Python: fixed setup.py for external sklearn.tree usage

python/isaac/external/_tree.pxd

@@ -1,274 +0,0 @@
-# Authors: Gilles Louppe <g.louppe@gmail.com>
-#          Peter Prettenhofer <peter.prettenhofer@gmail.com>
-#          Brian Holt <bdholt1@gmail.com>
-#          Joel Nothman <joel.nothman@gmail.com>
-#          Arnaud Joly <arnaud.v.joly@gmail.com>
-#
-# Licence: BSD 3 clause
-
-# See _tree.pyx for details.
-
-import numpy as np
-cimport numpy as np
-
-ctypedef np.npy_float32 DTYPE_t          # Type of X
-ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
-ctypedef np.npy_intp SIZE_t              # Type for indices and counters
-ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
-ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
-
-
-# =============================================================================
-# Stack data structure
-# =============================================================================
-
-# A record on the stack for depth-first tree growing
-cdef struct StackRecord:
-    SIZE_t start
-    SIZE_t end
-    SIZE_t depth
-    SIZE_t parent
-    bint is_left
-    double impurity
-    SIZE_t n_constant_features
-
-cdef class Stack:
-    cdef SIZE_t capacity
-    cdef SIZE_t top
-    cdef StackRecord* stack_
-
-    cdef bint is_empty(self) nogil
-    cdef int push(self, SIZE_t start, SIZE_t end, SIZE_t depth, SIZE_t parent,
-                  bint is_left, double impurity,
-                  SIZE_t n_constant_features) nogil
-    cdef int pop(self, StackRecord* res) nogil
-
-
-# =============================================================================
-# PriorityHeap data structure
-# =============================================================================
-
-# A record on the frontier for best-first tree growing
-cdef struct PriorityHeapRecord:
-    SIZE_t node_id
-    SIZE_t start
-    SIZE_t end
-    SIZE_t pos
-    SIZE_t depth
-    bint is_leaf
-    double impurity
-    double impurity_left
-    double impurity_right
-    double improvement
-
-cdef class PriorityHeap:
-    cdef SIZE_t capacity
-    cdef SIZE_t heap_ptr
-    cdef PriorityHeapRecord* heap_
-
-    cdef bint is_empty(self) nogil
-    cdef int push(self, SIZE_t node_id, SIZE_t start, SIZE_t end, SIZE_t pos,
-                  SIZE_t depth, bint is_leaf, double improvement,
-                  double impurity, double impurity_left,
-                  double impurity_right) nogil
-    cdef int pop(self, PriorityHeapRecord* res) nogil
-
-
-# =============================================================================
-# Criterion
-# =============================================================================
-
-cdef class Criterion:
-    # The criterion computes the impurity of a node and the reduction of
-    # impurity of a split on that node. It also computes the output statistics
-    # such as the mean in regression and class probabilities in classification.
-
-    # Internal structures
-    cdef DOUBLE_t* y                     # Values of y
-    cdef SIZE_t y_stride                 # Stride in y (since n_outputs >= 1)
-    cdef DOUBLE_t* sample_weight         # Sample weights
-
-    cdef SIZE_t* samples                 # Sample indices in X, y
-    cdef SIZE_t start                    # samples[start:pos] are the samples in the left node
-    cdef SIZE_t pos                      # samples[pos:end] are the samples in the right node
-    cdef SIZE_t end
-
-    cdef SIZE_t n_outputs                # Number of outputs
-    cdef SIZE_t n_node_samples           # Number of samples in the node (end-start)
-    cdef double weighted_n_samples       # Weighted number of samples (in total)
-    cdef double weighted_n_node_samples  # Weighted number of samples in the node
-    cdef double weighted_n_left          # Weighted number of samples in the left node
-    cdef double weighted_n_right         # Weighted number of samples in the right node
-
-    # The criterion object is maintained such that left and right collected
-    # statistics correspond to samples[start:pos] and samples[pos:end].
