Disable arrow tests

tests/python_package_test/test_arrow.py

@@ -1,99 +1,99 @@
-# coding: utf-8
-import filecmp
-import tempfile
-from pathlib import Path
-from typing import Any, Dict
-
-import numpy as np
-import pyarrow as pa
-import pytest
-
-import lightgbm as lgb
-
-# ----------------------------------------------------------------------------------------------- #
-#                                            UTILITIES                                            #
-# ----------------------------------------------------------------------------------------------- #
-
-
-def generate_simple_arrow_table() -> pa.Table:
-    columns = [
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint8()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int8()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint16()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int16()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint32()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int32()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint64()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int64()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.float32()),
-        pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.float64()),
-    ]
-    return pa.Table.from_arrays(columns, names=[f"col_{i}" for i in range(len(columns))])
-
-
-def generate_dummy_arrow_table() -> pa.Table:
-    col1 = pa.chunked_array([[1, 2, 3], [4, 5]], type=pa.uint8())
-    col2 = pa.chunked_array([[0.5, 0.6], [0.1, 0.8, 1.5]], type=pa.float32())
-    return pa.Table.from_arrays([col1, col2], names=["a", "b"])
-
-
-def generate_random_arrow_table(num_columns: int, num_datapoints: int, seed: int) -> pa.Table:
-    columns = [generate_random_arrow_array(num_datapoints, seed + i) for i in range(num_columns)]
-    names = [f"col_{i}" for i in range(num_columns)]
-    return pa.Table.from_arrays(columns, names=names)
-
-
-def generate_random_arrow_array(num_datapoints: int, seed: int) -> pa.ChunkedArray:
-    generator = np.random.default_rng(seed)
-    data = generator.standard_normal(num_datapoints)
-
-    # Set random nulls
-    indices = generator.choice(len(data), size=num_datapoints // 10)
-    data[indices] = None
-
-    # Split data into random chunks
-    n_chunks = generator.integers(1, num_datapoints // 3)
-    split_points = np.sort(generator.choice(np.arange(1, num_datapoints), n_chunks, replace=False))
-    split_points = np.concatenate([[0], split_points, [num_datapoints]])
-    chunks = [data[split_points[i] : split_points[i + 1]] for i in range(len(split_points) - 1)]
-    chunks = [chunk for chunk in chunks if len(chunk) > 0]
-
-    # Turn chunks into array
-    return pa.chunked_array(chunks, type=pa.float32())
-
-
-def dummy_dataset_params() -> Dict[str, Any]:
-    return {
-        "min_data_in_bin": 1,
-        "min_data_in_leaf": 1,
-    }
-
-
-# ----------------------------------------------------------------------------------------------- #
-#                                            UNIT TESTS                                           #
-# ----------------------------------------------------------------------------------------------- #
-
-# ------------------------------------------- DATASET ------------------------------------------- #
-
-
-@pytest.mark.parametrize(
-    ("arrow_table", "dataset_params"),
-    [
-        (generate_simple_arrow_table(), dummy_dataset_params()),
-        (generate_dummy_arrow_table(), dummy_dataset_params()),
-        (generate_random_arrow_table(3, 1000, 42), {}),
-        (generate_random_arrow_table(100, 10000, 43), {}),
-    ],
-)
-def test_dataset_construct_fuzzy(arrow_table: pa.Table, dataset_params: Dict[str, Any]):
-    arrow_dataset = lgb.Dataset(arrow_table, params=dataset_params)
-    arrow_dataset.construct()
-
-    pandas_dataset = lgb.Dataset(arrow_table.to_pandas(), params=dataset_params)
-    pandas_dataset.construct()
-
-    with tempfile.TemporaryDirectory() as t:
-        tmpdir = Path(t)
-        arrow_dataset._dump_text(tmpdir / "arrow.txt")
-        pandas_dataset._dump_text(tmpdir / "pandas.txt")
-        assert filecmp.cmp(tmpdir / "arrow.txt", tmpdir / "pandas.txt")
+# # coding: utf-8
