500 lines
19 KiB
Python
500 lines
19 KiB
Python
![]() |
"""This module implements a loader and dumper for the svmlight format
|
||
|
|
||
|
This format is a text-based format, with one sample per line. It does
|
||
|
not store zero valued features hence is suitable for sparse dataset.
|
||
|
|
||
|
The first element of each line can be used to store a target variable to
|
||
|
predict.
|
||
|
|
||
|
This format is used as the default format for both svmlight and the
|
||
|
libsvm command line programs.
|
||
|
"""
|
||
|
|
||
|
# Authors: Mathieu Blondel <mathieu@mblondel.org>
|
||
|
# Lars Buitinck
|
||
|
# Olivier Grisel <olivier.grisel@ensta.org>
|
||
|
# License: BSD 3 clause
|
||
|
|
||
|
from contextlib import closing
|
||
|
import io
|
||
|
import os.path
|
||
|
|
||
|
import numpy as np
|
||
|
import scipy.sparse as sp
|
||
|
|
||
|
from .. import __version__
|
||
|
|
||
|
from ..utils import check_array, IS_PYPY
|
||
|
from ..utils.validation import _deprecate_positional_args
|
||
|
|
||
|
if not IS_PYPY:
|
||
|
from ._svmlight_format_fast import _load_svmlight_file
|
||
|
else:
|
||
|
def _load_svmlight_file(*args, **kwargs):
|
||
|
raise NotImplementedError(
|
||
|
'load_svmlight_file is currently not '
|
||
|
'compatible with PyPy (see '
|
||
|
'https://github.com/scikit-learn/scikit-learn/issues/11543 '
|
||
|
'for the status updates).')
|
||
|
|
||
|
|
||
|
@_deprecate_positional_args
|
||
|
def load_svmlight_file(f, *, n_features=None, dtype=np.float64,
|
||
|
multilabel=False, zero_based="auto", query_id=False,
|
||
|
offset=0, length=-1):
|
||
|
"""Load datasets in the svmlight / libsvm format into sparse CSR matrix
|
||
|
|
||
|
This format is a text-based format, with one sample per line. It does
|
||
|
not store zero valued features hence is suitable for sparse dataset.
|
||
|
|
||
|
The first element of each line can be used to store a target variable
|
||
|
to predict.
|
||
|
|
||
|
This format is used as the default format for both svmlight and the
|
||
|
libsvm command line programs.
|
||
|
|
||
|
Parsing a text based source can be expensive. When working on
|
||
|
repeatedly on the same dataset, it is recommended to wrap this
|
||
|
loader with joblib.Memory.cache to store a memmapped backup of the
|
||
|
CSR results of the first call and benefit from the near instantaneous
|
||
|
loading of memmapped structures for the subsequent calls.
|
||
|
|
||
|
In case the file contains a pairwise preference constraint (known
|
||
|
as "qid" in the svmlight format) these are ignored unless the
|
||
|
query_id parameter is set to True. These pairwise preference
|
||
|
constraints can be used to constraint the combination of samples
|
||
|
when using pairwise loss functions (as is the case in some
|
||
|
learning to rank problems) so that only pairs with the same
|
||
|
query_id value are considered.
|
||
|
|
||
|
This implementation is written in Cython and is reasonably fast.
|
||
|
However, a faster API-compatible loader is also available at:
|
||
|
|
||
|
https://github.com/mblondel/svmlight-loader
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
f : str, file-like or int
|
||
|
(Path to) a file to load. If a path ends in ".gz" or ".bz2", it will
|
||
|
be uncompressed on the fly. If an integer is passed, it is assumed to
|
||
|
be a file descriptor. A file-like or file descriptor will not be closed
|
||
|
by this function. A file-like object must be opened in binary mode.
|
||
|
|
||
|
n_features : int, default=None
|
||
|
The number of features to use. If None, it will be inferred. This
|
||
|
argument is useful to load several files that are subsets of a
|
||
|
bigger sliced dataset: each subset might not have examples of
|
||
|
every feature, hence the inferred shape might vary from one
|
||
|
slice to another.
|
||
|
n_features is only required if ``offset`` or ``length`` are passed a
|
||
|
non-default value.
|
||
|
|
||
|
dtype : numpy data type, default=np.float64
|
||
|
Data type of dataset to be loaded. This will be the data type of the
|
||
|
output numpy arrays ``X`` and ``y``.