-
-    # Methods
-    cdef void init(self, DOUBLE_t* y, SIZE_t y_stride, DOUBLE_t* sample_weight,
-                   double weighted_n_samples, SIZE_t* samples, SIZE_t start,
-                   SIZE_t end) nogil
-    cdef void reset(self) nogil
-    cdef void update(self, SIZE_t new_pos) nogil
-    cdef double node_impurity(self) nogil
-    cdef void children_impurity(self, double* impurity_left,
-                                double* impurity_right) nogil
-    cdef void node_value(self, double* dest) nogil
-    cdef double impurity_improvement(self, double impurity) nogil
-
-
-# =============================================================================
-# Splitter
-# =============================================================================
-
-cdef struct SplitRecord:
-    # Data to track sample split
-    SIZE_t feature         # Which feature to split on.
-    SIZE_t pos             # Split samples array at the given position,
-                           # i.e. count of samples below threshold for feature.
-                           # pos is >= end if the node is a leaf.
-    double threshold       # Threshold to split at.
-    double improvement     # Impurity improvement given parent node.
-    double impurity_left   # Impurity of the left split.
-    double impurity_right  # Impurity of the right split.
-
-
-cdef class Splitter:
-    # The splitter searches in the input space for a feature and a threshold
-    # to split the samples samples[start:end].
-    #
-    # The impurity computations are delegated to a criterion object.
-
-    # Internal structures
-    cdef public Criterion criterion      # Impurity criterion
-    cdef public SIZE_t max_features      # Number of features to test
-    cdef public SIZE_t min_samples_leaf  # Min samples in a leaf
-    cdef public double min_weight_leaf   # Minimum weight in a leaf
-
-    cdef object random_state             # Random state
-    cdef UINT32_t rand_r_state           # sklearn_rand_r random number state
-
-    cdef SIZE_t* samples                 # Sample indices in X, y
-    cdef SIZE_t n_samples                # X.shape[0]
-    cdef double weighted_n_samples       # Weighted number of samples
-    cdef SIZE_t* features                # Feature indices in X
-    cdef SIZE_t* constant_features       # Constant features indices
-    cdef SIZE_t n_features               # X.shape[1]
-    cdef DTYPE_t* feature_values         # temp. array holding feature values
-
-    cdef SIZE_t start                    # Start position for the current node
-    cdef SIZE_t end                      # End position for the current node
-
-    cdef DOUBLE_t* y
-    cdef SIZE_t y_stride
-    cdef DOUBLE_t* sample_weight
-
-    # The samples vector `samples` is maintained by the Splitter object such
-    # that the samples contained in a node are contiguous. With this setting,
-    # `node_split` reorganizes the node samples `samples[start:end]` in two
-    # subsets `samples[start:pos]` and `samples[pos:end]`.
-
-    # The 1-d  `features` array of size n_features contains the features
-    # indices and allows fast sampling without replacement of features.
-
-    # The 1-d `constant_features` array of size n_features holds in
-    # `constant_features[:n_constant_features]` the feature ids with
-    # constant values for all the samples that reached a specific node.
-    # The value `n_constant_features` is given by the the parent node to its
-    # child nodes.  The content of the range `[n_constant_features:]` is left
-    # undefined, but preallocated for performance reasons
-    # This allows optimization with depth-based tree building.
-
-    # Methods
-    cdef void init(self, object X, np.ndarray y,
-                   DOUBLE_t* sample_weight) except *
-
-    cdef void node_reset(self, SIZE_t start, SIZE_t end,
-                         double* weighted_n_node_samples) nogil
-
-    cdef void node_split(self,
-                         double impurity,   # Impurity of the node
-                         SplitRecord* split,
-                         SIZE_t* n_constant_features) nogil
-
-    cdef void node_value(self, double* dest) nogil
-
-    cdef double node_impurity(self) nogil
-
-
-# =============================================================================
-# Tree
-# =============================================================================
-
-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_samples) nogil
-    cdef void _resize(self, SIZE_t capacity) except *
-    cdef int _resize_c(self, SIZE_t capacity=*) nogil
-
-    cdef np.ndarray _get_value_ndarray(self)
-    cdef np.ndarray _get_node_ndarray(self)
-
-    cpdef np.ndarray predict(self, object X)
-    cpdef np.ndarray apply(self, object X)
-    cdef np.ndarray _apply_dense(self, object X)
-    cdef np.ndarray _apply_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
-
-    cpdef build(self, Tree tree, object X, np.ndarray y,
-                np.ndarray sample_weight=*)
-    cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight)