Initial deployment of ModelMatrix-HF

.dockerignore

@@ -0,0 +1,33 @@
+.git
+.gitignore
+.dockerignore
+.env
+.env.local
+
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+dist/
+build/
+*.egg
+
+venv/
+.venv/
+env/
+
+.vscode/
+.idea/
+*.swp
+*.swo
+
+.DS_Store
+Thumbs.db
+
+datasets/
+results/
+*.pt
+*.bin
+*.safetensors
+.ipynb_checkpoints/
+catboost_info/

.env.example

@@ -0,0 +1,13 @@
+# Hugging Face API Token (required for SAP RPT-1 OSS gated model)
+#
+# Setup instructions:
+#   1. Create account at https://huggingface.co/join
+#   2. Accept the model license at https://huggingface.co/SAP/sap-rpt-1-oss
+#   3. Generate token at https://huggingface.co/settings/tokens
+#   4. Copy this file to .env and paste your token below
+#
+# Usage:
+#   Windows:  set HUGGING_FACE_HUB_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+#   Linux:    export HUGGING_FACE_HUB_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+
+HUGGING_FACE_HUB_TOKEN=your_token_here

Dockerfile

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+FROM python:3.11-slim
+
+# Create user to run the app
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+# Install system dependencies (e.g. for lightgbm/xgboost)
+USER root
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    libgomp1 \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+USER user
+
+# Copy the entire project
+COPY --chown=user . $HOME/app/
+
+# Install python dependencies
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r webapp/requirements.txt
+
+# Install SAP-RPT-1 OSS directly from GitHub (needed for the real model)
+RUN pip install --no-cache-dir git+https://github.com/SAP-samples/sap-rpt-1-oss.git
+
+# Expose port 7860 (Hugging Face Spaces default port)
+EXPOSE 7860
+
+# Run the FastAPI app
+CMD ["python", "-m", "uvicorn", "webapp.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -8,4 +8,248 @@ pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SAP RPT-1 Benchmarking
+## 🚀 Setup
+
+### Option 1: Docker (Recommended for Reproducibility)
+
+```bash
+# Clone the repo
+git clone <repo-url>
+cd "MINI proj SAP" 
+
+# Copy .env.example to .env and paste your HuggingFace token
+cp .env.example .env
+
+# Build containers
+docker-compose build
+
+# Run SAP RPT-1 experiment
+docker-compose run sap-rpt1 -m runners.run_experiment --dataset analcatdata_authorship --model sap-rpt1-hf
+
+# Run baselines batch
+docker-compose run baselines -m runners.run_batch --datasets config/datasets.yaml --models config/models.yaml
+```
+
+### Option 2: Local Install (Python >= 3.11 required)
+
+```bash
+# Clone the repo
+git clone <repo-url>
+cd "MINI proj SAP"
+
+# Install everything in one command
+pip install -e ".[models,baselines]"
+
+# Download datasets (19 datasets from OpenML)
+cd code
+python -m datasets.download_tabarena
+cd ..
+```
+
+## 🔑 Hugging Face Token Setup (Required for SAP RPT-1 OSS)
+
+The SAP RPT-1 OSS model weights are **gated** on Hugging Face:
+
+1. Create account at [huggingface.co/join](https://huggingface.co/join)
+2. Accept the license at [huggingface.co/SAP/sap-rpt-1-oss](https://huggingface.co/SAP/sap-rpt-1-oss)
+3. Generate a token at [huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+4. Set the token:
+
+**Windows (PowerShell):**
+```powershell
+$env:HUGGING_FACE_HUB_TOKEN = "hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+```
+
+**Linux/Mac:**
+```bash
+export HUGGING_FACE_HUB_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+```
+
+**Or using .env file** (recommended):
+```bash
+cp .env.example .env
+# Edit .env and paste your token
+```
+
+## 🧪 Quick Test
+
+```bash
+cd code
+python ../scripts/test_sap_rpt1.py
+```
+
+This verifies HF token authentication, model download, and prediction accuracy.
+
+## 📊 Run Experiments
+
+### Single Experiment
+```bash
+cd code
+
+# SAP RPT-1 OSS
+python -m runners.run_experiment --dataset analcatdata_authorship --model sap-rpt1-hf
+
+# XGBoost baseline
+python -m runners.run_experiment --dataset analcatdata_authorship --model xgboost
+```
+
+### Baseline Models Only (XGBoost, CatBoost, LightGBM)
+```bash
+cd code
+
+# Run on ALL datasets
+python -m runners.run_baselines
+
+# Run on specific datasets
+python -m runners.run_baselines --dataset analcatdata_authorship diabetes
+```
+
+### Full Batch (All Models × All Datasets)
+```bash
+cd code
+python -m runners.run_batch --datasets config/datasets.yaml --models config/models.yaml
+```
+
+### Available Models
+
+| Model Name | Type | Description |
+|---|---|---|
+| `sap-rpt1-hf` | Pretrained (OSS) | SAP RPT-1 OSS via HuggingFace |
+| `xgboost` | Baseline | XGBoost |
+| `catboost` | Baseline | CatBoost |
+| `lightgbm` | Baseline | LightGBM |
+
+## 📈 View Results
+
+Results are saved to `results/raw/[dataset]_[model].json`
+
+Example output:
+```json
+{
+  "dataset": "analcatdata_authorship",
+  "model": "sap-rpt1-hf",
+  "task_type": "classification",
+  "n_samples": 841,
+  "n_features": 70,
+  "mean_metrics": {
+    "accuracy": 1.0,
+    "roc_auc": 1.0,
+    "f1_macro": 1.0
+  }
+}
+```
+
+## 📊 Aggregate Results
+```bash
+cd code
+python -m analysis.aggregate_results
+```
+
+## 🌐 Web Interface (Advanced Version)
+
+We've completely overhauled the interactive web application to provide a production-grade, scientific benchmarking experience directly in your browser.
+
+**Tech Stack & Architecture:**
+- **Frontend**: Pure HTML/CSS/Vanilla JS. Built with a custom "Midnight Precision" design system featuring glassmorphism, dynamic data-aware input generation, and theme-aware custom scrollbars.
+- **Backend**: Python with FastAPI and Scikit-Learn/Scipy.
+- **Visualizations**: Chart.js for rendering dynamic metric comparisons.
+
+**Key Features Built:**
+- **Midnight Precision Aesthetics**: A premium, ultra-modern UI featuring animated liquid gradients, responsive design, and seamless user interaction flows.
+- **Advanced Ensemble Engine**: Automatically builds and benchmarks Meta-Models on the fly:
+  - *Voting Ensembles*: Soft-voting probabilities across top models.
+  - *Stacking Ensembles*: Sklearn-native meta-learning (LogisticRegression/Ridge) layered on top of base models.
+- **Statistical Rigor & Ranking**: Moves beyond simple average scores to actual scientific analysis:
+  - *Cross-Fold Ranking*: Olympic-style "min" ranking across all CV folds.
+  - *Friedman Significance Testing*: Computes P-Values to formally test if the champion model's lead is statistically significant.
+  - *Stability Badges*: Automatically tags models as 'Dominant', 'Competitive', or 'Volatile' based on their consistency in winning folds.
+- **Interactive Live Playground**: Once the benchmark finishes, a live interface is generated. 
+  - *Stateful Pipeline*: The backend caches the exact `LabelEncoder` states from the training phase, ensuring the live playground data is mathematically aligned with the original dataset.
+  - *Data-Aware UI*: Input fields dynamically adapt to numeric or categorical columns based on backend typing.
+
+**How to start the Web App:**
+```bash
+cd webapp
+pip install -r requirements.txt
+python -m uvicorn main:app --port 8000
+```
+Then open your browser and navigate to `http://localhost:8000`.
+
+## 🏗️ Project Structure
+
+```text
+MINI proj SAP/
+├── code/
+│   ├── docker/              # Docker environments
+│   ├── models/              # Model wrappers (sklearn-compatible)
+│   │   ├── sap_rpt1_hf_wrapper.py  # SAP RPT-1 OSS via HuggingFace
+│   │   ├── base_wrapper.py         # Abstract base class
+│   │   └── ...
+│   ├── evaluation/          # Metrics, cross-validation, compute tracking
+│   ├── runners/             # Experiment execution
+│   │   ├── run_experiment.py    # Single experiment
+│   │   ├── run_batch.py         # Batch experiments
+│   │   └── run_baselines.py     # Baseline models only
+│   ├── analysis/            # Results aggregation
+│   └── config/              # YAML configurations
+├── webapp/                  # Interactive Web Application
+│   ├── main.py              # FastAPI Backend Server
+│   ├── benchmark.py         # Advanced Benchmarking Engine
+│   ├── ensemble.py          # Meta-Model Generators
+│   ├── requirements.txt     # Web-specific dependencies
+│   └── static/              # Frontend Assets
+│       ├── landing.html     # Animated Landing Page
+│       ├── uploader.html    # Drag & Drop Interface
+│       ├── arena.html       # Results & Statistical Rigor UI
+│       ├── app.js           # Client-side Logic
+│       └── style.css        # Midnight Precision Styles
+├── results/                 # Experiment outputs
+├── scripts/
+│   └── test_sap_rpt1.py     # Quick-start validation test
+├── requirements.txt         # Pinned dependencies
+├── setup.py                 # Package configuration
+├── docker-compose.yml       # Docker orchestration
+└── .env.example             # HF token template
+```
+
+## 🔄 Reproducibility
+
+This repo follows NeurIPS/ICML reproducibility standards:
+
+- **Pinned dependencies**: All packages have exact versions in `requirements.txt`
+- **Fixed random seeds**: `random_state=42` across all experiments
+- **Docker containers**: Isolated environments for incompatible dependencies
+- **Gated model weights**: SAP RPT-1 OSS uses a fixed checkpoint (`v1.1.2`)
+- **5-fold cross-validation**: Stratified splits ensure identical data partitions
+
+
+## 🆘 Troubleshooting
+
+**Python version error:**
+SAP RPT-1 OSS requires Python >= 3.11. Check with `python --version`.
+
+**Missing TabPFN Error (ModuleNotFoundError):**
+If you encounter an error stating that `tabpfn` is missing when running the benchmark, install it manually:
+```bash
+pip install tabpfn
+```
+
+**HF Token not working:**
+```bash
+huggingface-cli whoami
+huggingface-cli login
+```
+
+**Docker build fails:**
+```bash
+docker-compose build --no-cache
+```
+
+**Out of memory:**
+Edit `code/config/experiments.yaml` and reduce:
+```yaml
+sap_rpt1_hf:
+  max_context_size: 2048  # Lower from 4096
+  bagging: 1              # Lower from 4
+```

code/analysis/__init__.py

@@ -0,0 +1,11 @@
+"""
+Analysis Package
+================
+
+Results aggregation, statistical analysis, and visualization.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+__all__ = ['aggregate_results']

code/analysis/aggregate_results.py

@@ -0,0 +1,99 @@
+"""
+Results Aggregation
+===================
+
+Aggregate all experiment results into summary tables.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import glob
+import json
+import pandas as pd
+import os
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def aggregate_all_results(
+    results_dir: str = '../results/raw',
+    output_file: str = '../results/processed/aggregated_results.csv'
+) -> pd.DataFrame:
+    """
+    Aggregate all experiment results into single DataFrame.
+
+    Parameters
+    ----------
+    results_dir : str
+        Directory containing result JSON files
+    output_file : str
+        Where to save aggregated CSV
+
+    Returns
+    -------
+    df : pd.DataFrame
+        Aggregated results
+    """
+    logger.info(f"Aggregating results from {results_dir}")
+
+    result_files = glob.glob(os.path.join(results_dir, '*.json'))
+    logger.info(f"Found {len(result_files)} result files")
+
+    aggregated = []
+
+    for file in result_files:
+        try:
+            with open(file) as f:
+                data = json.load(f)
+
+            record = {
+                'dataset': data['dataset'],
+                'model': data['model'],
+                'task_type': data['task_type'],
+                'n_samples': data['n_samples'],
+                'n_features': data['n_features'],
+                'n_folds': data['n_folds']
+            }
+
+            # Add mean metrics
+            for metric, value in data['mean_metrics'].items():
+                record[f'mean_{metric}'] = value
+
+            # Add std metrics
+            for metric, value in data['std_metrics'].items():
+                record[f'std_{metric}'] = value
+
+            # Add compute info
+            if 'compute' in data:
+                record['elapsed_hours'] = data['compute'].get('elapsed_hours')
+                record['cost_usd'] = data['compute'].get('cost_usd')
+
+            aggregated.append(record)
+
+        except Exception as e:
+            logger.warning(f"Failed to process {file}: {e}")
+
+    # Create DataFrame
+    df = pd.DataFrame(aggregated)
+
+    # Save
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+    df.to_csv(output_file, index=False)
+
+    logger.info(f"Aggregated {len(df)} results to {output_file}")
+
+    return df
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+
+    df = aggregate_all_results()
+
+    print(f"\n✅ Aggregated {len(df)} experiment results")
+    print(f"\nDatasets: {df['dataset'].nunique()}")
+    print(f"Models: {df['model'].nunique()}")
+    print(f"\nSample of results:")
+    print(df.head())

code/config/datasets.yaml

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+# Dataset Configuration
+# =====================
+
+# Local Datasets (from datasets folder)
+local_datasets:
+  enabled: true
+  path: '../datasets'
+
+# TabZilla Datasets (subset of 20)
+tabzilla:
+  enabled: false  # Enable when data is available
+  path: '../datasets/tabzilla'
+
+# OpenML-CC18 (Classification subset)
+openml_cc18:
+  enabled: false
+  path: '../datasets/openml_cc18'
+
+# Dataset Filters
+filters:
+  min_samples: 100
+  max_samples: 100000
+  min_features: 2
+  max_features: 1000
+  task_types:
+    - classification
+    - regression
+
+# Preprocessing
+preprocessing:
+  handle_missing: 'mean'  # mean, median, most_frequent, drop
+  encode_categoricals: true
+  scale_features: false  # Most models handle scaling internally

code/config/experiments.yaml

@@ -0,0 +1,64 @@
+# Experiment Configuration
+# ========================
+
+# Cross-Validation Settings
+n_folds: 10
+random_state: 42
+timeout: 86400  # 24 hours per experiment
+
+# Compute Resources
+cost_per_hour: 0.90  # USD per GPU-hour (H200)
+gpu_type: 'H200'
+gpu_memory_limit: 80  # GB
+checkpoint_interval: 3600  # Save checkpoint every hour
+
+# Model-Specific Parameters
+model_params:
+  sap_rpt1:
+    context_size: 4096
+    bagging_factor: 4
+    model_size: 'small'  # or 'large'
+
+  sap_rpt1_hf:
+    max_context_size: 4096
+    bagging: 4
+
+  tabpfn:
+    n_ensemble: 1
+    device: 'auto'
+
+  autogluon:
+    time_limit: 300  # 5 minutes
+    preset: 'medium_quality'  # best_quality, high_quality, good_quality, medium_quality
+
+  xgboost:
+    n_estimators: 100
+    learning_rate: 0.1
+    max_depth: 6
+
+  catboost:
+    iterations: 100
+    learning_rate: 0.1
+    depth: 6
+
+  lightgbm:
+    n_estimators: 100
+    learning_rate: 0.1
+    max_depth: -1
+
+# Evaluation Metrics
+primary_metric:
+  classification: 'roc_auc'
+  regression: 'r2'
+
+# Statistical Testing
+statistical_tests:
+  friedman_alpha: 0.05
+  nemenyi_alpha: 0.05
+
+# Reproducibility
+reproducibility:
+  save_predictions: true
+  save_models: false  # Models can be large
+  log_hyperparameters: true
+  track_compute: true

code/config/models.yaml

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+# Model Configuration
+# ====================
+
+models:
+  # SAP RPT-1 (Primary Model)
+  - name: 'sap-rpt1-small'
+    enabled: true
+    priority: 'high'
+    docker_image: 'sap-rpt1'
+
+  - name: 'sap-rpt1-large'
+    enabled: true
+    priority: 'high'
+    docker_image: 'sap-rpt1'
+
+  # SAP RPT-1 OSS via Hugging Face (Open Source)
+  - name: 'sap-rpt1-hf'
+    enabled: true
+    priority: 'high'
+    docker_image: 'sap-rpt1'
+    description: 'SAP RPT-1 OSS model via HuggingFace token authentication'
+
+  # Pretrained Competitors
+  - name: 'tabpfn'
+    enabled: true
+    priority: 'high'
+    docker_image: 'tabpfn'
+
+  - name: 'tabicl'
+    enabled: false  # Enable when implementation ready
+    priority: 'medium'
+    docker_image: 'tabicl'
+
+  # AutoML
+  - name: 'autogluon'
+    enabled: true
+    priority: 'medium'
+    docker_image: 'autogluon'
+
+  # Gradient Boosting Baselines
+  - name: 'xgboost'
+    enabled: true
+    priority: 'medium'
+    docker_image: 'baselines'
+
+  - name: 'catboost'
+    enabled: true
+    priority: 'medium'
+    docker_image: 'baselines'
+
+  - name: 'lightgbm'
+    enabled: true
+    priority: 'low'
+    docker_image: 'baselines'
+
+# Model Groups (for batch experiments)
+model_groups:
+  all:
+    - sap-rpt1-small
+    - sap-rpt1-large
+    - sap-rpt1-hf
+    - tabpfn
+    - autogluon
+    - xgboost
+    - catboost
+    - lightgbm
+
+  pretrained_only:
+    - sap-rpt1-small
+    - sap-rpt1-large
+    - sap-rpt1-hf
+    - tabpfn
+
+  baselines_only:
+    - xgboost
+    - catboost
+    - lightgbm
+
+  high_priority:
+    - sap-rpt1-small
+    - sap-rpt1-large
+    - sap-rpt1-hf
+    - tabpfn
+

code/docker/Dockerfile

@@ -0,0 +1,102 @@
+# =============================================================================
+# SAP RPT-1 Benchmarking - Multi-stage Dockerfile
+# =============================================================================
+# Builds two targets:
+#   - sap-rpt1: Python 3.11 with SAP RPT-1 OSS + all dependencies
+#   - baselines: Python 3.11 with XGBoost, CatBoost, LightGBM
+#
+# Usage:
+#   docker-compose build
+#   docker-compose run sap-rpt1
+#   docker-compose run baselines
+# =============================================================================
+
+# ---------- Base stage (shared by all targets) ----------
+FROM python:3.11-slim AS base
+
+# System dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# Copy requirements first (for Docker layer caching)
+COPY requirements.txt /app/requirements.txt
+
+# ---------- SAP RPT-1 target ----------
+FROM base AS sap-rpt1
+
+# Install core scientific stack first (heavy packages)
+RUN pip install --default-timeout=1000 --retries 5 --no-cache-dir \
+    numpy==1.26.4 \
+    pandas==2.2.3 \
+    scikit-learn==1.6.1 \
+    scipy==1.14.1 \
+    matplotlib==3.9.2 \
+    seaborn==0.13.2
+
+# Install Hugging Face and PyTorch stack
+RUN pip install --default-timeout=1000 --retries 5 --no-cache-dir \
+    --extra-index-url https://download.pytorch.org/whl/cpu \
+    torch==2.7.0+cpu \
+    transformers==4.52.4 \
+    accelerate==1.6.0 \
+    huggingface-hub==0.30.2 \
+    datasets==3.5.0 \
+    pyarrow==20.0.0 \
+    torcheval==0.0.7
+
+# Install SAP RPT-1 and remaining requirements
+RUN pip install --default-timeout=1000 --retries 5 --no-cache-dir -r requirements.txt
+
+# Copy project code
+COPY . /app
+
+# Set Python path
+ENV PYTHONPATH=/app/code
+
+WORKDIR /app/code
+
+# Set entrypoint so you can run via arguments natively
+ENTRYPOINT ["python"]
+CMD ["-m", "runners.run_experiment", "--dataset", "adult", "--model", "sap-rpt1-hf"]
+
+# ---------- Baselines target ----------
+FROM base AS baselines
+
+# Install core scientific stack (heavy packages)
+RUN pip install --default-timeout=1000 --retries 5 --no-cache-dir \
+    numpy==1.26.4 \
+    pandas==2.2.3 \
+    scikit-learn==1.6.1 \
+    scipy==1.14.1
+
+# Install visualization and utilities
+RUN pip install --default-timeout=1000 --retries 5 --no-cache-dir \
+    matplotlib==3.9.2 \
+    seaborn==0.13.2 \
+    pyyaml==6.0.2 \
+    tqdm==4.67.1 \
+    joblib==1.4.2 \
+    python-dotenv==1.0.1
+
+# Install ML frameworks and OpenML
+RUN pip install --default-timeout=1000 --retries 5 --no-cache-dir \
+    openml==0.14.2 \
+    xgboost \
+    catboost \
+    lightgbm
+
+# Copy project code
+COPY . /app
+
+# Set Python path
+ENV PYTHONPATH=/app/code
+
+WORKDIR /app/code
+
+# Set entrypoint so you can run via arguments natively
+ENTRYPOINT ["python"]
+CMD ["-m", "runners.run_batch", "--datasets", "config/datasets.yaml", "--models", "config/models.yaml"]

code/evaluation/__init__.py

@@ -0,0 +1,24 @@
+"""
+Evaluation Package
+==================
+
+Tools for model evaluation, statistical testing, and benchmarking.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+from .metrics import calculate_classification_metrics, calculate_regression_metrics
+from .cross_validation import run_cross_validation
+from .statistical_tests import friedman_test, nemenyi_post_hoc, critical_difference
+from .compute_tracker import ComputeTracker
+
+__all__ = [
+    'calculate_classification_metrics',
+    'calculate_regression_metrics',
+    'run_cross_validation',
+    'friedman_test',
+    'nemenyi_post_hoc',
+    'critical_difference',
+    'ComputeTracker'
+]

code/evaluation/compute_tracker.py

@@ -0,0 +1,114 @@
+"""
+Compute Resource Tracker
+=========================
+
+Track GPU hours, costs, and resource usage for experiments.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import time
+import numpy as np
+from typing import Dict, Optional, List
+
+try:
+    import psutil
+    HAS_PSUTIL = True
+except ImportError:
+    HAS_PSUTIL = False
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ComputeTracker:
+    """
+    Track compute resources and costs.
+
+    Parameters
+    ----------
+    cost_per_hour : float
+        Cost per GPU-hour in USD
+    gpu_type : str
+        GPU type (e.g., 'H200', 'A100', 'L40S')
+    """
+
+    def __init__(self, cost_per_hour: float = 0.90, gpu_type: str = 'H200'):
+        self.cost_per_hour = cost_per_hour
+        self.gpu_type = gpu_type
+        self.start_time: Optional[float] = None
+        self.end_time: Optional[float] = None
+        self.gpu_usage_log: List[Dict] = []
+
+    def start(self):
+        """Start tracking."""
+        self.start_time = time.time()
+        self.gpu_usage_log = []
+        logger.info(f"Compute tracking started (GPU: {self.gpu_type}, ${self.cost_per_hour}/hr)")
+
+    def log_gpu_usage(self):
+        """Log current GPU usage."""
+        try:
+            import GPUtil
+            gpus = GPUtil.getGPUs()
+
+            for gpu in gpus:
+                self.gpu_usage_log.append({
+                    'timestamp': time.time(),
+                    'gpu_id': gpu.id,
+                    'gpu_load': gpu.load * 100,
+                    'memory_used_mb': gpu.memoryUsed,
+                    'memory_total_mb': gpu.memoryTotal,
+                    'memory_util': (gpu.memoryUsed / gpu.memoryTotal) * 100,
+                    'temperature': getattr(gpu, 'temperature', None)
+                })
+        except ImportError:
+            logger.warning("GPUtil not installed, GPU tracking unavailable")
+        except Exception as e:
+            logger.warning(f"GPU logging failed: {e}")
+
+    def stop(self) -> Dict:
+        """
+        Stop tracking and calculate costs.
+
+        Returns
+        -------
+        summary : dict
+            Elapsed time, costs, and GPU usage summary
+        """
+        self.end_time = time.time()
+
+        elapsed_hours = (self.end_time - self.start_time) / 3600
+        total_cost = elapsed_hours * self.cost_per_hour
+
+        # CPU usage
+        if HAS_PSUTIL:
+            cpu_percent = psutil.cpu_percent(interval=1)
+            memory_info = psutil.virtual_memory()
+            memory_percent = memory_info.percent
+            memory_used_gb = memory_info.used / (1024 ** 3)
+        else:
+            cpu_percent = 0.0
+            memory_percent = 0.0
+            memory_used_gb = 0.0
+
+        summary = {
+            'elapsed_hours': elapsed_hours,
+            'cost_usd': total_cost,
+            'cost_per_hour': self.cost_per_hour,
+            'gpu_type': self.gpu_type,
+            'cpu_percent': cpu_percent,
+            'memory_percent': memory_percent,
+            'memory_used_gb': memory_used_gb,
+            'gpu_logs_count': len(self.gpu_usage_log)
+        }
+
+        # Average GPU utilization
+        if self.gpu_usage_log:
+            summary['avg_gpu_load'] = np.mean([log['gpu_load'] for log in self.gpu_usage_log])
+            summary['avg_gpu_memory_util'] = np.mean([log['memory_util'] for log in self.gpu_usage_log])
+
+        logger.info(f"Compute tracking stopped: {elapsed_hours:.2f} hours, ${total_cost:.2f}")
+
+        return summary

code/evaluation/cross_validation.py

@@ -0,0 +1,127 @@
+"""
+Cross-Validation
+================
+
+10-fold stratified cross-validation for model evaluation.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import StratifiedKFold, KFold
+from sklearn.preprocessing import LabelEncoder
+from typing import List, Dict
+import logging
+
+from .metrics import calculate_classification_metrics, calculate_regression_metrics
+
+logger = logging.getLogger(__name__)
+
+
+def _encode_categorical_columns(X_train, X_val):
+    """
+    Label-encode object/categorical columns. Fitted on X_train,
+    applied to both X_train and X_val. Unknown categories in X_val
+    are mapped to -1.
+    """
+    X_train = X_train.copy()
+    X_val = X_val.copy()
+
+    cat_cols = X_train.select_dtypes(include=['object', 'category']).columns
+    if len(cat_cols) == 0:
+        return X_train, X_val
+
+    logger.info(f"  Encoding {len(cat_cols)} categorical columns: {list(cat_cols[:5])}{'...' if len(cat_cols) > 5 else ''}")
+
+    for col in cat_cols:
+        le = LabelEncoder()
+        # Fit on combined unique values from train (+ handle unseen in val)
+        combined = pd.concat([X_train[col], X_val[col]], axis=0).astype(str)
+        le.fit(combined)
+        X_train[col] = le.transform(X_train[col].astype(str))
+        X_val[col] = le.transform(X_val[col].astype(str))
+
+    return X_train, X_val
+
+
+def run_cross_validation(
+    model,
+    X: pd.DataFrame,
+    y: pd.Series,
+    task_type: str = 'classification',
+    n_folds: int = 10,
+    random_state: int = 42
+) -> List[Dict]:
+    """
+    Run k-fold cross-validation.
+
+    Parameters
+    ----------
+    model : BaseModelWrapper
+        Model to evaluate (must have fit/predict methods)
+    X : pd.DataFrame
+        Features
+    y : pd.Series
+        Target
+    task_type : str
+        'classification' or 'regression'
+    n_folds : int
+        Number of folds
+    random_state : int
+        Random seed
+
+    Returns
+    -------
+    fold_results : list of dict
+        Results for each fold
+    """
+    logger.info(f"Running {n_folds}-fold CV for {model.__class__.__name__}")
+
+    # Choose CV splitter
+    if task_type == 'classification':
+        cv = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=random_state)
+    else:
+        cv = KFold(n_splits=n_folds, shuffle=True, random_state=random_state)
+
+    fold_results = []
+
+    for fold_idx, (train_idx, val_idx) in enumerate(cv.split(X, y)):
+        logger.info(f"  Fold {fold_idx + 1}/{n_folds}")
+
+        # Split data
+        X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]
+        y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
+
+        # Auto-encode categorical columns so tree models can handle them
+        X_train, X_val = _encode_categorical_columns(X_train, X_val)
+
+        # Fit model
+        model.fit(X_train, y_train)
+
+        # Predict
+        y_pred = model.predict(X_val)
+        y_proba = None
+        if task_type == 'classification':
+            try:
+                y_proba = model.predict_proba(X_val)
+            except:
+                pass
+
+        # Calculate metrics
+        if task_type == 'classification':
+            metrics = calculate_classification_metrics(y_val, y_pred, y_proba)
+        else:
+            metrics = calculate_regression_metrics(y_val, y_pred)
+
+        # Add timing info
+        metrics.update({
+            'fold': fold_idx,
+            'fit_time': model.fit_time,
+            'predict_time': model.predict_time
+        })
+
+        fold_results.append(metrics)
+
+    return fold_results

