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from pathlib import Path
import joblib
import pandas as pd
import sklearn
from datasets import load_dataset
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
accuracy_score,
classification_report,
confusion_matrix,
)
from sklearn.model_selection import StratifiedKFold, cross_val_predict
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
DATASET_ID = "aigovdev/ai-governance-scenarios"
MODEL_PATH = Path("artifacts/model.joblib")
METRICS_PATH = Path("artifacts/metrics.json")
FEATURES = [
"sector",
"impact",
"decision_autonomy",
"human_oversight",
"monitoring",
"traceability",
"technical_documentation",
]
def map_risk(label: str) -> str:
if label in {"low", "limited"}:
return "lower"
if label == "high":
return "high"
if label == "unacceptable":
return "unacceptable"
raise ValueError(f"Unexpected governance_risk label: {label}")
def build_pipeline() -> Pipeline:
preprocessor = ColumnTransformer(
transformers=[
(
"categorical",
OneHotEncoder(
handle_unknown="ignore",
),
FEATURES,
)
],
remainder="drop",
)
classifier = LogisticRegression(
max_iter=2000,
class_weight="balanced",
random_state=42,
)
return Pipeline(
[
("preprocessor", preprocessor),
("classifier", classifier),
]
)
def main():
MODEL_PATH.parent.mkdir(parents=True, exist_ok=True)
dataset = load_dataset(
DATASET_ID,
split="train",
)
df = dataset.to_pandas()
df["risk_tier"] = df["governance_risk"].map(map_risk)
X = df[FEATURES].copy()
y = df["risk_tier"].copy()
print("Dataset:", DATASET_ID)
print("Examples:", len(df))
print()
print("Target distribution:")
print(y.value_counts().sort_index())
print()
cv = StratifiedKFold(
n_splits=3,
shuffle=True,
random_state=42,
)
pipeline = build_pipeline()
predictions = cross_val_predict(
pipeline,
X,
y,
cv=cv,
)
labels = [
"lower",
"high",
"unacceptable",
]
accuracy = accuracy_score(
y,
predictions,
)
report = classification_report(
y,
predictions,
labels=labels,
output_dict=True,
zero_division=0,
)
matrix = confusion_matrix(
y,
predictions,
labels=labels,
)
print("Stratified 3-Fold Cross-Validation")
print("=" * 42)
print(
classification_report(
y,
predictions,
labels=labels,
digits=3,
zero_division=0,
)
)
print("Confusion matrix")
print("Labels:", labels)
print(matrix)
print()
pipeline.fit(
X,
y,
)
joblib.dump(
pipeline,
MODEL_PATH,
)
metrics = {
"dataset": DATASET_ID,
"examples": int(len(df)),
"target_distribution": {
key: int(value)
for key, value in y.value_counts().to_dict().items()
},
"evaluation": {
"method": "stratified_3_fold_cross_validation",
"accuracy": float(accuracy),
"macro_precision": float(
report["macro avg"]["precision"]
),
"macro_recall": float(
report["macro avg"]["recall"]
),
"macro_f1": float(
report["macro avg"]["f1-score"]
),
"confusion_matrix_labels": labels,
"confusion_matrix": matrix.tolist(),
},
"features": FEATURES,
"risk_mapping": {
"low": "lower",
"limited": "lower",
"high": "high",
"unacceptable": "unacceptable",
},
"runtime": {
"scikit_learn": sklearn.__version__,
},
"limitations": [
"Dataset contains only 12 synthetic scenarios.",
"Evaluation is illustrative and not a production benchmark.",
"Risk tiers are engineering labels, not legal classifications.",
],
}
METRICS_PATH.write_text(
json.dumps(
metrics,
indent=2,
),
encoding="utf-8",
)
print("Saved model:", MODEL_PATH.resolve())
print("Saved metrics:", METRICS_PATH.resolve())
if __name__ == "__main__":
main()
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