{{py:
"""
Dataset abstractions for sequential data access.
Template file for easily generate fused types consistent code using Tempita
(https://github.com/cython/cython/blob/master/Cython/Tempita/_tempita.py).
Generated file: _seq_dataset.pyx
Each class is duplicated for all dtypes (float and double). The keywords
between double braces are substituted in setup.py.
Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
Arthur Imbert <arthurimbert05@gmail.com>
Joan Massich <mailsik@gmail.com>
License: BSD 3 clause
"""
# name_suffix, c_type, np_type
dtypes = [('64', 'float64_t', 'np.float64'),
('32', 'float32_t', 'np.float32')]
}}
"""Dataset abstractions for sequential data access."""
import numpy as np
cimport cython
from libc.limits cimport INT_MAX
from ._random cimport our_rand_r
from ._typedefs cimport float32_t, float64_t, uint32_t
{{for name_suffix, c_type, np_type in dtypes}}
#------------------------------------------------------------------------------
cdef class SequentialDataset{{name_suffix}}:
"""Base class for datasets with sequential data access.
SequentialDataset is used to iterate over the rows of a matrix X and
corresponding target values y, i.e. to iterate over samples.
There are two methods to get the next sample:
- next : Iterate sequentially (optionally randomized)
- random : Iterate randomly (with replacement)
Attributes
----------
index : np.ndarray
Index array for fast shuffling.
index_data_ptr : int
Pointer to the index array.
current_index : int
Index of current sample in ``index``.
The index of current sample in the data is given by
index_data_ptr[current_index].
n_samples : Py_ssize_t
Number of samples in the dataset.
seed : uint32_t
Seed used for random sampling. This attribute is modified at each call to the
`random` method.
"""
cdef void next(self, {{c_type}} **x_data_ptr, int **x_ind_ptr,
int *nnz, {{c_type}} *y, {{c_type}} *sample_weight) noexcept nogil:
"""Get the next example ``x`` from the dataset.
This method gets the next sample looping sequentially over all samples.
The order can be shuffled with the method ``shuffle``.
Shuffling once before iterating over all samples corresponds to a
random draw without replacement. It is used for instance in SGD solver.
Parameters
----------
x_data_ptr : {{c_type}}**
A pointer to the {{c_type}} array which holds the feature
values of the next example.
x_ind_ptr : np.intc**
A pointer to the int array which holds the feature
indices of the next example.
nnz : int*
A pointer to an int holding the number of non-zero
values of the next example.
y : {{c_type}}*
The target value of the next example.
sample_weight : {{c_type}}*
The weight of the next example.
"""
cdef int current_index = self._get_next_index()
self._sample(x_data_ptr, x_ind_ptr, nnz, y, sample_weight,
current_index)
cdef int random(self, {{c_type}} **x_data_ptr, int **x_ind_ptr,
int *nnz, {{c_type}} *y, {{c_type}} *sample_weight) noexcept nogil:
"""Get a random example ``x`` from the dataset.
This method gets next sample chosen randomly over a uniform
distribution. It corresponds to a random draw with replacement.
It is used for instance in SAG solver.
Parameters
----------
x_data_ptr : {{c_type}}**
A pointer to the {{c_type}} array which holds the feature
values of the next example.
x_ind_ptr : np.intc**
A pointer to the int array which holds the feature
indices of the next example.
nnz : int*
A pointer to an int holding the number of non-zero
values of the next example.
y : {{c_type}}*
The target value of the next example.
sample_weight : {{c_type}}*
The weight of the next example.
Returns
-------
current_index : int
Index of current sample.
"""
cdef int current_index = self._get_random_index()
self._sample(x_data_ptr, x_ind_ptr, nnz, y, sample_weight,
current_index)
return current_index
cdef void shuffle(self, uint32_t seed) noexcept nogil:
"""Permutes the ordering of examples."""
# Fisher-Yates shuffle
cdef int *ind = self.index_data_ptr
cdef int n = self.n_samples
cdef unsigned i, j
for i in range(n - 1):
j = i + our_rand_r(&seed) % (n - i)
ind[i], ind[j] = ind[j], ind[i]
cdef int _get_next_index(self) noexcept nogil:
cdef int current_index = self.current_index
if current_index >= (self.n_samples - 1):
current_index = -1
current_index += 1
self.current_index = current_index
return self.current_index
cdef int _get_random_index(self) noexcept nogil:
cdef int n = self.n_samples
cdef int current_index = our_rand_r(&self.seed) % n
self.current_index = current_index
return current_index
cdef void _sample(self, {{c_type}} **x_data_ptr, int **x_ind_ptr,
int *nnz, {{c_type}} *y, {{c_type}} *sample_weight,
int current_index) noexcept nogil:
pass
def _shuffle_py(self, uint32_t seed):
"""python function used for easy testing"""
self.shuffle(seed)
def _next_py(self):
"""python function used for easy testing"""
cdef int current_index = self._get_next_index()
return self._sample_py(current_index)
def _random_py(self):
"""python function used for easy testing"""
cdef int current_index = self._get_random_index()
return self._sample_py(current_index)
def _sample_py(self, int current_index):
"""python function used for easy testing"""
cdef {{c_type}}* x_data_ptr
cdef int* x_indices_ptr
cdef int nnz, j
cdef {{c_type}} y, sample_weight
# call _sample in cython
self._sample(&x_data_ptr, &x_indices_ptr, &nnz, &y, &sample_weight,
current_index)
# transform the pointed data in numpy CSR array
cdef {{c_type}}[:] x_data = np.empty(nnz, dtype={{np_type}})
cdef int[:] x_indices = np.empty(nnz, dtype=np.int32)
cdef int[:] x_indptr = np.asarray([0, nnz], dtype=np.int32)
for j in range(nnz):
x_data[j] = x_data_ptr[j]
x_indices[j] = x_indices_ptr[j]
cdef int sample_idx = self.index_data_ptr[current_index]
return (
(np.asarray(x_data), np.asarray(x_indices), np.asarray(x_indptr)),
y,
sample_weight,
sample_idx,
)
cdef class ArrayDataset{{name_suffix}}(SequentialDataset{{name_suffix}}):
"""Dataset backed by a two-dimensional numpy array.