code/evaluation/metrics.py

@@ -0,0 +1,116 @@
+"""
+Evaluation Metrics
+==================
+
+Comprehensive metrics for classification and regression tasks.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import numpy as np
+from sklearn.metrics import (
+    roc_auc_score, accuracy_score, f1_score, precision_score, recall_score,
+    r2_score, mean_squared_error, mean_absolute_error, log_loss
+)
+from typing import Dict, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def calculate_classification_metrics(
+    y_true: np.ndarray,
+    y_pred: np.ndarray,
+    y_proba: Optional[np.ndarray] = None
+) -> Dict[str, float]:
+    """
+    Calculate all classification metrics.
+
+    Parameters
+    ----------
+    y_true : np.ndarray
+        True labels
+    y_pred : np.ndarray
+        Predicted labels
+    y_proba : np.ndarray, optional
+        Predicted probabilities (n_samples, n_classes)
+
+    Returns
+    -------
+    metrics : dict
+        Dictionary of metric names and values
+    """
+    metrics = {
+        'accuracy': accuracy_score(y_true, y_pred),
+        'f1_macro': f1_score(y_true, y_pred, average='macro', zero_division=0),
+        'f1_weighted': f1_score(y_true, y_pred, average='weighted', zero_division=0),
+        'precision_macro': precision_score(y_true, y_pred, average='macro', zero_division=0),
+        'recall_macro': recall_score(y_true, y_pred, average='macro', zero_division=0)
+    }
+
+    # ROC-AUC (if probabilities available)
+    if y_proba is not None:
+        try:
+            n_classes = len(np.unique(y_true))
+
+            if n_classes == 2:
+                # Binary classification
+                metrics['roc_auc'] = roc_auc_score(y_true, y_proba[:, 1])
+            else:
+                # Multi-class classification
+                metrics['roc_auc'] = roc_auc_score(
+                    y_true, y_proba,
+                    multi_class='ovr',
+                    average='macro'
+                )
+
+            # Log loss
+            metrics['log_loss'] = log_loss(y_true, y_proba)
+
+        except Exception as e:
+            logger.warning(f"ROC-AUC calculation failed: {e}")
+            metrics['roc_auc'] = np.nan
+            metrics['log_loss'] = np.nan
+
+    return metrics
+
+
+def calculate_regression_metrics(
+    y_true: np.ndarray,
+    y_pred: np.ndarray
+) -> Dict[str, float]:
+    """
+    Calculate all regression metrics.
+
+    Parameters
+    ----------
+    y_true : np.ndarray
+        True values
+    y_pred : np.ndarray
+        Predicted values
+
+    Returns
+    -------
+    metrics : dict
+        Dictionary of metric names and values
+    """
+    metrics = {
+        'r2': r2_score(y_true, y_pred),
+        'rmse': np.sqrt(mean_squared_error(y_true, y_pred)),
+        'mae': mean_absolute_error(y_true, y_pred),
+        'mse': mean_squared_error(y_true, y_pred)
+    }
+
+    # MAPE (avoid division by zero)
+    try:
+        non_zero_mask = y_true != 0
+        if np.any(non_zero_mask):
+            mape = np.mean(np.abs((y_true[non_zero_mask] - y_pred[non_zero_mask]) / y_true[non_zero_mask])) * 100
+            metrics['mape'] = mape
+        else:
+            metrics['mape'] = np.nan
+    except:
+        metrics['mape'] = np.nan
+
+    return metrics

code/evaluation/statistical_tests.py

@@ -0,0 +1,109 @@
+"""
+Statistical Tests
+=================
+
+Statistical significance testing for model comparisons.
+
+Implements:
+- Friedman test (non-parametric ANOVA)
+- Nemenyi post-hoc test
+- Critical difference calculation
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+from typing import Dict, Tuple
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def friedman_test(results_df: pd.DataFrame) -> Dict:
+    """
+    Friedman test for comparing multiple models.
+
+    Parameters
+    ----------
+    results_df : pd.DataFrame
+        Rows = datasets, columns = models, values = metric scores
+
+    Returns
+    -------
+    results : dict
+        Test statistic, p-value, and significance
+    """
+    # Rank models for each dataset (higher is better)
+    ranks = results_df.rank(axis=1, ascending=False)
+
+    # Friedman test
+    stat, p_value = stats.friedmanchisquare(*[ranks[col] for col in ranks.columns])
+
+    logger.info(f"Friedman Test: statistic={stat:.4f}, p-value={p_value:.4e}")
+
+    return {
+        'statistic': stat,
+        'p_value': p_value,
+        'significant': p_value < 0.05,
+        'avg_ranks': ranks.mean().to_dict()
+    }
+
+
+def nemenyi_post_hoc(results_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Nemenyi post-hoc test (pairwise comparisons).
+
+    Parameters
+    ----------
+    results_df : pd.DataFrame
+        Rows = datasets, columns = models, values = metric scores
+
+    Returns
+    -------
+    p_values : pd.DataFrame
+        Pairwise p-values
+    """
+    try:
+        import scikit_posthocs as sp
+        ranks = results_df.rank(axis=1, ascending=False)
+        p_values = sp.posthoc_nemenyi_friedman(ranks.T)
+        return p_values
+    except ImportError:
+        logger.error("scikit-posthocs not installed. Install with: pip install scikit-posthocs")
+        raise
+
+
+def critical_difference(
+    n_datasets: int,
+    n_models: int,
+    alpha: float = 0.05
+) -> float:
+    """
+    Calculate critical difference for CD diagrams.
+
+    Parameters
+    ----------
+    n_datasets : int
+        Number of datasets
+    n_models : int
+        Number of models
+    alpha : float
+        Significance level
+
+    Returns
+    -------
+    cd : float
+        Critical difference value
+    """
+    # Critical value from Nemenyi distribution
+    # Approximation using normal distribution
+    q_alpha = stats.norm.ppf(1 - alpha / 2)
+
+    cd = q_alpha * np.sqrt((n_models * (n_models + 1)) / (6 * n_datasets))
+
+    logger.info(f"Critical Difference: {cd:.4f} (alpha={alpha})")
+
+    return cd

code/models/__init__.py

@@ -0,0 +1,42 @@
+"""
+Model Wrappers Package
+======================
+
+Provides sklearn-compatible wrappers for all benchmarking models.
+
+Available Models:
+- SAP RPT-1 (sap_rpt1_wrapper)
+- TabPFN (tabpfn_wrapper)
+- TabICL (tabicl_wrapper)
+- AutoGluon (autogluon_wrapper)
+- XGBoost (baseline_wrappers)
+- CatBoost (baseline_wrappers)
+- LightGBM (baseline_wrappers)
+
+All models implement the sklearn API:
+    - fit(X, y)
+    - predict(X)
+    - predict_proba(X)  # for classification
+"""
+
+from .base_wrapper import BaseModelWrapper
+from .sap_rpt1_wrapper import SAPRPT1Wrapper
+from .sap_rpt1_hf_wrapper import SAPRPT1HFWrapper
+from .tabpfn_wrapper import TabPFNWrapper
+from .tabicl_wrapper import TabICLWrapper
+from .autogluon_wrapper import AutoGluonWrapper
+from .baseline_wrappers import XGBoostWrapper, CatBoostWrapper, LightGBMWrapper
+
+__all__ = [
+    'BaseModelWrapper',
+    'SAPRPT1Wrapper',
+    'SAPRPT1HFWrapper',
+    'TabPFNWrapper',
+    'TabICLWrapper',
+    'AutoGluonWrapper',
+    'XGBoostWrapper',
+    'CatBoostWrapper',
+    'LightGBMWrapper'
+]
+
+__version__ = '1.0.0'

code/models/autogluon_wrapper.py

@@ -0,0 +1,210 @@
+"""
+AutoGluon Wrapper
+=================
+
+Sklearn-compatible wrapper for AutoGluon Tabular.
+
+AutoGluon is an AutoML framework that automatically
+trains and ensembles multiple models.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import time
+import logging
+from typing import Optional, Union
+import numpy as np
+import pandas as pd
+import tempfile
+import shutil
+
+from .base_wrapper import BaseModelWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class AutoGluonWrapper(BaseModelWrapper):
+    """
+    AutoGluon Tabular wrapper.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    time_limit : int, default=300
+        Time limit for training in seconds
+    preset : str, default='medium_quality'
+        Preset: 'best_quality', 'high_quality', 'good_quality', 'medium_quality'
+    eval_metric : str, optional
+        Evaluation metric (auto-detected if None)
+    random_state : int, default=42
+        Random seed
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        time_limit: int = 300,
+        preset: str = 'medium_quality',
+        eval_metric: Optional[str] = None,
+        random_state: int = 42
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.time_limit = time_limit
+        self.preset = preset
+        self.eval_metric = eval_metric
+        self._temp_dir = None
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'AutoGluonWrapper':
+        """
+        Fit AutoGluon model.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Training features
+        y : pd.Series or np.ndarray, shape (n_samples,)
+            Training target
+
+        Returns
+        -------
+        self : AutoGluonWrapper
+            Fitted model
+        """
+        self._validate_input(X, y)
+
+        logger.info(f"Fitting AutoGluon ({self.preset}) on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            from autogluon.tabular import TabularPredictor
+
+            # Convert to DataFrame if needed
+            if isinstance(X, np.ndarray):
+                X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
+
+            if isinstance(y, np.ndarray):
+                y = pd.Series(y, name='target')
+
+            # Combine X and y for AutoGluon
+            train_data = X.copy()
+            train_data['target'] = y.values
+
+            # Create temporary directory for model
+            self._temp_dir = tempfile.mkdtemp(prefix='autogluon_')
+
+            # Auto-detect problem type
+            problem_type = 'binary' if self.task_type == 'classification' and len(np.unique(y)) == 2 else None
+            if self.task_type == 'regression':
+                problem_type = 'regression'
+            elif self.task_type == 'classification' and len(np.unique(y)) > 2:
+                problem_type = 'multiclass'
+
+            # Initialize predictor
+            self.model = TabularPredictor(
+                label='target',
+                problem_type=problem_type,
+                eval_metric=self.eval_metric,
+                path=self._temp_dir,
+                verbosity=2
+            )
+
+            # Fit model
+            self.model.fit(
+                train_data=train_data,
+                time_limit=self.time_limit,
+                presets=self.preset
+            )
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            # Log leaderboard
+            leaderboard = self.model.leaderboard(silent=True)
+            best_model = leaderboard.iloc[0]['model']
+            logger.info(f"AutoGluon fitted in {self.fit_time:.2f} seconds. Best model: {best_model}")
+
+        except ImportError:
+            logger.error("AutoGluon not installed")
+            raise ImportError("Install AutoGluon with: pip install autogluon.tabular[all]")
+        except Exception as e:
+            logger.error(f"Error fitting AutoGluon: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Make predictions with AutoGluon.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        predictions : np.ndarray, shape (n_samples,)
+            Predicted values or class labels
+        """
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        logger.info(f"Predicting on {X.shape[0]} samples with AutoGluon...")
+        start_time = time.time()
+
+        try:
+            # Convert to DataFrame if needed
+            if isinstance(X, np.ndarray):
+                X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
+
+            predictions = self.model.predict(X).values
+            self.predict_time = time.time() - start_time
+
+            logger.info(f"Predictions complete in {self.predict_time:.2f} seconds")
+
+            return predictions
+
+        except Exception as e:
+            logger.error(f"Error during prediction: {e}")
+            raise
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Predict class probabilities with AutoGluon.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+        """
+        if isinstance(X, np.ndarray):
+            X = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
+
+        return self.model.predict_proba(X).values
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters for this estimator."""
+        params = super().get_params(deep)
+        params.update({
+            'time_limit': self.time_limit,
+            'preset': self.preset,
+            'eval_metric': self.eval_metric
+        })
+        return params
+
+    def __del__(self):
+        """Clean up temporary directory on deletion."""
+        if self._temp_dir and self._temp_dir.startswith('/tmp'):
+            try:
+                shutil.rmtree(self._temp_dir)
+            except:
+                pass

code/models/base_wrapper.py

@@ -0,0 +1,208 @@
+"""
+Base Model Wrapper
+==================
+
+Abstract base class for all model wrappers.
+Ensures sklearn-compatible interface for consistent evaluation.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+from abc import ABC, abstractmethod
+from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
+import time
+import logging
+from typing import Any, Optional
+import numpy as np
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+
+
+class BaseModelWrapper(BaseEstimator, ABC):
+    """
+    Base class for all model wrappers.
+
+    Ensures sklearn-compatible interface with:
+    - fit(X, y): Train the model
+    - predict(X): Make predictions
+    - predict_proba(X): Predict class probabilities (classification only)
+
+    Also tracks timing information:
+    - fit_time: Time spent in training
+    - predict_time: Time spent in prediction
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Type of task: 'classification' or 'regression'
+    random_state : int, optional
+        Random seed for reproducibility
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        random_state: Optional[int] = 42
+    ):
+        self.task_type = task_type
+        self.random_state = random_state
+        self.model = None
+        self.fit_time: Optional[float] = None
+        self.predict_time: Optional[float] = None
+        self.is_fitted: bool = False
+
+    @abstractmethod
+    def fit(self, X: Any, y: Any) -> 'BaseModelWrapper':
+        """
+        Train the model on provided data.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Training features
+        y : pd.Series or np.ndarray, shape (n_samples,)
+            Training target
+
+        Returns
+        -------
+        self : BaseModelWrapper
+            Returns self for method chaining
+        """
+        pass
+
+    @abstractmethod
+    def predict(self, X: Any) -> np.ndarray:
+        """
+        Make predictions on new data.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        predictions : np.ndarray, shape (n_samples,)
+            Predicted values or class labels
+        """
+        pass
+
+    def predict_proba(self, X: Any) -> np.ndarray:
+        """
+        Predict class probabilities (classification only).
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+
+        Raises
+        ------
+        NotImplementedError
+            If task_type is not 'classification'
+        ValueError
+            If model is not fitted
+        """
+        if self.task_type != 'classification':
+            raise NotImplementedError(
+                f"predict_proba only available for classification tasks, "
+                f"got task_type='{self.task_type}'"
+            )
+
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        start_time = time.time()
+        proba = self._predict_proba_impl(X)
+        self.predict_time = time.time() - start_time
+
+        return proba
+
+    @abstractmethod
+    def _predict_proba_impl(self, X: Any) -> np.ndarray:
+        """
+        Implementation of predict_proba (model-specific).
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+        """
+        pass
+
+    def get_params(self, deep: bool = True) -> dict:
+        """
+        Get parameters for this estimator (sklearn compatibility).
+
+        Parameters
+        ----------
+        deep : bool, default=True
+            If True, return parameters for sub-estimators
+
+        Returns
+        -------
+        params : dict
+            Parameter names mapped to their values
+        """
+        return {
+            'task_type': self.task_type,
+            'random_state': self.random_state
+        }
+
+    def set_params(self, **params) -> 'BaseModelWrapper':
+        """
+        Set parameters for this estimator (sklearn compatibility).
+
+        Parameters
+        ----------
+        **params : dict
+            Estimator parameters
+
+        Returns
+        -------
+        self : BaseModelWrapper
+            Returns self
+        """
+        for key, value in params.items():
+            setattr(self, key, value)
+        return self
+
+    def _validate_input(self, X: Any, y: Optional[Any] = None):
+        """
+        Validate input data format.
+
+        Parameters
+        ----------
+        X : any
+            Features
+        y : any, optional
+            Target (if provided)
+        """
+        # Convert to pandas if needed
+        if not isinstance(X, (pd.DataFrame, np.ndarray)):
+            raise TypeError(
+                f"X must be pd.DataFrame or np.ndarray, got {type(X)}"
+            )
+
+        if y is not None and not isinstance(y, (pd.Series, np.ndarray)):
+            raise TypeError(
+                f"y must be pd.Series or np.ndarray, got {type(y)}"
+            )
+
+    def __repr__(self) -> str:
+        """String representation of the model."""
+        params = self.get_params()
+        param_str = ', '.join(f"{k}={v}" for k, v in params.items())
+        return f"{self.__class__.__name__}({param_str})"

code/models/baseline_wrappers.py

@@ -0,0 +1,353 @@
+"""
+Baseline Model Wrappers
+========================
+
+Sklearn-compatible wrappers for traditional gradient boosting models:
+- XGBoost
+- CatBoost
+- LightGBM
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import time
+import logging
+from typing import Optional, Union, Dict, Any
+import numpy as np
+import pandas as pd
+
+from .base_wrapper import BaseModelWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class XGBoostWrapper(BaseModelWrapper):
+    """
+    XGBoost wrapper.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    n_estimators : int, default=100
+        Number of boosting rounds
+    learning_rate : float, default=0.1
+        Step size shrinkage
+    max_depth : int, default=6
+        Maximum tree depth
+    random_state : int, default=42
+        Random seed
+    **kwargs : dict
+        Additional XGBoost parameters
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        n_estimators: int = 100,
+        learning_rate: float = 0.1,
+        max_depth: int = 6,
+        random_state: int = 42,
+        **kwargs
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.kwargs = kwargs
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'XGBoostWrapper':
+        """Fit XGBoost model."""
+        from sklearn.preprocessing import LabelEncoder
+        self._label_encoder = None
+        self._validate_input(X, y)
+
+        logger.info(f"Fitting XGBoost on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            import xgboost as xgb
+
+            if self.task_type == 'classification':
+                self.model = xgb.XGBClassifier(
+                    n_estimators=self.n_estimators,
+                    learning_rate=self.learning_rate,
+                    max_depth=self.max_depth,
+                    random_state=self.random_state,
+                    **self.kwargs
+                )
+            else:
+                self.model = xgb.XGBRegressor(
+                    n_estimators=self.n_estimators,
+                    learning_rate=self.learning_rate,
+                    max_depth=self.max_depth,
+                    random_state=self.random_state,
+                    **self.kwargs
+                )
+
+            if self.task_type == 'classification':
+                self._label_encoder = LabelEncoder()
+                y_encoded = self._label_encoder.fit_transform(y)
+                self.model.fit(X, y_encoded)
+            else:
+                self.model.fit(X, y)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"XGBoost fitted in {self.fit_time:.2f} seconds")
+
+        except ImportError:
+            raise ImportError("Install XGBoost with: pip install xgboost")
+        except Exception as e:
+            logger.error(f"Error fitting XGBoost: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """Make predictions with XGBoost."""
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        start_time = time.time()
+        predictions = self.model.predict(X)
+        if self.task_type == 'classification' and self._label_encoder is not None:
+            predictions = self._label_encoder.inverse_transform(predictions)
+        self.predict_time = time.time() - start_time
+
+        return predictions
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """Predict class probabilities."""
+        return self.model.predict_proba(X)
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters."""
+        params = super().get_params(deep)
+        params.update({
+            'n_estimators': self.n_estimators,
+            'learning_rate': self.learning_rate,
+            'max_depth': self.max_depth,
+            **self.kwargs
+        })
+        return params
+
+
+class CatBoostWrapper(BaseModelWrapper):
+    """
+    CatBoost wrapper.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    iterations : int, default=100
+        Number of boosting iterations
+    learning_rate : float, default=0.1
+        Step size shrinkage
+    depth : int, default=6
+        Tree depth
+    random_state : int, default=42
+        Random seed
+    **kwargs : dict
+        Additional CatBoost parameters
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        iterations: int = 100,
+        learning_rate: float = 0.1,
+        depth: int = 6,
+        random_state: int = 42,
+        **kwargs
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.iterations = iterations
+        self.learning_rate = learning_rate
+        self.depth = depth
+        self.kwargs = kwargs
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'CatBoostWrapper':
+        """Fit CatBoost model."""
+        self._validate_input(X, y)
+
+        logger.info(f"Fitting CatBoost on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            from catboost import CatBoostClassifier, CatBoostRegressor
+
+            if self.task_type == 'classification':
+                self.model = CatBoostClassifier(
+                    iterations=self.iterations,
+                    learning_rate=self.learning_rate,
+                    depth=self.depth,
+                    random_state=self.random_state,
+                    verbose=False,
+                    **self.kwargs
+                )
+            else:
+                self.model = CatBoostRegressor(
+                    iterations=self.iterations,
+                    learning_rate=self.learning_rate,
+                    depth=self.depth,
+                    random_state=self.random_state,
+                    verbose=False,
+                    **self.kwargs
+                )
+
+            self.model.fit(X, y)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"CatBoost fitted in {self.fit_time:.2f} seconds")
+
+        except ImportError:
+            raise ImportError("Install CatBoost with: pip install catboost")
+        except Exception as e:
+            logger.error(f"Error fitting CatBoost: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """Make predictions with CatBoost."""
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        start_time = time.time()
+        predictions = self.model.predict(X)
+        self.predict_time = time.time() - start_time
+
+        return predictions
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """Predict class probabilities."""
+        return self.model.predict_proba(X)
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters."""
+        params = super().get_params(deep)
+        params.update({
+            'iterations': self.iterations,
+            'learning_rate': self.learning_rate,
+            'depth': self.depth,
+            **self.kwargs
+        })
+        return params
+
+
+class LightGBMWrapper(BaseModelWrapper):
+    """
+    LightGBM wrapper.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    n_estimators : int, default=100
+        Number of boosting rounds
+    learning_rate : float, default=0.1
+        Step size shrinkage
+    max_depth : int, default=-1
+        Maximum tree depth (-1 for unlimited)
+    random_state : int, default=42
+        Random seed
+    **kwargs : dict
+        Additional LightGBM parameters
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        n_estimators: int = 100,
+        learning_rate: float = 0.1,
+        max_depth: int = -1,
+        random_state: int = 42,
+        **kwargs
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.kwargs = kwargs
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'LightGBMWrapper':
+        """Fit LightGBM model."""
+        self._validate_input(X, y)
+
+        logger.info(f"Fitting LightGBM on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            import lightgbm as lgb
+
+            if self.task_type == 'classification':
+                self.model = lgb.LGBMClassifier(
+                    n_estimators=self.n_estimators,
+                    learning_rate=self.learning_rate,
+                    max_depth=self.max_depth,
+                    random_state=self.random_state,
+                    verbose=-1,
+                    **self.kwargs
+                )
+            else:
+                self.model = lgb.LGBMRegressor(
+                    n_estimators=self.n_estimators,
+                    learning_rate=self.learning_rate,
+                    max_depth=self.max_depth,
+                    random_state=self.random_state,
+                    verbose=-1,
+                    **self.kwargs
+                )
+
+            self.model.fit(X, y)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"LightGBM fitted in {self.fit_time:.2f} seconds")
+
+        except ImportError:
+            raise ImportError("Install LightGBM with: pip install lightgbm")
+        except Exception as e:
+            logger.error(f"Error fitting LightGBM: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """Make predictions with LightGBM."""
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        start_time = time.time()
+        predictions = self.model.predict(X)
+        self.predict_time = time.time() - start_time
+
+        return predictions
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """Predict class probabilities."""
+        return self.model.predict_proba(X)
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters."""
+        params = super().get_params(deep)
+        params.update({
+            'n_estimators': self.n_estimators,
+            'learning_rate': self.learning_rate,
+            'max_depth': self.max_depth,
+            **self.kwargs
+        })
+        return params

code/models/sap_rpt1_hf_wrapper.py

@@ -0,0 +1,314 @@
+"""
+SAP RPT-1 OSS Wrapper (Hugging Face Authenticated)
+====================================================
+
+Sklearn-compatible wrapper for SAP RPT-1-OSS via Hugging Face.
+
+This wrapper uses the official `sap_rpt_oss` package with HF token
+authentication for downloading gated model weights.
+
+SAP RPT-1 OSS is a tabular in-context learning model — it does NOT
+use text generation. It accepts DataFrames/arrays and produces
+predictions directly on structured tabular data.
+
+Requirements:
+    - Python >= 3.11
+    - pip install git+https://github.com/SAP-samples/sap-rpt-1-oss.git
+    - Hugging Face token with access to SAP/sap-rpt-1-oss
+
+Author: UW MSIM Team
+Date: April 2026
+"""
+
+import os
+import time
+import logging
+from typing import Optional, Union
+
+import numpy as np
+import pandas as pd
+
+from .base_wrapper import BaseModelWrapper
+
+logger = logging.getLogger(__name__)
+
+
+def _authenticate_huggingface(token: Optional[str] = None) -> str:
+    """
+    Authenticate with Hugging Face Hub using token.
+
+    Token resolution order:
+        1. Explicit `token` parameter
+        2. HUGGING_FACE_HUB_TOKEN environment variable
+        3. HF_TOKEN environment variable
+        4. Previously saved token via `huggingface-cli login`
+
+    Parameters
+    ----------
+    token : str, optional
+        Explicit HF token to use
+
+    Returns
+    -------
+    str
+        The resolved token
+
+    Raises
+    ------
+    RuntimeError
+        If no valid token is found
+    """
+    from huggingface_hub import login, HfApi
+
+    # Resolve token from multiple sources
+    resolved_token = (
+        token
+        or os.getenv("HUGGING_FACE_HUB_TOKEN")
+        or os.getenv("HF_TOKEN")
+    )
+
+    if resolved_token:
+        try:
+            login(token=resolved_token, add_to_git_credential=False)
+            logger.info("✅ Hugging Face authentication successful (via token)")
+            return resolved_token
+        except Exception as e:
+            raise RuntimeError(
+                f"Hugging Face authentication failed: {e}\n"
+                "Ensure your token is valid and you have accepted the license at:\n"
+                "  https://huggingface.co/SAP/sap-rpt-1-oss"
+            )
+
+    # Check if already logged in via CLI
+    try:
+        api = HfApi()
+        user_info = api.whoami()
+        logger.info(f"✅ Hugging Face authenticated as: {user_info.get('name', 'unknown')}")
+        return ""  # Already authenticated
+    except Exception:
+        pass
+
+    raise RuntimeError(
+        "No Hugging Face token found. Please set one of:\n"
+        "  1. Environment variable: set HUGGING_FACE_HUB_TOKEN=hf_xxx\n"
+        "  2. Environment variable: set HF_TOKEN=hf_xxx\n"
+        "  3. Run: huggingface-cli login\n\n"
+        "You must also accept the model license at:\n"
+        "  https://huggingface.co/SAP/sap-rpt-1-oss"
+    )
+
+
+class SAPRPT1HFWrapper(BaseModelWrapper):
+    """
+    SAP RPT-1 OSS (Hugging Face) wrapper for tabular prediction.
+
+    Uses the official `sap_rpt_oss` package with in-context learning.
+    The model automatically handles:
+        - Column/cell embeddings via built-in LLM
+        - Missing values
+        - CPU/GPU auto-detection (GPU not required)
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    max_context_size : int, default=4096
+        Maximum number of context rows for in-context learning.
+        Higher = better accuracy but more memory/time.
+        Recommended: 2048 (light), 4096 (balanced), 8192 (best)
+    bagging : int or 'auto', default=4
+        Number of bagging iterations for prediction stability.
+        Use 1 for fast inference, 4-8 for best accuracy.
+        'auto' = automatically determined based on dataset size.
+    hf_token : str, optional
+        Explicit Hugging Face token. If not provided, reads from
+        HUGGING_FACE_HUB_TOKEN or HF_TOKEN environment variable.
+    random_state : int, default=42
+        Random seed for reproducibility
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        max_context_size: int = 4096,
+        bagging: Union[int, str] = 4,
+        hf_token: Optional[str] = None,
+        random_state: int = 42
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.max_context_size = max_context_size
+        self.bagging = bagging
+        self.hf_token = hf_token
+
+    def fit(
+        self,
+        X: Union[pd.DataFrame, np.ndarray],
+        y: Union[pd.Series, np.ndarray]
+    ) -> 'SAPRPT1HFWrapper':
+        """
+        Fit SAP RPT-1 OSS model.
+
+        Note: SAP RPT-1 uses in-context learning, so "fitting" stores
+        the training data for retrieval during inference. The model
+        weights are pretrained and NOT updated.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Training features
+        y : pd.Series or np.ndarray, shape (n_samples,)
+            Training target
+
+        Returns
+        -------
+        self : SAPRPT1HFWrapper
+            Fitted model
+        """
+        self._validate_input(X, y)
+
+        logger.info(
+            f"Fitting SAP RPT-1 OSS on {X.shape[0]} samples, "
+            f"{X.shape[1]} features (max_context={self.max_context_size}, "
+            f"bagging={self.bagging})..."
+        )
+        start_time = time.time()
+
+        try:
+            # Authenticate with Hugging Face (downloads gated model weights)
+            _authenticate_huggingface(self.hf_token)
+
+            # Import here to avoid import errors in environments without sap_rpt_oss
+            from sap_rpt_oss import SAP_RPT_OSS_Classifier, SAP_RPT_OSS_Regressor
+
+            # Initialize appropriate model based on task type
+            if self.task_type == 'classification':
+                self.model = SAP_RPT_OSS_Classifier(
+                    max_context_size=self.max_context_size,
+                    bagging=self.bagging
+                )
+            else:
+                self.model = SAP_RPT_OSS_Regressor(
+                    max_context_size=self.max_context_size,
+                    bagging=self.bagging
+                )
+
+            # Fit model (stores training data for in-context learning)
+            self.model.fit(X, y)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"✅ SAP RPT-1 OSS fitted in {self.fit_time:.2f} seconds")
+
+        except ImportError as e:
+            logger.error(f"SAP RPT-1 OSS package not installed: {e}")
+            raise ImportError(
+                "sap-rpt-1-oss not found. Install with:\n"
+                "  pip install git+https://github.com/SAP-samples/sap-rpt-1-oss.git\n\n"
+                "Requires Python >= 3.11"
+            )
+        except Exception as e:
+            logger.error(f"Error fitting SAP RPT-1 OSS: {e}")
+            raise
+
+        return self
+
+    def predict(
+        self,
+        X: Union[pd.DataFrame, np.ndarray]
+    ) -> np.ndarray:
+        """
+        Make predictions with SAP RPT-1 OSS.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        predictions : np.ndarray, shape (n_samples,)
+            Predicted values or class labels
+        """
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        logger.info(f"Predicting on {X.shape[0]} samples with SAP RPT-1 OSS...")
+        start_time = time.time()
+
+        try:
+            predictions = self.model.predict(X)
+
+            # Convert list to numpy array if needed
+            if isinstance(predictions, list):
+                predictions = np.array(predictions)
+
+            self.predict_time = time.time() - start_time
+            logger.info(f"✅ Predictions complete in {self.predict_time:.2f} seconds")
+
+            return predictions
+
+        except Exception as e:
+            logger.error(f"Error during prediction: {e}")
+            raise
+
+    def _predict_proba_impl(
+        self,
+        X: Union[pd.DataFrame, np.ndarray]
+    ) -> np.ndarray:
+        """
+        Predict class probabilities with SAP RPT-1 OSS.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+        """
+        if self.task_type != 'classification':
+            raise ValueError("predict_proba only available for classification")
+
+        try:
+            proba = self.model.predict_proba(X)
+
+            # Convert to numpy if needed
+            if not isinstance(proba, np.ndarray):
+                proba = np.array(proba)
+
+            return proba
+
+        except AttributeError:
+            # Fallback: one-hot encode predictions if predict_proba unavailable
+            logger.warning(
+                "predict_proba not available, using one-hot encoding of predictions"
+            )
+            predictions = self.model.predict(X)
+            if isinstance(predictions, list):
+                predictions = np.array(predictions)
+
+            classes = np.unique(predictions)
+            n_samples = len(predictions)
+            n_classes = len(classes)
+            proba = np.zeros((n_samples, n_classes))
+
+            class_to_idx = {c: i for i, c in enumerate(classes)}
+            for i, pred in enumerate(predictions):
+                proba[i, class_to_idx[pred]] = 1.0
+
+            return proba
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters for this estimator (sklearn compatibility)."""
+        params = super().get_params(deep)
+        params.update({
+            'max_context_size': self.max_context_size,
+            'bagging': self.bagging,
+            'hf_token': self.hf_token
+        })
+        return params

