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sklearn/utils/_weight_vector.pyx.tp
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kostayScr
FIX an issue with SGD models(SGDRegressor etc.) convergence criteria (#31856)
03 окт 2025, 13:46
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03 окт 2025, 13:46
eb6dd0a
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{{py: """ Efficient (dense) parameter vector implementation for linear models. Template file for easily generate fused types consistent code using Tempita (https://github.com/cython/cython/blob/master/Cython/Tempita/_tempita.py). Generated file: weight_vector.pxd Each class is duplicated for all dtypes (float and double). The keywords between double braces are substituted during the build. """ # name_suffix, c_type, reset_wscale_threshold dtypes = [('64', 'double', 1e-9), ('32', 'float', 1e-6)] }} # cython: binding=False # # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause cimport cython from libc.limits cimport INT_MAX from libc.math cimport sqrt, fabs from sklearn.utils._cython_blas cimport _dot, _scal, _axpy, _asum {{for name_suffix, c_type, reset_wscale_threshold in dtypes}} cdef class WeightVector{{name_suffix}}(object): """Dense vector represented by a scalar and a numpy array. The class provides methods to ``add`` a sparse vector and scale the vector. Representing a vector explicitly as a scalar times a vector allows for efficient scaling operations. Attributes ---------- w : ndarray, dtype={{c_type}}, order='C' The numpy array which backs the weight vector. aw : ndarray, dtype={{c_type}}, order='C' The numpy array which backs the average_weight vector. w_data_ptr : {{c_type}}* A pointer to the data of the numpy array. wscale : {{c_type}} The scale of the vector. n_features : int The number of features (= dimensionality of ``w``). sq_norm : {{c_type}} The squared norm of ``w``. l1_norm : {{c_type}} The L1 norm of ``w``. """ def __cinit__(self, {{c_type}}[::1] w, {{c_type}}[::1] aw): if w.shape[0] > INT_MAX: raise ValueError("More than %d features not supported; got %d." % (INT_MAX, w.shape[0])) self.w = w self.w_data_ptr = &w[0] self.wscale = 1.0 self.n_features = w.shape[0] self.sq_norm = _dot(self.n_features, self.w_data_ptr, 1, self.w_data_ptr, 1) self.l1_norm = _asum(self.n_features, self.w_data_ptr, 1) self.aw = aw if self.aw is not None: self.aw_data_ptr = &aw[0] self.average_a = 0.0 self.average_b = 1.0 cdef void add(self, {{c_type}} *x_data_ptr, int *x_ind_ptr, int xnnz, {{c_type}} c) noexcept nogil: """Scales sample x by constant c and adds it to the weight vector. This operation updates ``sq_norm`` and ``l1_norm``. Parameters ---------- x_data_ptr : {{c_type}}* The array which holds the feature values of ``x``. x_ind_ptr : np.intc* The array which holds the feature indices of ``x``. xnnz : int The number of non-zero features of ``x``. c : {{c_type}} The scaling constant for the example. """ cdef int j cdef int idx cdef double val cdef double l2norm_accumulator = 0.0 cdef double l1norm_accumulator = 0.0 # the next two lines save a factor of 2! cdef {{c_type}} wscale = self.wscale cdef {{c_type}}* w_data_ptr = self.w_data_ptr for j in range(xnnz): idx = x_ind_ptr[j] val = x_data_ptr[j] w_data_ptr[idx] += val * (c / wscale) l2norm_accumulator += w_data_ptr[idx] * w_data_ptr[idx] l1norm_accumulator += fabs(w_data_ptr[idx]) self.sq_norm = l2norm_accumulator * (wscale * wscale) self.l1_norm = l1norm_accumulator * wscale # Update the average weights according to the sparse trick defined # here: https://research.microsoft.com/pubs/192769/tricks-2012.pdf # by Leon Bottou cdef void add_average(self, {{c_type}} *x_data_ptr, int *x_ind_ptr, int xnnz, {{c_type}} c, {{c_type}} num_iter) noexcept nogil: """Updates the average weight vector. Parameters ---------- x_data_ptr : {{c_type}}* The array which holds the feature values of ``x``. x_ind_ptr : np.intc* The array which holds the feature indices of ``x``. xnnz : int The number of non-zero features of ``x``. c : {{c_type}} The scaling constant for the example. num_iter : {{c_type}} The total number of iterations. """ cdef int j cdef int idx cdef double val cdef double mu = 1.0 / num_iter cdef double average_a = self.average_a cdef double wscale = self.wscale cdef {{c_type}}* aw_data_ptr = self.aw_data_ptr for j in range(xnnz): idx = x_ind_ptr[j] val = x_data_ptr[j] aw_data_ptr[idx] += (self.average_a * val * (-c / wscale)) # Once the sample has been processed # update the average_a and average_b if num_iter > 1: self.average_b /= (1.0 - mu) self.average_a += mu * self.average_b * wscale cdef {{c_type}} dot(self, {{c_type}} *x_data_ptr, int *x_ind_ptr, int xnnz) noexcept nogil: """Computes the dot product of a sample x and the weight vector. Parameters ---------- x_data_ptr : {{c_type}}* The array which holds the feature values of ``x``. x_ind_ptr : np.intc* The array which holds the feature indices of ``x``. xnnz : int The number of non-zero features of ``x`` (length of x_ind_ptr). Returns ------- innerprod : {{c_type}} The inner product of ``x`` and ``w``. """ cdef int j cdef int idx cdef double innerprod = 0.0 cdef {{c_type}}* w_data_ptr = self.w_data_ptr for j in range(xnnz): idx = x_ind_ptr[j] innerprod += w_data_ptr[idx] * x_data_ptr[j] innerprod *= self.wscale return innerprod cdef void scale(self, {{c_type}} c) noexcept nogil: """Scales the weight vector by a constant ``c``. It updates ``wscale``, ``sq_norm``, and ``l1_norm``. If ``wscale`` gets too small we call ``reset_wscale``.""" self.wscale *= c self.sq_norm *= (c * c) self.l1_norm *= fabs(c) if self.wscale < {{reset_wscale_threshold}}: self.reset_wscale() cdef void reset_wscale(self) noexcept nogil: """Scales each coef of ``w`` by ``wscale`` and resets it to 1. """ if self.aw_data_ptr != NULL: _axpy(self.n_features, self.average_a, self.w_data_ptr, 1, self.aw_data_ptr, 1) _scal(self.n_features, 1.0 / self.average_b, self.aw_data_ptr, 1) self.average_a = 0.0 self.average_b = 1.0 _scal(self.n_features, self.wscale, self.w_data_ptr, 1) self.wscale = 1.0 cdef {{c_type}} norm(self) noexcept nogil: """The L2 norm of the weight vector. """ return sqrt(self.sq_norm) cdef {{c_type}} l1norm(self) noexcept nogil: """The L1 norm of the weight vector. """ return self.l1_norm {{endfor}}