forked from 170010011/fr
80 lines
3.7 KiB
Cython
80 lines
3.7 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 _criterion.pyx for implementation details.
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import numpy as np
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cimport numpy as np
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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 class Criterion:
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# The criterion computes the impurity of a node and the reduction of
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# impurity of a split on that node. It also computes the output statistics
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# such as the mean in regression and class probabilities in classification.
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# Internal structures
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cdef const DOUBLE_t[:, ::1] y # Values of y
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cdef DOUBLE_t* sample_weight # Sample weights
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cdef SIZE_t* samples # Sample indices in X, y
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cdef SIZE_t start # samples[start:pos] are the samples in the left node
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cdef SIZE_t pos # samples[pos:end] are the samples in the right node
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cdef SIZE_t end
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cdef SIZE_t n_outputs # Number of outputs
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cdef SIZE_t n_samples # Number of samples
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cdef SIZE_t n_node_samples # Number of samples in the node (end-start)
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cdef double weighted_n_samples # Weighted number of samples (in total)
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cdef double weighted_n_node_samples # Weighted number of samples in the node
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cdef double weighted_n_left # Weighted number of samples in the left node
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cdef double weighted_n_right # Weighted number of samples in the right node
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cdef double* sum_total # For classification criteria, the sum of the
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# weighted count of each label. For regression,
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# the sum of w*y. sum_total[k] is equal to
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# sum_{i=start}^{end-1} w[samples[i]]*y[samples[i], k],
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# where k is output index.
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cdef double* sum_left # Same as above, but for the left side of the split
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cdef double* sum_right # same as above, but for the right side of the split
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# The criterion object is maintained such that left and right collected
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# statistics correspond to samples[start:pos] and samples[pos:end].
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# Methods
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cdef int init(self, const DOUBLE_t[:, ::1] y, DOUBLE_t* sample_weight,
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double weighted_n_samples, SIZE_t* samples, SIZE_t start,
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SIZE_t end) nogil except -1
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cdef int reset(self) nogil except -1
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cdef int reverse_reset(self) nogil except -1
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cdef int update(self, SIZE_t new_pos) nogil except -1
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cdef double node_impurity(self) nogil
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cdef void children_impurity(self, double* impurity_left,
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double* impurity_right) nogil
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cdef void node_value(self, double* dest) nogil
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cdef double impurity_improvement(self, double impurity_parent,
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double impurity_left,
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double impurity_right) nogil
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cdef double proxy_impurity_improvement(self) nogil
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cdef class ClassificationCriterion(Criterion):
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"""Abstract criterion for classification."""
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cdef SIZE_t* n_classes
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cdef SIZE_t sum_stride
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cdef class RegressionCriterion(Criterion):
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"""Abstract regression criterion."""
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cdef double sq_sum_total
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