Repository for Multimodal AutoML Benchmark

Overview

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

Repository for the NeurIPS 2021 Dataset Track Submission "Benchmarking Multimodal AutoML for Tabular Data with Text Fields" (Link, Full Paper with Appendix). An earlier version of the paper, called "Multimodal AutoML on Structured Tables with Text Fields" (Link) has been accepted by ICML 2021 AutoML workshop as Oral. As we have since updated the benchmark with more datasets, the version used in the AutoML workshop paper has been archived at the icml_workshop branch.

This benchmark contains a diverse collection of tabular datasets. Each dataset contains numeric/categorical as well as text columns. The goal is to evaluate the performance of (automated) ML systems for supervised learning (classification and regression) with such multimodal data. The folder multimodal_text_benchmark/scripts/benchmark/ provides Python scripts to run different variants of the AutoGluon and H2O AutoML tools on the benchmark.

Datasets used in the Benchmark

Here's a brief summary of the datasets in our benchmark. Each dataset is described in greater detail in the multimodal_text_benchmark/ folder.

ID key #Train #Test Task Metric Prediction Target
prod product_sentiment_machine_hack 5,091 1,273 multiclass accuracy sentiment related to product
salary data_scientist_salary 15,84 3961 multiclass accuracy salary range in data scientist job listings
airbnb melbourne_airbnb 18,316 4,579 multiclass accuracy price of Airbnb listing
channel news_channel 20,284 5,071 multiclass accuracy category of news article
wine wine_reviews 84,123 21,031 multiclass accuracy variety of wine
imdb imdb_genre_prediction 800 200 binary roc_auc whether film is a drama
fake fake_job_postings2 12,725 3,182 binary roc_auc whether job postings are fake
kick kick_starter_funding 86,052 21,626 binary roc_auc will Kickstarter get funding
jigsaw jigsaw_unintended_bias100K 100,000 25,000 binary roc_auc whether comments are toxic
qaa google_qa_answer_type_reason_explanation 4,863 1,216 regression r2 type of answer
qaq google_qa_question_type_reason_explanation 4,863 1,216 regression r2 type of question
book bookprice_prediction 4,989 1,248 regression r2 price of books
jc jc_penney_products 10,860 2,715 regression r2 price of JC Penney products
cloth women_clothing_review 18,788 4,698 regression r2 review score
ae ae_price_prediction 22,662 5,666 regression r2 American-Eagle item prices
pop news_popularity2 24,007 6,002 regression r2 news article popularity online
house california_house_price 24,007 6,002 regression r2 sale price of houses in California
mercari mercari_price_suggestion100K 100,000 25,000 regression r2 price of Mercari products

License

The versions of datasets in this benchmark are released under the CC BY-NC-SA license. Note that the datasets in this benchmark are modified versions of previously publicly-available original copies and we do not own any of the datasets in the benchmark. Any data from this benchmark which has previously been published elsewhere falls under the original license from which the data originated. Please refer to the licenses of each original source linked in the multimodal_text_benchmark/README.md.

Install the Benchmark Suite

cd multimodal_text_benchmark
# Install the benchmarking suite
python3 -m pip install -U -e .

You can do a quick test of the installation by going to the test folder

cd multimodal_text_benchmark/tests
python3 -m pytest test_datasets.py

To work with one of the datasets, use the following code:

from auto_mm_bench.datasets import dataset_registry

print(dataset_registry.list_keys())  # list of all dataset names
dataset_name = 'product_sentiment_machine_hack'

train_dataset = dataset_registry.create(dataset_name, 'train')
test_dataset = dataset_registry.create(dataset_name, 'test')
print(train_dataset.data)
print(test_dataset.data)

To access all datasets that comprise the benchmark:

from auto_mm_bench.datasets import create_dataset, TEXT_BENCHMARK_ALIAS_MAPPING

for dataset_name in list(TEXT_BENCHMARK_ALIAS_MAPPING.values()):
    print(dataset_name)
    dataset = create_dataset(dataset_name)

Run Experiments

Go to multimodal_text_benchmark/scripts/benchmark to see how to run some baseline ML methods over the benchmark.

References

BibTeX entry of the ICML Workshop Version:

@article{agmultimodaltext,
  title={Multimodal AutoML on Structured Tables with Text Fields},
  author={Shi, Xingjian and Mueller, Jonas and Erickson, Nick and Li, Mu and Smola, Alexander},
  journal={8th ICML Workshop on Automated Machine Learning (AutoML)},
  year={2021}
}
Owner
Xingjian Shi
Xingjian Shi
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