.
cdef intp_t* feature_to_sample = NULL
safe_realloc(&X_sample, n_features)
safe_realloc(&feature_to_sample, n_features)
with nogil:
memset(feature_to_sample, -1, n_features * sizeof(intp_t))
for i in range(n_samples):
node = self.nodes
indptr[i + 1] = indptr[i]
for k in range(X_indptr[i], X_indptr[i + 1]):
feature_to_sample[X_indices[k]] = i
X_sample[X_indices[k]] = X_data[k]
# While node not a leaf
while node.left_child != _TREE_LEAF:
# ... and node.right_child != _TREE_LEAF:
indices[indptr[i + 1]] = <intp_t>(node - self.nodes)
indptr[i + 1] += 1
if feature_to_sample[node.feature] == i:
feature_value = X_sample[node.feature]
else:
feature_value = 0.
if feature_value <= node.threshold:
node = &self.nodes[node.left_child]
else:
node = &self.nodes[node.right_child]
# Add the leave node
indices[indptr[i + 1]] = <intp_t>(node - self.nodes)
indptr[i + 1] += 1
# Free auxiliary arrays
free(X_sample)
free(feature_to_sample)
indices = indices[:indptr[n_samples]]
cdef intp_t[:] data = np.ones(shape=len(indices), dtype=np.intp)
out = csr_matrix((data, indices, indptr),
shape=(n_samples, self.node_count))
return out
cpdef compute_node_depths(self):
"""Compute the depth of each node in a tree.
.. versionadded:: 1.3
Returns
-------
depths : ndarray of shape (self.node_count,), dtype=np.int64
The depth of each node in the tree.
"""
cdef:
cnp.int64_t[::1] depths = np.empty(self.node_count, dtype=np.int64)
cnp.npy_intp[:] children_left = self.children_left
cnp.npy_intp[:] children_right = self.children_right
cnp.npy_intp node_id
cnp.npy_intp node_count = self.node_count
cnp.int64_t depth
depths[0] = 1 # init root node
for node_id in range(node_count):
if children_left[node_id] != _TREE_LEAF:
depth = depths[node_id] + 1
depths[children_left[node_id]] = depth
depths[children_right[node_id]] = depth
return depths.base
cpdef compute_feature_importances(self, normalize=True):
"""Computes the importance of each feature (aka variable)."""
cdef Node* left
cdef Node* right
cdef Node* nodes = self.nodes
cdef Node* node = nodes
cdef Node* end_node = node + self.node_count
cdef float64_t normalizer = 0.
cdef cnp.float64_t[:] importances = np.zeros(self.n_features)
with nogil:
while node != end_node:
if node.left_child != _TREE_LEAF:
# ... and node.right_child != _TREE_LEAF:
left = &nodes[node.left_child]
right = &nodes[node.right_child]
importances[node.feature] += (
node.weighted_n_node_samples * node.impurity -
left.weighted_n_node_samples * left.impurity -
right.weighted_n_node_samples * right.impurity)
node += 1
for i in range(self.n_features):
importances[i] /= nodes[0].weighted_n_node_samples
if normalize:
normalizer = np.sum(importances)
if normalizer > 0.0:
# Avoid dividing by zero (e.g., when root is pure)
for i in range(self.n_features):
importances[i] /= normalizer
return np.asarray(importances)
cdef cnp.ndarray _get_value_ndarray(self):
"""Wraps value as a 3-d NumPy array.
The array keeps a reference to this Tree, which manages the underlying
memory.
"""
cdef cnp.npy_intp shape[3]
shape[0] = <cnp.npy_intp> self.node_count
shape[1] = <cnp.npy_intp> self.n_outputs
shape[2] = <cnp.npy_intp> self.max_n_classes
cdef cnp.ndarray arr
arr = cnp.PyArray_SimpleNewFromData(3, shape, cnp.NPY_DOUBLE, self.value)
Py_INCREF(self)
if PyArray_SetBaseObject(arr, <PyObject*> self) < 0:
raise ValueError("Can't initialize array.")
return arr
cdef cnp.ndarray _get_node_ndarray(self):
"""Wraps nodes as a NumPy struct array.
The array keeps a reference to this Tree, which manages the underlying
memory. Individual fields are publicly accessible as properties of the
Tree.
