Jv branch

Examples/API_workflow/robert_api_full_workflow.py

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+#!/usr/bin/env python
+"""
+Full ROBERT workflow via the Python API.
+
+Runs CURATE, GENERATE (Bayesian hyperparameter optimization), VERIFY,
+PREDICT, and REPORT; prints sklearn and ROBERT scores and artifact paths.
+
+Usage (from repository root)::
+
+    python Examples/API_workflow/robert_api_full_workflow.py
+
+Requires WeasyPrint system libraries for PDF generation (see ROBERT docs).
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+_REPO = Path(__file__).resolve().parents[2]
+if str(_REPO) not in sys.path:
+    sys.path.insert(0, str(_REPO))
+
+from robert import RobertModel  # noqa: E402
+
+_CSV = _REPO / "tests" / "Robert_example.csv"
+_WORKDIR = Path(__file__).resolve().parent / "robert_api_run"
+
+
+def main() -> None:
+    df = pd.read_csv(_CSV, encoding="utf-8")
+    X = df.drop(columns=["Target_values"])
+    y = df["Target_values"]
+    n_fit = 25
+
+    model = RobertModel(
+        problem_type="reg",
+        filter_mode="no_pfi",
+        workdir=_WORKDIR,
+        names="Name",
+        report=True,
+        plot_verbosity=2,
+        model=["RF", "GB"],
+        n_iter=2,
+        init_points=2,
+        repeat_kfolds=2,
+        kfold=3,
+        seed=42,
+    )
+
+    print("Fitting (CURATE → GENERATE → VERIFY → PREDICT → REPORT)...")
+    model.fit(X.iloc[:n_fit], y.iloc[:n_fit])
+
+    info = model.best_model_info()
+    print(f"Best model: {info['model']}  descriptors: {len(info['descriptors'])}")
+
+    X_test = X.iloc[n_fit:].drop_duplicates(subset=["Name"], keep="first")
+    y_test = y.loc[X_test.index]
+    preds = model.predict(X_test)
+    r2 = model.score(X_test, y_test)
+    print(f"Holdout R² (sklearn): {r2:.3f}")
+    print(f"Holdout predictions shape: {preds.shape}")
+
+    scores = model.robert_scores()
+    print(f"\nROBERT score ({scores['suffix']}): {scores['robert_score']}")
+    print("Sub-scores:")
+    for name, value in scores["components"].items():
+        print(f"  - {name}: {value}")
+
+    workdir = Path(model.workdir_)
+    print("\nKey outputs:")
+    for pattern in (
+        "CURATE/*.png",
+        "GENERATE/Heatmap*.png",
+        "VERIFY/VERIFY_tests_*.png",
+        "PREDICT/*.png",
+        "ROBERT_report.pdf",
+    ):
+        matches = sorted(workdir.glob(pattern))
+        for path in matches[:3]:
+            print(f"  {path.relative_to(workdir)}")
+        if len(matches) > 3:
+            print(f"  ... ({len(matches)} files matching {pattern})")
+
+    if scores["pdf_path"]:
+        print(f"\nReport PDF: {scores['pdf_path']}")
+    else:
+        print("\nx  ROBERT_report.pdf was not created (WeasyPrint may be missing).")
+
+
+if __name__ == "__main__":
+    main()