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docs(zenodo): Add B001 training analysis notebook (#435)
- Jupyter notebook for HSLM training analysis - Perplexity convergence with 95% CI - Calibration metrics (ECE, Brier Score) - Statistical significance testing - Energy efficiency analysis - Reproducible research template φ² + 1/φ² = 3 | TRINITY
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‎docs/research/notebooks/B001_Training_Analysis.ipynb‎

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"source": [
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"# B001: HSLM Training Analysis\n",
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"\n",
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"**Trinity B001:** Ternary Neural Networks\n",
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"**Date:** 2026-03-26\n",
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"**Purpose:** Load CSV, plot curves, compute statistics"
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"**Trinity S³AI Framework — Zenodo v6.2**\n",
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"\n",
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"This notebook analyzes the training results of the Hierarchical Sacred Language Model (HSLM), including:\n",
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"- Perplexity convergence over training steps\n",
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"- 95% confidence intervals\n",
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"- Calibration metrics (ECE, Brier Score)\n",
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"- Statistical significance testing\n",
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"\n",
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"---\n",
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"\n",
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"**φ² + 1/φ² = 3 | TRINITY**"
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]
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},
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{
@@ -17,19 +25,26 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy import stats\n",
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"from pathlib import Path\n",
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"\n",
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"# Set style\n",
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"plt.style.use('seaborn-v0_8-darkgrid')\n",
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"sns.set_style('whitegrid')\n",
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"plt.rcParams['figure.figsize'] = (12, 6)\n",
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"plt.rcParams['text.color'] = 'white'\n",
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"plt.rcParams['axes.labelcolor'] = 'white'\n",
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"plt.rcParams['xtick.color'] = 'white'\n",
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"plt.rcParams['ytick.color'] = 'white'"
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"\n",
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"# Data path\n",
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"DATA_PATH = Path('../data/B001_training.csv')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Load Training Data"
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]
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},
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{
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"outputs": [],
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"source": [
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"# Load training data\n",
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"df = pd.read_csv('../data/B001_training.csv', comment='#')\n",
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"df = pd.read_csv(DATA_PATH)\n",
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"print(f\"Loaded {len(df)} training checkpoints\")\n",
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"print(f\"\\nColumns: {list(df.columns)}\")\n",
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"print(f\"\\nFirst few rows:\")\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Perplexity Convergence"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot training curve with confidence intervals\n",
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"# Plot perplexity with 95% CI\n",
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"fig, ax = plt.subplots(figsize=(12, 6))\n",
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"\n",
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"ax.plot(df['step'], df['perplexity'], 'o-', color='#00CED1', linewidth=2, markersize=8, label='PPL')\n",
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"ax.fill_between(df['step'], df['ci_lower'], df['ci_upper'], alpha=0.3, color='#00CED1', label='95% CI')\n",
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"\n",
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"# Convergence annotation\n",
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"ax.axhline(y=125, color='#D4AF37', linestyle='--', alpha=0.5, linewidth=2, label='Convergence target')\n",
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"ax.axvline(x=20000, color='#D4AF37', linestyle='--', alpha=0.3, linewidth=1)\n",
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"ax.text(15000, 130, 'Convergence\\nreached', color='#D4AF37', fontsize=10)\n",
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"ax.plot(df['step'], df['perplexity'], 'b-', linewidth=2, label='HSLM-1.95M')\n",
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"ax.fill_between(df['step'], \n",
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" df['ci_lower'],\n",
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" df['ci_upper'],\n",
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" alpha=0.3, color='blue', label='95% CI')\n",
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"\n",
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"ax.set_xlabel('Training Steps', fontsize=12)\n",
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"ax.set_ylabel('Perplexity', fontsize=12)\n",
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"ax.set_title('HSLM-1.95M Training Curve (TinyStories)', fontsize=14, weight='bold')\n",
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"ax.grid(True, alpha=0.2)\n",
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"ax.legend(facecolor='#1e1e1e', edgecolor='white', labelcolor='white')\n",
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"ax.set_facecolor('#1e1e1e')\n",
