# Authors: Gilles Louppe # Peter Prettenhofer # Brian Holt # Joel Nothman # Arnaud Joly # Jacob Schreiber # Nelson Liu # # License: BSD 3 clause # See _tree.pyx for details. import numpy as np cimport numpy as cnp ctypedef cnp.npy_float32 DTYPE_t # Type of X ctypedef cnp.npy_float64 DOUBLE_t # Type of y, sample_weight ctypedef cnp.npy_intp SIZE_t # Type for indices and counters ctypedef cnp.npy_int32 INT32_t # Signed 32 bit integer ctypedef cnp.npy_uint32 UINT32_t # Unsigned 32 bit integer from ._splitter cimport Splitter from ._splitter cimport SplitRecord cdef struct Node: # Base storage structure for the nodes in a Tree object SIZE_t left_child # id of the left child of the node SIZE_t right_child # id of the right child of the node SIZE_t feature # Feature used for splitting the node DOUBLE_t threshold # Threshold value at the node DOUBLE_t impurity # Impurity of the node (i.e., the value of the criterion) SIZE_t n_node_samples # Number of samples at the node DOUBLE_t weighted_n_node_samples # Weighted number of samples at the node cdef class Tree: # The Tree object is a binary tree structure constructed by the # TreeBuilder. The tree structure is used for predictions and # feature importances. # Input/Output layout cdef public SIZE_t n_features # Number of features in X cdef SIZE_t* n_classes # Number of classes in y[:, k] cdef public SIZE_t n_outputs # Number of outputs in y cdef public SIZE_t max_n_classes # max(n_classes) # Inner structures: values are stored separately from node structure, # since size is determined at runtime. cdef public SIZE_t max_depth # Max depth of the tree cdef public SIZE_t node_count # Counter for node IDs cdef public SIZE_t capacity # Capacity of tree, in terms of nodes cdef Node* nodes # Array of nodes cdef double* value # (capacity, n_outputs, max_n_classes) array of values cdef SIZE_t value_stride # = n_outputs * max_n_classes # Methods cdef SIZE_t _add_node(self, SIZE_t parent, bint is_left, bint is_leaf, SIZE_t feature, double threshold, double impurity, SIZE_t n_node_samples, double weighted_n_node_samples) nogil except -1 cdef int _resize(self, SIZE_t capacity) nogil except -1 cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1 cdef cnp.ndarray _get_value_ndarray(self) cdef cnp.ndarray _get_node_ndarray(self) cpdef cnp.ndarray predict(self, object X) cpdef cnp.ndarray apply(self, object X) cdef cnp.ndarray _apply_dense(self, object X) cdef cnp.ndarray _apply_sparse_csr(self, object X) cpdef object decision_path(self, object X) cdef object _decision_path_dense(self, object X) cdef object _decision_path_sparse_csr(self, object X) cpdef compute_feature_importances(self, normalize=*) # ============================================================================= # Tree builder # ============================================================================= cdef class TreeBuilder: # The TreeBuilder recursively builds a Tree object from training samples, # using a Splitter object for splitting internal nodes and assigning # values to leaves. # # This class controls the various stopping criteria and the node splitting # evaluation order, e.g. depth-first or best-first. cdef Splitter splitter # Splitting algorithm cdef SIZE_t min_samples_split # Minimum number of samples in an internal node cdef SIZE_t min_samples_leaf # Minimum number of samples in a leaf cdef double min_weight_leaf # Minimum weight in a leaf cdef SIZE_t max_depth # Maximal tree depth cdef double min_impurity_decrease # Impurity threshold for early stopping cpdef build(self, Tree tree, object X, cnp.ndarray y, cnp.ndarray sample_weight=*) cdef _check_input(self, object X, cnp.ndarray y, cnp.ndarray sample_weight)