116 lines
4.6 KiB
Cython
116 lines
4.6 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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from ..utils._typedefs cimport float64_t, int8_t, intp_t
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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 float64_t[:, ::1] y # Values of y
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cdef const float64_t[:] sample_weight # Sample weights
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cdef const intp_t[:] sample_indices # Sample indices in X, y
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cdef intp_t start # samples[start:pos] are the samples in the left node
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cdef intp_t pos # samples[pos:end] are the samples in the right node
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cdef intp_t end
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cdef intp_t n_missing # Number of missing values for the feature being evaluated
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cdef bint missing_go_to_left # Whether missing values go to the left node
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cdef intp_t n_outputs # Number of outputs
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cdef intp_t n_samples # Number of samples
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cdef intp_t n_node_samples # Number of samples in the node (end-start)
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cdef float64_t weighted_n_samples # Weighted number of samples (in total)
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cdef float64_t weighted_n_node_samples # Weighted number of samples in the node
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cdef float64_t weighted_n_left # Weighted number of samples in the left node
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cdef float64_t weighted_n_right # Weighted number of samples in the right node
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cdef float64_t weighted_n_missing # Weighted number of samples that are missing
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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(
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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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cdef void init_sum_missing(self)
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cdef void init_missing(self, intp_t n_missing) noexcept nogil
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cdef int reset(self) except -1 nogil
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cdef int reverse_reset(self) except -1 nogil
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cdef int update(self, intp_t new_pos) except -1 nogil
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cdef float64_t node_impurity(self) noexcept nogil
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cdef void children_impurity(
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self,
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float64_t* impurity_left,
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float64_t* impurity_right
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) noexcept nogil
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cdef void node_value(
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self,
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float64_t* dest
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) noexcept nogil
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cdef void clip_node_value(
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self,
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float64_t* dest,
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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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cdef float64_t middle_value(self) noexcept nogil
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cdef float64_t impurity_improvement(
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self,
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float64_t impurity_parent,
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float64_t impurity_left,
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float64_t impurity_right
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) noexcept nogil
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cdef float64_t proxy_impurity_improvement(self) noexcept nogil
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cdef bint check_monotonicity(
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self,
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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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cdef inline bint _check_monotonicity(
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self,
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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 sum_left,
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float64_t sum_right,
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) noexcept nogil
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cdef class ClassificationCriterion(Criterion):
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"""Abstract criterion for classification."""
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cdef intp_t[::1] n_classes
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cdef intp_t max_n_classes
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cdef float64_t[:, ::1] sum_total # The sum of the weighted count of each label.
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cdef float64_t[:, ::1] sum_left # Same as above, but for the left side of the split
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cdef float64_t[:, ::1] sum_right # Same as above, but for the right side of the split
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cdef float64_t[:, ::1] sum_missing # Same as above, but for missing values in X
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cdef class RegressionCriterion(Criterion):
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"""Abstract regression criterion."""
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cdef float64_t sq_sum_total
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cdef float64_t[::1] sum_total # The sum of w*y.
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cdef float64_t[::1] sum_left # Same as above, but for the left side of the split
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cdef float64_t[::1] sum_right # Same as above, but for the right side of the split
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cdef float64_t[::1] sum_missing # Same as above, but for missing values in X
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