{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ⚡ Algorithm Performance Comparison\n",
    "\n",
    "Welcome to the **OnlineRake Performance Laboratory!** 🧪\n",
    "\n",
    "This notebook provides a comprehensive comparison of the two core algorithms:\n",
    "- **SGD Raking**: Stochastic Gradient Descent with additive updates\n",
    "- **MWU Raking**: Multiplicative Weights Update with exponential updates\n",
    "\n",
    "We'll test them across different bias scenarios and see which performs better! 🏁"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:17.291286Z",
     "iopub.status.busy": "2025-12-28T04:11:17.291142Z",
     "iopub.status.idle": "2025-12-28T04:11:17.777979Z",
     "shell.execute_reply": "2025-12-28T04:11:17.777201Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔬 Performance Laboratory initialized!\n",
      "📊 Ready for comprehensive algorithm comparison!\n"
     ]
    }
   ],
   "source": [
    "# Import required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from dataclasses import dataclass\n",
    "from typing import Any\n",
    "\n",
    "from onlinerake import OnlineRakingSGD, OnlineRakingMWU, Targets\n",
    "\n",
    "# Set up plotting style\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")\n",
    "np.random.seed(42)\n",
    "\n",
    "print(\"🔬 Performance Laboratory initialized!\")\n",
    "print(\"📊 Ready for comprehensive algorithm comparison!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🏗️ Setting Up the Performance Testing Framework\n",
    "\n",
    "Let's create a sophisticated framework to test both algorithms across different bias scenarios!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:17.808460Z",
     "iopub.status.busy": "2025-12-28T04:11:17.808212Z",
     "iopub.status.idle": "2025-12-28T04:11:17.816130Z",
     "shell.execute_reply": "2025-12-28T04:11:17.815427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏗️ Performance testing framework ready!\n",
      "📋 Available bias scenarios:\n",
      "   📈 Linear shift - Gradual bias changes\n",
      "   ⚡ Sudden shift - Abrupt bias changes\n",
      "   🌊 Oscillating - Cyclical bias patterns\n"
     ]
    }
   ],
   "source": [
    "@dataclass\n",
    "class FeatureObservation:\n",
    "    \"\"\"Container for a single set of binary feature indicators.\"\"\"\n",
    "    feature_a: int\n",
    "    feature_b: int  \n",
    "    feature_c: int\n",
    "    feature_d: int\n",
    "\n",
    "    def as_dict(self) -> dict[str, int]:\n",
    "        return {\n",
    "            \"feature_a\": self.feature_a,\n",
    "            \"feature_b\": self.feature_b,\n",
    "            \"feature_c\": self.feature_c,\n",
    "            \"feature_d\": self.feature_d,\n",
    "        }\n",
    "\n",
    "class BiasSimulator:\n",
    "    \"\"\"Simulate streams of feature observations with evolving bias.\"\"\"\n",
    "\n",
    "    @staticmethod\n",
    "    def linear_shift(\n",
    "        n_obs: int, start_probs: dict[str, float], end_probs: dict[str, float]\n",
    "    ) -> list[FeatureObservation]:\n",
    "        \"\"\"Generate a linear drift from start_probs to end_probs.\"\"\"\n",
    "        data = []\n",
    "        for i in range(n_obs):\n",
    "            progress = i / (n_obs - 1) if n_obs > 1 else 0.0\n",
    "            probs = {\n",
    "                name: start_probs[name] + progress * (end_probs[name] - start_probs[name])\n",
    "                for name in start_probs\n",
    "            }\n",
    "            obs = FeatureObservation(\n",
    "                feature_a=np.random.binomial(1, probs[\"feature_a\"]),\n",
    "                feature_b=np.random.binomial(1, probs[\"feature_b\"]),\n",
    "                feature_c=np.random.binomial(1, probs[\"feature_c\"]),\n",
    "                feature_d=np.random.binomial(1, probs[\"feature_d\"]),\n",
    "            )\n",
    "            data.append(obs)\n",
    "        return data\n",
    "\n",
    "    @staticmethod\n",
    "    def sudden_shift(\n",
    "        n_obs: int, shift_point: float, before_probs: dict[str, float], after_probs: dict[str, float]\n",
    "    ) -> list[FeatureObservation]:\n",
    "        \"\"\"Generate a sudden shift at shift_point fraction of the stream.\"\"\"\n",
    "        data = []\n",
    "        shift_index = int(shift_point * n_obs)\n",
    "        for i in range(n_obs):\n",
    "            probs = before_probs if i < shift_index else after_probs\n",
    "            obs = FeatureObservation(\n",
    "                feature_a=np.random.binomial(1, probs[\"feature_a\"]),\n",
    "                feature_b=np.random.binomial(1, probs[\"feature_b\"]),\n",
    "                feature_c=np.random.binomial(1, probs[\"feature_c\"]),\n",
    "                feature_d=np.random.binomial(1, probs[\"feature_d\"]),\n",
    "            )\n",
    "            data.append(obs)\n",
    "        return data\n",
    "\n",
    "    @staticmethod\n",
    "    def oscillating_bias(\n",
    "        n_obs: int, base_probs: dict[str, float], amplitude: float, period: int\n",
    "    ) -> list[FeatureObservation]:\n",
    "        \"\"\"Generate oscillating bias around base_probs.\"\"\"\n",
    "        data = []\n",
    "        for i in range(n_obs):\n",
    "            phase = 2 * np.pi * i / period\n",
    "            osc = amplitude * np.sin(phase)\n",
    "            probs = {\n",
    "                name: float(np.clip(base_probs[name] + osc, 0.1, 0.9))\n",
    "                for name in base_probs\n",
    "            }\n",
    "            obs = FeatureObservation(\n",
    "                feature_a=np.random.binomial(1, probs[\"feature_a\"]),\n",
    "                feature_b=np.random.binomial(1, probs[\"feature_b\"]),\n",
    "                feature_c=np.random.binomial(1, probs[\"feature_c\"]),\n",
    "                feature_d=np.random.binomial(1, probs[\"feature_d\"]),\n",
    "            )\n",
    "            data.append(obs)\n",
    "        return data\n",
    "\n",
    "print(\"🏗️ Performance testing framework ready!\")\n",
    "print(\"📋 Available bias scenarios:\")\n",
    "print(\"   📈 Linear shift - Gradual bias changes\")\n",
    "print(\"   ⚡ Sudden shift - Abrupt bias changes\")\n",
    "print(\"   🌊 Oscillating - Cyclical bias patterns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:17.817582Z",
     "iopub.status.busy": "2025-12-28T04:11:17.817434Z",
     "iopub.status.idle": "2025-12-28T04:11:17.821142Z",
     "shell.execute_reply": "2025-12-28T04:11:17.820512Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎯 Test Configuration:\n",
      "   Target margins: {'feature_a': 0.5, 'feature_b': 0.51, 'feature_c': 0.4, 'feature_d': 0.3}\n",
      "   Seeds per scenario: 3\n",
      "   Observations per run: 200\n",
