Split (1)

train

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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 7 new columns ({'sd_log2TPM', 'is_housekeeping', 'gene_id', 'pct_expressed', 'gene_name', 'mean_log2TPM', 'cv'}) and 10 missing columns ({'ancestry_superpop', 'sequencing_type', 'mean_coverage', 'pct_bases_20x', 'age', 'cohort_id', 'sample_id', 'sex', 'consent_tier', 'genome_build'}).

This happened while the csv dataset builder was generating data using

hf://datasets/xpertsystems/hlt013-sample/gene_expression.csv (at revision f5452c4cce8b566c1c2bd860e5bd9eba69ab9181), [/tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/cohort_manifest.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/cohort_manifest.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/gene_expression.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/gene_expression.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/pharmacogenomics.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/pharmacogenomics.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/polygenic_risk_scores.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/polygenic_risk_scores.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/scrna_pbmc.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/scrna_pbmc.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/variants_annotated.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/variants_annotated.csv)]

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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              gene_id: string
              gene_name: string
              mean_log2TPM: double
              sd_log2TPM: double
              cv: double
              pct_expressed: double
              is_housekeeping: bool
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1097
              to
              {'sample_id': Value('string'), 'ancestry_superpop': Value('string'), 'sex': Value('string'), 'age': Value('int64'), 'cohort_id': Value('string'), 'genome_build': Value('string'), 'sequencing_type': Value('string'), 'mean_coverage': Value('float64'), 'pct_bases_20x': Value('float64'), 'consent_tier': Value('string')}
              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 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, 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 7 new columns ({'sd_log2TPM', 'is_housekeeping', 'gene_id', 'pct_expressed', 'gene_name', 'mean_log2TPM', 'cv'}) and 10 missing columns ({'ancestry_superpop', 'sequencing_type', 'mean_coverage', 'pct_bases_20x', 'age', 'cohort_id', 'sample_id', 'sex', 'consent_tier', 'genome_build'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/xpertsystems/hlt013-sample/gene_expression.csv (at revision f5452c4cce8b566c1c2bd860e5bd9eba69ab9181), [/tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/cohort_manifest.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/cohort_manifest.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/gene_expression.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/gene_expression.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/pharmacogenomics.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/pharmacogenomics.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/polygenic_risk_scores.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/polygenic_risk_scores.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/scrna_pbmc.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/scrna_pbmc.csv), /tmp/hf-datasets-cache/medium/datasets/48864965097651-config-parquet-and-info-xpertsystems-hlt013-sampl-16ebd919/hub/datasets--xpertsystems--hlt013-sample/snapshots/f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/variants_annotated.csv (origin=hf://datasets/xpertsystems/hlt013-sample@f5452c4cce8b566c1c2bd860e5bd9eba69ab9181/variants_annotated.csv)]
              
              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.

