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# Author: Tom Dupre la Tour
# Joan Massich <mailsik@gmail.com>
#
# License: BSD 3 clause
from itertools import product
import numpy as np
import pytest
from numpy.testing import assert_array_equal
from sklearn.datasets import load_iris
from sklearn.utils._seq_dataset import (
ArrayDataset32,
ArrayDataset64,
CSRDataset32,
CSRDataset64,
)
from sklearn.utils._testing import assert_allclose
from sklearn.utils.fixes import CSR_CONTAINERS
iris = load_iris()
X64 = iris.data.astype(np.float64)
y64 = iris.target.astype(np.float64)
sample_weight64 = np.arange(y64.size, dtype=np.float64)
X32 = iris.data.astype(np.float32)
y32 = iris.target.astype(np.float32)
sample_weight32 = np.arange(y32.size, dtype=np.float32)
floating = [np.float32, np.float64]
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(float_dtype):
if float_dtype == np.float32:
return ArrayDataset32(X32, y32, sample_weight32, seed=42)
return ArrayDataset64(X64, y64, sample_weight64, seed=42)
def _make_sparse_dataset(csr_container, float_dtype):
if float_dtype == np.float32:
X, y, sample_weight, csr_dataset = X32, y32, sample_weight32, CSRDataset32
else:
X, y, sample_weight, csr_dataset = X64, y64, sample_weight64, CSRDataset64
X = csr_container(X)
return csr_dataset(X.data, X.indptr, X.indices, y, sample_weight, seed=42)
def _make_dense_datasets():
return [_make_dense_dataset(float_dtype) for float_dtype in floating]
def _make_sparse_datasets():
return [
_make_sparse_dataset(csr_container, float_dtype)
for csr_container, float_dtype in product(CSR_CONTAINERS, floating)
]
def _make_fused_types_datasets():
all_datasets = _make_dense_datasets() + _make_sparse_datasets()
# group dataset by array types to get a tuple (float32, float64)
return (all_datasets[idx : idx + 2] for idx in range(0, len(all_datasets), 2))
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
@pytest.mark.parametrize("dataset", _make_dense_datasets() + _make_sparse_datasets())
def test_seq_dataset_basic_iteration(dataset, csr_container):
NUMBER_OF_RUNS = 5
X_csr64 = csr_container(X64)
for _ in range(NUMBER_OF_RUNS):
# next sample
xi_, yi, swi, idx = dataset._next_py()
xi = csr_container(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 = csr_container(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(
"dense_dataset,sparse_dataset",
[
(
_make_dense_dataset(float_dtype),
_make_sparse_dataset(csr_container, float_dtype),
)
for float_dtype, csr_container in product(floating, CSR_CONTAINERS)
],
)
def test_seq_dataset_shuffle(dense_dataset, 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("dataset_32,dataset_64", _make_fused_types_datasets())
def test_fused_types_consistency(dataset_32, 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),
for csr_container in CSR_CONTAINERS:
X_csr32 = csr_container(X32)
X_csr64 = csr_container(X64)
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,
),
|
