File size: 4,584 Bytes
8fde187 42f78dd 6b4ca98 42f78dd 6b4ca98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
---
license: mit
---
## Metrics:
```python
Train:
({'accuracy': 0.9406146072672105,
'precision': 0.2947122459102886,
'recall': 0.952624323712029,
'f1': 0.4501592605994876,
'auc': 0.9464622170085311,
'mcc': 0.5118390407598565},
Test:
{'accuracy': 0.9266827008067329,
'precision': 0.22378953253253775,
'recall': 0.7790246675002842,
'f1': 0.3476966444342296,
'auc': 0.8547531675185658,
'mcc': 0.3930283737012391})
```
## Using the Model
Head over to [here](https://huggingface.co/datasets/AmelieSchreiber/binding_sites_random_split_by_family)
to download the dataset first. Once you have the pickle files downloaded locally, run the following:
```python
from datasets import Dataset
from transformers import AutoTokenizer
import pickle
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t12_35M_UR50D")
# Function to truncate labels
def truncate_labels(labels, max_length):
"""Truncate labels to the specified max_length."""
return [label[:max_length] for label in labels]
# Set the maximum sequence length
max_sequence_length = 1000
# Load the data from pickle files
with open("train_sequences_chunked_by_family.pkl", "rb") as f:
train_sequences = pickle.load(f)
with open("test_sequences_chunked_by_family.pkl", "rb") as f:
test_sequences = pickle.load(f)
with open("train_labels_chunked_by_family.pkl", "rb") as f:
train_labels = pickle.load(f)
with open("test_labels_chunked_by_family.pkl", "rb") as f:
test_labels = pickle.load(f)
# Tokenize the sequences
train_tokenized = tokenizer(train_sequences, padding=True, truncation=True, max_length=max_sequence_length, return_tensors="pt", is_split_into_words=False)
test_tokenized = tokenizer(test_sequences, padding=True, truncation=True, max_length=max_sequence_length, return_tensors="pt", is_split_into_words=False)
# Truncate the labels to match the tokenized sequence lengths
train_labels = truncate_labels(train_labels, max_sequence_length)
test_labels = truncate_labels(test_labels, max_sequence_length)
# Create train and test datasets
train_dataset = Dataset.from_dict({k: v for k, v in train_tokenized.items()}).add_column("labels", train_labels)
test_dataset = Dataset.from_dict({k: v for k, v in test_tokenized.items()}).add_column("labels", test_labels)
```
Then run the following to get the train/test metrics:
```python
from sklearn.metrics import(
matthews_corrcoef,
accuracy_score,
precision_recall_fscore_support,
roc_auc_score
)
from peft import PeftModel
from transformers import DataCollatorForTokenClassification, AutoModelForTokenClassification
from transformers import Trainer
from accelerate import Accelerator
# Instantiate the accelerator
accelerator = Accelerator()
# Define paths to the LoRA and base models
base_model_path = "facebook/esm2_t12_35M_UR50D"
lora_model_path = "AmelieSchreiber/esm2_t12_35M_lora_binding_sites_cp1" # "path/to/your/lora/model" Replace with the correct path to your LoRA model
# Load the base model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
# Load the LoRA model
model = PeftModel.from_pretrained(base_model, lora_model_path)
model = accelerator.prepare(model) # Prepare the model using the accelerator
# Define label mappings
id2label = {0: "No binding site", 1: "Binding site"}
label2id = {v: k for k, v in id2label.items()}
# Create a data collator
data_collator = DataCollatorForTokenClassification(tokenizer)
# Define a function to compute the metrics
def compute_metrics(dataset):
# Get the predictions using the trained model
trainer = Trainer(model=model, data_collator=data_collator)
predictions, labels, _ = trainer.predict(test_dataset=dataset)
# Remove padding and special tokens
mask = labels != -100
true_labels = labels[mask].flatten()
flat_predictions = np.argmax(predictions, axis=2)[mask].flatten().tolist()
# Compute the metrics
accuracy = accuracy_score(true_labels, flat_predictions)
precision, recall, f1, _ = precision_recall_fscore_support(true_labels, flat_predictions, average='binary')
auc = roc_auc_score(true_labels, flat_predictions)
mcc = matthews_corrcoef(true_labels, flat_predictions) # Compute the MCC
return {"accuracy": accuracy, "precision": precision, "recall": recall, "f1": f1, "auc": auc, "mcc": mcc} # Include the MCC in the returned dictionary
# Get the metrics for the training and test datasets
train_metrics = compute_metrics(train_dataset)
test_metrics = compute_metrics(test_dataset)
train_metrics, test_metrics
```
|