95 lines
4.1 KiB
Cython
95 lines
4.1 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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#
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# License: BSD 3 clause
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# See _splitter.pyx for details.
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import numpy as np
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cimport numpy as np
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from ._criterion cimport Criterion
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from ._tree cimport DTYPE_t # Type of X
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from ._tree cimport DOUBLE_t # Type of y, sample_weight
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from ._tree cimport SIZE_t # Type for indices and counters
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from ._tree cimport INT32_t # Signed 32 bit integer
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from ._tree cimport UINT32_t # Unsigned 32 bit integer
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cdef struct SplitRecord:
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# Data to track sample split
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SIZE_t feature # Which feature to split on.
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SIZE_t pos # Split samples array at the given position,
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# i.e. count of samples below threshold for feature.
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# pos is >= end if the node is a leaf.
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double threshold # Threshold to split at.
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double improvement # Impurity improvement given parent node.
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double impurity_left # Impurity of the left split.
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double impurity_right # Impurity of the right split.
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cdef class Splitter:
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# The splitter searches in the input space for a feature and a threshold
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# to split the samples samples[start:end].
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#
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# The impurity computations are delegated to a criterion object.
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# Internal structures
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cdef public Criterion criterion # Impurity criterion
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cdef public SIZE_t max_features # Number of features to test
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cdef public SIZE_t min_samples_leaf # Min samples in a leaf
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cdef public double min_weight_leaf # Minimum weight in a leaf
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cdef object random_state # Random state
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cdef UINT32_t rand_r_state # sklearn_rand_r random number state
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cdef SIZE_t* samples # Sample indices in X, y
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cdef SIZE_t n_samples # X.shape[0]
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cdef double weighted_n_samples # Weighted number of samples
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cdef SIZE_t* features # Feature indices in X
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cdef SIZE_t* constant_features # Constant features indices
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cdef SIZE_t n_features # X.shape[1]
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cdef DTYPE_t* feature_values # temp. array holding feature values
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cdef SIZE_t start # Start position for the current node
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cdef SIZE_t end # End position for the current node
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cdef const DOUBLE_t[:, ::1] y
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cdef DOUBLE_t* sample_weight
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# The samples vector `samples` is maintained by the Splitter object such
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# that the samples contained in a node are contiguous. With this setting,
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# `node_split` reorganizes the node samples `samples[start:end]` in two
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# subsets `samples[start:pos]` and `samples[pos:end]`.
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# The 1-d `features` array of size n_features contains the features
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# indices and allows fast sampling without replacement of features.
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# The 1-d `constant_features` array of size n_features holds in
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# `constant_features[:n_constant_features]` the feature ids with
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# constant values for all the samples that reached a specific node.
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# The value `n_constant_features` is given by the parent node to its
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# child nodes. The content of the range `[n_constant_features:]` is left
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# undefined, but preallocated for performance reasons
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# This allows optimization with depth-based tree building.
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# Methods
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cdef int init(self, object X, const DOUBLE_t[:, ::1] y,
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DOUBLE_t* sample_weight,
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np.ndarray X_idx_sorted=*) except -1
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cdef int node_reset(self, SIZE_t start, SIZE_t end,
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double* weighted_n_node_samples) nogil except -1
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cdef int node_split(self,
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double impurity, # Impurity of the node
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SplitRecord* split,
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SIZE_t* n_constant_features) nogil except -1
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cdef void node_value(self, double* dest) nogil
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cdef double node_impurity(self) nogil
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