1709 lines
61 KiB
Cython
1709 lines
61 KiB
Cython
# 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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# Noel Dawe <noel@dawe.me>
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# Satrajit Gosh <satrajit.ghosh@gmail.com>
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# Lars Buitinck
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# Arnaud Joly <arnaud.v.joly@gmail.com>
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# Joel Nothman <joel.nothman@gmail.com>
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# Fares Hedayati <fares.hedayati@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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from libc.string cimport memcpy
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from libc.string cimport memset
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from libc.math cimport fabs, INFINITY
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import numpy as np
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cimport numpy as cnp
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cnp.import_array()
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from scipy.special.cython_special cimport xlogy
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from ._utils cimport log
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from ._utils cimport WeightedMedianCalculator
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# EPSILON is used in the Poisson criterion
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cdef float64_t EPSILON = 10 * np.finfo('double').eps
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cdef class Criterion:
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"""Interface for impurity criteria.
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This object stores methods on how to calculate how good a split is using
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different metrics.
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"""
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def __getstate__(self):
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return {}
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def __setstate__(self, d):
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pass
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cdef int init(
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self,
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const float64_t[:, ::1] y,
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const float64_t[:] sample_weight,
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float64_t weighted_n_samples,
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const intp_t[:] sample_indices,
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intp_t start,
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intp_t end,
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) except -1 nogil:
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"""Placeholder for a method which will initialize the criterion.
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Returns -1 in case of failure to allocate memory (and raise MemoryError)
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or 0 otherwise.
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Parameters
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----------
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y : ndarray, dtype=float64_t
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y is a buffer that can store values for n_outputs target variables
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stored as a Cython memoryview.
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sample_weight : ndarray, dtype=float64_t
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The weight of each sample stored as a Cython memoryview.
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weighted_n_samples : float64_t
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The total weight of the samples being considered
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sample_indices : ndarray, dtype=intp_t
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A mask on the samples. Indices of the samples in X and y we want to use,
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where sample_indices[start:end] correspond to the samples in this node.
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start : intp_t
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The first sample to be used on this node
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end : intp_t
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The last sample used on this node
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"""
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pass
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cdef void init_missing(self, intp_t n_missing) noexcept nogil:
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"""Initialize sum_missing if there are missing values.
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This method assumes that caller placed the missing samples in
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self.sample_indices[-n_missing:]
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Parameters
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----------
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n_missing: intp_t
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Number of missing values for specific feature.
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"""
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pass
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cdef int reset(self) except -1 nogil:
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"""Reset the criterion at pos=start.
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This method must be implemented by the subclass.
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"""
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pass
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cdef int reverse_reset(self) except -1 nogil:
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"""Reset the criterion at pos=end.
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This method must be implemented by the subclass.
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"""
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pass
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cdef int update(self, intp_t new_pos) except -1 nogil:
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"""Updated statistics by moving sample_indices[pos:new_pos] to the left child.
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This updates the collected statistics by moving sample_indices[pos:new_pos]
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from the right child to the left child. It must be implemented by
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the subclass.
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Parameters
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----------
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new_pos : intp_t
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New starting index position of the sample_indices in the right child
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"""
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pass
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cdef float64_t node_impurity(self) noexcept nogil:
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"""Placeholder for calculating the impurity of the node.
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Placeholder for a method which will evaluate the impurity of
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the current node, i.e. the impurity of sample_indices[start:end]. This is the
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primary function of the criterion class. The smaller the impurity the
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better.
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"""
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pass
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cdef void children_impurity(self, float64_t* impurity_left,
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float64_t* impurity_right) noexcept nogil:
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"""Placeholder for calculating the impurity of children.
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Placeholder for a method which evaluates the impurity in
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children nodes, i.e. the impurity of sample_indices[start:pos] + the impurity
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of sample_indices[pos:end].
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Parameters
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----------
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impurity_left : float64_t pointer
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The memory address where the impurity of the left child should be
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stored.
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impurity_right : float64_t pointer
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The memory address where the impurity of the right child should be
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stored
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"""
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pass
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cdef void node_value(self, float64_t* dest) noexcept nogil:
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"""Placeholder for storing the node value.
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Placeholder for a method which will compute the node value
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of sample_indices[start:end] and save the value into dest.
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Parameters
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----------
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dest : float64_t pointer
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The memory address where the node value should be stored.
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"""
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pass
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cdef void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
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pass
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cdef float64_t middle_value(self) noexcept nogil:
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"""Compute the middle value of a split for monotonicity constraints
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This method is implemented in ClassificationCriterion and RegressionCriterion.
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"""
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pass
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cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
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"""Compute a proxy of the impurity reduction.
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This method is used to speed up the search for the best split.
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It is a proxy quantity such that the split that maximizes this value
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also maximizes the impurity improvement. It neglects all constant terms
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of the impurity decrease for a given split.
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The absolute impurity improvement is only computed by the
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impurity_improvement method once the best split has been found.
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"""
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cdef float64_t impurity_left
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cdef float64_t impurity_right
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self.children_impurity(&impurity_left, &impurity_right)
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return (- self.weighted_n_right * impurity_right
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- self.weighted_n_left * impurity_left)
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cdef float64_t impurity_improvement(self, float64_t impurity_parent,
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float64_t impurity_left,
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float64_t impurity_right) noexcept nogil:
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"""Compute the improvement in impurity.
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This method computes the improvement in impurity when a split occurs.
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The weighted impurity improvement equation is the following:
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N_t / N * (impurity - N_t_R / N_t * right_impurity
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- N_t_L / N_t * left_impurity)
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where N is the total number of samples, N_t is the number of samples
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at the current node, N_t_L is the number of samples in the left child,
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and N_t_R is the number of samples in the right child,
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Parameters
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----------
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impurity_parent : float64_t
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The initial impurity of the parent node before the split
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impurity_left : float64_t
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The impurity of the left child
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impurity_right : float64_t
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The impurity of the right child
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Return
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------
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float64_t : improvement in impurity after the split occurs
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"""
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return ((self.weighted_n_node_samples / self.weighted_n_samples) *
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(impurity_parent - (self.weighted_n_right /
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self.weighted_n_node_samples * impurity_right)
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- (self.weighted_n_left /
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self.weighted_n_node_samples * impurity_left)))
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cdef bint check_monotonicity(
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self,
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cnp.int8_t monotonic_cst,
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float64_t lower_bound,
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float64_t upper_bound,
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) noexcept nogil:
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pass
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cdef inline bint _check_monotonicity(
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self,
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cnp.int8_t monotonic_cst,
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float64_t lower_bound,
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float64_t upper_bound,
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float64_t value_left,
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float64_t value_right,
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) noexcept nogil:
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cdef:
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bint check_lower_bound = (
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(value_left >= lower_bound) &
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(value_right >= lower_bound)
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)
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bint check_upper_bound = (
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(value_left <= upper_bound) &
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(value_right <= upper_bound)
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)
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bint check_monotonic_cst = (
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(value_left - value_right) * monotonic_cst <= 0
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)
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return check_lower_bound & check_upper_bound & check_monotonic_cst
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cdef void init_sum_missing(self):
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"""Init sum_missing to hold sums for missing values."""
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cdef inline void _move_sums_classification(
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ClassificationCriterion criterion,
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float64_t[:, ::1] sum_1,
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float64_t[:, ::1] sum_2,
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float64_t* weighted_n_1,
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float64_t* weighted_n_2,
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bint put_missing_in_1,
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) noexcept nogil:
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"""Distribute sum_total and sum_missing into sum_1 and sum_2.
