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107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
import lance
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import numpy as np
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import pyarrow as pa
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import pytest
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from datasets import load_dataset
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@pytest.fixture
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def lance_dataset(tmp_path) -> str:
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data = pa.table(
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{
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"id": pa.array([1, 2, 3, 4]),
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"value": pa.array([10.0, 20.0, 30.0, 40.0]),
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"text": pa.array(["a", "b", "c", "d"]),
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"vector": pa.FixedSizeListArray.from_arrays(pa.array([0.1] * 16, pa.float32()), list_size=4),
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}
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)
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dataset_path = tmp_path / "test_dataset.lance"
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lance.write_dataset(data, dataset_path)
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return str(dataset_path)
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@pytest.fixture
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def lance_hf_dataset(tmp_path) -> str:
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data = pa.table(
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{
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"id": pa.array([1, 2, 3, 4]),
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"value": pa.array([10.0, 20.0, 30.0, 40.0]),
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"text": pa.array(["a", "b", "c", "d"]),
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"vector": pa.FixedSizeListArray.from_arrays(pa.array([0.1] * 16, pa.float32()), list_size=4),
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}
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)
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dataset_dir = tmp_path / "data" / "train.lance"
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dataset_dir.parent.mkdir(parents=True, exist_ok=True)
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lance.write_dataset(data, dataset_dir)
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lance.write_dataset(data[:2], tmp_path / "data" / "test.lance")
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with open(tmp_path / "README.md", "w") as f:
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f.write("""---
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size_categories:
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- 1M<n<10M
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source_datasets:
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- lance_test
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---
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# Test Lance Dataset\n\n
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# My Markdown is fancier\n
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""")
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return str(tmp_path)
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def test_load_lance_dataset(lance_dataset):
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dataset_dict = load_dataset(lance_dataset)
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assert "train" in dataset_dict.keys()
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dataset = dataset_dict["train"]
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assert "id" in dataset.column_names
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assert "value" in dataset.column_names
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assert "text" in dataset.column_names
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assert "vector" in dataset.column_names
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ids = dataset["id"]
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assert ids == [1, 2, 3, 4]
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@pytest.mark.parametrize("streaming", [False, True])
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def test_load_hf_dataset(lance_hf_dataset, streaming):
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dataset_dict = load_dataset(lance_hf_dataset, columns=["id", "text"], streaming=streaming)
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assert "train" in dataset_dict.keys()
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assert "test" in dataset_dict.keys()
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dataset = dataset_dict["train"]
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assert "id" in dataset.column_names
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assert "text" in dataset.column_names
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assert "value" not in dataset.column_names
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assert "vector" not in dataset.column_names
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ids = list(dataset["id"])
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assert ids == [1, 2, 3, 4]
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text = list(dataset["text"])
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assert text == ["a", "b", "c", "d"]
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assert "value" not in dataset.column_names
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def test_load_vectors(lance_hf_dataset):
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dataset_dict = load_dataset(lance_hf_dataset, columns=["vector"])
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assert "train" in dataset_dict.keys()
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dataset = dataset_dict["train"]
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assert "vector" in dataset.column_names
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vectors = dataset.data["vector"].combine_chunks().values.to_numpy(zero_copy_only=False)
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assert np.allclose(vectors, np.full(16, 0.1))
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@pytest.mark.parametrize("streaming", [False, True])
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def test_load_lance_streaming_modes(lance_hf_dataset, streaming):
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"""Test loading Lance dataset in both streaming and non-streaming modes."""
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from datasets import IterableDataset
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ds = load_dataset(lance_hf_dataset, split="train", streaming=streaming)
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if streaming:
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assert isinstance(ds, IterableDataset)
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items = list(ds)
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else:
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items = list(ds)
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assert len(items) == 4
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assert all("id" in item for item in items)
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