sanidhyaanand/selcon

SELCON

Modularised Implementation of the SELCON subset selection method from the paper:
Training Data Subset Selection for Regression with Controlled Generalization Error
by Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan, Abir De

Installation

You can install the package using
pip install selcon

Dependencies

To run this code fully, you'll need PyTorch (we're using version 1.4.0) and scikit-learn. We've been running our code in Python 3.7.

Usage

SELCON package can be utilised in Linear Subset Selection or Deep Subset Selection methods as:

Linear

from SELCON.datasets import load_def_data, get_data
from SELCON.linear import Regression

load_def_data provides functionality for using the datasets used for the experiments in the paper (provided you have them available in the 'Dataset' directory)

reg = Regression()

# Converts specified numpy arrays to torch tensors (assuming data has been split previously)
X_trn, X_val, Y_trn, Y_val = get_data(x_train, x_val, y_train, y_val)
# Trains SELCON model for a subset fraction of 0.03 on the training subset (no fairness)
reg.train_model(X_trn, Y_trn, X_val, Y_val, fraction = 0.03)
# Return optimal subset indices
subset_idxs = reg.return_subset()

# Returns the optimal subset of the training data for further use
X_sub = X_trn[subset_idxs]
y_sub = Y_trn[subset_idxs]

Deep

from SELCON.datasets import load_def_data, get_data
from SELCON.deep import DeepSelection

reg = DeepSelection()

# Converts specified numpy arrays to torch tensors (assuming data has been split into train-val sets previously)
X_trn, X_val, Y_trn, Y_val = get_data(x_train, x_val, y_train, y_val)
# Trains SELCON model for a subset fraction of 0.03 on the training subset (with fairness)
reg.train_model_fair(X_trn, Y_trn, X_val, Y_val, fraction = 0.03)
# Return optimal subset indices
subset_idxs = reg.return_subset()

# Returns the optimal subset of the training data for further use
X_sub = X_trn[subset_idxs]
y_sub = Y_trn[subset_idxs]