      "   SGD learning rate: 5.0\n",
      "   MWU learning rate: 1.0\n",
      "   Update steps: 3\n"
     ]
    }
   ],
   "source": [
    "# Set up test parameters\n",
    "targets = Targets(feature_a=0.5, feature_b=0.51, feature_c=0.4, feature_d=0.3)\n",
    "n_seeds = 3  # Multiple runs for statistical robustness\n",
    "n_obs = 200  # Observations per scenario\n",
    "learning_rate_sgd = 5.0\n",
    "learning_rate_mwu = 1.0\n",
    "n_steps = 3\n",
    "\n",
    "print(\"🎯 Test Configuration:\")\n",
    "print(f\"   Target margins: {targets.as_dict()}\")\n",
    "print(f\"   Seeds per scenario: {n_seeds}\")\n",
    "print(f\"   Observations per run: {n_obs}\")\n",
    "print(f\"   SGD learning rate: {learning_rate_sgd}\")\n",
    "print(f\"   MWU learning rate: {learning_rate_mwu}\")\n",
    "print(f\"   Update steps: {n_steps}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🧪 Running Comprehensive Performance Tests\n",
    "\n",
    "Time to put both algorithms through their paces! We'll test three challenging scenarios..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:17.822776Z",
     "iopub.status.busy": "2025-12-28T04:11:17.822633Z",
     "iopub.status.idle": "2025-12-28T04:11:17.826906Z",
     "shell.execute_reply": "2025-12-28T04:11:17.826193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎮 Test Scenarios Configured:\n",
      "   📈 Linear Shift: Gradual bias evolution\n",
      "   ⚡ Sudden Shift: Abrupt bias change at midpoint\n",
      "   🌊 Oscillating: Cyclical bias patterns\n",
      "\n",
      "🚀 Starting performance comparison across 3 scenarios...\n"
     ]
    }
   ],
   "source": [
    "# Define test scenarios\n",
    "scenarios = {\n",
    "    \"linear\": {\n",
    "        \"sim_fn\": BiasSimulator.linear_shift,\n",
    "        \"params\": {\n",
    "            \"n_obs\": n_obs,\n",
    "            \"start_probs\": {\"feature_a\": 0.2, \"feature_b\": 0.3, \"feature_c\": 0.2, \"feature_d\": 0.1},\n",
    "            \"end_probs\": {\"feature_a\": 0.8, \"feature_b\": 0.7, \"feature_c\": 0.6, \"feature_d\": 0.5},\n",
    "        },\n",
    "        \"description\": \"📈 Linear Shift: Gradual bias evolution\"\n",
    "    },\n",
    "    \"sudden\": {\n",
    "        \"sim_fn\": BiasSimulator.sudden_shift,\n",
    "        \"params\": {\n",
    "            \"n_obs\": n_obs,\n",
    "            \"shift_point\": 0.5,\n",
    "            \"before_probs\": {\"feature_a\": 0.2, \"feature_b\": 0.2, \"feature_c\": 0.2, \"feature_d\": 0.2},\n",
    "            \"after_probs\": {\"feature_a\": 0.8, \"feature_b\": 0.8, \"feature_c\": 0.6, \"feature_d\": 0.4},\n",
    "        },\n",
    "        \"description\": \"⚡ Sudden Shift: Abrupt bias change at midpoint\"\n",
    "    },\n",
    "    \"oscillating\": {\n",
    "        \"sim_fn\": BiasSimulator.oscillating_bias,\n",
    "        \"params\": {\n",
    "            \"n_obs\": n_obs,\n",
    "            \"base_probs\": {\"feature_a\": 0.5, \"feature_b\": 0.5, \"feature_c\": 0.4, \"feature_d\": 0.3},\n",
    "            \"amplitude\": 0.2,\n",
    "            \"period\": max(50, n_obs // 4),\n",
    "        },\n",
    "        \"description\": \"🌊 Oscillating: Cyclical bias patterns\"\n",
    "    },\n",
    "}\n",
    "\n",
    "print(\"🎮 Test Scenarios Configured:\")\n",
    "for name, config in scenarios.items():\n",
    "    print(f\"   {config['description']}\")\n",
    "\n",
    "print(f\"\\n🚀 Starting performance comparison across {len(scenarios)} scenarios...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:17.828323Z",
     "iopub.status.busy": "2025-12-28T04:11:17.828179Z",
     "iopub.status.idle": "2025-12-28T04:11:18.738076Z",
     "shell.execute_reply": "2025-12-28T04:11:18.737287Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🔬 Testing scenario: 📈 Linear Shift: Gradual bias evolution\n",
      "   🎲 Seed 1/3... ✅\n",
      "   🎲 Seed 2/3... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅\n",
      "   🎲 Seed 3/3... ✅\n",
      "\n",
      "🔬 Testing scenario: ⚡ Sudden Shift: Abrupt bias change at midpoint\n",
      "   🎲 Seed 1/3... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅\n",
      "   🎲 Seed 2/3... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅\n",
      "   🎲 Seed 3/3... ✅\n",
      "\n",
      "🔬 Testing scenario: 🌊 Oscillating: Cyclical bias patterns\n",
      "   🎲 Seed 1/3... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅\n",
      "   🎲 Seed 2/3... "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅\n",
      "   🎲 Seed 3/3... ✅\n",
      "\n",
      "🎉 Performance comparison complete!\n",
      "📊 Collected 18 performance measurements\n",
      "📋 Results shape: (18, 14)\n"
     ]
    }
   ],
   "source": [
    "# Run the comprehensive performance test\n",
    "results = []\n",
    "detailed_history = {}  # Store detailed results for visualization\n",
    "\n",
    "for scenario_name, config in scenarios.items():\n",
    "    print(f\"\\n🔬 Testing scenario: {config['description']}\")\n",
    "    sim_fn = config[\"sim_fn\"]\n",
    "    params = config[\"params\"]\n",
    "    \n",
    "    scenario_history = {\"SGD\": [], \"MWU\": []}\n",
    "    \n",
    "    for seed in range(n_seeds):\n",
    "        print(f\"   🎲 Seed {seed + 1}/{n_seeds}...\", end=\" \")\n",
    "        \n",
    "        np.random.seed(seed)\n",
    "        # Generate data stream\n",
    "        stream = sim_fn(**params)\n",
    "        \n",
    "        # Initialize rakers\n",
    "        sgd_raker = OnlineRakingSGD(\n",
    "            targets=targets, learning_rate=learning_rate_sgd, n_sgd_steps=n_steps,\n",
    "            min_weight=1e-3, max_weight=100.0\n",
    "        )\n",
    "        mwu_raker = OnlineRakingMWU(\n",
    "            targets=targets, learning_rate=learning_rate_mwu, n_steps=n_steps,\n",
    "            min_weight=1e-3, max_weight=100.0\n",
    "        )\n",
    "        \n",
    "        # Track progress for visualization (first seed only)\n",
    "        if seed == 0:\n",
    "            sgd_progress = []\n",
    "            mwu_progress = []\n",
    "            step_numbers = []\n",
    "        \n",
    "        # Run both algorithms on the same stream\n",
    "        for i, obs in enumerate(stream):\n",
    "            sgd_raker.partial_fit(obs.as_dict())\n",
    "            mwu_raker.partial_fit(obs.as_dict())\n",
    "            \n",
    "            # Track progress every 20 steps for visualization\n",
    "            if seed == 0 and (i + 1) % 20 == 0:\n",
    "                step_numbers.append(i + 1)\n",
    "                sgd_progress.append({\n",
    "                    'margins': sgd_raker.margins.copy(),\n",
    "                    'loss': sgd_raker.loss,\n",
    "                    'ess': sgd_raker.effective_sample_size\n",
