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running make to fix Circle CI errors
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Sasha committed May 3, 2022
1 parent 027a847 commit ed75f87
Showing 1 changed file with 3 additions and 3 deletions.
6 changes: 3 additions & 3 deletions metrics/pearsonr/pearsonr.py
Original file line number Diff line number Diff line change
Expand Up @@ -64,11 +64,11 @@
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
Expand Down Expand Up @@ -103,4 +103,4 @@ def _compute(self, predictions, references, return_pvalue=False):
results = pearsonr(references, predictions)
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(references, predictions)[0])}
return {"pearsonr": float(pearsonr(references, predictions)[0])}

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Show benchmarks

PyArrow==5.0.0

Show updated benchmarks!

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) 0.010332 / 0.011353 (-0.001021) 0.004340 / 0.011008 (-0.006668) 0.031642 / 0.038508 (-0.006866) 0.036592 / 0.023109 (0.013483) 0.299295 / 0.275898 (0.023397) 0.324699 / 0.323480 (0.001219) 0.008566 / 0.007986 (0.000580) 0.005039 / 0.004328 (0.000711) 0.009297 / 0.004250 (0.005047) 0.044544 / 0.037052 (0.007492) 0.281537 / 0.258489 (0.023048) 0.321138 / 0.293841 (0.027297) 0.032242 / 0.128546 (-0.096304) 0.009957 / 0.075646 (-0.065689) 0.253970 / 0.419271 (-0.165302) 0.051954 / 0.043533 (0.008421) 0.284753 / 0.255139 (0.029614) 0.313255 / 0.283200 (0.030055) 0.106561 / 0.141683 (-0.035121) 1.743462 / 1.452155 (0.291307) 1.809697 / 1.492716 (0.316981)

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) 0.301673 / 0.018006 (0.283667) 0.560437 / 0.000490 (0.559947) 0.005505 / 0.000200 (0.005305) 0.000323 / 0.000054 (0.000268)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.030757 / 0.037411 (-0.006655) 0.106794 / 0.014526 (0.092268) 0.117668 / 0.176557 (-0.058889) 0.161089 / 0.737135 (-0.576047) 0.119756 / 0.296338 (-0.176582)

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) 0.407992 / 0.215209 (0.192783) 4.084853 / 2.077655 (2.007199) 1.725958 / 1.504120 (0.221838) 1.520212 / 1.541195 (-0.020982) 1.616909 / 1.468490 (0.148419) 0.439154 / 4.584777 (-4.145623) 4.513152 / 3.745712 (0.767440) 2.152842 / 5.269862 (-3.117019) 0.925235 / 4.565676 (-3.640442) 0.053376 / 0.424275 (-0.370899) 0.012372 / 0.007607 (0.004765) 0.513771 / 0.226044 (0.287727) 5.143179 / 2.268929 (2.874251) 2.185553 / 55.444624 (-53.259072) 1.824803 / 6.876477 (-5.051674) 1.954894 / 2.142072 (-0.187178) 0.566734 / 4.805227 (-4.238493) 0.124229 / 6.500664 (-6.376435) 0.063791 / 0.075469 (-0.011678)

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) 1.586121 / 1.841788 (-0.255667) 14.367101 / 8.074308 (6.292793) 26.601542 / 10.191392 (16.410150) 0.851030 / 0.680424 (0.170606) 0.509904 / 0.534201 (-0.024297) 0.486052 / 0.579283 (-0.093231) 0.491955 / 0.434364 (0.057591) 0.319711 / 0.540337 (-0.220626) 0.324322 / 1.386936 (-1.062614)
PyArrow==latest
Show updated benchmarks!

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) 0.008640 / 0.011353 (-0.002713) 0.004062 / 0.011008 (-0.006946) 0.029482 / 0.038508 (-0.009026) 0.034149 / 0.023109 (0.011040) 0.324033 / 0.275898 (0.048135) 0.332740 / 0.323480 (0.009260) 0.006376 / 0.007986 (-0.001609) 0.005682 / 0.004328 (0.001354) 0.007457 / 0.004250 (0.003207) 0.039660 / 0.037052 (0.002608) 0.302596 / 0.258489 (0.044107) 0.337107 / 0.293841 (0.043266) 0.031251 / 0.128546 (-0.097295) 0.009885 / 0.075646 (-0.065761) 0.251404 / 0.419271 (-0.167867) 0.050458 / 0.043533 (0.006925) 0.304993 / 0.255139 (0.049854) 0.327243 / 0.283200 (0.044043) 0.093810 / 0.141683 (-0.047873) 1.747471 / 1.452155 (0.295317) 1.807877 / 1.492716 (0.315160)

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) 0.348174 / 0.018006 (0.330167) 0.547061 / 0.000490 (0.546571) 0.005261 / 0.000200 (0.005061) 0.000119 / 0.000054 (0.000064)

Benchmark: benchmark_indices_mapping.json

metric select shard shuffle sort train_test_split
new / old (diff) 0.027037 / 0.037411 (-0.010375) 0.101862 / 0.014526 (0.087337) 0.113984 / 0.176557 (-0.062572) 0.160264 / 0.737135 (-0.576872) 0.114260 / 0.296338 (-0.182078)

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) 0.415990 / 0.215209 (0.200781) 4.176756 / 2.077655 (2.099101) 1.773122 / 1.504120 (0.269002) 1.568447 / 1.541195 (0.027252) 1.698804 / 1.468490 (0.230314) 0.440293 / 4.584777 (-4.144484) 4.609927 / 3.745712 (0.864215) 3.332725 / 5.269862 (-1.937137) 0.935902 / 4.565676 (-3.629775) 0.052825 / 0.424275 (-0.371450) 0.012019 / 0.007607 (0.004412) 0.520724 / 0.226044 (0.294680) 5.190817 / 2.268929 (2.921888) 2.210803 / 55.444624 (-53.233821) 1.865673 / 6.876477 (-5.010803) 2.029830 / 2.142072 (-0.112243) 0.551965 / 4.805227 (-4.253262) 0.121072 / 6.500664 (-6.379592) 0.061565 / 0.075469 (-0.013904)

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) 1.603469 / 1.841788 (-0.238319) 14.566057 / 8.074308 (6.491749) 26.432373 / 10.191392 (16.240981) 0.824357 / 0.680424 (0.143934) 0.512685 / 0.534201 (-0.021516) 0.496389 / 0.579283 (-0.082894) 0.498035 / 0.434364 (0.063671) 0.318700 / 0.540337 (-0.221638) 0.334533 / 1.386936 (-1.052403)

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