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If there are missing values and:
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- put_missing_in_1 is True, then missing values to go sum_1. Specifically:
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sum_1 = sum_missing
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sum_2 = sum_total - sum_missing
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- put_missing_in_1 is False, then missing values go to sum_2. Specifically:
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sum_1 = 0
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sum_2 = sum_total
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"""
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cdef intp_t k, c, n_bytes
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if criterion.n_missing != 0 and put_missing_in_1:
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for k in range(criterion.n_outputs):
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n_bytes = criterion.n_classes[k] * sizeof(float64_t)
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memcpy(&sum_1[k, 0], &criterion.sum_missing[k, 0], n_bytes)
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for k in range(criterion.n_outputs):
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for c in range(criterion.n_classes[k]):
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sum_2[k, c] = criterion.sum_total[k, c] - criterion.sum_missing[k, c]
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weighted_n_1[0] = criterion.weighted_n_missing
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weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
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else:
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# Assigning sum_2 = sum_total for all outputs.
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for k in range(criterion.n_outputs):
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n_bytes = criterion.n_classes[k] * sizeof(float64_t)
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memset(&sum_1[k, 0], 0, n_bytes)
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memcpy(&sum_2[k, 0], &criterion.sum_total[k, 0], n_bytes)
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weighted_n_1[0] = 0.0
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weighted_n_2[0] = criterion.weighted_n_node_samples
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cdef class ClassificationCriterion(Criterion):
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"""Abstract criterion for classification."""
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def __cinit__(self, intp_t n_outputs,
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cnp.ndarray[intp_t, ndim=1] n_classes):
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"""Initialize attributes for this criterion.
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Parameters
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----------
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n_outputs : intp_t
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The number of targets, the dimensionality of the prediction
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n_classes : numpy.ndarray, dtype=intp_t
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The number of unique classes in each target
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"""
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self.start = 0
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self.pos = 0
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self.end = 0
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self.missing_go_to_left = 0
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self.n_outputs = n_outputs
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self.n_samples = 0
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self.n_node_samples = 0
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self.weighted_n_node_samples = 0.0
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self.weighted_n_left = 0.0
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self.weighted_n_right = 0.0
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self.weighted_n_missing = 0.0
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self.n_classes = np.empty(n_outputs, dtype=np.intp)
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cdef intp_t k = 0
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cdef intp_t max_n_classes = 0
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# For each target, set the number of unique classes in that target,
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# and also compute the maximal stride of all targets
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for k in range(n_outputs):
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self.n_classes[k] = n_classes[k]
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if n_classes[k] > max_n_classes:
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max_n_classes = n_classes[k]
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self.max_n_classes = max_n_classes
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# Count labels for each output
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self.sum_total = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
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self.sum_left = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
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self.sum_right = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
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def __reduce__(self):
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return (type(self),
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(self.n_outputs, np.asarray(self.n_classes)), self.__getstate__())
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cdef int init(
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self,
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const float64_t[:, ::1] y,
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const float64_t[:] sample_weight,
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float64_t weighted_n_samples,
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const intp_t[:] sample_indices,
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intp_t start,
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intp_t end
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) except -1 nogil:
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"""Initialize the criterion.
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This initializes the criterion at node sample_indices[start:end] and children
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sample_indices[start:start] and sample_indices[start:end].
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Returns -1 in case of failure to allocate memory (and raise MemoryError)
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or 0 otherwise.
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Parameters
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----------
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y : ndarray, dtype=float64_t
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The target stored as a buffer for memory efficiency.
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sample_weight : ndarray, dtype=float64_t
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The weight of each sample stored as a Cython memoryview.
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weighted_n_samples : float64_t
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The total weight of all samples
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sample_indices : ndarray, dtype=intp_t
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A mask on the samples. Indices of the samples in X and y we want to use,
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where sample_indices[start:end] correspond to the samples in this node.
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start : intp_t
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The first sample to use in the mask
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end : intp_t
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The last sample to use in the mask
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"""
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self.y = y
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self.sample_weight = sample_weight
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self.sample_indices = sample_indices
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self.start = start
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self.end = end
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self.n_node_samples = end - start
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self.weighted_n_samples = weighted_n_samples
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self.weighted_n_node_samples = 0.0
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cdef intp_t i
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cdef intp_t p
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cdef intp_t k
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cdef intp_t c
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cdef float64_t w = 1.0
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for k in range(self.n_outputs):
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memset(&self.sum_total[k, 0], 0, self.n_classes[k] * sizeof(float64_t))
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for p in range(start, end):
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i = sample_indices[p]
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# w is originally set to be 1.0, meaning that if no sample weights
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# are given, the default weight of each sample is 1.0.
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if sample_weight is not None:
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w = sample_weight[i]
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# Count weighted class frequency for each target
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for k in range(self.n_outputs):
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c = <intp_t> self.y[i, k]
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self.sum_total[k, c] += w
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self.weighted_n_node_samples += w
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# Reset to pos=start
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self.reset()
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return 0
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cdef void init_sum_missing(self):
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"""Init sum_missing to hold sums for missing values."""
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self.sum_missing = np.zeros((self.n_outputs, self.max_n_classes), dtype=np.float64)
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cdef void init_missing(self, intp_t n_missing) noexcept nogil:
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"""Initialize sum_missing if there are missing values.
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This method assumes that caller placed the missing samples in
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self.sample_indices[-n_missing:]
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"""
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cdef intp_t i, p, k, c
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cdef float64_t w = 1.0
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self.n_missing = n_missing
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if n_missing == 0:
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return
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memset(&self.sum_missing[0, 0], 0, self.max_n_classes * self.n_outputs * sizeof(float64_t))
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self.weighted_n_missing = 0.0
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# The missing samples are assumed to be in self.sample_indices[-n_missing:]
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for p in range(self.end - n_missing, self.end):
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i = self.sample_indices[p]
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if self.sample_weight is not None:
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w = self.sample_weight[i]
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for k in range(self.n_outputs):
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c = <intp_t> self.y[i, k]
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self.sum_missing[k, c] += w
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self.weighted_n_missing += w
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cdef int reset(self) except -1 nogil:
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"""Reset the criterion at pos=start.
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Returns -1 in case of failure to allocate memory (and raise MemoryError)
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or 0 otherwise.
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"""
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self.pos = self.start
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_move_sums_classification(
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self,
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self.sum_left,
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self.sum_right,
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&self.weighted_n_left,
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&self.weighted_n_right,
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self.missing_go_to_left,
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)
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return 0
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cdef int reverse_reset(self) except -1 nogil:
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"""Reset the criterion at pos=end.
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Returns -1 in case of failure to allocate memory (and raise MemoryError)
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or 0 otherwise.
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"""
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self.pos = self.end
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_move_sums_classification(
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self,
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self.sum_right,
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self.sum_left,
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&self.weighted_n_right,
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&self.weighted_n_left,
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not self.missing_go_to_left
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)
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return 0
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cdef int update(self, intp_t new_pos) except -1 nogil:
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"""Updated statistics by moving sample_indices[pos:new_pos] to the left child.
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Returns -1 in case of failure to allocate memory (and raise MemoryError)
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or 0 otherwise.
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Parameters
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----------
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new_pos : intp_t
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The new ending position for which to move sample_indices from the right
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child to the left child.
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"""
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cdef intp_t pos = self.pos
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# The missing samples are assumed to be in
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# self.sample_indices[-self.n_missing:] that is
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# self.sample_indices[end_non_missing:self.end].