    "                })\n",
    "                mwu_progress.append({\n",
    "                    'margins': mwu_raker.margins.copy(),\n",
    "                    'loss': mwu_raker.loss,\n",
    "                    'ess': mwu_raker.effective_sample_size\n",
    "                })\n",
    "        \n",
    "        # Store detailed history for visualization\n",
    "        if seed == 0:\n",
    "            scenario_history[\"SGD\"] = sgd_progress\n",
    "            scenario_history[\"MWU\"] = mwu_progress\n",
    "            scenario_history[\"steps\"] = step_numbers\n",
    "        \n",
    "        # Compute summary metrics\n",
    "        for method_name, raker in [(\"SGD\", sgd_raker), (\"MWU\", mwu_raker)]:\n",
    "            # Calculate temporal errors\n",
    "            temporal_errors = {}\n",
    "            baseline_errors = {}\n",
    "            \n",
    "            for feature in [\"feature_a\", \"feature_b\", \"feature_c\", \"feature_d\"]:\n",
    "                target_val = targets[feature]\n",
    "                weighted_errors = [\n",
    "                    abs(h[\"weighted_margins\"][feature] - target_val) for h in raker.history\n",
    "                ]\n",
    "                raw_errors = [\n",
    "                    abs(h[\"raw_margins\"][feature] - target_val) for h in raker.history\n",
    "                ]\n",
    "                temporal_errors[f\"{feature}_temporal_error\"] = float(np.mean(weighted_errors))\n",
    "                baseline_errors[f\"{feature}_temporal_baseline_error\"] = float(np.mean(raw_errors))\n",
    "            \n",
    "            final_state = raker.history[-1]\n",
    "            result = {\n",
    "                \"scenario\": scenario_name,\n",
    "                \"seed\": seed,\n",
    "                \"method\": method_name,\n",
    "                \"final_loss\": float(final_state[\"loss\"]),\n",
    "                \"final_ess\": float(final_state[\"ess\"]),\n",
    "                \"avg_temporal_loss\": float(np.mean([h[\"loss\"] for h in raker.history])),\n",
    "            }\n",
    "            result.update(temporal_errors)\n",
    "            result.update(baseline_errors)\n",
    "            results.append(result)\n",
    "        \n",
    "        print(\"✅\")\n",
    "    \n",
    "    detailed_history[scenario_name] = scenario_history\n",
    "\n",
    "# Convert results to DataFrame\n",
    "df = pd.DataFrame(results)\n",
    "print(f\"\\n🎉 Performance comparison complete!\")\n",
    "print(f\"📊 Collected {len(results)} performance measurements\")\n",
    "print(f\"📋 Results shape: {df.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📊 Comprehensive Results Analysis\n",
    "\n",
    "Let's analyze the results and see which algorithm performs better in each scenario!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:18.739661Z",
     "iopub.status.busy": "2025-12-28T04:11:18.739510Z",
     "iopub.status.idle": "2025-12-28T04:11:18.755342Z",
     "shell.execute_reply": "2025-12-28T04:11:18.754686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📈 PERFORMANCE SUMMARY BY SCENARIO\n",
      "============================================================\n",
      "\n",
      "🔬 Scenario: LINEAR\n",
      "\n",
      "   🎯 Method: SGD\n",
      "     feature_a   : baseline 0.1478 → weighted 0.0278 (+81.2% imp)\n",
      "     feature_b   : baseline 0.1483 → weighted 0.0350 (+76.4% imp)\n",
      "     feature_c   : baseline 0.1334 → weighted 0.0390 (+70.7% imp)\n",
      "     feature_d   : baseline 0.0880 → weighted 0.0419 (+52.3% imp)\n",
      "     Overall improvement: +72.2%\n",
      "     Final ESS   : 165.3 ± 1.9\n",
      "     Final loss  : 0.001342 ± 0.000172\n",
      "\n",
      "   🎯 Method: MWU\n",
      "     feature_a   : baseline 0.1478 → weighted 0.0614 (+58.5% imp)\n",
      "     feature_b   : baseline 0.1483 → weighted 0.0805 (+45.7% imp)\n",
      "     feature_c   : baseline 0.1334 → weighted 0.0778 (+41.7% imp)\n",
      "     feature_d   : baseline 0.0880 → weighted 0.0710 (+19.3% imp)\n",
      "     Overall improvement: +43.8%\n",
      "     Final ESS   : 163.8 ± 7.9\n",
      "     Final loss  : 0.006450 ± 0.003192\n",
      "\n",
      "🔬 Scenario: SUDDEN\n",
      "\n",
      "   🎯 Method: SGD\n",
      "     feature_a   : baseline 0.2047 → weighted 0.0449 (+78.1% imp)\n",
      "     feature_b   : baseline 0.2419 → weighted 0.0540 (+77.7% imp)\n",
      "     feature_c   : baseline 0.1757 → weighted 0.0428 (+75.7% imp)\n",
      "     feature_d   : baseline 0.0804 → weighted 0.0383 (+52.4% imp)\n",
      "     Overall improvement: +74.4%\n",
      "     Final ESS   : 141.1 ± 5.5\n",
      "     Final loss  : 0.000954 ± 0.000198\n",
      "\n",
      "   🎯 Method: MWU\n",
      "     feature_a   : baseline 0.2047 → weighted 0.1085 (+47.0% imp)\n",
      "     feature_b   : baseline 0.2419 → weighted 0.1374 (+43.2% imp)\n",
      "     feature_c   : baseline 0.1757 → weighted 0.0979 (+44.3% imp)\n",
      "     feature_d   : baseline 0.0804 → weighted 0.0644 (+19.9% imp)\n",
      "     Overall improvement: +41.9%\n",
      "     Final ESS   : 120.1 ± 19.2\n",
      "     Final loss  : 0.010641 ± 0.000717\n",
      "\n",
      "🔬 Scenario: OSCILLATING\n",
      "\n",
      "   🎯 Method: SGD\n",
      "     feature_a   : baseline 0.0641 → weighted 0.0168 (+73.7% imp)\n",
      "     feature_b   : baseline 0.0695 → weighted 0.0196 (+71.8% imp)\n",
      "     feature_c   : baseline 0.0586 → weighted 0.0219 (+62.7% imp)\n",
      "     feature_d   : baseline 0.0497 → weighted 0.0168 (+66.2% imp)\n",
      "     Overall improvement: +69.0%\n",
      "     Final ESS   : 183.1 ± 4.6\n",
      "     Final loss  : 0.000262 ± 0.000070\n",
      "\n",
      "   🎯 Method: MWU\n",
      "     feature_a   : baseline 0.0641 → weighted 0.0298 (+53.5% imp)\n",
      "     feature_b   : baseline 0.0695 → weighted 0.0382 (+45.1% imp)\n",
      "     feature_c   : baseline 0.0586 → weighted 0.0420 (+28.4% imp)\n",
      "     feature_d   : baseline 0.0497 → weighted 0.0295 (+40.5% imp)\n",
      "     Overall improvement: +42.3%\n",
      "     Final ESS   : 185.4 ± 6.8\n",
      "     Final loss  : 0.001114 ± 0.000534\n",
      "\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# Display summary statistics\n",
    "print(\"📈 PERFORMANCE SUMMARY BY SCENARIO\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "feature_names = [\"feature_a\", \"feature_b\", \"feature_c\", \"feature_d\"]\n",
    "\n",
    "for scenario in df[\"scenario\"].unique():\n",
    "    print(f\"\\n🔬 Scenario: {scenario.upper()}\")\n",
    "    scen_df = df[df[\"scenario\"] == scenario]\n",
    "    \n",
    "    for method in scen_df[\"method\"].unique():\n",
    "        mdf = scen_df[scen_df[\"method\"] == method]\n",
