Spaces:
Running
Running
File size: 9,499 Bytes
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 | """Integrity validation for the multisource feature pipeline.
Produces JSON and Markdown reports covering:
- feature-group statistics
- sparsity / dead-dimension diagnostics
- mapping coverage
- train/inference parity
- label distribution checks
"""
from __future__ import annotations
import json
import sys
from dataclasses import asdict
from pathlib import Path
from typing import Any
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT))
from .feature_pipeline_multisource import build_feature_pipeline, transform_pair_features
MODELS_DIR = ROOT / 'models'
REPORT_DIR = MODELS_DIR / 'reports'
REPORT_DIR.mkdir(parents=True, exist_ok=True)
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 _group_statistics(X: np.ndarray, group_slices: dict[str, tuple[int, int]]) -> tuple[pd.DataFrame, dict[str, Any]]:
rows = []
diagnostics: dict[str, Any] = {}
for group_name, (start, end) in group_slices.items():
block = X[:, start:end]
variances = np.var(block, axis=0)
zero_var = int(np.sum(variances <= 1e-10))
rows.append(
{
'group': group_name,
'dims': int(end - start),
'mean': float(np.mean(block)),
'std': float(np.std(block)),
'min': float(np.min(block)),
'max': float(np.max(block)),
'sparsity': float(np.mean(block == 0.0)),
'non_zero_rate': float(np.mean(block != 0.0)),
'zero_var_dims': zero_var,
}
)
diagnostics[group_name] = {
'dims': int(end - start),
'zero_var_dims': zero_var,
'sparsity': float(np.mean(block == 0.0)),
'non_zero_rate': float(np.mean(block != 0.0)),
'all_zero': bool(np.allclose(block, 0.0)),
}
return pd.DataFrame(rows), diagnostics
def _consistency_check(pairs_df: pd.DataFrame, artifacts: dict[str, Any], sample_size: int = 50) -> dict[str, Any]:
rng = np.random.default_rng(2026)
sample_size = min(sample_size, len(pairs_df))
indices = rng.choice(len(pairs_df), size=sample_size, replace=False)
mismatches = []
max_diff = 0.0
for idx in indices:
row = pairs_df.iloc[int(idx)]
train_vector = np.asarray(row['_X'], dtype=np.float32)
inference_vector = transform_pair_features(row['drug_a'], row['drug_b'], artifacts)
if train_vector.shape != inference_vector.shape:
mismatches.append(
{
'index': int(idx),
'drug_a': row['drug_a'],
'drug_b': row['drug_b'],
'issue': 'dimension_mismatch',
'train_dim': int(train_vector.shape[0]),
'inference_dim': int(inference_vector.shape[0]),
}
)
continue
diff = np.abs(train_vector - inference_vector)
sample_max = float(np.max(diff))
max_diff = max(max_diff, sample_max)
if sample_max > 1e-6:
mismatches.append(
{
'index': int(idx),
'drug_a': row['drug_a'],
'drug_b': row['drug_b'],
'issue': 'value_mismatch',
'max_diff': sample_max,
'mean_diff': float(np.mean(diff)),
}
)
return {
'samples_checked': int(sample_size),
'mismatches': mismatches,
'mismatch_count': int(len(mismatches)),
'max_diff': float(max_diff),
}
def _plot_statistics(group_df: pd.DataFrame, label_counts: pd.Series) -> tuple[Path, Path]:
stats_path = REPORT_DIR / 'feature_group_statistics_multisource.png'
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
axes = axes.ravel()
axes[0].bar(group_df['group'], group_df['dims'], color='#4C78A8')
axes[0].set_title('Group Dimensions')
axes[0].tick_params(axis='x', rotation=20)
axes[1].bar(group_df['group'], group_df['sparsity'], color='#F58518')
axes[1].set_title('Group Sparsity')
axes[1].tick_params(axis='x', rotation=20)
axes[2].bar(group_df['group'], group_df['zero_var_dims'], color='#E45756')
axes[2].set_title('Zero-Variance Dims')
axes[2].tick_params(axis='x', rotation=20)
axes[3].bar(label_counts.index.tolist(), label_counts.values.tolist(), color='#72B7B2')
axes[3].set_title('Label Distribution')
axes[3].tick_params(axis='x', rotation=20)