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cdef intp_t end_non_missing = self.end - self.n_missing
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cdef const intp_t[:] sample_indices = self.sample_indices
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cdef const float64_t[:] sample_weight = self.sample_weight
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cdef intp_t i
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cdef intp_t p
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cdef intp_t k
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cdef intp_t c
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cdef float64_t w = 1.0
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# Update statistics up to new_pos
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#
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# Given that
|
|
# sum_left[x] + sum_right[x] = sum_total[x]
|
|
# and that sum_total is known, we are going to update
|
|
# sum_left from the direction that require the least amount
|
|
# of computations, i.e. from pos to new_pos or from end to new_po.
|
|
if (new_pos - pos) <= (end_non_missing - new_pos):
|
|
for p in range(pos, new_pos):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
self.sum_left[k, <intp_t> self.y[i, k]] += w
|
|
|
|
self.weighted_n_left += w
|
|
|
|
else:
|
|
self.reverse_reset()
|
|
|
|
for p in range(end_non_missing - 1, new_pos - 1, -1):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
self.sum_left[k, <intp_t> self.y[i, k]] -= w
|
|
|
|
self.weighted_n_left -= w
|
|
|
|
# Update right part statistics
|
|
self.weighted_n_right = self.weighted_n_node_samples - self.weighted_n_left
|
|
for k in range(self.n_outputs):
|
|
for c in range(self.n_classes[k]):
|
|
self.sum_right[k, c] = self.sum_total[k, c] - self.sum_left[k, c]
|
|
|
|
self.pos = new_pos
|
|
return 0
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
pass
|
|
|
|
cdef void children_impurity(self, float64_t* impurity_left,
|
|
float64_t* impurity_right) noexcept nogil:
|
|
pass
|
|
|
|
cdef void node_value(self, float64_t* dest) noexcept nogil:
|
|
"""Compute the node value of sample_indices[start:end] and save it into dest.
|
|
|
|
Parameters
|
|
----------
|
|
dest : float64_t pointer
|
|
The memory address which we will save the node value into.
|
|
"""
|
|
cdef intp_t k, c
|
|
|
|
for k in range(self.n_outputs):
|
|
for c in range(self.n_classes[k]):
|
|
dest[c] = self.sum_total[k, c] / self.weighted_n_node_samples
|
|
dest += self.max_n_classes
|
|
|
|
cdef inline void clip_node_value(
|
|
self, float64_t * dest, float64_t lower_bound, float64_t upper_bound
|
|
) noexcept nogil:
|
|
"""Clip the values in dest such that predicted probabilities stay between
|
|
`lower_bound` and `upper_bound` when monotonic constraints are enforced.
|
|
Note that monotonicity constraints are only supported for:
|
|
- single-output trees and
|
|
- binary classifications.
|
|
"""
|
|
if dest[0] < lower_bound:
|
|
dest[0] = lower_bound
|
|
elif dest[0] > upper_bound:
|
|
dest[0] = upper_bound
|
|
|
|
# Values for binary classification must sum to 1.
|
|
dest[1] = 1 - dest[0]
|
|
|
|
cdef inline float64_t middle_value(self) noexcept nogil:
|
|
"""Compute the middle value of a split for monotonicity constraints as the simple average
|
|
of the left and right children values.
|
|
|
|
Note that monotonicity constraints are only supported for:
|
|
- single-output trees and
|
|
- binary classifications.
|
|
"""
|
|
return (
|
|
(self.sum_left[0, 0] / (2 * self.weighted_n_left)) +
|
|
(self.sum_right[0, 0] / (2 * self.weighted_n_right))
|
|
)
|
|
|
|
cdef inline bint check_monotonicity(
|
|
self,
|
|
cnp.int8_t monotonic_cst,
|
|
float64_t lower_bound,
|
|
float64_t upper_bound,
|
|
) noexcept nogil:
|
|
"""Check monotonicity constraint is satisfied at the current classification split"""
|
|
cdef:
|
|
float64_t value_left = self.sum_left[0][0] / self.weighted_n_left
|
|
float64_t value_right = self.sum_right[0][0] / self.weighted_n_right
|
|
|
|
return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
|
|
|
|
|
|
cdef class Entropy(ClassificationCriterion):
|
|
r"""Cross Entropy impurity criterion.
|
|
|
|
This handles cases where the target is a classification taking values
|
|
0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
|
|
then let
|
|
|
|
count_k = 1 / Nm \sum_{x_i in Rm} I(yi = k)
|
|
|
|
be the proportion of class k observations in node m.
|
|
|
|
The cross-entropy is then defined as
|
|
|
|
cross-entropy = -\sum_{k=0}^{K-1} count_k log(count_k)
|
|
"""
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
"""Evaluate the impurity of the current node.
|
|
|
|
Evaluate the cross-entropy criterion as impurity of the current node,
|
|
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
|
|
better.
|
|
"""
|
|
cdef float64_t entropy = 0.0
|
|
cdef float64_t count_k
|
|
cdef intp_t k
|
|
cdef intp_t c
|
|
|
|
for k in range(self.n_outputs):
|
|
for c in range(self.n_classes[k]):
|
|
count_k = self.sum_total[k, c]
|
|
if count_k > 0.0:
|
|
count_k /= self.weighted_n_node_samples
|
|
entropy -= count_k * log(count_k)
|
|
|
|
return entropy / self.n_outputs
|
|
|
|
cdef void children_impurity(self, float64_t* impurity_left,
|
|
float64_t* impurity_right) noexcept nogil:
|
|
"""Evaluate the impurity in children nodes.
|
|
|
|
i.e. the impurity of the left child (sample_indices[start:pos]) and the
|
|
impurity the right child (sample_indices[pos:end]).
|
|
|
|
Parameters
|
|
----------
|
|
impurity_left : float64_t pointer
|
|
The memory address to save the impurity of the left node
|
|
impurity_right : float64_t pointer
|
|
The memory address to save the impurity of the right node
|
|
"""
|
|
cdef float64_t entropy_left = 0.0
|
|
cdef float64_t entropy_right = 0.0
|
|
cdef float64_t count_k
|
|
cdef intp_t k
|
|
cdef intp_t c
|
|
|
|
for k in range(self.n_outputs):
|
|
for c in range(self.n_classes[k]):
|
|
count_k = self.sum_left[k, c]
|
|
if count_k > 0.0:
|
|
count_k /= self.weighted_n_left
|
|
entropy_left -= count_k * log(count_k)
|
|
|
|
count_k = self.sum_right[k, c]
|
|
if count_k > 0.0:
|
|
count_k /= self.weighted_n_right
|
|
entropy_right -= count_k * log(count_k)
|
|
|
|
impurity_left[0] = entropy_left / self.n_outputs
|
|
impurity_right[0] = entropy_right / self.n_outputs
|
|
|
|
|
|
cdef class Gini(ClassificationCriterion):
|
|
r"""Gini Index impurity criterion.
|
|
|
|
This handles cases where the target is a classification taking values
|
|
0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
|
|
then let
|
|
|
|
count_k = 1/ Nm \sum_{x_i in Rm} I(yi = k)
|
|
|
|
be the proportion of class k observations in node m.
|
|
|
|
The Gini Index is then defined as:
|
|
|
|
index = \sum_{k=0}^{K-1} count_k (1 - count_k)
|
|
= 1 - \sum_{k=0}^{K-1} count_k ** 2
|
|
"""
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
"""Evaluate the impurity of the current node.
|
|
|
|
Evaluate the Gini criterion as impurity of the current node,
|
|
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
|
|
better.