sample_id string	ancestry_superpop string	sex string	age int64	cohort_id string	genome_build string	sequencing_type string	mean_coverage float64	pct_bases_20x float64	consent_tier string
HLT013_000001	EAS	Female	31	COHORT_A	GRCh38	WES	55.1	96.79	TIER1
HLT013_000002	AFR	Female	61	COHORT_A	GRCh38	WGS	29.9	95.94	TIER2
HLT013_000003	AMR	Female	20	COHORT_B	GRCh38	WGS	45.9	91.15	TIER2
HLT013_000004	EAS	Male	20	COHORT_A	GRCh38	WGS	48.1	96.16	TIER2
HLT013_000005	EUR	Female	49	COHORT_B	GRCh38	WES	25.3	92.95	TIER1
HLT013_000006	SAS	Female	61	COHORT_A	GRCh38	WGS	33.5	92.89	TIER1
HLT013_000007	EAS	Male	27	COHORT_C	GRCh38	WGS	30.7	98.39	TIER1
HLT013_000008	EAS	Male	19	COHORT_B	GRCh38	WES	26.9	94.45	TIER2
HLT013_000009	EUR	Male	32	COHORT_A	GRCh38	WES	57.4	89.95	TIER1
HLT013_000010	AFR	Female	58	COHORT_A	GRCh38	WGS	31.9	97.58	TIER1
HLT013_000011	EUR	Male	79	COHORT_B	GRCh38	WES	55.5	92.56	TIER3
HLT013_000012	SAS	Male	29	COHORT_C	GRCh38	WES	51.3	90.27	TIER3
HLT013_000013	EAS	Female	50	COHORT_B	GRCh38	WGS	28.3	95.07	TIER3
HLT013_000014	AMR	Female	39	COHORT_B	GRCh38	WGS	48	91.61	TIER2
HLT013_000015	AFR	Female	30	COHORT_A	GRCh38	WGS	31	94.67	TIER3
HLT013_000016	EUR	Male	77	COHORT_C	GRCh38	WGS	26.9	96.09	TIER1
HLT013_000017	AFR	Male	37	COHORT_C	GRCh38	WES	48.9	89.79	TIER3
HLT013_000018	EUR	Male	58	COHORT_A	GRCh38	WES	50.9	95.83	TIER2
HLT013_000019	AMR	Female	25	COHORT_C	GRCh38	WGS	45.4	94.92	TIER2
HLT013_000020	EAS	Male	21	COHORT_C	GRCh38	WGS	27.3	96.9	TIER2
HLT013_000021	EAS	Female	36	COHORT_A	GRCh38	WGS	59.4	98.85	TIER2
HLT013_000022	EUR	Female	42	COHORT_B	GRCh38	WES	42.9	95.35	TIER2
HLT013_000023	SAS	Female	50	COHORT_B	GRCh38	WES	39.9	93.34	TIER3
HLT013_000024	AMR	Male	53	COHORT_C	GRCh38	WES	58	98.13	TIER1
HLT013_000025	EAS	Female	31	COHORT_B	GRCh38	WGS	53.1	88.26	TIER2
HLT013_000026	EUR	Male	86	COHORT_B	GRCh38	WES	58.4	96.44	TIER1
HLT013_000027	AFR	Male	43	COHORT_A	GRCh38	WGS	30	94.02	TIER1
HLT013_000028	EUR	Male	85	COHORT_B	GRCh38	WGS	44.7	96	TIER3
HLT013_000029	EUR	Male	59	COHORT_C	GRCh38	WGS	41.7	95.07	TIER3
HLT013_000030	EAS	Female	27	COHORT_C	GRCh38	WGS	42.7	96.56	TIER1
HLT013_000031	EAS	Male	43	COHORT_A	GRCh38	WGS	41.5	89.28	TIER1
HLT013_000032	SAS	Female	25	COHORT_A	GRCh38	WGS	59.6	93.45	TIER3
HLT013_000033	EUR	Male	42	COHORT_C	GRCh38	WES	53.1	98.37	TIER3
HLT013_000034	EUR	Male	33	COHORT_A	GRCh38	WGS	54.8	94.31	TIER1
HLT013_000035	AFR	Female	86	COHORT_B	GRCh38	WGS	43.6	89.65	TIER3
HLT013_000036	EUR	Female	62	COHORT_B	GRCh38	WES	37.9	95.43	TIER2
HLT013_000037	EUR	Male	67	COHORT_B	GRCh38	WGS	34.5	97.56	TIER3
HLT013_000038	AFR	Male	87	COHORT_B	GRCh38	WGS	51.6	95.82	TIER2
HLT013_000039	EUR	Female	36	COHORT_C	GRCh38	WGS	54.2	92.52	TIER3
HLT013_000040	EAS	Female	36	COHORT_B	GRCh38	WES	49.4	96.82	TIER1
HLT013_000041	AFR	Female	61	COHORT_A	GRCh38	WES	27	93.82	TIER1
HLT013_000042	AMR	Female	68	COHORT_C	GRCh38	WGS	32	90.1	TIER1
HLT013_000043	EAS	Female	53	COHORT_B	GRCh38	WGS	44.9	96.56	TIER3
HLT013_000044	EUR	Male	21	COHORT_C	GRCh38	WES	40.4	93.38	TIER2
HLT013_000045	AMR	Male	72	COHORT_B	GRCh38	WGS	32.3	94.45	TIER1
HLT013_000046	AMR	Male	59	COHORT_C	GRCh38	WES	56.4	97.04	TIER2
HLT013_000047	EUR	Male	71	COHORT_C	GRCh38	WES	31.4	89.18	TIER3
HLT013_000048	EUR	Female	43	COHORT_A	GRCh38	WGS	55.7	92.72	TIER3
HLT013_000049	EAS	Male	65	COHORT_B	GRCh38	WGS	40.1	98.93	TIER1