    "        print(f\"\\n   🎯 Method: {method}\")\n",
    "        \n",
    "        # Compute average errors and improvements\n",
    "        for feature in feature_names:\n",
    "            mean_w = mdf[f\"{feature}_temporal_error\"].mean()\n",
    "            mean_b = mdf[f\"{feature}_temporal_baseline_error\"].mean()\n",
    "            impr = (mean_b - mean_w) / mean_b * 100 if mean_b != 0 else 0.0\n",
    "            print(f\"     {feature:<12}: baseline {mean_b:.4f} → weighted {mean_w:.4f} ({impr:+.1f}% imp)\")\n",
    "        \n",
    "        # Overall improvement\n",
    "        mean_w_overall = mdf[[f\"{f}_temporal_error\" for f in feature_names]].values.mean()\n",
    "        mean_b_overall = mdf[[f\"{f}_temporal_baseline_error\" for f in feature_names]].values.mean()\n",
    "        overall_impr = (mean_b_overall - mean_w_overall) / mean_b_overall * 100 if mean_b_overall != 0 else 0.0\n",
    "        \n",
    "        print(f\"     {'Overall improvement':<12}: {overall_impr:+.1f}%\")\n",
    "        print(f\"     {'Final ESS':<12}: {mdf['final_ess'].mean():.1f} ± {mdf['final_ess'].std():.1f}\")\n",
    "        print(f\"     {'Final loss':<12}: {mdf['final_loss'].mean():.6f} ± {mdf['final_loss'].std():.6f}\")\n",
    "\n",
    "print(\"\\n\" + \"=\" * 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:18.756907Z",
     "iopub.status.busy": "2025-12-28T04:11:18.756738Z",
     "iopub.status.idle": "2025-12-28T04:11:19.805834Z",
     "shell.execute_reply": "2025-12-28T04:11:19.804977Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127942 (\\N{TROPHY}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128201 (\\N{CHART WITH DOWNWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128300 (\\N{MICROSCOPE}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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27Uv2eJv4b+7cuaZ1OPqMJvbnn38aRYsWdXo948ePd7g9ieuFhIQkue0pmT9/vsPflPv1zTffONyWn3/++b6XGc/R5yKp72e82bNnO5wnufMNR65du2Y6N6pWrVqS9ROfr1gsFuPPP/+0Tf/5558NX19fpz8LqY03nqNjRq1atYz8+fPbXru7uxt//fWXbZ6oqCjDz8/PNj1z5sxGy5YtTctxdA3g6Jiyf//+JOuULVvWGDlypF39o0eP2tV/4oknTMv8448/TOt2tN+Su04JDQ011R89enSq9i8A4L+BBAwAwCUeVgLm9u3bDpMA77//fuoCTmTz5s2Gl5eX0xeykowOHTo4XFbiepkzZ3YYc+KL8MGDB6e4zjfffNOpdfr5+RkBAQEpLq9jx46mZTm6QZEzZ07D09MzxRsXgwYNStU+LFeunHHjxg1TDI4SMIULF05xeSEhIQ4TcZcvX07VTStJxrhx4xzua0c3yZy58TZixAjTsrp27ZqqmO4nAePopk/dunUdbtuUKVNMy5o1a5ZdnRMnThg5cuRwOmaLxWLMmTPH4fqckZoEzF9//eWw7nfffWdXb+nSpYabm5vT25A1a1aHSRhHsSWX1HmQBEzfvn1TNa+7u7vx448/PnDM8Rx9zrJnz55sDB4eHsbXX3+dYtKoUKFCRkxMjCnW1CRg4v8mTpzo8LPhyu+tYRjGypUrk/xNdPSX+Abv7du3jWrVqqVq23r27OkwltR65ZVXTMv+/PPPjdjYWCMoKMiu3MfHx7h27VqSy3L0uXBmv1avXt3h8lKTgJFkeHp6OkzgpZSAMQzDePPNN011Nm/ebFrWrVu3jCxZstjVq127tm16bGysU8enhH/3m4CpU6dOqtbzMBIwa9ascbjuevXqGdOnTzeOHz9uWK1Wp5fn6DNVsmTJ+44voftJwMTExDj8rRs1alSq19+5c2fTcsLDw031rl69ajonTfj5jYiIcOocL+GfKxMwoaGhxvjx4+3K3nrrLds8X3zxhd20F154IcVzlXj9+vVz+PsU7/r163YPC/Tp08dYtWqVXf2vvvrKVv/KlSumJHdwcLDDbXW030jAAABcgS7IAAAZyqFDhxx2xfUgfXFHRUWpbdu2pu4Z/Pz81KFDB/Xp00dly5Y1zbdw4UKnBhi9deuW7ty5o/z58+uFF15QvXr1THV27dplG9OiSpUq6t27t4oXL26qN3Xq1GS7RIp3/fp1RUZGKnv27Hr++efVq1cv5c+f31RvwYIFKXYxI93r5uLu3bvy8PBQo0aN1LdvX7Vp08aua46lS5c67E6jSpUqeuGFF9ShQwdTVx6HDh3S4MGDU1y/dK+fcUl68skn1bdvX1WvXt1U58yZM1qxYoWpvHv37jp58qRdWaZMmfTss8+qT58+ql+/vqlLk9GjRzvdf/nFixfl7e2t1q1bq3fv3goODjbVmTZtml03RHfv3tXixYvt6nh7e+vpp59W37591bt3b7Vo0UKFChVyKoakZM2aVS1btrQr27x5s8PuzBYuXGj3OkuWLHbdElmtVrVp00aXL1+2qxcQEKC2bduqd+/epvfFMAz17dvXtP/TwrJlyxyWFy5c2Pb/v/76S88//7ysVqupTpcuXdS9e3cVLVrUblr8b4Qz371///1XkuTj46MWLVqob9++atGihbJkyaJSpUpp0KBBGjRokGk+Pz8/27TEdb799luHYxqUKFFCL774op577jlly5bNblpcXJw6d+6sCxcuPFDMyYmIiJCXl5fatGmjLl26mMbZiY2N1QsvvKDo6GgFBQWpZ8+eevrpp03LOX36tMPvbbzAwECFhobavl/9+vVTp06dVKxYMVPdt99+W1FRUSlus3R/31tJOnv2rNq3b++wW7EKFSqoV69ett+VpMb1GT58uHbt2mVX5uHhoSZNmqhPnz5q1qyZPDzsh+ucOXOmqeuw1LJarfr222/tytzc3NS6dWu5u7ubfivu3LmT5PcqKRcvXpQkhYaGqm/fvipTpoypzs6dO7V///4kl1GwYEE1aNBAHTp00Msvv6yXXnpJLVu2NHUxevfu3fseEL1Pnz6m7o5mzJhhqvfzzz+bugzr2rWr7f87d+60HZ/i5ciRQ+3bt1e/fv3Us2dP1a9f32H3qKnxzz//aMuWLXZlfn5+atWqlV5++WX16tVLTZo0Ud68eR9oPalVrVo1ZcqUyVS+YcMGvfjiiypRooSyZcumBg0aaPz48Tp06FCyy3PUZWR6jrfi6enpcGySxOPeOKNbt26mssTHXUn67rvvTOek8V1FSvc+k4m7+MybN6+ee+459evXT926dVPt2rXTdJD5F154wW58qK+//tr2m5j43Pill15yermO9nXCLsR2795t1yVZjRo1VK1aNbm5/f9bWwm7KNuxY4epSzJH548AAKSp9M4AAQAeDw+rBUz8fIn/kurKyhnvvPOOaXn58+c3zpw5Y6tjtVqNIUOGmOrlyZPHuHv3rt3yHMVXoUIFIyoqylanQ4cODuv16dPHVuf69etG7ty5TXUOHDhg2gZHy3riiSeMS5cu2ercunXLqFevnqlew4YN7ZaV1BOi2bNnN3755Re7utHR0bYn1xN38eDh4WFqeXDlyhWjXLlydvXc3d3tunIxDMctYCTZdTEUFxdntGvXzlRn0KBBdsvatWuXqU7p0qWN8+fP29VbvXq14eHhYVfPUUsRR08p+/r62u2b06dPO3xiNuF79++//5qmr1ixwrQ+wzCMf/75x5g2bZrxxRdfmKY581Tp6tWrTXU+++wzuzr//vuvqVVI165d7eosXLjQtJy6devafbYNwzBmzJhhqtejRw+H25YSZ1rAnD171vj8889NT6lL97r4S/j09UsvvWSq8+qrr9p173T37l2jR48epnqJW/IkFVuRIkXsuooxjHvf54QSz5PcE+jFixc31e/Zs6ddN4gXLlxwWG/48OEuidnR58zd3d3us7Zy5UqHy86fP7/d923AgAEpfm8NwzCOHj1q7NmzJ8mut6xWq8NWd8uXLzfVddX31jAcP6GdJUsW44cffjCtNyIiwnjrrbeMf//911b2999/m1rP5MmTxzh+/LjdvAcPHrTrxkeSUbRo0QfqimzdunWm2GvVqmWb7ui3on79+kkuz9HnQpIxbdo0W53bt28bNWvWNNX55JNPTMvbvHmzcfLkySTXd+PGDVP3hxaLxbhy5YpdPWdawBiGYbRq1cquTubMmU0tfjp27GhXJ1OmTHZ1EnfBlSlTJrtjb0JHjhwxxo8f77CLwJRawGzfvt00PanuEU+ePGl8/PHHxsKFCx1OT83vjzNGjBjh8HOQ1F+1atWS7HrQUQtLR58VwzCMypUrJ7uexC0476cFjGEYDrs/rVy5cqr3U1xcnKnFYaVKlUz1GjdubPp9Sfh7nPi8tUCBAg67S4yLizP27t1rvP7668bhw4dTHa9hJN0CxjAM0znYokWLjC1bttiVValSxTAM585VDMMwwsPDTfUKFChgmz5u3Di7aSdOnDAMw7A7tyxRooStvqPPZlKt5h19NmgBAwBwBRIwAACXeFgJmHnz5jm8QEpqPBZnOLqAd9RlUnR0tJEzZ05T3e3bt9vVcxRf4hvrM2fONNXx9vY2JZIc3QRetmyZKTZH63TU/dDRo0dN9Tw9Pe3GgknqBkVy3Ug5Wm779u0d1nXUX/xHH31kV8dRAiZ37tym7sXWrl1rqvfMM8/Y1Rk+fLipTlJ9ySceX8hisRiXL1+2q+PoJtkrr7xiWtZzzz2X7HsXFRVlmp7wpqWznLmp4eimT40aNezqTJo0ybScDRs22NVxlPD67bffHMaVeGyMgICA+7pxnFTCwNm/6dOn25ZltVpN3SPlzp3bYVyOEmQtWrRwKjZH3RgllniepG6AHj582FTXz8/PlNAxDMNYvny5qW7icS3uN2ZHn7O2bdva1YmJiXE4jkzixOHmzZtT/N4mFBsba2zfvt2YPn26MXr0aGPYsGHGoEGDjEGDBhmtW7c2LctR10Cu+t5arVYjV65cKW5jchx19ZfU/L179zbVTZwITw1Hx5RJkybZpt+9e9cIDAy0m+7m5maXQErI0efC0Y3pr776ylRv4MCBScZ5/fp1Y9WqVcakSZOMkSNHGkOHDrW9547GzUr8W+VsAmb9+vWmegnHyLp9+7YpCdapUye7Zfzwww920728vIxDhw4luW1JSSkBc+jQIdP01atXp3o9aSGlMewc/Xl4eBiLFi0yLSvxgxCSfZdSCT2sBIyj7uqSGzMoOa+//rppWQnHI7l8+bJpHyR+GOKzzz6zmx4cHGw3BosrJZeASTxuVN26dU0Jy/j3wNkEjGEYDrvF/OeffwzDMOzGl0rYlVjChysSnrs5SpJs27bN4XodfTZIwAAAXIEuyAAAGUpS3SncuHHjvpYXGxurAwcOmMqbNWtmKvPy8lKDBg1M5Y66y0jIzc3N1H1Grly5TPUqV66sgICAFOtdv3492fVJ97qyadiwoam8VKlSpq7I7t69q6NHjya7PB8fH3Xo0CHJ6bt37zaVLVq0SBaLxfT33HPPmeru3Lkz2fVLUtu2beXl5WVXljt3blO9xPvHUWxPP/20w9jWrl1rV88wDFM3QY507tzZVJZSbH5+fnrqqafspvfp00d58+ZVo0aN1L9/f33++efasWOHqSuS1HJzc9Pzzz9vV7Zz5079/ffftteJu0EpWLCgwsLC7Moc7ctSpUo53JcnTpywqxcZGanff//9gbYjtV566SX17t3b9jo8PNzWPVK8c+fOyd3d3RS/o258nPmcFipUSHXq1Hnw4P+Po9+X0NBQ+fr6msobN25s6lLpxIkTpq5qErvfmBs1amT32tPTU9mzZzfVS/y76ezvWmxsrN555x3lzJlTNWvW1IsvvqixY8fqww8/1KeffqpPP/3UYfdYibvIS8r9fG//+usvU/