scikit-learn/sklearn/datasets/_svmlight_format.pyx

101 lines
2.9 KiB
Cython

# Optimized inner loop of load_svmlight_file.
#
# Authors: Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Olivier Grisel <olivier.grisel@ensta.org>
# License: Simple BSD.
from libc.string cimport strchr
cimport numpy as np
import numpy as np
import scipy.sparse as sp
from ..utils.arraybuilder import ArrayBuilder
np.import_array()
# csr_matrix.indices and .indptr's dtypes are undocumented. We derive them
# empirically.
_temp_csr = sp.csr_matrix(0)
_INDICES_DTYPE = _temp_csr.indices.dtype
_INDPTR_DTYPE = _temp_csr.indptr.dtype
del _temp_csr
cdef bytes COMMA = u','.encode('ascii')
cdef bytes COLON = u':'.encode('ascii')
def _load_svmlight_file(f, dtype, bint multilabel, bint zero_based, bint query_id):
cdef bytes line
cdef char *hash_ptr, *line_cstr
cdef np.int32_t idx, prev_idx
cdef Py_ssize_t i
data = ArrayBuilder(dtype=dtype)
indptr = ArrayBuilder(dtype=_INDPTR_DTYPE)
indices = ArrayBuilder(dtype=_INDICES_DTYPE)
query_values = ArrayBuilder(dtype=np.int)
if multilabel:
labels = []
else:
labels = ArrayBuilder(dtype=np.double)
for line in f:
# skip comments
line_cstr = line
hash_ptr = strchr(line_cstr, '#')
if hash_ptr != NULL:
line = line[:hash_ptr - line_cstr]
line_parts = line.split()
if len(line_parts) == 0:
continue
target, features = line_parts[0], line_parts[1:]
if multilabel:
target = [float(y) for y in target.split(COMMA)]
target.sort()
labels.append(tuple(target))
else:
labels.append(float(target))
indptr.append(len(data))
prev_idx = -1
n_features = len(features)
if n_features and line_parts[1].startswith('qid'):
_, value = line_parts[1].split(COLON, 1)
if query_id:
query_values.append(int(value))
line_parts.pop(1)
n_features -= 1
for i in xrange(1, n_features + 1):
idx_s, value = line_parts[i].split(COLON, 1)
# XXX if we replace int with np.int32 in the line below, this
# function becomes twice as slow.
idx = int(idx_s)
if idx < 0 or not zero_based and idx == 0:
raise ValueError(
"Invalid index %d in SVMlight/LibSVM data file." % idx)
if idx <= prev_idx:
raise ValueError("Feature ndices in SVMlight/LibSVM data "
"file should be sorted and unique.")
indices.append(idx)
data.append(dtype(value))
prev_idx = idx
indptr.append(len(data))
indptr = indptr.get()
data = data.get()
indices = indices.get()
query_values = query_values.get()
if not multilabel:
labels = labels.get()
return data, indices, indptr, labels, query_values