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