| --- |
| license: apache-2.0 |
| language: |
| - en |
| metrics: |
| - accuracy |
| pipeline_tag: tabular-regression |
| tags: |
| - code |
| --- |
| |
| # RRF-Savant Meta-State Logistic Regression |
|
|
| ## Model Summary |
|
|
| This repository contains a lightweight **logistic regression** classifier implemented with **scikit-learn**. |
| The model operates on **15-dimensional RRF-Savant meta-state features**, derived from the RRF / SavantEngine pipeline, and outputs a binary prediction with associated probabilities. |
|
|
| It is designed as a fast, interpretable decision layer on top of the richer RRF-Savant embedding and resonance machinery. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Model type:** Logistic Regression (binary classifier) |
| - **Framework:** scikit-learn |
| - **Input dimensionality:** 15 |
| - **Source notebook:** `RRFSavant_AGI_Core_Colab.ipynb` |
| - **File format (recommended):** `joblib` (`.joblib`) |
|
|
| ### Input Features |
|
|
| Each input is a 15-dimensional feature vector: |
|
|
| - **RRF-Savant meta-state features**, including: |
| - φ / Φ phase indicators (RRF-Savant “phi” level) |
| - Ω / omega dynamics or cycle index |
| - Global coherence / resonance scores |
| - Spectral features: |
| - `S_RRF`: spectral smoothness |
| - `C_RRF`: spectral concentration |
| - Energy-like measures (e.g. `E_H`) |
| - Dominant frequency or harmonic index |
| - One-hot encoded Φ nodes / states |
|
|
| > Exact semantics and preprocessing are defined in the source notebook |
| > `RRFSavant_AGI_Core_Colab.ipynb`. |
| |
| ### Outputs |
| |
| - `y_pred`: binary class label (e.g. `0` vs `1`) |
| - `proba`: probability estimates for each class via `predict_proba` |
|
|
| The precise interpretation of class `0` and class `1` (e.g. baseline vs. “RRF-aligned” state, safe vs. risky, etc.) should be documented alongside your use-case. |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| - **Primary use:** |
| - As a **meta-controller** for RRF-Savant systems, mapping high-level meta-state features to a simple decision (binary label). |
| - As a **fast screening / routing head** deciding whether to: |
| - escalate to a heavier RRF/Savant pipeline, |
| - trigger a specific operating mode, |
| - log / flag certain states. |
|
|
| - **Not intended for:** |
| - Standalone critical decision-making (medical, legal, safety-critical applications) without human oversight. |
| - Direct real-world risk scoring without proper calibration and validation. |
|
|
| --- |
|
|
| ## Training |
|
|
| - **Training framework:** scikit-learn `LogisticRegression` |
| - **Data source:** |
| - Internal RRF-Savant meta-state dataset, generated and curated in |
| `RRFSavant_AGI_Core_Colab.ipynb`. |
| - **Preprocessing (typical):** |
| - Numeric features scaled (e.g. `StandardScaler`) |
| - Categorical / discrete Φ nodes one-hot encoded |
| - Train/validation split performed inside the notebook |
| |
| > For exact data splits, preprocessing, and hyperparameters, refer to the Colab notebook. |
|
|
| --- |
|
|
| ## Evaluation |
|
|
| Typical metrics for this model family include: |
|
|
| - Accuracy |
| - ROC-AUC |
| - Precision / Recall / F1 |
| - Calibration of probabilities |
|
|
| You should log and report: |
|
|
| - Metrics on a **held-out test set** |
| - Any **class imbalance** handling performed (e.g. `class_weight="balanced"`) |
|
|
| --- |
|
|
| ## How to Use Assuming model is loaded |
| import joblib |
| import numpy as np |
|
|
| # Load model |
| clf = joblib.load("rrf_savant_meta_logit.joblib") |
| |
| # Example: single feature vector (15 dims) |
| x = np.array([ |
| 0.85087634, 0.67296168, 0.74652746, |
| 0.03735409, 0.72399869, 0.66076596, |
| 0.30312352, 0.69585885, 0.98531076, |
| 0.28866375, 0.99602791, 0.69072907, |
| 0.05884264, 0.74298728, 0.75928443 |
| ]).reshape(1, -1) |
| |
| # Prediction |
| y_pred = clf.predict(x)[0] |
| proba = clf.predict_proba(x)[0] # [P(class 0), P(class 1)] |
| |
| print("Predicted label:", y_pred) |
| print("Probabilities:", proba) |
|
|
|
|
| ### Install Dependencies |
|
|
| ```bash |
| pip install scikit-learn numpy joblib |
