mirror of
https://github.com/ghndrx/kubeflow-pipelines.git
synced 2026-02-10 06:45:13 +00:00
feat: add ADE, Triage, and Symptom-Disease training pipelines
New tasks supported: - task=ade: Adverse Drug Event classification (ADE Corpus V2, 30K samples) - task=triage: Medical Triage classification (urgency levels) - task=symptom_disease: Symptom-to-Disease prediction (40+ diseases) All use HuggingFace datasets, Bio_ClinicalBERT, and S3 model storage.
This commit is contained in:
@@ -445,23 +445,483 @@ def train_bert_classifier(job_input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
shutil.rmtree(work_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def train_ade_classifier(job_input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Train Adverse Drug Event (ADE) binary classifier.
|
||||
Dataset: ade-benchmark-corpus/ade_corpus_v2 (30K samples)
|
||||
Labels: 0=No ADE, 1=ADE Present
|
||||
"""
|
||||
import torch
|
||||
import tempfile
|
||||
import shutil
|
||||
from datasets import load_dataset
|
||||
from transformers import (
|
||||
AutoTokenizer, AutoModelForSequenceClassification,
|
||||
TrainingArguments, Trainer
|
||||
)
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
work_dir = tempfile.mkdtemp()
|
||||
|
||||
try:
|
||||
model_name = job_input.get('model_name', 'emilyalsentzer/Bio_ClinicalBERT')
|
||||
max_samples = job_input.get('max_samples', 10000)
|
||||
epochs = job_input.get('epochs', 3)
|
||||
batch_size = job_input.get('batch_size', 16)
|
||||
learning_rate = job_input.get('learning_rate', 2e-5)
|
||||
eval_split = job_input.get('eval_split', 0.1)
|
||||
|
||||
print(f"Loading ADE Corpus V2 dataset...")
|
||||
dataset = load_dataset("ade-benchmark-corpus/ade_corpus_v2", "Ade_corpus_v2_classification")
|
||||
|
||||
# Prepare data
|
||||
training_data = []
|
||||
for item in dataset['train']:
|
||||
if max_samples and len(training_data) >= max_samples:
|
||||
break
|
||||
training_data.append({
|
||||
'text': item['text'],
|
||||
'label': item['label'] # 0 or 1
|
||||
})
|
||||
|
||||
print(f"Loaded {len(training_data)} ADE samples")
|
||||
|
||||
# Split
|
||||
if eval_split > 0:
|
||||
train_data, eval_data = train_test_split(
|
||||
training_data, test_size=eval_split, random_state=42,
|
||||
stratify=[d['label'] for d in training_data]
|
||||
)
|
||||
else:
|
||||
train_data, eval_data = training_data, None
|
||||
|
||||
from datasets import Dataset
|
||||
train_dataset = Dataset.from_list(train_data)
|
||||
eval_dataset = Dataset.from_list(eval_data) if eval_data else None
|
||||
|
||||
# Load model (binary: ADE / No ADE)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForSequenceClassification.from_pretrained(
|
||||
model_name, num_labels=2
|
||||
)
|
||||
|
||||
def tokenize(examples):
|
||||
return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=256)
|
||||
|
||||
train_dataset = train_dataset.map(tokenize, batched=True)
|
||||
if eval_dataset:
|
||||
eval_dataset = eval_dataset.map(tokenize, batched=True)
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir=work_dir,
|
||||
num_train_epochs=epochs,
|
||||
learning_rate=learning_rate,
|
||||
per_device_train_batch_size=batch_size,
|
||||
eval_strategy='epoch' if eval_dataset else 'no',
|
||||
save_strategy='no',
|
||||
fp16=torch.cuda.is_available(),
|
||||
report_to='none',
