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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 22 new columns ({'path_loader_kwargs', 'version', 'rollout_fn', 'qf_kwargs', 'snapshot_gap', 'snapshot_mode', 'alg_class', 'policy_kwargs', 'algorithm_kwargs', 'IQN', 'trainer_cls', 'algorithm', 'replay_buffer_size', 'trainer_kwargs', 'env_id', 'pipeline', 'seed', 'replay_buffer_class', 'qf_class', 'policy_class', 'd4rl', 'normalize_env'}) and 9 missing columns ({'hopper-medium-expert-v2', 'hopper-medium-v2', 'halfcheetah-medium-v2', 'halfcheetah-medium-replay-v2', 'walker2d-medium-expert-v2', 'halfcheetah-medium-expert-v2', 'hopper-medium-replay-v2', 'walker2d-medium-replay-v2', 'walker2d-medium-v2'}).

This happened while the json dataset builder was generating data using

hf://datasets/ezipe/cfpi/mg-12-no-normalize/antmaze-large-diverse-v0/0/variant.json (at revision aa0e3614ecef0eaa16cd220df9459d5344bb3064)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              IQN: bool
              alg_class: struct<$class: string>
                child 0, $class: string
              algorithm: string
              algorithm_kwargs: struct<batch_size: int64, max_path_length: int64, num_epochs: int64, num_eval_steps_per_epoch: int64, num_trains_per_train_loop: int64, start_epoch: int64>
                child 0, batch_size: int64
                child 1, max_path_length: int64
                child 2, num_epochs: int64
                child 3, num_eval_steps_per_epoch: int64
                child 4, num_trains_per_train_loop: int64
                child 5, start_epoch: int64
              d4rl: bool
              env_id: string
              normalize_env: bool
              path_loader_kwargs: struct<>
              pipeline: struct<$class: string>
                child 0, $class: string
              policy_class: struct<$class: string>
                child 0, $class: string
              policy_kwargs: struct<hidden_sizes: list<item: int64>, num_gaussians: int64>
                child 0, hidden_sizes: list<item: int64>
                    child 0, item: int64
                child 1, num_gaussians: int64
              qf_class: struct<$class: string>
                child 0, $class: string
              qf_kwargs: struct<hidden_sizes: list<item: int64>>
                child 0, hidden_sizes: list<item: int64>
                    child 0, item: int64
              replay_buffer_class: struct<$class: string>
                child 0, $class: string
              replay_buffer_size: int64
              rollout_fn: struct<$function: string>
                child 0, $function: string
              seed: int64
              snapshot_gap: int64
              snapshot_mode: string
              trainer_cls: struct<$class: string>
                child 0, $class: string
              trainer_kwargs: struct<discount: double, policy_lr: double, qf_lr: double, reward_scale: int64, soft_target_tau: double, target_update_period: int64>
                child 0, discount: double
                child 1, policy_lr: double
                child 2, qf_lr: double
                child 3, reward_scale: int64
                child 4, soft_target_tau: double
                child 5, target_update_period: int64
              version: string
              to
              {'halfcheetah-medium-expert-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'walker2d-medium-expert-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'hopper-medium-expert-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'halfcheetah-medium-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'walker2d-medium-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'hopper-medium-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'halfcheetah-medium-replay-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'walker2d-medium-replay-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'hopper-medium-replay-v2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1316, in compute_config_parquet_and_info_response
                  parquet_operations, partial = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 22 new columns ({'path_loader_kwargs', 'version', 'rollout_fn', 'qf_kwargs', 'snapshot_gap', 'snapshot_mode', 'alg_class', 'policy_kwargs', 'algorithm_kwargs', 'IQN', 'trainer_cls', 'algorithm', 'replay_buffer_size', 'trainer_kwargs', 'env_id', 'pipeline', 'seed', 'replay_buffer_class', 'qf_class', 'policy_class', 'd4rl', 'normalize_env'}) and 9 missing columns ({'hopper-medium-expert-v2', 'hopper-medium-v2', 'halfcheetah-medium-v2', 'halfcheetah-medium-replay-v2', 'walker2d-medium-expert-v2', 'halfcheetah-medium-expert-v2', 'hopper-medium-replay-v2', 'walker2d-medium-replay-v2', 'walker2d-medium-v2'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/ezipe/cfpi/mg-12-no-normalize/antmaze-large-diverse-v0/0/variant.json (at revision aa0e3614ecef0eaa16cd220df9459d5344bb3064)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

halfcheetah-medium-expert-v2
sequence
walker2d-medium-expert-v2
sequence
hopper-medium-expert-v2
sequence
halfcheetah-medium-v2
sequence
walker2d-medium-v2
sequence
hopper-medium-v2
sequence
halfcheetah-medium-replay-v2
sequence
walker2d-medium-replay-v2
sequence
hopper-medium-replay-v2
sequence
IQN
bool
alg_class
dict
algorithm
string
algorithm_kwargs
dict
d4rl
bool
env_id
string
normalize_env
bool
path_loader_kwargs
dict
pipeline
dict
policy_class
dict
policy_kwargs
dict
qf_class
dict
qf_kwargs
dict
replay_buffer_class
dict
replay_buffer_size
int64
rollout_fn
dict
seed
int64
snapshot_gap
int64
snapshot_mode
string
trainer_cls
dict
trainer_kwargs
dict
version
string
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[ 110.02591474429295, 112.61279352093601, 111.95078194622808, 113.10647582158053, 112.08290549621707, 113.59268597575524, 113.60908406597778, 109.82460119665856, 112.52619479873307, 112.99173611815195, 112.93959941841901, 113.82539724590171, 112.84894699761925, 113.68314294969177, 112.1325...