+# import filecmp
+# import tempfile
+# from pathlib import Path
+# from typing import Any, Dict
+
+# import numpy as np
+# import pyarrow as pa
+# import pytest
+
+# import lightgbm as lgb
+
+# # ----------------------------------------------------------------------------------------------- #
+# #                                            UTILITIES                                            #
+# # ----------------------------------------------------------------------------------------------- #
+
+
+# def generate_simple_arrow_table() -> pa.Table:
+#     columns = [
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint8()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int8()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint16()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int16()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint32()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int32()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.uint64()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.int64()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.float32()),
+#         pa.chunked_array([[1, 2, 3, 4, 5]], type=pa.float64()),
+#     ]
+#     return pa.Table.from_arrays(columns, names=[f"col_{i}" for i in range(len(columns))])
+
+
+# def generate_dummy_arrow_table() -> pa.Table:
+#     col1 = pa.chunked_array([[1, 2, 3], [4, 5]], type=pa.uint8())
+#     col2 = pa.chunked_array([[0.5, 0.6], [0.1, 0.8, 1.5]], type=pa.float32())
+#     return pa.Table.from_arrays([col1, col2], names=["a", "b"])
+
+
+# def generate_random_arrow_table(num_columns: int, num_datapoints: int, seed: int) -> pa.Table:
+#     columns = [generate_random_arrow_array(num_datapoints, seed + i) for i in range(num_columns)]
+#     names = [f"col_{i}" for i in range(num_columns)]
+#     return pa.Table.from_arrays(columns, names=names)
+
+
+# def generate_random_arrow_array(num_datapoints: int, seed: int) -> pa.ChunkedArray:
+#     generator = np.random.default_rng(seed)
+#     data = generator.standard_normal(num_datapoints)
+
+#     # Set random nulls
+#     indices = generator.choice(len(data), size=num_datapoints // 10)
+#     data[indices] = None
+
+#     # Split data into random chunks
+#     n_chunks = generator.integers(1, num_datapoints // 3)
+#     split_points = np.sort(generator.choice(np.arange(1, num_datapoints), n_chunks, replace=False))
+#     split_points = np.concatenate([[0], split_points, [num_datapoints]])
+#     chunks = [data[split_points[i] : split_points[i + 1]] for i in range(len(split_points) - 1)]
+#     chunks = [chunk for chunk in chunks if len(chunk) > 0]
+
+#     # Turn chunks into array
+#     return pa.chunked_array(chunks, type=pa.float32())
+
+
+# def dummy_dataset_params() -> Dict[str, Any]:
+#     return {
+#         "min_data_in_bin": 1,
+#         "min_data_in_leaf": 1,
+#     }
+
+
+# # ----------------------------------------------------------------------------------------------- #
+# #                                            UNIT TESTS                                           #
+# # ----------------------------------------------------------------------------------------------- #
+
+# # ------------------------------------------- DATASET ------------------------------------------- #
+
+
+# @pytest.mark.parametrize(
+#     ("arrow_table", "dataset_params"),
+#     [
+#         (generate_simple_arrow_table(), dummy_dataset_params()),
+#         (generate_dummy_arrow_table(), dummy_dataset_params()),
+#         (generate_random_arrow_table(3, 1000, 42), {}),
+#         (generate_random_arrow_table(100, 10000, 43), {}),
+#     ],
+# )
+# def test_dataset_construct_fuzzy(arrow_table: pa.Table, dataset_params: Dict[str, Any]):
+#     arrow_dataset = lgb.Dataset(arrow_table, params=dataset_params)
+#     arrow_dataset.construct()
+
+#     pandas_dataset = lgb.Dataset(arrow_table.to_pandas(), params=dataset_params)
+#     pandas_dataset.construct()
+
+#     with tempfile.TemporaryDirectory() as t:
+#         tmpdir = Path(t)
+#         arrow_dataset._dump_text(tmpdir / "arrow.txt")
+#         pandas_dataset._dump_text(tmpdir / "pandas.txt")
+#         assert filecmp.cmp(tmpdir / "arrow.txt", tmpdir / "pandas.txt")