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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 | """Multisource ablation study for DDInter + DrugBank + TWOSIDES fusion."""
from __future__ import annotations
import gc
import json
import sys
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT))
from .feature_pipeline_multisource import build_feature_pipeline
from .retrain_production_model import TrainConfig, train_and_evaluate
REPORT_DIR = ROOT / 'models' / 'reports'
REPORT_DIR.mkdir(parents=True, exist_ok=True)
def _mask_groups(X: np.ndarray, group_slices: dict[str, tuple[int, int]], enabled_groups: list[str]) -> np.ndarray:
masked = np.zeros_like(X)
for group_name in enabled_groups:
if group_name not in group_slices:
continue
start, end = group_slices[group_name]
masked[:, start:end] = X[:, start:end]
return masked
def _markdown_table(df: pd.DataFrame) -> str:
headers = list(df.columns)
rows = [headers]
for _, row in df.iterrows():
rows.append([str(row[col]) for col in headers])
widths = [max(len(cell) for cell in column) for column in zip(*rows)]
lines = []
lines.append('| ' + ' | '.join(header.ljust(widths[idx]) for idx, header in enumerate(headers)) + ' |')
lines.append('| ' + ' | '.join('-' * widths[idx] for idx in range(len(headers))) + ' |')
for row in rows[1:]:
lines.append('| ' + ' | '.join(cell.ljust(widths[idx]) for idx, cell in enumerate(row)) + ' |')
return '\n'.join(lines)
def _save_confusion_matrix(cm: list[list[int]], labels: list[str], out_path: Path) -> None:
matrix = np.asarray(cm, dtype=np.int64)
fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(matrix, cmap='Blues')
fig.colorbar(im, ax=ax)
ax.set_xticks(range(len(labels)))
ax.set_yticks(range(len(labels)))
ax.set_xticklabels(labels, rotation=45, ha='right')
ax.set_yticklabels(labels)
ax.set_xlabel('Predicted')
ax.set_ylabel('True')
ax.set_title('Confusion Matrix')
threshold = matrix.max() / 2.0 if matrix.size else 0
for i in range(matrix.shape[0]):
for j in range(matrix.shape[1]):
ax.text(j, i, str(matrix[i, j]), ha='center', va='center', color='white' if matrix[i, j] > threshold else 'black')
fig.tight_layout()
fig.savefig(out_path, dpi=160)
plt.close(fig)
def main() -> None:
print('Building multisource feature pipeline...', flush=True)
pairs_df, artifacts = build_feature_pipeline(save_artifacts=True, sample_size=3000, seed=2026)
X = np.asarray(list(pairs_df['_X'].values), dtype=np.float32)
group_slices = artifacts.group_slices
base_groups = ['semantic_embeddings', 'graph_topology', 'source_flags']
arms = {
'ddinter_only': base_groups,
'ddinter_drugbank': base_groups + ['drugbank_metadata'],
'ddinter_twosides': base_groups + ['twosides_signals'],
'full_fusion': base_groups + ['drugbank_metadata', 'twosides_signals'],
'full_no_semantic': ['drugbank_metadata', 'twosides_signals', 'graph_topology', 'source_flags'],
'full_no_graph': ['semantic_embeddings', 'drugbank_metadata', 'twosides_signals', 'source_flags'],
}
config = TrainConfig(
seed=2026,
embedding_dim=64,
hidden_dim=128,
dropout=0.2,
lr=1e-3,
batch_size=128,
weight_decay=1e-5,
epochs=3,
loss_type='focal',
sampler='weighted',
class_weights=[],
)
results: list[dict[str, object]] = []
for arm_name, enabled_groups in arms.items():
arm_X = _mask_groups(X, group_slices, enabled_groups)
arm_df = pairs_df[['drug_a', 'drug_b', 'label']].copy()
arm_df['_X'] = list(arm_X.tolist())
train_df, temp_df = train_test_split(arm_df, test_size=0.2, stratify=arm_df['label'], random_state=2026)
val_df, test_df = train_test_split(temp_df, test_size=0.5, stratify=temp_df['label'], random_state=2026)
print(f'Running arm={arm_name} with groups={enabled_groups}', flush=True)
report = train_and_evaluate(config, train_df, val_df, test_df, vocab={})
result = {
'arm': arm_name,
'accuracy': report['accuracy'],
'macro_f1': report['macro_f1'],
'severe_recall': report['severe_recall'],
'num_test_examples': report['num_test_examples'],
'enabled_groups': enabled_groups,
}
results.append(result)
_save_confusion_matrix(report['confusion_matrix'], report['label_names'], REPORT_DIR / f'ablation_confusion_matrix_{arm_name}.png')
del arm_X, arm_df, train_df, val_df, test_df, report
gc.collect()
summary_df = pd.DataFrame(results).sort_values(by=['severe_recall', 'macro_f1'], ascending=False)
summary_csv = REPORT_DIR / 'ablation_summary_multisource.csv'
summary_json = REPORT_DIR / 'ablation_summary_multisource.json'
summary_md = REPORT_DIR / 'ablation_report_multisource.md'
chart_path = REPORT_DIR / 'ablation_metrics_multisource.png'
summary_df.to_csv(summary_csv, index=False)
summary_json.write_text(json.dumps(results, indent=2), encoding='utf-8')
fig, axes = plt.subplots(1, 3, figsize=(15, 4), sharex=True)
for ax, metric in zip(axes, ['accuracy', 'macro_f1', 'severe_recall']):
ax.bar(summary_df['arm'], summary_df[metric], color=['#4C78A8', '#72B7B2', '#F58518', '#54A24B', '#E45756', '#B279A2'])
ax.set_title(metric.replace('_', ' ').title())
ax.set_ylim(0, 1)
ax.tick_params(axis='x', rotation=20)
fig.tight_layout()
fig.savefig(chart_path, dpi=160)
plt.close(fig)
md_lines = [
'# Multisource Ablation Study',
'',
_markdown_table(summary_df),
'',
'## Arms',
'',
'- `ddinter_only`: semantic_embeddings + graph_topology + source_flags',
'- `ddinter_drugbank`: ddinter_only + drugbank_metadata',
'- `ddinter_twosides`: ddinter_only + twosides_signals',
'- `full_fusion`: all groups',
'- `full_no_semantic`: full fusion without semantic_embeddings',
'- `full_no_graph`: full fusion without graph_topology',
'',
'## Artifacts',
'',
f'- CSV: {summary_csv}',
f'- JSON: {summary_json}',
f'- Chart: {chart_path}',
]
summary_md.write_text('\n'.join(md_lines), encoding='utf-8')
print('Ablation complete.')
print(f'Summary CSV: {summary_csv}')
print(f'Summary JSON: {summary_json}')
print(f'Report: {summary_md}')
if __name__ == '__main__':
main()
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