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stochs.pyx
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# distutils: include_dirs = /scratch/clear/abietti/local/include
# cython: boundscheck=False
import numpy as np
import scipy.sparse as sp
cimport numpy as np
from cpython.ref cimport PyObject, Py_INCREF
from cython.operator cimport dereference as deref
from libc.stdint cimport int32_t, int64_t
from libcpp.string cimport string
from libcpp cimport bool
np.import_array()
IF USE_FLOAT:
ctypedef float Double
npDOUBLE = np.NPY_FLOAT32
dtype = np.float32
ELSE:
ctypedef double Double
npDOUBLE = np.NPY_FLOAT64
dtype = np.float64
cdef extern from "numpy/arrayobject.h":
void PyArray_SetBaseObject(np.ndarray, PyObject*)
cdef extern from "solvers/common.h" namespace "solvers":
void _center "solvers::center"(
Double* const XData, const size_t rows, const size_t cols) nogil
void _normalize "solvers::normalize"(
Double* const XData, const size_t rows, const size_t cols) nogil
void _normalizeSparse "solvers::normalize"(
const size_t rows, const size_t cols, const size_t nnz,
const int32_t* const indptr, const int32_t* const indices,
Double* const values) nogil
def center(Double[:,::1] X not None):
_center(&X[0,0], X.shape[0], X.shape[1])
def normalize_dense(Double[:,::1] X not None):
_normalize(&X[0,0], X.shape[0], X.shape[1])
def normalize_sparse(X):
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
_normalizeSparse(X.shape[0], X.shape[1], X.nnz,
&indptr[0], &indices[0], &values[0])
def normalize(X):
if isinstance(X, sp.csr_matrix):
normalize_sparse(X)
else:
normalize_dense(X)
cdef extern from "solvers/Solver.h" namespace "solvers":
void iterateBlock "solvers::Solver::iterateBlock"[SolverT](
SolverT& solver,
const size_t blockSize,
const Double* const XData,
const Double* const yData,
const int64_t* const idxData) nogil
void iterateBlockIndexed "solvers::Solver::iterateBlockIndexed"[SolverT](
SolverT& solver,
const size_t dataSize,
const Double* const XData,
const Double* const yData,
const size_t blockSize,
const int64_t* const idxData) nogil
void setQ "solvers::Solver::setQ"[SolverT](
SolverT& solver,
const size_t n,
const Double* const qData)
# for sparse data
void iterateBlockSparse "solvers::Solver::iterateBlock"[SolverT](
SolverT& solver,
const size_t blockSize,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues,
const Double* const yData,
const int64_t* const idxData) nogil
void iterateBlockIndexedSparse "solvers::Solver::iterateBlockIndexed"[SolverT](
SolverT& solver,
const size_t dataSize,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues,
const Double* const yData,
const size_t blockSize,
const int64_t* const idxData) nogil
void initFromX "solvers::Solver::initFromX"[SolverT](
SolverT& solver,
const size_t dataSize,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues)
void initQ "solvers::Solver::initQ"[SolverT](
SolverT& solver,
const size_t dataSize,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues)
cdef cppclass OneVsRest[SolverT]:
OneVsRest(size_t nclasses, ...)
size_t nclasses()
void startDecay()
void decay(Double mult)
void iterateBlock(...)
void iterateBlockIndexed(...)
