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Inductive-bias Learning

Project description

IBLM:Inductive-bias Learning Models

What is IBL?

IBL (Inductive-bias Learning) is a new machine learning modeling method that uses LLM to infer the structure of the model itself from the data set and outputs it as Python code. The learned model (code model) can be used as a machine learning model to predict a new dataset.In this repository, you can try different learning methods with IBL.(Currently only binary classification with simple methods is available.)

ibl

How to Use

  • Installation and Import
pip install iblm

import iblm
  • Setting
    • OpenAI
#
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"

model = iblm.IBLModel(api_type="openai", model_name="gpt-4-0125-preview", objective="binary")
  • Azure OpenAI
# AZURE_OPENAI_API
os.environ["AZURE_OPENAI_KEY"] = "xxx"
os.environ["AZURE_OPENAI_ENDPOINT"] = "xxx"
os.environ["OPENAI_API_VERSION"] = "xxx"

model = iblm.IBLModel(api_type="azure", model_name="gpt-4-0125-preview", objective="binary")
  • Google API
os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY"
model = iblm.IBLModel(api_type="gemini", model_name="gemini-pro", objective="binary")
  • Anthropic API
os.environ["ANTHROPIC_API_KEY"] = "YOUR_API_KEY"
model = iblm.IBLModel(api_type="", model_name="", objective="binary")
  • Model Learning Currently, only small amounts of data can be executed.
code_model = model.fit(x_train, y_train)

print(code_model)
  • Model Predictions
y_proba = model.predict(x_test)

Examples

Use the link below to try it out immediately on Google colab.

  • Binary classification
    • Pseudo dataset:Open In Colab
    • Moon dataset:Open In Colab

Supported Models

Contributor

Cite

If you find this repo helpful, please cite the following papers:

@article{tanaka2023inductive,
  title={Inductive-bias Learning: Generating Code Models with Large Language Model},
  author={Tanaka, Toma and Emoto, Naofumi and Yumibayashi, Tsukasa},
  journal={arXiv preprint arXiv:2308.09890},
  year={2023}
}

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