Inzynierka/Lib/site-packages/sklearn/datasets/tests/test_svmlight_format.py

from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import scipy.sparse as sp
import os
import shutil
from tempfile import NamedTemporaryFile

import pytest

from sklearn.utils.fixes import _open_binary, _path
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal, assert_allclose
from sklearn.utils._testing import fails_if_pypy

import sklearn
from sklearn.datasets import load_svmlight_file, load_svmlight_files, dump_svmlight_file


TEST_DATA_MODULE = "sklearn.datasets.tests.data"
datafile = "svmlight_classification.txt"
multifile = "svmlight_multilabel.txt"
invalidfile = "svmlight_invalid.txt"
invalidfile2 = "svmlight_invalid_order.txt"

pytestmark = fails_if_pypy


def _load_svmlight_local_test_file(filename, **kwargs):
    """
    Helper to load resource `filename` with `importlib.resources`
    """
    with _open_binary(TEST_DATA_MODULE, filename) as f:
        return load_svmlight_file(f, **kwargs)


def test_load_svmlight_file():
    X, y = _load_svmlight_local_test_file(datafile)

    # test X's shape
    assert X.indptr.shape[0] == 7
    assert X.shape[0] == 6
    assert X.shape[1] == 21
    assert y.shape[0] == 6

    # test X's non-zero values
    for i, j, val in (
        (0, 2, 2.5),
        (0, 10, -5.2),
        (0, 15, 1.5),
        (1, 5, 1.0),
        (1, 12, -3),
        (2, 20, 27),
    ):

        assert X[i, j] == val

    # tests X's zero values
    assert X[0, 3] == 0
    assert X[0, 5] == 0
    assert X[1, 8] == 0
    assert X[1, 16] == 0
    assert X[2, 18] == 0

    # test can change X's values
    X[0, 2] *= 2
    assert X[0, 2] == 5

    # test y
    assert_array_equal(y, [1, 2, 3, 4, 1, 2])


def test_load_svmlight_file_fd():
    # test loading from file descriptor

    # GH20081: testing equality between path-based and
    # fd-based load_svmlight_file
    with _path(TEST_DATA_MODULE, datafile) as data_path:
        data_path = str(data_path)
        X1, y1 = load_svmlight_file(data_path)

        fd = os.open(data_path, os.O_RDONLY)
        try:
            X2, y2 = load_svmlight_file(fd)
            assert_array_almost_equal(X1.data, X2.data)
            assert_array_almost_equal(y1, y2)
        finally:
            os.close(fd)


def test_load_svmlight_pathlib():
    # test loading from file descriptor
    with _path(TEST_DATA_MODULE, datafile) as data_path:
        X1, y1 = load_svmlight_file(str(data_path))
        X2, y2 = load_svmlight_file(data_path)

    assert_allclose(X1.data, X2.data)
    assert_allclose(y1, y2)


def test_load_svmlight_file_multilabel():
    X, y = _load_svmlight_local_test_file(multifile, multilabel=True)
    assert y == [(0, 1), (2,), (), (1, 2)]


def test_load_svmlight_files():
    with _path(TEST_DATA_MODULE, datafile) as data_path:
        X_train, y_train, X_test, y_test = load_svmlight_files(
            [str(data_path)] * 2, dtype=np.float32
        )
    assert_array_equal(X_train.toarray(), X_test.toarray())
    assert_array_almost_equal(y_train, y_test)
    assert X_train.dtype == np.float32
    assert X_test.dtype == np.float32

    with _path(TEST_DATA_MODULE, datafile) as data_path:
        X1, y1, X2, y2, X3, y3 = load_svmlight_files(
            [str(data_path)] * 3, dtype=np.float64
        )
    assert X1.dtype == X2.dtype
    assert X2.dtype == X3.dtype
    assert X3.dtype == np.float64


def test_load_svmlight_file_n_features():
    X, y = _load_svmlight_local_test_file(datafile, n_features=22)