void predict(const size_t sz,
int32_t* const out,
const Double* const XData)
Double computeLoss(const size_t sz,
const Double* const XData,
const int32_t* const yData) nogil
Double computeSquaredNorm() nogil
Double computeProxPenalty()
cdef extern from "solvers/Loss.h" namespace "solvers":
void setGradSigma "solvers::Loss::setGradSigma"(
const Double gradSigma)
Double gradSigma "solvers::Loss::gradSigma"()
cdef extern from "solvers/SGD.h" namespace "solvers":
cdef cppclass _SGD "solvers::SGD":
_SGD(size_t dim, Double lr, Double lmbda, string loss,
string prox, Double proxWeight, bool average)
void startDecay()
void decay(Double mult)
size_t t()
size_t nfeatures()
Double* wdata()
Double computeLoss(const size_t sz,
const Double* const XData,
const Double* const yData) nogil
Double computeSquaredNorm() nogil
Double computeProxPenalty() nogil
cdef cppclass _SparseSGD "solvers::SparseSGD":
_SparseSGD(size_t dim, Double lr, Double lmbda, string loss)
void startDecay()
void decay(Double mult)
size_t t()
size_t nfeatures()
Double* wdata()
Double computeLoss(const size_t sz,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues,
const Double* const yData) nogil
Double computeSquaredNorm() nogil
def set_grad_sigma(Double gradSigma):
setGradSigma(gradSigma)
def grad_sigma():
return gradSigma()
cdef class SGD:
cdef _SGD* solver
def __cinit__(self, size_t dim, Double lr=0.1,
Double lmbda=0., string loss="logistic",
string prox="none", Double prox_weight=0., bool average=False):
self.solver = new _SGD(dim, lr, lmbda, loss, prox, prox_weight, average)
def __dealloc__(self):
del self.solver
property nfeatures:
def __get__(self):
return self.solver.nfeatures()
property w:
def __get__(self):
cdef np.npy_intp shape[1]
shape[0] = self.nfeatures
cdef np.ndarray[Double, ndim=1] arr = \
np.PyArray_SimpleNewFromData(1, shape, npDOUBLE,
self.solver.wdata())
Py_INCREF(self)
PyArray_SetBaseObject(arr, <PyObject*>self)
return arr
def start_decay(self):
self.solver.startDecay()
def decay(self, Double multiplier=0.5):
self.solver.decay(multiplier)
def compute_loss(self, Double[:,::1] X not None, Double[::1] y not None):
return self.solver.computeLoss(X.shape[0], &X[0,0], &y[0])
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def compute_prox_penalty(self):
return self.solver.computeProxPenalty()
def iterate(self,
Double[:,::1] X not None,
Double[::1] y not None,
int64_t[::1] idx not None):
iterateBlock[_SGD](deref(self.solver),
X.shape[0],
&X[0,0],
&y[0],
&idx[0])
def iterate_indexed(self,
Double[:,::1] X not None,
Double[::1] y not None,
int64_t[::1] idx not None):
iterateBlockIndexed[_SGD](deref(self.solver),
X.shape[0],
&X[0,0],
&y[0],
idx.shape[0],
&idx[0])
cdef class SparseSGD:
cdef _SparseSGD* solver
def __cinit__(self, size_t dim, Double lr=0.1,
Double lmbda=0., string loss="logistic"):
self.solver = new _SparseSGD(dim, lr, lmbda, loss)
def __dealloc__(self):
del self.solver
property nfeatures:
def __get__(self):
return self.solver.nfeatures()
property w:
def __get__(self):
cdef np.npy_intp shape[1]
shape[0] = self.nfeatures
cdef np.ndarray[Double, ndim=1] arr = \
np.PyArray_SimpleNewFromData(1, shape, npDOUBLE,
self.solver.wdata())
Py_INCREF(self)
PyArray_SetBaseObject(arr, <PyObject*>self)
return arr
def start_decay(self):
self.solver.startDecay()
def decay(self, Double multiplier=0.5):
self.solver.decay(multiplier)
def set_q(self, Double[::1] q not None):
setQ[_SparseSGD](deref(self.solver), q.shape[0], &q[0])
