feat: add train/val gap tracking and bootstrap insights

plexe/CODE_INDEX.md

@@ -1,6 +1,6 @@
 # Code Index: plexe
 
-> Generated on 2026-03-02 22:03:39
+> Generated on 2026-03-03 00:06:47
 
 Code structure and public interface documentation for the **plexe** package.
 
@@ -222,7 +222,7 @@ Helper functions for workflow.
 
 **Functions:**
 - `select_viable_model_types(data_layout: DataLayout, selected_frameworks: list[str] | None) -> list[str]` - Select viable model types using three-tier filtering.
-- `evaluate_on_sample(spark: SparkSession, sample_uri: str, model_artifacts_path: Path, model_type: str, metric: str, target_columns: list[str], group_column: str | None) -> float` - Evaluate model on sample (fast).
+- `evaluate_on_sample(spark: SparkSession, sample_uri: str, model_artifacts_path: Path, model_type: str, metric: str, target_columns: list[str], group_column: str | None, train_sample_uri: str | None) -> tuple[float, float | None]` - Evaluate model on validation sample, optionally also on training sample.
 - `compute_metric_hardcoded(y_true, y_pred, metric_name: str) -> float` - Compute metric using hardcoded sklearn implementations.
 - `compute_metric(y_true, y_pred, metric_name: str, group_ids) -> float` - Compute metric value.
 

plexe/agents/hypothesiser.py

@@ -189,7 +189,13 @@ def _summarize_node(node) -> str:
         if not node:
             return "No node"
 
-        summary = f"  Performance: {node.performance:.4f}\n" if node.performance else "  Performance: N/A\n"
+        if node.performance is not None:
+            summary = f"  Val Performance: {node.performance:.4f}\n"
+            if node.train_performance is not None:
+                gap = node.train_performance - node.performance
+                summary += f"  Train Performance: {node.train_performance:.4f} (train-val gap: {gap:+.4f})\n"
+        else:
+            summary = "  Performance: N/A\n"
 
         if node.plan:
             summary += f"  Features: {node.plan.features.strategy}\n"
@@ -236,6 +242,11 @@ def _summarize_history(history: list[dict]) -> str:
             status = f"✓ {perf:.4f}" if success and perf else ("✗ FAILED" if not success else "pending")
             summary += f"  Solution {solution_id} ({stage}): {status}\n"
 
+            train_perf = entry.get("train_performance")
+            if success and perf and train_perf is not None:
+                gap = train_perf - perf
+                summary += f"    Train/val gap: {gap:+.4f}\n"
+
             if entry.get("error"):
                 summary += f"    Error: {entry['error'][:80]}\n"
 

plexe/agents/insight_extractor.py

@@ -209,7 +209,10 @@ def _summarize_results(self, parent_perf: float | None) -> str:
                 if perf_delta is not None:
                     perf_pct = (perf_delta / parent_perf * 100) if parent_perf else 0
                     perf_str += f" ({perf_delta:+.4f}, {perf_pct:+.1f}%)"
-                summary += f"    Performance: {perf_str}\n"
+                summary += f"    Val Performance: {perf_str}\n"
+                if sol.train_performance is not None:
+                    gap = sol.train_performance - sol.performance
+                    summary += f"    Train Performance: {sol.train_performance:.4f} (train-val gap: {gap:+.4f})\n"
             else:
                 summary += f"    Status: {status}\n"
 

plexe/helpers.py

@@ -8,7 +8,7 @@
 
 import logging
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import Any, TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
@@ -93,68 +93,85 @@ def evaluate_on_sample(
     metric: str,
     target_columns: list[str],
     group_column: str | None = None,
-) -> float:
+    train_sample_uri: str | None = None,
+) -> tuple[float, float | None]:
     """
-    Evaluate model on sample (fast).
+    Evaluate model on validation sample, optionally also on training sample.
 