|
||
|
|
||
|
multilabel : bool, default=False
|
||
|
Samples may have several labels each (see
|
||
|
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)
|
||
|
|
||
|
zero_based : bool or "auto", default="auto"
|
||
|
Whether column indices in f are zero-based (True) or one-based
|
||
|
(False). If column indices are one-based, they are transformed to
|
||
|
zero-based to match Python/NumPy conventions.
|
||
|
If set to "auto", a heuristic check is applied to determine this from
|
||
|
the file contents. Both kinds of files occur "in the wild", but they
|
||
|
are unfortunately not self-identifying. Using "auto" or True should
|
||
|
always be safe when no ``offset`` or ``length`` is passed.
|
||
|
If ``offset`` or ``length`` are passed, the "auto" mode falls back
|
||
|
to ``zero_based=True`` to avoid having the heuristic check yield
|
||
|
inconsistent results on different segments of the file.
|
||
|
|
||
|
query_id : bool, default=False
|
||
|
If True, will return the query_id array for each file.
|
||
|
|
||
|
offset : int, default=0
|
||
|
Ignore the offset first bytes by seeking forward, then
|
||
|
discarding the following bytes up until the next new line
|
||
|
character.
|
||
|
|
||
|
length : int, default=-1
|
||
|
If strictly positive, stop reading any new line of data once the
|
||
|
position in the file has reached the (offset + length) bytes threshold.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
X : scipy.sparse matrix of shape (n_samples, n_features)
|
||
|
|
||
|
y : ndarray of shape (n_samples,), or, in the multilabel a list of
|
||
|
tuples of length n_samples.
|
||
|
|
||
|
query_id : array of shape (n_samples,)
|
||
|
query_id for each sample. Only returned when query_id is set to
|
||
|
True.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
load_svmlight_files : Similar function for loading multiple files in this
|
||
|
format, enforcing the same number of features/columns on all of them.
|
||
|
|
||
|
Examples
|
||
|
--------
|
||
|
To use joblib.Memory to cache the svmlight file::
|
||
|
|
||
|
from joblib import Memory
|
||
|
from .datasets import load_svmlight_file
|
||
|
mem = Memory("./mycache")
|
||
|
|
||
|
@mem.cache
|
||
|
def get_data():
|
||
|
data = load_svmlight_file("mysvmlightfile")
|
||
|
return data[0], data[1]
|
||
|
|
||
|
X, y = get_data()
|
||
|
"""
|
||
|
return tuple(load_svmlight_files([f], n_features=n_features,
|
||
|
dtype=dtype,
|
||
|
multilabel=multilabel,
|
||
|
zero_based=zero_based,
|
||
|
query_id=query_id,
|
||
|
offset=offset,
|
||
|
length=length))
|
||
|
|
||
|
|
||
|
def _gen_open(f):
|
||
|
if isinstance(f, int): # file descriptor
|
||
|
return io.open(f, "rb", closefd=False)
|
||
|
elif not isinstance(f, str):
|
||
|
raise TypeError("expected {str, int, file-like}, got %s" % type(f))
|
||
|
|
||
|
_, ext = os.path.splitext(f)
|
||
|
if ext == ".gz":
|
||
|
import gzip
|
||
|
return gzip.open(f, "rb")
|
||
|
elif ext == ".bz2":
|
||
|
from bz2 import BZ2File
|
||
|
return BZ2File(f, "rb")
|
||
|
else:
|
||
|
return open(f, "rb")
|
||
|
|
||
|
|
||
|
def _open_and_load(f, dtype, multilabel, zero_based, query_id,
|
||
|
offset=0, length=-1):
|
||
|
if hasattr(f, "read"):
|
||
|
actual_dtype, data, ind, indptr, labels, query = \
|
||
|
_load_svmlight_file(f, dtype, multilabel, zero_based, query_id,
|
||
|
offset, length)
|
||
|
else:
|
||
|
with closing(_gen_open(f)) as f:
|
||
|
actual_dtype, data, ind, indptr, labels, query = \
|
||
|
_load_svmlight_file(f, dtype, multilabel, zero_based, query_id,
|
||
|
offset, length)
|
||
|
|
||
|
# convert from array.array, give data the right dtype
|
||
|
if not multilabel:
|
||
|
labels = np.frombuffer(labels, np.float64)
|
||
|
data = np.frombuffer(data, actual_dtype)
|
||
|
indices = np.frombuffer(ind, np.longlong)
|
||
|
indptr = np.frombuffer(indptr, dtype=np.longlong) # never empty
|
||
|
query = np.frombuffer(query, np.int64)
|
||
|
|
||
|
data = np.asarray(data, dtype=dtype) # no-op for float{32,64}
|
||
|
return data, indices, indptr, labels, query
|
||
|
|
||
|
|
||
|
@_deprecate_positional_args
|
||
|
def load_svmlight_files(files, *, n_features=None, dtype=np.float64,
|
||
|
multilabel=False, zero_based="auto", query_id=False,
|
||
|
offset=0, length=-1):
|
||
|
"""Load dataset from multiple files in SVMlight format
|
||
|
|
||
|
This function is equivalent to mapping load_svmlight_file over a list of
|
||
|
files, except that the results are concatenated into a single, flat list
|
||
|
and the samples vectors are constrained to all have the same number of
|
||
|
features.