|
||||
)
|
||||
|
||||
def compute_metrics(eval_pred):
|
||||
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
|
||||
preds = eval_pred.predictions.argmax(-1)
|
||||
return {
|
||||
'accuracy': accuracy_score(eval_pred.label_ids, preds),
|
||||
'f1': f1_score(eval_pred.label_ids, preds),
|
||||
'precision': precision_score(eval_pred.label_ids, preds),
|
||||
'recall': recall_score(eval_pred.label_ids, preds),
|
||||
}
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
compute_metrics=compute_metrics if eval_dataset else None,
|
||||
)
|
||||
|
||||
train_result = trainer.train()
|
||||
|
||||
metrics = {
|
||||
'task': 'ade_classification',
|
||||
'train_loss': float(train_result.training_loss),
|
||||
'epochs': epochs,
|
||||
'model_name': model_name,
|
||||
'samples': len(train_data),
|
||||
'gpu': torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU',
|
||||
'data_source': 'ade_corpus_v2',
|
||||
}
|
||||
|
||||
if eval_dataset:
|
||||
eval_result = trainer.evaluate()
|
||||
metrics.update({
|
||||
'eval_accuracy': float(eval_result['eval_accuracy']),
|
||||
'eval_f1': float(eval_result['eval_f1']),
|
||||
'eval_precision': float(eval_result['eval_precision']),
|
||||
'eval_recall': float(eval_result['eval_recall']),
|
||||
})
|
||||
|
||||
# Save to S3
|
||||
s3_uri = None
|
||||
s3_bucket = job_input.get('s3_bucket')
|
||||
if s3_bucket:
|
||||
save_dir = os.path.join(work_dir, 'saved_model')
|
||||
trainer.save_model(save_dir)
|
||||
tokenizer.save_pretrained(save_dir)
|
||||
|
||||
aws_creds = {
|
||||
'aws_access_key_id': job_input.get('aws_access_key_id'),
|
||||
'aws_secret_access_key': job_input.get('aws_secret_access_key'),
|
||||
'aws_session_token': job_input.get('aws_session_token'),
|
||||
'aws_region': job_input.get('aws_region', 'us-east-1'),
|
||||
}
|
||||
s3_prefix = job_input.get('s3_prefix', 'ade-models/bert')
|
||||
s3_uri = upload_to_s3(save_dir, s3_bucket, s3_prefix, aws_creds)
|
||||
metrics['s3_uri'] = s3_uri
|
||||
|
||||
return {'status': 'success', 'metrics': metrics, 'model_uri': s3_uri, 'message': 'ADE classifier trained on ADE Corpus V2'}
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
return {'status': 'error', 'error': str(e), 'traceback': traceback.format_exc()}
|
||||
finally:
|
||||
shutil.rmtree(work_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def train_triage_classifier(job_input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Train Medical Triage classifier.
|
||||
Dataset: shubham212/Medical_Triage_Classification
|
||||
Labels: Triage urgency levels
|
||||
"""
|
||||
import torch
|
||||
import tempfile
|
||||
import shutil
|
||||
from datasets import load_dataset, Dataset
|
||||
from transformers import (
|
||||
AutoTokenizer, AutoModelForSequenceClassification,
|
||||
TrainingArguments, Trainer
|
||||
)
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
work_dir = tempfile.mkdtemp()
|
||||
|
||||
try:
|
||||
model_name = job_input.get('model_name', 'emilyalsentzer/Bio_ClinicalBERT')
|
||||
max_samples = job_input.get('max_samples', 5000)
|
||||
epochs = job_input.get('epochs', 3)
|
||||
batch_size = job_input.get('batch_size', 8)
|
||||
learning_rate = job_input.get('learning_rate', 2e-5)
|
||||
eval_split = job_input.get('eval_split', 0.1)
|
||||
|
||||
print(f"Loading Medical Triage dataset...")