def compute_loss(self, X, Double[::1] y not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
cdef size_t n = X.shape[0], nnz = X.nnz
cdef Double loss
with nogil:
loss = self.solver.computeLoss(n, nnz, &indptr[0],
&indices[0], &values[0], &y[0])
return loss
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def iterate(self, X,
Double[::1] y not None,
int64_t[::1] idx not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
cdef size_t n = X.shape[0], nnz = X.nnz
with nogil:
iterateBlockSparse[_SparseSGD](deref(self.solver),
n, nnz,
&indptr[0],
&indices[0],
&values[0],
&y[0],
&idx[0])
def iterate_indexed(self, X,
Double[::1] y not None,
int64_t[::1] idx not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indices = X.indices
cdef int32_t[:] indptr = X.indptr
cdef size_t n = X.shape[0], nnz = X.nnz, blockSize = idx.shape[0]
with nogil:
iterateBlockIndexedSparse[_SparseSGD](deref(self.solver),
n, nnz,
&indptr[0],
&indices[0],
&values[0],
&y[0],
blockSize,
&idx[0])
cdef class SGDOneVsRest:
cdef OneVsRest[_SGD]* solver
def __cinit__(self, size_t nclasses, size_t dim, Double lr=0.1,
Double lmbda=0., string loss="logistic",
string prox="none", Double prox_weight=0., bool average=False):
self.solver = new OneVsRest[_SGD](nclasses, dim, lr, lmbda, loss, prox, prox_weight, average)
def __dealloc__(self):
del self.solver
def start_decay(self):
self.solver.startDecay()
def decay(self, Double multiplier=0.5):
self.solver.decay(multiplier)
def iterate(self,
Double[:,::1] X not None,
int32_t[::1] y not None,
int64_t[::1] idx not None):
self.solver.iterateBlock(X.shape[0], &X[0,0], &y[0], &idx[0])
def iterate_indexed(self,
Double[:,::1] X not None,
int32_t[::1] y not None,
int64_t[::1] idx not None):
self.solver.iterateBlockIndexed(
X.shape[0], &X[0,0], &y[0], idx.shape[0], &idx[0])
def predict(self, Double[:,::1] X not None):
preds = np.empty(X.shape[0], dtype=np.int32)
cdef int32_t[:] out = preds
self.solver.predict(out.shape[0], &out[0], &X[0,0])
return preds
def compute_loss(self, Double[:,::1] X not None, int32_t[::1] y not None):
return self.solver.computeLoss(X.shape[0], &X[0,0], &y[0])
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def compute_prox_penalty(self):
return self.solver.computeProxPenalty()
cdef extern from "solvers/MISO.h" namespace "solvers":
cdef cppclass _MISO "solvers::MISO":
_MISO(size_t dim, size_t n, Double alpha, Double lmbda, string loss,
bool computeLB, string prox, Double proxWeight, bool average)
void startDecay()
void decay(Double mult)
size_t t()
size_t nfeatures()
size_t nexamples()
Double* wdata()
Double lowerBound()
Double computeLoss(const size_t sz,
const Double* const XData,
const Double* const yData) nogil
Double computeSquaredNorm() nogil
Double computeProxPenalty() nogil
cdef cppclass _SparseMISO "solvers::SparseMISO":
_SparseMISO(size_t dim, size_t n, Double alpha, Double lmbda, string loss, bool computeLB)
void startDecay()
void decay(Double mult)
size_t t()
size_t nfeatures()
size_t nexamples()
Double* wdata()
Double lowerBound()
Double computeLoss(const size_t sz,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues,
const Double* const yData) nogil
Double computeSquaredNorm() nogil
cdef class MISO:
cdef _MISO* solver
def __cinit__(self, size_t dim, size_t n, Double alpha=1.0,
Double lmbda=0.1, string loss="logistic", bool compute_lb=False,
string prox="none", Double prox_weight=0., bool average=False):
self.solver = new _MISO(dim, n, alpha, lmbda, loss, compute_lb, prox, prox_weight, average)
def __dealloc__(self):