    # test X'shape
    assert X.indptr.shape[0] == 7
    assert X.shape[0] == 6
    assert X.shape[1] == 22

    # test X's non-zero values
    for i, j, val in ((0, 2, 2.5), (0, 10, -5.2), (1, 5, 1.0), (1, 12, -3)):

        assert X[i, j] == val

    # 21 features in file
    with pytest.raises(ValueError):
        _load_svmlight_local_test_file(datafile, n_features=20)


def test_load_compressed():
    X, y = _load_svmlight_local_test_file(datafile)

    with NamedTemporaryFile(prefix="sklearn-test", suffix=".gz") as tmp:
        tmp.close()  # necessary under windows
        with _open_binary(TEST_DATA_MODULE, datafile) as f:
            with gzip.open(tmp.name, "wb") as fh_out:
                shutil.copyfileobj(f, fh_out)
        Xgz, ygz = load_svmlight_file(tmp.name)
        # because we "close" it manually and write to it,
        # we need to remove it manually.
        os.remove(tmp.name)
    assert_array_almost_equal(X.toarray(), Xgz.toarray())
    assert_array_almost_equal(y, ygz)

    with NamedTemporaryFile(prefix="sklearn-test", suffix=".bz2") as tmp:
        tmp.close()  # necessary under windows
        with _open_binary(TEST_DATA_MODULE, datafile) as f:
            with BZ2File(tmp.name, "wb") as fh_out:
                shutil.copyfileobj(f, fh_out)
        Xbz, ybz = load_svmlight_file(tmp.name)
        # because we "close" it manually and write to it,
        # we need to remove it manually.
        os.remove(tmp.name)
    assert_array_almost_equal(X.toarray(), Xbz.toarray())
    assert_array_almost_equal(y, ybz)


def test_load_invalid_file():
    with pytest.raises(ValueError):
        _load_svmlight_local_test_file(invalidfile)


def test_load_invalid_order_file():
    with pytest.raises(ValueError):
        _load_svmlight_local_test_file(invalidfile2)


def test_load_zero_based():
    f = BytesIO(b"-1 4:1.\n1 0:1\n")
    with pytest.raises(ValueError):
        load_svmlight_file(f, zero_based=False)


def test_load_zero_based_auto():
    data1 = b"-1 1:1 2:2 3:3\n"
    data2 = b"-1 0:0 1:1\n"

    f1 = BytesIO(data1)
    X, y = load_svmlight_file(f1, zero_based="auto")
    assert X.shape == (1, 3)

    f1 = BytesIO(data1)
    f2 = BytesIO(data2)
    X1, y1, X2, y2 = load_svmlight_files([f1, f2], zero_based="auto")
    assert X1.shape == (1, 4)
    assert X2.shape == (1, 4)


def test_load_with_qid():
    # load svmfile with qid attribute
    data = b"""
    3 qid:1 1:0.53 2:0.12
    2 qid:1 1:0.13 2:0.1
    7 qid:2 1:0.87 2:0.12"""
    X, y = load_svmlight_file(BytesIO(data), query_id=False)
    assert_array_equal(y, [3, 2, 7])
    assert_array_equal(X.toarray(), [[0.53, 0.12], [0.13, 0.1], [0.87, 0.12]])
    res1 = load_svmlight_files([BytesIO(data)], query_id=True)
    res2 = load_svmlight_file(BytesIO(data), query_id=True)
    for X, y, qid in (res1, res2):
        assert_array_equal(y, [3, 2, 7])
        assert_array_equal(qid, [1, 1, 2])
        assert_array_equal(X.toarray(), [[0.53, 0.12], [0.13, 0.1], [0.87, 0.12]])


@pytest.mark.skip(
    "testing the overflow of 32 bit sparse indexing requires a large amount of memory"
)
def test_load_large_qid():
    """
    load large libsvm / svmlight file with qid attribute. Tests 64-bit query ID
    """
    data = b"\n".join(
        (
            "3 qid:{0} 1:0.53 2:0.12\n2 qid:{0} 1:0.13 2:0.1".format(i).encode()
            for i in range(1, 40 * 1000 * 1000)
        )
    )
    X, y, qid = load_svmlight_file(BytesIO(data), query_id=True)
    assert_array_equal(y[-4:], [3, 2, 3, 2])
    assert_array_equal(np.unique(qid), np.arange(1, 40 * 1000 * 1000))


def test_load_invalid_file2():
    with pytest.raises(ValueError):
        with _path(TEST_DATA_MODULE, datafile) as data_path, _path(
            TEST_DATA_MODULE, invalidfile
        ) as invalid_path:
            load_svmlight_files([str(data_path), str(invalid_path), str(data_path)])


def test_not_a_filename():
    # in python 3 integers are valid file opening arguments (taken as unix
    # file descriptors)
    with pytest.raises(TypeError):
        load_svmlight_file(0.42)


def test_invalid_filename():
    with pytest.raises(IOError):
        load_svmlight_file("trou pic nic douille")


def test_dump():
    X_sparse, y_dense = _load_svmlight_local_test_file(datafile)
    X_dense = X_sparse.toarray()
    y_sparse = sp.csr_matrix(y_dense)