|
||
|
|
||
|
In case the file contains a pairwise preference constraint (known
|
||
|
as "qid" in the svmlight format) these are ignored unless the
|
||
|
query_id parameter is set to True. These pairwise preference
|
||
|
constraints can be used to constraint the combination of samples
|
||
|
when using pairwise loss functions (as is the case in some
|
||
|
learning to rank problems) so that only pairs with the same
|
||
|
query_id value are considered.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
files : array-like, dtype=str, file-like or int
|
||
|
(Paths of) files to load. If a path ends in ".gz" or ".bz2", it will
|
||
|
be uncompressed on the fly. If an integer is passed, it is assumed to
|
||
|
be a file descriptor. File-likes and file descriptors will not be
|
||
|
closed by this function. File-like objects must be opened in binary
|
||
|
mode.
|
||
|
|
||
|
n_features : int, default=None
|
||
|
The number of features to use. If None, it will be inferred from the
|
||
|
maximum column index occurring in any of the files.
|
||
|
|
||
|
This can be set to a higher value than the actual number of features
|
||
|
in any of the input files, but setting it to a lower value will cause
|
||
|
an exception to be raised.
|
||
|
|
||
|
dtype : numpy data type, default=np.float64
|
||
|
Data type of dataset to be loaded. This will be the data type of the
|
||
|
output numpy arrays ``X`` and ``y``.
|
||
|
|
||
|
multilabel : bool, default=False
|
||
|
Samples may have several labels each (see
|
||
|
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)
|
||
|
|
||
|
zero_based : bool or "auto", default="auto"
|
||
|
Whether column indices in f are zero-based (True) or one-based
|
||
|
(False). If column indices are one-based, they are transformed to
|
||
|
zero-based to match Python/NumPy conventions.
|
||
|
If set to "auto", a heuristic check is applied to determine this from
|
||
|
the file contents. Both kinds of files occur "in the wild", but they
|
||
|
are unfortunately not self-identifying. Using "auto" or True should
|
||
|
always be safe when no offset or length is passed.
|
||
|
If offset or length are passed, the "auto" mode falls back
|
||
|
to zero_based=True to avoid having the heuristic check yield
|
||
|
inconsistent results on different segments of the file.
|
||
|
|
||
|
query_id : bool, default=False
|
||
|
If True, will return the query_id array for each file.
|
||
|
|
||
|
offset : int, default=0
|
||
|
Ignore the offset first bytes by seeking forward, then
|
||
|
discarding the following bytes up until the next new line
|
||
|
character.
|
||
|
|
||
|
length : int, default=-1
|
||
|
If strictly positive, stop reading any new line of data once the
|
||
|
position in the file has reached the (offset + length) bytes threshold.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
[X1, y1, ..., Xn, yn]
|
||
|
where each (Xi, yi) pair is the result from load_svmlight_file(files[i]).
|
||
|
|
||
|
If query_id is set to True, this will return instead [X1, y1, q1,
|
||
|
..., Xn, yn, qn] where (Xi, yi, qi) is the result from
|
||
|
load_svmlight_file(files[i])
|
||
|
|
||
|
Notes
|
||
|
-----
|
||
|
When fitting a model to a matrix X_train and evaluating it against a
|
||
|
matrix X_test, it is essential that X_train and X_test have the same
|
||
|
number of features (X_train.shape[1] == X_test.shape[1]). This may not
|
||
|
be the case if you load the files individually with load_svmlight_file.
|
||
|
|
||
|
See Also
|
||
|
--------
|
||
|
load_svmlight_file
|
||
|
"""
|
||
|
if (offset != 0 or length > 0) and zero_based == "auto":
|
||
|
# disable heuristic search to avoid getting inconsistent results on
|
||
|
# different segments of the file
|
||
|
zero_based = True
|
||
|
|
||
|
if (offset != 0 or length > 0) and n_features is None:
|
||
|
raise ValueError(
|
||
|
"n_features is required when offset or length is specified.")
