futureframe 0.1.4

Accelerate your data science workflow from months to days with foundation models for tabular data.

💠 Future Frame

Accelerate your data science workflow from months to days with foundation models for tabular data

Installation

pip install futureframe

Quick-start guide

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

import futureframe as ff

dataset_name = "tests/data/churn.csv"
target_variable = "Churn"
df = pd.read_csv(dataset_name)

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, random_state=42)

##############
# Future Frame
##############
model = ff.models.CM2Classifier()
model.finetune(X_train, y_train)

y_pred = model.predict(X_test)
##############

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	Link

More to come!

Important links

• Future Frame Website
• futureframe Pypi package index
• futureframe Github repository

Contributing

We are currently under heavy development. If you want to contribute, please send us an email at eduardo(at)futureframe.ai.

If you find any bugs, please write us an issue :).