|
|
"""
|
|
cdef float64_t gini = 0.0
|
|
cdef float64_t sq_count
|
|
cdef float64_t count_k
|
|
cdef intp_t k
|
|
cdef intp_t c
|
|
|
|
for k in range(self.n_outputs):
|
|
sq_count = 0.0
|
|
|
|
for c in range(self.n_classes[k]):
|
|
count_k = self.sum_total[k, c]
|
|
sq_count += count_k * count_k
|
|
|
|
gini += 1.0 - sq_count / (self.weighted_n_node_samples *
|
|
self.weighted_n_node_samples)
|
|
|
|
return gini / self.n_outputs
|
|
|
|
cdef void children_impurity(self, float64_t* impurity_left,
|
|
float64_t* impurity_right) noexcept nogil:
|
|
"""Evaluate the impurity in children nodes.
|
|
|
|
i.e. the impurity of the left child (sample_indices[start:pos]) and the
|
|
impurity the right child (sample_indices[pos:end]) using the Gini index.
|
|
|
|
Parameters
|
|
----------
|
|
impurity_left : float64_t pointer
|
|
The memory address to save the impurity of the left node to
|
|
impurity_right : float64_t pointer
|
|
The memory address to save the impurity of the right node to
|
|
"""
|
|
cdef float64_t gini_left = 0.0
|
|
cdef float64_t gini_right = 0.0
|
|
cdef float64_t sq_count_left
|
|
cdef float64_t sq_count_right
|
|
cdef float64_t count_k
|
|
cdef intp_t k
|
|
cdef intp_t c
|
|
|
|
for k in range(self.n_outputs):
|
|
sq_count_left = 0.0
|
|
sq_count_right = 0.0
|
|
|
|
for c in range(self.n_classes[k]):
|
|
count_k = self.sum_left[k, c]
|
|
sq_count_left += count_k * count_k
|
|
|
|
count_k = self.sum_right[k, c]
|
|
sq_count_right += count_k * count_k
|
|
|
|
gini_left += 1.0 - sq_count_left / (self.weighted_n_left *
|
|
self.weighted_n_left)
|
|
|
|
gini_right += 1.0 - sq_count_right / (self.weighted_n_right *
|
|
self.weighted_n_right)
|
|
|
|
impurity_left[0] = gini_left / self.n_outputs
|
|
impurity_right[0] = gini_right / self.n_outputs
|
|
|
|
|
|
cdef inline void _move_sums_regression(
|
|
RegressionCriterion criterion,
|
|
float64_t[::1] sum_1,
|
|
float64_t[::1] sum_2,
|
|
float64_t* weighted_n_1,
|
|
float64_t* weighted_n_2,
|
|
bint put_missing_in_1,
|
|
) noexcept nogil:
|
|
"""Distribute sum_total and sum_missing into sum_1 and sum_2.
|
|
|
|
If there are missing values and:
|
|
- put_missing_in_1 is True, then missing values to go sum_1. Specifically:
|
|
sum_1 = sum_missing
|
|
sum_2 = sum_total - sum_missing
|
|
|
|
- put_missing_in_1 is False, then missing values go to sum_2. Specifically:
|
|
sum_1 = 0
|
|
sum_2 = sum_total
|
|
"""
|
|
cdef:
|
|
intp_t i
|
|
intp_t n_bytes = criterion.n_outputs * sizeof(float64_t)
|
|
bint has_missing = criterion.n_missing != 0
|
|
|
|
if has_missing and put_missing_in_1:
|
|
memcpy(&sum_1[0], &criterion.sum_missing[0], n_bytes)
|
|
for i in range(criterion.n_outputs):
|
|
sum_2[i] = criterion.sum_total[i] - criterion.sum_missing[i]
|
|
weighted_n_1[0] = criterion.weighted_n_missing
|
|
weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
|
|
else:
|
|
memset(&sum_1[0], 0, n_bytes)
|
|
# Assigning sum_2 = sum_total for all outputs.
|
|
memcpy(&sum_2[0], &criterion.sum_total[0], n_bytes)
|
|
weighted_n_1[0] = 0.0
|
|
weighted_n_2[0] = criterion.weighted_n_node_samples
|
|
|
|
|
|
cdef class RegressionCriterion(Criterion):
|
|
r"""Abstract regression criterion.
|
|
|
|
This handles cases where the target is a continuous value, and is
|
|
evaluated by computing the variance of the target values left and right
|
|
of the split point. The computation takes linear time with `n_samples`
|
|
by using ::
|
|
|
|
var = \sum_i^n (y_i - y_bar) ** 2
|
|
= (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2
|
|
"""
|
|
|
|
def __cinit__(self, intp_t n_outputs, intp_t n_samples):
|
|
"""Initialize parameters for this criterion.
|
|
|
|
Parameters
|
|
----------
|
|
n_outputs : intp_t
|
|
The number of targets to be predicted
|
|
|
|
n_samples : intp_t
|
|
The total number of samples to fit on
|
|
"""
|
|
# Default values
|
|
self.start = 0
|
|
self.pos = 0
|
|
self.end = 0
|
|
|
|
self.n_outputs = n_outputs
|
|
self.n_samples = n_samples
|
|
self.n_node_samples = 0
|
|
self.weighted_n_node_samples = 0.0
|
|
self.weighted_n_left = 0.0
|
|
self.weighted_n_right = 0.0
|
|
self.weighted_n_missing = 0.0
|
|
|
|
self.sq_sum_total = 0.0
|
|
|
|
self.sum_total = np.zeros(n_outputs, dtype=np.float64)
|
|
self.sum_left = np.zeros(n_outputs, dtype=np.float64)
|
|
self.sum_right = np.zeros(n_outputs, dtype=np.float64)
|
|
|
|
def __reduce__(self):
|
|
return (type(self), (self.n_outputs, self.n_samples), self.__getstate__())
|
|
|
|
cdef int init(
|
|
self,
|
|
const float64_t[:, ::1] y,
|
|
const float64_t[:] sample_weight,
|
|
float64_t weighted_n_samples,
|
|
const intp_t[:] sample_indices,
|
|
intp_t start,
|
|
intp_t end,
|
|
) except -1 nogil:
|
|
"""Initialize the criterion.
|
|
|
|
This initializes the criterion at node sample_indices[start:end] and children
|
|
sample_indices[start:start] and sample_indices[start:end].
|
|
"""
|
|
# Initialize fields
|
|
self.y = y
|
|
self.sample_weight = sample_weight
|
|
self.sample_indices = sample_indices
|
|
self.start = start
|
|
self.end = end
|
|
self.n_node_samples = end - start
|
|
self.weighted_n_samples = weighted_n_samples
|
|
self.weighted_n_node_samples = 0.
|
|
|
|
cdef intp_t i
|
|
cdef intp_t p
|
|
cdef intp_t k
|
|
cdef float64_t y_ik
|
|
cdef float64_t w_y_ik
|
|
cdef float64_t w = 1.0
|
|
self.sq_sum_total = 0.0
|
|
memset(&self.sum_total[0], 0, self.n_outputs * sizeof(float64_t))
|
|
|
|
for p in range(start, end):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
y_ik = self.y[i, k]
|
|
w_y_ik = w * y_ik
|
|
self.sum_total[k] += w_y_ik
|
|
self.sq_sum_total += w_y_ik * y_ik
|
|
|
|
self.weighted_n_node_samples += w
|
|
|
|
# Reset to pos=start
|
|
self.reset()
|
|
return 0
|
|
|
|
cdef void init_sum_missing(self):
|
|
"""Init sum_missing to hold sums for missing values."""
|
|
self.sum_missing = np.zeros(self.n_outputs, dtype=np.float64)
|
|
|
|
cdef void init_missing(self, intp_t n_missing) noexcept nogil:
|
|
"""Initialize sum_missing if there are missing values.
