Set load_from_disk path type as PathLike
Opened by albertvillanova (closed)

Set `load_from_disk` path type as `PathLike`. This way it is aligned with `save_to_disk`.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7081). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005665 / 0.011353 (-0.005688) | 0.004130 / 0.011008 (-0.006878) | 0.064231 / 0.038508 (0.025723) | 0.030738 / 0.023109 (0.007628) | 0.251896 / 0.275898 (-0.024002) | 0.275182 / 0.323480 (-0.048298) | 0.003364 / 0.007986 (-0.004621) | 0.003569 / 0.004328 (-0.000759) | 0.049407 / 0.004250 (0.045157) | 0.048177 / 0.037052 (0.011124) | 0.253739 / 0.258489 (-0.004751) | 0.304087 / 0.293841 (0.010246) | 0.030457 / 0.128546 (-0.098089) | 0.012762 / 0.075646 (-0.062885) | 0.214312 / 0.419271 (-0.204959) | 0.036673 / 0.043533 (-0.006860) | 0.251838 / 0.255139 (-0.003301) | 0.274049 / 0.283200 (-0.009151) | 0.021133 / 0.141683 (-0.120550) | 1.143743 / 1.452155 (-0.308412) | 1.203681 / 1.492716 (-0.289036) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.094668 / 0.018006 (0.076662) | 0.300323 / 0.000490 (0.299833) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 0.000054 (-0.000010) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018565 / 0.037411 (-0.018846) | 0.066096 / 0.014526 (0.051570) | 0.075700 / 0.176557 (-0.100857) | 0.122185 / 0.737135 (-0.614950) | 0.077688 / 0.296338 (-0.218651) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.288804 / 0.215209 (0.073595) | 2.838336 / 2.077655 (0.760681) | 1.530575 / 1.504120 (0.026455) | 1.406716 / 1.541195 (-0.134478) | 1.438885 / 1.468490 (-0.029605) | 0.744809 / 4.584777 (-3.839968) | 2.447992 / 3.745712 (-1.297721) | 3.126261 / 5.269862 (-2.143601) | 1.999687 / 4.565676 (-2.565990) | 0.081536 / 0.424275 (-0.342739) | 0.005827 / 0.007607 (-0.001780) | 0.346367 / 0.226044 (0.120323) | 3.373268 / 2.268929 (1.104339) | 1.890293 / 55.444624 (-53.554332) | 1.590384 / 6.876477 (-5.286093) | 1.652101 / 2.142072 (-0.489971) | 0.805888 / 4.805227 (-3.999339) | 0.137687 / 6.500664 (-6.362977) | 0.044536 / 0.075469 (-0.030933) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.998393 / 1.841788 (-0.843395) | 12.392241 / 8.074308 (4.317933) | 10.055638 / 10.191392 (-0.135754) | 0.132347 / 0.680424 (-0.548077) | 0.014635 / 0.534201 (-0.519566) | 0.301939 / 0.579283 (-0.277344) | 0.266756 / 0.434364 (-0.167608) | 0.342730 / 0.540337 (-0.197608) | 0.435463 / 1.386936 (-0.951473) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006421 / 0.011353 (-0.004932) | 0.004494 / 0.011008 (-0.006514) | 0.051315 / 0.038508 (0.012806) | 0.035570 / 0.023109 (0.012460) | 0.271635 / 0.275898 (-0.004263) | 0.297082 / 0.323480 (-0.026398) | 0.004572 / 0.007986 (-0.003414) | 0.002886 / 0.004328 (-0.001443) | 0.049152 / 0.004250 (0.044902) | 0.043000 / 0.037052 (0.005948) | 0.281921 / 0.258489 (0.023432) | 0.321097 / 0.293841 (0.027256) | 0.033488 / 0.128546 (-0.095058) | 0.012835 / 0.075646 (-0.062811) | 0.061831 / 0.419271 (-0.357441) | 0.034674 / 0.043533 (-0.008858) | 0.272885 / 0.255139 (0.017746) | 0.292726 / 0.283200 (0.009527) | 0.019906 / 0.141683 (-0.121777) | 1.132234 / 1.452155 (-0.319920) | 1.155359 / 1.492716 (-0.337357) |

### Benchmark: benchmark_getitem\_100B.json

| metric | get_batch_of\_1024\_random_rows | get_batch_of\_1024\_rows | get_first_row | get_last_row |
|--------|---|---|---|---|
| new / old (diff) | 0.096943 / 0.018006 (0.078937) | 0.308980 / 0.000490 (0.308490) | 0.000225 / 0.000200 (0.000025) | 0.000047 / 0.000054 (-0.000008) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023551 / 0.037411 (-0.013861) | 0.081682 / 0.014526 (0.067156) | 0.090987 / 0.176557 (-0.085569) | 0.132542 / 0.737135 (-0.604593) | 0.092844 / 0.296338 (-0.203494) |

### Benchmark: benchmark_iterating.json

| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.304190 / 0.215209 (0.088981) | 2.958591 / 2.077655 (0.880936) | 1.610211 / 1.504120 (0.106091) | 1.488216 / 1.541195 (-0.052978) | 1.525429 / 1.468490 (0.056939) | 0.752811 / 4.584777 (-3.831966) | 0.967887 / 3.745712 (-2.777825) | 2.982760 / 5.269862 (-2.287102) | 1.996623 / 4.565676 (-2.569053) | 0.080783 / 0.424275 (-0.343492) | 0.005337 / 0.007607 (-0.002270) | 0.354996 / 0.226044 (0.128951) | 3.540788 / 2.268929 (1.271860) | 1.997445 / 55.444624 (-53.447179) | 1.682232 / 6.876477 (-5.194245) | 1.883198 / 2.142072 (-0.258875) | 0.814444 / 4.805227 (-3.990783) | 0.135798 / 6.500664 (-6.364867) | 0.041750 / 0.075469 (-0.033719) |

### Benchmark: benchmark_map_filter.json

| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |
|--------|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 1.048688 / 1.841788 (-0.793099) | 13.122809 / 8.074308 (5.048501) | 10.893354 / 10.191392 (0.701962) | 0.133710 / 0.680424 (-0.546713) | 0.016357 / 0.534201 (-0.517844) | 0.304364 / 0.579283 (-0.274919) | 0.126457 / 0.434364 (-0.307907) | 0.345747 / 0.540337 (-0.194591) | 0.441620 / 1.386936 (-0.945316) |

</details>
</details>

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