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"{html.escape(item)}" for item in run["executive_summary"])
recommendations = "".join(f"{html.escape(item)}" for item in run["recommendations"])
issues = (
"".join(
f"| {html.escape(item['severity'])} | {html.escape(item['code'])} | "
f"{html.escape(item['message'])} |
"
for item in run["quality_issues"]
)
or "| No material flags |
"
)
metrics = "".join(
f"| {html.escape(name)} | {value:.4f} |
"
for name, value in best["final_test_metrics"].items()
)
return f"""
DataPilot AI report
DataPilot AI Analysis Report
{html.escape(run["dataset_name"])} · Run {html.escape(run["run_id"])}
Selected model
{html.escape(run["best_model"])}
Test {html.escape(best["primary_metric"])}
{best["final_test_score"]:.3f}
Rows analyzed
{run["profile"]["rows"]:,}
Executive findings
Evaluation
Data quality
| Severity | Code | Observation |
|---|
{issues}
Recommendations
{recommendations}
Generated from computed evidence. Predictive findings do not establish causality.
"""