del self.solver
property nfeatures:
def __get__(self):
return self.solver.nfeatures()
property nexamples:
def __get__(self):
return self.solver.nexamples()
property w:
def __get__(self):
cdef np.npy_intp shape[1]
shape[0] = self.nfeatures
cdef np.ndarray[Double, ndim=1] arr = \
np.PyArray_SimpleNewFromData(1, shape, npDOUBLE,
self.solver.wdata())
Py_INCREF(self)
PyArray_SetBaseObject(arr, <PyObject*>self)
return arr
def start_decay(self):
self.solver.startDecay()
def decay(self, Double multiplier=0.5):
self.solver.decay(multiplier)
def lower_bound(self):
return self.solver.lowerBound()
def compute_loss(self, Double[:,::1] X not None, Double[::1] y not None):
return self.solver.computeLoss(X.shape[0], &X[0,0], &y[0])
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def compute_prox_penalty(self):
return self.solver.computeProxPenalty()
def iterate(self,
Double[:,::1] X not None,
Double[::1] y not None,
int64_t[::1] idx not None):
iterateBlock[_MISO](deref(self.solver),
X.shape[0],
&X[0,0],
&y[0],
&idx[0])
def iterate_indexed(self,
Double[:,::1] X not None,
Double[::1] y not None,
int64_t[::1] idx not None):
iterateBlockIndexed[_MISO](deref(self.solver),
X.shape[0],
&X[0,0],
&y[0],
idx.shape[0],
&idx[0])
cdef class SparseMISO:
cdef _SparseMISO* solver
def __cinit__(self, size_t dim, size_t n, Double alpha=1.0,
Double lmbda=0.1, string loss="logistic", bool compute_lb=False):
self.solver = new _SparseMISO(dim, n, alpha, lmbda, loss, compute_lb)
def __dealloc__(self):
del self.solver
property nfeatures:
def __get__(self):
return self.solver.nfeatures()
property nexamples:
def __get__(self):
return self.solver.nexamples()
property w:
def __get__(self):
cdef np.npy_intp shape[1]
shape[0] = self.nfeatures
cdef np.ndarray[Double, ndim=1] arr = \
np.PyArray_SimpleNewFromData(1, shape, npDOUBLE,
self.solver.wdata())
Py_INCREF(self)
PyArray_SetBaseObject(arr, <PyObject*>self)
return arr
def start_decay(self):
self.solver.startDecay()
def decay(self, Double multiplier=0.5):
self.solver.decay(multiplier)
def lower_bound(self):
return self.solver.lowerBound()
def init(self, X):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
initFromX[_SparseMISO](deref(self.solver), X.shape[0], X.nnz, &indptr[0],
&indices[0], &values[0])
def set_q(self, Double[::1] q not None):
setQ[_SparseMISO](deref(self.solver), q.shape[0], &q[0])
def compute_loss(self, X, Double[::1] y not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
cdef size_t n = X.shape[0], nnz = X.nnz
cdef Double loss
with nogil:
loss = self.solver.computeLoss(n, nnz, &indptr[0],
&indices[0], &values[0], &y[0])
return loss
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def iterate(self, X,
Double[::1] y not None,
int64_t[::1] idx not None):
assert isinstance(X, sp.csr_matrix)
assert X.has_sorted_indices, "The Sparse MISO implementation requires sorted indices." \
"Use X.sort_indices() to sort them."
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
cdef size_t n = X.shape[0], nnz = X.nnz
with nogil:
iterateBlockSparse[_SparseMISO](deref(self.solver),
n,
nnz,
&indptr[0],
&indices[0],
&values[0],
&y[0],
&idx[0])
def iterate_indexed(self, X,
Double[::1] y not None,
int64_t[::1] idx not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indices = X.indices
cdef int32_t[:] indptr = X.indptr
cdef size_t n = X.shape[0], nnz = X.nnz, blockSize = idx.shape[0]
with nogil:
iterateBlockIndexedSparse[_SparseMISO](deref(self.solver),
n, nnz,
&indptr[0],
&indices[0],
&values[0],
&y[0],
