IBLM:Inductive-bias Learning Models

What is IBL
Examples
How to Use
Inductive-bias Learning Models
Supported LLMs
Contributor
Backstory

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:
  - Claude:
- IBLbagging
  - OpenAI:
  - Claude:

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}
}

Metadata

Release files for iblm 1.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for iblm 1.0.6
File	Size	Uploaded
iblm-1.0.6.tar.gz	11.9 kB	Mar 16, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for iblm 1.0.6
File	Interpreter	ABI	Platform	Reset
iblm-1.0.6-py3-none-any.whl 19.7 kB Mar 16, 2024	Python 3	none	any	Details

Total release size: 31.6 kB

Release files / iblm-1.0.6.tar.gz

Download URL	iblm-1.0.6.tar.gz
Size	11.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`ad4eb7c5e384a91b6dfe8a736e91614b5b207d280b4b5953d19cb171bb163e21`
BLAKE2b-256 checksum How to use checksums	`10423b65f8ba80e07716337d943680c1a85433d89057f15736fd53d134ed256e`
Upload date	Mar 16, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.8.2 CPython/3.9.18 Darwin/23.3.0`

Release files / iblm-1.0.6-py3-none-any.whl

Download URL	iblm-1.0.6-py3-none-any.whl
Size	19.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`fe8661dfba6794518a8e91cdc0503bc1c3e77977530d2e5a1acc9ad7ff70fd23`
BLAKE2b-256 checksum How to use checksums	`4588b818304763ac83fc25f99278f163f2b4433f9dc02af3c1ba8b7f4970f384`
Upload date	Mar 16, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/1.8.2 CPython/3.9.18 Darwin/23.3.0`