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9c1c0ef | 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 | from __future__ import annotations
import html
import json
from pathlib import Path
from typing import Any
import joblib
def export_artifacts(
run_id: str,
state: dict[str, Any],
run_directory: Path,
) -> dict[str, str]:
run_directory.mkdir(parents=True, exist_ok=True)
bundle = state["model_bundle"]
summary = state["summary_payload"]
pipeline_path = run_directory / "model_pipeline.joblib"
joblib.dump(bundle.pipeline, pipeline_path)
metrics_path = run_directory / "metrics.json"
metrics_path.write_text(json.dumps(summary, indent=2, default=str), encoding="utf-8")
model_card_path = run_directory / "MODEL_CARD.md"
model_card_path.write_text(_model_card(summary), encoding="utf-8")
report_path = run_directory / "analysis_report.html"
report_path.write_text(_html_report(summary), encoding="utf-8")
requirements_path = run_directory / "reproduction.json"
requirements_path.write_text(
json.dumps(
{
"run_id": run_id,
"random_state": state["settings"].random_state,
"test_size": state["settings"].test_size,
"target": summary["profile"]["target"],
"task_type": summary["profile"]["task_type"],
"best_model": summary["best_model"],
},
indent=2,
),
encoding="utf-8",
)
return {
"pipeline": str(pipeline_path),
"metrics": str(metrics_path),
"model_card": str(model_card_path),
"report": str(report_path),
"reproduction": str(requirements_path),
}
def _model_card(run: dict[str, Any]) -> str:
best = run["model_results"][0]
profile = run["profile"]
issues = (
"\n".join(f"- {item['message']}" for item in run["quality_issues"]) or "- None detected"
)
return f"""# Model Card — {run["dataset_name"]}
## Model details
- Run ID: `{run["run_id"]}`
- Task: {profile["task_type"]}
- Target: `{profile["target"]}`
- Selected model: **{run["best_model"]}**
- Training-CV selection metric: `{best["primary_metric"]} = {best["selection_score"]:.4f}`
- One-time untouched test metric: `{best["primary_metric"]} = {best["final_test_score"]:.4f}`
- Training rows before split: {profile["rows"]:,}
## Intended use
Exploratory decision support and portfolio demonstration. Validate with domain-specific,
out-of-time data before any consequential or production use.
## Evaluation
```json
{json.dumps(best["final_test_metrics"], indent=2)}
```
## Data-quality observations
{issues}
## Explainability
Method: **{run["explainability"]["method"]}**. Importance values are predictive associations,
not evidence of causation.
## Limitations
- Results depend on the uploaded dataset and chosen target.
- Automated task inference can be wrong; a domain owner should confirm the objective.
- Fairness, privacy, and legal review are outside the automatic approval gate.
"""
def _html_report(run: dict[str, Any]) -> str:
best = run["model_results"][0]
summary_items = "".join(f"<li>{html.escape(item)}</li>" for item in run["executive_summary"])
recommendations = "".join(f"<li>{html.escape(item)}</li>" for item in run["recommendations"])
issues = (
"".join(
f"<tr><td>{html.escape(item['severity'])}</td><td>{html.escape(item['code'])}</td>"
f"<td>{html.escape(item['message'])}</td></tr>"
for item in run["quality_issues"]
)
or "<tr><td colspan='3'>No material flags</td></tr>"
)
metrics = "".join(
f"<tr><td>{html.escape(name)}</td><td>{value:.4f}</td></tr>"
for name, value in best["final_test_metrics"].items()
)
return f"""<!doctype html>
<html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width">
<title>DataPilot AI report</title>
<style>
body{{font-family:Inter,system-ui,sans-serif;max-width:1000px;margin:40px auto;padding:0 24px;color:#172033}}
h1{{color:#5537d8}} .hero{{background:#f4f1ff;border:1px solid #d9d0ff;padding:24px;border-radius:18px}}
.grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(220px,1fr));gap:16px;margin:20px 0}}
.card{{border:1px solid #e2e6ef;border-radius:14px;padding:18px}} table{{border-collapse:collapse;width:100%}}
td,th{{border-bottom:1px solid #e2e6ef;padding:10px;text-align:left}} small{{color:#667085}}
</style></head><body>
<div class="hero"><h1>DataPilot AI Analysis Report</h1>
<p>{html.escape(run["dataset_name"])} · Run {html.escape(run["run_id"])}</p></div>
<div class="grid">
<div class="card"><small>Selected model</small><h2>{html.escape(run["best_model"])}</h2></div>
<div class="card"><small>Test {html.escape(best["primary_metric"])}</small><h2>{best["final_test_score"]:.3f}</h2></div>
<div class="card"><small>Rows analyzed</small><h2>{run["profile"]["rows"]:,}</h2></div>
</div>
<h2>Executive findings</h2><ul>{summary_items}</ul>
<h2>Evaluation</h2><table><tr><th>Metric</th><th>Value</th></tr>{metrics}</table>
<h2>Data quality</h2><table><tr><th>Severity</th><th>Code</th><th>Observation</th></tr>{issues}</table>
<h2>Recommendations</h2><ol>{recommendations}</ol>
<p><small>Generated from computed evidence. Predictive findings do not establish causality.</small></p>
</body></html>"""
|