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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

  • Currently, only binary classification is supported.

Examples

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

  • Binary classification
    • IBL

      • OpenAI:Open In Colab
      • Claude:Open In Colab
    • IBLbagging

      • OpenAI:Open In Colab
      • Claude:Open In Colab

How to Use

  • Installation and Import
pip install iblm

import iblm
  • Setting

    • OpenAI

      os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
      
      ibl = iblm.IBLModel(api_type="openai", model_name="gpt-4-0125-preview", objective="binary")
      
    • Azure OpenAI

      os.environ["AZURE_OPENAI_KEY"] = "YOUR_API_KEY"
      os.environ["AZURE_OPENAI_ENDPOINT"] = "xxx"
      os.environ["OPENAI_API_VERSION"] = "xxx"
      
      ibl = iblm.IBLModel(api_type="azure", model_name="gpt-4-0125-preview", objective="binary")
      
    • Google API

      os.environ["GOOGLE_API_KEY"] = "YOUR_API_KEY"
      ibl = iblm.IBLModel(api_type="gemini", model_name="gemini-pro", objective="binary")
      
    • Anthropic API

      os.environ["ANTHROPIC_API_KEY"] = "YOUR_API_KEY"
      ibl = iblm.IBLModel(api_type="", model_name="", objective="binary")
      
  • Model Learning
    Currently, only small amounts of data can be executed.

    code_model = ibl.fit(x_train, y_train)
    
    print(code_model)
    
  • Model Predictions

    y_proba = ibl.predict(x_test)
    

Inductive-bias Learning Models

  • Inductive-bias Learning
    Normal Inductive-bias Learning

    from iblm import IBLBaggingModel
    
    iblm = IBLModel(
        api_type="openai",
        model_name="gpt-4-0125-preview",
        objective="binary"
        )
    
  • Inductive-bias Learning bagging
    Sampling data from a given dataset, we create multiple models, and the average of these models is used as the predicted value.

    from iblm import IBLBaggingModel
    
    iblbagging = IBLBaggingModel(
        api_type="openai",
        model_name="gpt-4-0125-preview",
        objective="binary",
        num_model=20,  # Number of models to create
        max_sample = 2000,  # Maximum number of samples from the data set
        min_sample = 300,  # Minimum number of samples from the data set
        )
    

Supported LLMs

  • OpenAI
    • gpt-4-0125-preview
    • gpt-3.5-turbo-0125
  • Azure OpenAI
    • gpt-4-0125-preview
    • gpt-3.5-turbo-0125
  • Google
    • gemini-pro
  • Anthropic
    • claude-3-opus-20240229
    • claude-3-sonnet-20240229

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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