code/models/sap_rpt1_wrapper.py

@@ -0,0 +1,196 @@
+"""
+SAP RPT-1 Wrapper
+=================
+
+Sklearn-compatible wrapper for SAP RPT-1-OSS.
+
+SAP RPT-1 uses in-context learning with pretrained transformers.
+Requires Python 3.11 and Hugging Face model access.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import time
+import logging
+from typing import Optional, Union
+import numpy as np
+import pandas as pd
+
+from .base_wrapper import BaseModelWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class SAPRPT1Wrapper(BaseModelWrapper):
+    """
+    SAP RPT-1 (Retrieval Pretrained Transformer) wrapper.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    context_size : int, default=4096
+        Maximum context window size in tokens
+    bagging_factor : int, default=4
+        Number of bagging iterations for prediction stability
+    model_size : str, default='small'
+        Model size: 'small' or 'large'
+    device : str, default='auto'
+        Device to use: 'cpu', 'cuda', or 'auto'
+    random_state : int, default=42
+        Random seed for reproducibility
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        context_size: int = 4096,
+        bagging_factor: int = 4,
+        model_size: str = 'small',
+        device: str = 'auto',
+        random_state: int = 42
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.context_size = context_size
+        self.bagging_factor = bagging_factor
+        self.model_size = model_size
+        self.device = device
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'SAPRPT1Wrapper':
+        """
+        Train SAP RPT-1 model.
+
+        Note: SAP RPT-1 uses in-context learning, so "training" is primarily
+        about storing the training data for retrieval during inference.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Training features
+        y : pd.Series or np.ndarray, shape (n_samples,)
+            Training target
+
+        Returns
+        -------
+        self : SAPRPT1Wrapper
+            Fitted model
+        """
+        self._validate_input(X, y)
+
+        logger.info(f"Fitting SAP RPT-1 ({self.model_size}) on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            # Import here to avoid import errors in environments without SAP RPT-1
+            from sap_rpt_1_oss import SAP_RPT_OSS_Classifier, SAP_RPT_OSS_Regressor
+
+            # Initialize appropriate model
+            if self.task_type == 'classification':
+                self.model = SAP_RPT_OSS_Classifier(
+                    context_size=self.context_size,
+                    bagging_factor=self.bagging_factor,
+                    model_size=self.model_size,
+                    device=self.device
+                )
+            else:
+                self.model = SAP_RPT_OSS_Regressor(
+                    context_size=self.context_size,
+                    bagging_factor=self.bagging_factor,
+                    model_size=self.model_size,
+                    device=self.device
+                )
+
+            # Fit model (stores training data for in-context learning)
+            self.model.fit(X, y)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"SAP RPT-1 fitted in {self.fit_time:.2f} seconds")
+
+        except ImportError as e:
+            logger.error(f"SAP RPT-1 not installed: {e}")
+            raise ImportError(
+                "SAP RPT-1 not found. Install with: "
+                "pip install git+https://github.com/SAP-samples/sap-rpt-1-oss.git"
+            )
+        except Exception as e:
+            logger.error(f"Error fitting SAP RPT-1: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Make predictions with SAP RPT-1.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        predictions : np.ndarray, shape (n_samples,)
+            Predicted values or class labels
+        """
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        logger.info(f"Predicting on {X.shape[0]} samples with SAP RPT-1...")
+        start_time = time.time()
+
+        try:
+            predictions = self.model.predict(X)
+            self.predict_time = time.time() - start_time
+
+            logger.info(f"Predictions complete in {self.predict_time:.2f} seconds")
+
+            return predictions
+
+        except Exception as e:
+            logger.error(f"Error during prediction: {e}")
+            raise
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Implementation of predict_proba for SAP RPT-1.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+        """
+        if self.task_type != 'classification':
+            raise ValueError("predict_proba only available for classification")
+
+        try:
+            return self.model.predict_proba(X)
+        except AttributeError:
+            # Fallback if predict_proba not available
+            logger.warning("predict_proba not available, using one-hot encoding of predictions")
+            predictions = self.model.predict(X)
+            n_samples = len(predictions)
+            n_classes = len(np.unique(predictions))
+            proba = np.zeros((n_samples, n_classes))
+            proba[np.arange(n_samples), predictions] = 1.0
+            return proba
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters for this estimator."""
+        params = super().get_params(deep)
+        params.update({
+            'context_size': self.context_size,
+            'bagging_factor': self.bagging_factor,
+            'model_size': self.model_size,
+            'device': self.device
+        })
+        return params

code/models/tabicl_wrapper.py

@@ -0,0 +1,191 @@
+"""
+TabICL Wrapper
+==============
+
+Sklearn-compatible wrapper for TabICL (Tabular In-Context Learning).
+
+TabICL uses language models for tabular prediction via in-context learning.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import time
+import logging
+from typing import Optional, Union
+import numpy as np
+import pandas as pd
+
+from .base_wrapper import BaseModelWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class TabICLWrapper(BaseModelWrapper):
+    """
+    TabICL (Tabular In-Context Learning) wrapper.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type: 'classification' or 'regression'
+    model_name : str, default='gpt2'
+        Base language model to use
+    max_samples : int, default=100
+        Maximum number of in-context examples
+    device : str, default='auto'
+        Device: 'cpu', 'cuda', or 'auto'
+    random_state : int, default=42
+        Random seed
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        model_name: str = 'gpt2',
+        max_samples: int = 100,
+        device: str = 'auto',
+        random_state: int = 42
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+        self.model_name = model_name
+        self.max_samples = max_samples
+        self.device = device
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'TabICLWrapper':
+        """
+        Fit TabICL (stores training data for in-context learning).
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Training features
+        y : pd.Series or np.ndarray, shape (n_samples,)
+            Training target
+
+        Returns
+        -------
+        self : TabICLWrapper
+            Fitted model
+        """
+        self._validate_input(X, y)
+
+        logger.info(f"Fitting TabICL with {self.model_name} on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            # Note: Actual TabICL implementation may vary
+            # This is a template; adjust imports based on actual TabICL package
+
+            # Store training data for in-context learning
+            if isinstance(X, pd.DataFrame):
+                self.X_train_ = X.copy()
+            else:
+                self.X_train_ = pd.DataFrame(X)
+
+            if isinstance(y, pd.Series):
+                self.y_train_ = y.copy()
+            else:
+                self.y_train_ = pd.Series(y)
+
+            # Limit to max_samples for efficiency
+            if len(self.X_train_) > self.max_samples:
+                logger.info(f"Sampling {self.max_samples} from {len(self.X_train_)} training samples")
+                sample_idx = np.random.RandomState(self.random_state).choice(
+                    len(self.X_train_), self.max_samples, replace=False
+                )
+                self.X_train_ = self.X_train_.iloc[sample_idx]
+                self.y_train_ = self.y_train_.iloc[sample_idx]
+
+            # Initialize TabICL model (placeholder - adjust for actual implementation)
+            # from tabicl import TabICLModel
+            # self.model = TabICLModel(model_name=self.model_name, device=self.device)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"TabICL fitted in {self.fit_time:.2f} seconds")
+            logger.warning("TabICL wrapper is a template. Adjust for actual TabICL implementation.")
+
+        except Exception as e:
+            logger.error(f"Error fitting TabICL: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Make predictions with TabICL.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        predictions : np.ndarray, shape (n_samples,)
+            Predicted values or class labels
+        """
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        logger.info(f"Predicting on {X.shape[0]} samples with TabICL...")
+        start_time = time.time()
+
+        try:
+            # Placeholder implementation
+            # In actual TabICL, this would use the language model with in-context examples
+            logger.warning("Using placeholder predictions. Integrate actual TabICL model.")
+
+            # Fallback: predict the majority class for classification to ensure valid type
+            if self.task_type == 'classification':
+                majority_class = self.y_train_.mode()[0]
+                predictions = np.full(len(X), majority_class)
+            else:
+                predictions = np.zeros(len(X))
+
+            self.predict_time = time.time() - start_time
+
+            logger.info(f"Predictions complete in {self.predict_time:.2f} seconds")
+
+            return predictions
+
+        except Exception as e:
+            logger.error(f"Error during prediction: {e}")
+            raise
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Predict class probabilities with TabICL.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+        """
+        # Placeholder implementation
+        n_samples = len(X)
+        n_classes = len(np.unique(self.y_train_))
+        proba = np.ones((n_samples, n_classes)) / n_classes
+
+        logger.warning("Using uniform probability distribution. Integrate actual TabICL model.")
+
+        return proba
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters for this estimator."""
+        params = super().get_params(deep)
+        params.update({
+            'model_name': self.model_name,
+            'max_samples': self.max_samples,
+            'device': self.device
+        })
+        return params

code/models/tabpfn_wrapper.py

@@ -0,0 +1,238 @@
+"""
+TabPFN Wrapper
+==============
+
+Sklearn-compatible wrapper for TabPFN (Tabular Pre-trained Transformers).
+
+TabPFN is a pretrained model for tabular classification using
+in-context learning (no training required).
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import time
+import logging
+import os
+from typing import Optional, Union
+import numpy as np
+import pandas as pd
+
+# Automatically accept the TabPFN license to prevent browser/socket crashes on Windows
+os.environ["TABPFN_LICENSE"] = "accept"
+os.environ["TABPFN_ACCEPT_LICENSE"] = "1"
+
+# ── Patch for old TabPFN compatibility with newer torch ──────────────────────
+try:
+    import torch.nn.modules.transformer
+    if not hasattr(torch.nn.modules.transformer, 'Optional'):
+        import typing
+        torch.nn.modules.transformer.Optional = typing.Optional
+        torch.nn.modules.transformer.Any = typing.Any
+        torch.nn.modules.transformer.Tuple = typing.Tuple
+        torch.nn.modules.transformer.List = typing.List
+except (ImportError, AttributeError):
+    pass
+
+# ── Patch for old TabPFN compatibility with newer sklearn ────────────────────
+try:
+    import sklearn.utils.validation
+    def _patch_validation(func):
+        from functools import wraps
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            if 'force_all_finite' in kwargs:
+                kwargs['ensure_all_finite'] = kwargs.pop('force_all_finite')
+            return func(*args, **kwargs)
+        return wrapper
+    sklearn.utils.validation.check_X_y = _patch_validation(sklearn.utils.validation.check_X_y)
+    sklearn.utils.validation.check_array = _patch_validation(sklearn.utils.validation.check_array)
+except (ImportError, AttributeError):
+    pass
+
+from .base_wrapper import BaseModelWrapper
+
+logger = logging.getLogger(__name__)
+
+
+class TabPFNWrapper(BaseModelWrapper):
+    """
+    TabPFN (Tabular Prior-Fitted Networks) wrapper.
+
+    TabPFN uses pretrained transformers for zero-shot tabular prediction.
+    Works best on datasets with <1000 samples and <100 features.
+
+    Parameters
+    ----------
+    task_type : str, default='classification'
+        Task type (only 'classification' supported by TabPFN)
+    n_ensemble : int, default=1
+        Number of ensemble members
+    device : str, default='auto'
+        Device: 'cpu', 'cuda', or 'auto'
+    random_state : int, default=42
+        Random seed
+    """
+
+    def __init__(
+        self,
+        task_type: str = 'classification',
+        n_ensemble: int = 1,
+        device: str = 'auto',
+        random_state: int = 42
+    ):
+        super().__init__(task_type=task_type, random_state=random_state)
+
+        if task_type != 'classification':
+            raise ValueError("TabPFN only supports classification tasks")
+
+        self.n_ensemble = n_ensemble
+        self.device = device
+
+    def fit(self, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.Series, np.ndarray]) -> 'TabPFNWrapper':
+        """
+        Fit TabPFN (stores training data for in-context learning).
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Training features (max 1000 samples, 100 features)
+        y : pd.Series or np.ndarray, shape (n_samples,)
+            Training target
+
+        Returns
+        -------
+        self : TabPFNWrapper
+            Fitted model
+        """
+        self._validate_input(X, y)
+
+        # Check TabPFN constraints
+        if X.shape[0] > 1024:
+            logger.warning(f"TabPFN strictly requires <= 1024 samples to avoid Memory OOM. Subsampling {X.shape[0]} to 1024 samples.")
+            sample_idx = np.random.RandomState(self.random_state).choice(
+                len(X), 1024, replace=False
+            )
+            if isinstance(X, pd.DataFrame):
+                X = X.iloc[sample_idx]
+            else:
+                X = X[sample_idx]
+            
+            if isinstance(y, pd.Series):
+                y = y.iloc[sample_idx]
+            else:
+                y = y[sample_idx]
+
+        if X.shape[1] > 100:
+            logger.warning(f"TabPFN strictly requires <= 100 features. Truncating {X.shape[1]} to 100 features.")
+            if isinstance(X, pd.DataFrame):
+                X = X.iloc[:, :100]
+            else:
+                X = X[:, :100]
+            self.truncated_features_ = True
+        else:
+            self.truncated_features_ = False
+
+        logger.info(f"Fitting TabPFN on {X.shape[0]} samples...")
+        start_time = time.time()
+
+        try:
+            from tabpfn import TabPFNClassifier
+
+            import torch
+            import tabpfn
+            
+            actual_device = 'cuda' if (self.device == 'auto' and torch.cuda.is_available()) else ('cpu' if self.device == 'auto' else self.device)
+            
+            if hasattr(tabpfn, '__version__') and tabpfn.__version__.startswith('0.1'):
+                self.model = TabPFNClassifier(device=actual_device, N_ensemble_configurations=self.n_ensemble)
+            else:
+                self.model = TabPFNClassifier(device=actual_device)
+
+            # Fit model
+            self.model.fit(X, y)
+
+            self.is_fitted = True
+            self.fit_time = time.time() - start_time
+
+            logger.info(f"TabPFN fitted in {self.fit_time:.2f} seconds")
+
+        except ImportError:
+            logger.error("TabPFN not installed")
+            raise ImportError("Install TabPFN with: pip install tabpfn")
+        except Exception as e:
+            logger.error(f"Error fitting TabPFN: {e}")
+            raise
+
+        return self
+
+    def predict(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Make predictions with TabPFN.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        predictions : np.ndarray, shape (n_samples,)
+            Predicted class labels
+        """
+        if not self.is_fitted:
+            raise ValueError("Model not fitted. Call fit() first.")
+
+        self._validate_input(X)
+
+        if getattr(self, 'truncated_features_', False) and X.shape[1] > 100:
+            if isinstance(X, pd.DataFrame):
+                X = X.iloc[:, :100]
+            else:
+                X = X[:, :100]
+
+        logger.info(f"Predicting on {X.shape[0]} samples with TabPFN...")
+        start_time = time.time()
+
+        try:
+            predictions = self.model.predict(X)
+            self.predict_time = time.time() - start_time
+
+            logger.info(f"Predictions complete in {self.predict_time:.2f} seconds")
+
+            return predictions
+
+        except Exception as e:
+            logger.error(f"Error during prediction: {e}")
+            raise
+
+    def _predict_proba_impl(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
+        """
+        Predict class probabilities with TabPFN.
+
+        Parameters
+        ----------
+        X : pd.DataFrame or np.ndarray, shape (n_samples, n_features)
+            Test features
+
+        Returns
+        -------
+        probabilities : np.ndarray, shape (n_samples, n_classes)
+            Class probabilities
+        """
+        if getattr(self, 'truncated_features_', False) and X.shape[1] > 100:
+            if isinstance(X, pd.DataFrame):
+                X = X.iloc[:, :100]
+            else:
+                X = X[:, :100]
+
+        return self.model.predict_proba(X)
+
+    def get_params(self, deep: bool = True) -> dict:
+        """Get parameters for this estimator."""
+        params = super().get_params(deep)
+        params.update({
+            'n_ensemble': self.n_ensemble,
+            'device': self.device
+        })
+        return params

code/runners/__init__.py

@@ -0,0 +1,11 @@
+"""
+Experiment Runners Package
+===========================
+
+Tools for executing benchmarking experiments.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+__all__ = ['run_experiment', 'run_batch']

code/runners/run_baselines.py

@@ -0,0 +1,50 @@
+"""
+Baseline Models Batch Runner
+==============================
+
+Run all baseline models (XGBoost, CatBoost, LightGBM) on all or specific datasets.
+
+Usage:
+    # Run on ALL datasets
+    py -3.12 -m runners.run_baselines
+
+    # Run on specific datasets
+    py -3.12 -m runners.run_baselines --dataset analcatdata_authorship diabetes
+
+Author: UW MSIM Team
+Date: April 2026
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from runners.run_batch import main as run_batch_main
+
+
+BASELINE_MODELS = ['xgboost', 'catboost', 'lightgbm']
+
+
+def main():
+    """Run all baseline models on all or specific datasets."""
+    parser = argparse.ArgumentParser(description='Run baseline models')
+    parser.add_argument('--dataset', nargs='*', default=None,
+                        help='Specific dataset(s) to run (e.g., --dataset analcatdata_authorship diabetes)')
+
+    args = parser.parse_args()
+
+    # Build sys.argv for run_batch
+    batch_args = ['run_baselines', '--model-filter', *BASELINE_MODELS]
+
+    if args.dataset:
+        batch_args.extend(['--dataset-filter', *args.dataset])
+
+    sys.argv = batch_args
+    run_batch_main()
+
+
+if __name__ == '__main__':
+    main()

code/runners/run_batch.py

@@ -0,0 +1,289 @@
+"""
+Batch Experiment Runner
+========================
+
+Run multiple models on multiple datasets.
+
+Usage:
+    python -m runners.run_batch \
+        --datasets config/datasets.yaml \
+        --models config/models.yaml
+
+Author: UW MSIM Team
+Date: April 2026
+"""
+
+import argparse
+import yaml
+import logging
+import sys
+import os
+import json
+import time
+from pathlib import Path
+from typing import List, Dict, Optional
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from runners.run_experiment import run_single_experiment, get_model
+
+logger = logging.getLogger(__name__)
+
+
+def get_dataset_list(datasets_config: dict, dataset_dir: str = None) -> List[str]:
+    """
+    Get list of available dataset names from the download directory.
+
+    Parameters
+    ----------
+    datasets_config : dict
+        Datasets YAML configuration
+    dataset_dir : str
+        Directory containing downloaded datasets
+
+    Returns
+    -------
+    datasets : list of str
+        List of dataset names
+    """
+    datasets = []
+
+    if dataset_dir is None:
+        dataset_dir = str(Path(__file__).parent.parent.parent / 'datasets')
+
+    if os.path.isdir(dataset_dir):
+        # Find all *_X.csv files and extract dataset names
+        for f in sorted(os.listdir(dataset_dir)):
+            if f.endswith('_X.csv'):
+                name = f[:-6]  # Remove '_X.csv'
+                # Verify y file also exists
+                y_file = os.path.join(dataset_dir, f"{name}_y.csv")
+                if os.path.exists(y_file):
+                    datasets.append(name)
+
+        logger.info(f"Found {len(datasets)} datasets in {dataset_dir}")
+    else:
+        logger.warning(f"Dataset directory not found: {dataset_dir}")
+
+    return datasets
+
+
+def get_model_list(models_config: dict) -> List[str]:
+    """
+    Get list of enabled model names from configuration.
+
+    Parameters
+    ----------
+    models_config : dict
+        Models YAML configuration
+
+    Returns
+    -------
+    models : list of str
+        List of enabled model names
+    """
+    models = []
+
+    for model_entry in models_config.get('models', []):
+        if model_entry.get('enabled', True):
+            models.append(model_entry['name'])
+
+    return models
+
+
+def run_batch_experiments(
+    datasets: List[str],
+    models: List[str],
+    experiment_config: dict,
+    output_dir: str = '../results/raw',
+    skip_existing: bool = True
+) -> dict:
+    """
+    Run experiments for all dataset × model combinations.
+
+    Parameters
+    ----------
+    datasets : list of str
+        Dataset names
+    models : list of str
+        Model names
+    experiment_config : dict
+        Experiment configuration (n_folds, random_state, etc.)
+    output_dir : str
+        Where to save results
+    skip_existing : bool
+        If True, skip experiments that already have result files
+
+    Returns
+    -------
+    summary : dict
+        Batch run summary with successes and failures
+    """
+    total_experiments = len(datasets) * len(models)
+    logger.info(f"\n{'='*60}")
+    logger.info(f"BATCH RUN: {len(datasets)} datasets × {len(models)} models = {total_experiments} experiments")
+    logger.info(f"{'='*60}\n")
+
+    summary = {
+        'total': total_experiments,
+        'completed': 0,
+        'skipped': 0,
+        'failed': 0,
+        'results': [],
+        'errors': []
+    }
+
+    batch_start_time = time.time()
+
+    for i, dataset_name in enumerate(datasets):
+        for j, model_name in enumerate(models):
+            experiment_num = i * len(models) + j + 1
+            output_file = os.path.join(output_dir, f"{dataset_name}_{model_name}.json")
+
+            # Skip existing results
+            if skip_existing and os.path.exists(output_file):
+                logger.info(
+                    f"[{experiment_num}/{total_experiments}] "
+                    f"SKIP {model_name} on {dataset_name} (result exists)"
+                )
+                summary['skipped'] += 1
+                continue
+
+            logger.info(
+                f"\n[{experiment_num}/{total_experiments}] "
+                f"Running {model_name} on {dataset_name}..."
+            )
+
+            try:
+                result = run_single_experiment(
+                    dataset_name=dataset_name,
+                    model_name=model_name,
+                    config=experiment_config,
+                    output_dir=output_dir
+                )
+                summary['completed'] += 1
+                summary['results'].append({
+                    'dataset': dataset_name,
+                    'model': model_name,
+                    'status': 'success'
+                })
+
+            except Exception as e:
+                logger.error(f"FAILED: {model_name} on {dataset_name}: {e}")
+                summary['failed'] += 1
+                summary['errors'].append({
+                    'dataset': dataset_name,
+                    'model': model_name,
+                    'error': str(e)
+                })
+
+    batch_elapsed = time.time() - batch_start_time
+
+    # Print summary
+    logger.info(f"\n{'='*60}")
+    logger.info(f"BATCH RUN COMPLETE")
+    logger.info(f"{'='*60}")
+    logger.info(f"  Total experiments: {summary['total']}")
+    logger.info(f"  Completed: {summary['completed']}")
+    logger.info(f"  Skipped: {summary['skipped']}")
+    logger.info(f"  Failed: {summary['failed']}")
+    logger.info(f"  Total time: {batch_elapsed / 3600:.2f} hours")
+    logger.info(f"{'='*60}\n")
+
+    # Save batch summary
+    os.makedirs(output_dir, exist_ok=True)
+    summary_file = os.path.join(output_dir, '_batch_summary.json')
+    summary['elapsed_hours'] = batch_elapsed / 3600
+    with open(summary_file, 'w') as f:
+        json.dump(summary, f, indent=2)
+    logger.info(f"Batch summary saved to {summary_file}")
+
+    return summary
+
+
+def main():
+    """Entry point for batch runner."""
+    # Setup logging
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+
+    # Parse arguments
+    parser = argparse.ArgumentParser(description='Run batch benchmarking experiments')
+    parser.add_argument('--datasets', default='config/datasets.yaml',
+                        help='Datasets config file')
+    parser.add_argument('--models', default='config/models.yaml',
+                        help='Models config file')
+    parser.add_argument('--config', default='config/experiments.yaml',
+                        help='Experiment config file')
+    parser.add_argument('--output-dir', default='../results/raw',
+                        help='Output directory')
+    parser.add_argument('--dataset-dir', default=None,
+                        help='Directory containing downloaded datasets')
+    parser.add_argument('--no-skip', action='store_true',
+                        help='Re-run experiments even if results exist')
+    parser.add_argument('--model-filter', nargs='*', default=None,
+                        help='Only run specific models (e.g., --model-filter sap-rpt1-hf xgboost)')
+    parser.add_argument('--dataset-filter', nargs='*', default=None,
+                        help='Only run specific datasets')
+
+    args = parser.parse_args()
+
+    # Load configs
+    if os.path.exists(args.datasets):
+        with open(args.datasets) as f:
+            datasets_config = yaml.safe_load(f)
+    else:
+        datasets_config = {}
+
+    if os.path.exists(args.models):
+        with open(args.models) as f:
+            models_config = yaml.safe_load(f)
+    else:
+        models_config = {}
+
+    if os.path.exists(args.config):
+        with open(args.config) as f:
+            experiment_config = yaml.safe_load(f)
+    else:
+        experiment_config = {
+            'n_folds': 10,
+            'random_state': 42,
+            'cost_per_hour': 0.90,
+            'gpu_type': 'H200'
+        }
+
+    # Get dataset and model lists
+    dataset_list = args.dataset_filter or get_dataset_list(datasets_config, args.dataset_dir)
+    model_list = args.model_filter or get_model_list(models_config)
+
+    if not dataset_list:
+        print("[ERROR] No datasets found in the datasets directory.")
+        sys.exit(1)
+
+    if not model_list:
+        print("[ERROR] No models enabled in config. Check config/models.yaml")
+        sys.exit(1)
+
+    print(f"\n[INFO] Datasets ({len(dataset_list)}): {dataset_list[:5]}{'...' if len(dataset_list) > 5 else ''}")
+    print(f"[INFO] Models ({len(model_list)}): {model_list}")
+
+    # Add dataset_dir to config for run_experiment to use
+    experiment_config['dataset_dir'] = args.dataset_dir if args.dataset_dir else str(Path(__file__).parent.parent.parent / 'datasets')
+
+    # Run batch
+    summary = run_batch_experiments(
+        datasets=dataset_list,
+        models=model_list,
+        experiment_config=experiment_config,
+        output_dir=args.output_dir,
+        skip_existing=not args.no_skip
+    )
+
+    print(f"\n[SUCCESS] Batch complete! {summary['completed']} succeeded, {summary['failed']} failed")
+
+
+if __name__ == "__main__":
+    main()

code/runners/run_experiment.py

@@ -0,0 +1,260 @@
+"""
+Single Experiment Runner
+=========================
+
+Run a single model on a single dataset.
+
+Usage:
+    python -m runners.run_experiment --dataset adult --model sap-rpt1
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import argparse
+import json
+import yaml
+import logging
+import sys
+import os
+from pathlib import Path
+
+# Add parent directory to path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from models import *
+from datasets.preprocessors import load_dataset
+from datasets.dataset_catalog import DatasetCatalog
+from evaluation import run_cross_validation, ComputeTracker
+
+logger = logging.getLogger(__name__)
+
+
+def get_model(model_name: str, task_type: str, config: dict):
+    """
+    Initialize model by name.
+
+    Parameters
+    ----------
+    model_name : str
+        Model identifier
+    task_type : str
+        'classification' or 'regression'
+    config : dict
+        Model configuration
+
+    Returns
+    -------
+    model : BaseModelWrapper
+        Initialized model
+    """
+    model_map = {
+        'sap-rpt1': SAPRPT1Wrapper,
+        'sap-rpt1-small': lambda **kwargs: SAPRPT1Wrapper(model_size='small', **kwargs),
+        'sap-rpt1-large': lambda **kwargs: SAPRPT1Wrapper(model_size='large', **kwargs),
+        'sap-rpt1-hf': SAPRPT1HFWrapper,
+        'tabpfn': TabPFNWrapper,
+        'tabicl': TabICLWrapper,
+        'autogluon': AutoGluonWrapper,
+        'xgboost': XGBoostWrapper,
+        'catboost': CatBoostWrapper,
+        'lightgbm': LightGBMWrapper
+    }
+
+    if model_name not in model_map:
+        raise ValueError(f"Unknown model: {model_name}. Choose from {list(model_map.keys())}")
+
+    model_class = model_map[model_name]
+    
+    # Get specific parameters for this model
+    model_config_key = model_name.replace('-', '_')
+    # Special handling for size variants like sap-rpt1-small -> sap_rpt1
+    if model_name.startswith('sap-rpt1-') and model_name not in ['sap-rpt1-hf']:
+        model_config_key = 'sap_rpt1'
+
+    model_params = config.get('model_params', {}).get(model_config_key, {})
+    
+    model = model_class(task_type=task_type, **model_params)
+
+    logger.info(f"Initialized {model_name} for {task_type}")
+
+    return model
+
+
+def run_single_experiment(
+    dataset_name: str,
+    model_name: str,
+    config: dict,
+    output_dir: str = '../results/raw'
+) -> dict:
+    """
+    Run experiment on single dataset with single model.
+
+    Parameters
+    ----------
+    dataset_name : str
+        Dataset name
+    model_name : str
+        Model name
+    config : dict
+        Experiment configuration
+    output_dir : str
+        Where to save results
+
+    Returns
+    -------
+    summary : dict
+        Experiment results
+    """
+    logger.info(f"\n{'='*60}")
+    logger.info(f"Experiment: {model_name} on {dataset_name}")
+    logger.info(f"{'='*60}\n")
+
+    # Create output directory
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Start compute tracking
+    tracker = ComputeTracker(
+        cost_per_hour=config.get('cost_per_hour', 0.90),
+        gpu_type=config.get('gpu_type', 'H200')
+    )
+    tracker.start()
+
+    try:
+        # Load dataset
+        logger.info("Loading dataset...")
+        default_dataset_dir = str(Path(__file__).parent.parent.parent / 'datasets')
+        dataset_dir = config.get('dataset_dir', default_dataset_dir)
+        dataset_path = config.get('dataset_path', None)
+
+        if dataset_path and os.path.exists(dataset_path):
+            # Explicit path provided
+            X, y, task_type = load_dataset(dataset_path)
+        elif os.path.isdir(dataset_dir):
+            # Search for dataset files in the download directory
+            X_file = None
+            y_file = None
+            for f in os.listdir(dataset_dir):
+                fname_lower = f.lower()
+                dname_lower = dataset_name.lower()
+                if fname_lower == f"{dname_lower}_x.csv" or (fname_lower.endswith('_x.csv') and dname_lower in fname_lower):
+                    X_file = os.path.join(dataset_dir, f)
+                if fname_lower == f"{dname_lower}_y.csv" or (fname_lower.endswith('_y.csv') and dname_lower in fname_lower):
+                    y_file = os.path.join(dataset_dir, f)
+
+            if X_file and y_file:
+                import pandas as pd_load
+                X = pd_load.read_csv(X_file)
+                y = pd_load.read_csv(y_file).iloc[:, 0]
+                # Determine task type
+                if y.dtype == 'object' or len(y.unique()) < 20:
+                    task_type = 'classification'
+                else:
+                    task_type = 'regression'
+                logger.info(f"Loaded {dataset_name}: {X.shape[0]} samples, {X.shape[1]} features, task={task_type}")
+            else:
+                # Fallback: try as a single CSV file
+                csv_path = os.path.join(dataset_dir, f"{dataset_name}.csv")
+                if os.path.exists(csv_path):
+                    X, y, task_type = load_dataset(csv_path)
+                else:
+                    raise FileNotFoundError(
+                        f"Dataset '{dataset_name}' not found in {dataset_dir}.\n"
+                        f"Available files: {os.listdir(dataset_dir)[:10]}..."
+                    )
+        else:
+            raise FileNotFoundError(
+                f"Dataset directory not found: {dataset_dir}"
+            )
+
+        # Initialize model
+        model = get_model(model_name, task_type, config)
+
+        # Run cross-validation
+        fold_results = run_cross_validation(
+            model=model,
+            X=X,
+            y=y,
+            task_type=task_type,
+            n_folds=config.get('n_folds', 10),
+            random_state=config.get('random_state', 42)
+        )
+
+        # Stop tracking
+        compute_summary = tracker.stop()
+
+        # Aggregate results
+        import pandas as pd
+        results_df = pd.DataFrame(fold_results)
+
+        summary = {
+            'dataset': dataset_name,
+            'model': model_name,
+            'task_type': task_type,
+            'n_samples': len(X),
+            'n_features': X.shape[1],
+            'n_folds': config.get('n_folds', 10),
+            'mean_metrics': results_df.mean().to_dict(),
+            'std_metrics': results_df.std().to_dict(),
+            'fold_results': fold_results,
+            'compute': compute_summary
+        }
+
+        # Save results
+        output_file = os.path.join(output_dir, f"{dataset_name}_{model_name}.json")
+        with open(output_file, 'w') as f:
+            json.dump(summary, f, indent=2)
+
+        logger.info(f"\n[SUCCESS] Results saved to {output_file}")
+
+        # Print summary
+        primary_metric = 'roc_auc' if task_type == 'classification' else 'r2'
+        if primary_metric in summary['mean_metrics']:
+            mean_val = summary['mean_metrics'][primary_metric]
+            std_val = summary['std_metrics'][primary_metric]
+            logger.info(f"\nPrimary Metric ({primary_metric}): {mean_val:.4f} ± {std_val:.4f}")
+
+        return summary
+
+    except Exception as e:
+        logger.error(f"Experiment failed: {e}", exc_info=True)
+        raise
+
+
+if __name__ == "__main__":
+    # Setup logging
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+
+    # Parse arguments
+    parser = argparse.ArgumentParser(description='Run single benchmarking experiment')
+    parser.add_argument('--dataset', required=True, help='Dataset name')
+    parser.add_argument('--model', required=True, help='Model name')
+    parser.add_argument('--config', default='../config/experiments.yaml', help='Config file')
+    parser.add_argument('--output-dir', default='../results/raw', help='Output directory')
+
+    args = parser.parse_args()
+
+    # Load config
+    if os.path.exists(args.config):
+        with open(args.config) as f:
+            config = yaml.safe_load(f)
+    else:
+        config = {
+            'n_folds': 10,
+            'random_state': 42,
+            'cost_per_hour': 0.90,
+            'gpu_type': 'H200'
+        }
+
+    # Run experiment
+    results = run_single_experiment(
+        dataset_name=args.dataset,
+        model_name=args.model,
+        config=config,
+        output_dir=args.output_dir
+    )
+
+    print("\n[SUCCESS] Experiment complete!")

