LoRA Inference

In [2]:

from dnallm import load_config, load_model_and_tokenizer
from dnallm import DNAInference

from dnallm import load_config, load_model_and_tokenizer from dnallm import DNAInference

In [3]:

# Load the config file
configs = load_config("inference_config.yaml")

# Load the config file configs = load_config("inference_config.yaml")

In [4]:

# Load the model and tokenizer
model_name = "kuleshov-group/PlantCAD2-Small-l24-d0768"
# from Hugging Face
model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'], source="huggingface")
# from ModelScope
# model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'], source="modelscope")

# Load the model and tokenizer model_name = "kuleshov-group/PlantCAD2-Small-l24-d0768" # from Hugging Face model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'], source="huggingface") # from ModelScope # model, tokenizer = load_model_and_tokenizer(model_name, task_config=configs['task'], source="modelscope")

Fetching 10 files:   0%|          | 0/10 [00:00<?, ?it/s]

15:24:53 - dnallm.utils.support - INFO - Model files are stored in /home/liuguanqing/.cache/huggingface/hub/models--kuleshov-group--PlantCAD2-Small-l24-d0768/snapshots/f756c255cb76e9f538c3acec04acf4214ed03fb3

Some weights of the model checkpoint at /home/liuguanqing/.cache/huggingface/hub/models--kuleshov-group--PlantCAD2-Small-l24-d0768/snapshots/f756c255cb76e9f538c3acec04acf4214ed03fb3 were not used when initializing CaduceusForSequenceClassification: ['lm_head.complement_map', 'lm_head.lm_head.weight']
- This IS expected if you are initializing CaduceusForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing CaduceusForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of CaduceusForSequenceClassification were not initialized from the model checkpoint at /home/liuguanqing/.cache/huggingface/hub/models--kuleshov-group--PlantCAD2-Small-l24-d0768/snapshots/f756c255cb76e9f538c3acec04acf4214ed03fb3 and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

In [5]:

# Create inference engine
lora_adapter_path = "plantcad/cross_species_acr_train_on_arabidopsis_plantcad2_small"
inference_engine = DNAInference(
    model=model,
    tokenizer=tokenizer,
    config=configs,
    lora_adapter=lora_adapter_path
)

# Create inference engine lora_adapter_path = "plantcad/cross_species_acr_train_on_arabidopsis_plantcad2_small" inference_engine = DNAInference( model=model, tokenizer=tokenizer, config=configs, lora_adapter=lora_adapter_path )

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15:25:33 - dnallm.utils.support - INFO - Model files are stored in /home/liuguanqing/.cache/huggingface/hub/models--plantcad--cross_species_acr_train_on_arabidopsis_plantcad2_small/snapshots/a76b001f89a3be2e12990158bb1903ccddd17625
15:25:46 - dnallm.utils.support - INFO - Loaded LoRA adapter from plantcad/cross_species_acr_train_on_arabidopsis_plantcad2_small
15:25:46 - dnallm.utils.support - INFO - Using device: cuda

In [6]:

seqs = [(
    "AAAAATTTAAATATCGTCTGTAGATATTTTATGGGATGCTTTGAGAATGGGCTTCGTTTTAATGGGCCTC"
    "CTCTGCAATCATTGTCCAGAGTCGAGAAACCACCTCTTCTTCTCTTGTTCTTTCTCCAAATCGATTTGGT"
    "CCCAACTCTCTTCAAGCAAAGGAGAGATATGAAAATGAAAGCTCTTACGGCGAACAAGTTTTTCCGATTG"
    "AAGAAGAGAAGAATCTAGAAGATGAAGACAACACTAGTGCACCAAACAGTTTTGCGCGTCTTGAGAGGAA"
    "ACAAAAAACTATTCAGAGTTCAGAGAGAGTCAACCCCCAAACGAGACTTAAACGATGAGCCCACTATAAT"
    "TTTATAATTTATGGGCCATCAGGCCCAAATGATCAGTAGTAGTTATTATTTGACTTTTGACATGGTGGAT"
    "TTGGTTTAACCACCAAACCGAACGAGTAAAACACTATTGGATTGGGTGATGATATCCCGGTTTTATTTGG"
    "TTAAAATCACAAAATCCTGATTTTGGTTCGCGGCTTGATTCTGCCGCTCTCTCGTCTTTAACCTAACTAA"
    "AGACGTAGAATGATTCTGGTTATTGAATTAGTTTGATACA"
)]
results = inference_engine.infer_seqs(seqs)