    # slicing a csr_matrix can unsort its .indices, so test that we sort
    # those correctly
    X_sliced = X_sparse[np.arange(X_sparse.shape[0])]
    y_sliced = y_sparse[np.arange(y_sparse.shape[0])]

    for X in (X_sparse, X_dense, X_sliced):
        for y in (y_sparse, y_dense, y_sliced):
            for zero_based in (True, False):
                for dtype in [np.float32, np.float64, np.int32, np.int64]:
                    f = BytesIO()
                    # we need to pass a comment to get the version info in;
                    # LibSVM doesn't grok comments so they're not put in by
                    # default anymore.

                    if sp.issparse(y) and y.shape[0] == 1:
                        # make sure y's shape is: (n_samples, n_labels)
                        # when it is sparse
                        y = y.T

                    # Note: with dtype=np.int32 we are performing unsafe casts,
                    # where X.astype(dtype) overflows. The result is
                    # then platform dependent and X_dense.astype(dtype) may be
                    # different from X_sparse.astype(dtype).asarray().
                    X_input = X.astype(dtype)

                    dump_svmlight_file(
                        X_input, y, f, comment="test", zero_based=zero_based
                    )
                    f.seek(0)

                    comment = f.readline()
                    comment = str(comment, "utf-8")

                    assert "scikit-learn %s" % sklearn.__version__ in comment

                    comment = f.readline()
                    comment = str(comment, "utf-8")

                    assert ["one", "zero"][zero_based] + "-based" in comment

                    X2, y2 = load_svmlight_file(f, dtype=dtype, zero_based=zero_based)
                    assert X2.dtype == dtype
                    assert_array_equal(X2.sorted_indices().indices, X2.indices)

                    X2_dense = X2.toarray()
                    if sp.issparse(X_input):
                        X_input_dense = X_input.toarray()
                    else:
                        X_input_dense = X_input

                    if dtype == np.float32:
                        # allow a rounding error at the last decimal place
                        assert_array_almost_equal(X_input_dense, X2_dense, 4)
                        assert_array_almost_equal(
                            y_dense.astype(dtype, copy=False), y2, 4
                        )
                    else:
                        # allow a rounding error at the last decimal place
                        assert_array_almost_equal(X_input_dense, X2_dense, 15)
                        assert_array_almost_equal(
                            y_dense.astype(dtype, copy=False), y2, 15
                        )


def test_dump_multilabel():
    X = [[1, 0, 3, 0, 5], [0, 0, 0, 0, 0], [0, 5, 0, 1, 0]]
    y_dense = [[0, 1, 0], [1, 0, 1], [1, 1, 0]]
    y_sparse = sp.csr_matrix(y_dense)
    for y in [y_dense, y_sparse]:
        f = BytesIO()
        dump_svmlight_file(X, y, f, multilabel=True)
        f.seek(0)
        # make sure it dumps multilabel correctly
        assert f.readline() == b"1 0:1 2:3 4:5\n"
        assert f.readline() == b"0,2 \n"
        assert f.readline() == b"0,1 1:5 3:1\n"


def test_dump_concise():
    one = 1
    two = 2.1
    three = 3.01
    exact = 1.000000000000001
    # loses the last decimal place
    almost = 1.0000000000000001
    X = [
        [one, two, three, exact, almost],
        [1e9, 2e18, 3e27, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0],
    ]
    y = [one, two, three, exact, almost]
    f = BytesIO()
    dump_svmlight_file(X, y, f)
    f.seek(0)
    # make sure it's using the most concise format possible
    assert f.readline() == b"1 0:1 1:2.1 2:3.01 3:1.000000000000001 4:1\n"
    assert f.readline() == b"2.1 0:1000000000 1:2e+18 2:3e+27\n"
    assert f.readline() == b"3.01 \n"
    assert f.readline() == b"1.000000000000001 \n"
    assert f.readline() == b"1 \n"
    f.seek(0)
    # make sure it's correct too :)
    X2, y2 = load_svmlight_file(f)
    assert_array_almost_equal(X, X2.toarray())
    assert_array_almost_equal(y, y2)


def test_dump_comment():
    X, y = _load_svmlight_local_test_file(datafile)
    X = X.toarray()

    f = BytesIO()
    ascii_comment = "This is a comment\nspanning multiple lines."
    dump_svmlight_file(X, y, f, comment=ascii_comment, zero_based=False)
    f.seek(0)