The dtype of the numpy array is expected to be ``{{np_type}}`` ({{c_type}})
and C-style memory layout.
"""
def __cinit__(
self,
const {{c_type}}[:, ::1] X,
const {{c_type}}[::1] Y,
const {{c_type}}[::1] sample_weights,
uint32_t seed=1,
):
"""A ``SequentialDataset`` backed by a two-dimensional numpy array.
Parameters
----------
X : ndarray, dtype={{c_type}}, ndim=2, mode='c'
The sample array, of shape(n_samples, n_features)
Y : ndarray, dtype={{c_type}}, ndim=1, mode='c'
The target array, of shape(n_samples, )
sample_weights : ndarray, dtype={{c_type}}, ndim=1, mode='c'
The weight of each sample, of shape(n_samples,)
"""
if X.shape[0] > INT_MAX or X.shape[1] > INT_MAX:
raise ValueError("More than %d samples or features not supported;"
" got (%d, %d)."
% (INT_MAX, X.shape[0], X.shape[1]))
# keep a reference to the data to prevent garbage collection
self.X = X
self.Y = Y
self.sample_weights = sample_weights
self.n_samples = X.shape[0]
self.n_features = X.shape[1]
self.feature_indices = np.arange(0, self.n_features, dtype=np.intc)
self.feature_indices_ptr = <int *> &self.feature_indices[0]
self.current_index = -1
self.X_stride = X.strides[0] // X.itemsize
self.X_data_ptr = <{{c_type}} *> &X[0, 0]
self.Y_data_ptr = <{{c_type}} *> &Y[0]
self.sample_weight_data = <{{c_type}} *> &sample_weights[0]
# Use index array for fast shuffling
self.index = np.arange(0, self.n_samples, dtype=np.intc)
self.index_data_ptr = <int *> &self.index[0]
# seed should not be 0 for our_rand_r
self.seed = max(seed, 1)
cdef void _sample(self, {{c_type}} **x_data_ptr, int **x_ind_ptr,
int *nnz, {{c_type}} *y, {{c_type}} *sample_weight,
int current_index) noexcept nogil:
cdef long long sample_idx = self.index_data_ptr[current_index]
cdef long long offset = sample_idx * self.X_stride
y[0] = self.Y_data_ptr[sample_idx]
x_data_ptr[0] = self.X_data_ptr + offset
x_ind_ptr[0] = self.feature_indices_ptr
nnz[0] = self.n_features
sample_weight[0] = self.sample_weight_data[sample_idx]
cdef class CSRDataset{{name_suffix}}(SequentialDataset{{name_suffix}}):
"""A ``SequentialDataset`` backed by a scipy sparse CSR matrix. """
def __cinit__(
self,
const {{c_type}}[::1] X_data,
const int[::1] X_indptr,
const int[::1] X_indices,
const {{c_type}}[::1] Y,
const {{c_type}}[::1] sample_weights,
uint32_t seed=1,
):
"""Dataset backed by a scipy sparse CSR matrix.
The feature indices of ``x`` are given by x_ind_ptr[0:nnz].
The corresponding feature values are given by
x_data_ptr[0:nnz].
Parameters
----------
X_data : ndarray, dtype={{c_type}}, ndim=1, mode='c'
The data array of the CSR features matrix.
X_indptr : ndarray, dtype=np.intc, ndim=1, mode='c'
The index pointer array of the CSR features matrix.
X_indices : ndarray, dtype=np.intc, ndim=1, mode='c'
The column indices array of the CSR features matrix.
Y : ndarray, dtype={{c_type}}, ndim=1, mode='c'
The target values.
sample_weights : ndarray, dtype={{c_type}}, ndim=1, mode='c'
The weight of each sample.
"""
# keep a reference to the data to prevent garbage collection
self.X_data = X_data
self.X_indptr = X_indptr
self.X_indices = X_indices
self.Y = Y
self.sample_weights = sample_weights
self.n_samples = Y.shape[0]
self.current_index = -1
self.X_data_ptr = <{{c_type}} *> &X_data[0]
self.X_indptr_ptr = <int *> &X_indptr[0]
self.X_indices_ptr = <int *> &X_indices[0]
self.Y_data_ptr = <{{c_type}} *> &Y[0]
self.sample_weight_data = <{{c_type}} *> &sample_weights[0]
# Use index array for fast shuffling
self.index = np.arange(self.n_samples, dtype=np.intc)
self.index_data_ptr = <int *> &self.index[0]
# seed should not be 0 for our_rand_r
self.seed = max(seed, 1)
cdef void _sample(self, {{c_type}} **x_data_ptr, int **x_ind_ptr,
int *nnz, {{c_type}} *y, {{c_type}} *sample_weight,
int current_index) noexcept nogil:
cdef long long sample_idx = self.index_data_ptr[current_index]
cdef long long offset = self.X_indptr_ptr[sample_idx]
y[0] = self.Y_data_ptr[sample_idx]
x_data_ptr[0] = self.X_data_ptr + offset
x_ind_ptr[0] = self.X_indices_ptr + offset
nnz[0] = self.X_indptr_ptr[sample_idx + 1] - offset
sample_weight[0] = self.sample_weight_data[sample_idx]
{{endfor}}