|
||
|
|
||
|
r = [_open_and_load(f, dtype, multilabel, bool(zero_based), bool(query_id),
|
||
|
offset=offset, length=length)
|
||
|
for f in files]
|
||
|
|
||
|
if (zero_based is False or
|
||
|
zero_based == "auto" and all(len(tmp[1]) and np.min(tmp[1]) > 0
|
||
|
for tmp in r)):
|
||
|
for _, indices, _, _, _ in r:
|
||
|
indices -= 1
|
||
|
|
||
|
n_f = max(ind[1].max() if len(ind[1]) else 0 for ind in r) + 1
|
||
|
|
||
|
if n_features is None:
|
||
|
n_features = n_f
|
||
|
elif n_features < n_f:
|
||
|
raise ValueError("n_features was set to {},"
|
||
|
" but input file contains {} features"
|
||
|
.format(n_features, n_f))
|
||
|
|
||
|
result = []
|
||
|
for data, indices, indptr, y, query_values in r:
|
||
|
shape = (indptr.shape[0] - 1, n_features)
|
||
|
X = sp.csr_matrix((data, indices, indptr), shape)
|
||
|
X.sort_indices()
|
||
|
result += X, y
|
||
|
if query_id:
|
||
|
result.append(query_values)
|
||
|
|
||
|
return result
|
||
|
|
||
|
|
||
|
def _dump_svmlight(X, y, f, multilabel, one_based, comment, query_id):
|
||
|
X_is_sp = int(hasattr(X, "tocsr"))
|
||
|
y_is_sp = int(hasattr(y, "tocsr"))
|
||
|
if X.dtype.kind == 'i':
|
||
|
value_pattern = "%d:%d"
|
||
|
else:
|
||
|
value_pattern = "%d:%.16g"
|
||
|
|
||
|
if y.dtype.kind == 'i':
|
||
|
label_pattern = "%d"
|
||
|
else:
|
||
|
label_pattern = "%.16g"
|
||
|
|
||
|
line_pattern = "%s"
|
||
|
if query_id is not None:
|
||
|
line_pattern += " qid:%d"
|
||
|
line_pattern += " %s\n"
|
||
|
|
||
|
if comment:
|
||
|
f.write(("# Generated by dump_svmlight_file from scikit-learn %s\n"
|
||
|
% __version__).encode())
|
||
|
f.write(("# Column indices are %s-based\n"
|
||
|
% ["zero", "one"][one_based]).encode())
|
||
|
|
||
|
f.write(b"#\n")
|
||
|
f.writelines(b"# %s\n" % line for line in comment.splitlines())
|
||
|
|
||
|
for i in range(X.shape[0]):
|
||
|
if X_is_sp:
|
||
|
span = slice(X.indptr[i], X.indptr[i + 1])
|
||
|
row = zip(X.indices[span], X.data[span])
|
||
|
else:
|
||
|
nz = X[i] != 0
|
||
|
row = zip(np.where(nz)[0], X[i, nz])
|
||
|
|
||
|
s = " ".join(value_pattern % (j + one_based, x) for j, x in row)
|
||
|
|
||
|
if multilabel:
|
||
|
if y_is_sp:
|
||
|
nz_labels = y[i].nonzero()[1]
|
||
|
else:
|
||
|
nz_labels = np.where(y[i] != 0)[0]
|
||
|
labels_str = ",".join(label_pattern % j for j in nz_labels)
|
||
|
else:
|
||
|
if y_is_sp:
|
||
|
labels_str = label_pattern % y.data[i]
|
||
|
else:
|
||
|
labels_str = label_pattern % y[i]
|
||
|
|
||
|
if query_id is not None:
|
||
|
feat = (labels_str, query_id[i], s)
|
||
|
else:
|
||
|
feat = (labels_str, s)
|
||
|
|
||
|
f.write((line_pattern % feat).encode('ascii'))
|
||
|
|
||
|
|
||
|
@_deprecate_positional_args
|
||
|
def dump_svmlight_file(X, y, f, *, zero_based=True, comment=None,
|
||
|
query_id=None,
|
||
|
multilabel=False):
|
||
|
"""Dump the dataset in svmlight / libsvm file format.
|
||
|
|
||
|
This format is a text-based format, with one sample per line. It does
|
||
|
not store zero valued features hence is suitable for sparse dataset.
|
||
|
|
||
|
The first element of each line can be used to store a target variable
|
||
|
to predict.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
||
|
Training vectors, where n_samples is the number of samples and
|
||
|
n_features is the number of features.