|
|
|
|
This method assumes that caller placed the missing samples in
|
|
self.sample_indices[-n_missing:]
|
|
"""
|
|
cdef intp_t i, p, k
|
|
cdef float64_t y_ik
|
|
cdef float64_t w_y_ik
|
|
cdef float64_t w = 1.0
|
|
|
|
self.n_missing = n_missing
|
|
if n_missing == 0:
|
|
return
|
|
|
|
memset(&self.sum_missing[0], 0, self.n_outputs * sizeof(float64_t))
|
|
|
|
self.weighted_n_missing = 0.0
|
|
|
|
# The missing samples are assumed to be in self.sample_indices[-n_missing:]
|
|
for p in range(self.end - n_missing, self.end):
|
|
i = self.sample_indices[p]
|
|
if self.sample_weight is not None:
|
|
w = self.sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
y_ik = self.y[i, k]
|
|
w_y_ik = w * y_ik
|
|
self.sum_missing[k] += w_y_ik
|
|
|
|
self.weighted_n_missing += w
|
|
|
|
cdef int reset(self) except -1 nogil:
|
|
"""Reset the criterion at pos=start."""
|
|
self.pos = self.start
|
|
_move_sums_regression(
|
|
self,
|
|
self.sum_left,
|
|
self.sum_right,
|
|
&self.weighted_n_left,
|
|
&self.weighted_n_right,
|
|
self.missing_go_to_left
|
|
)
|
|
return 0
|
|
|
|
cdef int reverse_reset(self) except -1 nogil:
|
|
"""Reset the criterion at pos=end."""
|
|
self.pos = self.end
|
|
_move_sums_regression(
|
|
self,
|
|
self.sum_right,
|
|
self.sum_left,
|
|
&self.weighted_n_right,
|
|
&self.weighted_n_left,
|
|
not self.missing_go_to_left
|
|
)
|
|
return 0
|
|
|
|
cdef int update(self, intp_t new_pos) except -1 nogil:
|
|
"""Updated statistics by moving sample_indices[pos:new_pos] to the left."""
|
|
cdef const float64_t[:] sample_weight = self.sample_weight
|
|
cdef const intp_t[:] sample_indices = self.sample_indices
|
|
|
|
cdef intp_t pos = self.pos
|
|
|
|
# The missing samples are assumed to be in
|
|
# self.sample_indices[-self.n_missing:] that is
|
|
# self.sample_indices[end_non_missing:self.end].
|
|
cdef intp_t end_non_missing = self.end - self.n_missing
|
|
cdef intp_t i
|
|
cdef intp_t p
|
|
cdef intp_t k
|
|
cdef float64_t w = 1.0
|
|
|
|
# Update statistics up to new_pos
|
|
#
|
|
# Given that
|
|
# sum_left[x] + sum_right[x] = sum_total[x]
|
|
# and that sum_total is known, we are going to update
|
|
# sum_left from the direction that require the least amount
|
|
# of computations, i.e. from pos to new_pos or from end to new_pos.
|
|
if (new_pos - pos) <= (end_non_missing - new_pos):
|
|
for p in range(pos, new_pos):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
self.sum_left[k] += w * self.y[i, k]
|
|
|
|
self.weighted_n_left += w
|
|
else:
|
|
self.reverse_reset()
|
|
|
|
for p in range(end_non_missing - 1, new_pos - 1, -1):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
self.sum_left[k] -= w * self.y[i, k]
|
|
|
|
self.weighted_n_left -= w
|
|
|
|
self.weighted_n_right = (self.weighted_n_node_samples -
|
|
self.weighted_n_left)
|
|
for k in range(self.n_outputs):
|
|
self.sum_right[k] = self.sum_total[k] - self.sum_left[k]
|
|
|
|
self.pos = new_pos
|
|
return 0
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
pass
|
|
|
|
cdef void children_impurity(self, float64_t* impurity_left,
|
|
float64_t* impurity_right) noexcept nogil:
|
|
pass
|
|
|
|
cdef void node_value(self, float64_t* dest) noexcept nogil:
|
|
"""Compute the node value of sample_indices[start:end] into dest."""
|
|
cdef intp_t k
|
|
|
|
for k in range(self.n_outputs):
|
|
dest[k] = self.sum_total[k] / self.weighted_n_node_samples
|
|
|
|
cdef inline void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
|
|
"""Clip the value in dest between lower_bound and upper_bound for monotonic constraints."""
|
|
if dest[0] < lower_bound:
|
|
dest[0] = lower_bound
|
|
elif dest[0] > upper_bound:
|
|
dest[0] = upper_bound
|
|
|
|
cdef float64_t middle_value(self) noexcept nogil:
|
|
"""Compute the middle value of a split for monotonicity constraints as the simple average
|
|
of the left and right children values.
|
|
|
|
Monotonicity constraints are only supported for single-output trees we can safely assume
|
|
n_outputs == 1.
|
|
"""
|
|
return (
|
|
(self.sum_left[0] / (2 * self.weighted_n_left)) +
|
|
(self.sum_right[0] / (2 * self.weighted_n_right))
|
|
)
|
|
|
|
cdef bint check_monotonicity(
|
|
self,
|
|
cnp.int8_t monotonic_cst,
|
|
float64_t lower_bound,
|
|
float64_t upper_bound,
|
|
) noexcept nogil:
|
|
"""Check monotonicity constraint is satisfied at the current regression split"""
|
|
cdef:
|
|
float64_t value_left = self.sum_left[0] / self.weighted_n_left
|
|
float64_t value_right = self.sum_right[0] / self.weighted_n_right
|
|
|
|
return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
|
|
|
|
cdef class MSE(RegressionCriterion):
|
|
"""Mean squared error impurity criterion.
|
|
|
|
MSE = var_left + var_right
|
|
"""
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
"""Evaluate the impurity of the current node.
|
|
|
|
Evaluate the MSE criterion as impurity of the current node,
|
|
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
|
|
better.
|
|
"""
|
|
cdef float64_t impurity
|
|
cdef intp_t k
|
|
|
|
impurity = self.sq_sum_total / self.weighted_n_node_samples
|
|
for k in range(self.n_outputs):
|
|
impurity -= (self.sum_total[k] / self.weighted_n_node_samples)**2.0
|
|
|
|
return impurity / self.n_outputs
|
|
|
|
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
|
|
"""Compute a proxy of the impurity reduction.
|
|
|
|
This method is used to speed up the search for the best split.
|
|
It is a proxy quantity such that the split that maximizes this value
|
|
also maximizes the impurity improvement. It neglects all constant terms
|
|
of the impurity decrease for a given split.
|
|
|
|
The absolute impurity improvement is only computed by the
|
|
impurity_improvement method once the best split has been found.
|
|
|
|
The MSE proxy is derived from
|
|
|
|
sum_{i left}(y_i - y_pred_L)^2 + sum_{i right}(y_i - y_pred_R)^2
|
|
= sum(y_i^2) - n_L * mean_{i left}(y_i)^2 - n_R * mean_{i right}(y_i)^2
|
|
|
|
Neglecting constant terms, this gives:
|
|
|
|
- 1/n_L * sum_{i left}(y_i)^2 - 1/n_R * sum_{i right}(y_i)^2
|
|
"""
|
|
cdef intp_t k
|
|
cdef float64_t proxy_impurity_left = 0.0
|
|
cdef float64_t proxy_impurity_right = 0.0
|
|
|
|
for k in range(self.n_outputs):
|
|
proxy_impurity_left += self.sum_left[k] * self.sum_left[k]
|
|
proxy_impurity_right += self.sum_right[k] * self.sum_right[k]
|
|
|
|
return (proxy_impurity_left / self.weighted_n_left +
|
|
proxy_impurity_right / self.weighted_n_right)
|
|
|
|
cdef void children_impurity(self, float64_t* impurity_left,
|
|
float64_t* impurity_right) noexcept nogil:
|
|
"""Evaluate the impurity in children nodes.