[ 51.52076578806856, 20.349991343084962, 55.917268110735094, 30.58406154443876, 31.84519804243542, 40.18721143677254, 45.87390095057244, 43.264668544224726, 27.0952065264286, 35.07375033023292, 31.935560669066597, 77.93819991419562, 57.70177708908314, 18.608872653524347, 39.44574287020114,...
[ 52.73918568029165, 54.76397534285906, 55.32698155418605, 53.79723908526993, 55.74595801500457, 54.73681133887749, 54.93035529868495, 54.179151362651304, 53.32338967698536, 52.53207661926091, 54.9384607214588, 54.610068189171926, 54.449027350413935, 53.72505532357277, 54.56896307100322, ...
[ 97.1181108707864, 91.39554857235616, 95.28926988502633, 94.04439585568039, 49.31952373815005, 20.17365646969431, 58.576570032187426, 94.76958827277984, 91.57612504274516, 89.47439937693473, 97.38462578703074, 93.80439613727174, 93.04402375842388, 93.12529964946599, 58.36650331340206, 9...
[ 101.57765567656187, 102.55663016135262, 50.47411315919352, 85.91313188528757, 100.52202735331517, 92.77472598589253, 101.764709477966, 102.02677565726668, 101.07989333925505, 101.78847057754726, 59.717109943283575, 83.92471409162826, 94.63337680297929, 101.97321311092897, 101.95677790481...
[ 41.58363691409069, 42.452111752244456, 44.30648229464782, 45.09812649107605, 45.83731631469691, 44.335065736797965, 45.685514109685904, 45.48345593325222, 45.933116872374946, 45.951757087358494, 34.43497960503411, 45.45656846399581, 43.96112763836391, 45.868584537241006, 45.0135507522375...
[ 97.01160279118756, 6.346051118881994, 94.72852616247904, 92.40206020675633, 91.34838298022976, 91.3187571579656, 91.01242809555777, 94.76076702626743, 17.74647356030324, 98.64880358282436, 97.61486300932944, 91.80419605146419, 93.74647674227788, 5.6221617128952825, 97.27354818203162, 3...
[ 104.63853291328537, 101.71837147758534, 101.33428948567014, 102.11010257340564, 102.21209317211508, 102.26385694290536, 101.55397844453786, 32.4823665791399, 101.7656770593256, 102.17267131149394, 61.43471468987885, 33.16419355503395, 101.75894998255899, 71.08199150203004, 29.56881496110...
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[ 24.554648539569808, 24.787293593595393, 29.761460541945052, 101.5805924557756, 96.31020332540082, 98.23661825669264, 99.21798198212272, 99.935638793933, 99.63081178489753, 100.7628946535576, 99.75622959810747, 79.58238789775845, 99.55522178760106, 86.04548445740437, 98.43794429085699, ...
[ 111.69078295689039, 112.28141427327614, 111.6262029560881, 111.80209008298452, 112.06572857479358, 112.48391901235154, 111.87398172340066, 112.2740368221936, 111.50499917953994, 112.48301580986595, 112.20866049532015, 112.15974397051909, 112.01635569690396, 111.90083246292532, 112.275772...
[ 100.88416182911526, 113.99469186574802, 57.16248484756699, 65.38506812042294, 35.752767326769266, 49.087756658909996, 51.56225286970644, 53.26689190251744, 56.406154658847726, 70.99145737145321, 38.94213254959256, 49.57648797503945, 65.79094803485897, 57.933239643576115, 55.8830048585425...
[ 52.18253586570691, 52.61960390985445, 53.04146481131548, 53.80206028135617, 53.27215686611355, 52.75245161352146, 53.79994649064367, 54.04771237712228, 52.82635491733123, 53.38776869574268, 52.43849569240802, 53.116684977888525, 53.948071847183, 53.25232758985224, 53.343470193845775, 5...
