Datasets:

Machine-Earning
/

CISIR-datasets

Error code:   DatasetGenerationError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 8, saw 2

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1815, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 190, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2
              
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, 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 1858, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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Highly Imbalanced Regression with Tabular Data in SEP and Other Applications

This repository contains the datasets used in the paper "Highly Imbalanced Regression with Tabular Data in SEP and Other Applications". This work investigates highly imbalanced regression with tabular data, where imbalance ratios exceed 1,000. It's particularly relevant for applications like forecasting the intensity of rare harmful Solar Energetic Particle (SEP) events.

The associated code and method implementation can be found on GitHub: https://github.com/Machine-Earning/CISIR

Overview

This repository contains the official implementation of CISIR (Correlation, Involution, Stratified Importance Regression), a novel method for highly imbalanced regression with tabular data. The method is particularly applicable to SEP (Solar Energetic Particle) forecasting research for NASA and other domains requiring accurate prediction of rare, high-impact events.

Key Contributions

Correlation-aware loss function that considers the correlation between predicted and actual values
Monotonically Decreasing Involution (MDI) importance weighting that outperforms traditional convex importance functions
Stratified sampling strategy that ensures rare instances are included in mini-batches
Comprehensive evaluation on five highly imbalanced datasets with imbalance ratios > 1,000

Datasets

We evaluate our method on five highly imbalanced datasets. All datasets exhibit high imbalance ratios (ρ > 1,000), making them ideal for evaluating highly imbalanced regression methods.

SEP Datasets

SEP-EC: Forecasts the change (delta) in proton intensity based on features from electron intensity and CMEs (Coronal Mass Ejections)
SEP-C: Forecasts peak proton intensity based on CME characteristics

Other Datasets

SARCOS: Estimates the torque vector based on joint-state inputs for a 7-DOF robot arm
Blog Feedback (BF): Forecasts the number of comments based on textual, temporal, and engagement features
Online News Popularity (ONP): Estimates the number of shares of an article based on content, topic, and sentiment attributes

Data Availability

The datasets are available for direct download at: https://huggingface.co/datasets/Machine-Earning/CISIR-datasets/resolve/main/CISIR-data.zip

Citation

If you find this repository useful in your research, please consider giving a star ⭐ and a citation:

@inproceedings{moukpe2024cisir,
  title={Highly Imbalanced Regression with Tabular Data in SEP and Other Applications},
  author={Moukpe, Josias K. and Chan, Philip K. and Zhang, Ming},
  booktitle={Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2024},
  organization={IEEE}
}

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