mirror of
https://github.com/ghndrx/kubeflow-pipelines.git
synced 2026-02-10 06:45:13 +00:00
Greg Hendrickson
4ff491f847
- Switch to self-hosted runner on compute-01 for faster builds - Replace PyTDC with curated DDI dataset (no heavy deps) - 60+ real drug interaction patterns based on clinical guidelines - Generates up to 10K training samples with text variations - Maintains 5-level severity classification
325 lines
14 KiB
Python
325 lines
14 KiB
Python
"""
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RunPod Serverless Handler for DDI Model Training
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This runs on RunPod GPU instances and trains the Bio_ClinicalBERT model
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for drug-drug interaction detection using real DDI data.
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"""
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import os
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import json
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import runpod
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from typing import Dict, Any, List, Optional
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# DDI severity labels
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DDI_LABELS = {
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0: "none", # No significant interaction
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1: "minor", # Minor interaction
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2: "moderate", # Moderate interaction
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3: "major", # Major interaction
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4: "contraindicated" # Contraindicated
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}
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def get_real_ddi_data(max_samples: int = 10000) -> List[Dict[str, Any]]:
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"""
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Generate real DDI training data from DrugBank patterns.
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Uses curated drug interaction patterns based on clinical guidelines.
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"""
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import random
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random.seed(42)
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# Real drug pairs with known interactions (based on clinical data)
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ddi_patterns = [
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# Contraindicated (4)
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{"drugs": ["fluoxetine", "tramadol"], "type": "serotonin syndrome risk", "label": 4},
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{"drugs": ["fluoxetine", "monoamine oxidase inhibitor"], "type": "serotonin syndrome risk", "label": 4},
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{"drugs": ["simvastatin", "itraconazole"], "type": "rhabdomyolysis risk", "label": 4},
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{"drugs": ["methotrexate", "trimethoprim"], "type": "severe bone marrow suppression", "label": 4},
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{"drugs": ["warfarin", "miconazole"], "type": "severe bleeding risk", "label": 4},
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{"drugs": ["cisapride", "erythromycin"], "type": "QT prolongation cardiac arrest", "label": 4},
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{"drugs": ["pimozide", "clarithromycin"], "type": "QT prolongation risk", "label": 4},
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{"drugs": ["ergotamine", "ritonavir"], "type": "ergot toxicity risk", "label": 4},
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{"drugs": ["sildenafil", "nitrates"], "type": "severe hypotension", "label": 4},
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{"drugs": ["linezolid", "serotonergic agents"], "type": "serotonin syndrome", "label": 4},
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# Major (3)
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{"drugs": ["warfarin", "aspirin"], "type": "increased bleeding risk", "label": 3},
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{"drugs": ["digoxin", "amiodarone"], "type": "digoxin toxicity elevated", "label": 3},
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{"drugs": ["lithium", "ibuprofen"], "type": "lithium toxicity risk", "label": 3},
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{"drugs": ["metformin", "contrast media"], "type": "lactic acidosis risk", "label": 3},
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{"drugs": ["potassium", "ACE inhibitor"], "type": "hyperkalemia risk", "label": 3},
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{"drugs": ["opioid", "benzodiazepine"], "type": "respiratory depression", "label": 3},
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{"drugs": ["theophylline", "ciprofloxacin"], "type": "theophylline toxicity", "label": 3},
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{"drugs": ["phenytoin", "fluconazole"], "type": "phenytoin toxicity", "label": 3},
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{"drugs": ["carbamazepine", "verapamil"], "type": "carbamazepine toxicity", "label": 3},
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{"drugs": ["cyclosporine", "ketoconazole"], "type": "nephrotoxicity risk", "label": 3},
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{"drugs": ["methotrexate", "NSAIDs"], "type": "methotrexate toxicity", "label": 3},
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{"drugs": ["quinidine", "digoxin"], "type": "digoxin toxicity", "label": 3},
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{"drugs": ["clopidogrel", "omeprazole"], "type": "reduced antiplatelet effect", "label": 3},
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{"drugs": ["warfarin", "vitamin K"], "type": "reduced anticoagulation", "label": 3},
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{"drugs": ["dabigatran", "rifampin"], "type": "reduced anticoagulant effect", "label": 3},
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# Moderate (2)
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{"drugs": ["simvastatin", "amlodipine"], "type": "increased statin exposure", "label": 2},
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{"drugs": ["metformin", "cimetidine"], "type": "increased metformin levels", "label": 2},
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{"drugs": ["levothyroxine", "calcium"], "type": "reduced thyroid absorption", "label": 2},
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{"drugs": ["gabapentin", "antacids"], "type": "reduced gabapentin absorption", "label": 2},
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{"drugs": ["furosemide", "gentamicin"], "type": "ototoxicity risk", "label": 2},
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{"drugs": ["prednisone", "NSAIDs"], "type": "GI bleeding risk", "label": 2},
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{"drugs": ["metoprolol", "verapamil"], "type": "bradycardia risk", "label": 2},
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{"drugs": ["sertraline", "tramadol"], "type": "seizure threshold lowered", "label": 2},
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{"drugs": ["losartan", "potassium supplements"], "type": "hyperkalemia risk", "label": 2},
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{"drugs": ["alprazolam", "ketoconazole"], "type": "increased sedation", "label": 2},
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{"drugs": ["atorvastatin", "grapefruit"], "type": "increased statin levels", "label": 2},
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{"drugs": ["ciprofloxacin", "iron"], "type": "reduced antibiotic absorption", "label": 2},
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{"drugs": ["warfarin", "acetaminophen"], "type": "slight INR increase", "label": 2},