|
||||
dataset = load_dataset("shubham212/Medical_Triage_Classification")
|
||||
|
||||
# Get unique labels
|
||||
labels = sorted(set(item['label'] for item in dataset['train']))
|
||||
label2id = {l: i for i, l in enumerate(labels)}
|
||||
id2label = {i: l for l, i in label2id.items()}
|
||||
num_labels = len(labels)
|
||||
|
||||
print(f"Found {num_labels} triage levels: {labels}")
|
||||
|
||||
training_data = []
|
||||
for item in dataset['train']:
|
||||
if max_samples and len(training_data) >= max_samples:
|
||||
break
|
||||
training_data.append({
|
||||
'text': item['text'],
|
||||
'label': label2id[item['label']]
|
||||
})
|
||||
|
||||
print(f"Loaded {len(training_data)} triage samples")
|
||||
|
||||
if eval_split > 0:
|
||||
train_data, eval_data = train_test_split(
|
||||
training_data, test_size=eval_split, random_state=42,
|
||||
stratify=[d['label'] for d in training_data]
|
||||
)
|
||||
else:
|
||||
train_data, eval_data = training_data, None
|
||||
|
||||
train_dataset = Dataset.from_list(train_data)
|
||||
eval_dataset = Dataset.from_list(eval_data) if eval_data else None
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForSequenceClassification.from_pretrained(
|
||||
model_name, num_labels=num_labels, id2label=id2label, label2id=label2id
|
||||
)
|
||||
|
||||
def tokenize(examples):
|
||||
return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=512)
|
||||
|
||||
train_dataset = train_dataset.map(tokenize, batched=True)
|
||||
if eval_dataset:
|
||||
eval_dataset = eval_dataset.map(tokenize, batched=True)
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir=work_dir,
|
||||
num_train_epochs=epochs,
|
||||
learning_rate=learning_rate,
|
||||
per_device_train_batch_size=batch_size,
|
||||
eval_strategy='epoch' if eval_dataset else 'no',
|
||||
save_strategy='no',
|
||||
fp16=torch.cuda.is_available(),
|
||||
report_to='none',
|
||||
)
|
||||
|
||||
def compute_metrics(eval_pred):
|
||||
from sklearn.metrics import accuracy_score, f1_score
|
||||
preds = eval_pred.predictions.argmax(-1)
|
||||
return {
|
||||
'accuracy': accuracy_score(eval_pred.label_ids, preds),
|
||||
'f1_weighted': f1_score(eval_pred.label_ids, preds, average='weighted'),
|
||||
}
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
compute_metrics=compute_metrics if eval_dataset else None,
|
||||
)
|
||||
|
||||
train_result = trainer.train()
|
||||
|
||||
metrics = {
|
||||
'task': 'triage_classification',
|
||||
'train_loss': float(train_result.training_loss),
|
||||
'epochs': epochs,
|
||||
'model_name': model_name,
|
||||
'samples': len(train_data),
|
||||
'num_labels': num_labels,
|
||||
'labels': id2label,
|
||||
'gpu': torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU',
|
||||
'data_source': 'medical_triage_classification',
|
||||
}
|
||||
|
||||
if eval_dataset:
|
||||
eval_result = trainer.evaluate()
|
||||
metrics.update({
|
||||
'eval_accuracy': float(eval_result['eval_accuracy']),
|
||||
'eval_f1_weighted': float(eval_result['eval_f1_weighted']),
|
||||
})
|
||||
|
||||
s3_uri = None
|
||||
s3_bucket = job_input.get('s3_bucket')
|
||||
if s3_bucket:
|
||||
save_dir = os.path.join(work_dir, 'saved_model')
|
||||
trainer.save_model(save_dir)
|
||||
tokenizer.save_pretrained(save_dir)
|
||||
|
||||
aws_creds = {
|
||||
'aws_access_key_id': job_input.get('aws_access_key_id'),
|
||||
'aws_secret_access_key': job_input.get('aws_secret_access_key'),
|
||||
'aws_session_token': job_input.get('aws_session_token'),
|
||||
'aws_region': job_input.get('aws_region', 'us-east-1'),
|
||||
}
|
||||
s3_prefix = job_input.get('s3_prefix', 'triage-models/bert')
|
||||
s3_uri = upload_to_s3(save_dir, s3_bucket, s3_prefix, aws_creds)
|
||||
metrics['s3_uri'] = s3_uri
|
||||
|
||||
return {'status': 'success', 'metrics': metrics, 'model_uri': s3_uri, 'message': 'Triage classifier trained'}
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
return {'status': 'error', 'error': str(e), 'traceback': traceback.format_exc()}
|
||||
finally:
|
||||
shutil.rmtree(work_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def train_symptom_disease_classifier(job_input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Train Symptom-to-Disease classifier.