    X2, y2 = load_svmlight_file(f, zero_based=False)
    assert_array_almost_equal(X, X2.toarray())
    assert_array_almost_equal(y, y2)

    # XXX we have to update this to support Python 3.x
    utf8_comment = b"It is true that\n\xc2\xbd\xc2\xb2 = \xc2\xbc"
    f = BytesIO()
    with pytest.raises(UnicodeDecodeError):
        dump_svmlight_file(X, y, f, comment=utf8_comment)

    unicode_comment = utf8_comment.decode("utf-8")
    f = BytesIO()
    dump_svmlight_file(X, y, f, comment=unicode_comment, zero_based=False)
    f.seek(0)

    X2, y2 = load_svmlight_file(f, zero_based=False)
    assert_array_almost_equal(X, X2.toarray())
    assert_array_almost_equal(y, y2)

    f = BytesIO()
    with pytest.raises(ValueError):
        dump_svmlight_file(X, y, f, comment="I've got a \0.")


def test_dump_invalid():
    X, y = _load_svmlight_local_test_file(datafile)

    f = BytesIO()
    y2d = [y]
    with pytest.raises(ValueError):
        dump_svmlight_file(X, y2d, f)

    f = BytesIO()
    with pytest.raises(ValueError):
        dump_svmlight_file(X, y[:-1], f)


def test_dump_query_id():
    # test dumping a file with query_id
    X, y = _load_svmlight_local_test_file(datafile)
    X = X.toarray()
    query_id = np.arange(X.shape[0]) // 2
    f = BytesIO()
    dump_svmlight_file(X, y, f, query_id=query_id, zero_based=True)

    f.seek(0)
    X1, y1, query_id1 = load_svmlight_file(f, query_id=True, zero_based=True)
    assert_array_almost_equal(X, X1.toarray())
    assert_array_almost_equal(y, y1)
    assert_array_almost_equal(query_id, query_id1)


def test_load_with_long_qid():
    # load svmfile with longint qid attribute
    data = b"""
    1 qid:0 0:1 1:2 2:3
    0 qid:72048431380967004 0:1440446648 1:72048431380967004 2:236784985
    0 qid:-9223372036854775807 0:1440446648 1:72048431380967004 2:236784985
    3 qid:9223372036854775807  0:1440446648 1:72048431380967004 2:236784985"""
    X, y, qid = load_svmlight_file(BytesIO(data), query_id=True)

    true_X = [
        [1, 2, 3],
        [1440446648, 72048431380967004, 236784985],
        [1440446648, 72048431380967004, 236784985],
        [1440446648, 72048431380967004, 236784985],
    ]

    true_y = [1, 0, 0, 3]
    trueQID = [0, 72048431380967004, -9223372036854775807, 9223372036854775807]
    assert_array_equal(y, true_y)
    assert_array_equal(X.toarray(), true_X)
    assert_array_equal(qid, trueQID)

    f = BytesIO()
    dump_svmlight_file(X, y, f, query_id=qid, zero_based=True)
    f.seek(0)
    X, y, qid = load_svmlight_file(f, query_id=True, zero_based=True)
    assert_array_equal(y, true_y)
    assert_array_equal(X.toarray(), true_X)
    assert_array_equal(qid, trueQID)

    f.seek(0)
    X, y = load_svmlight_file(f, query_id=False, zero_based=True)
    assert_array_equal(y, true_y)
    assert_array_equal(X.toarray(), true_X)


def test_load_zeros():
    f = BytesIO()
    true_X = sp.csr_matrix(np.zeros(shape=(3, 4)))
    true_y = np.array([0, 1, 0])
    dump_svmlight_file(true_X, true_y, f)

    for zero_based in ["auto", True, False]:
        f.seek(0)
        X, y = load_svmlight_file(f, n_features=4, zero_based=zero_based)
        assert_array_almost_equal(y, true_y)
        assert_array_almost_equal(X.toarray(), true_X.toarray())


