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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 | """Corrected ablation study using fixed feature pipeline."""
from __future__ import annotations
import json
import sys
import gc
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 src.training.feature_pipeline_corrected import build_feature_pipeline
from src.training.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:
"""Mask features to enable/disable groups."""
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 _save_confusion_matrix(cm: list[list[int]], labels: list[str], out_path: Path) -> None:
"""Save confusion matrix as PNG."""
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 _markdown_table(df: pd.DataFrame) -> str:
"""Render DataFrame as markdown table."""
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 main() -> None:
"""Run corrected ablation study."""
print('Building corrected feature pipeline (DDInter-only)...', flush=True)
pairs_df, artifacts = build_feature_pipeline(save_artifacts=True, sample_size=3000, seed=2026)
group_slices = artifacts.group_slices
X = np.asarray(list(pairs_df['_X'].values), dtype=np.float32)
base_groups = ['pair_encoding', 'semantic_embeddings']
arms = {
'pair_encoding_only': ['pair_encoding'],
'pair_encoding_semantic': ['pair_encoding', 'semantic_embeddings'],
'pair_encoding_support': ['pair_encoding', 'pair_support'],
'full': ['pair_encoding', 'semantic_embeddings', 'pair_support'],
}
results: list[dict[str, object]] = []
summary_by_arm: dict[str, 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', 'pair_id']].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)
config = TrainConfig(
seed=2026,
embedding_dim=32,
hidden_dim=64,
dropout=0.15,
lr=1e-3,
batch_size=128,
weight_decay=1e-5,
epochs=2,
loss_type='focal',
sampler='weighted',
class_weights=[],
)
print(f'Running arm={arm_name} with groups={enabled_groups} and samples={len(arm_df)}', flush=True)
report = train_and_evaluate(config, train_df, val_df, test_df, vocab={})
summary = {
'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(summary)
summary_by_arm[arm_name] = report
cm_path = REPORT_DIR / f'ablation_confusion_matrix_{arm_name}.png'
_save_confusion_matrix(report['confusion_matrix'], report['label_names'], cm_path)
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_corrected.csv'
summary_df.to_csv(summary_csv, index=False)
summary_json = REPORT_DIR / 'ablation_summary_corrected.json'
summary_json.write_text(json.dumps(results, indent=2), encoding='utf-8')
chart_path = REPORT_DIR / 'ablation_metrics_corrected.png'
fig, axes = plt.subplots(1, 3, figsize=(14, 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'])
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)
report_md = REPORT_DIR / 'ablation_report_corrected.md'
lines = [
'# Corrected Ablation Study Report',
'',
'## Summary',
'',
_markdown_table(summary_df),
'',
'## Interpretation',
'',
'- **pair_encoding_only**: Baseline using only hashed pair names.',
'- **pair_encoding_semantic**: Adds drug name n-gram embeddings.',
'- **pair_encoding_support**: Adds frequency of pair occurrence.',
'- **full**: All three feature groups combined.',
'',
'If ablation shows meaningful differences now, the features are working correctly.',
'',
'## Artifacts',
'',
f'- CSV: {summary_csv}',
f'- JSON: {summary_json}',
f'- Chart: {chart_path}',
]
report_md.write_text('\n'.join(lines), encoding='utf-8')
print('Corrected ablation complete.')
print(f'Summary CSV: {summary_csv}')
print(f'Summary JSON: {summary_json}')
print(f'Report: {report_md}')
if __name__ == '__main__':
main()
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