197 lines
7.6 KiB
Cython
197 lines
7.6 KiB
Cython
# cython: cdivision=True
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# cython: boundscheck=False
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# cython: wraparound=False
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#
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# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
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#
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# Licence: BSD 3 clause
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cimport cython
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from libc.limits cimport INT_MAX
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cimport numpy as np
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import numpy as np
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np.import_array()
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cdef class SequentialDataset:
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"""Base class for datasets with sequential data access. """
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cdef void next(self, double **x_data_ptr, int **x_ind_ptr,
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int *nnz, double *y, double *sample_weight) nogil:
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"""Get the next example ``x`` from the dataset.
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Parameters
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----------
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x_data_ptr : double**
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A pointer to the double array which holds the feature
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values of the next example.
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x_ind_ptr : np.intc**
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A pointer to the int array which holds the feature
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indices of the next example.
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nnz : int*
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A pointer to an int holding the number of non-zero
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values of the next example.
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y : double*
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The target value of the next example.
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sample_weight : double*
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The weight of the next example.
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"""
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with gil:
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raise NotImplementedError()
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cdef void shuffle(self, np.uint32_t seed) nogil:
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"""Permutes the ordering of examples."""
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# Fisher-Yates shuffle
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cdef int *ind = self.index_data_ptr
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cdef int n = self.n_samples
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cdef unsigned i, j
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for i in range(n - 1):
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j = i + our_rand_r(&seed) % (n - i)
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ind[i], ind[j] = ind[j], ind[i]
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cdef class ArrayDataset(SequentialDataset):
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"""Dataset backed by a two-dimensional numpy array.
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The dtype of the numpy array is expected to be ``np.float64`` (double)
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and C-style memory layout.
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"""
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def __cinit__(self, np.ndarray[double, ndim=2, mode='c'] X,
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np.ndarray[double, ndim=1, mode='c'] Y,
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np.ndarray[double, ndim=1, mode='c'] sample_weights):
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"""A ``SequentialDataset`` backed by a two-dimensional numpy array.
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Parameters
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----------
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X : ndarray, dtype=double, ndim=2, mode='c'
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The samples; a two-dimensional c-continuous numpy array of
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dtype double.
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Y : ndarray, dtype=double, ndim=1, mode='c'
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The target values; a one-dimensional c-continuous numpy array of
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dtype double.
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sample_weights : ndarray, dtype=double, ndim=1, mode='c'
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The weight of each sample; a one-dimensional c-continuous numpy
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array of dtype double.
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"""
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if X.shape[0] > INT_MAX or X.shape[1] > INT_MAX:
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raise ValueError("More than %d samples or features not supported;"
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" got (%d, %d)."
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% (INT_MAX, X.shape[0], X.shape[1]))
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self.n_samples = X.shape[0]
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self.n_features = X.shape[1]
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cdef np.ndarray[int, ndim=1,
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mode='c'] feature_indices = np.arange(0, self.n_features,
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dtype=np.intc)
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self.feature_indices = feature_indices
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self.feature_indices_ptr = <int *> feature_indices.data
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self.current_index = -1
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self.stride = X.strides[0] / X.itemsize
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self.X_data_ptr = <double *>X.data
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self.Y_data_ptr = <double *>Y.data
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self.sample_weight_data = <double *>sample_weights.data
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# Use index array for fast shuffling
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cdef np.ndarray[int, ndim=1, mode='c'] index = \
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np.arange(0, self.n_samples, dtype=np.intc)
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self.index = index
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self.index_data_ptr = <int *>index.data
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cdef void next(self, double **x_data_ptr, int **x_ind_ptr,
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int *nnz, double *y, double *sample_weight) nogil:
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cdef int current_index = self.current_index
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if current_index >= (self.n_samples - 1):
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current_index = -1
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current_index += 1
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cdef int sample_idx = self.index_data_ptr[current_index]
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cdef int offset = sample_idx * self.stride
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y[0] = self.Y_data_ptr[sample_idx]
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x_data_ptr[0] = self.X_data_ptr + offset
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x_ind_ptr[0] = self.feature_indices_ptr
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nnz[0] = self.n_features
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sample_weight[0] = self.sample_weight_data[sample_idx]
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self.current_index = current_index
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cdef class CSRDataset(SequentialDataset):
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"""A ``SequentialDataset`` backed by a scipy sparse CSR matrix. """
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def __cinit__(self, np.ndarray[double, ndim=1, mode='c'] X_data,
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np.ndarray[int, ndim=1, mode='c'] X_indptr,
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np.ndarray[int, ndim=1, mode='c'] X_indices,
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np.ndarray[double, ndim=1, mode='c'] Y,
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np.ndarray[double, ndim=1, mode='c'] sample_weight):
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"""Dataset backed by a scipy sparse CSR matrix.