[ 93.52143776868141, 94.76283566089087, 93.95660011110292, 92.56920339039569, 92.40835043349026, 91.93751951818938, 93.28170107630923, 95.22760005221905, 88.70283437580092, 93.38785031419033, 94.08915194891703, 90.81348869860378, 92.4760290622329, 75.92125649713014, 89.81703647655355, 92...
[ 59.10728927032537, 102.93655630104466, 102.81418598289281, 102.7103123306953, 102.63451315995, 52.898546330454806, 75.8117376439245, 105.08244172831802, 103.102861522507, 68.96723501052085, 96.83133048750636, 58.56350853253751, 68.81405589283887, 54.02411149901315, 69.33728613784581, 6...
[ 44.10938239771767, 44.250365275624745, 44.766226927820114, 43.725143125123424, 44.18607015427576, 44.66063316791924, 46.356657877920895, 45.078880140554176, 45.24080005588879, 45.99460877629535, 44.048980249348865, 44.05741554567329, 44.64129067022182, 43.5794157522514, 44.22198901792998...
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[ 100.68927397410188, 102.37456942352765, 101.32207265157899, 100.88544508719525, 100.52812411491017, 100.69503280102954, 100.86831622329183, 101.57646485711224, 100.75680613260889, 100.60489753894298, 100.65332819830527, 100.67978450650521, 100.79588638183313, 101.78984530087662, 100.0425...
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[87.30306269296739,83.90261457453286,91.48420279931702,39.2658890207759,68.86289493133997,53.9299889(...TRUNCATED)
[112.17411126603854,111.34176461748058,111.2587001072742,111.36333743927548,111.48705293767726,111.7(...TRUNCATED)
[30.048938275912707,53.175952471155355,32.20424345261726,48.496383694682564,31.67357506996693,50.857(...TRUNCATED)
[52.276845947115355,52.975048026850814,52.895708347326476,53.67493465401918,53.85489064796719,53.990(...TRUNCATED)
[39.98948096723127,90.61949911040583,92.48141291957036,89.95622971886937,92.12456668613244,94.361120(...TRUNCATED)
[58.31284684045912,103.06270733947129,97.11260300878449,102.41251929601982,77.26483495577384,85.5076(...TRUNCATED)
[43.37495474452912,40.88942347180704,42.958169736265276,43.51197226433958,41.65088424464633,46.13404(...TRUNCATED)
[45.903140940159496,65.42085028462024,97.67104310921818,62.77635189221667,93.25430134794186,11.27543(...TRUNCATED)
[102.53208195935086,101.57468595209582,102.60641562600863,101.49279996832313,101.95494183066039,102.(...TRUNCATED)
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[92.59230539206611,98.66960701242188,21.31549616869439,23.149664629842917,92.83809102279403,51.49905(...TRUNCATED)
[109.85714643825753,109.41736133352381,110.6115533695409,110.90682577667783,110.39542385274291,110.6(...TRUNCATED)
[53.65253577454692,31.864293785814095,46.78213218481977,54.39869529809776,100.89191197840164,54.4901(...TRUNCATED)
[54.47976113824349,54.48048696668665,54.61948342622234,55.30659876443453,54.98377470810855,54.231915(...TRUNCATED)
[93.08800622285015,93.3750761294721,91.42471826225194,91.50390857819373,94.94606736961921,9.57770698(...TRUNCATED)
[51.53304579099745,102.61770023937693,95.39730718341477,69.88321330325469,86.55574750330642,101.0972(...TRUNCATED)
[44.05061253685321,41.54296516522307,45.73120385017107,41.82201603402944,44.481127514800455,44.64455(...TRUNCATED)
[95.16458286729592,93.01870696465849,4.321601197663494,7.7303201510475485,91.99808695280815,81.74950(...TRUNCATED)
[100.04672008495044,100.8703492329317,100.76625892754436,101.39919817898515,101.06976571914346,101.7(...TRUNCATED)
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true
{ "$class": "rlkit.torch.torch_rl_algorithm.OfflineTorchBatchRLAlgorithm" }
mg-behavior-cloning
{"batch_size":256,"max_path_length":1000,"num_epochs":0,"num_eval_steps_per_epoch":1000,"num_trains_(...TRUNCATED)
true
antmaze-large-diverse-v0
false
{}
{"$class":"rlkit.launchers.pipeline.<Pipeline offline_bc_pipeline>:\nbc_sanity,\noffline_init,\ncrea(...TRUNCATED)