blockSize,
&idx[0])
cdef class MISOOneVsRest:
cdef OneVsRest[_MISO]* solver
def __cinit__(self, size_t nclasses, size_t dim, size_t n, Double alpha=1.0,
Double lmbda=0.1, string loss="logistic", bool compute_lb=False,
string prox="none", Double prox_weight=0., bool average=False):
self.solver = new OneVsRest[_MISO](nclasses, dim, n, alpha, lmbda, loss,
compute_lb, prox, prox_weight, average)
def __dealloc__(self):
del self.solver
def start_decay(self):
self.solver.startDecay()
def decay(self, Double multiplier=0.5):
self.solver.decay(multiplier)
def iterate(self,
Double[:,::1] X not None,
int32_t[::1] y not None,
int64_t[::1] idx not None):
self.solver.iterateBlock(X.shape[0], &X[0,0], &y[0], &idx[0])
def iterate_indexed(self,
Double[:,::1] X not None,
int32_t[::1] y not None,
int64_t[::1] idx not None):
self.solver.iterateBlockIndexed(
X.shape[0], &X[0,0], &y[0], idx.shape[0], &idx[0])
def predict(self, Double[:,::1] X not None):
preds = np.empty(X.shape[0], dtype=np.int32)
cdef int32_t[:] out = preds
self.solver.predict(out.shape[0], &out[0], &X[0,0])
return preds
def compute_loss(self, Double[:,::1] X not None, int32_t[::1] y not None):
return self.solver.computeLoss(X.shape[0], &X[0,0], &y[0])
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def compute_prox_penalty(self):
return self.solver.computeProxPenalty()
cdef extern from "solvers/SAGA.h" namespace "solvers":
cdef cppclass _SAGA "solvers::SAGA":
_SAGA(size_t dim, size_t n, Double lr, Double lmbda,
string loss, string prox, Double proxWeight)
size_t t()
size_t nfeatures()
size_t nexamples()
Double* wdata()
Double computeLoss(const size_t sz,
const Double* const XData,
const Double* const yData) nogil
Double computeSquaredNorm() nogil
Double computeProxPenalty()
cdef cppclass _SparseSAGA "solvers::SparseSAGA":
_SparseSAGA(size_t dim, size_t n, Double lr, Double lmbda, string loss)
size_t t()
size_t nfeatures()
size_t nexamples()
Double* wdata()
Double computeLoss(const size_t sz,
const size_t nnz,
const int32_t* const Xindptr,
const int32_t* const Xindices,
const Double* const Xvalues,
const Double* const yData) nogil
Double computeSquaredNorm() nogil
cdef class SAGA:
cdef _SAGA* solver
def __cinit__(self, size_t dim, size_t n, Double lr=1.0,
Double lmbda=0.1, string loss="logistic",
string prox="none", Double prox_weight=0.):
self.solver = new _SAGA(dim, n, lr, lmbda, loss, prox, prox_weight)
def __dealloc__(self):
del self.solver
property nfeatures:
def __get__(self):
return self.solver.nfeatures()
property nexamples:
def __get__(self):
return self.solver.nexamples()
property w:
def __get__(self):
cdef np.npy_intp shape[1]
shape[0] = self.nfeatures
cdef np.ndarray[Double, ndim=1] arr = \
np.PyArray_SimpleNewFromData(1, shape, npDOUBLE,
self.solver.wdata())
Py_INCREF(self)
PyArray_SetBaseObject(arr, <PyObject*>self)
return arr
def start_decay(self):
pass # self.solver.startDecay()
def decay(self, Double multiplier=0.5):
pass # self.solver.decay(multiplier)
def compute_loss(self, Double[:,::1] X not None, Double[::1] y not None):
return self.solver.computeLoss(X.shape[0], &X[0,0], &y[0])
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def compute_prox_penalty(self):
return self.solver.computeProxPenalty()
def iterate(self,
Double[:,::1] X not None,
Double[::1] y not None,
int64_t[::1] idx not None):
iterateBlock[_SAGA](deref(self.solver),
X.shape[0],
&X[0,0],
&y[0],
&idx[0])
def iterate_indexed(self,
Double[:,::1] X not None,
Double[::1] y not None,
int64_t[::1] idx not None):