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The feature indices of ``x`` are given by x_ind_ptr[0:nnz].
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The corresponding feature values are given by
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x_data_ptr[0:nnz].
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Parameters
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----------
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X_data : ndarray, dtype=double, ndim=1, mode='c'
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The data array of the CSR matrix; a one-dimensional c-continuous
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numpy array of dtype double.
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X_indptr : ndarray, dtype=np.intc, ndim=1, mode='c'
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The index pointer array of the CSR matrix; a one-dimensional
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c-continuous numpy array of dtype np.intc.
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X_indices : ndarray, dtype=np.intc, ndim=1, mode='c'
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The column indices array of the CSR matrix; a one-dimensional
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c-continuous numpy array of dtype np.intc.
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Y : ndarray, dtype=double, ndim=1, mode='c'
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The target values; a one-dimensional c-continuous numpy array of
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dtype double.
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sample_weights : ndarray, dtype=double, ndim=1, mode='c'
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The weight of each sample; a one-dimensional c-continuous numpy
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array of dtype double.
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"""
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self.n_samples = Y.shape[0]
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self.current_index = -1
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self.X_data_ptr = <double *>X_data.data
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self.X_indptr_ptr = <int *>X_indptr.data
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self.X_indices_ptr = <int *>X_indices.data
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self.Y_data_ptr = <double *>Y.data
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self.sample_weight_data = <double *>sample_weight.data
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# Use index array for fast shuffling
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cdef np.ndarray[int, ndim=1, mode='c'] idx = np.arange(self.n_samples,
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dtype=np.intc)
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self.index = idx
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self.index_data_ptr = <int *>idx.data
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cdef void next(self, double **x_data_ptr, int **x_ind_ptr,
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int *nnz, double *y, double *sample_weight) nogil:
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cdef int current_index = self.current_index
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if current_index >= (self.n_samples - 1):
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current_index = -1
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current_index += 1
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cdef int sample_idx = self.index_data_ptr[current_index]
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cdef int offset = self.X_indptr_ptr[sample_idx]
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y[0] = self.Y_data_ptr[sample_idx]
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x_data_ptr[0] = self.X_data_ptr + offset
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x_ind_ptr[0] = self.X_indices_ptr + offset
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nnz[0] = self.X_indptr_ptr[sample_idx + 1] - offset
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sample_weight[0] = self.sample_weight_data[sample_idx]
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self.current_index = current_index
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cdef enum:
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RAND_R_MAX = 0x7FFFFFFF
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# rand_r replacement using a 32bit XorShift generator
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# See http://www.jstatsoft.org/v08/i14/paper for details
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# XXX copied over from sklearn/tree/_tree.pyx, should refactor
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cdef inline np.uint32_t our_rand_r(np.uint32_t* seed) nogil:
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seed[0] ^= <np.uint32_t>(seed[0] << 13)
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seed[0] ^= <np.uint32_t>(seed[0] >> 17)
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seed[0] ^= <np.uint32_t>(seed[0] << 5)
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return seed[0] % (<np.uint32_t>RAND_R_MAX + 1)
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