Update README.md

README.md

@@ -1,112 +1,272 @@
-import csv
-import json
-import sys
-from typing import Dict, List
-
-
-DEFAULT_INPUT_PATH = "data/tester.csv"
-
-
-def _safe_float(value, default: float = 0.0) -> float:
-    try:
-        return float(value)
-    except (TypeError, ValueError):
-        return default
-
-
-def _read_csv(path: str) -> List[Dict[str, str]]:
-    with open(path, "r", encoding="utf-8") as f:
-        return list(csv.DictReader(f))
-
-
-def _detect_id_column(fieldnames: List[str]) -> str:
-    preferred = ["id", "row_id", "sample_id", "case_id", "record_id"]
-    for col in preferred:
-        if col in fieldnames:
-            return col
-    return ""
-
-
-def heuristic_score(row: Dict[str, str]) -> float:
-    oxygen_delivery_score = _safe_float(row.get("oxygen_delivery_score"))
-    buffer_capacity_index = _safe_float(row.get("buffer_capacity_index"))
-    latent_coupling_pressure = _safe_float(row.get("latent_coupling_pressure"))
-    compensation_fatigue_score = _safe_float(row.get("compensation_fatigue_score"))
-    drift_gradient = _safe_float(row.get("drift_gradient"))
-    coherence_stability_score = _safe_float(row.get("coherence_stability_score"))
-    context_integrity_score = _safe_float(row.get("context_integrity_score"))
-    decision_readiness_score = _safe_float(row.get("decision_readiness_score"))
-
-    score = 0.0
-    score += max(0.0, 1.0 - oxygen_delivery_score) * 1.1
-    score += max(0.0, 1.0 - buffer_capacity_index) * 1.2
-    score += latent_coupling_pressure * 1.3
-    score += compensation_fatigue_score * 1.1
-    score += max(0.0, drift_gradient) * 1.4
-    score += max(0.0, 1.0 - coherence_stability_score) * 1.1
-    score += max(0.0, 1.0 - context_integrity_score) * 1.0
-    score += max(0.0, 1.0 - decision_readiness_score) * 1.0
-
-    if score < 0.0:
-        return 0.0
-    if score > 1.0:
-        return 1.0
-    return round(score, 6)
-
-
-def generate_predictions(
-    input_path: str = DEFAULT_INPUT_PATH,
-    output_path: str = "predictions.csv",
-    threshold: float = 0.5,
-) -> Dict[str, object]:
-    rows = _read_csv(input_path)
-    if not rows:
-        raise ValueError("Input file is empty.")
-
-    fieldnames = list(rows[0].keys())
-    id_col = _detect_id_column(fieldnames)
-
-    output_rows = []
-    positive_predictions = 0
-
-    for idx, row in enumerate(rows):
-        row_id = row[id_col] if id_col else str(idx)
-        pred_score = heuristic_score(row)
-        pred_label = 1 if pred_score >= threshold else 0
-        positive_predictions += pred_label
-
-        output_rows.append(
-            {
-                "id": row_id,
-                "prediction_score": pred_score,
-                "prediction": pred_label,
-            }
-        )
-
-    with open(output_path, "w", encoding="utf-8", newline="") as f:
-        writer = csv.DictWriter(f, fieldnames=["id", "prediction_score", "prediction"])
-        writer.writeheader()
-        writer.writerows(output_rows)
-
-    return {
-        "input_path": input_path,
-        "output_path": output_path,
-        "rows_processed": len(rows),
-        "threshold_used": threshold,
-        "predicted_positive_support": positive_predictions,
-        "predicted_negative_support": len(rows) - positive_predictions,
-        "note": "This is a dataset-specific baseline heuristic, not the canonical evaluation scorer.",
-    }
-
-
-if __name__ == "__main__":
-    input_path = sys.argv[1] if len(sys.argv) > 1 else DEFAULT_INPUT_PATH
-    output_path = sys.argv[2] if len(sys.argv) > 2 else "predictions.csv"
-    threshold = float(sys.argv[3]) if len(sys.argv) > 3 else 0.5
-
-    result = generate_predictions(
-        input_path=input_path,
-        output_path=output_path,
-        threshold=threshold,
-    )
-    print(json.dumps(result, indent=2))
+---
+language: en
+license: mit
+task_categories:
+  - text-classification
+tags:
+  - clinical-trials
+  - trajectory-aware
+  - clarus
+  - latent-cross-coupling
+  - oxygen
+  - buffer
+size_categories:
+  - n<1K
+pretty_name: Clinical Latent Cross Coupling Oxygen Buffer Instability v0.2
+---
+
+# Clinical Latent Cross Coupling Oxygen Buffer Instability v0.2
+
+## What this is
+
+A small dataset that tests one question:
+
+Can you detect when an oxygen-buffer system is moving toward hidden instability, not just carrying visible strain?