seqs = [( "AAAAATTTAAATATCGTCTGTAGATATTTTATGGGATGCTTTGAGAATGGGCTTCGTTTTAATGGGCCTC" "CTCTGCAATCATTGTCCAGAGTCGAGAAACCACCTCTTCTTCTCTTGTTCTTTCTCCAAATCGATTTGGT" "CCCAACTCTCTTCAAGCAAAGGAGAGATATGAAAATGAAAGCTCTTACGGCGAACAAGTTTTTCCGATTG" "AAGAAGAGAAGAATCTAGAAGATGAAGACAACACTAGTGCACCAAACAGTTTTGCGCGTCTTGAGAGGAA" "ACAAAAAACTATTCAGAGTTCAGAGAGAGTCAACCCCCAAACGAGACTTAAACGATGAGCCCACTATAAT" "TTTATAATTTATGGGCCATCAGGCCCAAATGATCAGTAGTAGTTATTATTTGACTTTTGACATGGTGGAT" "TTGGTTTAACCACCAAACCGAACGAGTAAAACACTATTGGATTGGGTGATGATATCCCGGTTTTATTTGG" "TTAAAATCACAAAATCCTGATTTTGGTTCGCGGCTTGATTCTGCCGCTCTCTCGTCTTTAACCTAACTAA" "AGACGTAGAATGATTCTGGTTATTGAATTAGTTTGATACA" )] results = inference_engine.infer_seqs(seqs)

Encoding inputs:   0%|          | 0/1 [00:00<?, ? examples/s]

Inferring: 100%|██████████| 1/1 [00:33<00:00, 33.73s/it]

In [7]:

print(results)

print(results)

{0: {'sequence': 'AAAAATTTAAATATCGTCTGTAGATATTTTATGGGATGCTTTGAGAATGGGCTTCGTTTTAATGGGCCTCCTCTGCAATCATTGTCCAGAGTCGAGAAACCACCTCTTCTTCTCTTGTTCTTTCTCCAAATCGATTTGGTCCCAACTCTCTTCAAGCAAAGGAGAGATATGAAAATGAAAGCTCTTACGGCGAACAAGTTTTTCCGATTGAAGAAGAGAAGAATCTAGAAGATGAAGACAACACTAGTGCACCAAACAGTTTTGCGCGTCTTGAGAGGAAACAAAAAACTATTCAGAGTTCAGAGAGAGTCAACCCCCAAACGAGACTTAAACGATGAGCCCACTATAATTTTATAATTTATGGGCCATCAGGCCCAAATGATCAGTAGTAGTTATTATTTGACTTTTGACATGGTGGATTTGGTTTAACCACCAAACCGAACGAGTAAAACACTATTGGATTGGGTGATGATATCCCGGTTTTATTTGGTTAAAATCACAAAATCCTGATTTTGGTTCGCGGCTTGATTCTGCCGCTCTCTCGTCTTTAACCTAACTAAAGACGTAGAATGATTCTGGTTATTGAATTAGTTTGATACA', 'label': 'positive', 'scores': {'negative': 0.06456661224365234, 'positive': 0.9354333281517029}}}

In [8]:

infer_file = "./test.csv"
results, metrics = inference_engine.infer_file(
    infer_file, seq_col="sequence", label_col="label", evaluate=True
)

infer_file = "./test.csv" results, metrics = inference_engine.infer_file( infer_file, seq_col="sequence", label_col="label", evaluate=True )

Encoding inputs:   0%|          | 0/250 [00:00<?, ? examples/s]

Inferring: 100%|██████████| 16/16 [00:08<00:00,  1.86it/s]

In [9]:

for i, res in results.items():
    print(res['label'], res['scores'], sep="\n")
    break

print(metrics)

for i, res in results.items(): print(res['label'], res['scores'], sep="\n") break print(metrics)

negative
{'negative': 0.9995535016059875, 'positive': 0.000446514313807711}
{'accuracy': 0.816, 'precision': 0.8888888888888888, 'recall': 0.26666666666666666, 'f1': 0.41025641025641024, 'mcc': 0.423204406266162, 'AUROC': 0.8621929824561403, 'AUPRC': 0.7100068048890062, 'TPR': 0.26666666666666666, 'TNR': 0.9894736842105263, 'FPR': 0.010526315789473684, 'FNR': 0.7333333333333333}

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