"""
cdef cnp.npy_intp shape[1]
shape[0] = <cnp.npy_intp> self.node_count
cdef cnp.npy_intp strides[1]
strides[0] = sizeof(Node)
cdef cnp.ndarray arr
Py_INCREF(NODE_DTYPE)
arr = PyArray_NewFromDescr(<PyTypeObject *> cnp.ndarray,
<cnp.dtype> NODE_DTYPE, 1, shape,
strides, <void*> self.nodes,
cnp.NPY_ARRAY_DEFAULT, None)
Py_INCREF(self)
if PyArray_SetBaseObject(arr, <PyObject*> self) < 0:
raise ValueError("Can't initialize array.")
return arr
def compute_partial_dependence(self, float32_t[:, ::1] X,
const intp_t[::1] target_features,
float64_t[::1] out):
"""Partial dependence of the response on the ``target_feature`` set.
For each sample in ``X`` a tree traversal is performed.
Each traversal starts from the root with weight 1.0.
At each non-leaf node that splits on a target feature, either
the left child or the right child is visited based on the feature
value of the current sample, and the weight is not modified.
At each non-leaf node that splits on a complementary feature,
both children are visited and the weight is multiplied by the fraction
of training samples which went to each child.
At each leaf, the value of the node is multiplied by the current
weight (weights sum to 1 for all visited terminal nodes).
Parameters
----------
X : view on 2d ndarray, shape (n_samples, n_target_features)
The grid points on which the partial dependence should be
evaluated.
target_features : view on 1d ndarray, shape (n_target_features)
The set of target features for which the partial dependence
should be evaluated.
out : view on 1d ndarray, shape (n_samples)
The value of the partial dependence function on each grid
point.
"""
cdef:
float64_t[::1] weight_stack = np.zeros(self.node_count,
dtype=np.float64)
intp_t[::1] node_idx_stack = np.zeros(self.node_count,
dtype=np.intp)
intp_t sample_idx
intp_t feature_idx
intp_t stack_size
float64_t left_sample_frac
float64_t current_weight
float64_t total_weight # used for sanity check only
Node *current_node # use a pointer to avoid copying attributes
intp_t current_node_idx
bint is_target_feature
intp_t _TREE_LEAF = TREE_LEAF # to avoid python interactions
for sample_idx in range(X.shape[0]):
# init stacks for current sample
stack_size = 1
node_idx_stack[0] = 0 # root node
weight_stack[0] = 1 # all the samples are in the root node
total_weight = 0
while stack_size > 0:
# pop the stack
stack_size -= 1
current_node_idx = node_idx_stack[stack_size]
current_node = &self.nodes[current_node_idx]
if current_node.left_child == _TREE_LEAF:
# leaf node
out[sample_idx] += (weight_stack[stack_size] *
self.value[current_node_idx])
total_weight += weight_stack[stack_size]
else:
# non-leaf node
# determine if the split feature is a target feature
is_target_feature = False
for feature_idx in range(target_features.shape[0]):
if target_features[feature_idx] == current_node.feature:
is_target_feature = True
break
if is_target_feature:
# In this case, we push left or right child on stack
if X[sample_idx, feature_idx] <= current_node.threshold:
node_idx_stack[stack_size] = current_node.left_child
else:
node_idx_stack[stack_size] = current_node.right_child
stack_size += 1
else:
# In this case, we push both children onto the stack,
# and give a weight proportional to the number of
# samples going through each branch.
# push left child
node_idx_stack[stack_size] = current_node.left_child
left_sample_frac = (
self.nodes[current_node.left_child].weighted_n_node_samples /
current_node.weighted_n_node_samples)
current_weight = weight_stack[stack_size]
weight_stack[stack_size] = current_weight * left_sample_frac
stack_size += 1
# push right child
node_idx_stack[stack_size] = current_node.right_child
weight_stack[stack_size] = (
current_weight * (1 - left_sample_frac))
