hf-tcr 0.2.0

HuggingFace-based inference and evaluation library for TCR-pMHC sequence translation models

hf-tcr

HuggingFace-based inference and evaluation library for TCR-pMHC sequence translation models.

Installation

pip install hf-tcr

Or install from source:

git clone https://github.com/pirl-unc/hf-tcr.git
cd hf-tcr
pip install .

Quick Start

Loading Data

from hf_tcr import TCRpMHCdataset

# Create dataset for pMHC -> TCR translation
dataset = TCRpMHCdataset(
    source="pmhc",
    target="tcr",
    use_pseudo=True,
    use_cdr3=True
)

# Load from CSV file
dataset.load_data_from_file("path/to/data.csv")

Running Inference

from hf_tcr import HuggingFaceModelAdapter, TCRBartTokenizer
from transformers import BartForConditionalGeneration

# Load your trained model and tokenizer
tokenizer = TCRBartTokenizer()
model = BartForConditionalGeneration.from_pretrained("path/to/model")

# Create adapter
adapter = HuggingFaceModelAdapter(
    hf_tokenizer=tokenizer,
    hf_model=model,
    device="cuda"
)

# Get a source from your dataset
source = dataset[0][0]  # Get source from first example

# Generate translations
translations = adapter.sample_translations(
    source=source,
    n=10,
    max_len=25,
    mode="top_k",
    top_k=50,
    temperature=1.0
)

Evaluating Models

from hf_tcr import ModelEvaluator

# Create evaluator (extends HuggingFaceModelAdapter)
evaluator = ModelEvaluator(
    hf_tokenizer=tokenizer,
    hf_model=model,
    device="cuda"
)

# Compute dataset-level metrics
metrics = evaluator.dataset_metrics_at_k(
    dataset=dataset,
    k=100,
    max_len=25,
    mode="top_k",
    top_k=50
)

print(f"BLEU: {metrics['char-bleu']:.4f}")
print(f"Precision@100: {metrics['precision']:.4f}")
print(f"Recall@100: {metrics['recall']:.4f}")
print(f"F1@100: {metrics['f1']:.4f}")
print(f"Mean Edit Distance: {metrics['d_edit']:.2f}")
print(f"Sequence Recovery: {metrics['seq_recovery']:.4f}")
print(f"Diversity: {metrics['diversity']:.4f}")
print(f"Perplexity: {metrics['perplexity']:.2f}")

Available Decoding Strategies

The adapter supports multiple decoding strategies:

• greedy: Deterministic greedy decoding
• ancestral: Multinomial sampling
• top_k: Top-k sampling with temperature
• top_p: Nucleus (top-p) sampling
• beam: Deterministic beam search
• stochastic_beam: Stochastic beam search
• diverse_beam: Diverse beam search
• contrastive: Contrastive decoding
• typical: Typical sampling

Metrics

The ModelEvaluator provides the following metrics:

• Char-BLEU: Character-level BLEU score
• Precision@K: Fraction of generated sequences that match references
• Recall@K: Fraction of reference sequences recovered
• F1@K: Harmonic mean of precision and recall
• Mean Edit Distance: Average Levenshtein distance to closest reference
• Sequence Recovery: Position-wise match percentage
• Diversity: Ratio of unique to total generated sequences
• Perplexity: Model perplexity on the dataset

Data Format

CSV files should contain the following columns:

Required:

• CDR3b: CDR3 beta sequence
• TRBV: TRBV gene (IMGT format)
• TRBJ: TRBJ gene (IMGT format)
• Epitope: Peptide sequence
• Allele: HLA allele
• Reference: Data source reference

Optional:

• CDR3a, TRAV, TRAJ, TRAD, TRBD
• TRA_stitched, TRB_stitched
• Pseudo, MHC

Dependencies

• torch >= 2.0.0
• transformers >= 4.30.0
• numpy, pandas, tqdm
• python-Levenshtein
• nltk
• einops
• tidytcells >= 2.0.0
• mhcgnomes >= 1.8.0
• tcrpmhcdataset >= 0.2.0

License

Apache-2.0