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https://github.com/ghndrx/kubeflow-pipelines.git
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feat: Use self-hosted runner + curated DDI dataset
- 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
This commit is contained in:
@@ -2,7 +2,7 @@
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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 DrugBank data via TDC.
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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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@@ -10,117 +10,125 @@ import runpod
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from typing import Dict, Any, List, Optional
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# DrugBank DDI type mapping to severity categories
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# TDC DrugBank has 86 interaction types - we map to 5 severity levels
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DDI_SEVERITY_MAP = {
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# 0 = No significant interaction / safe
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'no known interaction': 0,
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# 1 = Minor interaction (mechanism-based, low clinical impact)
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'the metabolism of drug1 can be increased': 1,
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'the metabolism of drug1 can be decreased': 1,
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'the absorption of drug1 can be affected': 1,
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'the bioavailability of drug1 can be affected': 1,
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'drug1 may affect the excretion rate': 1,
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# 2 = Moderate interaction (effect-based, monitor patient)
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'the serum concentration of drug1 can be increased': 2,
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'the serum concentration of drug1 can be decreased': 2,
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'the therapeutic efficacy of drug1 can be decreased': 2,
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'the therapeutic efficacy of drug1 can be increased': 2,
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'the protein binding of drug1 can be affected': 2,
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# 3 = Major interaction (significant risk, avoid if possible)
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'the risk or severity of adverse effects can be increased': 3,
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'the risk of bleeding can be increased': 3,
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'the risk of hypotension can be increased': 3,
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'the risk of hypertension can be increased': 3,
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'the risk of hypoglycemia can be increased': 3,
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'the risk of hyperglycemia can be increased': 3,
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'the risk of QTc prolongation can be increased': 3,
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'the risk of cardiotoxicity can be increased': 3,
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'the risk of nephrotoxicity can be increased': 3,
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'the risk of hepatotoxicity can be increased': 3,
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# 4 = Contraindicated (avoid combination)
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'the risk of serotonin syndrome can be increased': 4,
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'the risk of rhabdomyolysis can be increased': 4,
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'the risk of severe hypotension can be increased': 4,
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'the risk of life-threatening arrhythmias can be increased': 4,
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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_severity_label(ddi_type: str) -> int:
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"""Map DDI type string to severity label (0-4)."""
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ddi_lower = ddi_type.lower()
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# Check exact matches first
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for pattern, label in DDI_SEVERITY_MAP.items():
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if pattern in ddi_lower:
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return label
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# Default heuristics based on keywords
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if any(x in ddi_lower for x in ['contraindicated', 'life-threatening', 'fatal', 'death']):
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return 4
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elif any(x in ddi_lower for x in ['severe', 'serious', 'major', 'toxic']):
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return 3
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elif any(x in ddi_lower for x in ['increased', 'decreased', 'risk', 'adverse']):
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return 2
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elif any(x in ddi_lower for x in ['may', 'can', 'affect', 'metabolism']):
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return 1
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else:
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return 0 # Unknown/no interaction
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def load_drugbank_ddi(max_samples: int = 50000) -> List[Dict[str, Any]]:
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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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Load DrugBank DDI dataset from TDC (Therapeutics Data Commons).
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Returns list of {"text": "drug1 drug2 interaction_description", "label": severity}
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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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from tdc.multi_pred import DDI
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import pandas as pd
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import random
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random.seed(42)
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print("Loading DrugBank DDI dataset from TDC...")
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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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# Load the DrugBank DDI dataset
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data = DDI(name='DrugBank')
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df = data.get_data()
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print(f"Total DDI pairs in DrugBank: {len(df)}")
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# Sample if dataset is too large
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if len(df) > max_samples:
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print(f"Sampling {max_samples} examples...")
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df = df.sample(n=max_samples, random_state=42)
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# Convert to training format
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# Expand with variations
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training_data = []
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for _, row in df.iterrows():
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drug1 = row['Drug1']
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drug2 = row['Drug2']
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ddi_type = row['Y'] # Interaction type string
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# Create text input
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text = f"{drug1} {drug2} {ddi_type}"
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# Map to severity label
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label = get_severity_label(ddi_type)
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training_data.append({
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'text': text,
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'label': label
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})
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# Log 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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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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print(f"Label distribution: {label_counts}")
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print(f"Total training samples: {len(training_data)}")
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# Shuffle and limit
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random.shuffle(training_data)
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return 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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@@ -130,13 +138,13 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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Expected input:
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{
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"model_name": "emilyalsentzer/Bio_ClinicalBERT",
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"use_drugbank": true, # Use real DrugBank data
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"max_samples": 50000, # Max samples to use
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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 # Validation split ratio
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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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@@ -153,8 +161,8 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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_drugbank = job_input.get('use_drugbank', True)
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max_samples = job_input.get('max_samples', 50000)
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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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@@ -162,18 +170,12 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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_drugbank and not training_data:
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print("Loading real DrugBank DDI dataset...")
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training_data = load_drugbank_ddi(max_samples=max_samples)
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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 = [
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{"text": "warfarin aspirin the risk of bleeding can be increased", "label": 3},
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{"text": "metformin lisinopril no known interaction", "label": 0},
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{"text": "fluoxetine tramadol the risk of serotonin syndrome can be increased", "label": 4},
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{"text": "simvastatin amiodarone the risk of rhabdomyolysis can be increased", "label": 4},
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{"text": "omeprazole clopidogrel the therapeutic efficacy of drug1 can be decreased", "label": 2},
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] * 30 # 150 samples
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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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@@ -185,6 +187,12 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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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@@ -216,7 +224,7 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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 # Longer for drug names + interaction text
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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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@@ -233,7 +241,7 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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', # Don't save checkpoints
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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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@@ -270,7 +278,7 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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': 'DrugBank' if use_drugbank else 'custom'
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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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@@ -291,7 +299,7 @@ def train_ddi_model(job_input: Dict[str, Any]) -> Dict[str, Any]:
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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 DrugBank DDI data'
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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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