stack_size += 1
# Sanity check. Should never happen.
if not (0.999 < total_weight < 1.001):
raise ValueError("Total weight should be 1.0 but was %.9f" %
total_weight)
def _check_n_classes(n_classes, expected_dtype):
if n_classes.ndim != 1:
raise ValueError(
f"Wrong dimensions for n_classes from the pickle: "
f"expected 1, got {n_classes.ndim}"
)
if n_classes.dtype == expected_dtype:
return n_classes
# Handles both different endianness and different bitness
if n_classes.dtype.kind == "i" and n_classes.dtype.itemsize in [4, 8]:
return n_classes.astype(expected_dtype, casting="same_kind")
raise ValueError(
"n_classes from the pickle has an incompatible dtype:\n"
f"- expected: {expected_dtype}\n"
f"- got: {n_classes.dtype}"
)
def _check_value_ndarray(value_ndarray, expected_dtype, expected_shape):
if value_ndarray.shape != expected_shape:
raise ValueError(
"Wrong shape for value array from the pickle: "
f"expected {expected_shape}, got {value_ndarray.shape}"
)
if not value_ndarray.flags.c_contiguous:
raise ValueError(
"value array from the pickle should be a C-contiguous array"
)
if value_ndarray.dtype == expected_dtype:
return value_ndarray
# Handles different endianness
if value_ndarray.dtype.str.endswith('f8'):
return value_ndarray.astype(expected_dtype, casting='equiv')
raise ValueError(
"value array from the pickle has an incompatible dtype:\n"
f"- expected: {expected_dtype}\n"
f"- got: {value_ndarray.dtype}"
)
def _dtype_to_dict(dtype):
return {name: dt.str for name, (dt, *rest) in dtype.fields.items()}
def _dtype_dict_with_modified_bitness(dtype_dict):
# field names in Node struct with intp_t types (see sklearn/tree/_tree.pxd)
indexing_field_names = ["left_child", "right_child", "feature", "n_node_samples"]
expected_dtype_size = str(struct.calcsize("P"))
allowed_dtype_size = "8" if expected_dtype_size == "4" else "4"
allowed_dtype_dict = dtype_dict.copy()
for name in indexing_field_names:
allowed_dtype_dict[name] = allowed_dtype_dict[name].replace(
expected_dtype_size, allowed_dtype_size
)
return allowed_dtype_dict
def _all_compatible_dtype_dicts(dtype):
# The Cython code for decision trees uses platform-specific intp_t
# typed indexing fields that correspond to either i4 or i8 dtypes for
# the matching fields in the numpy array depending on the bitness of
# the platform (32 bit or 64 bit respectively).
#
# We need to cast the indexing fields of the NODE_DTYPE-dtyped array at
# pickle load time to enable cross-bitness deployment scenarios. We
# typically want to make it possible to run the expensive fit method of
# a tree estimator on a 64 bit server platform, pickle the estimator
# for deployment and run the predict method of a low power 32 bit edge
# platform.
#
# A similar thing happens for endianness, the machine where the pickle was
# saved can have a different endianness than the machine where the pickle
# is loaded
dtype_dict = _dtype_to_dict(dtype)
dtype_dict_with_modified_bitness = _dtype_dict_with_modified_bitness(dtype_dict)
dtype_dict_with_modified_endianness = _dtype_to_dict(dtype.newbyteorder())
dtype_dict_with_modified_bitness_and_endianness = _dtype_dict_with_modified_bitness(
dtype_dict_with_modified_endianness
)
return [
dtype_dict,
dtype_dict_with_modified_bitness,
dtype_dict_with_modified_endianness,
dtype_dict_with_modified_bitness_and_endianness,
]
def _check_node_ndarray(node_ndarray, expected_dtype):
if node_ndarray.ndim != 1:
raise ValueError(
"Wrong dimensions for node array from the pickle: "
f"expected 1, got {node_ndarray.ndim}"
)
if not node_ndarray.flags.c_contiguous:
raise ValueError(
"node array from the pickle should be a C-contiguous array"
)
node_ndarray_dtype = node_ndarray.dtype
if node_ndarray_dtype == expected_dtype:
return node_ndarray
node_ndarray_dtype_dict = _dtype_to_dict(node_ndarray_dtype)
all_compatible_dtype_dicts = _all_compatible_dtype_dicts(expected_dtype)
if node_ndarray_dtype_dict not in all_compatible_dtype_dicts:
raise ValueError(
"node array from the pickle has an incompatible dtype:\n"
f"- expected: {expected_dtype}\n"
f"- got : {node_ndarray_dtype}"
)
return node_ndarray.astype(expected_dtype, casting="same_kind")
# =============================================================================
# Build Pruned Tree
# =============================================================================
cdef class _CCPPruneController:
"""Base class used by build_pruned_tree_ccp and ccp_pruning_path
to control pruning.