iterateBlockIndexed[_SAGA](deref(self.solver),
X.shape[0],
&X[0,0],
&y[0],
idx.shape[0],
&idx[0])
cdef class SparseSAGA:
cdef _SparseSAGA* solver
def __cinit__(self, size_t dim, size_t n, Double lr=1.0,
Double lmbda=0.1, string loss="logistic"):
self.solver = new _SparseSAGA(dim, n, lr, lmbda, loss)
def __dealloc__(self):
del self.solver
property nfeatures:
def __get__(self):
return self.solver.nfeatures()
property nexamples:
def __get__(self):
return self.solver.nexamples()
property w:
def __get__(self):
cdef np.npy_intp shape[1]
shape[0] = self.nfeatures
cdef np.ndarray[Double, ndim=1] arr = \
np.PyArray_SimpleNewFromData(1, shape, npDOUBLE,
self.solver.wdata())
Py_INCREF(self)
PyArray_SetBaseObject(arr, <PyObject*>self)
return arr
def init(self, X):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
return initFromX[_SparseSAGA](deref(self.solver), X.shape[0], X.nnz, &indptr[0],
&indices[0], &values[0])
def start_decay(self):
pass # self.solver.startDecay()
def decay(self, Double multiplier=0.5):
pass # self.solver.decay(multiplier)
def compute_loss(self, X, Double[::1] y not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
cdef size_t n = X.shape[0], nnz = X.nnz
cdef Double loss
with nogil:
loss = self.solver.computeLoss(n, nnz, &indptr[0],
&indices[0], &values[0], &y[0])
return loss
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def iterate(self, X,
Double[::1] y not None,
int64_t[::1] idx not None):
assert isinstance(X, sp.csr_matrix)
assert X.has_sorted_indices, "The Sparse SAGA implementation requires sorted indices." \
"Use X.sort_indices() to sort them."
cdef Double[:] values = X.data
cdef int32_t[:] indptr = X.indptr
cdef int32_t[:] indices = X.indices
cdef size_t n = X.shape[0], nnz = X.nnz
with nogil:
iterateBlockSparse[_SparseSAGA](deref(self.solver),
n, nnz,
&indptr[0],
&indices[0],
&values[0],
&y[0],
&idx[0])
def iterate_indexed(self, X,
Double[::1] y not None,
int64_t[::1] idx not None):
assert isinstance(X, sp.csr_matrix)
cdef Double[:] values = X.data
cdef int32_t[:] indices = X.indices
cdef int32_t[:] indptr = X.indptr
cdef size_t n = X.shape[0], nnz = X.nnz, blockSize = idx.shape[0]
with nogil:
iterateBlockIndexedSparse[_SparseSAGA](deref(self.solver),
n, nnz,
&indptr[0],
&indices[0],
&values[0],
&y[0],
blockSize,
&idx[0])
cdef class SAGAOneVsRest:
cdef OneVsRest[_SAGA]* solver
def __cinit__(self, size_t nclasses, size_t dim, size_t n, Double lr=1.0,
Double lmbda=0.1, string loss="logistic",
string prox="none", Double prox_weight=0.):
self.solver = new OneVsRest[_SAGA](nclasses, dim, n, lr, lmbda, loss, prox, prox_weight)
def __dealloc__(self):
del self.solver
def iterate(self,
Double[:,::1] X not None,
int32_t[::1] y not None,
int64_t[::1] idx not None):
self.solver.iterateBlock(X.shape[0], &X[0,0], &y[0], &idx[0])
def iterate_indexed(self,
Double[:,::1] X not None,
int32_t[::1] y not None,
int64_t[::1] idx not None):
self.solver.iterateBlockIndexed(
X.shape[0], &X[0,0], &y[0], idx.shape[0], &idx[0])
def predict(self, Double[:,::1] X not None):
preds = np.empty(X.shape[0], dtype=np.int32)
cdef int32_t[:] out = preds
self.solver.predict(out.shape[0], &out[0], &X[0,0])
return preds
def compute_loss(self, Double[:,::1] X not None, int32_t[::1] y not None):
return self.solver.computeLoss(X.shape[0], &X[0,0], &y[0])
def compute_squared_norm(self):
cdef Double norm
with nogil:
norm = self.solver.computeSquaredNorm()
return norm
def compute_prox_penalty(self):
return self.solver.computeProxPenalty()