     Args:
         spark: SparkSession
-        sample_uri: Sample data URI
+        sample_uri: Validation sample data URI
         model_artifacts_path: Path to model artifacts
         model_type: "xgboost", "catboost", "lightgbm", "keras", or "pytorch"
         metric: Metric name
         target_columns: Target column names
         group_column: Optional group column for ranking metrics (query_id, session_id)
+        train_sample_uri: Optional training sample URI (for train/val gap computation)
 
     Returns:
-        Performance value
+        Tuple of (val_performance, train_performance). train_performance is None
+        when train_sample_uri is not provided.
     """
+    predictor = _load_predictor(model_artifacts_path, model_type)
+    val_performance = _evaluate_predictor(spark, predictor, sample_uri, metric, target_columns, group_column)
+    logger.info(f"Val sample performance ({metric}): {val_performance:.4f}")
+
+    # TODO: Computing secondary metrics (e.g. per-class breakdown, calibration) per solution during search.
+    train_performance = None
+    if train_sample_uri:
+        train_performance = _evaluate_predictor(
+            spark, predictor, train_sample_uri, metric, target_columns, group_column
+        )
+        gap = train_performance - val_performance
+        logger.info(f"Train sample performance ({metric}): {train_performance:.4f} (train-val gap: {gap:+.4f})")
 
-    logger.info(f"Evaluating on sample with metric: {metric}")
-
-    # Load Sample
-    sample_df = spark.read.parquet(sample_uri).toPandas()
-
-    # Extract group IDs if ranking task
-    group_ids = sample_df[group_column].values if group_column and group_column in sample_df.columns else None
-
-    # Use column names instead of positional indexing to handle target columns in any position
-    columns_to_drop = list(target_columns)
-    if group_column and group_column in sample_df.columns:
-        columns_to_drop.append(group_column)
+    return val_performance, train_performance
 
-    X_sample = sample_df.drop(columns=columns_to_drop)
-    y_sample = sample_df[target_columns[0]]
 
-    # Load Predictor
+def _load_predictor(model_artifacts_path: Path, model_type: str) -> Any:
+    """Load the appropriate predictor for a model type."""
     if model_type == ModelType.XGBOOST:
         from plexe.templates.inference.xgboost_predictor import XGBoostPredictor
 
-        predictor = XGBoostPredictor(str(model_artifacts_path))
+        return XGBoostPredictor(str(model_artifacts_path))
     elif model_type == ModelType.CATBOOST:
         from plexe.templates.inference.catboost_predictor import CatBoostPredictor
 
-        predictor = CatBoostPredictor(str(model_artifacts_path))
+        return CatBoostPredictor(str(model_artifacts_path))
     elif model_type == ModelType.LIGHTGBM:
         from plexe.templates.inference.lightgbm_predictor import LightGBMPredictor
 
-        predictor = LightGBMPredictor(str(model_artifacts_path))
+        return LightGBMPredictor(str(model_artifacts_path))
     elif model_type == ModelType.KERAS:
         from plexe.templates.inference.keras_predictor import KerasPredictor
 
-        predictor = KerasPredictor(str(model_artifacts_path))
+        return KerasPredictor(str(model_artifacts_path))
     else:
         from plexe.templates.inference.pytorch_predictor import PyTorchPredictor
 
-        predictor = PyTorchPredictor(str(model_artifacts_path))
+        return PyTorchPredictor(str(model_artifacts_path))
 
-    # Predict and compute metric on predictions
-    predictions = predictor.predict(X_sample)["prediction"].values
-    performance = compute_metric(y_sample, predictions, metric, group_ids=group_ids)
 
-    logger.info(f"Sample performance ({metric}): {performance:.4f}")
+def _evaluate_predictor(
+    spark: "SparkSession",
+    predictor: Any,
+    data_uri: str,
+    metric: str,
+    target_columns: list[str],
+    group_column: str | None,
+) -> float:
+    """Run predictor on a dataset and compute metric."""
+    df = spark.read.parquet(data_uri).toPandas()
+    group_ids = df[group_column].values if group_column and group_column in df.columns else None
+
+    columns_to_drop = list(target_columns)
+    if group_column and group_column in df.columns:
+        columns_to_drop.append(group_column)
 
