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d29b763 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 | from __future__ import annotations
import json
import random
from collections import Counter, defaultdict
from pathlib import Path
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
from .predictor import DDIEmbeddingMLP, canonical_pair_key, normalize_name
BASE_DIR = Path(__file__).resolve().parents[2]
DATA_PATH = BASE_DIR / 'data' / 'processed' / 'ddinter_combined.parquet'
MODEL_DIR = BASE_DIR / 'models'
MODEL_PATH = MODEL_DIR / 'ddi_mlp_best.pt'
LABEL_NAMES = ['unknown', 'minor', 'moderate', 'major']
LABEL_TO_INDEX = {label: index for index, label in enumerate(LABEL_NAMES)}
class PairDataset(Dataset):
def __init__(self, examples: list[tuple[int, int, int]]) -> None:
self.examples = examples
def __len__(self) -> int:
return len(self.examples)
def __getitem__(self, index: int) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
drug_a_id, drug_b_id, label_id = self.examples[index]
return (
torch.tensor(drug_a_id, dtype=torch.long),
torch.tensor(drug_b_id, dtype=torch.long),
torch.tensor(label_id, dtype=torch.long),
)
def load_and_aggregate_dataset() -> list[dict[str, str]]:
from preprocessing.artifact_manager import manager
import pandas as pd
df = manager.load_artifact('ddinter_combined')
pair_records: dict[tuple[str, str], Counter] = defaultdict(Counter)
support_records: dict[tuple[str, str], int] = defaultdict(int)
canonical_names: dict[tuple[str, str], tuple[str, str]] = {}
for _, row in df.iterrows():
try:
drug_a = str(row.get('canonical_drug_a') or row.get('Drug_A', '')).strip()
drug_b = str(row.get('canonical_drug_b') or row.get('Drug_B', '')).strip()
severity = str(row.get('Level') or row.get('level', 'Unknown')).strip().lower()
if severity not in LABEL_NAMES:
severity = 'unknown'
key = canonical_pair_key(drug_a, drug_b)
pair_records[key][severity] += 1
support_records[key] += 1
canonical_names.setdefault(key, (drug_a, drug_b))
except Exception:
continue
examples: list[dict[str, str]] = []
for key, counter in pair_records.items():
severity = max(counter.items(), key=lambda item: (item[1], LABEL_TO_INDEX.get(item[0], 0)))[0]
drug_a, drug_b = canonical_names[key]
examples.append(
{
'drug_a': drug_a,
'drug_b': drug_b,
'severity': severity,
'support_count': str(support_records[key]),
}
)
return examples
def build_vocabulary(examples: list[dict[str, str]]) -> dict[str, int]:
vocab: dict[str, int] = {}
for example in examples:
for drug_name in (example['drug_a'], example['drug_b']):
normalized = normalize_name(drug_name)
if normalized not in vocab:
vocab[normalized] = len(vocab) + 1
return vocab
def encode_examples(examples: list[dict[str, str]], vocab: dict[str, int]) -> list[tuple[int, int, int]]:
encoded_examples: list[tuple[int, int, int]] = []
for example in examples:
drug_a_id = vocab.get(normalize_name(example['drug_a']), 0)
drug_b_id = vocab.get(normalize_name(example['drug_b']), 0)
label_id = LABEL_TO_INDEX.get(example['severity'], 0)
encoded_examples.append((drug_a_id, drug_b_id, label_id))
return encoded_examples
def compute_class_weights(labels: list[int]) -> torch.Tensor:
counts = Counter(labels)
total = sum(counts.values())
weights = []
for index in range(len(LABEL_NAMES)):
class_count = max(counts.get(index, 1), 1)
weight = total / (len(LABEL_NAMES) * class_count)
weights.append(weight)
return torch.tensor(weights, dtype=torch.float32)
def split_examples(examples: list[tuple[int, int, int]], seed: int = 42) -> tuple[list, list]:
shuffled = examples[:]
random.Random(seed).shuffle(shuffled)
split_index = max(1, int(len(shuffled) * 0.9))
return shuffled[:split_index], shuffled[split_index:]