|
||||
Dataset: shanover/disease_symptoms_prec_full
|
||||
Task: Predict disease from symptoms
|
||||
"""
|
||||
import torch
|
||||
import tempfile
|
||||
import shutil
|
||||
from datasets import load_dataset, Dataset
|
||||
from transformers import (
|
||||
AutoTokenizer, AutoModelForSequenceClassification,
|
||||
TrainingArguments, Trainer
|
||||
)
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
work_dir = tempfile.mkdtemp()
|
||||
|
||||
try:
|
||||
model_name = job_input.get('model_name', 'emilyalsentzer/Bio_ClinicalBERT')
|
||||
max_samples = job_input.get('max_samples', 5000)
|
||||
epochs = job_input.get('epochs', 3)
|
||||
batch_size = job_input.get('batch_size', 16)
|
||||
learning_rate = job_input.get('learning_rate', 2e-5)
|
||||
eval_split = job_input.get('eval_split', 0.1)
|
||||
|
||||
print(f"Loading Symptom-Disease dataset...")
|
||||
dataset = load_dataset("shanover/disease_symptoms_prec_full")
|
||||
|
||||
# Build label mapping from diseases
|
||||
diseases = sorted(set(item['disease'] for item in dataset['train']))
|
||||
label2id = {d: i for i, d in enumerate(diseases)}
|
||||
id2label = {i: d for d, i in label2id.items()}
|
||||
num_labels = len(diseases)
|
||||
|
||||
print(f"Found {num_labels} diseases")
|
||||
|
||||
training_data = []
|
||||
for item in dataset['train']:
|
||||
if max_samples and len(training_data) >= max_samples:
|
||||
break
|
||||
# Format symptoms as natural text
|
||||
symptoms = item['symptoms'].replace('_', ' ').replace(',', ', ')
|
||||
training_data.append({
|
||||
'text': f"Patient presents with: {symptoms}",
|
||||
'label': label2id[item['disease']]
|
||||
})
|
||||
|
||||
print(f"Loaded {len(training_data)} symptom-disease samples")
|
||||
|
||||
if eval_split > 0:
|
||||
train_data, eval_data = train_test_split(
|
||||
training_data, test_size=eval_split, random_state=42
|
||||
)
|
||||
else:
|
||||
train_data, eval_data = training_data, None
|
||||
|
||||
train_dataset = Dataset.from_list(train_data)
|
||||
eval_dataset = Dataset.from_list(eval_data) if eval_data else None
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
model = AutoModelForSequenceClassification.from_pretrained(
|
||||
model_name, num_labels=num_labels, id2label=id2label, label2id=label2id
|
||||
)
|
||||
|
||||
def tokenize(examples):
|
||||
return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=256)
|
||||
|
||||
train_dataset = train_dataset.map(tokenize, batched=True)
|
||||
if eval_dataset:
|
||||
eval_dataset = eval_dataset.map(tokenize, batched=True)
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir=work_dir,
|
||||
num_train_epochs=epochs,
|
||||
learning_rate=learning_rate,
|
||||
per_device_train_batch_size=batch_size,
|
||||
eval_strategy='epoch' if eval_dataset else 'no',
|
||||
save_strategy='no',
|
||||
fp16=torch.cuda.is_available(),
|
||||
report_to='none',
|
||||
)
|
||||
|
||||
def compute_metrics(eval_pred):
|
||||
from sklearn.metrics import accuracy_score, f1_score, top_k_accuracy_score
|
||||
preds = eval_pred.predictions.argmax(-1)
|
||||
metrics = {
|
||||
'accuracy': accuracy_score(eval_pred.label_ids, preds),
|
||||
'f1_weighted': f1_score(eval_pred.label_ids, preds, average='weighted'),
|
||||
}
|
||||
# Top-5 accuracy (important for diagnosis)
|
||||
try:
|
||||
metrics['top5_accuracy'] = top_k_accuracy_score(
|
||||
eval_pred.label_ids, eval_pred.predictions, k=5
|
||||
)
|
||||
except:
|
||||
pass
|
||||
return metrics
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
compute_metrics=compute_metrics if eval_dataset else None,
|
||||
)
|
||||
|
||||
train_result = trainer.train()
|
||||
|
||||
metrics = {
|
||||
