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Inductive-bias Learning

Project description

IBLM:Inductive-bias Learning Models

[[ArXiv]]()
※ I am currently writing a paper. Please contact ulti4929@gmail.com if you are interested.

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

Setting

  • Installation
pip install iblm
  • OpenAI API key settings
os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"

Binary classification

Currently, only small amounts of data can be executed. The same seed value may or may not generate a successful code model.

  • Model Definition
from iblm import IBLModel

# Load LLM via LangChain. (GPT-4 recommended)
llm_model = OpenAI(temperature=0, model_name = 'gpt-4-0613')

params = {
    'columns_name': True,
    'objective': 'classification',
    }

iblm = IBLModel(llm_model = llm_model, params=params)
  • Model Learning
model = iblm.fit(x_train, y_train)
  • Model Predictions
y_proba = iblm.predict(x_test)

Notebooks

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

  • Binary classification:Open In Colab

Supported Models

Currently, the recommended model is GPT-4

Contributor

Backstory

This idea is based on langchain-tools, which was created in an attempt to make LLM learn LightGBM.

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