Create evaluation_framework.md

evaluation_framework.md

@@ -0,0 +1,178 @@
+# Clarus Evaluation Framework
+
+The Clarus Stability Benchmark evaluates whether machine learning models can detect **latent instability dynamics** rather than relying on simple correlations.
+
+The evaluation framework measures model capability across multiple reasoning levels.
+
+These levels test progressively more difficult forms of stability reasoning.
+
+---
+
+# Evaluation Levels
+
+## Level 1 — Single Dataset Evaluation
+
+Models are trained and evaluated on the same dataset.
+
+Purpose:
+
+Measure whether the model can detect instability patterns within a single system.
+
+Procedure:
+
+1. Train model on `train.csv`
+2. Generate predictions for `test.csv`
+3. Evaluate using the dataset scorer
+
+Metrics:
+
+- accuracy
+- precision
+- recall
+- f1 score
+- confusion matrix
+
+Limitations:
+
+High performance at this level does not necessarily indicate true stability reasoning.
+
+Models may rely on dataset-specific correlations.
+
+---
+
+## Level 2 — Within-Domain Transfer
+
+Models are trained on one dataset and evaluated on a different dataset within the same domain.
+
+Example:
+
+Train on:
+
+protein-folding-pathway-instability
+
+Test on:
+
+protein-aggregation-risk-instability
+
+Purpose:
+
+Evaluate whether models can generalize stability reasoning across related systems.
+
+Evaluation procedure:
+
+1. Train on source dataset
+2. Predict on target dataset
+3. Score using target dataset scorer
+
+---
+
+## Level 3 — Cross-Domain Transfer
+
+Models are trained on one system domain and evaluated on another.
+
+Domains in the Clarus benchmark include:
+
+- clinical systems
+- molecular / protein systems
+- quantum systems
+
+Example transfer tasks:
+
+| Train Domain | Test Domain |
+|---|---|
+| clinical | clinical |
+| protein | protein |
+| quantum | quantum |
+| clinical | protein |
+| protein | quantum |
+| quantum | clinical |
+
+Purpose:
+
+Determine whether models learn general **stability geometry** rather than domain-specific patterns.
+
+---
+
+# Robustness Evaluation
+
+The benchmark includes robustness tests that simulate real-world system conditions.
+
+---
+
+## Missing Data Evaluation
+
+Real systems often contain incomplete observations.
+
+Trajectory datasets may include variants where timepoints are missing.
+
+Variants include:
+
+- missing t0
+- missing t1
+- missing t2
+- random missing
+
+Purpose:
+
+Evaluate whether models can infer stability dynamics when observations are incomplete.
+
+The prediction task remains unchanged.
+
+---
+
+## Class Imbalance Evaluation
+
+Many real-world systems exhibit rare failure events.
+
+Datasets may include variants with different class distributions.
+
+Supported imbalance regimes:
+
+- balanced (50 / 50)
+- mild imbalance (70 / 30)
+- severe imbalance (90 / 10)
+- extreme imbalance (99 / 1)
+
+Purpose:
+
+Evaluate whether models detect true instability rather than relying on class prevalence.
+
+Accuracy alone is insufficient under imbalance conditions.
+
+Recommended metrics:
+
+- precision
+- recall
+- F1 score
+
+---
+
+# Transfer Stability Score
+
+To summarize transfer performance, the benchmark defines the **Transfer Stability Score (TSS)**.
+
+Definition:
+
+TSS = mean F1 score across all transfer evaluation tasks.
+
+Interpretation:
+
+High TSS indicates that the model has learned stability reasoning that generalizes across systems.
+
+Low TSS suggests the model relies on dataset-specific patterns.
+
+---
+
+# Benchmark Objective
+
+The Clarus benchmark evaluates whether models can detect instability dynamics across complex systems.
+
+The benchmark tests five core capabilities:
+
+1. pattern detection within individual datasets
+2. interaction reasoning across variables
+3. trajectory reasoning over time
+4. robustness to incomplete observation
+5. cross-domain stability reasoning
+
+Models that succeed across all levels demonstrate the ability to reason about **latent stability geometry** rather than simple statistical correlations.