'task': 'symptom_disease_classification',
|
||||
'train_loss': float(train_result.training_loss),
|
||||
'epochs': epochs,
|
||||
'model_name': model_name,
|
||||
'samples': len(train_data),
|
||||
'num_diseases': num_labels,
|
||||
'gpu': torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU',
|
||||
'data_source': 'disease_symptoms_prec_full',
|
||||
}
|
||||
|
||||
if eval_dataset:
|
||||
eval_result = trainer.evaluate()
|
||||
metrics.update({
|
||||
'eval_accuracy': float(eval_result['eval_accuracy']),
|
||||
'eval_f1_weighted': float(eval_result['eval_f1_weighted']),
|
||||
})
|
||||
if 'eval_top5_accuracy' in eval_result:
|
||||
metrics['eval_top5_accuracy'] = float(eval_result['eval_top5_accuracy'])
|
||||
|
||||
s3_uri = None
|
||||
s3_bucket = job_input.get('s3_bucket')
|
||||
if s3_bucket:
|
||||
save_dir = os.path.join(work_dir, 'saved_model')
|
||||
trainer.save_model(save_dir)
|
||||
tokenizer.save_pretrained(save_dir)
|
||||
|
||||
# Save label mapping
|
||||
with open(os.path.join(save_dir, 'disease_labels.json'), 'w') as f:
|
||||
json.dump({'id2label': id2label, 'label2id': label2id}, f)
|
||||
|
||||
aws_creds = {
|
||||
'aws_access_key_id': job_input.get('aws_access_key_id'),
|
||||
'aws_secret_access_key': job_input.get('aws_secret_access_key'),
|
||||
'aws_session_token': job_input.get('aws_session_token'),
|
||||
'aws_region': job_input.get('aws_region', 'us-east-1'),
|
||||
}
|
||||
s3_prefix = job_input.get('s3_prefix', 'symptom-disease-models/bert')
|
||||
s3_uri = upload_to_s3(save_dir, s3_bucket, s3_prefix, aws_creds)
|
||||
metrics['s3_uri'] = s3_uri
|
||||
|
||||
return {'status': 'success', 'metrics': metrics, 'model_uri': s3_uri, 'message': 'Symptom-Disease classifier trained'}
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
return {'status': 'error', 'error': str(e), 'traceback': traceback.format_exc()}
|
||||
finally:
|
||||
shutil.rmtree(work_dir, ignore_errors=True)
|
||||
|
||||
|
||||
def handler(job):
|
||||
"""RunPod serverless handler."""
|
||||
"""RunPod serverless handler with multi-task support."""
|
||||
job_input = job.get('input', {})
|
||||
|
||||
model_name = job_input.get('model_name', 'meta-llama/Llama-3.1-8B-Instruct')
|
||||
use_lora = job_input.get('use_lora', True)
|
||||
# Task routing
|
||||
task = job_input.get('task', 'ddi')
|
||||
|
||||
# Auto-detect: use LoRA for large models
|
||||
if any(x in model_name.lower() for x in ['gemma', 'llama', 'mistral', 'qwen']):
|
||||
use_lora = True
|
||||
elif 'bert' in model_name.lower():
|
||||
use_lora = False
|
||||
|
||||
if use_lora:
|
||||
if task == 'ade':
|
||||
return train_ade_classifier(job_input)
|
||||
elif task == 'triage':
|
||||
return train_triage_classifier(job_input)
|
||||
elif task == 'symptom_disease':
|
||||
return train_symptom_disease_classifier(job_input)
|
||||
elif task == 'ddi' or task == 'bert':
|
||||
# Original DDI training
|
||||
model_name = job_input.get('model_name', 'emilyalsentzer/Bio_ClinicalBERT')
|
||||
if 'bert' in model_name.lower():
|
||||
return train_bert_classifier(job_input)
|
||||
else:
|
||||
return train_llm_lora(job_input)
|
||||
elif task == 'llm':
|
||||
return train_llm_lora(job_input)
|
||||
else:
|
||||
return train_bert_classifier(job_input)
|
||||
# Auto-detect based on model
|
||||
model_name = job_input.get('model_name', 'meta-llama/Llama-3.1-8B-Instruct')
|
||||
if any(x in model_name.lower() for x in ['gemma', 'llama', 'mistral', 'qwen']):
|
||||
return train_llm_lora(job_input)
|
||||
else:
|
||||
return train_bert_classifier(job_input)
|
||||
|
||||
|
||||
# RunPod serverless entrypoint
|
||||
|
||||
Reference in New Issue
Block a user