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Welcome to autoBOT's documentation and source pages. Here, you can find simple examples to get you started, but also delve deeper into autoBOT's capabilities. To explore autoBOT's functionality, start with the TUTORIAL.
TLDR:
pip install autobotlib
and then,
import autoBOTLib
import pandas as pd
# Load TSV-based data ("text_a" field are texts, "label" are labels)
dataframe = pd.read_csv("train.tsv", sep="\t")
train_sequences = dataframe["text_a"]
train_targets = dataframe["label"]
# Initialize the object + conduct model search (cross-val is default evaluation)
autoBOTLibObj = autoBOTLib.GAlearner(
train_sequences,
train_targets,
time_constraint=1).evolve()
# Summarize run
autoBOTLibObj.generate_report(output_folder="report", job_id="testJobId123")
# Store model
autoBOTLib.store_autobot_model(autoBOTLibObj, "model.pickle")
# Load an existing model
autoBOTObj = autoBOTLib.load_autobot_model("model.pickle")
# Predict on new data
dataframe_new = pd.read_csv("test.tsv", sep="\t")
test_sequences = dataframe_new["text_a"]
predictions_non_prob = autoBOTObj.predict(test_sequences)
predictions_prob = autoBOTObj.predict_proba(test_sequences)If you use this work, please cite:
@Article{Škrlj2021,
author={{\v{S}}krlj, Bla{\v{z}}
and Martinc, Matej
and Lavra{\v{c}}, Nada
and Pollak, Senja},
title={autoBOT: evolving neuro-symbolic representations for explainable low resource text classification},
journal={Machine Learning},
year={2021},
month={Apr},
day={14},
abstract={Learning from texts has been widely adopted throughout industry and science. While state-of-the-art neural language models have shown very promising results for text classification, they are expensive to (pre-)train, require large amounts of data and tuning of hundreds of millions or more parameters. This paper explores how automatically evolved text representations can serve as a basis for explainable, low-resource branch of models with competitive performance that are subject to automated hyperparameter tuning. We present autoBOT (automatic Bags-Of-Tokens), an autoML approach suitable for low resource learning scenarios, where both the hardware and the amount of data required for training are limited. The proposed approach consists of an evolutionary algorithm that jointly optimizes various sparse representations of a given text (including word, subword, POS tag, keyword-based, knowledge graph-based and relational features) and two types of document embeddings (non-sparse representations). The key idea of autoBOT is that, instead of evolving at the learner level, evolution is conducted at the representation level. The proposed method offers competitive classification performance on fourteen real-world classification tasks when compared against a competitive autoML approach that evolves ensemble models, as well as state-of-the-art neural language models such as BERT and RoBERTa. Moreover, the approach is explainable, as the importance of the parts of the input space is part of the final solution yielded by the proposed optimization procedure, offering potential for meta-transfer learning.},
issn={1573-0565},
doi={10.1007/s10994-021-05968-x},
url={https://doi.org/10.1007/s10994-021-05968-x}
}