d93t7edt0DpcTR9+jll192+D366quvTHWd+Rw6cufOHS1dutSuzGKxqE2bNrbXHh4eeuaZZ+zqOOq2LDn3s1/jXbx4Ub169VKOHDnUpEkTDR48WOPHj9fHH39se88ddf3k7HueWL169fTEE0/YlSXshmz16tWmOBN2Pybd6zIpYVdzMTExKl++vEqUKKGWLVvq1Vdf1cyZM3X06FFTV0ipUapUKdN3p3HjxipUqJCaNWumIUOGaNq0adq/f79dF00Pg4eHh+bOnasVK1YoNDTU1KWnI/FdFF69etWu3NF53v2e47mKo8/q/Xbv5ei3IuHxd+nSpabuLhPPk7gr24sXL6pQoUIqW7as2rZtqzfffFPz5s3TqVOn7itGZ9WtW1clS5a0vd64caOWLFliex0YGJjsuWNSkuqGLPE5WcKuxBLOYxiGdu7cqbt375p+L7y8vPTkk0+mOiYAAB4ECRgAQIbiqI9+Sfd9kXnlyhXTWBCZMmVyeBNRksNxVBLf0E0sKCjINA6Ao3EBHC07ccJBkinepNbp7e3tcFq+fPlMZSndvCpevHiSy5OkS5cupRhTchyNR5JYSEiIqczRfky8fx7l2CZNmmTqO//s2bNau3atpkyZogEDBqhmzZoKDg7WoEGDTDeqUiNx3/OGYdhu+pw5c8aUaOratavpJtrD2JeuULx4cc2bN09ffPGFXfmDxh8REZHiODCOxot6EI5idvRbId37zDkaZyKl7b7fmB3F4cxvm7O/ax07dtRbb72liIiIVMV169Ytp+rdz/fW0Zg6ISEhDsfASEp6fY9+/PFH0/g41apVMx0TEo77FC81Y8/c7+/h1atXVbNmTc2cOVPR0dFOr09y/j135OWXX7Z7vXfvXh05ckSS7G4mS/cSSYkfbggMDNS7775rV2YYhv744w99//33mjhxonr16qUyZcqoUKFCmjRp0n0lSNzd3TV58mRTkjU8PFw///yzJk2apL59+6py5crKkyePRo0apdu3b6d6PQ+iWbNm2rRpk86dO6dvv/1W/fv3V6VKlUwxx4uKijIlUR2d550+fdrh/J07d7aNmZU4keZKjs4x73dMnxIlSqhq1ap2ZQsWLHD4f+ne9ynxwxClS5c2jSUWFxenI0eOaOnSpXr33Xf1/PPPq2jRoipdurT+97//3Veszkg8vkvCY2T37t1T9dsYr0aNGqay7du36+jRo3YPFCRMuiRO2mzfvl0HDhwwfQcqVaqU5PlsUp/TpDhKqCYeuwsAAIkEDAAggylXrpzDGzkbNmxIh2ic4yheR0+HJjVY86MgccscV3Pm6VZHSbHUXizfj7SMrUaNGjp48KC6du1qGsA8oWvXrumzzz5TvXr1HA787YySJUuabvrEJ2AWLVpkdyPBYrGYnvJ2BVc+xZxwsPqhQ4dq9OjRmj59ug4cOKDjx487bGn1oAzDMA3GnVhaf1fSwv3GnJa/bT/99JOptYaznG1lkF6/KQ/qfr9HjpIoMTExGjx4sN3fTz/9ZKp34MABp1uw3e9+fffdd3Xy5Emn1pHYg7Qs6dq1q6lF2YwZMxQTE6Mff/zRrrxLly4Ot2XIkCFas2aNGjVqZDcweWJnzpzRkCFDNHDgwPuKtW3bttq5c6datWqV7I3tixcvavz48Q6TaQ9Dzpw51bFjR02ePFm//PKLLly4oPfee8/hvjt8+LDd6ypVqpjqbNy40eF6hg4dqkmTJmnSpEkO53OFmJgYu0Hd4z3I+hK3aDl69KiOHj2q8+fPa/PmzXbTHD0MId17gGPhwoWqUaOG3QD0if3222/q1q2bJk6ceN/xJqdr167KnDmzqdxisahv3773tcykWsAkfh8SJmoKFixo19Jux44d2r59u2k5jpI78Ry1akouuetoWrZs2ZKsDwD47yI9DwDIUHx8fFS7dm1TV1HTpk3TwIEDHT5ZnZzAwEC5ubnZPYl7+/ZtRUREOLyJlLDLpnhJtcpJT5cuXVJ0dLTDp/z++ecfU1mOHDmSXV5K3Yk42gfVqlUz3fBPiqPunlwlODjYdOOwZ8+e8vPzc2r+ChUqpEFU/1/x4sU1Z84c29Orv//+u06dOqWjR49q5cqVdk97Hjx4UEuWLFGnTp3ua13dunWz6/5o3759OnXqlKn7sVq1aqlIkSKm+YODg3XmzBnbazc3N/Xv39+p7mYkqXDhwvcVtyOTJk1K9TyOPqf58uWz64IpJSn9xji7L5zl6ClrR79DkhQdHe2wdUVKT2q7OmZX+P77701l1apV0/vvv6/y5cvbbpStXr1aTZo0eWhxOfoMnTlzRnfu3HE60eRoGW3btnX6d/B+uou7evWqVq5caSr/5Zdf9Msvvzi1jHnz5umdd95J9bqd5eg97969u1555RUVLlzYdpN3xIgReu+991y23qxZs+r555/Xl19+aSubN2+e6tSpo2vXrtnVTS4x3bBhQzVs2FA3b97Ur7/+quPHj+vkyZM6cOCA1q1bZ5c8nzp1qt544437OvY99dRTWrZsmaKjo3X48GHbMePQoUNatWqV3RP/P//8s3bu3GnXVVN6yJ49u1577TWdOXPGbj9LMrVQaNSokebMmWNXdvjwYW3ZssWl3Ts6a+7cuQ5bnzrq5tVZHTt21ODBg+1aei1YsEA5c+a0Ox+1WCym1qsJtW/fXu3bt1dkZKTdZ27v3r3avHmzXWJy3LhxGjRoUKrPk1MSEBCgTp062XXdJ0n169dXsWLF7muZFSpUUObMme0SHL/++qvWrVtne+2oK7GaNWvaWq3t27fPYTLEUXInXu7cuXXlyhW7sqSOt9K9LikTc9TFJgAAJGAAABnOwIEDTQmYkydP6rXXXtMnn3yS4vw7duywPQHn4eGhihUrmm5A/fzzz6YxM2JiYuwu/uIlHsfjURAbG6t169aZxrL5/fffTReTnp6eKlWq1AOtz9E+8PX1deomuWEYD/T0ckqeeuop0xOl9erVc6qFRFxc3EN7It7d3V3ly5dX+fLlbWUnT5403cDYvXv3fSdgOnbsqCFDhtjd9HnnnXdMn/+kxrN46qmn7BIwVqtV7du3T/aGRryHuS+TEhISoqCgILskxbVr1zRhwgSnuklJj21w9N3asmWLbt68qSxZstiVr1692tS1UbFixTJkqxxHN7Y+//xzVa5c2a7M0XgqaSkkJES5cuWy6wYsOjpas2fPdvpp76eeesp0g7lChQp68803U5z3fj+DixcvfuCxpL755hu9/fbbaZawS/yeZ82aVTNmzDA93Z8W73m/fv3sEgOXL182tVKpVKmSypQpk+KysmTJoho1atg9af/111/bjUdltVq1d+/eB3r4wNvbW08++aTdTeh169aZEgO7d+9O0wTM6dOntWPHDnXs2DHFz6aj5GNgYKDd69atWytv3rz6999/7cpfeOEF7dq1y1Q/Lf3xxx8aNmyYqfzJJ5906riXlICAAD377LNatGiRrWzhwoXKmTOnXb2kHoZwtLzQ0FCFhobayt566y27hOmNGzf022+/pclDJS+99JIpAZO4a7LU8PDwUJUqVbRp0yZbWWxsrL777jvba0ddiSVMwNy6dctha77kWsBUrVrVNCbi+vXr1adPH1Pdw4cPO+yCuFq1akkuHwDw30UXZACADKd58+aqXbu2qXzSpEnq2bOnw/FMDMPQunXrVL9+fVN/761btzbVf/PNN+0SFYZh6PXXXzf1/587d+406/biQQ0fPtxu7IQ7d+447PYkLCzsvvroTqh06dIqUaKEXdm6des0duzYJG/6nThxQhMnTlTJkiUd3mx1FUfv76BBg5IcyPru3bvauHGjXnzxRbVs2TLN4pKkHj16aNGiRaanrOM5Gm8itWMjJJQtWzbTANuzZs2ye505c2a1a9fO4fyO9mXXrl117Ngxh/Vv376tn376SZ06dTJ979KDxWJRq1at7MquX7+u9u3bm556jXf58mXNmTNHoaGhmjdv3sMI006ZMmVUvHhxu7KoqCgNGjTILtly8eJFDR8+3DR/Wn+G04qjp7R//fVXu9ebN2/W+++//7BCkmQetD7esGHDtGLFClN5VFSUxo0bZ3czuUWLFqZxAt5++21Td1fxrFardu3apVdeecXpVoWJueKzGx4e7rBLH1dJ/J7fuHHDbuwNwzD00UcfJdkd1YMoXbq0aZyNxK1Fk2qJ8Ndff6lnz57auHFjkl1EuuK3PC4uTu3atdNPP/2U5Nguzq7HYrHY/RUsWDBVsSQUERGhLl26qESJEnr33XcVHh7usN6ff/5pOt5IMiVVfXx8NHbsWFO9EydOqEaNGnY35dNKXFyc5s6dqxo1ati1QpXu7TtXtMBK/Hk6ceKEtm3bZleW1MMQu3fv1sCBA7V79+4kxwV09flDcipXrqyaNWvK29tb3t7eKliwoOlcI7UcJbgSji/jKJGSeJ7EY7YVKlQo2RYqzz77rKls2bJlWr16tV3ZrVu3NHjwYFPdypUrp2mLbgBAxkULGABAhrRw4UJVqVLFdINk1qxZ+uabb1SrVi1bd0fnz5/Xrl27bE+9J2xhIEn9+/fXpEmT7J6K//vvv1W6dGk1a9ZMAQEB2rFjhw4dOmSKY+TIkY/sgJu///67SpQooWbNmsnLy0tr1qxxmOhIPJDr/Ro3bpw6dOhgVzZmzBh9+eWXqlGjhnLlyqWYmBidO3dOhw4dctgVWlqoXr26mjZtatf9TkREhGrUqKHKlSurZMmSCggI0LVr1/Tnn3/q0KFDtjEWEj5NmhbWrl2r2bNny8PDQ6VKlVLx4sWVPXt2ubm56cyZMw7HNkp8Mz61unfvrsWLFyc5vXXr1kl2z9ahQwe9++67tgGqpXs31UqVKqUaNWqoaNGiypIli65evaoTJ07oyJEjunPnjqSkb14+bG+88Ybmzp1rdwNzxYoVypcvn0JDQ5U/f35ZLBZFRETot99+0/Hjx203uHr06JEuMY8ZM0adO3e2K5sxY4a2bdum0NBQ25O+ibvJ8fPz0yuvvPIwQ3WZypU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",
      "text/plain": [
       "<Figure size 2000x1600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎨 Performance visualization complete!\n",
      "📊 Clear evidence of algorithm performance differences across scenarios!\n"
     ]
    }
   ],
   "source": [
    "# Create comprehensive performance visualization\n",
    "fig = plt.figure(figsize=(20, 16))\n",
    "gs = fig.add_gridspec(4, 4, hspace=0.3, wspace=0.3)\n",
    "\n",
    "# Color scheme\n",
    "colors = {'SGD': '#2E8B57', 'MWU': '#4169E1'}  # SeaGreen and RoyalBlue\n",
    "\n",
    "# 1. Overall Performance Comparison (Top Row)\n",
    "ax1 = fig.add_subplot(gs[0, :2])\n",
    "performance_summary = df.groupby(['scenario', 'method']).agg({\n",
    "    'final_loss': 'mean',\n",
    "    'final_ess': 'mean'\n",
    "}).reset_index()\n",
    "\n",
    "scenarios_list = list(scenarios.keys())\n",
    "x = np.arange(len(scenarios_list))\n",
    "width = 0.35\n",
    "\n",
    "sgd_losses = [performance_summary[(performance_summary['scenario'] == s) & \n",
    "                                 (performance_summary['method'] == 'SGD')]['final_loss'].iloc[0] \n",
    "              for s in scenarios_list]\n",
    "mwu_losses = [performance_summary[(performance_summary['scenario'] == s) & \n",
    "                                 (performance_summary['method'] == 'MWU')]['final_loss'].iloc[0] \n",
    "              for s in scenarios_list]\n",
    "\n",
    "ax1.bar(x - width/2, sgd_losses, width, label='SGD', alpha=0.8, color=colors['SGD'])\n",
    "ax1.bar(x + width/2, mwu_losses, width, label='MWU', alpha=0.8, color=colors['MWU'])\n",
    "ax1.set_xlabel('Scenarios')\n",
    "ax1.set_ylabel('Final Loss')\n",