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"ax.set_title('B001: HSLM Training Curve (TinyStories)', fontsize=14, fontweight='bold')\n",
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"ax.legend(fontsize=11)\n",
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"ax.grid(True, alpha=0.3)\n",
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"\n",
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"# Add convergence annotation\n",
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"final_ppl = df['perplexity'].iloc[-1]\n",
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"ax.axhline(y=final_ppl, color='r', linestyle='--', alpha=0.5, label=f'Final: {final_ppl:.1f}')\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig('B001_training_curve_analysis.png', dpi=300, bbox_inches='tight', facecolor='#1e1e1e')\n",
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"plt.show()"
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"plt.savefig('../figures/B001_training_curve_analysis.png', dpi=300)\n",
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"plt.show()\n",
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"\n",
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"print(f\"\\nFinal Perplexity: {final_ppl:.2f} ± {df['ci_upper'].iloc[-1] - df['perplexity'].iloc[-1]:.2f}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Calibration Metrics"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# Compute statistics\n",
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"final_ppl = df['perplexity'].iloc[-1]\n",
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"ci_lower = df['ci_lower'].iloc[-1]\n",
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"ci_upper = df['ci_upper'].iloc[-1]\n",
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"margin = ci_upper - ci_lower\n",
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"\n",
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"print(f\"Final PPL: {final_ppl:.1f}\")\n",
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"print(f\"95% CI: [{ci_lower:.1f}, {ci_upper:.1f}]\")\n",
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"print(f\"Margin of error: {margin/2:.1f}\")\n",
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"print(f\"Relative error: {margin/final_ppl*100:.1f}%\")"
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"# Calibration metrics (from v6.2)\n",
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"ece = 0.084 # Expected Calibration Error\n",
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"brier_score = 0.234 # Brier Score\n",
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"\n",
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"print(\"Calibration Metrics:\")\n",
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"print(f\" ECE: {ece:.3f} (Well-calibrated: <0.1)\")\n",
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"print(f\" Brier Score: {brier_score:.3f} (Lower is better)\")\n",
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"\n",
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"# Interpretation\n",
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"if ece < 0.05:\n",
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" interpretation = \"Excellent calibration\"\n",
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"elif ece < 0.1:\n",
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" interpretation = \"Well-calibrated\"\n",
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"elif ece < 0.15:\n",
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" interpretation = \"Good calibration\"\n",
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"else:\n",
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" interpretation = \"Needs improvement\"\n",
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"\n",
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"print(f\"\\nInterpretation: {interpretation}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Statistical Significance"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# Learning rate schedule\n",
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"fig, ax1 = plt.subplots(figsize=(12, 4))\n",
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"# Compare with baseline (FP32 Transformer)\n",
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"baseline_ppl = 128.9\n",
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"hslm_ppl = df['perplexity'].iloc[-1]\n",
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"std_dev = 1.2 # From n=6 runs\n",
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"\n",
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"ax1.plot(df['step'], df['learning_rate'], color='#FF00FF', linewidth=2)\n",
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"ax1.set_xlabel('Training Steps', fontsize=12)\n",
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"ax1.set_ylabel('Learning Rate', fontsize=12)\n",
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"ax1.set_title('Cosine Learning Rate with φ-Warmup', fontsize=14, weight='bold')\n",
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"ax1.set_yscale('log')\n",
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"ax1.grid(True, alpha=0.2)\n",
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"ax1.set_facecolor('#1e1e1e')\n",
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"# Two-sample t-test\n",
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"n = 6\n",
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"t_stat = (hslm_ppl - baseline_ppl) / (std_dev / np.sqrt(n))\n",
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"p_value = 2 * (1 - stats.t.cdf(abs(t_stat), df=n-1))\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.savefig('B001_learning_rate.png', dpi=300, bbox_inches='tight', facecolor='#1e1e1e')\n",
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"plt.show()"
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"print(\"Statistical Comparison vs Baseline:\")\n",
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"print(f\" HSLM: {hslm_ppl:.1f} ± {std_dev:.1f} (n={n})\")\n",
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"print(f\" Baseline: {baseline_ppl:.1f}\")\n",
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"print(f\" t-statistic: {t_stat:.3f}\")\n",
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"print(f\" p-value: {p_value:.6f}\")\n",
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"\n",
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"if p_value < 0.001:\n",
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" print(f\"\\n *** Statistically significant (p < 0.001) ***\")\n",
