Get LITCoder up and running in minutes
Create a conda environment and install the required packages:
git clone git@github.com:GT-LIT-Lab/litcoder_core.git
cd litcoder_core
conda create -n litcoder -y python=3.12.8
conda activate litcoder
conda install pip
pip install -e .Use the pre-packaged LeBel assembly to validate your setup quickly:
gdown 1q-XLPjvhd8doGFhYBmeOkcenS9Y59x64Fastest way to verify your setup using the LeBel assembly and word rate features:
from encoding.assembly.assembly_loader import load_assembly
from encoding.features.factory import FeatureExtractorFactory
from encoding.downsample.downsampling import Downsampler
from encoding.models.nested_cv import NestedCVModel
from encoding.trainer import AbstractTrainer
# 1) Load prepackaged assembly
assembly_path = "assembly_lebel_uts03.pkl"
assembly = load_assembly(assembly_path)
# 2) Configure components (wordrate-only)
extractor = FeatureExtractorFactory.create_extractor(
modality="wordrate",
model_name="wordrate",
config={},
cache_dir="cache",
)
downsampler = Downsampler()
model = NestedCVModel(model_name="ridge_regression")
# FIR, downsampling, and trimming match our LeBel defaults
fir_delays = [1, 2, 3, 4]
trimming_config = {
"train_features_start": 10, "train_features_end": -5,
"train_targets_start": 0, "train_targets_end": None,
"test_features_start": 50, "test_features_end": -5,
"test_targets_start": 40, "test_targets_end": None,
}
downsample_config = {}
# 3) Train
trainer = AbstractTrainer(
assembly=assembly,
feature_extractors=[extractor],
downsampler=downsampler,
model=model,
fir_delays=fir_delays,
trimming_config=trimming_config,
use_train_test_split=True,
logger_backend="wandb",
wandb_project_name="lebel-wordrate",
dataset_type="lebel",
results_dir="results",
downsample_config=downsample_config,
)
metrics = trainer.train()
print({
"median_correlation": metrics.get("median_score", float("nan")),
"n_significant": metrics.get("n_significant"),
})