Metadata-Version: 2.1
Name: futureframe
Version: 0.2.5
Summary: Accelerate your data science workflow from months to days with foundation models for tabular data.
Home-page: https://futureframe.ai
Keywords: data science,foundation models,tabular data
Author: Future Frame
Maintainer: Eduardo Dadalto
Maintainer-email: eduardo@futureframe.ai
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: catboost (>=1.2.5,<2.0.0)
Requires-Dist: fire (>=0.6.0,<0.7.0)
Requires-Dist: numpy (>=1.17,<2.0)
Requires-Dist: openml (>=0.14.2,<0.15.0)
Requires-Dist: pandas (>=2.2.2,<3.0.0)
Requires-Dist: pyarrow (>=16.1.0,<17.0.0)
Requires-Dist: pydantic (>=2.7.2,<3.0.0)
Requires-Dist: rich (>=13.7.1,<14.0.0)
Requires-Dist: scikit-learn (>=1.4.2,<2.0.0)
Requires-Dist: sentence-transformers (>=3.0.1,<4.0.0)
Requires-Dist: torch (>=2,<3)
Requires-Dist: torchmetrics (>=1.4.0.post0,<2.0.0)
Requires-Dist: transformers (>=4.41.0,<5.0.0)
Requires-Dist: xgboost (>=2.1.0,<3.0.0)
Project-URL: Documentation, https://futureframe.ai/docs
Project-URL: Repository, https://github.com/futureframeai/futureframe
Description-Content-Type: text/markdown

# 💠 Future Frame

Empowering Data Scientists with Foundation Models for Tabular Data

- This Python package allows you to interact with pre-trained foundation models for tabular data.
- Easily fine-tune them on your classification and regression use cases in a single line of code.
- Interested in what we're building? Join our [waitlist](https://futureframe.ai/)!

## Installation

1. Install Future Frame with `pip` – more details on our [PyPI page](https://pypi.org/project/futureframe/).

```bash
pip install futureframe
```

## Quick Start

Use Future Frame to fine-tune a pre-trained foundation model on a classification task.

```python linenums="1"
# Import standard libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

# Import Future Frame
import futureframe as ff

# Import data
dataset_name = "https://raw.githubusercontent.com/futureframeai/futureframe/main/tests/data/churn.csv"
target_variable = "Churn"
df = pd.read_csv(dataset_name)

# Split data
X, y = df.drop(columns=[target_variable]), df[target_variable]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Fine-tune a pre-trained classifier with Future Frame
model = ff.models.cm2.CM2Classifier()
model.finetune(X_train, y_train)

# Make predictions with Future Frame
y_pred = model.predict(X_test)

# Evaluate your model
auc = roc_auc_score(y_test, y_pred)
print(f"AUC: {auc:0.2f}")
```

## Models

| Model Name | Paper Title                                                | Paper                                               | GitHub                                 |
| ---------- | ---------------------------------------------------------- | --------------------------------------------------- | -------------------------------------- |
| CM2        | Towards Cross-Table Masked Pretraining for Web Data Mining | [Ye et al., 2024](https://arxiv.org/abs/2307.04308) | [Link](https://github.com/Chao-Ye/CM2) |

More foundation models will be integrated into the library soon. Stay tuned by joining our [waitlist](https://futureframe.ai/)!

## Links

- [Future Frame Official Website](https://futureframe.ai/)
- [Future Frame API Reference](https://futureframe.ai/api-reference/)
- [`futureframe` PyPI Page](https://pypi.python.org/pypi/futureframe)
- [`futureframe` GitHub Repository](https://github.com/futureframeai/futureframe)
- [`futureframe` Documentation](https://futureframe.ai/docs/)

## Contributing

- We are currently under heavy development.
- If you'd like to contribute, please send us an email at <i>eduardo(at)futureframe.ai</i>.
- To report a bug, please write an [issue](https://github.com/futureframeai/futureframe/issues/new).