code/utils/__init__.py

@@ -0,0 +1,11 @@
+"""
+Utilities Package
+=================
+
+Logging, result export, and helper functions.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+__all__ = []

code/utils/logging_utils.py

@@ -0,0 +1,63 @@
+"""
+Logging Utilities
+=================
+
+Structured logging for experiments.
+
+Author: UW MSIM Team
+Date: November 2025
+"""
+
+import logging
+import sys
+from pathlib import Path
+
+
+def setup_logger(
+    name: str,
+    log_file: str = None,
+    level: int = logging.INFO,
+    format_string: str = None
+) -> logging.Logger:
+    """
+    Setup logger with file and console handlers.
+
+    Parameters
+    ----------
+    name : str
+        Logger name
+    log_file : str, optional
+        Log file path
+    level : int
+        Logging level
+    format_string : str, optional
+        Custom format string
+
+    Returns
+    -------
+    logger : logging.Logger
+        Configured logger
+    """
+    if format_string is None:
+        format_string = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+
+    # Create logger
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    logger.handlers = []  # Clear existing handlers
+
+    # Console handler
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(level)
+    console_handler.setFormatter(logging.Formatter(format_string))
+    logger.addHandler(console_handler)
+
+    # File handler (if specified)
+    if log_file:
+        Path(log_file).parent.mkdir(parents=True, exist_ok=True)
+        file_handler = logging.FileHandler(log_file)
+        file_handler.setLevel(level)
+        file_handler.setFormatter(logging.Formatter(format_string))
+        logger.addHandler(file_handler)
+
+    return logger

docker-compose.yml

@@ -0,0 +1,28 @@
+services:
+  sap-rpt1:
+    build:
+      context: .
+      dockerfile: code/docker/Dockerfile
+      target: sap-rpt1
+    volumes:
+      - .:/app
+    environment:
+      - PYTHONPATH=/app/code
+      - HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}
+      - HF_TOKEN=${HF_TOKEN}
+    working_dir: /app/code
+    # Default to running single experiment as shown in README
+    command: -m runners.run_experiment --dataset analcatdata_authorship --model sap-rpt1-hf
+
+  baselines:
+    build:
+      context: .
+      dockerfile: code/docker/Dockerfile
+      target: baselines
+    volumes:
+      - .:/app
+    environment:
+      - PYTHONPATH=/app/code
+    working_dir: /app/code
+    # Default to running batch experiments as shown in GEMINI.md
+    command: -m runners.run_batch --datasets config/datasets.yaml --models config/models.yaml

fix_dataset.py

@@ -0,0 +1,9 @@
+import pandas as pd
+
+df = pd.read_csv("datasets/analcatdata_authorship.csv")
+
+df['target'] = df['target'].map({'N': 0, 'P': 1})
+
+df.to_csv("datasets/analcatdata_authorship.csv", index=False)
+
+print("Fixed target column ✅")

requirements.txt

@@ -0,0 +1,37 @@
+# =============================================================================
+# Pinned Dependencies for Reproducibility
+# =============================================================================
+# All versions are pinned to ensure identical results across machines.
+# To update: pip install <package> --upgrade, then update version here.
+# =============================================================================
+
+# Core scientific stack
+numpy==1.26.4
+pandas==2.2.3
+scikit-learn==1.6.1
+scipy==1.14.1
+matplotlib==3.9.2
+seaborn==0.13.2
+
+# Configuration & utilities
+pyyaml==6.0.2
+tqdm==4.67.1
+joblib==1.4.2
+python-dotenv==1.0.1
+psutil==6.1.1
+
+# Data sources
+openml==0.14.2
+
+# PyTorch & Hugging Face (for SAP RPT-1 OSS)
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch==2.7.0+cpu
+transformers==4.52.4
+accelerate==1.6.0
+huggingface-hub==0.30.2
+datasets==3.5.0
+pyarrow==20.0.0
+torcheval==0.0.7
+
+# SAP RPT-1 OSS model (pinned to release v1.1.2)
+sap-rpt-oss @ git+https://github.com/SAP-samples/sap-rpt-1-oss.git@v1.1.2

results/processed/.gitkeep

@@ -0,0 +1 @@
+# This file ensures the directory is tracked by Git

results/raw/.gitkeep

@@ -0,0 +1 @@
+# This file ensures the directory is tracked by Git

scripts/demo_benchmark.py

@@ -0,0 +1,580 @@
+"""
+SAP RPT-1 Benchmarking Demo
+============================
+Self-contained demo: runs XGBoost, LightGBM, CatBoost, and SAP RPT-1 (simulated)
+on classic sklearn datasets (Iris, Breast Cancer, Diabetes regression) using
+5-fold cross-validation. Saves JSON results and a beautiful HTML report.
+
+Run from repo root:
+    python scripts/demo_benchmark.py
+"""
+
+import os, sys, json, time, warnings
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from datetime import datetime
+from sklearn.model_selection import StratifiedKFold, KFold
+from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, r2_score, mean_absolute_error
+from sklearn.preprocessing import LabelEncoder
+from sklearn.datasets import load_iris, load_breast_cancer, load_diabetes
+from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+
+warnings.filterwarnings("ignore")
+
+RESULTS_DIR = Path(__file__).parent.parent / "results" / "raw"
+RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+
+N_FOLDS = 5
+RANDOM_STATE = 42
+
+# ─────────────────────────────────────────────
+# Helpers
+# ─────────────────────────────────────────────
+
+def timer():
+    return time.perf_counter()
+
+
+def load_datasets():
+    datasets = {}
+
+    # 1. Iris  (multi-class classification)
+    d = load_iris(as_frame=True)
+    datasets["iris"] = {
+        "X": d.data,
+        "y": d.target,
+        "task": "classification",
+        "desc": "Iris flower species (3 classes, 150 rows, 4 features)"
+    }
+
+    # 2. Breast Cancer (binary classification)
+    d = load_breast_cancer(as_frame=True)
+    datasets["breast_cancer"] = {
+        "X": d.data,
+        "y": d.target,
+        "task": "classification",
+        "desc": "Wisconsin Breast Cancer (binary, 569 rows, 30 features)"
+    }
+
+    # 3. Diabetes (regression)
+    d = load_diabetes(as_frame=True)
+    datasets["diabetes"] = {
+        "X": d.data,
+        "y": d.target,
+        "task": "regression",
+        "desc": "Diabetes progression (regression, 442 rows, 10 features)"
+    }
+
+    return datasets
+
+
+# ─────────────────────────────────────────────
+# Model builders
+# ─────────────────────────────────────────────
+
+def build_xgboost(task):
+    import xgboost as xgb
+    if task == "classification":
+        return xgb.XGBClassifier(n_estimators=100, max_depth=6, learning_rate=0.1,
+                                  random_state=RANDOM_STATE, use_label_encoder=False,
+                                  eval_metric="logloss", verbosity=0)
+    return xgb.XGBRegressor(n_estimators=100, max_depth=6, learning_rate=0.1,
+                             random_state=RANDOM_STATE, verbosity=0)
+
+
+def build_lightgbm(task):
+    import lightgbm as lgb
+    if task == "classification":
+        return lgb.LGBMClassifier(n_estimators=100, learning_rate=0.1,
+                                   random_state=RANDOM_STATE, verbose=-1)
+    return lgb.LGBMRegressor(n_estimators=100, learning_rate=0.1,
+                              random_state=RANDOM_STATE, verbose=-1)
+
+
+def build_catboost(task):
+    from catboost import CatBoostClassifier, CatBoostRegressor
+    if task == "classification":
+        return CatBoostClassifier(iterations=100, learning_rate=0.1,
+                                   random_state=RANDOM_STATE, verbose=False)
+    return CatBoostRegressor(iterations=100, learning_rate=0.1,
+                              random_state=RANDOM_STATE, verbose=False)
+
+
+class SAPSimulator:
+    """
+    SAP RPT-1 Simulator.
+    Mimics SAP RPT-1's in-context learning behaviour using a fast
+    k-NN retrieval backbone (conceptually similar to how RPT-1 retrieves
+    nearest context rows and predicts via its pretrained head).
+    
+    NOTE: This is a *demonstration substitute* for the real SAP RPT-1 OSS
+    model which requires a gated HuggingFace token + pip install of the
+    SAP-samples package. The real wrapper is in code/models/sap_rpt1_hf_wrapper.py.
+    """
+    def __init__(self, task, k=15):
+        self.task = task
+        self.k = k
+        if task == "classification":
+            self.model = KNeighborsClassifier(n_neighbors=k)
+        else:
+            self.model = KNeighborsRegressor(n_neighbors=k)
+        self.le = LabelEncoder() if task == "classification" else None
+
+    def fit(self, X, y):
+        if self.task == "classification":
+            y_enc = self.le.fit_transform(y)
+            self.model.fit(X, y_enc)
+        else:
+            self.model.fit(X, y)
+        return self
+
+    def predict(self, X):
+        preds = self.model.predict(X)
+        if self.task == "classification":
+            return self.le.inverse_transform(preds)
+        return preds
+
+    def predict_proba(self, X):
+        return self.model.predict_proba(X)
+
+
+MODELS = {
+    "XGBoost":   build_xgboost,
+    "LightGBM":  build_lightgbm,
+    "CatBoost":  build_catboost,
+    "SAP-RPT1 (sim)": lambda task: SAPSimulator(task),
+}
+
+
+# ─────────────────────────────────────────────
+# Evaluation
+# ─────────────────────────────────────────────
+
+def eval_fold_classification(model, X_train, y_train, X_val, y_val):
+    t0 = timer()
+    model.fit(X_train, y_train)
+    fit_time = timer() - t0
+
+    t0 = timer()
+    y_pred = model.predict(X_val)
+    pred_time = timer() - t0
+
+    acc = accuracy_score(y_val, y_pred)
+    f1  = f1_score(y_val, y_pred, average="macro", zero_division=0)
+
+    try:
+        proba = model.predict_proba(X_val)
+        n_cls = len(np.unique(y_val))
+        if n_cls == 2:
+            auc = roc_auc_score(y_val, proba[:, 1])
+        else:
+            auc = roc_auc_score(y_val, proba, multi_class="ovr", average="macro")
+    except Exception:
+        auc = float("nan")
+
+    return {"accuracy": acc, "f1_macro": f1, "roc_auc": auc,
+            "fit_time": fit_time, "pred_time": pred_time}
+
+
+def eval_fold_regression(model, X_train, y_train, X_val, y_val):
+    t0 = timer()
+    model.fit(X_train, y_train)
+    fit_time = timer() - t0
+
+    t0 = timer()
+    y_pred = model.predict(X_val)
+    pred_time = timer() - t0
+
+    r2  = r2_score(y_val, y_pred)
+    mae = mean_absolute_error(y_val, y_pred)
+
+    return {"r2": r2, "mae": mae, "fit_time": fit_time, "pred_time": pred_time}
+
+
+def run_cv(model_fn, dataset_name, ds):
+    X, y, task = ds["X"], ds["y"], ds["task"]
+
+    if task == "classification":
+        cv = StratifiedKFold(n_splits=N_FOLDS, shuffle=True, random_state=RANDOM_STATE)
+        splits = list(cv.split(X, y))
+    else:
+        cv = KFold(n_splits=N_FOLDS, shuffle=True, random_state=RANDOM_STATE)
+        splits = list(cv.split(X))
+
+    fold_results = []
+    for fold_i, (train_idx, val_idx) in enumerate(splits):
+        X_tr, X_val = X.iloc[train_idx], X.iloc[val_idx]
+        y_tr, y_val = y.iloc[train_idx], y.iloc[val_idx]
+
+        model = model_fn(task)
+
+        if task == "classification":
+            fold_results.append(eval_fold_classification(model, X_tr, y_tr, X_val, y_val))
+        else:
+            fold_results.append(eval_fold_regression(model, X_tr, y_tr, X_val, y_val))
+
+    df = pd.DataFrame(fold_results)
+    return {"mean": df.mean().to_dict(), "std": df.std().to_dict(), "folds": fold_results}
+
+
+# ─────────────────────────────────────────────
+# Main
+# ─────────────────────────────────────────────
+
+def main():
+    print("\n" + "="*65)
+    print("  SAP RPT-1 Benchmarking Demo")
+    print(f"  Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print("="*65)
+
+    datasets = load_datasets()
+    all_results = {}
+
+    for ds_name, ds in datasets.items():
+        print(f"\n[DATASET] {ds_name}  ({ds['desc']})")
+        all_results[ds_name] = {"task": ds["task"], "models": {}}
+
+        for model_name, model_fn in MODELS.items():
+            try:
+                print(f"   >> Running {model_name}...", end=" ", flush=True)
+                t_total = timer()
+                cv_res = run_cv(model_fn, ds_name, ds)
+                elapsed = timer() - t_total
+
+                all_results[ds_name]["models"][model_name] = cv_res
+                task = ds["task"]
+                if task == "classification":
+                    primary = cv_res["mean"].get("roc_auc", cv_res["mean"]["accuracy"])
+                    print(f"ROC-AUC={primary:.4f}  ({elapsed:.1f}s)")
+                else:
+                    primary = cv_res["mean"]["r2"]
+                    print(f"R²={primary:.4f}  ({elapsed:.1f}s)")
+
+            except Exception as e:
+                print(f"  ✗ FAILED: {e}")
+                all_results[ds_name]["models"][model_name] = {"error": str(e)}
+
+    # Save JSON
+    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+    json_path = RESULTS_DIR / f"demo_results_{ts}.json"
+    with open(json_path, "w") as f:
+        json.dump(all_results, f, indent=2, default=str)
+    print(f"\n[OK] JSON saved -> {json_path}")
+
+    # Generate HTML dashboard
+    html_path = Path(__file__).parent.parent / "results" / f"demo_dashboard_{ts}.html"
+    generate_html(all_results, html_path, ts)
+    print(f"[OK] HTML dashboard -> {html_path}")
+    print("\nOpen the HTML file in your browser to see the results!\n")
+
+    return all_results, html_path
+
+
+# ─────────────────────────────────────────────
+# HTML Report Generator
+# ──────────���──────────────────────────────────
+
+def color_for_metric(val, task):
+    """Return a CSS color class based on metric value."""
+    if task == "classification":  # ROC-AUC or Accuracy
+        if val >= 0.97: return "excellent"
+        if val >= 0.92: return "good"
+        if val >= 0.85: return "fair"
+        return "poor"
+    else:  # R²
+        if val >= 0.55: return "excellent"
+        if val >= 0.40: return "good"
+        if val >= 0.20: return "fair"
+        return "poor"
+
+
+def generate_html(results, out_path, ts):
+    MODEL_COLORS = {
+        "XGBoost":        "#f59e0b",
+        "LightGBM":       "#10b981",
+        "CatBoost":       "#6366f1",
+        "SAP-RPT1 (sim)": "#ec4899",
+    }
+
+    # Build chart data JSON
+    chart_datasets = {}
+    for ds_name, ds_data in results.items():
+        task = ds_data["task"]
+        metric = "roc_auc" if task == "classification" else "r2"
+        fallback = "accuracy"
+        chart_datasets[ds_name] = {
+            "task": task,
+            "models": {},
+        }
+        for m_name, m_data in ds_data["models"].items():
+            if "error" in m_data:
+                continue
+            val = m_data["mean"].get(metric, m_data["mean"].get(fallback, 0))
+            std = m_data["std"].get(metric, m_data["std"].get(fallback, 0))
+            chart_datasets[ds_name]["models"][m_name] = {"val": round(val, 4), "std": round(std, 4)}
+
+    chart_json = json.dumps(chart_datasets)
+    colors_json = json.dumps(MODEL_COLORS)
+
+    # Table rows
+    table_rows = ""
+    for ds_name, ds_data in results.items():
+        task = ds_data["task"]
+        metric_key = "roc_auc" if task == "classification" else "r2"
+        for m_name, m_data in ds_data["models"].items():
+            if "error" in m_data:
+                table_rows += f"""<tr><td>{ds_name}</td><td>{m_name}</td>
+                    <td>{task}</td><td colspan="4" style="color:#ef4444">ERROR: {m_data['error'][:60]}</td></tr>"""
+                continue
+            acc  = m_data["mean"].get("accuracy", "-")
+            f1   = m_data["mean"].get("f1_macro", "-")
+            auc  = m_data["mean"].get("roc_auc", "-")
+            r2   = m_data["mean"].get("r2", "-")
+            mae  = m_data["mean"].get("mae", "-")
+            ft   = m_data["mean"].get("fit_time", 0)
+            prim = m_data["mean"].get(metric_key, m_data["mean"].get("accuracy", 0))
+            cls  = color_for_metric(prim, task)
+
+            def fmt(v): return f"{v:.4f}" if isinstance(v, float) else "-"
+            color = MODEL_COLORS.get(m_name, "#888")
+            dot = f'<span style="display:inline-block;width:10px;height:10px;border-radius:50%;background:{color};margin-right:6px"></span>'
+            table_rows += f"""<tr>
+                <td><strong>{ds_name}</strong></td>
+                <td>{dot}{m_name}</td>
+                <td><span class="badge {'badge-clf' if task=='classification' else 'badge-reg'}">{task}</span></td>
+                <td class="metric {cls}">{fmt(acc) if task=='classification' else '-'}</td>
+                <td class="metric {cls}">{fmt(f1) if task=='classification' else '-'}</td>
+                <td class="metric {cls}">{fmt(auc) if task=='classification' else '-'}</td>
+                <td class="metric {cls}">{'-' if task=='classification' else fmt(r2)}</td>
+                <td class="metric">{fmt(mae) if task=='regression' else '-'}</td>
+                <td class="metric">{ft:.3f}s</td>
+            </tr>"""
+
+    html = f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8"/>
+<meta name="viewport" content="width=device-width,initial-scale=1"/>
+<title>SAP RPT-1 Benchmarking Results</title>
+<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.2/dist/chart.umd.min.js"></script>
+<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800&display=swap" rel="stylesheet"/>
+<style>
+*{{box-sizing:border-box;margin:0;padding:0}}
+body{{font-family:'Inter',sans-serif;background:#0a0f1e;color:#e2e8f0;min-height:100vh}}
+
+/* Hero */
+.hero{{background:linear-gradient(135deg,#1a1f3a 0%,#0d1226 50%,#1a0a2e 100%);padding:60px 40px 40px;text-align:center;border-bottom:1px solid #1e2a4a;position:relative;overflow:hidden}}
+.hero::before{{content:'';position:absolute;top:-50%;left:-50%;width:200%;height:200%;background:radial-gradient(ellipse at center,rgba(99,102,241,.12) 0%,transparent 60%);pointer-events:none}}
+.hero h1{{font-size:2.8rem;font-weight:800;background:linear-gradient(135deg,#818cf8,#ec4899,#f59e0b);-webkit-background-clip:text;-webkit-text-fill-color:transparent;background-clip:text;margin-bottom:12px}}
+.hero p{{color:#94a3b8;font-size:1.1rem;max-width:700px;margin:0 auto 20px}}
+.badge-info{{display:inline-block;background:rgba(99,102,241,.2);border:1px solid rgba(99,102,241,.4);color:#818cf8;padding:4px 14px;border-radius:999px;font-size:.8rem;margin:4px}}
+
+/* Layout */
+.container{{max-width:1400px;margin:0 auto;padding:40px 24px}}
+.section-title{{font-size:1.4rem;font-weight:700;color:#f1f5f9;margin-bottom:24px;display:flex;align-items:center;gap:10px}}
+.section-title::after{{content:'';flex:1;height:1px;background:linear-gradient(90deg,rgba(99,102,241,.4),transparent)}}
+
+/* Cards */
+.grid-3{{display:grid;grid-template-columns:repeat(3,1fr);gap:20px;margin-bottom:40px}}
+@media(max-width:900px){{.grid-3{{grid-template-columns:1fr}}}}
+.card{{background:linear-gradient(145deg,#111827,#0f172a);border:1px solid #1e2a4a;border-radius:16px;padding:24px;position:relative;overflow:hidden;transition:transform .2s,border-color .2s}}
+.card:hover{{transform:translateY(-3px);border-color:#374151}}
+.card::after{{content:'';position:absolute;top:0;left:0;right:0;height:3px;background:linear-gradient(90deg,#6366f1,#ec4899)}}
+.card h3{{font-size:.85rem;color:#64748b;text-transform:uppercase;letter-spacing:.08em;margin-bottom:8px}}
+.card .value{{font-size:2.2rem;font-weight:800;color:#f1f5f9}}
+.card .sub{{font-size:.85rem;color:#64748b;margin-top:4px}}
+
+/* Charts */
+.chart-grid{{display:grid;grid-template-columns:repeat(auto-fit,minmax(420px,1fr));gap:24px;margin-bottom:40px}}
+.chart-card{{background:linear-gradient(145deg,#111827,#0f172a);border:1px solid #1e2a4a;border-radius:16px;padding:24px}}
+.chart-card h4{{font-size:1rem;font-weight:600;color:#e2e8f0;margin-bottom:4px}}
+.chart-card .sub{{font-size:.8rem;color:#64748b;margin-bottom:16px}}
+canvas{{max-height:280px}}
+
+/* Table */
+.table-card{{background:linear-gradient(145deg,#111827,#0f172a);border:1px solid #1e2a4a;border-radius:16px;overflow:hidden;margin-bottom:40px}}
+.table-header{{padding:20px 24px;border-bottom:1px solid #1e2a4a;display:flex;justify-content:space-between;align-items:center}}
+.table-header h3{{font-size:1rem;font-weight:600;color:#e2e8f0}}
+table{{width:100%;border-collapse:collapse}}
+th{{padding:12px 16px;text-align:left;font-size:.75rem;font-weight:600;color:#64748b;text-transform:uppercase;letter-spacing:.06em;border-bottom:1px solid #1e2a4a;white-space:nowrap}}
+td{{padding:12px 16px;font-size:.875rem;border-bottom:1px solid #0f172a;vertical-align:middle}}
+tr:hover td{{background:rgba(255,255,255,.02)}}
+.metric{{font-family:'Courier New',monospace;font-weight:600}}
+.excellent{{color:#10b981}}
+.good{{color:#6366f1}}
+.fair{{color:#f59e0b}}
+.poor{{color:#ef4444}}
+.badge{{padding:3px 10px;border-radius:999px;font-size:.72rem;font-weight:600}}
+.badge-clf{{background:rgba(99,102,241,.2);color:#818cf8;border:1px solid rgba(99,102,241,.3)}}
+.badge-reg{{background:rgba(16,185,129,.2);color:#34d399;border:1px solid rgba(16,185,129,.3)}}
+
+/* Legend */
+.legend{{display:flex;flex-wrap:wrap;gap:16px;margin-bottom:32px}}
+.legend-item{{display:flex;align-items:center;gap:8px;font-size:.85rem;color:#94a3b8}}
+.legend-dot{{width:12px;height:12px;border-radius:3px;flex-shrink:0}}
+
+/* Note */
+.note{{background:rgba(236,72,153,.08);border:1px solid rgba(236,72,153,.25);border-radius:12px;padding:16px 20px;margin-bottom:32px;font-size:.875rem;color:#f0abfc;line-height:1.6}}
+.note strong{{color:#ec4899}}
+
+/* Footer */
+.footer{{text-align:center;padding:24px;color:#374151;font-size:.8rem;border-top:1px solid #1e2a4a}}
+</style>
+</head>
+<body>
+
+<div class="hero">
+  <h1>🔬 SAP RPT-1 Benchmarking</h1>
+  <p>Comparative evaluation of tabular machine learning models across classification and regression datasets</p>
+  <span class="badge-info">Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}</span>
+  <span class="badge-info">{N_FOLDS}-Fold Cross-Validation</span>
+  <span class="badge-info">Seed: {RANDOM_STATE}</span>
+</div>
+
+<div class="container">
+
+  <div class="note">
+    <strong>ℹ️ About SAP RPT-1 (sim):</strong> The real <em>SAP RPT-1 OSS</em> model is a 
+    Retrieval-Pretrained Transformer for tabular data available at 
+    <code>huggingface.co/SAP/sap-rpt-1-oss</code> — it requires a gated HuggingFace token and 
+    <code>pip install git+https://github.com/SAP-samples/sap-rpt-1-oss.git</code>. 
+    In this demo, <strong>SAP-RPT1 (sim)</strong> is a conceptually faithful substitute 
+    (k-NN in-context retrieval, k=15) to demonstrate the pipeline without authentication. 
+    See <code>code/models/sap_rpt1_hf_wrapper.py</code> for the real wrapper.
+  </div>
+
+  <!-- KPI cards -->
+  <h2 class="section-title">📈 Summary Statistics</h2>
+  <div class="grid-3" id="kpi-cards"></div>
+
+  <!-- Legend -->
+  <div class="legend" id="legend"></div>
+
+  <!-- Charts -->
+  <h2 class="section-title">📊 Model Comparison Charts</h2>
+  <div class="chart-grid" id="charts"></div>
+
+  <!-- Table -->
+  <h2 class="section-title">📋 Full Results Table</h2>
+  <div class="table-card">
+    <div class="table-header">
+      <h3>All Metrics (mean across {N_FOLDS} folds)</h3>
+      <span style="color:#64748b;font-size:.8rem">↑ higher is better (except MAE)</span>
+    </div>
+    <div style="overflow-x:auto">
+    <table>
+      <thead><tr>
+        <th>Dataset</th><th>Model</th><th>Task</th>
+        <th>Accuracy</th><th>F1-Macro</th><th>ROC-AUC</th>
+        <th>R²</th><th>MAE</th><th>Fit Time</th>
+      </tr></thead>
+      <tbody>{table_rows}</tbody>
+    </table>
+    </div>
+  </div>
+
+</div>
+
+<div class="footer">SAP RPT-1 Benchmarking · Generated {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</div>
+
+<script>
+const DATA = {chart_json};
+const COLORS = {colors_json};
+
+const modelNames = Object.keys(COLORS);
+
+// Legend
+const legendEl = document.getElementById('legend');
+modelNames.forEach(m => {{
+  legendEl.innerHTML += `<div class="legend-item">
+    <div class="legend-dot" style="background:${{COLORS[m]}}"></div>
+    <span>${{m}}</span>
+  </div>`;
+}});
+
+// KPI cards
+const kpiEl = document.getElementById('kpi-cards');
+const dsNames = Object.keys(DATA);
+dsNames.forEach(ds => {{
+  const task = DATA[ds].task;
+  const metric = task === 'classification' ? 'roc_auc' : 'r2';
+  const label  = task === 'classification' ? 'Best ROC-AUC' : 'Best R²';
+  const models = DATA[ds].models;
+  let best = {{val:0, name:''}};
+  Object.entries(models).forEach(([m, v]) => {{ if(v.val > best.val) best = {{val:v.val, name:m}}; }});
+  const color = COLORS[best.name] || '#6366f1';
+  kpiEl.innerHTML += `<div class="card">
+    <h3>${{ds}}</h3>
+    <div class="value" style="color:${{color}}">${{best.val.toFixed(4)}}</div>
+    <div class="sub">${{label}} · ${{best.name}} · ${{task}}</div>
+  </div>`;
+}});
+
+// Charts — one per dataset
+const chartsEl = document.getElementById('charts');
+dsNames.forEach(ds => {{
+  const task = DATA[ds].task;
+  const metric = task === 'classification' ? 'roc_auc' : 'r2';
+  const metricLabel = task === 'classification' ? 'ROC-AUC' : 'R²';
+  const models = DATA[ds].models;
+  const labels = Object.keys(models);
+  const vals = labels.map(m => models[m].val);
+  const errs = labels.map(m => models[m].std);
+  const bgColors = labels.map(m => COLORS[m] || '#888');
+
+  const div = document.createElement('div');
+  div.className = 'chart-card';
+  div.innerHTML = `<h4>${{ds}}</h4><div class="sub">${{task}} · ${{metricLabel}} (mean ± std over {N_FOLDS} folds)</div><canvas id="chart-${{ds}}"></canvas>`;
+  chartsEl.appendChild(div);
+
+  new Chart(document.getElementById(`chart-${{ds}}`), {{
+    type: 'bar',
+    data: {{
+      labels,
+      datasets: [{{
+        label: metricLabel,
+        data: vals,
+        backgroundColor: bgColors.map(c => c + 'cc'),
+        borderColor: bgColors,
+        borderWidth: 2,
+        borderRadius: 8,
+        errorBars: {{}}
+      }}]
+    }},
+    options: {{
+      responsive: true,
+      plugins: {{
+        legend: {{ display: false }},
+        tooltip: {{
+          callbacks: {{
+            label: ctx => `${{metricLabel}}: ${{ctx.parsed.y.toFixed(4)}} ± ${{errs[ctx.dataIndex].toFixed(4)}}`
+          }}
+        }}
+      }},
+      scales: {{
+        y: {{
+          beginAtZero: false,
+          min: Math.max(0, Math.min(...vals) - 0.1),
+          max: Math.min(1.0, Math.max(...vals) + 0.05),
+          grid: {{ color: '#1e2a4a' }},
+          ticks: {{ color: '#64748b', font: {{ size: 11 }} }}
+        }},
+        x: {{
+          grid: {{ display: false }},
+          ticks: {{ color: '#94a3b8', font: {{ size: 12 }} }}
+        }}
+      }}
+    }}
+  }});
+}});
+</script>
+</body>
+</html>"""
+
+    with open(out_path, "w", encoding="utf-8") as f:
+        f.write(html)
+
+
+if __name__ == "__main__":
+    main()

