124 lines
5.0 KiB
Cython
124 lines
5.0 KiB
Cython
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# Authors: Gilles Louppe <g.louppe@gmail.com>
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# Peter Prettenhofer <peter.prettenhofer@gmail.com>
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# Brian Holt <bdholt1@gmail.com>
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# Joel Nothman <joel.nothman@gmail.com>
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# Arnaud Joly <arnaud.v.joly@gmail.com>
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# Jacob Schreiber <jmschreiber91@gmail.com>
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# Nelson Liu <nelson@nelsonliu.me>
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#
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# License: BSD 3 clause
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# See _tree.pyx for details.
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import numpy as np
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cimport numpy as cnp
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from ..utils._typedefs cimport float32_t, float64_t, intp_t, int32_t, uint32_t
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from ._splitter cimport Splitter
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from ._splitter cimport SplitRecord
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cdef struct Node:
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# Base storage structure for the nodes in a Tree object
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intp_t left_child # id of the left child of the node
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intp_t right_child # id of the right child of the node
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intp_t feature # Feature used for splitting the node
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float64_t threshold # Threshold value at the node
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float64_t impurity # Impurity of the node (i.e., the value of the criterion)
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intp_t n_node_samples # Number of samples at the node
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float64_t weighted_n_node_samples # Weighted number of samples at the node
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unsigned char missing_go_to_left # Whether features have missing values
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cdef struct ParentInfo:
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# Structure to store information about the parent of a node
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# This is passed to the splitter, to provide information about the previous split
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float64_t lower_bound # the lower bound of the parent's impurity
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float64_t upper_bound # the upper bound of the parent's impurity
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float64_t impurity # the impurity of the parent
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intp_t n_constant_features # the number of constant features found in parent
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cdef class Tree:
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# The Tree object is a binary tree structure constructed by the
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# TreeBuilder. The tree structure is used for predictions and
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# feature importances.
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# Input/Output layout
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cdef public intp_t n_features # Number of features in X
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cdef intp_t* n_classes # Number of classes in y[:, k]
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cdef public intp_t n_outputs # Number of outputs in y
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cdef public intp_t max_n_classes # max(n_classes)
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# Inner structures: values are stored separately from node structure,
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# since size is determined at runtime.
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cdef public intp_t max_depth # Max depth of the tree
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cdef public intp_t node_count # Counter for node IDs
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cdef public intp_t capacity # Capacity of tree, in terms of nodes
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cdef Node* nodes # Array of nodes
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cdef float64_t* value # (capacity, n_outputs, max_n_classes) array of values
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cdef intp_t value_stride # = n_outputs * max_n_classes
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# Methods
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cdef intp_t _add_node(self, intp_t parent, bint is_left, bint is_leaf,
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intp_t feature, float64_t threshold, float64_t impurity,
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intp_t n_node_samples,
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float64_t weighted_n_node_samples,
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unsigned char missing_go_to_left) except -1 nogil
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cdef int _resize(self, intp_t capacity) except -1 nogil
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cdef int _resize_c(self, intp_t capacity=*) except -1 nogil
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cdef cnp.ndarray _get_value_ndarray(self)
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cdef cnp.ndarray _get_node_ndarray(self)
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cpdef cnp.ndarray predict(self, object X)
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cpdef cnp.ndarray apply(self, object X)
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cdef cnp.ndarray _apply_dense(self, object X)
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cdef cnp.ndarray _apply_sparse_csr(self, object X)
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cpdef object decision_path(self, object X)
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cdef object _decision_path_dense(self, object X)
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cdef object _decision_path_sparse_csr(self, object X)
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cpdef compute_node_depths(self)
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cpdef compute_feature_importances(self, normalize=*)
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# =============================================================================
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# Tree builder
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# =============================================================================
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cdef class TreeBuilder:
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# The TreeBuilder recursively builds a Tree object from training samples,
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# using a Splitter object for splitting internal nodes and assigning
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# values to leaves.
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#
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# This class controls the various stopping criteria and the node splitting
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# evaluation order, e.g. depth-first or best-first.
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cdef Splitter splitter # Splitting algorithm
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cdef intp_t min_samples_split # Minimum number of samples in an internal node
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cdef intp_t min_samples_leaf # Minimum number of samples in a leaf
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cdef float64_t min_weight_leaf # Minimum weight in a leaf
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cdef intp_t max_depth # Maximal tree depth
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cdef float64_t min_impurity_decrease # Impurity threshold for early stopping
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cpdef build(
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self,
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Tree tree,
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object X,
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const float64_t[:, ::1] y,
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const float64_t[:] sample_weight=*,
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const unsigned char[::1] missing_values_in_feature_mask=*,
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)
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cdef _check_input(
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self,
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object X,
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const float64_t[:, ::1] y,
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const float64_t[:] sample_weight,
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)
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