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https://github.com/soda-inria/carte
Repository for CARTE: Context-Aware Representation of Table Entries
https://github.com/soda-inria/carte
classification data-science graph-transformer machine-learning regression transformers
Last synced: 4 days ago
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Repository for CARTE: Context-Aware Representation of Table Entries
- Host: GitHub
- URL: https://github.com/soda-inria/carte
- Owner: soda-inria
- License: bsd-3-clause
- Created: 2024-05-31T07:03:55.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-01-06T20:38:26.000Z (24 days ago)
- Last Synced: 2025-01-19T06:06:18.358Z (11 days ago)
- Topics: classification, data-science, graph-transformer, machine-learning, regression, transformers
- Language: Python
- Homepage: https://soda-inria.github.io/carte/
- Size: 161 MB
- Stars: 97
- Watchers: 10
- Forks: 14
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
[](https://pypi.org/project/carte-ai/)
[](https://pypi.org/project/carte-ai/)
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[](https://github.com/psf/black)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://huggingface.co/datasets/inria-soda/carte-benchmark)
[](https://arxiv.org/pdf/2402.16785)
# CARTE:
Pretraining and Transfer for Tabular Learning
This repository contains the implementation of the paper CARTE: Pretraining and Transfer for Tabular Learning.
CARTE is a pretrained model for tabular data by treating each table row as a star graph and training a graph transformer on top of this representation.
## Colab Examples (Give it a test):
[](https://colab.research.google.com/drive/1PeltEmNLehQ26VQtFJhl7OxnzCS8rPMT?usp=sharing)
* CARTERegressor on Wine Poland dataset
* CARTEClassifier on Spotify dataset
Other datasets are available for testing: [datasets](https://huggingface.co/datasets/inria-soda/carte-benchmark/tree/main/data_raw)
### 01 Install 🚀
The library has been tested on Linux, MacOSX and Windows.
CARTE-AI can be installed from [PyPI](https://pypi.org/project/carte-ai):
pip install carte-ai
pip install huggingface_hub
#### Post installation check
After a correct installation, you should be able to import the module without errors:
```python
import carte_ai
```
### 02 CARTE-AI example on sampled data step by step ➡️
#### 1️⃣ Load the Data 💽
```python
import pandas as pd
from carte_ai.data.load_data import *
num_train = 128 # Example: set the number of training groups/entities
random_state = 1 # Set a random seed for reproducibility
X_train, X_test, y_train, y_test = wina_pl(num_train, random_state)
print("Wina Poland dataset:", X_train.shape, X_test.shape)
```
#### 2️⃣ Convert Table 2 Graph 🪵
The basic preparations are:
- preprocess raw data
- load the prepared data and configs; set train/test split
- generate graphs for each table entries (rows) using the Table2GraphTransformer
- create an estimator and make inference
```python
import fasttext
from huggingface_hub import hf_hub_download
from carte_ai import Table2GraphTransformer
model_path = hf_hub_download(repo_id="hi-paris/fastText", filename="cc.en.300.bin")
preprocessor = Table2GraphTransformer(fasttext_model_path=model_path)
# Fit and transform the training data
X_train = preprocessor.fit_transform(X_train, y=y_train)
# Transform the test data
X_test = preprocessor.transform(X_test)
```
#### 3️⃣ Make Predictions🔮
For learning, CARTE currently runs with the sklearn interface (fit/predict) and the process is:
- Define parameters
- Set the estimator
- Run 'fit' to train the model and 'predict' to make predictions
```python
from carte_ai import CARTERegressor, CARTEClassifier
# Define some parameters
fixed_params = dict()
fixed_params["num_model"] = 10 # 10 models for the bagging strategy
fixed_params["disable_pbar"] = False # True if you want cleanness
fixed_params["random_state"] = 0
fixed_params["device"] = "cpu"
fixed_params["n_jobs"] = 10
fixed_params["pretrained_model_path"] = config_directory["pretrained_model"]
# Define the estimator and run fit/predict
estimator = CARTERegressor(**fixed_params) # CARTERegressor for Regression
estimator.fit(X=X_train, y=y_train)
y_pred = estimator.predict(X_test)
# Obtain the r2 score on predictions
score = r2_score(y_test, y_pred)
print(f"\nThe R2 score for CARTE:", "{:.4f}".format(score))
```
### 03 Reproducing paper results ⚙️
➡️ [installation instructions setup paper](INSTALL.md)
### 04 Contribute to the package 🚀
➡️ [read the contributions guidelines](CONTRIBUTIONS.md)
### 05 Star History ⭐️
### 06 CARTE-AI references 📚
```
@article{kim2024carte,
title={CARTE: pretraining and transfer for tabular learning},
author={Kim, Myung Jun and Grinsztajn, L{\'e}o and Varoquaux, Ga{\"e}l},
journal={arXiv preprint arXiv:2402.16785},
year={2024}
}
```