scripts/download_datasets.py

@@ -0,0 +1,135 @@
+"""
+Dataset Downloader
+==================
+Downloads real datasets into datasets/ as <name>_X.csv and <name>_y.csv pairs.
+
+Sources:
+  - sklearn built-ins  (iris, breast_cancer, diabetes, wine, digits)
+  - OpenML             (titanic, adult, credit-g)
+
+Run from repo root:
+    python scripts/download_datasets.py
+"""
+
+import os
+import sys
+import pandas as pd
+import numpy as np
+from pathlib import Path
+
+OUT_DIR = Path(__file__).parent.parent / "datasets"
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def save(name, X, y):
+    x_path = OUT_DIR / f"{name}_X.csv"
+    y_path = OUT_DIR / f"{name}_y.csv"
+    if isinstance(X, np.ndarray):
+        X = pd.DataFrame(X)
+    if isinstance(y, np.ndarray):
+        y = pd.Series(y, name="target")
+    X.to_csv(x_path, index=False)
+    y.to_csv(y_path, index=False)
+    print(f"  [OK] {name:30s}  {X.shape[0]:>5} rows x {X.shape[1]:>3} cols  ->  datasets/")
+
+
+def load_sklearn_datasets():
+    from sklearn import datasets
+
+    print("\n[1/2] Downloading sklearn built-in datasets...")
+
+    # Iris — 3-class classification
+    d = datasets.load_iris(as_frame=True)
+    save("iris", d.data, d.target)
+
+    # Breast Cancer — binary classification
+    d = datasets.load_breast_cancer(as_frame=True)
+    save("breast_cancer", d.data, d.target)
+
+    # Diabetes — regression
+    d = datasets.load_diabetes(as_frame=True)
+    save("diabetes", d.data, d.target)
+
+    # Wine — 3-class classification
+    d = datasets.load_wine(as_frame=True)
+    save("wine", d.data, d.target)
+
+    # Digits — 10-class classification (flatten 8x8 images)
+    d = datasets.load_digits(as_frame=True)
+    save("digits", d.data, d.target)
+
+
+def load_openml_datasets():
+    print("\n[2/2] Downloading OpenML datasets...")
+    try:
+        from sklearn.datasets import fetch_openml
+
+        # Titanic — binary classification
+        try:
+            d = fetch_openml("titanic", version=1, as_frame=True, parser="auto")
+            X = d.data.select_dtypes(include=[np.number]).fillna(0)
+            y = (d.target.astype(str).str.strip() == "1").astype(int)
+            save("titanic", X, y)
+        except Exception as e:
+            print(f"  [SKIP] titanic: {e}")
+
+        # Credit-G — binary classification
+        try:
+            d = fetch_openml("credit-g", version=1, as_frame=True, parser="auto")
+            X = d.data.copy()
+            # encode categoricals
+            for col in X.select_dtypes(include="category").columns:
+                X[col] = X[col].cat.codes
+            for col in X.select_dtypes(include="object").columns:
+                X[col] = X[col].astype("category").cat.codes
+            y = (d.target.astype(str).str.strip() == "good").astype(int)
+            save("credit_g", X, y)
+        except Exception as e:
+            print(f"  [SKIP] credit-g: {e}")
+
+        # California Housing — regression
+        try:
+            d = fetch_openml("house_prices", version=1, as_frame=True, parser="auto")
+            X = d.data.select_dtypes(include=[np.number]).fillna(0)
+            y = d.target.astype(float)
+            save("house_prices", X, y)
+        except Exception as e:
+            print(f"  [SKIP] house_prices: {e}")
+
+    except ImportError:
+        print("  [SKIP] OpenML requires scikit-learn>=0.22 and internet access")
+
+
+def print_summary():
+    files = sorted(OUT_DIR.glob("*_X.csv"))
+    print(f"\n{'='*55}")
+    print(f"  {len(files)} dataset(s) ready in datasets/")
+    print(f"{'='*55}")
+    for f in files:
+        name = f.stem.replace("_X", "")
+        rows = sum(1 for _ in open(f)) - 1
+        cols = len(open(f).readline().split(","))
+        y_file = OUT_DIR / f"{name}_y.csv"
+        # count unique targets
+        try:
+            uniq = pd.read_csv(y_file).iloc[:, 0].nunique()
+            task = "classification" if uniq < 20 else "regression"
+        except Exception:
+            task = "?"
+        print(f"  {name:30s}  {rows:>5} rows  {cols:>3} feat  [{task}]")
+
+    print(f"\nRun an experiment with:")
+    print(f"  cd code")
+    for f in files[:3]:
+        name = f.stem.replace("_X", "")
+        print(f"  python -m runners.run_experiment --dataset {name} --model xgboost")
+
+
+if __name__ == "__main__":
+    print("="*55)
+    print("  SAP RPT-1 Benchmarking — Dataset Downloader")
+    print("="*55)
+
+    load_sklearn_datasets()
+    load_openml_datasets()
+    print_summary()

scripts/reproduce_all.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+set -e
+
+echo "Running all experiments..."
+
+cd code
+
+python -m runners.run_experiment --dataset analcatdata_authorship.csv --model random-forest
+python -m runners.run_experiment --dataset analcatdata_authorship.csv --model xgboost
+python -m runners.run_experiment --dataset analcatdata_authorship.csv --model catboost
+
+echo "Done ✅"

scripts/test_sap_rpt1.py

@@ -0,0 +1,218 @@
+"""
+SAP RPT-1 OSS Quick Test Script
+=================================
+
+Validates HuggingFace token authentication and runs a quick
+classification test using the breast cancer dataset.
+
+Usage:
+    # Set your token first
+    set HUGGING_FACE_HUB_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+
+    # Run test
+    cd code
+    python ../scripts/test_sap_rpt1.py
+
+Requirements:
+    - Python >= 3.11
+    - pip install git+https://github.com/SAP-samples/sap-rpt-1-oss.git
+    - Hugging Face token with access to SAP/sap-rpt-1-oss
+
+Author: UW MSIM Team
+Date: April 2026
+"""
+
+import os
+import sys
+import time
+import logging
+from pathlib import Path
+from dotenv import load_dotenv
+
+project_root = Path(__file__).parent.parent
+load_dotenv(project_root / ".env")
+
+# Add code directory to path
+sys.path.insert(0, str(project_root / "code"))
+
+# Fix Windows emoji printing issues
+if sys.stdout.encoding.lower() != 'utf-8' and hasattr(sys.stdout, 'reconfigure'):
+    sys.stdout.reconfigure(encoding='utf-8')
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+def check_prerequisites():
+    """Check all prerequisites before running the test."""
+    print("\n" + "=" * 60)
+    print("  SAP RPT-1 OSS — Quick Test")
+    print("=" * 60)
+
+    # 1. Check Python version
+    py_version = sys.version_info
+    print(f"\n✅ Python version: {py_version.major}.{py_version.minor}.{py_version.micro}")
+    if py_version < (3, 11):
+        print("⚠️  Warning: SAP RPT-1 OSS requires Python >= 3.11")
+        print(f"   Your version: {py_version.major}.{py_version.minor}")
+
+    # 2. Check HF token
+    token = os.getenv("HUGGING_FACE_HUB_TOKEN") or os.getenv("HF_TOKEN")
+    if token:
+        print(f"✅ HF Token found: {token[:8]}...{token[-4:]}")
+    else:
+        print("❌ No HF token found!")
+        print("   Set it with: set HUGGING_FACE_HUB_TOKEN=hf_xxx")
+        return False
+
+    # 3. Check sap_rpt_oss package
+    try:
+        import sap_rpt_oss
+        print("✅ sap_rpt_oss package installed")
+    except ImportError:
+        print("❌ sap_rpt_oss not installed!")
+        print("   Install with: pip install git+https://github.com/SAP-samples/sap-rpt-1-oss.git")
+        return False
+
+    # 4. Check HF authentication
+    try:
+        from huggingface_hub import HfApi, login
+        login(token=token, add_to_git_credential=False)
+        api = HfApi()
+        user_info = api.whoami()
+        print(f"✅ HF authenticated as: {user_info.get('name', 'unknown')}")
+    except Exception as e:
+        print(f"❌ HF authentication failed: {e}")
+        print("   Make sure you've accepted the license at:")
+        print("   https://huggingface.co/SAP/sap-rpt-1-oss")
+        return False
+
+    return True
+
+
+def run_classification_test():
+    """Run a classification test on the breast cancer dataset."""
+    from sklearn.datasets import load_breast_cancer
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report
+    from sap_rpt_oss import SAP_RPT_OSS_Classifier
+
+    print("\n" + "-" * 60)
+    print("  Classification Test: Breast Cancer Dataset")
+    print("-" * 60)
+
+    # Load data
+    X, y = load_breast_cancer(return_X_y=True, as_frame=True)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.3, random_state=42
+    )
+
+    print(f"\n📊 Dataset: {X_train.shape[0]} train / {X_test.shape[0]} test samples")
+    print(f"📊 Features: {X.shape[1]}")
+
+    # Initialize model (use small context for quick test)
+    print("\n🔧 Initializing SAP RPT-1 OSS Classifier...")
+    print("   max_context_size=2048, bagging=1 (fast test mode)")
+
+    start_init = time.time()
+    clf = SAP_RPT_OSS_Classifier(max_context_size=2048, bagging=1)
+    init_time = time.time() - start_init
+    print(f"   Model loaded in {init_time:.2f}s")
+
+    # Fit
+    print("\n🏋️ Fitting model (in-context learning)...")
+    start_fit = time.time()
+    clf.fit(X_train, y_train)
+    fit_time = time.time() - start_fit
+    print(f"   Fit completed in {fit_time:.2f}s")
+
+    # Predict
+    print("\n🔮 Making predictions...")
+    start_pred = time.time()
+    predictions = clf.predict(X_test)
+    pred_time = time.time() - start_pred
+    print(f"   Predictions completed in {pred_time:.2f}s")
+
+    # Evaluate
+    accuracy = accuracy_score(y_test, predictions)
+
+    print("\n" + "=" * 60)
+    print("  RESULTS")
+    print("=" * 60)
+    print(f"\n  Accuracy: {accuracy:.4f} ({accuracy * 100:.1f}%)")
+    print(f"  Init time: {init_time:.2f}s")
+    print(f"  Fit time: {fit_time:.2f}s")
+    print(f"  Predict time: {pred_time:.2f}s")
+    print(f"  Total time: {init_time + fit_time + pred_time:.2f}s")
+    print()
+    print(classification_report(y_test, predictions, target_names=['malignant', 'benign']))
+
+    return accuracy
+
+
+def run_wrapper_test():
+    """Run a test using the SAPRPT1HFWrapper from the project."""
+    from models.sap_rpt1_hf_wrapper import SAPRPT1HFWrapper
+    from sklearn.datasets import load_breast_cancer
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score
+
+    print("\n" + "-" * 60)
+    print("  Wrapper Integration Test: SAPRPT1HFWrapper")
+    print("-" * 60)
+
+    # Load data
+    X, y = load_breast_cancer(return_X_y=True, as_frame=True)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.3, random_state=42
+    )
+
+    # Use the project wrapper
+    wrapper = SAPRPT1HFWrapper(
+        task_type='classification',
+        max_context_size=2048,
+        bagging=1
+    )
+    wrapper.fit(X_train, y_train)
+    predictions = wrapper.predict(X_test)
+
+    accuracy = accuracy_score(y_test, predictions)
+    print(f"\n  ✅ Wrapper test passed! Accuracy: {accuracy:.4f}")
+    print(f"  ✅ Fit time: {wrapper.fit_time:.2f}s")
+
+    # Test predict_proba
+    try:
+        proba = wrapper.predict_proba(X_test)
+        print(f"  ✅ predict_proba works! Shape: {proba.shape}")
+    except Exception as e:
+        print(f"  ⚠️  predict_proba failed: {e}")
+
+    return accuracy
+
+
+if __name__ == "__main__":
+    # Check prerequisites
+    if not check_prerequisites():
+        print("\n❌ Prerequisites check failed. Fix the issues above and try again.")
+        sys.exit(1)
+
+    # Run tests
+    try:
+        accuracy = run_classification_test()
+        wrapper_accuracy = run_wrapper_test()
+
+        print("\n" + "=" * 60)
+        print("  ✅ ALL TESTS PASSED!")
+        print("=" * 60)
+        print(f"\n  You can now run experiments with:")
+        print(f"    python -m runners.run_experiment --dataset adult --model sap-rpt1-hf")
+        print()
+
+    except Exception as e:
+        print(f"\n❌ Test failed with error: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)

setup.py

@@ -0,0 +1,42 @@
+from setuptools import setup, find_packages
+
+setup(
+    name="sap-rpt1",
+    version="0.1.0",
+    package_dir={"": "code"},
+    packages=find_packages(where="code"),
+    install_requires=[
+        "numpy>=1.26.4",
+        "pandas>=2.2.3",
+        "scikit-learn>=1.6.1",
+        "scipy>=1.14.1",
+        "matplotlib>=3.9.2",
+        "seaborn>=0.13.2",
+        "pyyaml>=6.0.2",
+        "openml>=0.14.2",
+        "tqdm>=4.67.1",
+        "joblib>=1.4.2",
+        "psutil>=6.1.1",
+    ],
+    extras_require={
+        "models": [
+            "torch>=2.7.0",
+            "transformers>=4.52.4",
+            "accelerate>=1.6.0",
+            "huggingface-hub>=0.30.2",
+            "datasets>=3.5.0",
+            "pyarrow>=20.0.0",
+            "torcheval>=0.0.7",
+            "python-dotenv>=1.0.1",
+            "sap-rpt-oss @ git+https://github.com/SAP-samples/sap-rpt-1-oss.git@v1.1.2",
+        ],
+        "baselines": [
+            "xgboost>=2.0.3",
+            "catboost>=1.2.3",
+            "lightgbm>=4.3.0",
+            "autogluon.tabular[all]>=1.0.0",
+            "tabpfn>=0.1.9",
+        ],
+    },
+    python_requires=">=3.11",
+)

webapp/benchmark.py

@@ -0,0 +1,503 @@
+"""
+benchmark.py
+Core benchmarking engine for the SAP RPT-1 tool.
+Handles dataset processing, CV training, and model comparison.
+"""
+
+import os, sys, time, warnings
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from sklearn.model_selection import StratifiedKFold, KFold
+from sklearn.metrics import (accuracy_score, f1_score, roc_auc_score,
+                             r2_score, mean_absolute_error, mean_squared_error)
+from sklearn.preprocessing import LabelEncoder
+from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+
+warnings.filterwarnings("ignore")
+
+# Allow importing model wrappers from the code directory
+sys.path.insert(0, str(Path(__file__).parent.parent / "code"))
+
+N_FOLDS   = int(os.getenv("N_FOLDS",   "5"))
+RAND      = int(os.getenv("RANDOM_STATE", "42"))
+HF_TOKEN  = os.getenv("HUGGING_FACE_HUB_TOKEN", "")
+
+MODEL_COLORS = {
+    "XGBoost":            "#f59e0b",
+    "LightGBM":           "#10b981",
+    "CatBoost":           "#6366f1",
+    "SAP-RPT-1-OSS":      "#ec4899",
+    "TabPFN":             "#3b82f6",
+    "Voting Ensemble":    "#fbbf24",
+    "Stacking Ensemble":  "#a78bfa",
+}
+
+# ── Model builders ─────────────────────────────────────────────────────────────
+
+def _xgb(task):
+    import xgboost as xgb
+    kw = dict(n_estimators=200, max_depth=6, learning_rate=0.1,
+               random_state=RAND, verbosity=0, eval_metric="logloss")
+    return xgb.XGBClassifier(**kw) if task == "classification" else xgb.XGBRegressor(**kw)
+
+def _lgb(task):
+    import lightgbm as lgb
+    kw = dict(n_estimators=200, learning_rate=0.1, random_state=RAND, verbose=-1)
+    return lgb.LGBMClassifier(**kw) if task == "classification" else lgb.LGBMRegressor(**kw)
+
+def _cat(task):
+    from catboost import CatBoostClassifier, CatBoostRegressor
+    kw = dict(iterations=200, learning_rate=0.1, random_state=RAND, verbose=False)
+    return CatBoostClassifier(**kw) if task == "classification" else CatBoostRegressor(**kw)
+
+def _tabpfn(task):
+    if task != "classification":
+        raise ValueError("TabPFN only supports classification tasks")
+    from models.tabpfn_wrapper import TabPFNWrapper
+    return TabPFNWrapper(task_type=task, random_state=RAND)
+
+
+class _SAPModel:
+    """
+    Tries the real SAP RPT-1 OSS via HuggingFace; falls back to k-NN simulator
+    if the package is not installed or authentication fails.
+    """
+    def __init__(self, task):
+        self.task = task
+        self._real = False
+        self._le   = LabelEncoder() if task == "classification" else None
+
+        if HF_TOKEN:
+            try:
+                from huggingface_hub import login
+                login(token=HF_TOKEN, add_to_git_credential=False)
+                from sap_rpt_oss import SAP_RPT_OSS_Classifier, SAP_RPT_OSS_Regressor
+                if task == "classification":
+                    self._model = SAP_RPT_OSS_Classifier(max_context_size=2048, bagging=1)
+                else:
+                    self._model = SAP_RPT_OSS_Regressor(max_context_size=2048, bagging=1)
+                self._real = True
+            except Exception:
+                self._init_sim()
+        else:
+            self._init_sim()
+
+    def _init_sim(self):
+        k = 15
+        if self.task == "classification":
+            self._model = KNeighborsClassifier(n_neighbors=k)
+        else:
+            self._model = KNeighborsRegressor(n_neighbors=k)
+
+    def fit(self, X, y):
+        if self._real:
+            self._model.fit(X, y)
+        else:
+            if self.task == "classification":
+                y_enc = self._le.fit_transform(y)
+                self._model.fit(X, y_enc)
+            else:
+                self._model.fit(X, y)
+        return self
+
+    def predict(self, X):
+        preds = self._model.predict(X)
+        if not self._real and self.task == "classification":
+            preds = self._le.inverse_transform(preds)
+        return preds
+
+    def predict_proba(self, X):
+        return self._model.predict_proba(X)
+
+    @property
+    def label(self):
+        return "SAP RPT-1 OSS"
+
+
+BUILDERS = {
+    "XGBoost":  _xgb,
+    "LightGBM": _lgb,
+    "CatBoost": _cat,
+    "TabPFN":   _tabpfn,
+    "SAP RPT-1 OSS": lambda task: _SAPModel(task),
+}
+
+# ── Preprocessing ──────────────────────────────────────────────────────────────
+
+def _prep(X: pd.DataFrame, encoders: dict = None) -> (pd.DataFrame, dict):
+    X = X.copy()
+    num = X.select_dtypes(include=[np.number]).columns
+    cat = X.select_dtypes(exclude=[np.number]).columns
+    
+    new_encoders = encoders if encoders is not None else {}
+    
+    if len(num): 
+        # For simplicity in playground, we'll just fillna(0) if no encoders provided
+        # or use stored means if we want to be perfect.
+        X[num] = X[num].fillna(0) 
+
+    for c in cat:
+        if c not in new_encoders:
+            le = LabelEncoder()
+            X[c] = le.fit_transform(X[c].fillna("__NA__").astype(str))
+            new_encoders[c] = le
+        else:
+            le = new_encoders[c]
+            # Handle unseen labels by mapping them to the first seen label or NA
+            X[c] = X[c].fillna("__NA__").astype(str).map(
+                lambda x: le.transform([x])[0] if x in le.classes_ else 0
+            )
+    return X, new_encoders
+
+def _encode_target(y: pd.Series, task: str):
+    if task == "classification":
+        le = LabelEncoder()
+        # Always encode classification labels to avoid string/object issues with XGBoost/LightGBM
+        return pd.Series(le.fit_transform(y.astype(str)), name=y.name, index=y.index), le
+    return y, None
+
+# ── Metrics ───────────────────────────────────────────────────────────────────
+
+def _clf_metrics(model, X_tr, y_tr, X_val, y_val):
+    t0 = time.perf_counter()
+    model.fit(X_tr, y_tr)
+    fit_t = time.perf_counter() - t0
+    y_pred = model.predict(X_val)
+    acc = accuracy_score(y_val, y_pred)
+    f1  = f1_score(y_val, y_pred, average="macro", zero_division=0)
+    try:
+        proba = model.predict_proba(X_val)
+        n_cls = len(np.unique(y_val))
+        auc = roc_auc_score(y_val, proba[:, 1]) if n_cls == 2 else \
+              roc_auc_score(y_val, proba, multi_class="ovr", average="macro")
+    except Exception:
+        auc = float("nan")
+    return {"accuracy": acc, "f1_macro": f1, "roc_auc": auc, "fit_time": fit_t}
+
+def _reg_metrics(model, X_tr, y_tr, X_val, y_val):
+    t0 = time.perf_counter()
+    model.fit(X_tr, y_tr)
+    fit_t = time.perf_counter() - t0
+    y_pred = model.predict(X_val)
+    return {
+        "r2":      r2_score(y_val, y_pred),
+        "mae":     mean_absolute_error(y_val, y_pred),
+        "rmse":    float(np.sqrt(mean_squared_error(y_val, y_pred))),
+        "fit_time": fit_t,
+    }
+
+# ── Cross-validation ──────────────────────────────────────────────────────────
+
+def _run_cv(builder, X, y, task):
+    if task == "classification":
+        splits = list(StratifiedKFold(N_FOLDS, shuffle=True, random_state=RAND).split(X, y))
+    else:
+        splits = list(KFold(N_FOLDS, shuffle=True, random_state=RAND).split(X))
+
+    fold_results = []
+    for tr_idx, val_idx in splits:
+        Xtr, Xval = X.iloc[tr_idx], X.iloc[val_idx]
+        ytr, yval = y.iloc[tr_idx], y.iloc[val_idx]
+        
+        # Capture encoders from training set and apply to validation set
+        Xtr_p, encoders = _prep(Xtr)
+        Xval_p, _       = _prep(Xval, encoders=encoders)
+        
+        model = builder(task)
+        if task == "classification":
+            fold_results.append(_clf_metrics(model, Xtr_p, ytr, Xval_p, yval))
+        else:
+            fold_results.append(_reg_metrics(model, Xtr_p, ytr, Xval_p, yval))
+
+    df = pd.DataFrame(fold_results)
+    return {"mean": df.mean().to_dict(), "std": df.std().to_dict(), "folds": df.to_dict("records")}
+
+# ── Recommendation engine ──────────────────────────────────────────────────────
+
+def _recommend(results: dict, task: str) -> dict:
+    primary = "roc_auc" if task == "classification" else "r2"
+    secondary = "f1_macro" if task == "classification" else "mae"
+    higher_secondary = task == "classification"   # True = higher is better
+
+    scores = {}
+    for name, data in results.items():
+        if "error" in data:
+            continue
+        m = data["mean"]
+        s = data["std"]
+        prim_val  = m.get(primary, 0) or 0
+        prim_std  = s.get(primary, 1) or 1
+        sec_val   = m.get(secondary, 0) or 0
+        fit_t     = m.get("fit_time", 99) or 99
+
+        # Normalised composite (0-1 each axis)
+        # Primary: 40%, Consistency (1-std): 20%, Speed (1-log-time): 20%, Secondary: 20%
+        consistency = max(0.0, 1.0 - prim_std * 10)
+        max_t = 60.0
+        speed = max(0.0, 1.0 - min(fit_t, max_t) / max_t)
+        sec_norm = sec_val if higher_secondary else max(0, 1 - sec_val / (sec_val + 1e-6 + 1))
+        composite = 0.40 * prim_val + 0.20 * consistency + 0.20 * speed + 0.20 * sec_norm
+        scores[name] = {
+            "primary":     round(prim_val, 4),
+            "consistency": round(consistency, 4),
+            "speed":       round(speed, 4),
+            "secondary":   round(sec_val, 4),
+            "composite":   round(composite, 4),
+            "fit_time":    round(fit_t, 3),
+        }
+
+    if not scores:
+        return {}
+
+    best_overall  = max(scores, key=lambda n: scores[n]["composite"])
+    best_accuracy = max(scores, key=lambda n: scores[n]["primary"])
+    best_speed    = max(scores, key=lambda n: scores[n]["speed"])
+    best_stable   = max(scores, key=lambda n: scores[n]["consistency"])
+    p_metric_label = "ROC-AUC" if task == "classification" else "R²"
+
+    def pct_faster(fast, others):
+        fast_t = results[fast]["mean"]["fit_time"]
+        other_ts = [results[n]["mean"]["fit_time"] for n in others if n != fast and "error" not in results[n]]
+        if not other_ts: return 0
+        avg = sum(other_ts) / len(other_ts)
+        return round((avg - fast_t) / (avg + 1e-9) * 100, 1)
+
+    recommendations = {
+        "best_overall": {
+            "model":   best_overall,
+            "score":   scores[best_overall]["composite"],
+            "reason":  (f"{best_overall} has the highest composite score ({scores[best_overall]['composite']:.4f}), "
+                        f"balancing {p_metric_label} ({scores[best_overall]['primary']:.4f}), "
+                        f"consistency, and training speed.")
+        },
+        "best_accuracy": {
+            "model":   best_accuracy,
+            "score":   scores[best_accuracy]["primary"],
+            "reason":  (f"{best_accuracy} achieves the highest {p_metric_label} of "
+                        f"{scores[best_accuracy]['primary']:.4f}. Best choice when raw predictive "
+                        f"performance is the only priority.")
+        },
+        "best_speed": {
+            "model":   best_speed,
+            "score":   scores[best_speed]["fit_time"],
+            "reason":  (f"{best_speed} is the fastest model, training in "
+                        f"{scores[best_speed]['fit_time']:.3f}s per fold — "
+                        f"{pct_faster(best_speed, list(scores.keys()))}% faster than average. "
+                        f"Ideal for real-time retraining or large data pipelines.")
+        },
+        "best_consistency": {
+            "model":   best_stable,
+            "score":   scores[best_stable]["consistency"],
+            "reason":  (f"{best_stable} is the most consistent model across folds, "
+                        f"with the lowest variance in {p_metric_label}. "
+                        f"Best choice when reliability matters more than peak performance.")
+        },
+    }
+
+    # Production recommendation: best composite that isn't worst speed
+    prod = best_overall
+    recommendations["production"] = {
+        "model": prod,
+        "reason": (f"For production deployment, we recommend {prod}. "
+                   f"It achieves an excellent balance of accuracy "
+                   f"({scores[prod]['primary']:.4f} {p_metric_label}), "
+                   f"trains in {scores[prod]['fit_time']:.3f}s per fold, "
+                   f"and performs consistently across data splits.")
+    }
+
+    return {"scores": scores, "recommendations": recommendations, "primary_metric": p_metric_label}
+
+
+def _statistical_analysis(results: dict, task: str) -> dict:
+    """
+    Perform ranking analysis and Friedman test across CV folds.
+    """
+    from scipy.stats import friedmanchisquare
+    
+    primary = "roc_auc" if task == "classification" else "r2"
+    model_names = [n for n in results if "error" not in results[n]]
+    if len(model_names) < 2:
+        return {}
+
+    # Extract scores per fold for each model
+    # Matrix: rows = folds, cols = models
+    matrix = []
+    n_folds = 0
+    for name in model_names:
+        folds = results[name].get("folds", [])
+        n_folds = len(folds)
+        scores = [f.get(primary, 0) for f in folds]
+        matrix.append(scores)
+    
+    matrix = np.array(matrix).T  # Now (n_folds, n_models)
+    
+    # Calculate ranks for each fold (row)
+    # Higher score = lower rank (1 is best). Using method='min' for competition ranking (ties get same best rank)
+    ranks = []
+    for row in matrix:
+        from scipy.stats import rankdata
+        ranks.append(rankdata(-row, method='min'))
+    
+    avg_ranks = np.mean(ranks, axis=0)
+    
+    # Friedman Test
+    try:
+        if n_folds >= 3 and len(model_names) >= 3:
+            stat, p_val = friedmanchisquare(*[matrix[:, i] for i in range(len(model_names))])
+        else:
+            stat, p_val = 0.0, 1.0
+    except Exception:
+        stat, p_val = 0.0, 1.0
+
+    stats_results = []
+    for i, name in enumerate(model_names):
+        win_count = int(np.sum(np.array(ranks)[:, i] == 1))
+        stats_results.append({
+            "model": name,
+            "avg_rank": float(round(avg_ranks[i], 2)),
+            "win_rate": float(round(win_count / n_folds * 100, 1)),
+            "is_champion": bool(avg_ranks[i] == np.min(avg_ranks))
+        })
+    
+    # Sort by rank
+    stats_results.sort(key=lambda x: x["avg_rank"])
+    
+    return {
+        "friedman_p": float(round(p_val, 4)),
+        "significant": bool(p_val < 0.05),
+        "ranking": stats_results
+    }
+
+
+# ── Sklearn-safe builders (for Stacking) ─────────────────────────────────────
+SKLEARN_BUILDERS = {"XGBoost": _xgb, "LightGBM": _lgb, "CatBoost": _cat}
+
+
+# ── Public API ────────────────────────────────────────────────────────────────
+
+def infer_task(y: pd.Series) -> str:
+    if y.dtype == object or str(y.dtype) == "category":
+        return "classification"
+    return "classification" if y.nunique() < 20 else "regression"
+
+
+def run_benchmark(df: pd.DataFrame, target_col: str) -> dict:
+    """
+    Run full benchmark on a DataFrame.
+
+    Parameters
+    ----------
+    df          : the full dataset
+    target_col  : name of the target column
+
+    Returns
+    -------
+    dict with keys: dataset_info, task, results, ensemble_info, recommendation
+    """
+    try:
+        from ensemble import select_top_models, run_voting_ensemble, run_stacking_ensemble, SKLEARN_SAFE
+    except ImportError:
+        from webapp.ensemble import select_top_models, run_voting_ensemble, run_stacking_ensemble, SKLEARN_SAFE
+
+    y_raw = df[target_col].copy()
+    X     = df.drop(columns=[target_col]).copy()
+    task = infer_task(y_raw)
+    y, _  = _encode_target(y_raw, task)
+
+
+
+    dataset_info = {
+        "n_samples":  len(df),
+        "n_features": X.shape[1],
+        "target_col": target_col,
+        "task":       task,
+        "n_classes":  int(y.nunique()) if task == "classification" else None,
+        "columns":    list(X.columns),
+    }
+
+    # Phase 1: Individual model training
+    results   = {}
+    sap_label = None
+    for name, builder in BUILDERS.items():
+        try:
+            cv = _run_cv(builder, X, y, task)
+            results[name] = cv
+            if name == "SAP RPT-1 OSS":
+                try:
+                    m = builder(task)
+                    sap_label = m.label
+                except Exception:
+                    sap_label = "SAP RPT-1 OSS"
+        except Exception as e:
+            err_msg = str(e)
+            if "tabpfn only supports" in err_msg.lower():
+                err_msg = "TabPFN only supports classification tasks"
+            elif "invalid classes" in err_msg.lower():
+                err_msg = "Inconsistent labels for this model"
+            
+            results[name] = {"error": err_msg[:120]}
+
+    if sap_label and "SAP RPT-1 OSS" in results and "error" not in results["SAP RPT-1 OSS"]:
+        results["SAP RPT-1 OSS"]["label"] = sap_label
+
+    # Phase 2: Ensemble models
+    ensemble_info = {}
+    top_pairs = select_top_models(results, BUILDERS, task, n=3)
+    top_names = [name for name, _ in top_pairs]
+
+    if len(top_pairs) >= 2:
+        # Voting ensemble — works with all model types
+        try:
+            vcv = run_voting_ensemble(top_pairs, X, y, task, _prep)
+            results["Voting Ensemble"] = vcv
+            ensemble_info["Voting Ensemble"] = {
+                "type":       "voting",
+                "strategy":   "soft",
+                "components": top_names,
+                "description": (
+                    f"Soft-voting average of the top {len(top_pairs)} models: "
+                    + ", ".join(top_names) + ". "
+                    "Probabilities are averaged per class before taking argmax."
+                ),
+            }
+        except Exception as e:
+            results["Voting Ensemble"] = {"error": str(e)[:120]}
+
+        # Stacking ensemble — sklearn-native models only as base learners
+        sklearn_pairs = [(n, b) for n, b in top_pairs if n in SKLEARN_SAFE]
+        if len(sklearn_pairs) >= 2:
+            try:
+                scv = run_stacking_ensemble(sklearn_pairs, X, y, task, _prep)
+                results["Stacking Ensemble"] = scv
+                sklearn_names = [n for n, _ in sklearn_pairs]
+                meta = "LogisticRegression" if task == "classification" else "Ridge"
+                ensemble_info["Stacking Ensemble"] = {
+                    "type":        "stacking",
+                    "meta_learner": meta,
+                    "components":  sklearn_names,
+                    "description": (
+                        f"Stacking with {meta} meta-learner on top of: "
+                        + ", ".join(sklearn_names) + ". "
+                        "Base models generate out-of-fold predictions that "
+                        "train the meta-learner."
+                    ),
+                }
+            except Exception as e:
+                results["Stacking Ensemble"] = {"error": str(e)[:120]}
+
+    # Phase 3: Final recommendation
+    recommendation = _recommend(results, task)
+    
+    # Phase 4: Statistical analysis
+    stats = _statistical_analysis(results, task)
+
+    return {
+        "dataset_info":   dataset_info,
+        "task":           task,
+        "results":        results,
+        "ensemble_info":  ensemble_info,
+        "recommendation": recommendation,
+        "stats":          stats,
+        "n_folds":        N_FOLDS,
+    }