-    return performance
+    X = df.drop(columns=columns_to_drop)
+    y = df[target_columns[0]]
+    predictions = predictor.predict(X)["prediction"].values
+    return compute_metric(y, predictions, metric, group_ids=group_ids)
 
 
 def compute_metric_hardcoded(y_true, y_pred, metric_name: str) -> float:

plexe/models.py

@@ -294,7 +294,8 @@ class Solution:
 
     # Execution Results
     model_artifacts_path: Path | None = None
-    performance: float | None = None
+    performance: float | None = None  # Validation set metric
+    train_performance: float | None = None  # Training set metric (for overfitting/underfitting detection)
     training_time: float | None = None
 
     # Tree Structure
@@ -359,6 +360,7 @@ def to_dict(self) -> dict:
             "parent_solution_id": self.parent.solution_id if self.parent else None,
             "child_solution_ids": [c.solution_id for c in self.children],
             "performance": self.performance,
+            "train_performance": self.train_performance,
             "training_time": self.training_time,
             "is_buggy": self.is_buggy,
             "error": self.error,
@@ -398,6 +400,7 @@ def from_dict(d: dict, all_solutions: dict[int, "Solution"]) -> "Solution":
             model_type=d["model_type"],
             model_artifacts_path=Path(d["model_artifacts_path"]) if d.get("model_artifacts_path") else None,
             performance=d.get("performance"),
+            train_performance=d.get("train_performance"),
             training_time=d.get("training_time"),
             parent=None,  # Will be linked in second pass
             children=[],  # Will be linked in second pass

plexe/search/journal.py

@@ -130,6 +130,7 @@ def get_history(self, limit: int = 10) -> list[dict]:
                 "stage": _compute_stage(node),
                 "success": not node.is_buggy,
                 "performance": node.performance,
+                "train_performance": node.train_performance,
                 "error": node.error,
             }
 

plexe/workflow.py

@@ -499,6 +499,11 @@ def build_model(
                 logger.error("No search journal or good nodes available for fallback")
                 logger.error("Proceeding with failed model (will be marked as FAILED)")
 
+        # TODO(EVAL_REFINEMENT_LOOP): When verdict is CONDITIONAL_PASS or FAIL with actionable
+        # HIGH-priority recommendations, summarize evaluation findings into structured feedback,
+        # loop back to search_models() for 1-3 targeted iterations, then re-evaluate.
+        # This creates a closed loop: search -> evaluate -> refine -> re-evaluate.
+
         # Phase 6: Package Final Deliverables
         if start_phase <= 6:
             with tracer.start_as_current_span("Phase 6: Package Final Model"):
@@ -1260,19 +1265,21 @@ def _execute_variant(
             (src_dir / "__init__.py").write_text("# Auto-generated\n")
 
         # Evaluate
-        performance = evaluate_on_sample(
+        performance, train_performance = evaluate_on_sample(
             spark=spark,
             sample_uri=variant_context.val_sample_uri,
             model_artifacts_path=model_artifacts_path,
             model_type=plan.model.model_type,
             metric=variant_context.metric.name,
             target_columns=variant_context.output_targets,
             group_column=variant_context.group_column,
+            train_sample_uri=variant_context.train_sample_uri,
         )
 
         # Update solution
         new_solution.model_artifacts_path = model_artifacts_path
         new_solution.performance = float(performance)
+        new_solution.train_performance = float(train_performance) if train_performance is not None else None
         new_solution.training_time = time.time() - start_time
         new_solution.is_buggy = False
 
@@ -1405,6 +1412,17 @@ def search_models(
 
                     logger.info(f"Generated {len(plans)} bootstrap plan(s)")
 