+
+This repo focuses on latent cross coupling between oxygen delivery and buffer capacity.
+
+It models a system where:
+
+- oxygen delivery may weaken
+- buffer capacity may erode
+- latent coupling pressure may rise
+- compensation fatigue may accumulate before overt collapse appears
+
+## Run this first
+
+Generate baseline predictions:
+
+```bash
+python baseline_heuristic.py data/tester.csv predictions.csv
+
+Score them:
+
+python scorer.py data/tester.csv predictions.csv
+
+That is enough to see the full evaluation loop.
+
+You will get:
+
+standard metrics
+
+trajectory detection performance
+
+oxygen-buffer instability detection errors
+
+What to try next
+
+Replace the baseline.
+
+Build your own model.
+
+Output a file like:
+
+id,prediction_score
+0,0.12
+1,0.81
+2,0.67
+
+Then run:
+
+python scorer.py data/tester.csv your_predictions.csv
+What matters
+
+Not just accuracy.
+
+The key signals are:
+
+recall_trajectory_deterioration_detection
+
+false_stable_trajectory_rate
+
+These tell you:
+
+are you catching systems that are getting worse
+
+are you missing hidden oxygen-buffer collapse
+
+Data
+
+Each row represents a latent oxygen-buffer coupling state.
+
+Core variables:
+
+oxygen_delivery_score
+
+buffer_capacity_index
+
+latent_coupling_pressure
+
+compensation_fatigue_score
+
+drift_gradient
+
+coherence_stability_score
+
+context_integrity_score
+
+decision_readiness_score
+
+Target:
+
+label_oxygen_buffer_instability
+
+Important distinction
+
+There are two different components in this repo.
+
+scorer.py
+
+evaluates predictions
+
+domain-agnostic
+
+works across all v0.2 datasets
+
+does not generate predictions
+
+baseline_heuristic.py
+
+generates predictions
+
+domain-specific
+
+uses the variables in this dataset
+
+Do not reuse the heuristic across datasets.
+
+It is only a local reference.
+
+What changed from v0.1
+
+v0.1:
+
+static latent coupling classification
+
+v0.2:
+
+adds direction via drift_gradient
+
+This allows you to separate:
+
+strained but stabilizing coupling states
+
+strained and deteriorating coupling states
+
+Why this exists
+
+Most models answer:
+
+what is happening now
+
+This tests:
+
+where the hidden interaction is going
+
+That difference is where failure appears early.
+
+Files
+
+data/train.csv — training data
+
+data/tester.csv — evaluation data
+
+scorer.py — canonical evaluation script
+
+baseline_heuristic.py — dataset-specific reference model
+
+README.md — dataset card
+
+Evaluation
+
+Primary metric:
+
+recall_trajectory_deterioration_detection
+
+Secondary metric:
+
+false_stable_trajectory_rate
+
+Standard metrics are also reported:
+
+accuracy
+
+precision
+
+recall
+
+f1
+
+The scorer supports binary predictions or score-based predictions.
+
+License
+
+MIT
+
+Structural Note
+
+Clarus datasets are structural instruments.
+
+They are designed to expose instability geometry, not just predict isolated outcomes.
+
+This v0.2 repo adds directional state movement so the dataset can separate static oxygen-buffer strain from active deterioration in latent cross coupling.
+
+Production Deployment
+
+This dataset can be used in:
+
+respiratory instability research
+
+compensation failure monitoring
+
+hidden coupling benchmarking
+
+critical care trajectory modeling
+
+model benchmarking for trajectory-aware latent coupling reasoning
+
+It is suitable for research and prototyping.
+
+It is not a substitute for live clinical judgment.
+
+Enterprise & Research Collaboration
+
+Clarus builds datasets for:
+
+instability detection
+
+trajectory tracking
+
+intervention reasoning
+
+These structures are not domain-bound.
+
+They apply wherever systems move toward or away from failure.
+
+Applicable domains include:
+
+healthcare systems
+
+financial markets
+
+energy infrastructure
+
+logistics networks
+
+artificial intelligence systems
+
+manufacturing systems
+
+supply chains
+
+climate systems
+
+Any environment where:
+
+capacity and demand interact
+
+delays and coupling exist
+
+trajectory determines outcome
+
+This dataset is one instance of a general stability framework.