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{"drugs": ["insulin", "beta blocker"], "type": "masked hypoglycemia", "label": 2},
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{"drugs": ["digoxin", "spironolactone"], "type": "increased digoxin levels", "label": 2},
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# Minor (1)
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{"drugs": ["aspirin", "ibuprofen"], "type": "reduced cardioprotection", "label": 1},
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{"drugs": ["metformin", "vitamin B12"], "type": "reduced B12 absorption long-term", "label": 1},
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{"drugs": ["amoxicillin", "oral contraceptives"], "type": "theoretical reduced efficacy", "label": 1},
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{"drugs": ["proton pump inhibitor", "vitamin B12"], "type": "reduced absorption", "label": 1},
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{"drugs": ["caffeine", "fluoroquinolones"], "type": "increased caffeine effect", "label": 1},
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{"drugs": ["antacids", "iron"], "type": "timing interaction", "label": 1},
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{"drugs": ["statin", "niacin"], "type": "monitoring recommended", "label": 1},
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{"drugs": ["ACE inhibitor", "aspirin"], "type": "possible reduced effect", "label": 1},
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{"drugs": ["thiazide", "calcium"], "type": "hypercalcemia monitoring", "label": 1},
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{"drugs": ["beta blocker", "clonidine"], "type": "withdrawal monitoring", "label": 1},
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# No interaction (0)
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{"drugs": ["amlodipine", "atorvastatin"], "type": "safe combination", "label": 0},
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{"drugs": ["metformin", "lisinopril"], "type": "complementary therapy", "label": 0},
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{"drugs": ["omeprazole", "levothyroxine"], "type": "can be used together", "label": 0},
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{"drugs": ["aspirin", "atorvastatin"], "type": "standard combination", "label": 0},
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{"drugs": ["metoprolol", "lisinopril"], "type": "common combination", "label": 0},
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{"drugs": ["gabapentin", "acetaminophen"], "type": "no interaction", "label": 0},
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{"drugs": ["sertraline", "omeprazole"], "type": "generally safe", "label": 0},
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{"drugs": ["metformin", "glipizide"], "type": "complementary", "label": 0},
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{"drugs": ["hydrochlorothiazide", "lisinopril"], "type": "synergistic", "label": 0},
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{"drugs": ["pantoprazole", "amlodipine"], "type": "no known interaction", "label": 0},
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]
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# Expand with variations
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training_data = []
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for pattern in ddi_patterns:
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drug1, drug2 = pattern["drugs"]
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interaction_type = pattern["type"]
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label = pattern["label"]
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# Create multiple text variations
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variations = [
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f"{drug1} and {drug2} interaction: {interaction_type}",
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f"{drug2} combined with {drug1} causes {interaction_type}",
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f"Patient taking {drug1} with {drug2}: {interaction_type}",
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f"Concomitant use of {drug1} and {drug2} leads to {interaction_type}",
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f"{drug1} {drug2} drug-drug interaction {interaction_type}",
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]
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for text in variations:
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training_data.append({"text": text, "label": label})
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# Shuffle and limit
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random.shuffle(training_data)
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# Replicate to reach target size
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while len(training_data) < max_samples:
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training_data.extend(training_data[:min(len(training_data), max_samples - len(training_data))])
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return training_data[:max_samples]
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def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Train DDI detection model.
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Expected input:
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{
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"model_name": "emilyalsentzer/Bio_ClinicalBERT",
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"use_real_data": true,
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"max_samples": 5000,
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"training_data": [...], # Or provide inline data
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"epochs": 3,
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"learning_rate": 2e-5,
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"batch_size": 16,
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"eval_split": 0.1
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}
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"""
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import torch
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from transformers import (
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AutoTokenizer,
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AutoModelForSequenceClassification,
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TrainingArguments,
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Trainer
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)
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from datasets import Dataset
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from sklearn.model_selection import train_test_split
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import tempfile
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import shutil
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# Extract parameters
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model_name = job_input.get('model_name', 'emilyalsentzer/Bio_ClinicalBERT')
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use_real_data = job_input.get('use_real_data', True)
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max_samples = job_input.get('max_samples', 5000)
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training_data = job_input.get('training_data', None)
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epochs = job_input.get('epochs', 3)
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learning_rate = job_input.get('learning_rate', 2e-5)
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batch_size = job_input.get('batch_size', 16)
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eval_split = job_input.get('eval_split', 0.1)
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# Load data
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if use_real_data and not training_data:
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print("Loading curated DDI dataset...")
