| import timeit | |
| import numpy as np | |
| import datasets | |
| from datasets.arrow_writer import ArrowWriter | |
| from datasets.features.features import _ArrayXD | |
| def get_duration(func): | |
| def wrapper(*args, **kwargs): | |
| starttime = timeit.default_timer() | |
| _ = func(*args, **kwargs) | |
| delta = timeit.default_timer() - starttime | |
| return delta | |
| wrapper.__name__ = func.__name__ | |
| return wrapper | |
| def generate_examples(features: dict, num_examples=100, seq_shapes=None): | |
| dummy_data = [] | |
| seq_shapes = seq_shapes or {} | |
| for i in range(num_examples): | |
| example = {} | |
| for col_id, (k, v) in enumerate(features.items()): | |
| if isinstance(v, _ArrayXD): | |
| data = np.random.rand(*v.shape).astype(v.dtype) | |
| elif isinstance(v, datasets.Value): | |
| if v.dtype == "string": | |
| data = "The small grey turtle was surprisingly fast when challenged." | |
| else: | |
| data = np.random.randint(10, size=1).astype(v.dtype).item() | |
| elif isinstance(v, datasets.Sequence): | |
| while isinstance(v, datasets.Sequence): | |
| v = v.feature | |
| shape = seq_shapes[k] | |
| data = np.random.rand(*shape).astype(v.dtype) | |
| example[k] = data | |
| dummy_data.append((i, example)) | |
| return dummy_data | |
| def generate_example_dataset(dataset_path, features, num_examples=100, seq_shapes=None): | |
| dummy_data = generate_examples(features, num_examples=num_examples, seq_shapes=seq_shapes) | |
| with ArrowWriter(features=features, path=dataset_path) as writer: | |
| for key, record in dummy_data: | |
| example = features.encode_example(record) | |
| writer.write(example) | |
| num_final_examples, num_bytes = writer.finalize() | |
| if not num_final_examples == num_examples: | |
| raise ValueError( | |
| f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." | |
| ) | |
| dataset = datasets.Dataset.from_file(filename=dataset_path, info=datasets.DatasetInfo(features=features)) | |
| return dataset | |