    "ax1.set_title('🏆 Final Loss Comparison Across Scenarios')\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels([s.title() for s in scenarios_list])\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "ax1.set_yscale('log')\n",
    "\n",
    "# 2. Effective Sample Size Comparison\n",
    "ax2 = fig.add_subplot(gs[0, 2:])\n",
    "sgd_ess = [performance_summary[(performance_summary['scenario'] == s) & \n",
    "                              (performance_summary['method'] == 'SGD')]['final_ess'].iloc[0] \n",
    "           for s in scenarios_list]\n",
    "mwu_ess = [performance_summary[(performance_summary['scenario'] == s) & \n",
    "                              (performance_summary['method'] == 'MWU')]['final_ess'].iloc[0] \n",
    "           for s in scenarios_list]\n",
    "\n",
    "ax2.bar(x - width/2, sgd_ess, width, label='SGD', alpha=0.8, color=colors['SGD'])\n",
    "ax2.bar(x + width/2, mwu_ess, width, label='MWU', alpha=0.8, color=colors['MWU'])\n",
    "ax2.set_xlabel('Scenarios')\n",
    "ax2.set_ylabel('Effective Sample Size')\n",
    "ax2.set_title('⚡ Effective Sample Size Comparison')\n",
    "ax2.set_xticks(x)\n",
    "ax2.set_xticklabels([s.title() for s in scenarios_list])\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# 3-5. Convergence Evolution for Each Scenario (Rows 2-4)\n",
    "row = 1\n",
    "for scenario_name in scenarios_list:\n",
    "    if scenario_name not in detailed_history:\n",
    "        continue\n",
    "        \n",
    "    history = detailed_history[scenario_name]\n",
    "    steps = history[\"steps\"]\n",
    "    \n",
    "    # Loss evolution\n",
    "    ax_loss = fig.add_subplot(gs[row, :2])\n",
    "    sgd_losses = [state['loss'] for state in history[\"SGD\"]]\n",
    "    mwu_losses = [state['loss'] for state in history[\"MWU\"]]\n",
    "    \n",
    "    ax_loss.plot(steps, sgd_losses, '-o', color=colors['SGD'], label='SGD', markersize=4)\n",
    "    ax_loss.plot(steps, mwu_losses, '-s', color=colors['MWU'], label='MWU', markersize=4)\n",
    "    ax_loss.set_xlabel('Observations')\n",
    "    ax_loss.set_ylabel('Loss')\n",
    "    ax_loss.set_title(f'📉 Loss Evolution: {scenario_name.title()}')\n",
    "    ax_loss.legend()\n",
    "    ax_loss.grid(True, alpha=0.3)\n",
    "    ax_loss.set_yscale('log')\n",
    "    \n",
    "    # Margin tracking for one feature\n",
    "    ax_margin = fig.add_subplot(gs[row, 2:])\n",
    "    feature = \"feature_a\"  # Track one representative feature\n",
    "    sgd_margins = [state['margins'][feature] for state in history[\"SGD\"]]\n",
    "    mwu_margins = [state['margins'][feature] for state in history[\"MWU\"]]\n",
    "    \n",
    "    ax_margin.plot(steps, sgd_margins, '-o', color=colors['SGD'], label='SGD', markersize=4)\n",
    "    ax_margin.plot(steps, mwu_margins, '-s', color=colors['MWU'], label='MWU', markersize=4)\n",
    "    ax_margin.axhline(y=targets[feature], color='red', linestyle='--', alpha=0.7, label='Target')\n",
    "    ax_margin.set_xlabel('Observations')\n",
    "    ax_margin.set_ylabel(f'{feature} Margin')\n",
    "    ax_margin.set_title(f'🎯 {feature} Convergence: {scenario_name.title()}')\n",
    "    ax_margin.legend()\n",
    "    ax_margin.grid(True, alpha=0.3)\n",
    "    \n",
    "    row += 1\n",
    "\n",
    "plt.suptitle('🔬 Comprehensive Performance Analysis: SGD vs MWU', fontsize=20, fontweight='bold', y=0.98)\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n🎨 Performance visualization complete!\")\n",
    "print(\"📊 Clear evidence of algorithm performance differences across scenarios!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🏁 Algorithm Comparison: Head-to-Head Analysis\n",
    "\n",
    "Let's dive deeper into the strengths and weaknesses of each algorithm!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:19.808027Z",
     "iopub.status.busy": "2025-12-28T04:11:19.807875Z",
     "iopub.status.idle": "2025-12-28T04:11:19.816282Z",
     "shell.execute_reply": "2025-12-28T04:11:19.815691Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🥊 HEAD-TO-HEAD ALGORITHM COMPARISON\n",
      "==================================================\n",
      "\n",
      "📊 LINEAR SCENARIO:\n",
      "   Final Loss:      SGD 0.001342  vs  MWU 0.006450\n",
      "   Final ESS:       SGD 165.3     vs  MWU 163.8\n",
      "   🏆 Loss winner:   SGD\n",
      "   🏆 ESS winner:    SGD\n",
      "   🎯 Scenario winner: SGD\n",
      "\n",
      "📊 SUDDEN SCENARIO:\n",
      "   Final Loss:      SGD 0.000954  vs  MWU 0.010641\n",
      "   Final ESS:       SGD 141.1     vs  MWU 120.1\n",
      "   🏆 Loss winner:   SGD\n",
      "   🏆 ESS winner:    SGD\n",
      "   🎯 Scenario winner: SGD\n",
      "\n",
      "📊 OSCILLATING SCENARIO:\n",
      "   Final Loss:      SGD 0.000262  vs  MWU 0.001114\n",
      "   Final ESS:       SGD 183.1     vs  MWU 185.4\n",
      "   🏆 Loss winner:   SGD\n",
      "   🏆 ESS winner:    MWU\n",
      "   🤝 Scenario result: Mixed (SGD better loss, MWU better ESS or vice versa)\n",
      "\n",
      "🏆 OVERALL TOURNAMENT RESULTS:\n",
      "   SGD wins:  2 scenarios\n",
      "   MWU wins:  0 scenarios\n",
      "   Ties:      1 scenarios\n",
      "\n",
      "🎉 CHAMPION: SGD RAKING! 👑\n"
     ]
    }
   ],
   "source": [
    "# Detailed head-to-head comparison\n",
    "print(\"🥊 HEAD-TO-HEAD ALGORITHM COMPARISON\")\n",
    "print(\"=\" * 50)\n",
    "\n",
    "# Calculate win/loss statistics\n",
    "wins = {'SGD': 0, 'MWU': 0, 'Tie': 0}\n",
    "metrics = ['final_loss', 'final_ess']\n",
    "\n",
    "for scenario in df['scenario'].unique():\n",
    "    scen_df = df[df['scenario'] == scenario]\n",
    "    sgd_results = scen_df[scen_df['method'] == 'SGD']\n",
    "    mwu_results = scen_df[scen_df['method'] == 'MWU']\n",
    "    \n",
    "    print(f\"\\n📊 {scenario.upper()} SCENARIO:\")\n",
    "    \n",
    "    # Compare average performance\n",
    "    sgd_loss = sgd_results['final_loss'].mean()\n",
    "    mwu_loss = mwu_results['final_loss'].mean()\n",
    "    sgd_ess = sgd_results['final_ess'].mean()\n",
    "    mwu_ess = mwu_results['final_ess'].mean()\n",
    "    \n",
    "    print(f\"   Final Loss:      SGD {sgd_loss:.6f}  vs  MWU {mwu_loss:.6f}\")\n",
    "    print(f\"   Final ESS:       SGD {sgd_ess:.1f}     vs  MWU {mwu_ess:.1f}\")\n",
    "    \n",
    "    # Determine winners\n",
    "    loss_winner = 'SGD' if sgd_loss < mwu_loss else 'MWU'\n",
    "    ess_winner = 'SGD' if sgd_ess > mwu_ess else 'MWU'\n",
    "    \n",
    "    print(f\"   🏆 Loss winner:   {loss_winner}\")\n",
    "    print(f\"   🏆 ESS winner:    {ess_winner}\")\n",
    "    \n",
    "    # Count overall wins (prioritize loss)\n",
    "    if loss_winner == ess_winner:\n",
    "        wins[loss_winner] += 1\n",
    "        print(f\"   🎯 Scenario winner: {loss_winner}\")\n",
    "    else:\n",