fig.tight_layout()
fig.savefig(stats_path, dpi=160)
plt.close(fig)
consistency_path = REPORT_DIR / 'feature_consistency_multisource.png'
return stats_path, consistency_path
def run_validation(sample_size: int = 1000, consistency_sample: int = 50, seed: int = 2026) -> dict[str, Any]:
pairs_df, artifacts_obj = build_feature_pipeline(save_artifacts=True, sample_size=sample_size, seed=seed)
artifacts = {
'mapper_artifact': artifacts_obj.mapper_artifact,
'group_slices': artifacts_obj.group_slices,
'feature_names': artifacts_obj.feature_names,
'active_feature_mask': artifacts_obj.active_feature_mask,
'semantic_dim': artifacts_obj.semantic_dim,
'drugbank_dim': artifacts_obj.drugbank_dim,
'twosides_hash_dim': artifacts_obj.twosides_hash_dim,
'metadata': artifacts_obj.metadata,
'ddinter_name_to_canonical': artifacts_obj.ddinter_name_to_canonical,
'twosides_cid_to_canonical': artifacts_obj.twosides_cid_to_canonical,
'canonical_entities': artifacts_obj.canonical_entities,
'ddinter_adjacency': artifacts_obj.ddinter_adjacency,
'twosides_pair_stats': artifacts_obj.twosides_pair_stats,
'graph_scaler': artifacts_obj.graph_scaler,
'twosides_scaler': artifacts_obj.twosides_scaler,
'coverage_stats': artifacts_obj.coverage_stats,
}
X = np.asarray(list(pairs_df['_X'].values), dtype=np.float32)
group_df, diagnostics = _group_statistics(X, artifacts_obj.group_slices)
consistency = _consistency_check(pairs_df, artifacts, sample_size=consistency_sample)
label_counts = pairs_df['label'].value_counts()
report = {
'metadata': artifacts_obj.metadata,
'coverage_stats': artifacts_obj.coverage_stats,
'feature_dimension': int(X.shape[1]),
'sample_size': int(len(pairs_df)),
'group_statistics': group_df.to_dict(orient='records'),
'group_diagnostics': diagnostics,
'label_distribution': label_counts.to_dict(),
'train_inference_consistency': consistency,
'dead_groups': [name for name, diag in diagnostics.items() if diag['all_zero'] or diag['zero_var_dims'] >= diag['dims']],
}
md_lines = [
'# Multisource Feature Integrity Report',
'',
f"- **Feature dimension:** {report['feature_dimension']}",
f"- **Samples:** {report['sample_size']}",
f"- **Train/inference mismatches:** {consistency['mismatch_count']} / {consistency['samples_checked']}",
f"- **Max consistency diff:** {consistency['max_diff']:.6f}",
'',
'## Coverage',
'',
_markdown_table(pd.DataFrame([report['coverage_stats']['ddinter'] | {'source': 'ddinter'}, report['coverage_stats']['twosides'] | {'source': 'twosides'}])),
'',
'## Group Statistics',
'',
_markdown_table(group_df),
'',
'## Label Distribution',
'',
_markdown_table(pd.DataFrame([{'label': label, 'count': count} for label, count in label_counts.items()])),
'',
'## Consistency Diagnostics',
'',
]
if consistency['mismatch_count'] == 0:
md_lines.append('No train/inference mismatches detected.')
else:
md_lines.append(f"Detected {consistency['mismatch_count']} mismatches. See JSON for pair-level details.")
md_lines.append('')
for mismatch in consistency['mismatches'][:10]:
md_lines.append(f"- {mismatch['drug_a']} + {mismatch['drug_b']}: {mismatch['issue']}")
md_lines.extend([
'',
'## Dead Groups',
'',
', '.join(report['dead_groups']) if report['dead_groups'] else 'None detected.',
'',
])
json_path = REPORT_DIR / 'feature_integrity_multisource.json'
md_path = REPORT_DIR / 'feature_integrity_multisource.md'
json_path.write_text(json.dumps(report, indent=2, default=str), encoding='utf-8')
md_path.write_text('\n'.join(md_lines), encoding='utf-8')
_plot_statistics(group_df, label_counts)
report['artifacts'] = {
'json': str(json_path),
'markdown': str(md_path),
}
return report
if __name__ == '__main__':
validation = run_validation()
print(json.dumps(validation['metadata'], indent=2))
|