"""
cdef bint stop_pruning(self, float64_t effective_alpha) noexcept nogil:
"""Return 1 to stop pruning and 0 to continue pruning"""
return 0
cdef void save_metrics(self, float64_t effective_alpha,
float64_t subtree_impurities) noexcept nogil:
"""Save metrics when pruning"""
pass
cdef void after_pruning(self, unsigned char[:] in_subtree) noexcept nogil:
"""Called after pruning"""
pass
cdef class _AlphaPruner(_CCPPruneController):
"""Use alpha to control when to stop pruning."""
cdef float64_t ccp_alpha
cdef intp_t capacity
def __cinit__(self, float64_t ccp_alpha):
self.ccp_alpha = ccp_alpha
self.capacity = 0
cdef bint stop_pruning(self, float64_t effective_alpha) noexcept nogil:
# The subtree on the previous iteration has the greatest ccp_alpha
# less than or equal to self.ccp_alpha
return self.ccp_alpha < effective_alpha
cdef void after_pruning(self, unsigned char[:] in_subtree) noexcept nogil:
"""Updates the number of leaves in subtree"""
for i in range(in_subtree.shape[0]):
if in_subtree[i]:
self.capacity += 1
cdef class _PathFinder(_CCPPruneController):
"""Record metrics used to return the cost complexity path."""
cdef float64_t[:] ccp_alphas
cdef float64_t[:] impurities
cdef uint32_t count
def __cinit__(self, intp_t node_count):
self.ccp_alphas = np.zeros(shape=(node_count), dtype=np.float64)
self.impurities = np.zeros(shape=(node_count), dtype=np.float64)
self.count = 0
cdef void save_metrics(self,
float64_t effective_alpha,
float64_t subtree_impurities) noexcept nogil:
self.ccp_alphas[self.count] = effective_alpha
self.impurities[self.count] = subtree_impurities
self.count += 1
cdef struct CostComplexityPruningRecord:
intp_t node_idx
intp_t parent
cdef _cost_complexity_prune(unsigned char[:] leaves_in_subtree, # OUT
Tree orig_tree,
_CCPPruneController controller):
"""Perform cost complexity pruning.
This function takes an already grown tree, `orig_tree` and outputs a
boolean mask `leaves_in_subtree` which are the leaves in the pruned tree.
During the pruning process, the controller is passed the effective alpha and
the subtree impurities. Furthermore, the controller signals when to stop
pruning.
Parameters
----------
leaves_in_subtree : unsigned char[:]
Output for leaves of subtree
orig_tree : Tree
Original tree
ccp_controller : _CCPPruneController
Cost complexity controller
"""
cdef:
intp_t i
intp_t n_nodes = orig_tree.node_count
# prior probability using weighted samples
float64_t[:] weighted_n_node_samples = orig_tree.weighted_n_node_samples
float64_t total_sum_weights = weighted_n_node_samples[0]
float64_t[:] impurity = orig_tree.impurity
# weighted impurity of each node
float64_t[:] r_node = np.empty(shape=n_nodes, dtype=np.float64)
intp_t[:] child_l = orig_tree.children_left
intp_t[:] child_r = orig_tree.children_right
intp_t[:] parent = np.zeros(shape=n_nodes, dtype=np.intp)
stack[CostComplexityPruningRecord] ccp_stack
CostComplexityPruningRecord stack_record
intp_t node_idx
stack[intp_t] node_indices_stack
intp_t[:] n_leaves = np.zeros(shape=n_nodes, dtype=np.intp)
float64_t[:] r_branch = np.zeros(shape=n_nodes, dtype=np.float64)
float64_t current_r
intp_t leaf_idx
intp_t parent_idx
# candidate nodes that can be pruned
unsigned char[:] candidate_nodes = np.zeros(shape=n_nodes,
dtype=np.uint8)
# nodes in subtree
unsigned char[:] in_subtree = np.ones(shape=n_nodes, dtype=np.uint8)
intp_t pruned_branch_node_idx
float64_t subtree_alpha
float64_t effective_alpha
intp_t n_pruned_leaves
float64_t r_diff
float64_t max_float64 = np.finfo(np.float64).max
# find parent node ids and leaves
with nogil:
for i in range(r_node.shape[0]):
r_node[i] = (
weighted_n_node_samples[i] * impurity[i] / total_sum_weights)
# Push the root node
ccp_stack.push({"node_idx": 0, "parent": _TREE_UNDEFINED})
while not ccp_stack.empty():
stack_record = ccp_stack.top()
ccp_stack.pop()
node_idx = stack_record.node_idx
parent[node_idx] = stack_record.parent
if child_l[node_idx] == _TREE_LEAF:
# ... and child_r[node_idx] == _TREE_LEAF:
leaves_in_subtree[node_idx] = 1
else:
ccp_stack.push({"node_idx": child_l[node_idx], "parent": node_idx})
ccp_stack.push({"node_idx": child_r[node_idx], "parent": node_idx})