{ "$class": "rlkit.policies.gaussian_policy.TanhGaussianMixturePolicy" }
{ "hidden_sizes": [ 256, 256, 256 ], "num_gaussians": 12 }
{ "$class": "rlkit.torch.networks.mlp.ConcatMlp" }
{ "hidden_sizes": [ 1024, 1024 ] }
{ "$class": "rlkit.data_management.env_replay_buffer.EnvReplayBuffer" }
2,000,000
{ "$function": "rlkit.samplers.rollout_functions.rollout" }
0
100
gap_and_last
{ "$class": "rlkit.torch.algorithms.bc.BCTrainer" }
{"discount":0.99,"policy_lr":0.0001,"qf_lr":0.0001,"reward_scale":1,"soft_target_tau":0.005,"target_(...TRUNCATED)
12-gaussian-3-layers-all-data-iclr-rebuttal
[90.53264559107052,93.23387173395568,91.8853591231252,47.41039328526552,90.80426464034068,89.6936558(...TRUNCATED)
[110.02591474429295,112.61279352093601,111.95078194622808,113.10647582158053,112.08290549621707,113.(...TRUNCATED)
[51.52076578806856,20.349991343084962,55.917268110735094,30.58406154443876,31.84519804243542,40.1872(...TRUNCATED)
[52.73918568029165,54.76397534285906,55.32698155418605,53.79723908526993,55.74595801500457,54.736811(...TRUNCATED)
[97.1181108707864,91.39554857235616,95.28926988502633,94.04439585568039,49.31952373815005,20.1736564(...TRUNCATED)
[101.57765567656187,102.55663016135262,50.47411315919352,85.91313188528757,100.52202735331517,92.774(...TRUNCATED)
[41.58363691409069,42.452111752244456,44.30648229464782,45.09812649107605,45.83731631469691,44.33506(...TRUNCATED)
[97.01160279118756,6.346051118881994,94.72852616247904,92.40206020675633,91.34838298022976,91.318757(...TRUNCATED)
[104.63853291328537,101.71837147758534,101.33428948567014,102.11010257340564,102.21209317211508,102.(...TRUNCATED)
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[24.554648539569808,24.787293593595393,29.761460541945052,101.5805924557756,96.31020332540082,98.236(...TRUNCATED)
[111.69078295689039,112.28141427327614,111.6262029560881,111.80209008298452,112.06572857479358,112.4(...TRUNCATED)
[100.88416182911526,113.99469186574802,57.16248484756699,65.38506812042294,35.752767326769266,49.087(...TRUNCATED)
[52.18253586570691,52.61960390985445,53.04146481131548,53.80206028135617,53.27215686611355,52.752451(...TRUNCATED)
[93.52143776868141,94.76283566089087,93.95660011110292,92.56920339039569,92.40835043349026,91.937519(...TRUNCATED)
[59.10728927032537,102.93655630104466,102.81418598289281,102.7103123306953,102.63451315995,52.898546(...TRUNCATED)
[44.10938239771767,44.250365275624745,44.766226927820114,43.725143125123424,44.18607015427576,44.660(...TRUNCATED)
[87.31212733532914,97.72757755038113,94.35079767553088,94.80024777645201,90.04692164591285,92.430569(...TRUNCATED)
[100.68927397410188,102.37456942352765,101.32207265157899,100.88544508719525,100.52812411491017,100.(...TRUNCATED)
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true
{ "$class": "rlkit.torch.torch_rl_algorithm.OfflineTorchBatchRLAlgorithm" }
mg-behavior-cloning
{"batch_size":256,"max_path_length":1000,"num_epochs":0,"num_eval_steps_per_epoch":1000,"num_trains_(...TRUNCATED)
true
antmaze-large-diverse-v0
false
{}
{"$class":"rlkit.launchers.pipeline.<Pipeline offline_bc_pipeline>:\nbc_sanity,\noffline_init,\ncrea(...TRUNCATED)
{ "$class": "rlkit.policies.gaussian_policy.TanhGaussianMixturePolicy" }
{ "hidden_sizes": [ 256, 256, 256 ], "num_gaussians": 12 }
{ "$class": "rlkit.torch.networks.mlp.ConcatMlp" }
{ "hidden_sizes": [ 1024, 1024 ] }
{ "$class": "rlkit.data_management.env_replay_buffer.EnvReplayBuffer" }
2,000,000
{ "$function": "rlkit.samplers.rollout_functions.rollout" }
1
100
gap_and_last
{ "$class": "rlkit.torch.algorithms.bc.BCTrainer" }
{"discount":0.99,"policy_lr":0.0001,"qf_lr":0.0001,"reward_scale":1,"soft_target_tau":0.005,"target_(...TRUNCATED)
12-gaussian-3-layers-all-data-iclr-rebuttal
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