|
|
|
|
i.e. the impurity of the left child (sample_indices[start:pos]) and the
|
|
impurity the right child (sample_indices[pos:end]).
|
|
"""
|
|
cdef const float64_t[:] sample_weight = self.sample_weight
|
|
cdef const intp_t[:] sample_indices = self.sample_indices
|
|
cdef intp_t pos = self.pos
|
|
cdef intp_t start = self.start
|
|
|
|
cdef float64_t y_ik
|
|
|
|
cdef float64_t sq_sum_left = 0.0
|
|
cdef float64_t sq_sum_right
|
|
|
|
cdef intp_t i
|
|
cdef intp_t p
|
|
cdef intp_t k
|
|
cdef float64_t w = 1.0
|
|
|
|
cdef intp_t end_non_missing
|
|
|
|
for p in range(start, pos):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
y_ik = self.y[i, k]
|
|
sq_sum_left += w * y_ik * y_ik
|
|
|
|
if self.missing_go_to_left:
|
|
# add up the impact of these missing values on the left child
|
|
# statistics.
|
|
# Note: this only impacts the square sum as the sum
|
|
# is modified elsewhere.
|
|
end_non_missing = self.end - self.n_missing
|
|
|
|
for p in range(end_non_missing, self.end):
|
|
i = sample_indices[p]
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
y_ik = self.y[i, k]
|
|
sq_sum_left += w * y_ik * y_ik
|
|
|
|
sq_sum_right = self.sq_sum_total - sq_sum_left
|
|
|
|
impurity_left[0] = sq_sum_left / self.weighted_n_left
|
|
impurity_right[0] = sq_sum_right / self.weighted_n_right
|
|
|
|
for k in range(self.n_outputs):
|
|
impurity_left[0] -= (self.sum_left[k] / self.weighted_n_left) ** 2.0
|
|
impurity_right[0] -= (self.sum_right[k] / self.weighted_n_right) ** 2.0
|
|
|
|
impurity_left[0] /= self.n_outputs
|
|
impurity_right[0] /= self.n_outputs
|
|
|
|
|
|
cdef class MAE(RegressionCriterion):
|
|
r"""Mean absolute error impurity criterion.
|
|
|
|
MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
|
|
value and f_i is the predicted value."""
|
|
|
|
cdef cnp.ndarray left_child
|
|
cdef cnp.ndarray right_child
|
|
cdef void** left_child_ptr
|
|
cdef void** right_child_ptr
|
|
cdef float64_t[::1] node_medians
|
|
|
|
def __cinit__(self, intp_t n_outputs, intp_t n_samples):
|
|
"""Initialize parameters for this criterion.
|
|
|
|
Parameters
|
|
----------
|
|
n_outputs : intp_t
|
|
The number of targets to be predicted
|
|
|
|
n_samples : intp_t
|
|
The total number of samples to fit on
|
|
"""
|
|
# Default values
|
|
self.start = 0
|
|
self.pos = 0
|
|
self.end = 0
|
|
|
|
self.n_outputs = n_outputs
|
|
self.n_samples = n_samples
|
|
self.n_node_samples = 0
|
|
self.weighted_n_node_samples = 0.0
|
|
self.weighted_n_left = 0.0
|
|
self.weighted_n_right = 0.0
|
|
|
|
self.node_medians = np.zeros(n_outputs, dtype=np.float64)
|
|
|
|
self.left_child = np.empty(n_outputs, dtype='object')
|
|
self.right_child = np.empty(n_outputs, dtype='object')
|
|
# initialize WeightedMedianCalculators
|
|
for k in range(n_outputs):
|
|
self.left_child[k] = WeightedMedianCalculator(n_samples)
|
|
self.right_child[k] = WeightedMedianCalculator(n_samples)
|
|
|
|
self.left_child_ptr = <void**> cnp.PyArray_DATA(self.left_child)
|
|
self.right_child_ptr = <void**> cnp.PyArray_DATA(self.right_child)
|
|
|
|
cdef int init(
|
|
self,
|
|
const float64_t[:, ::1] y,
|
|
const float64_t[:] sample_weight,
|
|
float64_t weighted_n_samples,
|
|
const intp_t[:] sample_indices,
|
|
intp_t start,
|
|
intp_t end,
|
|
) except -1 nogil:
|
|
"""Initialize the criterion.
|
|
|
|
This initializes the criterion at node sample_indices[start:end] and children
|
|
sample_indices[start:start] and sample_indices[start:end].
|
|
"""
|
|
cdef intp_t i, p, k
|
|
cdef float64_t w = 1.0
|
|
|
|
# Initialize fields
|
|
self.y = y
|
|
self.sample_weight = sample_weight
|
|
self.sample_indices = sample_indices
|
|
self.start = start
|
|
self.end = end
|
|
self.n_node_samples = end - start
|
|
self.weighted_n_samples = weighted_n_samples
|
|
self.weighted_n_node_samples = 0.
|
|
|
|
cdef void** left_child = self.left_child_ptr
|
|
cdef void** right_child = self.right_child_ptr
|
|
|
|
for k in range(self.n_outputs):
|
|
(<WeightedMedianCalculator> left_child[k]).reset()
|
|
(<WeightedMedianCalculator> right_child[k]).reset()
|
|
|
|
for p in range(start, end):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
# push method ends up calling safe_realloc, hence `except -1`
|
|
# push all values to the right side,
|
|
# since pos = start initially anyway
|
|
(<WeightedMedianCalculator> right_child[k]).push(self.y[i, k], w)
|
|
|
|
self.weighted_n_node_samples += w
|
|
# calculate the node medians
|
|
for k in range(self.n_outputs):
|
|
self.node_medians[k] = (<WeightedMedianCalculator> right_child[k]).get_median()
|
|
|
|
# Reset to pos=start
|
|
self.reset()
|
|
return 0
|
|
|
|
cdef void init_missing(self, intp_t n_missing) noexcept nogil:
|
|
"""Raise error if n_missing != 0."""
|
|
if n_missing == 0:
|
|
return
|
|
with gil:
|
|
raise ValueError("missing values is not supported for MAE.")
|
|
|
|
cdef int reset(self) except -1 nogil:
|
|
"""Reset the criterion at pos=start.
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
"""
|
|
cdef intp_t i, k
|
|
cdef float64_t value
|
|
cdef float64_t weight
|
|
|
|
cdef void** left_child = self.left_child_ptr
|
|
cdef void** right_child = self.right_child_ptr
|
|
|
|
self.weighted_n_left = 0.0
|
|
self.weighted_n_right = self.weighted_n_node_samples
|
|
self.pos = self.start
|
|
|
|
# reset the WeightedMedianCalculators, left should have no
|
|
# elements and right should have all elements.
|
|
|
|
for k in range(self.n_outputs):
|
|
# if left has no elements, it's already reset
|
|
for i in range((<WeightedMedianCalculator> left_child[k]).size()):
|
|
# remove everything from left and put it into right
|
|
(<WeightedMedianCalculator> left_child[k]).pop(&value,
|
|
&weight)
|
|
# push method ends up calling safe_realloc, hence `except -1`
|
|
(<WeightedMedianCalculator> right_child[k]).push(value,
|
|
weight)
|
|
return 0
|
|
|
|
cdef int reverse_reset(self) except -1 nogil:
|
|
"""Reset the criterion at pos=end.