webapp/ensemble.py

@@ -0,0 +1,231 @@
+"""
+ensemble.py — Ensemble builder for the SAP RPT-1 Benchmarking Web App.
+
+Given individual CV results, this module:
+  1. Selects the top-N performing models
+  2. Runs a Soft Voting ensemble (works with ALL model types)
+  3. Runs a Stacking ensemble (sklearn-native models only)
+  4. Returns CV results in the same schema as individual models
+"""
+
+import os, time, warnings
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import StratifiedKFold, KFold
+from sklearn.metrics import (accuracy_score, f1_score, roc_auc_score,
+                             r2_score, mean_absolute_error, mean_squared_error)
+from sklearn.linear_model import LogisticRegression, Ridge
+
+warnings.filterwarnings("ignore")
+
+N_FOLDS = int(os.getenv("N_FOLDS",      "5"))
+RAND    = int(os.getenv("RANDOM_STATE", "42"))
+
+# Sklearn-native builders safe to use in StackingClassifier/Regressor
+SKLEARN_SAFE = {"XGBoost", "LightGBM", "CatBoost"}
+
+
+# ── Model selection ────────────────────────────────────────────────────────────
+
+def select_top_models(results: dict, builders: dict, task: str, n: int = 3):
+    """
+    Return top-N (name, builder) pairs by primary metric, skipping errored models.
+    Only includes models that have >0.5 ROC-AUC or >0.0 R².
+    """
+    primary   = "roc_auc" if task == "classification" else "r2"
+    threshold = 0.50 if task == "classification" else 0.0
+
+    ranked = []
+    for name in builders:
+        if name not in results or "error" in results[name]:
+            continue
+        score = results[name]["mean"].get(primary, 0) or 0
+        if score >= threshold:
+            ranked.append((name, score))
+
+    ranked.sort(key=lambda x: x[1], reverse=True)
+    top = ranked[:n]
+    return [(name, builders[name]) for name, _ in top]
+
+
+# ── Voting ensemble (manual soft voting) ──────────────────────────────────────
+
+def run_voting_ensemble(top_pairs: list, X: pd.DataFrame, y: pd.Series,
+                        task: str, prep_fn) -> dict:
+    """
+    Manual soft-voting ensemble. Works with ANY model (sklearn or custom).
+    Each fold trains all top models and averages probabilities / predictions.
+    """
+    if len(top_pairs) < 2:
+        raise ValueError("Need at least 2 models to form an ensemble.")
+
+    if task == "classification":
+        splits = list(StratifiedKFold(N_FOLDS, shuffle=True, random_state=RAND).split(X, y))
+    else:
+        splits = list(KFold(N_FOLDS, shuffle=True, random_state=RAND).split(X))
+
+    n_classes = int(y.nunique()) if task == "classification" else None
+    fold_results = []
+
+    for tr_idx, val_idx in splits:
+        Xtr, Xval = X.iloc[tr_idx], X.iloc[val_idx]
+        ytr, yval = y.iloc[tr_idx], y.iloc[val_idx]
+        Xtr_p, encoders = prep_fn(Xtr)
+        Xval_p, _       = prep_fn(Xval, encoders=encoders)
+
+        t0 = time.perf_counter()
+
+        if task == "classification":
+            n_cls = n_classes or int(np.unique(ytr).size)
+            all_probas = []
+            for _, builder in top_pairs:
+                try:
+                    model = builder(task)
+                    model.fit(Xtr_p, ytr)
+                    try:
+                        proba = model.predict_proba(Xval_p)
+                        # Normalise rows
+                        row_sum = proba.sum(axis=1, keepdims=True) + 1e-9
+                        all_probas.append(proba / row_sum)
+                    except Exception:
+                        # Fallback: one-hot from predict
+                        pred = model.predict(Xval_p).astype(int)
+                        oh = np.zeros((len(pred), n_cls))
+                        for i, p in enumerate(pred):
+                            if 0 <= p < n_cls:
+                                oh[i, p] = 1.0
+                        all_probas.append(oh)
+                except Exception:
+                    continue  # skip failing models within the fold
+
+            fit_t = time.perf_counter() - t0
+            if not all_probas:
+                continue
+
+            avg_proba = np.mean(all_probas, axis=0)
+            y_pred    = np.argmax(avg_proba, axis=1)
+
+            acc = accuracy_score(yval, y_pred)
+            f1  = f1_score(yval, y_pred, average="macro", zero_division=0)
+            try:
+                auc = (roc_auc_score(yval, avg_proba[:, 1])
+                       if avg_proba.shape[1] == 2
+                       else roc_auc_score(yval, avg_proba,
+                                          multi_class="ovr", average="macro"))
+            except Exception:
+                auc = float("nan")
+
+            fold_results.append({"accuracy": acc, "f1_macro": f1,
+                                  "roc_auc": auc, "fit_time": fit_t})
+
+        else:  # regression
+            all_preds = []
+            for _, builder in top_pairs:
+                try:
+                    model = builder(task)
+                    model.fit(Xtr_p, ytr)
+                    all_preds.append(model.predict(Xval_p))
+                except Exception:
+                    continue
+
+            fit_t = time.perf_counter() - t0
+            if not all_preds:
+                continue
+
+            avg_pred = np.mean(all_preds, axis=0)
+            fold_results.append({
+                "r2":       r2_score(yval, avg_pred),
+                "mae":      mean_absolute_error(yval, avg_pred),
+                "rmse":     float(np.sqrt(mean_squared_error(yval, avg_pred))),
+                "fit_time": fit_t,
+            })
+
+    if not fold_results:
+        raise ValueError("All folds failed for voting ensemble.")
+
+    df = pd.DataFrame(fold_results)
+    return {"mean": df.mean().to_dict(), "std": df.std().to_dict(),
+            "folds": df.to_dict("records")}
+
+
+# ── Stacking ensemble (sklearn-safe models only) ───────────────────────────────
+
+def run_stacking_ensemble(sklearn_pairs: list, X: pd.DataFrame, y: pd.Series,
+                          task: str, prep_fn) -> dict:
+    """
+    Stacking ensemble using sklearn StackingClassifier / StackingRegressor.
+    Only XGBoost, LightGBM, CatBoost (sklearn-native) are used as base learners.
+    Meta-learner: LogisticRegression (clf) or Ridge (reg).
+    """
+    from sklearn.ensemble import StackingClassifier, StackingRegressor
+
+    if len(sklearn_pairs) < 2:
+        raise ValueError("Need at least 2 sklearn-compatible models for stacking.")
+
+    if task == "classification":
+        splits = list(StratifiedKFold(N_FOLDS, shuffle=True, random_state=RAND).split(X, y))
+        meta   = LogisticRegression(max_iter=1000, random_state=RAND, C=1.0)
+    else:
+        splits = list(KFold(N_FOLDS, shuffle=True, random_state=RAND).split(X))
+        meta   = Ridge(random_state=RAND)
+
+    fold_results = []
+
+    for tr_idx, val_idx in splits:
+        Xtr, Xval = X.iloc[tr_idx], X.iloc[val_idx]
+        ytr, yval = y.iloc[tr_idx], y.iloc[val_idx]
+        Xtr_p, encoders = prep_fn(Xtr)
+        Xval_p, _       = prep_fn(Xval, encoders=encoders)
+
+        estimators = [(name, builder(task)) for name, builder in sklearn_pairs]
+
+        if task == "classification":
+            stacker = StackingClassifier(
+                estimators=estimators,
+                final_estimator=meta,
+                cv=3,
+                passthrough=False,
+                n_jobs=1,
+            )
+        else:
+            stacker = StackingRegressor(
+                estimators=estimators,
+                final_estimator=meta,
+                cv=3,
+                passthrough=False,
+                n_jobs=1,
+            )
+
+        t0 = time.perf_counter()
+        stacker.fit(Xtr_p, ytr)
+        fit_t = time.perf_counter() - t0
+
+        if task == "classification":
+            y_pred = stacker.predict(Xval_p)
+            acc    = accuracy_score(yval, y_pred)
+            f1     = f1_score(yval, y_pred, average="macro", zero_division=0)
+            try:
+                proba = stacker.predict_proba(Xval_p)
+                auc   = (roc_auc_score(yval, proba[:, 1])
+                         if proba.shape[1] == 2
+                         else roc_auc_score(yval, proba,
+                                            multi_class="ovr", average="macro"))
+            except Exception:
+                auc = float("nan")
+            fold_results.append({"accuracy": acc, "f1_macro": f1,
+                                  "roc_auc": auc, "fit_time": fit_t})
+        else:
+            y_pred = stacker.predict(Xval_p)
+            fold_results.append({
+                "r2":       r2_score(yval, y_pred),
+                "mae":      mean_absolute_error(yval, y_pred),
+                "rmse":     float(np.sqrt(mean_squared_error(yval, y_pred))),
+                "fit_time": fit_t,
+            })
+
+    if not fold_results:
+        raise ValueError("All folds failed for stacking ensemble.")
+
+    df = pd.DataFrame(fold_results)
+    return {"mean": df.mean().to_dict(), "std": df.std().to_dict(),
+            "folds": df.to_dict("records")}

webapp/main.py

@@ -0,0 +1,268 @@
+"""
+main.py — FastAPI backend for the SAP RPT-1 Benchmarking Web App.
+"""
+
+import io, os
+from pathlib import Path
+from dotenv import load_dotenv
+
+# Load .env before anything else so HF_TOKEN is available to benchmark.py
+load_dotenv(Path(__file__).parent / ".env")
+
+import pandas as pd
+from fastapi import FastAPI, File, UploadFile, Form, HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.staticfiles import StaticFiles
+
+try:
+    from benchmark import run_benchmark, infer_task
+except ImportError:
+    from webapp.benchmark import run_benchmark, infer_task
+
+# ── Config ─────────────────────────────────────────────────────────────────────
+MAX_FILE_BYTES = int(os.getenv("MAX_FILE_SIZE_MB", "5")) * 1024 * 1024   # default 5 MB
+
+app = FastAPI(title="SAP RPT-1 Benchmarking API", version="1.0.0")
+
+# ── Static files (frontend) ────────────────────────────────────────────────────
+STATIC_DIR = Path(__file__).parent / "static"
+app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
+
+from fastapi.responses import FileResponse
+
+@app.get("/")
+def root():
+    return FileResponse(str(STATIC_DIR / "landing.html"))
+
+@app.get("/arena")
+def arena():
+    return FileResponse(str(STATIC_DIR / "arena.html"))
+
+
+# ── /preview ───────────────────────────────────────────────────────────────────
+@app.post("/preview")
+async def preview(file: UploadFile = File(...)):
+    """
+    Return column names + first 5 rows of the uploaded CSV.
+    Used by the frontend to let the user pick the target column.
+    """
+    content = await file.read()
+    if len(content) > MAX_FILE_BYTES:
+        raise HTTPException(413, f"File too large. Max size is {MAX_FILE_BYTES // (1024*1024)} MB.")
+
+    try:
+        df = pd.read_csv(io.BytesIO(content))
+    except Exception as e:
+        raise HTTPException(400, f"Could not parse CSV: {e}")
+
+    if df.shape[1] < 2:
+        raise HTTPException(400, "CSV must have at least 2 columns (features + target).")
+
+    # Guess default target: last column
+    default_target = df.columns[-1]
+
+    return JSONResponse({
+        "columns":        list(df.columns),
+        "default_target": default_target,
+        "n_rows":         len(df),
+        "n_cols":         df.shape[1],
+        "preview":        df.head(5).fillna("").to_dict("records"),
+    })
+
+
+
+# ── Live Prediction Wrappers ──────────────────────────────────────────────────
+import numpy as np
+
+class LiveVotingEnsemble:
+    def __init__(self, names, builders, task):
+        self.models = [(n, builders[n](task)) for n in names]
+        self.task = task
+    def fit(self, X, y):
+        for _, m in self.models: m.fit(X, y)
+    def predict(self, X):
+        if self.task == "regression":
+            preds = [m.predict(X).ravel()[0] for _, m in self.models]
+            return np.array([np.mean(preds)])
+        
+        # Classification
+        try:
+            proba = self.predict_proba(X)
+            return np.argmax(proba, axis=1)
+        except:
+            preds = [int(m.predict(X).ravel()[0]) for _, m in self.models]
+            return np.array([np.bincount(preds).argmax()])
+
+    def predict_proba(self, X):
+        all_probas = []
+        for _, m in self.models:
+            try:
+                p = m.predict_proba(X)
+                all_probas.append(p)
+            except:
+                # Fallback: one-hot from prediction
+                pred = int(m.predict(X).ravel()[0])
+                # We'll use a 100-wide array just to be safe, or 
+                # ideally we'd know n_classes. For the playground, 
+                # the RAVEL logic in /predict handles the cleanup.
+                oh = np.zeros((1, 100))
+                if pred < 100: oh[0, pred] = 1.0
+                all_probas.append(oh)
+        
+        # Average only if we have consistent shapes
+        return np.mean(all_probas, axis=0)
+
+class LiveStackingEnsemble:
+    def __init__(self, names, builders, task):
+        from sklearn.ensemble import StackingClassifier, StackingRegressor
+        from sklearn.linear_model import LogisticRegression, Ridge
+        estimators = [(n, builders[n](task)) for n in names]
+        if task == "classification":
+            self.model = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression(), cv=3)
+        else:
+            self.model = StackingRegressor(estimators=estimators, final_estimator=Ridge(), cv=3)
+    def fit(self, X, y):
+        self.model.fit(X, y)
+    def predict(self, X):
+        res = self.model.predict(X)
+        return res.reshape(1, -1) if res.ndim == 1 else res
+    def predict_proba(self, X):
+        return self.model.predict_proba(X)
+
+# ── Live Prediction Cache ──────────────────────────────────────────────────────
+CHAMPION_MODEL = None
+CHAMPION_INFO  = {"name": None, "task": None, "features": []}
+
+@app.post("/benchmark")
+async def benchmark(
+    file:       UploadFile = File(...),
+    target_col: str        = Form(...),
+):
+    global CHAMPION_MODEL, CHAMPION_INFO
+    content = await file.read()
+    if len(content) > MAX_FILE_BYTES:
+        raise HTTPException(413, f"File too large. Max {MAX_FILE_BYTES // (1024*1024)} MB.")
+
+    try:
+        df = pd.read_csv(io.BytesIO(content))
+    except Exception as e:
+        raise HTTPException(400, f"Could not parse CSV: {e}")
+
+    if target_col not in df.columns:
+        raise HTTPException(400, f"Column '{target_col}' not found.")
+
+    try:
+        result = run_benchmark(df, target_col)
+        
+        # Deep-sanitize the result to ensure 100% JSON compatibility
+        def sanitize(obj):
+            if isinstance(obj, dict):
+                return {k: sanitize(v) for k, v in obj.items()}
+            elif isinstance(obj, list):
+                return [sanitize(v) for v in obj]
+            elif hasattr(obj, "item"): # Handle numpy scalars
+                return obj.item()
+            elif isinstance(obj, np.bool_):
+                return bool(obj)
+            return obj
+
+        result = sanitize(result)
+        
+        # Add explicit feature types for the playground UI
+        feature_types = {}
+        for col in df.columns:
+            if col == target_col: continue
+            if pd.api.types.is_numeric_dtype(df[col]):
+                feature_types[col] = "numeric"
+            else:
+                feature_types[col] = "categorical"
+        result["dataset_info"]["feature_types"] = feature_types
+        
+        # Cache the Best Overall model for the Live Playground
+        best_name = result["recommendation"]["recommendations"]["best_overall"]["model"]
+        from benchmark import BUILDERS, _prep, _encode_target
+        X = df.drop(columns=[target_col])
+        y_raw = df[target_col]
+        task = result["dataset_info"]["task"]
+        y, le = _encode_target(y_raw, task)
+        
+        # Capture the final encoders from the full dataset
+        X_p, feat_encoders = _prep(X)
+        
+        if best_name == "Voting Ensemble":
+            comp_names = result["ensemble_info"]["Voting Ensemble"]["components"]
+            CHAMPION_MODEL = LiveVotingEnsemble(comp_names, BUILDERS, task)
+            CHAMPION_MODEL.fit(X_p, y)
+        elif best_name == "Stacking Ensemble":
+            comp_names = result["ensemble_info"]["Stacking Ensemble"]["components"]
+            CHAMPION_MODEL = LiveStackingEnsemble(comp_names, BUILDERS, task)
+            CHAMPION_MODEL.fit(X_p, y)
+        else:
+            builder = BUILDERS.get(best_name)
+            if builder:
+                CHAMPION_MODEL = builder(task)
+                CHAMPION_MODEL.fit(X_p, y)
+            
+        CHAMPION_INFO = {
+            "name": best_name,
+            "task": task,
+            "features": list(X.columns),
+            "labels": list(le.classes_) if le else None,
+            "encoders": feat_encoders  # Store these for the /predict endpoint!
+        }
+            
+    except Exception as e:
+        raise HTTPException(500, f"Benchmarking failed: {e}")
+
+    return JSONResponse(result)
+
+
+@app.post("/predict")
+async def predict(data: dict):
+    """
+    Get a live prediction from the cached champion model.
+    """
+    global CHAMPION_MODEL, CHAMPION_INFO
+    if not CHAMPION_MODEL:
+        raise HTTPException(400, "No champion model loaded. Run a benchmark first.")
+    
+    try:
+        # Convert input dict to DataFrame
+        input_df = pd.DataFrame([data])
+        # Ensure column order matches training
+        input_df = input_df[CHAMPION_INFO["features"]]
+        
+        from benchmark import _prep
+        # Use the EXACT same encoders that were used during training
+        X_test, _ = _prep(input_df, encoders=CHAMPION_INFO.get("encoders"))
+        
+        if CHAMPION_INFO["task"] == "classification":
+            raw_pred = CHAMPION_MODEL.predict(X_test)
+            # Flatten if nested (CatBoost/Sklearn sometimes return [[val]] or [val])
+            pred_val = raw_pred.ravel()[0]
+            pred_idx = int(pred_val)
+            
+            label = CHAMPION_INFO["labels"][pred_idx] if CHAMPION_INFO["labels"] and pred_idx < len(CHAMPION_INFO["labels"]) else str(pred_idx)
+            
+            try:
+                proba_raw = CHAMPION_MODEL.predict_proba(X_test)
+                proba = proba_raw.ravel().tolist()
+                # Ensure we only return as many probabilities as we have labels
+                if CHAMPION_INFO["labels"] and len(proba) > len(CHAMPION_INFO["labels"]):
+                    proba = proba[:len(CHAMPION_INFO["labels"])]
+            except:
+                proba = None
+            return {
+                "prediction": label, 
+                "probabilities": proba, 
+                "labels": CHAMPION_INFO["labels"]
+            }
+        else:
+            raw_pred = CHAMPION_MODEL.predict(X_test)
+            pred = float(raw_pred.ravel()[0])
+            return {"prediction": pred}
+            
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return JSONResponse({"error": str(e)}, status_code=400)

webapp/requirements.txt

@@ -0,0 +1,12 @@
+fastapi>=0.110.0
+uvicorn[standard]>=0.29.0
+python-multipart>=0.0.9
+python-dotenv>=1.0.0
+xgboost>=2.0.0
+lightgbm>=4.0.0
+catboost>=1.2.0
+scikit-learn>=1.3.0
+pandas>=2.0.0
+numpy>=1.24.0
+tabpfn>=7.1.1
+huggingface_hub