@pytest.mark.parametrize("sparsity", [0, 0.1, 0.5, 0.99, 1])
@pytest.mark.parametrize("n_samples", [13, 101])
@pytest.mark.parametrize("n_features", [2, 7, 41])
def test_load_with_offsets(sparsity, n_samples, n_features):
    rng = np.random.RandomState(0)
    X = rng.uniform(low=0.0, high=1.0, size=(n_samples, n_features))
    if sparsity:
        X[X < sparsity] = 0.0
    X = sp.csr_matrix(X)
    y = rng.randint(low=0, high=2, size=n_samples)

    f = BytesIO()
    dump_svmlight_file(X, y, f)
    f.seek(0)

    size = len(f.getvalue())

    # put some marks that are likely to happen anywhere in a row
    mark_0 = 0
    mark_1 = size // 3
    length_0 = mark_1 - mark_0
    mark_2 = 4 * size // 5
    length_1 = mark_2 - mark_1

    # load the original sparse matrix into 3 independent CSR matrices
    X_0, y_0 = load_svmlight_file(
        f, n_features=n_features, offset=mark_0, length=length_0
    )
    X_1, y_1 = load_svmlight_file(
        f, n_features=n_features, offset=mark_1, length=length_1
    )
    X_2, y_2 = load_svmlight_file(f, n_features=n_features, offset=mark_2)

    y_concat = np.concatenate([y_0, y_1, y_2])
    X_concat = sp.vstack([X_0, X_1, X_2])
    assert_array_almost_equal(y, y_concat)
    assert_array_almost_equal(X.toarray(), X_concat.toarray())


def test_load_offset_exhaustive_splits():
    rng = np.random.RandomState(0)
    X = np.array(
        [
            [0, 0, 0, 0, 0, 0],
            [1, 2, 3, 4, 0, 6],
            [1, 2, 3, 4, 0, 6],
            [0, 0, 0, 0, 0, 0],
            [1, 0, 3, 0, 0, 0],
            [0, 0, 0, 0, 0, 1],
            [1, 0, 0, 0, 0, 0],
        ]
    )
    X = sp.csr_matrix(X)
    n_samples, n_features = X.shape
    y = rng.randint(low=0, high=2, size=n_samples)
    query_id = np.arange(n_samples) // 2

    f = BytesIO()
    dump_svmlight_file(X, y, f, query_id=query_id)
    f.seek(0)

    size = len(f.getvalue())

    # load the same data in 2 parts with all the possible byte offsets to
    # locate the split so has to test for particular boundary cases
    for mark in range(size):
        f.seek(0)
        X_0, y_0, q_0 = load_svmlight_file(
            f, n_features=n_features, query_id=True, offset=0, length=mark
        )
        X_1, y_1, q_1 = load_svmlight_file(
            f, n_features=n_features, query_id=True, offset=mark, length=-1
        )
        q_concat = np.concatenate([q_0, q_1])
        y_concat = np.concatenate([y_0, y_1])
        X_concat = sp.vstack([X_0, X_1])
        assert_array_almost_equal(y, y_concat)
        assert_array_equal(query_id, q_concat)
        assert_array_almost_equal(X.toarray(), X_concat.toarray())


def test_load_with_offsets_error():
    with pytest.raises(ValueError, match="n_features is required"):
        _load_svmlight_local_test_file(datafile, offset=3, length=3)


def test_multilabel_y_explicit_zeros(tmp_path):
    """
    Ensure that if y contains explicit zeros (i.e. elements of y.data equal to
    0) then those explicit zeros are not encoded.
    """
    save_path = str(tmp_path / "svm_explicit_zero")
    rng = np.random.RandomState(42)
    X = rng.randn(3, 5).astype(np.float64)
    indptr = np.array([0, 2, 3, 6])
    indices = np.array([0, 2, 2, 0, 1, 2])
    # The first and last element are explicit zeros.
    data = np.array([0, 1, 1, 1, 1, 0])
    y = sp.csr_matrix((data, indices, indptr), shape=(3, 3))
    # y as a dense array would look like
    # [[0, 0, 1],
    #  [0, 0, 1],
    #  [1, 1, 0]]

    dump_svmlight_file(X, y, save_path, multilabel=True)

    _, y_load = load_svmlight_file(save_path, multilabel=True)
    y_true = [(2.0,), (2.0,), (0.0, 1.0)]
    assert y_load == y_true