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