Datasets:
Looong
/
DMT_Assignment_2

Dataset card Data Studio Files Community
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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 4 new columns ({'date_time', 'click_bool', 'gross_bookings_usd', 'position'}) and 4 missing columns ({'day', 'hour', 'month', 'year'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Looong/DMT_Assignment_2/training_set_VU_DM.csv (at revision 81aed772be0935590e9959b8e399836b945cd928)

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
              srch_id: int64
              date_time: string
              site_id: int64
              visitor_location_country_id: int64
              visitor_hist_starrating: double
              visitor_hist_adr_usd: double
              prop_country_id: int64
              prop_id: int64
              prop_starrating: int64
              prop_review_score: double
              prop_brand_bool: int64
              prop_location_score1: double
              prop_location_score2: double
              prop_log_historical_price: double
              position: int64
              price_usd: double
              promotion_flag: int64
              srch_destination_id: int64
              srch_length_of_stay: int64
              srch_booking_window: int64
              srch_adults_count: int64
              srch_children_count: int64
              srch_room_count: int64
              srch_saturday_night_bool: int64
              srch_query_affinity_score: double
              orig_destination_distance: double
              random_bool: int64
              comp1_rate: double
              comp1_inv: double
              comp1_rate_percent_diff: double
              comp2_rate: double
              comp2_inv: double
              comp2_rate_percent_diff: double
              comp3_rate: double
              comp3_inv: double
              comp3_rate_percent_diff: double
              comp4_rate: double
              comp4_inv: double
              comp4_rate_percent_diff: double
              comp5_rate: double
              comp5_inv: double
              comp5_rate_percent_diff: double
              comp6_rate: double
              comp6_inv: double
              comp6_rate_percent_diff: double
              comp7_rate: double
              comp7_inv: double
              comp7_rate_percent_diff: double
              comp8_rate: double
              comp8_inv: double
              comp8_rate_percent_diff: double
              click_bool: int64
              gross_bookings_usd: double
              booking_bool: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 7294
              to
              {'srch_id': Value(dtype='int64', id=None), 'site_id': Value(dtype='int64', id=None), 'visitor_location_country_id': Value(dtype='int64', id=None), 'visitor_hist_starrating': Value(dtype='float64', id=None), 'visitor_hist_adr_usd': Value(dtype='float64', id=None), 'prop_country_id': Value(dtype='int64', id=None), 'prop_id': Value(dtype='int64', id=None), 'prop_starrating': Value(dtype='int64', id=None), 'prop_review_score': Value(dtype='float64', id=None), 'prop_brand_bool': Value(dtype='int64', id=None), 'prop_location_score1': Value(dtype='float64', id=None), 'prop_location_score2': Value(dtype='float64', id=None), 'prop_log_historical_price': Value(dtype='float64', id=None), 'price_usd': Value(dtype='float64', id=None), 'promotion_flag': Value(dtype='int64', id=None), 'srch_destination_id': Value(dtype='int64', id=None), 'srch_length_of_stay': Value(dtype='int64', id=None), 'srch_booking_window': Value(dtype='int64', id=None), 'srch_adults_count': Value(dtype='int64', id=None), 'srch_children_count': Value(dtype='int64', id=None), 'srch_room_count': Value(dtype='int64', id=None), 'srch_saturday_night_bool': Value(dtype='int64', id=None), 'srch_query_affinity_score': Value(dtype='float64', id=None), 'orig_destination_distance': Value(dtype='float64', id=None), 'random_bool': Value(dtype='int64', id=None), 'comp1_rate': Value(dtype='float64', id=None), 'comp1_inv': Value(dtype='float64', id=None), 'comp1_rate_percent_diff': Value(dtype='float64', id=None), 'comp2_rate': Value(dtype='float64', id=None), 'comp2_inv': Value(dtype='float64', id=None), 'comp2_rate_percent_diff': Value(dtype='float64', id=None), 'comp3_rate': Value(dtype='float64', id=None), 'comp3_inv': Value(dtype='float64', id=None), 'comp3_rate_percent_diff': Value(dtype='float64', id=None), 'comp4_rate': Value(dtype='float64', id=None), 'comp4_inv': Value(dtype='float64', id=None), 'comp4_rate_percent_diff': Value(dtype='float64', id=None), 'comp5_rate': Value(dtype='float64', id=None), 'comp5_inv': Value(dtype='float64', id=None), 'comp5_rate_percent_diff': Value(dtype='float64', id=None), 'comp6_rate': Value(dtype='float64', id=None), 'comp6_inv': Value(dtype='float64', id=None), 'comp6_rate_percent_diff': Value(dtype='float64', id=None), 'comp7_rate': Value(dtype='float64', id=None), 'comp7_inv': Value(dtype='float64', id=None), 'comp7_rate_percent_diff': Value(dtype='float64', id=None), 'comp8_rate': Value(dtype='float64', id=None), 'comp8_inv': Value(dtype='float64', id=None), 'comp8_rate_percent_diff': Value(dtype='float64', id=None), 'booking_bool': Value(dtype='float64', id=None), 'year': Value(dtype='int64', id=None), 'month': Value(dtype='int64', id=None), 'day': Value(dtype='int64', id=None), 'hour': Value(dtype='int64', 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 1319, 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 912, 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 4 new columns ({'date_time', 'click_bool', 'gross_bookings_usd', 'position'}) and 4 missing columns ({'day', 'hour', 'month', 'year'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Looong/DMT_Assignment_2/training_set_VU_DM.csv (at revision 81aed772be0935590e9959b8e399836b945cd928)
              
              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.

srch_id
int64
site_id
int64
visitor_location_country_id
int64
visitor_hist_starrating
float64
visitor_hist_adr_usd
float64
prop_country_id
int64
prop_id
int64
prop_starrating
int64
prop_review_score
float64
prop_brand_bool
int64
prop_location_score1
float64
prop_location_score2
float64
prop_log_historical_price
float64
price_usd
float64
promotion_flag
int64
srch_destination_id
int64
srch_length_of_stay
int64
srch_booking_window
int64
srch_adults_count
int64
srch_children_count
int64
srch_room_count
int64
srch_saturday_night_bool
int64
srch_query_affinity_score
float64
orig_destination_distance
float64
random_bool
int64
comp1_rate
float64
comp1_inv
float64
comp1_rate_percent_diff
float64
comp2_rate
float64
comp2_inv
float64
comp2_rate_percent_diff
float64
comp3_rate
float64
comp3_inv
float64
comp3_rate_percent_diff
float64
comp4_rate
float64
comp4_inv
float64
comp4_rate_percent_diff
float64
comp5_rate
float64
comp5_inv
float64
comp5_rate_percent_diff
float64
comp6_rate
float64
comp6_inv
float64
comp6_rate_percent_diff
float64
comp7_rate
float64
comp7_inv
float64
comp7_rate_percent_diff
float64
comp8_rate
float64
comp8_inv
float64
comp8_rate_percent_diff
float64
booking_bool
float64
year
int64
month
int64
day
int64
hour
int64
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