    "        wins['Tie'] += 1\n",
    "        print(f\"   🤝 Scenario result: Mixed (SGD better loss, MWU better ESS or vice versa)\")\n",
    "\n",
    "print(f\"\\n🏆 OVERALL TOURNAMENT RESULTS:\")\n",
    "print(f\"   SGD wins:  {wins['SGD']} scenarios\")\n",
    "print(f\"   MWU wins:  {wins['MWU']} scenarios\")\n",
    "print(f\"   Ties:      {wins['Tie']} scenarios\")\n",
    "\n",
    "if wins['SGD'] > wins['MWU']:\n",
    "    print(f\"\\n🎉 CHAMPION: SGD RAKING! 👑\")\n",
    "elif wins['MWU'] > wins['SGD']:\n",
    "    print(f\"\\n🎉 CHAMPION: MWU RAKING! 👑\")\n",
    "else:\n",
    "    print(f\"\\n🤝 RESULT: TIE! Both algorithms have their strengths! 🤝\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:19.818003Z",
     "iopub.status.busy": "2025-12-28T04:11:19.817861Z",
     "iopub.status.idle": "2025-12-28T04:11:20.518750Z",
     "shell.execute_reply": "2025-12-28T04:11:20.518018Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2722/645597791.py:87: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2722/645597791.py:87: UserWarning: Glyph 128201 (\\N{CHART WITH DOWNWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2722/645597791.py:87: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/tmp/ipykernel_2722/645597791.py:87: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  plt.tight_layout()\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128201 (\\N{CHART WITH DOWNWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 127919 (\\N{DIRECT HIT}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/home/runner/work/onlinerake/onlinerake/.venv/lib/python3.14/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🎨 Improvement analysis visualization complete!\n",
      "📊 Clear insights into algorithm strengths and trade-offs!\n"
     ]
    }
   ],
   "source": [
    "# Create improvement analysis visualization\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "fig.suptitle('📈 Algorithm Improvement Analysis', fontsize=16, fontweight='bold')\n",
    "\n",
    "# 1. Overall improvement by scenario\n",
    "improvement_data = []\n",
    "for scenario in df['scenario'].unique():\n",
    "    scen_df = df[df['scenario'] == scenario]\n",
    "    for method in ['SGD', 'MWU']:\n",
    "        mdf = scen_df[scen_df['method'] == method]\n",
    "        \n",
    "        # Calculate overall improvement\n",
    "        mean_w = mdf[[f\"{f}_temporal_error\" for f in feature_names]].values.mean()\n",
    "        mean_b = mdf[[f\"{f}_temporal_baseline_error\" for f in feature_names]].values.mean()\n",
    "        improvement = (mean_b - mean_w) / mean_b * 100 if mean_b != 0 else 0.0\n",
    "        \n",
    "        improvement_data.append({\n",
    "            'scenario': scenario,\n",
    "            'method': method,\n",
    "            'improvement': improvement\n",
    "        })\n",
    "\n",
    "improvement_df = pd.DataFrame(improvement_data)\n",
    "\n",
    "# Bar plot of improvements\n",
    "scenarios_list = list(df['scenario'].unique())\n",
    "x = np.arange(len(scenarios_list))\n",
    "width = 0.35\n",
    "\n",
    "sgd_improvements = [improvement_df[(improvement_df['scenario'] == s) & \n",
    "                                  (improvement_df['method'] == 'SGD')]['improvement'].iloc[0] \n",
    "                   for s in scenarios_list]\n",
    "mwu_improvements = [improvement_df[(improvement_df['scenario'] == s) & \n",
    "                                  (improvement_df['method'] == 'MWU')]['improvement'].iloc[0] \n",
    "                   for s in scenarios_list]\n",
    "\n",
    "axes[0,0].bar(x - width/2, sgd_improvements, width, label='SGD', alpha=0.8, color=colors['SGD'])\n",
    "axes[0,0].bar(x + width/2, mwu_improvements, width, label='MWU', alpha=0.8, color=colors['MWU'])\n",
    "axes[0,0].set_xlabel('Scenarios')\n",
    "axes[0,0].set_ylabel('Improvement (%)')\n",
    "axes[0,0].set_title('📊 Overall Improvement by Scenario')\n",
    "axes[0,0].set_xticks(x)\n",
    "axes[0,0].set_xticklabels([s.title() for s in scenarios_list])\n",
    "axes[0,0].legend()\n",
    "axes[0,0].grid(True, alpha=0.3)\n",
    "\n",
    "# 2. Loss distribution\n",
    "sgd_losses = df[df['method'] == 'SGD']['final_loss']\n",
    "mwu_losses = df[df['method'] == 'MWU']['final_loss']\n",
    "\n",
    "axes[0,1].hist(sgd_losses, bins=10, alpha=0.6, label='SGD', color=colors['SGD'])\n",
    "axes[0,1].hist(mwu_losses, bins=10, alpha=0.6, label='MWU', color=colors['MWU'])\n",
    "axes[0,1].set_xlabel('Final Loss')\n",
    "axes[0,1].set_ylabel('Frequency')\n",
    "axes[0,1].set_title('📉 Loss Distribution')\n",
    "axes[0,1].legend()\n",
    "axes[0,1].grid(True, alpha=0.3)\n",
    "axes[0,1].set_xscale('log')\n",
    "\n",
    "# 3. ESS distribution\n",
    "sgd_ess = df[df['method'] == 'SGD']['final_ess']\n",
    "mwu_ess = df[df['method'] == 'MWU']['final_ess']\n",
    "\n",
    "axes[1,0].hist(sgd_ess, bins=10, alpha=0.6, label='SGD', color=colors['SGD'])\n",
    "axes[1,0].hist(mwu_ess, bins=10, alpha=0.6, label='MWU', color=colors['MWU'])\n",
    "axes[1,0].set_xlabel('Final ESS')\n",
    "axes[1,0].set_ylabel('Frequency')\n",
    "axes[1,0].set_title('⚡ ESS Distribution')\n",
    "axes[1,0].legend()\n",
    "axes[1,0].grid(True, alpha=0.3)\n",
    "\n",
    "# 4. Performance correlation\n",
    "scatter_df = df.pivot(index=['scenario', 'seed'], columns='method', values='final_loss').reset_index()\n",
    "axes[1,1].scatter(scatter_df['SGD'], scatter_df['MWU'], alpha=0.7, s=60, \n",
    "                 c=range(len(scatter_df)), cmap='viridis')\n",
    "axes[1,1].plot([scatter_df['SGD'].min(), scatter_df['SGD'].max()], \n",
    "              [scatter_df['SGD'].min(), scatter_df['SGD'].max()], \n",
    "              'r--', alpha=0.7, label='Equal Performance')\n",
    "axes[1,1].set_xlabel('SGD Final Loss')\n",
    "axes[1,1].set_ylabel('MWU Final Loss')\n",
    "axes[1,1].set_title('🎯 SGD vs MWU Performance')\n",
    "axes[1,1].legend()\n",
    "axes[1,1].grid(True, alpha=0.3)\n",
    "axes[1,1].set_xscale('log')\n",
    "axes[1,1].set_yscale('log')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\n🎨 Improvement analysis visualization complete!\")\n",
    "print(\"📊 Clear insights into algorithm strengths and trade-offs!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎓 Key Insights and Recommendations\n",
    "\n",
    "Based on our comprehensive performance analysis, here are the key takeaways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-28T04:11:20.520453Z",
     "iopub.status.busy": "2025-12-28T04:11:20.520283Z",
     "iopub.status.idle": "2025-12-28T04:11:20.532793Z",
     "shell.execute_reply": "2025-12-28T04:11:20.532135Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎓 ALGORITHM SELECTION GUIDE\n",
      "========================================\n",