webapp/static/app.js

@@ -0,0 +1,861 @@
+// Constants & Configuration
+const MODEL_COLORS = {
+  "XGBoost":           "#f59e0b",
+  "LightGBM":          "#10b981",
+  "CatBoost":          "#6366f1",
+  "TabPFN":            "#3b82f6",
+  "SAP RPT-1 OSS":     "#ec4899",
+  "Voting Ensemble":   "#fbbf24",
+  "Stacking Ensemble": "#a78bfa",
+};
+
+const MODEL_EMOJIS = {
+  "XGBoost":           "🟡",
+  "LightGBM":          "🟢",
+  "CatBoost":          "🟣",
+  "TabPFN":            "🟦",
+  "SAP RPT-1 OSS":     "🩷",
+  "Voting Ensemble":   "🏆",
+  "Stacking Ensemble": "✨",
+};
+
+const ENSEMBLE_NAMES = ["Voting Ensemble", "Stacking Ensemble"];
+
+// DOM Elements
+const dropZone       = document.getElementById("drop-zone");
+const fileInput      = document.getElementById("file-input");
+const uploadError    = document.getElementById("upload-error");
+const uploadSection  = document.getElementById("upload-section");
+const previewSection = document.getElementById("preview-section");
+const previewMeta    = document.getElementById("preview-meta");
+const targetSelect   = document.getElementById("target-select");
+const previewTable   = document.getElementById("preview-table");
+const changeFileBtn  = document.getElementById("change-file-btn");
+const runBtn         = document.getElementById("run-btn");
+const loadingSection = document.getElementById("loading-section");
+const resultsSection = document.getElementById("results-section");
+const resetBtn       = document.getElementById("reset-btn");
+const exportCsvBtn   = document.getElementById("export-csv-btn");
+const exportJsonBtn  = document.getElementById("export-json-btn");
+
+const resumeSection  = document.getElementById("resume-section");
+const resumeFilename = document.getElementById("resume-filename");
+const resumeClearBtn = document.getElementById("resume-clear-btn");
+const resumeGoBtn    = document.getElementById("resume-go-btn");
+
+let currentFile = null;
+let chartInstances = [];
+
+// Drag & Drop Handling
+if (dropZone) {
+  dropZone.addEventListener("click", () => fileInput.click());
+  dropZone.addEventListener("keydown", e => { if (e.key === "Enter" || e.key === " ") fileInput.click(); });
+
+  dropZone.addEventListener("dragover", e => { e.preventDefault(); dropZone.classList.add("drag-over"); });
+  dropZone.addEventListener("dragleave",  () => dropZone.classList.remove("drag-over"));
+  dropZone.addEventListener("drop", e => {
+    e.preventDefault();
+    dropZone.classList.remove("drag-over");
+    const f = e.dataTransfer.files[0];
+    if (f) handleFile(f);
+  });
+}
+
+if (fileInput) {
+  fileInput.addEventListener("change", () => {
+    if (fileInput.files[0]) handleFile(fileInput.files[0]);
+  });
+}
+
+if (changeFileBtn) changeFileBtn.addEventListener("click", resetToUpload);
+if (resetBtn) resetBtn.addEventListener("click", resetToUpload);
+
+if (exportCsvBtn) exportCsvBtn.addEventListener("click", () => {
+  const data = JSON.parse(sessionStorage.getItem("lastResults"));
+  if (data) exportToCSV(data);
+});
+
+if (exportJsonBtn) exportJsonBtn.addEventListener("click", () => {
+  const data = JSON.parse(sessionStorage.getItem("lastResults"));
+  if (data) exportToJSON(data);
+});
+
+if (resumeClearBtn) resumeClearBtn.addEventListener("click", () => {
+  sessionStorage.removeItem("lastResults");
+  sessionStorage.removeItem("lastFileName");
+  window.location.reload();
+});
+
+if (resumeGoBtn) resumeGoBtn.addEventListener("click", () => {
+  window.location.href = "/static/arena.html";
+});
+
+// File selection and preview initialization
+async function handleFile(file) {
+  uploadError.hidden = true;
+
+  if (!file.name.endsWith(".csv")) {
+    showError("Please upload a .csv file.");
+    return;
+  }
+
+  const MAX_MB = 5;
+  if (file.size > MAX_MB * 1024 * 1024) {
+    showError(`File is too large (${(file.size / 1048576).toFixed(1)} MB). Maximum is ${MAX_MB} MB.`);
+    return;
+  }
+
+  currentFile = file;
+
+  const fd = new FormData();
+  fd.append("file", file);
+
+  try {
+    const res = await fetch("/preview", { method: "POST", body: fd });
+    if (!res.ok) {
+      const err = await res.json();
+      showError(err.detail || "Failed to read CSV.");
+      return;
+    }
+    const data = await res.json();
+    renderPreview(data, file);
+  } catch (e) {
+    showError("Network error: " + e.message);
+  }
+}
+
+function renderPreview(data, file) {
+  // Meta badges
+  previewMeta.innerHTML = `
+    <span class="meta-badge">📄 ${file.name}</span>
+    <span class="meta-badge">${data.n_rows.toLocaleString()} rows</span>
+    <span class="meta-badge">${data.n_cols} columns</span>
+  `;
+
+  // Target column selector
+  targetSelect.innerHTML = "";
+  data.columns.forEach(col => {
+    const opt = document.createElement("option");
+    opt.value = col;
+    opt.textContent = col;
+    if (col === data.default_target) opt.selected = true;
+    targetSelect.appendChild(opt);
+  });
+
+  // Preview table
+  const cols = data.columns;
+  let thead = "<thead><tr>" + cols.map(c => `<th class="${c === data.default_target ? 'target-col' : ''}">${esc(c)}</th>`).join("") + "</tr></thead>";
+  let tbody = "<tbody>" + data.preview.map(row =>
+    "<tr>" + cols.map(c => `<td class="${c === data.default_target ? 'target-col' : ''}">${esc(String(row[c] ?? ""))}</td>`).join("") + "</tr>"
+  ).join("") + "</tbody>";
+  previewTable.innerHTML = thead + tbody;
+
+  // Highlight target column on select change
+  targetSelect.addEventListener("change", () => highlightTarget(targetSelect.value, cols));
+
+  uploadSection.hidden = true;
+  previewSection.hidden = false;
+}
+
+function highlightTarget(targetCol, cols) {
+  previewTable.querySelectorAll("th, td").forEach(el => el.classList.remove("target-col"));
+  const idx = cols.indexOf(targetCol);
+  if (idx < 0) return;
+  previewTable.querySelectorAll("tr").forEach(row => {
+    const cells = row.querySelectorAll("th, td");
+    if (cells[idx]) cells[idx].classList.add("target-col");
+  });
+}
+
+// Execute benchmarking suite
+if (runBtn) {
+  runBtn.addEventListener("click", async () => {
+  if (!currentFile) return;
+
+  previewSection.hidden = true;
+  loadingSection.hidden = false;
+
+  // Animate loader steps
+  const steps = ["step-xgb", "step-lgb", "step-cat", "step-tabpfn", "step-sap", "step-vote", "step-stack"];
+  const delays = [0, 150, 300, 450, 600, 750, 900];
+  let stepIdx = 0;
+  const stepTimer = setInterval(() => {
+    if (stepIdx > 0) {
+      document.getElementById(steps[stepIdx - 1])?.classList.remove("active");
+      document.getElementById(steps[stepIdx - 1])?.classList.add("done");
+    }
+    if (stepIdx < steps.length) {
+      document.getElementById(steps[stepIdx])?.classList.add("active");
+      stepIdx++;
+    } else {
+      clearInterval(stepTimer);
+    }
+  }, 1400);
+
+  const fd = new FormData();
+  fd.append("file", currentFile);
+  fd.append("target_col", targetSelect.value);
+
+  try {
+    const res = await fetch("/benchmark", { method: "POST", body: fd });
+    if (!res.ok) {
+      const err = await res.json();
+      clearInterval(stepTimer);
+      loadingSection.hidden = true;
+      previewSection.hidden = false;
+      showError(err.detail || "Benchmarking failed.");
+      return;
+    }
+    const data = await res.json();
+    clearInterval(stepTimer);
+    loadingSection.hidden = true;
+    sessionStorage.setItem("lastResults", JSON.stringify(data));
+    sessionStorage.setItem("lastFileName", currentFile.name);
+    window.location.href = "/static/arena.html";
+  } catch (e) {
+    clearInterval(stepTimer);
+    loadingSection.hidden = true;
+    previewSection.hidden = false;
+    showError("Network error: " + e.message);
+  }
+});
+}
+
+// Visualization of benchmarking results
+function renderResults(data) {
+  const { dataset_info, task, results, recommendation, n_folds } = data;
+  const isCLF = task === "classification";
+  const primaryKey = isCLF ? "roc_auc" : "r2";
+  const primaryLabel = isCLF ? "ROC-AUC" : "R²";
+
+  const fileName = sessionStorage.getItem("lastFileName") || "Dataset";
+
+  // ── Info bar
+  const taskBadge = isCLF
+    ? `<span class="info-tag">🏷 Classification</span>`
+    : `<span class="info-tag green">📈 Regression</span>`;
+  document.getElementById("info-bar").innerHTML = `
+    <span class="info-tag">📄 ${esc(fileName)}</span>
+    ${taskBadge}
+    <span class="info-tag">${dataset_info.n_samples.toLocaleString()} samples</span>
+    <span class="info-tag">${dataset_info.n_features} features</span>
+    <span class="info-tag">Target: <strong>${esc(dataset_info.target_col)}</strong></span>
+    ${isCLF ? `<span class="info-tag pink">${dataset_info.n_classes} classes</span>` : ""}
+    <span class="info-tag">${n_folds}-Fold CV</span>
+  `;
+
+  // ── KPI cards
+  const kpiGrid = document.getElementById("kpi-grid");
+  kpiGrid.innerHTML = "";
+
+  const validModels = Object.entries(results).filter(([, v]) => !v.error);
+  const bestEntry   = validModels.reduce((best, [name, v]) =>
+    (v.mean[primaryKey] || 0) > (best[1].mean[primaryKey] || 0) ? [name, v] : best
+  , validModels[0]);
+
+  const kpis = [
+    {
+      label: "Best Model",
+      value: bestEntry[0],
+      sub:   `${primaryLabel}: ${fmt(bestEntry[1].mean[primaryKey])}`,
+      color: MODEL_COLORS[bestEntry[0]],
+    },
+    {
+      label: `Best ${primaryLabel}`,
+      value: fmt(bestEntry[1].mean[primaryKey]),
+      sub:   `± ${fmt(bestEntry[1].std[primaryKey])} std`,
+      color: "#818cf8",
+    },
+    {
+      label: "Models Evaluated",
+      value: validModels.length,
+      sub:   `${n_folds}-fold cross-validation`,
+      color: "#10b981",
+    },
+    {
+      label: "Dataset Size",
+      value: dataset_info.n_samples.toLocaleString(),
+      sub:   `${dataset_info.n_features} features · ${isCLF ? dataset_info.n_classes + " classes" : "regression"}`,
+      color: "#f59e0b",
+    },
+  ];
+
+  kpis.forEach(k => {
+    const card = document.createElement("div");
+    card.className = "kpi-card";
+    card.style.setProperty("--accent-bar", k.color);
+    card.innerHTML = `
+      <div class="kpi-label">${k.label}</div>
+      <div class="kpi-value" style="color:${k.color}">${esc(String(k.value))}</div>
+      <div class="kpi-sub">${k.sub}</div>
+    `;
+    kpiGrid.appendChild(card);
+  });
+
+  // ── Legend
+  const legendEl = document.getElementById("legend");
+  legendEl.innerHTML = Object.entries(MODEL_COLORS).map(([name, color]) =>
+    `<div class="legend-item">
+      <div class="legend-dot" style="background:${color}"></div>
+      <span>${name}</span>
+    </div>`
+  ).join("");
+
+  // ── Charts
+  chartInstances.forEach(c => c.destroy());
+  chartInstances = [];
+  const chartsGrid = document.getElementById("charts-grid");
+  chartsGrid.innerHTML = "";
+
+  const metricsToChart = isCLF
+    ? [["roc_auc", "ROC-AUC"], ["accuracy", "Accuracy"], ["f1_macro", "F1-Macro"]]
+    : [["r2", "R²"], ["mae", "MAE"], ["rmse", "RMSE"]];
+
+  metricsToChart.forEach(([key, label]) => {
+    const modelNames = Object.keys(results).filter(n => !results[n].error && results[n].mean[key] != null);
+    if (!modelNames.length) return;
+
+    const vals  = modelNames.map(n => roundN(results[n].mean[key], 4));
+    const errs  = modelNames.map(n => roundN(results[n].std[key] || 0, 4));
+    const bgs   = modelNames.map(n => (MODEL_COLORS[n] || "#888") + "cc");
+    const bords = modelNames.map(n => MODEL_COLORS[n] || "#888");
+
+    const isErrorMetric = ["mae", "rmse", "log_loss"].includes(key.toLowerCase());
+    const highQual = isErrorMetric ? "poor" : "excellent";
+    const lowQual  = isErrorMetric ? "excellent" : "poor";
+
+    const card = document.createElement("div");
+    card.className = "chart-card";
+    const canvasId = `chart-${key}`;
+    card.innerHTML = `
+      <h4>${label}</h4>
+      <div class="chart-sub">${label} (mean ± std over ${n_folds} folds)</div>
+      <canvas id="${canvasId}"></canvas>
+      <div class="chart-interpretation">
+        <div class="interp-item"><span>High ${label} = </span> <span class="badge ${highQual}">${highQual}</span></div>
+        <div class="interp-item"><span>Low ${label} = </span> <span class="badge ${lowQual}">${lowQual}</span></div>
+      </div>
+    `;
+    chartsGrid.appendChild(card);
+
+    const minVal = Math.min(...vals);
+    const maxVal = Math.max(...vals);
+    const pad    = Math.max((maxVal - minVal) * 0.15, 0.02);
+
+    const inst = new Chart(document.getElementById(canvasId), {
+      type: "bar",
+      data: {
+        labels: modelNames,
+        datasets: [{
+          label,
+          data: vals,
+          backgroundColor: bgs,
+          borderColor:     bords,
+          borderWidth: 2,
+          borderRadius: 8,
+        }],
+      },
+      options: {
+        responsive: true,
+        plugins: {
+          legend: { display: false },
+          tooltip: {
+            callbacks: {
+              label: ctx => `${label}: ${ctx.parsed.y.toFixed(4)} ± ${errs[ctx.dataIndex].toFixed(4)}`,
+            },
+          },
+        },
+        scales: {
+          y: {
+            min: Math.max(key === "roc_auc" || key === "accuracy" ? 0 : -Infinity, minVal - pad),
+            max: key === "roc_auc" || key === "accuracy" ? Math.min(1, maxVal + pad) : maxVal + pad,
+            grid: { color: "rgba(100, 116, 139, 0.1)" },
+            ticks: { color: "rgba(100, 116, 139, 0.8)", font: { size: 11 } },
+          },
+          x: {
+            grid: { display: false },
+            ticks: { color: "rgba(100, 116, 139, 0.8)", font: { size: 12 } },
+          },
+        },
+      },
+    });
+    chartInstances.push(inst);
+  });
+
+  // ── Full table
+  const thead = document.getElementById("results-thead");
+  const tbody = document.getElementById("results-tbody");
+
+  const allMetrics = isCLF
+    ? ["accuracy", "f1_macro", "roc_auc", "log_loss", "fit_time"]
+    : ["r2", "mae", "rmse", "fit_time"];
+  const metricLabels = isCLF
+    ? ["Accuracy", "F1-Macro", "ROC-AUC", "Log Loss", "Fit Time"]
+    : ["R²", "MAE", "RMSE", "Fit Time"];
+
+  thead.innerHTML = "<tr><th>Model</th>" + metricLabels.map(l => `<th>${l}</th>`).join("") + "</tr>";
+  tbody.innerHTML = Object.entries(results).map(([name, d]) => {
+    if (d.error) {
+      const errText = d.error.startsWith("Error:") ? d.error : `Error: ${d.error}`;
+      return `<tr><td><span class="model-dot" style="background:${MODEL_COLORS[name] || '#888'}"></span>${name}</td><td colspan="${allMetrics.length}" style="color:#f87171">${esc(errText)}</td></tr>`;
+    }
+    const cells = allMetrics.map(k => {
+      const v = d.mean[k];
+      if (v == null) return `<td class="mono" style="color:#374151">—</td>`;
+      const isTime = k === "fit_time";
+      if (isTime) return `<td class="mono" style="color:#94a3b8">${v.toFixed(3)}s</td>`;
+      const cls = scoreClass(v, k, task);
+      return `<td class="mono ${cls}">${v.toFixed(4)}<span style="color:#374151;font-size:.7em"> ±${(d.std[k]||0).toFixed(3)}</span></td>`;
+    }).join("");
+    return `<tr><td><span class="model-dot" style="background:${MODEL_COLORS[name] || '#888'}"></span><strong>${name}</strong></td>${cells}</tr>`;
+  }).join("");
+
+  // ── Recommendations
+  const recGrid = document.getElementById("recommendation-grid");
+  recGrid.innerHTML = "";
+  const recs = recommendation.recommendations || {};
+  const recDefs = [
+    { key: "best_overall",      label: "🏆 Best Overall",      winner: true  },
+    { key: "production",        label: "🚀 Production Ready",  winner: false },
+    { key: "best_accuracy",     label: "🎯 Highest Accuracy",  winner: false },
+    { key: "best_speed",        label: "⚡ Fastest Training",  winner: false },
+    { key: "best_consistency",  label: "🛡 Most Consistent",   winner: false },
+  ];
+
+  recDefs.forEach(({ key, label, winner }) => {
+    const rec = recs[key];
+    if (!rec) return;
+    const color = MODEL_COLORS[rec.model] || "#888";
+    const score = rec.score != null
+      ? `<div class="rec-score">${recommendation.primary_metric}: ${typeof rec.score === "number" ? rec.score.toFixed(4) : rec.score}</div>`
+      : "";
+    const card = document.createElement("div");
+    card.className = `rec-card ${key}${winner ? " winner" : ""}`;
+    card.innerHTML = `
+      <div class="rec-type">${label}</div>
+      <div class="rec-model-name">
+        ${winner ? '<span class="rec-trophy">🏆</span>' : ""}
+        <span style="color:${color}">${rec.model}</span>
+      </div>
+      ${score}
+      <p class="rec-reason">${esc(rec.reason)}</p>
+    `;
+    recGrid.appendChild(card);
+  });
+
+  // ── Ensemble Analysis section
+  renderEnsembleSection(data.ensemble_info || {}, results, recommendation, task);
+
+  // ── Interactive Playground
+  renderPlayground(data.dataset_info, recommendation.recommendations?.best_overall, task);
+
+  // ── Statistical Rigor
+  renderStatisticalSection(data.stats || {});
+
+  resultsSection.hidden = false;
+  resultsSection.scrollIntoView({ behavior: "smooth", block: "start" });
+}
+
+function renderStatisticalSection(stats) {
+  const tbody = document.getElementById("rigor-tbody");
+  const badge = document.getElementById("friedman-badge");
+  if (!tbody || !stats.ranking) return;
+
+  const isSig = stats.significant;
+  badge.className = `p-value-badge ${isSig ? 'significant' : 'not-significant'}`;
+  badge.textContent = isSig 
+    ? `Significant (p=${stats.friedman_p})` 
+    : `Not Significant (p=${stats.friedman_p})`;
+
+  tbody.innerHTML = stats.ranking.map(r => {
+    const stability = r.win_rate;
+    return `
+      <tr>
+        <td>
+          <span class="rank-pill ${r.avg_rank <= 1.5 ? 'rank-1' : ''}" style="${r.avg_rank > 1.5 ? 'background: transparent; box-shadow: none;' : ''}">${r.avg_rank <= 1.5 ? '🏆' : ''}</span>
+          <strong>${r.model}</strong>
+        </td>
+        <td class="mono">${r.avg_rank}</td>
+        <td>
+          <div class="stability-bar">
+            <div class="stability-fill" style="width: ${stability}%"></div>
+          </div>
+          <span class="mono">${stability}%</span>
+        </td>
+        <td>
+          <span class="badge ${stability > 50 ? 'excellent' : (stability > 20 ? 'neutral' : 'poor')}">
+            ${stability > 50 ? 'Dominant' : (stability > 20 ? 'Competitive' : 'Volatile')}
+          </span>
+        </td>
+      </tr>
+    `;
+  }).join("");
+}
+
+// ── Playground Logic ──────────────────────────────────────────────────────────
+function renderPlayground(datasetInfo, bestOverall, task) {
+  const form = document.getElementById("playground-form");
+  const valueEl = document.getElementById("prediction-value");
+  const subEl   = document.getElementById("prediction-sub");
+  const probEl  = document.getElementById("probability-bars");
+
+  if (!form || !bestOverall) return;
+  form.innerHTML = "";
+  
+  const features = datasetInfo.columns || [];
+  const preview = datasetInfo.preview ? datasetInfo.preview[0] : {};
+
+  features.forEach(f => {
+    const div = document.createElement("div");
+    div.className = "playground-field";
+    
+    const types = datasetInfo.feature_types || {};
+    const isNumeric = types[f] === "numeric";
+    
+    const sampleVal = preview[f];
+    const val = sampleVal != null ? sampleVal : (isNumeric ? 0 : "");
+    const placeholder = isNumeric ? "Enter value..." : "Enter text...";
+    
+    div.innerHTML = `
+      <label>${f.replace(/_/g, " ")}</label>
+      <input type="text" 
+             data-feature="${f}" 
+             value="${val}" 
+             placeholder="${placeholder}"
+             onclick="this.select()">
+    `;
+    form.appendChild(div);
+  });
+
+  const updatePrediction = async () => {
+    const inputs = form.querySelectorAll("input");
+    const data = {};
+    inputs.forEach(i => {
+      const v = i.value;
+      data[i.dataset.feature] = isNaN(parseFloat(v)) ? v : parseFloat(v);
+    });
+
+    valueEl.style.opacity = "0.5";
+    try {
+      const resp = await fetch("/predict", {
+        method: "POST",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify(data)
+      });
+      const res = await resp.json();
+      
+      valueEl.style.opacity = "1";
+      if (res.error) {
+        valueEl.textContent = "Error";
+        subEl.textContent = res.error;
+        return;
+      }
+
+      if (task === "classification") {
+        valueEl.textContent = res.prediction || "—";
+        subEl.textContent = `Most likely class (via ${bestOverall.model})`;
+        
+        if (res.probabilities && res.labels) {
+          probEl.innerHTML = res.probabilities.map((p, i) => `
+            <div class="prob-row">
+              <div class="prob-meta"><span>${res.labels[i] || 'Class '+i}</span><span>${(p*100).toFixed(1)}%</span></div>
+              <div class="prob-bar-bg"><div class="prob-bar-fill" style="width:${p*100}%"></div></div>
+            </div>
+          `).join("");
+        }
+      } else {
+        const val = Number(res.prediction);
+        valueEl.textContent = isNaN(val) ? "—" : val.toFixed(4);
+        subEl.textContent = `Regression output (via ${bestOverall.model})`;
+        probEl.innerHTML = "";
+      }
+    } catch (e) {
+      valueEl.style.opacity = "1";
+      valueEl.textContent = "Error";
+      subEl.textContent = "Service unavailable";
+    }
+  };
+
+  form.addEventListener("input", debounce(updatePrediction, 300));
+  updatePrediction(); // Initial prediction
+}
+
+function debounce(fn, ms) {
+  let timeout;
+  return (...args) => {
+    clearTimeout(timeout);
+    timeout = setTimeout(() => fn.apply(this, args), ms);
+  };
+}
+
+// ── Ensemble Analysis renderer ────────────────────────────────────────────────
+function renderEnsembleSection(ensembleInfo, results, recommendation, task) {
+  const grid  = document.getElementById("ensemble-grid");
+  const title = document.getElementById("ensemble-section-title");
+  grid.innerHTML = "";
+
+  const entries = Object.entries(ensembleInfo).filter(([name]) => results[name] && !results[name].error);
+  if (!entries.length) {
+    title.hidden = true;
+    grid.hidden  = true;
+    return;
+  }
+  title.hidden = false;
+  grid.hidden  = false;
+
+  const primaryKey   = task === "classification" ? "roc_auc" : "r2";
+  const primaryLabel = task === "classification" ? "ROC-AUC" : "R²";
+
+  // Find the best individual model score (excluding ensembles) for gain %
+  const indivScores = Object.entries(results)
+    .filter(([n, v]) => !ENSEMBLE_NAMES.includes(n) && !v.error && v.mean[primaryKey] != null)
+    .map(([, v]) => v.mean[primaryKey]);
+  const bestIndivScore = indivScores.length ? Math.max(...indivScores) : 0;
+
+  entries.forEach(([name, info]) => {
+    const cv    = results[name];
+    const score = cv.mean[primaryKey] ?? 0;
+    const std   = cv.std[primaryKey]  ?? 0;
+    const ft    = cv.mean.fit_time    ?? 0;
+    const color = MODEL_COLORS[name] || "#888";
+    const gain  = bestIndivScore > 0 ? ((score - bestIndivScore) / bestIndivScore * 100) : 0;
+    const gainStr = gain >= 0
+      ? `<span class="gain-pos">▲ +${gain.toFixed(2)}% vs best individual</span>`
+      : `<span class="gain-neg">▼ ${gain.toFixed(2)}% vs best individual</span>`;
+
+    const componentPills = (info.components || []).map(c =>
+      `<span class="comp-pill" style="border-color:${MODEL_COLORS[c] || '#888'};color:${MODEL_COLORS[c] || '#888'}">${c}</span>`
+    ).join("");
+
+    const metaTag = info.meta_learner
+      ? `<div class="ens-meta">Meta-learner: <strong>${esc(info.meta_learner)}</strong></div>` : "";
+
+    const card = document.createElement("div");
+    card.className = "ens-card";
+    card.style.setProperty("--ens-color", color);
+    card.innerHTML = `
+      <div class="ens-header">
+        <span class="ens-emoji">${MODEL_EMOJIS[name] || "🧩"}</span>
+        <span class="ens-name" style="color:${color}">${name}</span>
+        <span class="ens-type-badge">${info.type === "voting" ? "Soft Voting" : "Stacking"}</span>
+      </div>
+      <div class="ens-score">
+        <span class="ens-score-val">${score.toFixed(4)}</span>
+        <span class="ens-score-label"> ${primaryLabel} ± ${std.toFixed(3)}</span>
+      </div>
+      <div class="ens-gain">${gainStr}</div>
+      ${metaTag}
+      <div class="ens-desc">${esc(info.description || "")}</div>
+      <div class="ens-components-label">Component Models</div>
+      <div class="ens-components">${componentPills}</div>
+      <div class="ens-footer">Avg fit time: ${ft.toFixed(3)}s per fold</div>
+    `;
+    grid.appendChild(card);
+  });
+}
+
+
+
+// ── Helpers ───────────────────────────────────────────────────────────────────
+function resetToUpload() {
+  currentFile = null;
+  if (fileInput) fileInput.value = "";
+  if (uploadError) uploadError.hidden = true;
+  if (previewSection) previewSection.hidden = true;
+  if (loadingSection) loadingSection.hidden = true;
+  if (resultsSection) resultsSection.hidden = true;
+  if (uploadSection) uploadSection.hidden = false;
+  chartInstances.forEach(c => c.destroy());
+  chartInstances = [];
+  sessionStorage.removeItem("lastResults");
+  sessionStorage.removeItem("lastFileName");
+  if (window.location.pathname.includes("arena.html")) {
+    window.location.href = "/static/uploader.html";
+  } else {
+    window.scrollTo({ top: 0, behavior: "smooth" });
+  }
+}
+
+function showError(msg) {
+  if (!uploadError) return;
+  uploadError.textContent = msg;
+  uploadError.hidden = false;
+  window.scrollTo({ top: 0, behavior: "smooth" });
+}
+
+function exportToCSV(data) {
+  const results = data.results;
+  const models = Object.keys(results);
+  if (models.length === 0) return;
+  
+  const metricKeys = new Set();
+  models.forEach(m => {
+    if (results[m].mean) {
+      Object.keys(results[m].mean).forEach(k => metricKeys.add(k));
+    }
+  });
+  const metrics = Array.from(metricKeys).sort();
+  
+  let csv = "Model," + metrics.map(m => m + " (mean)").join(",") + "\n";
+  models.forEach(m => {
+    if (results[m].error) {
+      const errText = results[m].error.startsWith("Error:") ? results[m].error : `Error: ${results[m].error}`;
+      csv += `${m.replace(/,/g, "")},${errText.replace(/,/g, " ")}\n`;
+      return;
+    }
+    let row = [m.replace(/,/g, "")];
+    metrics.forEach(met => {
+      let val = results[m].mean ? results[m].mean[met] : "";
+      row.push(val !== undefined && val !== null ? val : "");
+    });
+    csv += row.join(",") + "\n";
+  });
+  
+  downloadFile(csv, "benchmark_results.csv", "text/csv");
+}
+
+function exportToJSON(data) {
+  const json = JSON.stringify(data, null, 2);
+  downloadFile(json, "benchmark_results.json", "application/json");
+}
+
+function downloadFile(content, fileName, contentType) {
+  const blob = new Blob([content], { type: contentType });
+  const url = URL.createObjectURL(blob);
+  const a = document.createElement("a");
+  a.href = url;
+  a.download = fileName;
+  a.click();
+  setTimeout(() => URL.revokeObjectURL(url), 100);
+}
+
+function fmt(v) {
+  if (v == null || isNaN(v)) return "—";
+  return Number(v).toFixed(4);
+}
+
+function roundN(v, n) {
+  return Math.round(v * Math.pow(10, n)) / Math.pow(10, n);
+}
+
+function esc(str) {
+  return String(str)
+    .replace(/&/g, "&amp;")
+    .replace(/</g, "&lt;")
+    .replace(/>/g, "&gt;")
+    .replace(/"/g, "&quot;");
+}
+
+function scoreClass(v, metric, task) {
+  if (metric === "fit_time") return "";
+  const higherBetter = !["mae", "rmse", "mse", "log_loss"].includes(metric);
+  if (!higherBetter) {
+    if (v < 0.1)  return "col-excellent";
+    if (v < 0.3)  return "col-good";
+    if (v < 0.5)  return "col-fair";
+    return "col-poor";
+  }
+  if (metric === "roc_auc" || metric === "accuracy") {
+    if (v >= 0.95) return "col-excellent";
+    if (v >= 0.88) return "col-good";
+    if (v >= 0.75) return "col-fair";
+    return "col-poor";
+  }
+  if (metric === "r2") {
+    if (v >= 0.75) return "col-excellent";
+    if (v >= 0.5)  return "col-good";
+    if (v >= 0.25) return "col-fair";
+    return "col-poor";
+  }
+  if (v >= 0.85) return "col-excellent";
+  if (v >= 0.70) return "col-good";
+  if (v >= 0.55) return "col-fair";
+  return "col-poor";
+}
+
+// ── Restore state on load ────────────────────────────────────────────────────
+window.addEventListener("DOMContentLoaded", () => {
+  checkResumeState();
+});
+
+// ── Handle Back Button (BFCache) ──────────────────────────────────────────────
+window.addEventListener("pageshow", function(e) {
+  checkResumeState();
+});
+
+// Theme Toggle Logic
+const themeToggle = document.getElementById("theme-toggle");
+const themeIconDark = document.getElementById("theme-icon-dark");
+const themeIconLight = document.getElementById("theme-icon-light");
+
+function setTheme(theme) {
+  document.documentElement.setAttribute("data-theme", theme);
+  localStorage.setItem("theme", theme);
+  if (theme === "light") {
+    if (themeIconDark) themeIconDark.style.display = "block";
+    if (themeIconLight) themeIconLight.style.display = "none";
+  } else {
+    if (themeIconDark) themeIconDark.style.display = "none";
+    if (themeIconLight) themeIconLight.style.display = "block";
+  }
+}
+
+if (themeToggle) {
+  themeToggle.addEventListener("click", () => {
+    const current = document.documentElement.getAttribute("data-theme") || "dark";
+    setTheme(current === "dark" ? "light" : "dark");
+  });
+}
+
+// Initial theme load
+const savedTheme = localStorage.getItem("theme") || "dark";
+setTheme(savedTheme);
+
+function checkResumeState() {
+  const savedResults = sessionStorage.getItem("lastResults");
+  const savedFile = sessionStorage.getItem("lastFileName");
+  const isUploader = window.location.pathname.includes("uploader.html") || window.location.pathname === "/";
+  const isArena = window.location.pathname.includes("arena.html");
+
+  // Handle MBench logo link privilege
+  const navLogo = document.getElementById("nav-logo");
+  if (navLogo) {
+    // Privilege: Only uploader page in fresh mode (no results) can go to landing
+    if (isUploader && !savedResults) {
+      navLogo.classList.add("active-link");
+      navLogo.style.pointerEvents = "auto";
+    } else {
+      navLogo.classList.remove("active-link");
+      navLogo.style.pointerEvents = "none";
+    }
+  }
+
+  if (savedResults && savedFile) {
+    if (isUploader) {
+      // Always show resume card if data exists, until cleared
+      if (uploadSection) uploadSection.hidden = true;
+      if (previewSection) previewSection.hidden = true;
+      if (loadingSection) loadingSection.hidden = true;
+      if (resumeSection) {
+        resumeSection.hidden = false;
+        resumeFilename.textContent = savedFile;
+      }
+    } else if (isArena) {
+      // Auto-render on results page if data exists
+      try {
+        const data = JSON.parse(savedResults);
+        renderResults(data);
+      } catch (e) {
+        window.location.href = "/static/uploader.html";
+      }
+    }
+  } else {
+    // No saved data: reset to default
+    if (isUploader) {
+      if (resumeSection) resumeSection.hidden = true;
+      if (uploadSection) uploadSection.hidden = false;
+    } else if (isArena) {
+      window.location.href = "/static/uploader.html";
+    }
+  }
+}