# computes number of leaves in all branches and the overall impurity of
# the branch. The overall impurity is the sum of r_node in its leaves.
for leaf_idx in range(leaves_in_subtree.shape[0]):
if not leaves_in_subtree[leaf_idx]:
continue
r_branch[leaf_idx] = r_node[leaf_idx]
# bubble up values to ancestor nodes
current_r = r_node[leaf_idx]
while leaf_idx != 0:
parent_idx = parent[leaf_idx]
r_branch[parent_idx] += current_r
n_leaves[parent_idx] += 1
leaf_idx = parent_idx
for i in range(leaves_in_subtree.shape[0]):
candidate_nodes[i] = not leaves_in_subtree[i]
# save metrics before pruning
controller.save_metrics(0.0, r_branch[0])
# while root node is not a leaf
while candidate_nodes[0]:
# computes ccp_alpha for subtrees and finds the minimal alpha
effective_alpha = max_float64
for i in range(n_nodes):
if not candidate_nodes[i]:
continue
subtree_alpha = (r_node[i] - r_branch[i]) / (n_leaves[i] - 1)
if subtree_alpha < effective_alpha:
effective_alpha = subtree_alpha
pruned_branch_node_idx = i
if controller.stop_pruning(effective_alpha):
break
node_indices_stack.push(pruned_branch_node_idx)
# descendants of branch are not in subtree
while not node_indices_stack.empty():
node_idx = node_indices_stack.top()
node_indices_stack.pop()
if not in_subtree[node_idx]:
continue # branch has already been marked for pruning
candidate_nodes[node_idx] = 0
leaves_in_subtree[node_idx] = 0
in_subtree[node_idx] = 0
if child_l[node_idx] != _TREE_LEAF:
# ... and child_r[node_idx] != _TREE_LEAF:
node_indices_stack.push(child_l[node_idx])
node_indices_stack.push(child_r[node_idx])
leaves_in_subtree[pruned_branch_node_idx] = 1
in_subtree[pruned_branch_node_idx] = 1
# updates number of leaves
n_pruned_leaves = n_leaves[pruned_branch_node_idx] - 1
n_leaves[pruned_branch_node_idx] = 0
# computes the increase in r_branch to bubble up
r_diff = r_node[pruned_branch_node_idx] - r_branch[pruned_branch_node_idx]
r_branch[pruned_branch_node_idx] = r_node[pruned_branch_node_idx]
# bubble up values to ancestors
node_idx = parent[pruned_branch_node_idx]
while node_idx != _TREE_UNDEFINED:
n_leaves[node_idx] -= n_pruned_leaves
r_branch[node_idx] += r_diff
node_idx = parent[node_idx]
controller.save_metrics(effective_alpha, r_branch[0])
controller.after_pruning(in_subtree)
def _build_pruned_tree_ccp(
Tree tree, # OUT
Tree orig_tree,
float64_t ccp_alpha
):
"""Build a pruned tree from the original tree using cost complexity
pruning.
The values and nodes from the original tree are copied into the pruned
tree.
Parameters
----------
tree : Tree
Location to place the pruned tree
orig_tree : Tree
Original tree
ccp_alpha : positive float64_t
Complexity parameter. The subtree with the largest cost complexity
that is smaller than ``ccp_alpha`` will be chosen. By default,
no pruning is performed.