+                    # Synthetic hypothesis for insight extraction on bootstrap results
+                    hypothesis = Hypothesis(
+                        expand_solution_id=-1,
+                        focus="both",
+                        vary="bootstrap_initial_solutions",
+                        num_variants=len(plans),
+                        rationale="Initial diverse solutions to seed the search tree",
+                        keep_from_parent=[],
+                        expected_impact="Establish baseline performance range across strategies",
+                    )
+
                 except Exception as e:
                     logger.error(f"Bootstrap planning failed: {e}")
                     continue  # Skip this iteration
@@ -1506,10 +1524,9 @@ def search_models(
             solution_id_counter += len(variant_ids)
 
             # ============================================
-            # Step 2e: Extract Insights from Variants (skip in bootstrap mode)
+            # Step 2e: Extract Insights from Variants
             # ============================================
-            if variant_solutions and expand_solution_id is not None:
-                # Only extract insights when we have a hypothesis to learn from
+            if variant_solutions:
                 try:
                     InsightExtractorAgent(
                         hypothesis=hypothesis,
@@ -1699,7 +1716,7 @@ def retrain_on_full_dataset(
     # ============================================
     logger.info("Evaluating final model on full validation set...")
 
-    final_val_performance = evaluate_on_sample(
+    final_val_performance, _ = evaluate_on_sample(
         spark=spark,
         sample_uri=context.val_uri,  # ← FULL validation set
         model_artifacts_path=final_artifacts_path,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "plexe"
-version = "1.4.0"
+version = "1.4.1"
 description = "An agentic framework for building ML models from natural language"
 authors = [
     "Marcello De Bernardi <mdebernardi@plexe.ai>",

tests/CODE_INDEX.md

@@ -1,6 +1,6 @@
 # Code Index: tests
 
-> Generated on 2026-03-02 22:03:39
+> Generated on 2026-03-03 00:06:47
 
 Test suite structure and test case documentation.
 
@@ -122,6 +122,8 @@ Unit tests for SearchJournal.
 - `test_journal_get_history()` - Test history returns recent entries.
 - `test_journal_improvement_trend_improving()` - Test improvement trend with steadily improving solutions.
 - `test_journal_improvement_trend_insufficient_data()` - Test improvement trend with fewer than 2 successful solutions.
+- `test_journal_get_history_includes_train_performance()` - get_history should include train_performance when set on a solution.
+- `test_journal_get_history_train_performance_none()` - get_history should include train_performance=None when not set.
 
 ---
 ## `unit/search/test_tree_policy_determinism.py`
@@ -208,6 +210,10 @@ Unit tests for core model dataclasses.
 
 **Functions:**
 - `test_build_context_update_and_unknown_key()` - Update should set known fields and reject unknown keys.
+- `test_solution_train_performance_defaults_to_none()` - New field should default to None for backward compatibility.
+- `test_solution_to_dict_includes_train_performance()` - to_dict should serialize train_performance.
+- `test_solution_from_dict_backward_compatible()` - Old checkpoints missing train_performance should deserialize cleanly.
+- `test_solution_from_dict_with_train_performance()` - Checkpoints with train_performance should round-trip correctly.
 
 ---
 ## `unit/test_submission_pytorch.py`

tests/unit/search/test_journal.py

@@ -170,3 +170,28 @@ def test_journal_improvement_trend_insufficient_data():
     trend = journal.get_improvement_trend()
 
     assert trend == 0.0
+
+
+# ============================================
+# Train Performance in History Tests
+# ============================================
+
+
+def test_journal_get_history_includes_train_performance():
+    """get_history should include train_performance when set on a solution."""
+    journal = SearchJournal()
+    sol = _make_solution(0, performance=0.85)
+    sol.train_performance = 0.92
+    journal.add_node(sol)
+
+    history = journal.get_history()
+    assert history[0]["train_performance"] == 0.92
+
+
+def test_journal_get_history_train_performance_none():
+    """get_history should include train_performance=None when not set."""
+    journal = SearchJournal()
+    journal.add_node(_make_solution(0, performance=0.85))
+
+    history = journal.get_history()
+    assert history[0]["train_performance"] is None