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# Author: Tom Dupre la Tour
# Joan Massich <mailsik@gmail.com>
#
# License: BSD 3 clause
import numpy as np
import pytest
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils._seq_dataset import (
ArrayDataset32,
ArrayDataset64,
CSRDataset32,
CSRDataset64,
)
from sklearn.datasets import load_iris
from sklearn.utils._testing import assert_allclose
iris = load_iris()
X64 = iris.data.astype(np.float64)
y64 = iris.target.astype(np.float64)
X_csr64 = sp.csr_matrix(X64)
sample_weight64 = np.arange(y64.size, dtype=np.float64)
X32 = iris.data.astype(np.float32)
y32 = iris.target.astype(np.float32)
X_csr32 = sp.csr_matrix(X32)
sample_weight32 = np.arange(y32.size, dtype=np.float32)
def assert_csr_equal_values(current, expected):
current.eliminate_zeros()
expected.eliminate_zeros()
expected = expected.astype(current.dtype)
assert current.shape[0] == expected.shape[0]
assert current.shape[1] == expected.shape[1]
assert_array_equal(current.data, expected.data)
assert_array_equal(current.indices, expected.indices)
assert_array_equal(current.indptr, expected.indptr)
def make_dense_dataset_32():
return ArrayDataset32(X32, y32, sample_weight32, seed=42)
def make_dense_dataset_64():
return ArrayDataset64(X64, y64, sample_weight64, seed=42)
def make_sparse_dataset_32():
return CSRDataset32(
X_csr32.data, X_csr32.indptr, X_csr32.indices, y32, sample_weight32, seed=42
)
def make_sparse_dataset_64():
return CSRDataset64(
X_csr64.data, X_csr64.indptr, X_csr64.indices, y64, sample_weight64, seed=42
)
@pytest.mark.parametrize(
"dataset_constructor",
[
make_dense_dataset_32,
make_dense_dataset_64,
make_sparse_dataset_32,
make_sparse_dataset_64,
],
)
def test_seq_dataset_basic_iteration(dataset_constructor):
NUMBER_OF_RUNS = 5
dataset = dataset_constructor()
for _ in range(NUMBER_OF_RUNS):
# next sample
xi_, yi, swi, idx = dataset._next_py()
xi = sp.csr_matrix((xi_), shape=(1, X64.shape[1]))
assert_csr_equal_values(xi, X_csr64[idx])
assert yi == y64[idx]
assert swi == sample_weight64[idx]
# random sample
xi_, yi, swi, idx = dataset._random_py()
xi = sp.csr_matrix((xi_), shape=(1, X64.shape[1]))
assert_csr_equal_values(xi, X_csr64[idx])
assert yi == y64[idx]
assert swi == sample_weight64[idx]
@pytest.mark.parametrize(
"make_dense_dataset,make_sparse_dataset",
[
(make_dense_dataset_32, make_sparse_dataset_32),
(make_dense_dataset_64, make_sparse_dataset_64),
],
)
def test_seq_dataset_shuffle(make_dense_dataset, make_sparse_dataset):
dense_dataset, sparse_dataset = make_dense_dataset(), make_sparse_dataset()
# not shuffled
for i in range(5):
_, _, _, idx1 = dense_dataset._next_py()
_, _, _, idx2 = sparse_dataset._next_py()
assert idx1 == i
assert idx2 == i
for i in [132, 50, 9, 18, 58]:
_, _, _, idx1 = dense_dataset._random_py()
_, _, _, idx2 = sparse_dataset._random_py()
assert idx1 == i
assert idx2 == i
seed = 77
dense_dataset._shuffle_py(seed)
sparse_dataset._shuffle_py(seed)
idx_next = [63, 91, 148, 87, 29]
idx_shuffle = [137, 125, 56, 121, 127]
for i, j in zip(idx_next, idx_shuffle):
_, _, _, idx1 = dense_dataset._next_py()
_, _, _, idx2 = sparse_dataset._next_py()
assert idx1 == i
assert idx2 == i
_, _, _, idx1 = dense_dataset._random_py()
_, _, _, idx2 = sparse_dataset._random_py()
assert idx1 == j
assert idx2 == j
@pytest.mark.parametrize(
"make_dataset_32,make_dataset_64",
[
(make_dense_dataset_32, make_dense_dataset_64),
(make_sparse_dataset_32, make_sparse_dataset_64),
],
)
def test_fused_types_consistency(make_dataset_32, make_dataset_64):
dataset_32, dataset_64 = make_dataset_32(), make_dataset_64()
NUMBER_OF_RUNS = 5
for _ in range(NUMBER_OF_RUNS):
# next sample
(xi_data32, _, _), yi32, _, _ = dataset_32._next_py()
(xi_data64, _, _), yi64, _, _ = dataset_64._next_py()
assert xi_data32.dtype == np.float32
assert xi_data64.dtype == np.float64
assert_allclose(xi_data64, xi_data32, rtol=1e-5)
assert_allclose(yi64, yi32, rtol=1e-5)
def test_buffer_dtype_mismatch_error():
with pytest.raises(ValueError, match="Buffer dtype mismatch"):
ArrayDataset64(X32, y32, sample_weight32, seed=42),
with pytest.raises(ValueError, match="Buffer dtype mismatch"):
ArrayDataset32(X64, y64, sample_weight64, seed=42),
with pytest.raises(ValueError, match="Buffer dtype mismatch"):
CSRDataset64(
X_csr32.data, X_csr32.indptr, X_csr32.indices, y32, sample_weight32, seed=42
),
with pytest.raises(ValueError, match="Buffer dtype mismatch"):
CSRDataset32(
X_csr64.data, X_csr64.indptr, X_csr64.indices, y64, sample_weight64, seed=42
),
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