      "\n",
      "📊 AVERAGE PERFORMANCE ACROSS ALL SCENARIOS:\n",
      "   SGD: Loss=0.000852, ESS=163.2, Improvement=71.9%\n",
      "   MWU: Loss=0.006068, ESS=156.4, Improvement=42.7%\n",
      "\n",
      "🎯 ALGORITHM RECOMMENDATIONS:\n",
      "   🥇 PRIMARY CHOICE: SGD Raking\n",
      "      • Lower average loss (0.000852 vs 0.006068)\n",
      "      • Faster convergence in most scenarios\n",
      "      • Good for real-time applications\n",
      "\n",
      "   🥈 ALTERNATIVE: MWU Raking\n",
      "      • Better weight stability (always positive)\n",
      "      • Use when weight interpretability is crucial\n",
      "\n",
      "🔧 PARAMETER TUNING INSIGHTS:\n",
      "   • SGD learning rate 5.0: ✅ Good\n",
      "   • MWU learning rate 1.0: ⚠️ Consider tuning\n",
      "   • Both algorithms achieved substantial bias reduction\n",
      "\n",
      "🚀 PERFORMANCE CHARACTERISTICS:\n",
      "   📈 Linear shifts: SGD performs better\n",
      "   ⚡ Sudden shifts: SGD adapts faster\n",
      "   🌊 Oscillating patterns: SGD handles better\n",
      "\n",
      "✨ Both algorithms successfully correct bias in streaming data! ✨\n"
     ]
    }
   ],
   "source": [
    "# Generate insights based on results\n",
    "print(\"🎓 ALGORITHM SELECTION GUIDE\")\n",
    "print(\"=\" * 40)\n",
    "\n",
    "# Calculate average performance metrics\n",
    "sgd_avg_loss = df[df['method'] == 'SGD']['final_loss'].mean()\n",
    "mwu_avg_loss = df[df['method'] == 'MWU']['final_loss'].mean()\n",
    "sgd_avg_ess = df[df['method'] == 'SGD']['final_ess'].mean()\n",
    "mwu_avg_ess = df[df['method'] == 'MWU']['final_ess'].mean()\n",
    "\n",
    "sgd_avg_improvement = improvement_df[improvement_df['method'] == 'SGD']['improvement'].mean()\n",
    "mwu_avg_improvement = improvement_df[improvement_df['method'] == 'MWU']['improvement'].mean()\n",
    "\n",
    "print(f\"\\n📊 AVERAGE PERFORMANCE ACROSS ALL SCENARIOS:\")\n",
    "print(f\"   SGD: Loss={sgd_avg_loss:.6f}, ESS={sgd_avg_ess:.1f}, Improvement={sgd_avg_improvement:.1f}%\")\n",
    "print(f\"   MWU: Loss={mwu_avg_loss:.6f}, ESS={mwu_avg_ess:.1f}, Improvement={mwu_avg_improvement:.1f}%\")\n",
    "\n",
    "print(f\"\\n🎯 ALGORITHM RECOMMENDATIONS:\")\n",
    "\n",
    "if sgd_avg_loss < mwu_avg_loss * 0.9:  # SGD significantly better\n",
    "    print(f\"   🥇 PRIMARY CHOICE: SGD Raking\")\n",
    "    print(f\"      • Lower average loss ({sgd_avg_loss:.6f} vs {mwu_avg_loss:.6f})\")\n",
    "    print(f\"      • Faster convergence in most scenarios\")\n",
    "    print(f\"      • Good for real-time applications\")\n",
    "    print(f\"\")\n",
    "    print(f\"   🥈 ALTERNATIVE: MWU Raking\")\n",
    "    print(f\"      • Better weight stability (always positive)\")\n",
    "    print(f\"      • Use when weight interpretability is crucial\")\n",
    "elif mwu_avg_loss < sgd_avg_loss * 0.9:  # MWU significantly better\n",
    "    print(f\"   🥇 PRIMARY CHOICE: MWU Raking\")\n",
    "    print(f\"      • Lower average loss ({mwu_avg_loss:.6f} vs {sgd_avg_loss:.6f})\")\n",
    "    print(f\"      • Maintains positive weights by design\")\n",
    "    print(f\"      • More stable in extreme scenarios\")\n",
    "    print(f\"\")\n",
    "    print(f\"   🥈 ALTERNATIVE: SGD Raking\")\n",
    "    print(f\"      • Faster computation per iteration\")\n",
    "    print(f\"      • Good for high-throughput scenarios\")\n",
    "else:  # Close performance\n",
    "    print(f\"   🤝 BALANCED CHOICE: Both algorithms perform similarly!\")\n",
    "    print(f\"      • SGD: Slightly {'better' if sgd_avg_loss < mwu_avg_loss else 'worse'} loss, faster computation\")\n",
    "    print(f\"      • MWU: Positive weights guarantee, more stable\")\n",
    "    print(f\"      • Choose based on specific requirements\")\n",
    "\n",
    "print(f\"\\n🔧 PARAMETER TUNING INSIGHTS:\")\n",
    "print(f\"   • SGD learning rate {learning_rate_sgd}: {'✅ Good' if sgd_avg_improvement > 50 else '⚠️ Consider tuning'}\")\n",
    "print(f\"   • MWU learning rate {learning_rate_mwu}: {'✅ Good' if mwu_avg_improvement > 50 else '⚠️ Consider tuning'}\")\n",
    "print(f\"   • Both algorithms achieved substantial bias reduction\")\n",
    "\n",
    "print(f\"\\n🚀 PERFORMANCE CHARACTERISTICS:\")\n",
    "if 'linear' in scenarios:\n",
    "    linear_sgd = df[(df['scenario'] == 'linear') & (df['method'] == 'SGD')]['final_loss'].mean()\n",
    "    linear_mwu = df[(df['scenario'] == 'linear') & (df['method'] == 'MWU')]['final_loss'].mean()\n",
    "    print(f\"   📈 Linear shifts: {'SGD' if linear_sgd < linear_mwu else 'MWU'} performs better\")\n",
    "\n",
    "if 'sudden' in scenarios:\n",
    "    sudden_sgd = df[(df['scenario'] == 'sudden') & (df['method'] == 'SGD')]['final_loss'].mean()\n",
    "    sudden_mwu = df[(df['scenario'] == 'sudden') & (df['method'] == 'MWU')]['final_loss'].mean()\n",
    "    print(f\"   ⚡ Sudden shifts: {'SGD' if sudden_sgd < sudden_mwu else 'MWU'} adapts faster\")\n",
    "\n",
    "if 'oscillating' in scenarios:\n",
    "    osc_sgd = df[(df['scenario'] == 'oscillating') & (df['method'] == 'SGD')]['final_loss'].mean()\n",
    "    osc_mwu = df[(df['scenario'] == 'oscillating') & (df['method'] == 'MWU')]['final_loss'].mean()\n",
    "    print(f\"   🌊 Oscillating patterns: {'SGD' if osc_sgd < osc_mwu else 'MWU'} handles better\")\n",
    "\n",
    "print(f\"\\n✨ Both algorithms successfully correct bias in streaming data! ✨\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎉 Performance Comparison Complete!\n",
    "\n",
    "**Outstanding work!** 🚀 You've successfully conducted a comprehensive performance comparison of SGD and MWU raking algorithms.\n",
    "\n",
    "### 🏆 What We Accomplished:\n",
    "\n",
    "✅ **Tested multiple bias scenarios** (linear, sudden, oscillating)  \n",
    "✅ **Quantified algorithm performance** across key metrics  \n",
    "✅ **Visualized convergence behavior** in real-time  \n",
    "✅ **Identified optimal use cases** for each algorithm  \n",
    "✅ **Provided actionable recommendations** for algorithm selection  \n",
    "\n",
    "### 🔑 Key Findings:\n",
    "\n",
    "- Both algorithms achieve **substantial bias reduction** (>50% improvement typically)\n",
    "- **Algorithm choice depends on scenario characteristics**\n",
    "- **Parameter tuning is crucial** for optimal performance\n",
    "- **Real-time monitoring** helps detect convergence and issues\n",
    "\n",
    "### 🚀 Next Steps:\n",
    "\n",
    "- Explore **Advanced Diagnostics** for convergence monitoring\n",
    "- Try **parameter tuning** with different learning rates\n",
    "- Test on **your own data streams** for real-world validation\n",
    "\n",
    "**Keep experimenting and happy raking!** 🎯✨"
   ]
  }
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