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training_data = get_real_ddi_data(max_samples=max_samples)
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elif not training_data:
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print("No training data provided, using sample DDI dataset...")
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training_data = get_real_ddi_data(max_samples=150)
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# Create temp directory
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work_dir = tempfile.mkdtemp()
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model_dir = os.path.join(work_dir, 'model')
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try:
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print(f"Training samples: {len(training_data)}")
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print(f"Model: {model_name}")
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print(f"Epochs: {epochs}, Batch size: {batch_size}")
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print(f"GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}")
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# Count label distribution
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label_counts = {}
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for item in training_data:
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label_counts[item['label']] = label_counts.get(item['label'], 0) + 1
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print(f"Label distribution: {label_counts}")
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# Split into train/eval
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if eval_split > 0 and len(training_data) > 100:
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train_data, eval_data = train_test_split(
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training_data,
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test_size=eval_split,
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random_state=42,
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stratify=[d['label'] for d in training_data]
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)
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print(f"Train: {len(train_data)}, Eval: {len(eval_data)}")
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else:
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train_data = training_data
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eval_data = None
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# Create datasets
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train_dataset = Dataset.from_list(train_data)
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eval_dataset = Dataset.from_list(eval_data) if eval_data else None
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# Load model and tokenizer
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print(f"Loading model: {model_name}")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_name,
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num_labels=5 # DDI severity: none(0), minor(1), moderate(2), major(3), contraindicated(4)
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)
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# Tokenize datasets
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def tokenize_function(examples):
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return tokenizer(
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examples['text'],
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padding='max_length',
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truncation=True,
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max_length=256
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)
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tokenized_train = train_dataset.map(tokenize_function, batched=True)
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tokenized_eval = eval_dataset.map(tokenize_function, batched=True) if eval_dataset else None
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# Training arguments
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training_args = TrainingArguments(
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output_dir=model_dir,
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num_train_epochs=epochs,
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learning_rate=learning_rate,
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per_device_train_batch_size=batch_size,
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per_device_eval_batch_size=batch_size,
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warmup_ratio=0.1,
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weight_decay=0.01,
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logging_steps=50,
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eval_strategy='epoch' if tokenized_eval else 'no',
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save_strategy='no',
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fp16=torch.cuda.is_available(),
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report_to='none',
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load_best_model_at_end=False,
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)
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# Compute metrics function
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def compute_metrics(eval_pred):
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from sklearn.metrics import accuracy_score, f1_score
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predictions, labels = eval_pred
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predictions = predictions.argmax(-1)
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return {
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'accuracy': accuracy_score(labels, predictions),
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'f1_macro': f1_score(labels, predictions, average='macro'),
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'f1_weighted': f1_score(labels, predictions, average='weighted'),
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}
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# Initialize trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_train,
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eval_dataset=tokenized_eval,
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compute_metrics=compute_metrics if tokenized_eval else None,
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)
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# Train
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print("Starting training...")
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train_result = trainer.train()
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# Get metrics
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metrics = {
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'train_loss': float(train_result.training_loss),
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'epochs': epochs,
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'model_name': model_name,
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'samples': len(train_data),
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'gpu': torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU',
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'data_source': 'curated_ddi'
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}
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# Run evaluation if we have eval data
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if tokenized_eval:
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print("Running evaluation...")
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eval_result = trainer.evaluate()
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metrics.update({
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'eval_loss': float(eval_result['eval_loss']),
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'eval_accuracy': float(eval_result['eval_accuracy']),
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'eval_f1_macro': float(eval_result['eval_f1_macro']),
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'eval_f1_weighted': float(eval_result['eval_f1_weighted']),
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})
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print(f"Training complete! Loss: {metrics['train_loss']:.4f}")
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if 'eval_accuracy' in metrics:
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print(f"Eval accuracy: {metrics['eval_accuracy']:.4f}, F1: {metrics['eval_f1_weighted']:.4f}")
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return {
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'status': 'success',
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'metrics': metrics,
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'message': 'Model trained successfully on curated DDI data'
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}
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except Exception as e:
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import traceback
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return {
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'status': 'error',
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'error': str(e),
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'traceback': traceback.format_exc()
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}
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finally:
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# Cleanup
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shutil.rmtree(work_dir, ignore_errors=True)
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def handler(job):
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"""RunPod serverless handler."""
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job_input = job.get('input', {})
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return train_ddi_model(job_input)
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# RunPod serverless entrypoint
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runpod.serverless.start({'handler': handler})
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