def evaluate(model: nn.Module, dataloader: DataLoader, loss_fn: nn.Module) -> tuple[float, float]:
model.eval()
total_loss = 0.0
total_correct = 0
total_items = 0
with torch.no_grad():
for drug_a_ids, drug_b_ids, labels in dataloader:
logits = model(drug_a_ids, drug_b_ids)
loss = loss_fn(logits, labels)
predictions = torch.argmax(logits, dim=-1)
total_loss += float(loss.item()) * labels.size(0)
total_correct += int((predictions == labels).sum().item())
total_items += int(labels.size(0))
average_loss = total_loss / max(total_items, 1)
accuracy = total_correct / max(total_items, 1)
return average_loss, accuracy
def train() -> dict[str, object]:
random.seed(42)
torch.manual_seed(42)
examples = load_and_aggregate_dataset()
vocab = build_vocabulary(examples)
encoded_examples = encode_examples(examples, vocab)
train_examples, valid_examples = split_examples(encoded_examples)
train_labels = [label_id for _, _, label_id in train_examples]
class_weights = compute_class_weights(train_labels)
train_dataset = PairDataset(train_examples)
valid_dataset = PairDataset(valid_examples)
train_loader = DataLoader(train_dataset, batch_size=4096, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=4096, shuffle=False)
model = DDIEmbeddingMLP(
vocab_size=len(vocab) + 1,
embedding_dim=64,
hidden_dim=128,
num_classes=len(LABEL_NAMES),
dropout=0.2,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=1e-4)
loss_fn = nn.CrossEntropyLoss(weight=class_weights)
best_state = None
best_accuracy = -1.0
history: list[dict[str, float]] = []
for epoch in range(4):
model.train()
running_loss = 0.0
running_correct = 0
running_items = 0
for drug_a_ids, drug_b_ids, labels in train_loader:
optimizer.zero_grad(set_to_none=True)
logits = model(drug_a_ids, drug_b_ids)
loss = loss_fn(logits, labels)
loss.backward()
optimizer.step()
predictions = torch.argmax(logits, dim=-1)
running_loss += float(loss.item()) * labels.size(0)
running_correct += int((predictions == labels).sum().item())
running_items += int(labels.size(0))
train_loss = running_loss / max(running_items, 1)
train_accuracy = running_correct / max(running_items, 1)
valid_loss, valid_accuracy = evaluate(model, valid_loader, loss_fn)
history.append(
{
'epoch': float(epoch + 1),
'train_loss': float(train_loss),
'train_accuracy': float(train_accuracy),
'valid_loss': float(valid_loss),
'valid_accuracy': float(valid_accuracy),
}
)
if valid_accuracy >= best_accuracy:
best_accuracy = valid_accuracy
best_state = {key: value.cpu() for key, value in model.state_dict().items()}
print(
f'epoch={epoch + 1} train_loss={train_loss:.4f} train_acc={train_accuracy:.4f} '
f'valid_loss={valid_loss:.4f} valid_acc={valid_accuracy:.4f}'
)
if best_state is None:
best_state = {key: value.cpu() for key, value in model.state_dict().items()}
MODEL_DIR.mkdir(parents=True, exist_ok=True)
checkpoint = {
'model_version': 'medcare-ddi-mlp-v1',
'embedding_dim': 64,
'hidden_dim': 128,
'label_names': LABEL_NAMES,
'label_to_index': LABEL_TO_INDEX,
'index_to_label': {index: label for index, label in enumerate(LABEL_NAMES)},
'drug_vocab': vocab,
'model_state_dict': best_state,
'training_history': history,
'best_validation_accuracy': float(best_accuracy),
'dataset_size': len(encoded_examples),
'vocab_size': len(vocab),
}
torch.save(checkpoint, MODEL_PATH)
summary_path = MODEL_DIR / 'ddi_mlp_best.summary.json'
summary_path.write_text(
json.dumps(
{
'model_version': checkpoint['model_version'],
'best_validation_accuracy': checkpoint['best_validation_accuracy'],
'dataset_size': checkpoint['dataset_size'],
'vocab_size': checkpoint['vocab_size'],
'training_history': history,
},
indent=2,
),
encoding='utf-8',
)
return checkpoint
if __name__ == '__main__':
train()
|