HLT013_000050	EUR	Female	29	COHORT_C	GRCh38	WES	34.2	92.84	TIER2
HLT013_000051	EUR	Male	34	COHORT_B	GRCh38	WES	51.2	96.99	TIER3
HLT013_000052	EUR	Male	27	COHORT_B	GRCh38	WGS	45.2	91.25	TIER2
HLT013_000053	EAS	Female	83	COHORT_A	GRCh38	WGS	59.5	88.34	TIER3
HLT013_000054	EAS	Male	56	COHORT_C	GRCh38	WGS	50.2	90.23	TIER1
HLT013_000055	EAS	Female	31	COHORT_C	GRCh38	WES	40.2	92.51	TIER1
HLT013_000056	EAS	Female	23	COHORT_C	GRCh38	WES	25.9	94.93	TIER2
HLT013_000057	AFR	Female	39	COHORT_C	GRCh38	WES	33.7	90.28	TIER2
HLT013_000058	AFR	Male	32	COHORT_B	GRCh38	WGS	40.9	89.02	TIER1
HLT013_000059	EUR	Male	61	COHORT_A	GRCh38	WGS	49.8	98.26	TIER3
HLT013_000060	EUR	Female	22	COHORT_C	GRCh38	WGS	59.9	94.86	TIER1
HLT013_000061	EAS	Male	38	COHORT_C	GRCh38	WGS	57	93.23	TIER2
HLT013_000062	AFR	Female	52	COHORT_B	GRCh38	WES	46.5	96.25	TIER1
HLT013_000063	AFR	Female	26	COHORT_A	GRCh38	WES	54.6	97.52	TIER3
HLT013_000064	EAS	Male	25	COHORT_A	GRCh38	WES	37.5	94.58	TIER2
HLT013_000065	EAS	Male	53	COHORT_C	GRCh38	WGS	33.8	89.79	TIER1
HLT013_000066	AFR	Male	88	COHORT_B	GRCh38	WES	57.2	99	TIER2
HLT013_000067	AFR	Male	36	COHORT_A	GRCh38	WGS	26.9	95.54	TIER1
HLT013_000068	EUR	Female	20	COHORT_C	GRCh38	WGS	31.4	94.71	TIER1
HLT013_000069	EUR	Male	37	COHORT_B	GRCh38	WGS	42.4	96.71	TIER1
HLT013_000070	AFR	Male	31	COHORT_A	GRCh38	WGS	48.2	93.3	TIER1
HLT013_000071	EUR	Male	46	COHORT_C	GRCh38	WGS	38.6	89.23	TIER3
HLT013_000072	AFR	Female	77	COHORT_B	GRCh38	WGS	48.8	93.67	TIER2
HLT013_000073	AMR	Male	53	COHORT_B	GRCh38	WES	36.8	88.25	TIER2
HLT013_000074	EUR	Female	89	COHORT_C	GRCh38	WGS	28.6	93.98	TIER2
HLT013_000075	EUR	Male	25	COHORT_B	GRCh38	WGS	59.2	88.66	TIER2
HLT013_000076	EUR	Male	33	COHORT_C	GRCh38	WES	54.4	89.25	TIER2
HLT013_000077	EUR	Male	36	COHORT_B	GRCh38	WES	55.6	95.95	TIER2
HLT013_000078	EAS	Female	62	COHORT_A	GRCh38	WES	47.6	95.78	TIER3
HLT013_000079	AFR	Female	27	COHORT_A	GRCh38	WGS	44	93.63	TIER1
HLT013_000080	EAS	Female	19	COHORT_C	GRCh38	WES	44.4	92.68	TIER1
HLT013_000081	EAS	Male	36	COHORT_C	GRCh38	WES	25.3	88.63	TIER3
HLT013_000082	AFR	Female	58	COHORT_C	GRCh38	WGS	29.3	90.62	TIER3
HLT013_000083	AMR	Male	67	COHORT_A	GRCh38	WGS	51.5	92.35	TIER3
HLT013_000084	EUR	Female	82	COHORT_B	GRCh38	WGS	58.5	92.94	TIER3
HLT013_000085	EUR	Male	85	COHORT_B	GRCh38	WES	34.6	93.39	TIER2
HLT013_000086	EUR	Female	41	COHORT_B	GRCh38	WGS	46.9	89.37	TIER3
HLT013_000087	EAS	Male	48	COHORT_B	GRCh38	WES	40	90.38	TIER2
HLT013_000088	AFR	Male	72	COHORT_C	GRCh38	WES	53.1	92.09	TIER1
HLT013_000089	EUR	Male	19	COHORT_C	GRCh38	WGS	45.3	88.27	TIER3
HLT013_000090	AFR	Male	86	COHORT_A	GRCh38	WES	45.1	95.3	TIER1
HLT013_000091	EUR	Male	30	COHORT_A	GRCh38	WES	36.1	96.24	TIER3
HLT013_000092	EAS	Female	54	COHORT_B	GRCh38	WGS	53.5	91.82	TIER3
HLT013_000093	AFR	Female	58	COHORT_C	GRCh38	WES	58.2	93.38	TIER1
HLT013_000094	EUR	Male	77	COHORT_B	GRCh38	WES	32.8	92.95	TIER3
HLT013_000095	EUR	Female	37	COHORT_B	GRCh38	WGS	56.2	88.31	TIER2
HLT013_000096	EAS	Female	50	COHORT_C	GRCh38	WGS	34.4	91.9	TIER2
HLT013_000097	EUR	Female	42	COHORT_C	GRCh38	WES	36.5	98.34	TIER2
HLT013_000098	EUR	Female	76	COHORT_A	GRCh38	WES	28.7	94.88	TIER3
HLT013_000099	EUR	Female	88	COHORT_C	GRCh38	WGS	54.1	96.43	TIER1
HLT013_000100	SAS	Male	83	COHORT_B	GRCh38	WGS	39.2	91.6	TIER2

End of preview.