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
"""
|
|
self.weighted_n_right = 0.0
|
|
self.weighted_n_left = self.weighted_n_node_samples
|
|
self.pos = self.end
|
|
|
|
cdef float64_t value
|
|
cdef float64_t weight
|
|
cdef void** left_child = self.left_child_ptr
|
|
cdef void** right_child = self.right_child_ptr
|
|
|
|
# reverse reset the WeightedMedianCalculators, right should have no
|
|
# elements and left should have all elements.
|
|
for k in range(self.n_outputs):
|
|
# if right has no elements, it's already reset
|
|
for i in range((<WeightedMedianCalculator> right_child[k]).size()):
|
|
# remove everything from right and put it into left
|
|
(<WeightedMedianCalculator> right_child[k]).pop(&value,
|
|
&weight)
|
|
# push method ends up calling safe_realloc, hence `except -1`
|
|
(<WeightedMedianCalculator> left_child[k]).push(value,
|
|
weight)
|
|
return 0
|
|
|
|
cdef int update(self, intp_t new_pos) except -1 nogil:
|
|
"""Updated statistics by moving sample_indices[pos:new_pos] to the left.
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
"""
|
|
cdef const float64_t[:] sample_weight = self.sample_weight
|
|
cdef const intp_t[:] sample_indices = self.sample_indices
|
|
|
|
cdef void** left_child = self.left_child_ptr
|
|
cdef void** right_child = self.right_child_ptr
|
|
|
|
cdef intp_t pos = self.pos
|
|
cdef intp_t end = self.end
|
|
cdef intp_t i, p, k
|
|
cdef float64_t w = 1.0
|
|
|
|
# Update statistics up to new_pos
|
|
#
|
|
# We are going to update right_child and left_child
|
|
# from the direction that require the least amount of
|
|
# computations, i.e. from pos to new_pos or from end to new_pos.
|
|
if (new_pos - pos) <= (end - new_pos):
|
|
for p in range(pos, new_pos):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
# remove y_ik and its weight w from right and add to left
|
|
(<WeightedMedianCalculator> right_child[k]).remove(self.y[i, k], w)
|
|
# push method ends up calling safe_realloc, hence except -1
|
|
(<WeightedMedianCalculator> left_child[k]).push(self.y[i, k], w)
|
|
|
|
self.weighted_n_left += w
|
|
else:
|
|
self.reverse_reset()
|
|
|
|
for p in range(end - 1, new_pos - 1, -1):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
for k in range(self.n_outputs):
|
|
# remove y_ik and its weight w from left and add to right
|
|
(<WeightedMedianCalculator> left_child[k]).remove(self.y[i, k], w)
|
|
(<WeightedMedianCalculator> right_child[k]).push(self.y[i, k], w)
|
|
|
|
self.weighted_n_left -= w
|
|
|
|
self.weighted_n_right = (self.weighted_n_node_samples -
|
|
self.weighted_n_left)
|
|
self.pos = new_pos
|
|
return 0
|
|
|
|
cdef void node_value(self, float64_t* dest) noexcept nogil:
|
|
"""Computes the node value of sample_indices[start:end] into dest."""
|
|
cdef intp_t k
|
|
for k in range(self.n_outputs):
|
|
dest[k] = <float64_t> self.node_medians[k]
|
|
|
|
cdef inline float64_t middle_value(self) noexcept nogil:
|
|
"""Compute the middle value of a split for monotonicity constraints as the simple average
|
|
of the left and right children values.
|
|
|
|
Monotonicity constraints are only supported for single-output trees we can safely assume
|
|
n_outputs == 1.
|
|
"""
|
|
return (
|
|
(<WeightedMedianCalculator> self.left_child_ptr[0]).get_median() +
|
|
(<WeightedMedianCalculator> self.right_child_ptr[0]).get_median()
|
|
) / 2
|
|
|
|
cdef inline bint check_monotonicity(
|
|
self,
|
|
cnp.int8_t monotonic_cst,
|
|
float64_t lower_bound,
|
|
float64_t upper_bound,
|
|
) noexcept nogil:
|
|
"""Check monotonicity constraint is satisfied at the current regression split"""
|
|
cdef:
|
|
float64_t value_left = (<WeightedMedianCalculator> self.left_child_ptr[0]).get_median()
|
|
float64_t value_right = (<WeightedMedianCalculator> self.right_child_ptr[0]).get_median()
|
|
|
|
return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
"""Evaluate the impurity of the current node.
|
|
|
|
Evaluate the MAE criterion as impurity of the current node,
|
|
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
|
|
better.
|
|
"""
|
|
cdef const float64_t[:] sample_weight = self.sample_weight
|
|
cdef const intp_t[:] sample_indices = self.sample_indices
|
|
cdef intp_t i, p, k
|
|
cdef float64_t w = 1.0
|
|
cdef float64_t impurity = 0.0
|
|
|
|
for k in range(self.n_outputs):
|
|
for p in range(self.start, self.end):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
impurity += fabs(self.y[i, k] - self.node_medians[k]) * w
|
|
|
|
return impurity / (self.weighted_n_node_samples * self.n_outputs)
|
|
|
|
cdef void children_impurity(self, float64_t* p_impurity_left,
|
|
float64_t* p_impurity_right) noexcept nogil:
|
|
"""Evaluate the impurity in children nodes.
|
|
|
|
i.e. the impurity of the left child (sample_indices[start:pos]) and the
|
|
impurity the right child (sample_indices[pos:end]).
|
|
"""
|
|
cdef const float64_t[:] sample_weight = self.sample_weight
|
|
cdef const intp_t[:] sample_indices = self.sample_indices
|
|
|
|
cdef intp_t start = self.start
|
|
cdef intp_t pos = self.pos
|
|
cdef intp_t end = self.end
|
|
|
|
cdef intp_t i, p, k
|
|
cdef float64_t median
|
|
cdef float64_t w = 1.0
|
|
cdef float64_t impurity_left = 0.0
|
|
cdef float64_t impurity_right = 0.0
|
|
|
|
cdef void** left_child = self.left_child_ptr
|
|
cdef void** right_child = self.right_child_ptr
|
|
|
|
for k in range(self.n_outputs):
|
|
median = (<WeightedMedianCalculator> left_child[k]).get_median()
|
|
for p in range(start, pos):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
impurity_left += fabs(self.y[i, k] - median) * w
|
|
p_impurity_left[0] = impurity_left / (self.weighted_n_left *
|
|
self.n_outputs)
|
|
|
|
for k in range(self.n_outputs):
|
|
median = (<WeightedMedianCalculator> right_child[k]).get_median()
|
|
for p in range(pos, end):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
impurity_right += fabs(self.y[i, k] - median) * w
|
|
p_impurity_right[0] = impurity_right / (self.weighted_n_right *
|
|
self.n_outputs)
|
|
|
|
|
|
cdef class FriedmanMSE(MSE):
|
|
"""Mean squared error impurity criterion with improvement score by Friedman.
|
|
|
|
Uses the formula (35) in Friedman's original Gradient Boosting paper:
|
|
|
|
diff = mean_left - mean_right
|
|
improvement = n_left * n_right * diff^2 / (n_left + n_right)
|
|
"""
|
|
|
|
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
|
|
"""Compute a proxy of the impurity reduction.
|
|
|
|
This method is used to speed up the search for the best split.
|
|
It is a proxy quantity such that the split that maximizes this value
|
|
also maximizes the impurity improvement. It neglects all constant terms
|
|
of the impurity decrease for a given split.
|
|
|
|
The absolute impurity improvement is only computed by the
|
|
impurity_improvement method once the best split has been found.