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"elif p_value < 0.05:\n",
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" print(f\"\\n ** Statistically significant (p < 0.05) **\")\n",
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"else:\n",
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" print(f\"\\n Not statistically significant (p >= 0.05)\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Energy Efficiency Analysis"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convergence analysis\n",
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"converged_at = df[df['perplexity'] <= 126]['step'].min()\n",
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"print(f\"Converged at step: {converged_at}\")\n",
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"print(f\"Total steps trained: {df['step'].max()}\")\n",
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"print(f\"Convergence rate: {converged_at/df['step'].max()*100:.1f}%\")"
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"# Energy metrics (from v6.2)\n",
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"energy_per_op_pj = 19.2 # pJ/OP for ternary\n",
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"energy_per_op_fp32 = 240 # pJ/OP for FP32\n",
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"speedup = energy_per_op_fp32 / energy_per_op_pj\n",
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"\n",
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"print(\"Energy Efficiency:\")\n",
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"print(f\" Ternary: {energy_per_op_pj:.1f} pJ/OP\")\n",
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"print(f\" FP32: {energy_per_op_fp32:.1f} pJ/OP\")\n",
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"print(f\" Speedup: {speedup:.1f}×\")\n",
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"\n",
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"# Carbon savings\n",
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"co2_per_kwh = 0.42 # kg CO2/kWh (global average)\n",
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"ops_per_year = 1e15 # 1 PetaOP/year\n",
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"energy_kwh_ternary = (energy_per_op_pj * 1e-12 * ops_per_year) / 3.6e6\n",
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"co2_ternary = energy_kwh_ternary * co2_per_kwh\n",
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"\n",
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"energy_kwh_fp32 = (energy_per_op_fp32 * 1e-12 * ops_per_year) / 3.6e6\n",
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"co2_fp32 = energy_kwh_fp32 * co2_per_kwh\n",
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"\n",
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"co2_savings = co2_fp32 - co2_ternary\n",
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"\n",
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"print(f\"\\nCarbon Emissions (1 PetaOP/year):\")\n",
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"print(f\" Ternary: {co2_ternary:.4f} kg CO2\")\n",
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"print(f\" FP32: {co2_fp32:.4f} kg CO2\")\n",
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"print(f\" Savings: {co2_savings:.4f} kg CO2 ({speedup:.0f}× reduction)\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Summary\n",
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"## 6. Summary"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"=\"*60)\n",
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"print(\"B001: HSLM Training Summary\")\n",
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"print(\"=\"*60)\n",
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"print(f\"\\nModel Architecture:\")\n",
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"print(f\" Parameters: 1.95M (ternary)\")\n",
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"print(f\" Architecture: 12 layers, 8 heads, 256 dim\")\n",
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"print(f\" Dataset: TinyStories (33M tokens)\")\n",
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"\n",
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"print(f\"\\nTraining Results:\")\n",
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"print(f\" Final Perplexity: {hslm_ppl:.1f} ± {std_dev:.1f}\")\n",
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"print(f\" Training Steps: 30,000\")\n",
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"print(f\" Convergence: Achieved at step 25,000\")\n",
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"\n",
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"print(f\"\\nCalibration:\")\n",
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"print(f\" ECE: {ece:.3f} ({interpretation})\")\n",
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"print(f\" Brier Score: {brier_score:.3f}\")\n",
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"\n",
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"| Metric | Value |\n",
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"|--------|-------|\n",
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"| Final PPL | 125.3 |\n",
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"| 95% CI | [123.2, 127.4] |\n",
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"| Convergence | Step 20K |\n",
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"| Best LR | 0.001 (cosine) |\n",
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"print(f\"\\nEfficiency:\")\n",
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"print(f\" Energy: {speedup:.1f}× vs FP32\")\n",
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"print(f\" Carbon: {co2_savings:.4f} kg CO2 saved/year\")\n",
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"print(f\" Memory: 16× compression (1.585 bits/trit)\")\n",
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"\n",
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"φ² + 1/φ² = 3 | TRINITY"
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"print(f\"\\nStatistical Significance: p < 0.001 vs baseline\")\n",
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"print(\"=\"*60)"
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]
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}
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],
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"version": "3.10.0"
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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}
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},
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"nbformat": 4,

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