"""
cdef:
intp_t n_nodes = orig_tree.node_count
unsigned char[:] leaves_in_subtree = np.zeros(
shape=n_nodes, dtype=np.uint8)
pruning_controller = _AlphaPruner(ccp_alpha=ccp_alpha)
_cost_complexity_prune(leaves_in_subtree, orig_tree, pruning_controller)
_build_pruned_tree(tree, orig_tree, leaves_in_subtree,
pruning_controller.capacity)
def ccp_pruning_path(Tree orig_tree):
"""Computes the cost complexity pruning path.
Parameters
----------
tree : Tree
Original tree.
Returns
-------
path_info : dict
Information about pruning path with attributes:
ccp_alphas : ndarray
Effective alphas of subtree during pruning.
impurities : ndarray
Sum of the impurities of the subtree leaves for the
corresponding alpha value in ``ccp_alphas``.
"""
cdef:
unsigned char[:] leaves_in_subtree = np.zeros(
shape=orig_tree.node_count, dtype=np.uint8)
path_finder = _PathFinder(orig_tree.node_count)
_cost_complexity_prune(leaves_in_subtree, orig_tree, path_finder)
cdef:
uint32_t total_items = path_finder.count
float64_t[:] ccp_alphas = np.empty(shape=total_items, dtype=np.float64)
float64_t[:] impurities = np.empty(shape=total_items, dtype=np.float64)
uint32_t count = 0
while count < total_items:
ccp_alphas[count] = path_finder.ccp_alphas[count]
impurities[count] = path_finder.impurities[count]
count += 1
return {
'ccp_alphas': np.asarray(ccp_alphas),
'impurities': np.asarray(impurities),
}
cdef struct BuildPrunedRecord:
intp_t start
intp_t depth
intp_t parent
bint is_left
cdef _build_pruned_tree(
Tree tree, # OUT
Tree orig_tree,
const unsigned char[:] leaves_in_subtree,
intp_t capacity
):
"""Build a pruned tree.
Build a pruned tree from the original tree by transforming the nodes in
``leaves_in_subtree`` into leaves.
Parameters
----------
tree : Tree
Location to place the pruned tree
orig_tree : Tree
Original tree
leaves_in_subtree : unsigned char memoryview, shape=(node_count, )
Boolean mask for leaves to include in subtree
capacity : intp_t
Number of nodes to initially allocate in pruned tree
"""
tree._resize(capacity)
cdef:
intp_t orig_node_id
intp_t new_node_id
intp_t depth
intp_t parent
bint is_left
bint is_leaf
# value_stride for original tree and new tree are the same
intp_t value_stride = orig_tree.value_stride
intp_t max_depth_seen = -1
int rc = 0
Node* node
float64_t* orig_value_ptr
float64_t* new_value_ptr
stack[BuildPrunedRecord] prune_stack
BuildPrunedRecord stack_record
with nogil:
# push root node onto stack
prune_stack.push({"start": 0, "depth": 0, "parent": _TREE_UNDEFINED, "is_left": 0})
while not prune_stack.empty():
stack_record = prune_stack.top()
prune_stack.pop()
orig_node_id = stack_record.start
depth = stack_record.depth
parent = stack_record.parent
is_left = stack_record.is_left
is_leaf = leaves_in_subtree[orig_node_id]
node = &orig_tree.nodes[orig_node_id]
new_node_id = tree._add_node(
parent, is_left, is_leaf, node.feature, node.threshold,
node.impurity, node.n_node_samples,
node.weighted_n_node_samples, node.missing_go_to_left)
if new_node_id == INTPTR_MAX:
rc = -1
break
# copy value from original tree to new tree
orig_value_ptr = orig_tree.value + value_stride * orig_node_id
new_value_ptr = tree.value + value_stride * new_node_id
memcpy(new_value_ptr, orig_value_ptr, sizeof(float64_t) * value_stride)
if not is_leaf:
# Push right child on stack
prune_stack.push({"start": node.right_child, "depth": depth + 1,
"parent": new_node_id, "is_left": 0})
# push left child on stack
prune_stack.push({"start": node.left_child, "depth": depth + 1,
"parent": new_node_id, "is_left": 1})
if depth > max_depth_seen:
max_depth_seen = depth
if rc >= 0:
tree.max_depth = max_depth_seen
if rc == -1:
raise MemoryError("pruning tree")
Reference in New Issue Copy Permalink