|
|
"""
|
|
cdef float64_t total_sum_left = 0.0
|
|
cdef float64_t total_sum_right = 0.0
|
|
|
|
cdef intp_t k
|
|
cdef float64_t diff = 0.0
|
|
|
|
for k in range(self.n_outputs):
|
|
total_sum_left += self.sum_left[k]
|
|
total_sum_right += self.sum_right[k]
|
|
|
|
diff = (self.weighted_n_right * total_sum_left -
|
|
self.weighted_n_left * total_sum_right)
|
|
|
|
return diff * diff / (self.weighted_n_left * self.weighted_n_right)
|
|
|
|
cdef float64_t impurity_improvement(self, float64_t impurity_parent, float64_t
|
|
impurity_left, float64_t impurity_right) noexcept nogil:
|
|
# Note: none of the arguments are used here
|
|
cdef float64_t total_sum_left = 0.0
|
|
cdef float64_t total_sum_right = 0.0
|
|
|
|
cdef intp_t k
|
|
cdef float64_t diff = 0.0
|
|
|
|
for k in range(self.n_outputs):
|
|
total_sum_left += self.sum_left[k]
|
|
total_sum_right += self.sum_right[k]
|
|
|
|
diff = (self.weighted_n_right * total_sum_left -
|
|
self.weighted_n_left * total_sum_right) / self.n_outputs
|
|
|
|
return (diff * diff / (self.weighted_n_left * self.weighted_n_right *
|
|
self.weighted_n_node_samples))
|
|
|
|
|
|
cdef class Poisson(RegressionCriterion):
|
|
"""Half Poisson deviance as impurity criterion.
|
|
|
|
Poisson deviance = 2/n * sum(y_true * log(y_true/y_pred) + y_pred - y_true)
|
|
|
|
Note that the deviance is >= 0, and since we have `y_pred = mean(y_true)`
|
|
at the leaves, one always has `sum(y_pred - y_true) = 0`. It remains the
|
|
implemented impurity (factor 2 is skipped):
|
|
1/n * sum(y_true * log(y_true/y_pred)
|
|
"""
|
|
# FIXME in 1.0:
|
|
# min_impurity_split with default = 0 forces us to use a non-negative
|
|
# impurity like the Poisson deviance. Without this restriction, one could
|
|
# throw away the 'constant' term sum(y_true * log(y_true)) and just use
|
|
# Poisson loss = - 1/n * sum(y_true * log(y_pred))
|
|
# = - 1/n * sum(y_true * log(mean(y_true))
|
|
# = - mean(y_true) * log(mean(y_true))
|
|
# With this trick (used in proxy_impurity_improvement()), as for MSE,
|
|
# children_impurity would only need to go over left xor right split, not
|
|
# both. This could be faster.
|
|
|
|
cdef float64_t node_impurity(self) noexcept nogil:
|
|
"""Evaluate the impurity of the current node.
|
|
|
|
Evaluate the Poisson criterion as impurity of the current node,
|
|
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
|
|
better.
|
|
"""
|
|
return self.poisson_loss(self.start, self.end, self.sum_total,
|
|
self.weighted_n_node_samples)
|
|
|
|
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
|
|
"""Compute a proxy of the impurity reduction.
|
|
|
|
This method is used to speed up the search for the best split.
|
|
It is a proxy quantity such that the split that maximizes this value
|
|
also maximizes the impurity improvement. It neglects all constant terms
|
|
of the impurity decrease for a given split.
|
|
|
|
The absolute impurity improvement is only computed by the
|
|
impurity_improvement method once the best split has been found.
|
|
|
|
The Poisson proxy is derived from:
|
|
|
|
sum_{i left }(y_i * log(y_i / y_pred_L))
|
|
+ sum_{i right}(y_i * log(y_i / y_pred_R))
|
|
= sum(y_i * log(y_i) - n_L * mean_{i left}(y_i) * log(mean_{i left}(y_i))
|
|
- n_R * mean_{i right}(y_i) * log(mean_{i right}(y_i))
|
|
|
|
Neglecting constant terms, this gives
|
|
|
|
- sum{i left }(y_i) * log(mean{i left}(y_i))
|
|
- sum{i right}(y_i) * log(mean{i right}(y_i))
|
|
"""
|
|
cdef intp_t k
|
|
cdef float64_t proxy_impurity_left = 0.0
|
|
cdef float64_t proxy_impurity_right = 0.0
|
|
cdef float64_t y_mean_left = 0.
|
|
cdef float64_t y_mean_right = 0.
|
|
|
|
for k in range(self.n_outputs):
|
|
if (self.sum_left[k] <= EPSILON) or (self.sum_right[k] <= EPSILON):
|
|
# Poisson loss does not allow non-positive predictions. We
|
|
# therefore forbid splits that have child nodes with
|
|
# sum(y_i) <= 0.
|
|
# Since sum_right = sum_total - sum_left, it can lead to
|
|
# floating point rounding error and will not give zero. Thus,
|
|
# we relax the above comparison to sum(y_i) <= EPSILON.
|
|
return -INFINITY
|
|
else:
|
|
y_mean_left = self.sum_left[k] / self.weighted_n_left
|
|
y_mean_right = self.sum_right[k] / self.weighted_n_right
|
|
proxy_impurity_left -= self.sum_left[k] * log(y_mean_left)
|
|
proxy_impurity_right -= self.sum_right[k] * log(y_mean_right)
|
|
|
|
return - proxy_impurity_left - proxy_impurity_right
|
|
|
|
cdef void children_impurity(self, float64_t* impurity_left,
|
|
float64_t* impurity_right) noexcept nogil:
|
|
"""Evaluate the impurity in children nodes.
|
|
|
|
i.e. the impurity of the left child (sample_indices[start:pos]) and the
|
|
impurity of the right child (sample_indices[pos:end]) for Poisson.
|
|
"""
|
|
cdef intp_t start = self.start
|
|
cdef intp_t pos = self.pos
|
|
cdef intp_t end = self.end
|
|
|
|
impurity_left[0] = self.poisson_loss(start, pos, self.sum_left,
|
|
self.weighted_n_left)
|
|
|
|
impurity_right[0] = self.poisson_loss(pos, end, self.sum_right,
|
|
self.weighted_n_right)
|
|
|
|
cdef inline float64_t poisson_loss(
|
|
self,
|
|
intp_t start,
|
|
intp_t end,
|
|
const float64_t[::1] y_sum,
|
|
float64_t weight_sum
|
|
) noexcept nogil:
|
|
"""Helper function to compute Poisson loss (~deviance) of a given node.
|
|
"""
|
|
cdef const float64_t[:, ::1] y = self.y
|
|
cdef const float64_t[:] sample_weight = self.sample_weight
|
|
cdef const intp_t[:] sample_indices = self.sample_indices
|
|
|
|
cdef float64_t y_mean = 0.
|
|
cdef float64_t poisson_loss = 0.
|
|
cdef float64_t w = 1.0
|
|
cdef intp_t i, k, p
|
|
cdef intp_t n_outputs = self.n_outputs
|
|
|
|
for k in range(n_outputs):
|
|
if y_sum[k] <= EPSILON:
|
|
# y_sum could be computed from the subtraction
|
|
# sum_right = sum_total - sum_left leading to a potential
|
|
# floating point rounding error.
|
|
# Thus, we relax the comparison y_sum <= 0 to
|
|
# y_sum <= EPSILON.
|
|
return INFINITY
|
|
|
|
y_mean = y_sum[k] / weight_sum
|
|
|
|
for p in range(start, end):
|
|
i = sample_indices[p]
|
|
|
|
if sample_weight is not None:
|
|
w = sample_weight[i]
|
|
|
|
poisson_loss += w * xlogy(y[i, k], y[i, k] / y_mean)
|
|
return poisson_loss / (weight_sum * n_outputs)
|