|
||
|
|
||
|
y : {array-like, sparse matrix}, shape = [n_samples (, n_labels)]
|
||
|
Target values. Class labels must be an
|
||
|
integer or float, or array-like objects of integer or float for
|
||
|
multilabel classifications.
|
||
|
|
||
|
f : string or file-like in binary mode
|
||
|
If string, specifies the path that will contain the data.
|
||
|
If file-like, data will be written to f. f should be opened in binary
|
||
|
mode.
|
||
|
|
||
|
zero_based : boolean, default=True
|
||
|
Whether column indices should be written zero-based (True) or one-based
|
||
|
(False).
|
||
|
|
||
|
comment : string, default=None
|
||
|
Comment to insert at the top of the file. This should be either a
|
||
|
Unicode string, which will be encoded as UTF-8, or an ASCII byte
|
||
|
string.
|
||
|
If a comment is given, then it will be preceded by one that identifies
|
||
|
the file as having been dumped by scikit-learn. Note that not all
|
||
|
tools grok comments in SVMlight files.
|
||
|
|
||
|
query_id : array-like of shape (n_samples,), default=None
|
||
|
Array containing pairwise preference constraints (qid in svmlight
|
||
|
format).
|
||
|
|
||
|
multilabel : boolean, default=False
|
||
|
Samples may have several labels each (see
|
||
|
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html)
|
||
|
|
||
|
.. versionadded:: 0.17
|
||
|
parameter *multilabel* to support multilabel datasets.
|
||
|
"""
|
||
|
if comment is not None:
|
||
|
# Convert comment string to list of lines in UTF-8.
|
||
|
# If a byte string is passed, then check whether it's ASCII;
|
||
|
# if a user wants to get fancy, they'll have to decode themselves.
|
||
|
# Avoid mention of str and unicode types for Python 3.x compat.
|
||
|
if isinstance(comment, bytes):
|
||
|
comment.decode("ascii") # just for the exception
|
||
|
else:
|
||
|
comment = comment.encode("utf-8")
|
||
|
if b"\0" in comment:
|
||
|
raise ValueError("comment string contains NUL byte")
|
||
|
|
||
|
yval = check_array(y, accept_sparse='csr', ensure_2d=False)
|
||
|
if sp.issparse(yval):
|
||
|
if yval.shape[1] != 1 and not multilabel:
|
||
|
raise ValueError("expected y of shape (n_samples, 1),"
|
||
|
" got %r" % (yval.shape,))
|
||
|
else:
|
||
|
if yval.ndim != 1 and not multilabel:
|
||
|
raise ValueError("expected y of shape (n_samples,), got %r"
|
||
|
% (yval.shape,))
|
||
|
|
||
|
Xval = check_array(X, accept_sparse='csr')
|
||
|
if Xval.shape[0] != yval.shape[0]:
|
||
|
raise ValueError(
|
||
|
"X.shape[0] and y.shape[0] should be the same, got"
|
||
|
" %r and %r instead." % (Xval.shape[0], yval.shape[0])
|
||
|
)
|
||
|
|
||
|
# We had some issues with CSR matrices with unsorted indices (e.g. #1501),
|
||
|
# so sort them here, but first make sure we don't modify the user's X.
|
||
|
# TODO We can do this cheaper; sorted_indices copies the whole matrix.
|
||
|
if yval is y and hasattr(yval, "sorted_indices"):
|
||
|
y = yval.sorted_indices()
|
||
|
else:
|
||
|
y = yval
|
||
|
if hasattr(y, "sort_indices"):
|
||
|
y.sort_indices()
|
||
|
|
||
|
if Xval is X and hasattr(Xval, "sorted_indices"):
|
||
|
X = Xval.sorted_indices()
|
||
|
else:
|
||
|
X = Xval
|
||
|
if hasattr(X, "sort_indices"):
|
||
|
X.sort_indices()
|
||
|
|
||
|
if query_id is not None:
|
||
|
query_id = np.asarray(query_id)
|
||
|
if query_id.shape[0] != y.shape[0]:
|
||
|
raise ValueError("expected query_id of shape (n_samples,), got %r"
|
||
|
% (query_id.shape,))
|
||
|
|
||
|
one_based = not zero_based
|
||
|
|
||
|
if hasattr(f, "write"):
|
||
|
_dump_svmlight(X, y, f, multilabel, one_based, comment, query_id)
|
||
|
else:
|
||
|
with open(f, "wb") as f:
|
||
|
_dump_svmlight(X, y, f, multilabel, one_based, comment, query_id)
|