Uploaded Test files

venv/Lib/site-packages/sklearn/utils/tests/test_seq_dataset.py

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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),