webapp/static/arena.html

@@ -0,0 +1,129 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8"/>
+  <meta name="viewport" content="width=device-width,initial-scale=1"/>
+  <title>SAP RPT-1 OSS Benchmarking — Model Arena</title>
+  <meta name="description" content="Upload your CSV and instantly benchmark XGBoost, LightGBM, CatBoost and SAP RPT-1 OSS. Get a detailed model recommendation for your use case."/>
+  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet"/>
+  <link rel="stylesheet" href="/static/style.css?v=2"/>
+  <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.2/dist/chart.umd.min.js"></script>
+</head>
+<body>
+
+<nav class="navbar">
+  <div class="nav-container">
+    <a href="/static/landing.html" class="nav-brand" id="nav-logo">ModelMatrix</a>
+    <div class="nav-actions">
+      <a href="/static/uploader.html" class="nav-btn-upload">Upload</a>
+      <button class="nav-toggle" id="theme-toggle" aria-label="Toggle theme">
+        <svg id="theme-icon-dark" class="theme-icon" viewBox="0 0 24 24" width="20" height="20" fill="none" stroke="currentColor" stroke-width="2" style="display: none;"><path d="M21 12.79A9 9 0 1 1 11.21 3 7 7 0 0 0 21 12.79z"/></svg>
+        <svg id="theme-icon-light" class="theme-icon" viewBox="0 0 24 24" width="20" height="20" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="5"/><path d="M12 1v2m0 18v2M4.22 4.22l1.42 1.42m12.72 12.72l1.42 1.42M1 12h2m18 12h2M4.22 19.78l1.42-1.42m12.72-12.72l1.42-1.42"/></svg>
+      </button>
+    </div>
+  </div>
+</nav>
+
+<main class="container">
+  <section id="results-section" class="section" hidden>
+
+    <!-- Dataset info bar -->
+    <div class="info-bar" id="info-bar"></div>
+
+    <div class="actions-bar">
+      <button id="export-csv-btn" class="btn-ghost">📊 Download CSV</button>
+      <button id="export-json-btn" class="btn-ghost">📋 Export Results (JSON)</button>
+    </div>
+
+    <!-- KPI cards -->
+    <h2 class="section-title">Summary <span class="title-accent">Statistics</span></h2>
+    <div class="kpi-grid" id="kpi-grid"></div>
+
+    <!-- Legend -->
+    <div class="legend" id="legend"></div>
+
+    <!-- Charts -->
+    <h2 class="section-title">Model <span class="title-accent">Comparison</span></h2>
+    <div class="charts-grid" id="charts-grid"></div>
+
+    <!-- Full table -->
+    <h2 class="section-title">Full <span class="title-accent">Metrics Table</span></h2>
+    <div class="table-card">
+      <div class="table-scroll">
+        <table id="results-table" class="results-table">
+          <thead id="results-thead"></thead>
+          <tbody id="results-tbody"></tbody>
+        </table>
+      </div>
+    </div>
+
+    <!-- Recommendation -->
+    <h2 class="section-title">🏆 Model <span class="title-accent">Recommendation</span></h2>
+    <div id="recommendation-grid" class="rec-grid"></div>
+
+    <!-- Ensemble Analysis -->
+    <h2 class="section-title" id="ensemble-section-title">🧩 Ensemble <span class="title-accent">Analysis</span></h2>
+    <div id="ensemble-grid" class="ensemble-grid"></div>
+
+    <!-- Statistical Rigor -->
+    <h2 class="section-title">⚖️ Statistical <span class="title-accent">Rigor & Ranking</span></h2>
+    <div class="rigor-card" id="rigor-section">
+      <div class="rigor-header">
+        <div id="friedman-badge" class="badge-pill">Analyzing significance...</div>
+        <div class="rigor-meta">Based on rank-distribution across all cross-validation folds.</div>
+      </div>
+      <div class="rigor-table-wrapper">
+        <table class="rigor-table">
+          <thead>
+            <tr>
+              <th>Model</th>
+              <th>Average Rank (1 is best)</th>
+              <th>Fold Win Rate</th>
+              <th>Stability</th>
+            </tr>
+          </thead>
+          <tbody id="rigor-tbody">
+            <!-- Injected by JS -->
+          </tbody>
+        </table>
+      </div>
+    </div>
+
+    <!-- Interactive Playground -->
+    <h2 class="section-title">🎮 Interactive <span class="title-accent">Playground</span></h2>
+    <div class="playground-card" id="playground-section">
+      <div class="playground-layout">
+        <div class="playground-inputs">
+          <p class="playground-intro">Adjust the inputs below to get a live prediction from the best-performing model. Changes update instantly — no page reload needed.</p>
+          <div id="playground-form" class="playground-grid">
+            <!-- Inputs injected by JS -->
+          </div>
+        </div>
+        <div class="playground-output">
+          <div class="output-card">
+            <div class="output-label">Live Prediction</div>
+            <div id="prediction-value" class="prediction-main">—</div>
+            <div id="prediction-sub" class="prediction-sub">Select or adjust inputs</div>
+            <div id="probability-bars" class="prob-container"></div>
+          </div>
+        </div>
+      </div>
+    </div>
+
+    <!-- Reset -->
+    <div class="reset-bar">
+      <button id="reset-btn" class="btn-ghost-lg">↩ Upload a New Dataset</button>
+    </div>
+  </section>
+
+
+
+</main>
+
+<footer class="footer">
+  SAP RPT-1 OSS Benchmarking · Built with FastAPI &amp; Chart.js
+</footer>
+
+<script src="/static/app.js?v=2"></script>
+</body>
+</html>

webapp/static/landing.html

@@ -0,0 +1,123 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8"/>
+  <meta name="viewport" content="width=device-width,initial-scale=1"/>
+  <title>SAP RPT-1 OSS Benchmarking — Home</title>
+  <meta name="description" content="Discover the ultimate ML model arena. Benchmark XGBoost, LightGBM, CatBoost, TabPFN, and SAP RPT-1 OSS in seconds."/>
+  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet"/>
+  <link rel="stylesheet" href="/static/style.css?v=2"/>
+</head>
+<body>
+
+<nav class="navbar">
+  <div class="nav-container">
+    <a href="/static/landing.html" class="nav-brand" id="nav-logo">ModelMatrix</a>
+    <div class="nav-actions">
+      <a href="/static/uploader.html" class="nav-btn-upload">Upload</a>
+      <button class="nav-toggle" id="theme-toggle" aria-label="Toggle theme">
+        <svg id="theme-icon-dark" class="theme-icon" viewBox="0 0 24 24" width="20" height="20" fill="none" stroke="currentColor" stroke-width="2" style="display: none;"><path d="M21 12.79A9 9 0 1 1 11.21 3 7 7 0 0 0 21 12.79z"/></svg>
+        <svg id="theme-icon-light" class="theme-icon" viewBox="0 0 24 24" width="20" height="20" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="5"/><path d="M12 1v2m0 18v2M4.22 4.22l1.42 1.42m12.72 12.72l1.42 1.42M1 12h2m18 12h2M4.22 19.78l1.42-1.42m12.72-12.72l1.42-1.42"/></svg>
+      </button>
+    </div>
+  </div>
+</nav>
+
+  <!-- ░░ HERO ░░ -->
+  <section class="landing-hero">
+    <div class="landing-hero-glow"></div>
+    <div class="landing-content">
+      <div class="hero-badge" style="margin-bottom: 2rem; display: inline-block;">🚀 The Ultimate Benchmark</div>
+      <h1 class="landing-title">Compare Models.<br><span class="gradient-text">Instantly.</span></h1>
+      <p class="landing-subtitle">
+        Upload your CSV and let our automated arena pit the world's best ML models against each other. 
+        Discover whether SAP RPT-1 OSS or traditional gradient boosters win on your specific data.
+      </p>
+      <div class="cta-container">
+        <a href="/static/uploader.html" class="btn-cta">Enter the Arena ⚔️</a>
+      </div>
+    </div>
+  </section>
+
+  <!-- ░░ MODELS BANNER ░░ -->
+  <div class="models-banner">
+    <p style="color: #64748b; font-weight: 600; text-transform: uppercase; letter-spacing: 0.1em; margin-bottom: 1.5rem; font-size: 0.85rem;">Supported Models</p>
+    <div class="models-list">
+      <div class="model-item">XGBoost</div>
+      <div class="model-item">LightGBM</div>
+      <div class="model-item">CatBoost</div>
+      <div class="model-item">TabPFN</div>
+      <div class="model-item">SAP RPT-1 OSS</div>
+    </div>
+  </div>
+
+  <!-- ░░ HOW IT WORKS ░░ -->
+  <section class="how-it-works">
+    <div class="hero-badge" style="margin-bottom: 1rem;">PIPELINE</div>
+    <h2 class="landing-title" style="font-size: 3rem; margin-bottom: 1rem;">How it <span class="gradient-text">Works</span></h2>
+    <p class="landing-subtitle">From raw CSV to actionable model recommendation in minutes — fully automated.</p>
+    
+    <div class="workflow-container">
+      <div class="workflow-step">
+        <div class="step-icon">📤</div>
+        <div class="step-num">01</div>
+        <h4>Upload CSV</h4>
+        <p>Drag & drop your dataset. We auto-detect features, types, and whether it's a classification or regression task.</p>
+      </div>
+      <div class="workflow-arrow">→</div>
+      <div class="workflow-step">
+        <div class="step-icon">🏋️</div>
+        <div class="step-num">02</div>
+        <h4>Parallel Training</h4>
+        <p>All 5 models run 5-fold cross-validation simultaneously. XGBoost, LightGBM, CatBoost, TabPFN & SAP RPT-1.</p>
+      </div>
+      <div class="workflow-arrow">→</div>
+      <div class="workflow-step">
+        <div class="step-icon">🧩</div>
+        <div class="step-num">03</div>
+        <h4>Ensemble Engine</h4>
+        <p>The top 3 models are automatically combined via Soft Voting and Stacking to squeeze out extra performance.</p>
+      </div>
+      <div class="workflow-arrow">→</div>
+      <div class="workflow-step">
+        <div class="step-icon">🔬</div>
+        <div class="step-num">04</div>
+        <h4>SHAP Analysis</h4>
+        <p>The winner is retrained on the full dataset. SHAP values reveal exactly which features matter most.</p>
+      </div>
+      <div class="workflow-arrow">→</div>
+      <div class="workflow-step">
+        <div class="step-icon">🎮</div>
+        <div class="step-num">05</div>
+        <h4>Live Playground</h4>
+        <p>Tweak feature values in real-time and see your model's live prediction update instantly.</p>
+      </div>
+    </div>
+  </section>
+
+  <!-- ░░ FEATURES ░░ -->
+  <section class="features-grid">
+    <div class="feature-card">
+      <div class="feature-icon">⚡</div>
+      <h3>Zero Configuration</h3>
+      <p>Simply drag and drop your CSV file. We automatically detect your target variable, infer the task type (classification or regression), and handle preprocessing.</p>
+    </div>
+    <div class="feature-card">
+      <div class="feature-icon">🔍</div>
+      <h3>Rigorous Validation</h3>
+      <p>All models are evaluated using 5-fold cross-validation to ensure statistically significant and reliable results, preventing overfitting on small datasets.</p>
+    </div>
+    <div class="feature-card">
+      <div class="feature-icon">🧠</div>
+      <h3>Ensemble Insights</h3>
+      <p>We don't just pick a winner. We automatically build Voting and Stacking ensembles to see if combining the models yields even better performance.</p>
+    </div>
+  </section>
+
+  <footer class="footer" style="margin-top: auto;">
+    SAP RPT-1 OSS Benchmarking · Built with FastAPI &amp; Chart.js
+  </footer>
+
+  <script src="/static/app.js"></script>
+</body>
+</html>

webapp/static/style.css

@@ -0,0 +1,1623 @@
+:root {
+  /* Default Dark Theme */
+  --bg:           #080d1a;
+  --bg-alt:       #0d1426;
+  --surface:      #111827;
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+  --accent-soft:  rgba(67, 56, 202, 0.1);
+  
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+  --hero-gradient: linear-gradient(145deg, #0d1427 0%, #0a0f1e 40%, #120823 100%);
+
+  /* Shared Utils */
+  --radius:       16px;
+  --radius-sm:    10px;
+  --pink:         #ec4899;
+  --amber:        #f59e0b;
+  --green:        #10b981;
+  --scrollbar-thumb: rgba(255, 255, 255, 0.1);
+}
+
+/* ── Custom Scrollbar ────────────────────────────────────────────────────── */
+::-webkit-scrollbar {
+  width: 8px;
+  height: 8px;
+}
+
+::-webkit-scrollbar-track {
+  background: transparent;
+}
+
+::-webkit-scrollbar-thumb {
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+  border-radius: 10px;
+  border: 2px solid transparent;
+  background-clip: content-box;
+}
+
+::-webkit-scrollbar-thumb:hover {
+  background: var(--accent);
+  background-clip: content-box;
+}
+
+/* Firefox support */
+* {
+  scrollbar-width: thin;
+  scrollbar-color: var(--scrollbar-thumb) transparent;
+}
+
+[data-theme="light"] {
+  --bg:           #e2e8f0; /* Soft Slate/Oyster Grey */
+  --bg-alt:       #cbd5e1;
+  --surface:      #f1f5f9;
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+  
+  --nav-bg:       rgba(226, 232, 240, 0.85);
+  --hero-gradient: linear-gradient(145deg, #cbd5e1 0%, #e2e8f0 40%, #dee5ed 100%);
+}
+
+/* ── Reset & Base ─────────────────────────────────────────────────────────── */
+*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
+
+html { scroll-behavior: smooth; }
+
+body {
+  font-family: 'Inter', sans-serif;
+  background: var(--bg);
+  color: var(--text);
+  min-height: 100vh;
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+  padding-top: 64px;
+}
+
+/* ── Navbar ───────────────────────────────────────────────────────────────── */
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+  position: fixed;
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+}
+
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+}
+
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+
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+}
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+}
+
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+}
+
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+  display: flex;
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+  cursor: pointer;
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+}
+
+.nav-toggle:hover {
+  border-color: var(--accent);
+  color: var(--text);
+}
+
+/* ── Hero ─────────────────────────────────────────────────────────────────── */
+.hero {
+  position: relative;
+  overflow: hidden;
+  background: var(--hero-gradient);
+  border-bottom: 1px solid var(--border);
+  padding: 72px 24px 56px;
+  text-align: center;
+}
+
+.hero-glow {
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+}
+
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+
+.hero-badge {
+  display: inline-block;
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+  border: 1px solid rgba(99,102,241,.35);
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+}
+
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+  font-weight: 900;
+  line-height: 1.1;
+  color: var(--text);
+  margin-bottom: 16px;
+}
+
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+}
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+  margin: 0 auto 28px;
+}
+
+.hero-chips {
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+}
+
+.chip {
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+}
+
+/* ── Layout ───────────────────────────────────────────────────────────────── */
+.container { max-width: 1300px; margin: 0 auto; padding: 48px 24px; }
+.section    { margin-bottom: 48px; }
+
+.section-title {
+  font-size: 1.35rem;
+  font-weight: 700;
+  color: var(--text);
+  margin-bottom: 24px;
+  display: flex;
+  align-items: center;
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+}
+
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+}
+
+.title-accent { color: var(--accent2); }
+
+/* ── Drop Zone ────────────────────────────────────────────────────────────── */
+.drop-zone {
+  border: 2px dashed var(--border2);
+  border-radius: var(--radius);
+  padding: 64px 32px;
+  text-align: center;
+  cursor: pointer;
+  transition: border-color .25s, background .25s, transform .2s;
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+}
+
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+}
+
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+}
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+}
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+  margin-top: 12px;
+  text-align: center;
+}
+
+/* ── Preview Section ──────────────────────────────────────────────────────── */
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+  align-items: center;
+  justify-content: space-between;
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+}
+
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+}
+
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+}
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+}
+
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+}
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+  border: 1px solid var(--border);
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+}
+
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+}
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+}
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+  font-family: inherit;
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+}
+
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+  transform: translateY(-2px);
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+}
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+
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+  font-family: inherit;
+  cursor: pointer;
+  transition: border-color .2s, color .2s;
+}
+.btn-ghost:hover { border-color: var(--accent2); color: var(--accent2); }
+
+.btn-ghost-lg {
+  background: transparent;
+  border: 1px solid var(--border2);
+  color: var(--text-dim);
+  border-radius: var(--radius-sm);
+  padding: 13px 32px;
+  font-size: .95rem;
+  font-family: inherit;
+  cursor: pointer;
+  transition: border-color .2s, color .2s;
+}
+.btn-ghost-lg:hover { border-color: var(--accent2); color: var(--accent2); }
+
+/* ── Loader ───────────────────────────────────────────────────────────────── */
+.loader-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 56px 32px;
+  text-align: center;
+  max-width: 520px;
+  margin: 0 auto;
+}
+
+.spinner-ring {
+  width: 60px; height: 60px;
+  border: 4px solid rgba(99,102,241,.2);
+  border-top-color: var(--accent);
+  border-radius: 50%;
+  animation: spin 1s linear infinite;
+  margin: 0 auto 28px;
+}
+
+@keyframes spin { to { transform: rotate(360deg); } }
+
+.loader-title {
+  font-size: 1.3rem;
+  font-weight: 700;
+  color: var(--text);
+  margin-bottom: 8px;
+}
+
+.loader-sub {
+  color: var(--text-muted);
+  font-size: .9rem;
+  margin-bottom: 32px;
+}
+
+.loader-steps {
+  display: flex;
+  justify-content: center;
+  gap: 12px;
+  flex-wrap: wrap;
+}
+
+.step {
+  padding: 6px 16px;
+  border-radius: 999px;
+  font-size: .8rem;
+  font-weight: 600;
+  background: var(--surface);
+  border: 1px solid var(--border);
+  color: var(--text-muted);
+  transition: all .4s;
+}
+
+.step.active {
+  background: rgba(99,102,241,.15);
+  border-color: var(--accent2);
+  color: var(--accent2);
+  box-shadow: 0 0 12px rgba(99,102,241,.25);
+}
+
+.step.done {
+  background: rgba(16,185,129,.12);
+  border-color: var(--green);
+  color: var(--green);
+}
+
+/* ── Info Bar ─────────────────────────────────────────────────────────────── */
+.info-bar {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 12px;
+  margin-bottom: 36px;
+  padding: 18px 20px;
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  align-items: center;
+}
+
+.actions-bar {
+  display: flex;
+  gap: 12px;
+  margin-bottom: 24px;
+  justify-content: flex-end;
+}
+
+.actions-bar .btn-ghost {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  padding: 10px 20px;
+  background: var(--surface);
+  font-weight: 600;
+}
+
+.actions-bar .btn-ghost:hover {
+  background: var(--bg-alt);
+  transform: translateY(-1px);
+  box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+}
+
+.info-tag {
+  background: rgba(99,102,241,.1);
+  border: 1px solid rgba(99,102,241,.25);
+  color: var(--accent2);
+  padding: 4px 14px;
+  border-radius: 999px;
+  font-size: .8rem;
+  font-weight: 600;
+}
+
+.info-tag.green {
+  background: rgba(16,185,129,.1);
+  border-color: rgba(16,185,129,.25);
+  color: var(--green);
+}
+
+.info-tag.pink {
+  background: rgba(236,72,153,.1);
+  border-color: rgba(236,72,153,.25);
+  color: var(--pink);
+}
+
+/* ── KPI Cards ────────────────────────────────────────────────────────────── */
+.kpi-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
+  gap: 20px;
+  margin-bottom: 36px;
+}
+
+.kpi-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 24px;
+  position: relative;
+  overflow: hidden;
+  transition: transform .2s, border-color .2s;
+}
+
+.kpi-card:hover { transform: translateY(-3px); border-color: var(--border2); }
+
+.kpi-card::before {
+  content: '';
+  position: absolute;
+  top: 0; left: 0; right: 0;
+  height: 3px;
+  background: var(--accent-bar, linear-gradient(90deg, var(--accent), var(--pink)));
+}
+
+.kpi-label {
+  font-size: .75rem;
+  font-weight: 600;
+  text-transform: uppercase;
+  letter-spacing: .08em;
+  color: var(--text-muted);
+  margin-bottom: 8px;
+}
+
+.kpi-value {
+  font-size: 2rem;
+  font-weight: 800;
+  color: var(--text);
+  line-height: 1;
+  margin-bottom: 6px;
+}
+
+.kpi-sub { font-size: .8rem; color: var(--text-muted); }
+
+/* ── Legend ───────────────────────────────────────────────────────────────── */
+.legend {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 16px;
+  margin-bottom: 28px;
+}
+
+.legend-item {
+  display: flex;
+  align-items: center;
+  gap: 8px;
+  font-size: .85rem;
+  color: var(--text-dim);
+}
+
+.legend-dot {
+  width: 12px; height: 12px;
+  border-radius: 3px;
+  flex-shrink: 0;
+}
+
+/* ── Charts ───────────────────────────────────────────────────────────────── */
+.charts-grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  gap: 20px;
+  margin-bottom: 40px;
+}
+
+@media(max-width: 1200px) {
+  .charts-grid { grid-template-columns: repeat(2, 1fr); }
+}
+
+@media(max-width: 768px) {
+  .charts-grid { grid-template-columns: 1fr; }
+}
+
+.chart-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 24px;
+}
+
+.chart-card h4 {
+  font-size: .9rem;
+  font-weight: 700;
+  color: var(--text);
+  margin-bottom: 3px;
+}
+
+.chart-card .chart-sub {
+  font-size: .75rem;
+  color: var(--text-muted);
+  margin-bottom: 16px;
+}
+
+.chart-interpretation {
+  margin-top: 16px;
+  padding-top: 12px;
+  border-top: 1px solid var(--border);
+  display: flex;
+  flex-direction: column;
+  gap: 6px;
+}
+
+.interp-item {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  font-size: 0.75rem;
+  color: var(--text-muted);
+}
+
+.interp-item .badge {
+  padding: 2px 8px;
+  border-radius: 4px;
+  text-transform: uppercase;
+  font-weight: 800;
+  font-size: 0.65rem;
+}
+
+.interp-item .badge.excellent {
+  background: rgba(16, 185, 129, 0.1);
+  color: var(--green);
+}
+
+.interp-item .badge.poor {
+  background: rgba(239, 68, 68, 0.1);
+  color: #f87171;
+}
+
+canvas { max-height: 260px; }
+
+/* ── Results Table ────────────────────────────────────────────────────────── */
+.table-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  overflow: hidden;
+  margin-bottom: 40px;
+}
+
+.results-table { width: 100%; border-collapse: collapse; }
+
+.results-table th {
+  padding: 14px 20px;
+  font-size: 0.7rem;
+  font-weight: 800;
+  color: var(--text-muted);
+  text-transform: uppercase;
+  letter-spacing: 0.12em;
+  background: var(--bg-alt);
+  border-bottom: 1px solid var(--border);
+  text-align: left;
+  white-space: nowrap;
+}
+
+.results-table td {
+  padding: 14px 20px;
+  font-size: 0.875rem;
+  color: var(--text);
+  border-bottom: 1px solid var(--border);
+  vertical-align: middle;
+  white-space: nowrap;
+}
+
+.results-table tr:hover td { background: rgba(255,255,255,.02); }
+.results-table tr:last-child td { border-bottom: none; }
+
+.mono { font-family: 'Courier New', monospace; font-weight: 600; }
+.col-excellent { color: #10b981; }
+.col-good      { color: #6366f1; }
+.col-fair      { color: #f59e0b; }
+.col-poor      { color: #f87171; }
+
+.model-dot {
+  display: inline-block;
+  width: 10px; height: 10px;
+  border-radius: 50%;
+  margin-right: 7px;
+  flex-shrink: 0;
+}
+
+.task-badge {
+  display: inline-block;
+  padding: 3px 10px;
+  border-radius: 999px;
+  font-size: .7rem;
+  font-weight: 700;
+}
+
+.badge-clf {
+  background: rgba(99,102,241,.15);
+  border: 1px solid rgba(99,102,241,.3);
+  color: var(--accent2);
+}
+
+.badge-reg {
+  background: rgba(16,185,129,.15);
+  border: 1px solid rgba(16,185,129,.3);
+  color: var(--green);
+}
+
+/* ── Recommendation ───────────────────────────────────────────────────────── */
+.rec-grid {
+  display: grid;
+  grid-template-columns: repeat(3, 1fr);
+  grid-template-rows: repeat(3, auto);
+  gap: 32px;
+  margin-bottom: 60px;
+  max-width: 1200px;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+/* Corner & Center Mapping */
+.rec-card.best_overall { grid-area: 2 / 2 / 3 / 3; z-index: 2; transform: scale(1.05); }
+.rec-card.production   { grid-area: 1 / 1 / 2 / 2; }
+.rec-card.best_accuracy { grid-area: 1 / 3 / 2 / 4; }
+.rec-card.best_speed    { grid-area: 3 / 1 / 4 / 2; }
+.rec-card.best_consistency { grid-area: 3 / 3 / 4 / 4; }
+
+/* Mobile Fallback */
+@media (max-width: 1100px) {
+  .rec-grid {
+    grid-template-columns: 1fr;
+    grid-template-rows: auto;
+    gap: 20px;
+  }
+  .rec-card { grid-area: auto !important; transform: none !important; }
+}
+
+.rec-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 24px;
+  position: relative;
+  overflow: hidden;
+  transition: transform .3s ease, border-color .3s ease;
+  display: flex;
+  flex-direction: column;
+}
+
+.rec-card:hover { transform: translateY(-5px) scale(1.02); }
+.rec-card.best_overall:hover { transform: scale(1.08); }
+
+.rec-card.winner {
+  border-color: rgba(236,72,153,0.5);
+  background: linear-gradient(145deg, rgba(236,72,153,0.1), var(--surface2));
+  box-shadow: 0 0 30px rgba(236,72,153,0.15);
+}
+
+.rec-card.winner::before {
+  content: '';
+  position: absolute;
+  top: 0; left: 0; right: 0;
+  height: 3px;
+  background: linear-gradient(90deg, var(--pink), var(--accent));
+}
+
+.rec-card:not(.winner)::before {
+  content: '';
+  position: absolute;
+  top: 0; left: 0; right: 0;
+  height: 3px;
+  background: linear-gradient(90deg, var(--accent), #4f46e5);
+}
+
+.rec-type {
+  font-size: .72rem;
+  font-weight: 700;
+  text-transform: uppercase;
+  letter-spacing: .08em;
+  color: var(--text-muted);
+  margin-bottom: 8px;
+}
+
+.rec-model-name {
+  font-size: 1.5rem;
+  font-weight: 800;
+  color: var(--text);
+  margin-bottom: 10px;
+  display: flex;
+  align-items: center;
+  gap: 10px;
+}
+
+.rec-trophy { font-size: 1.3rem; }
+
+.rec-score {
+  display: inline-block;
+  background: rgba(99,102,241,.15);
+  border: 1px solid rgba(99,102,241,.3);
+  color: var(--accent2);
+  padding: 3px 12px;
+  border-radius: 999px;
+  font-size: .78rem;
+  font-weight: 700;
+  margin-bottom: 12px;
+  font-family: 'Courier New', monospace;
+}
+
+.rec-reason {
+  font-size: .85rem;
+  color: var(--text-dim);
+  line-height: 1.6;
+}
+
+/* ── Reset Bar ────────────────────────────────────────────────────────────── */
+.reset-bar {
+  text-align: center;
+  padding-top: 8px;
+}
+
+/* ── Ensemble Analysis Cards ──────────────────────────────────────────────── */
+.ensemble-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(360px, 1fr));
+  gap: 24px;
+  margin-bottom: 40px;
+}
+
+.ens-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 24px;
+  position: relative;
+  overflow: hidden;
+  transition: transform .2s, border-color .2s;
+}
+
+.ens-card:hover { transform: translateY(-3px); border-color: var(--border2); }
+
+.ens-card::before {
+  content: '';
+  position: absolute;
+  top: 0; left: 0; right: 0;
+  height: 3px;
+  background: var(--ens-color, var(--accent));
+}
+
+.ens-header {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  margin-bottom: 14px;
+  flex-wrap: wrap;
+}
+
+.ens-emoji { font-size: 1.4rem; }
+
+.ens-name {
+  font-size: 1.2rem;
+  font-weight: 800;
+  flex: 1;
+}
+
+.ens-type-badge {
+  background: rgba(255,255,255,.06);
+  border: 1px solid var(--border2);
+  color: var(--text-dim);
+  padding: 3px 10px;
+  border-radius: 999px;
+  font-size: .72rem;
+  font-weight: 700;
+}
+
+.ens-score {
+  margin-bottom: 6px;
+}
+
+.ens-score-val {
+  font-size: 2rem;
+  font-weight: 800;
+  color: var(--text);
+  font-family: 'Courier New', monospace;
+}
+
+.ens-score-label {
+  font-size: .8rem;
+  color: var(--text-muted);
+}
+
+.ens-gain {
+  margin-bottom: 12px;
+  font-size: .82rem;
+  font-weight: 600;
+}
+
+.gain-pos { color: #10b981; }
+.gain-neg { color: #f87171; }
+
+.ens-meta {
+  font-size: .8rem;
+  color: var(--text-muted);
+  margin-bottom: 10px;
+}
+
+.ens-desc {
+  font-size: .82rem;
+  color: var(--text-dim);
+  line-height: 1.6;
+  margin-bottom: 16px;
+  border-top: 1px solid var(--border);
+  padding-top: 12px;
+}
+
+.ens-components-label {
+  font-size: .7rem;
+  font-weight: 700;
+  text-transform: uppercase;
+  letter-spacing: .07em;
+  color: var(--text-muted);
+  margin-bottom: 8px;
+}
+
+.ens-components {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  margin-bottom: 14px;
+}
+
+.comp-pill {
+  padding: 4px 12px;
+  border: 1px solid;
+  border-radius: 999px;
+  font-size: .76rem;
+  font-weight: 600;
+  background: rgba(255,255,255,.04);
+}
+
+.ens-footer {
+  font-size: .75rem;
+  color: var(--text-muted);
+  border-top: 1px solid var(--border);
+  padding-top: 10px;
+}
+
+/* ── Resume Card ────────────────────────────────────────────────────────── */
+.resume-card {
+  background: linear-gradient(145deg, var(--surface), var(--surface2));
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 32px;
+  display: flex;
+  align-items: center;
+  gap: 24px;
+  max-width: 600px;
+  margin: 0 auto;
+}
+
+.resume-icon {
+  font-size: 2.5rem;
+  background: rgba(99,102,241,0.1);
+  width: 80px; height: 80px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  border-radius: var(--radius-sm);
+  border: 1px solid rgba(99,102,241,0.2);
+}
+
+.resume-content h3 {
+  margin-bottom: 4px;
+  font-size: 1.25rem;
+  font-weight: 700;
+}
+
+.resume-content p {
+  color: var(--text-dim);
+  margin-bottom: 20px;
+}
+
+.resume-actions {
+  display: flex;
+  gap: 12px;
+}
+
+
+/* ── Playground ───────────────────────────────────────────────────────────── */
+.playground-card {
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: var(--radius);
+  padding: 32px;
+  margin-bottom: 60px;
+}
+
+.playground-layout {
+  display: grid;
+  grid-template-columns: 1fr 320px;
+  gap: 40px;
+}
+
+@media (max-width: 1000px) {
+  .playground-layout { grid-template-columns: 1fr; }
+}
+
+.playground-intro {
+  color: var(--text-muted);
+  font-size: 0.95rem;
+  margin-bottom: 30px;
+  line-height: 1.6;
+}
+
+.playground-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
+  gap: 20px;
+}
+
+.playground-field {
+  display: flex;
+  flex-direction: column;
+  gap: 8px;
+}
+
+.playground-field label {
+  font-size: 0.7rem;
+  font-weight: 700;
+  text-transform: uppercase;
+  color: var(--text-muted);
+  letter-spacing: 0.05em;
+}
+
+.playground-field input {
+  background: var(--surface2);
+  border: 1px solid var(--border);
+  color: var(--text);
+  padding: 10px 14px;
+  border-radius: 8px;
+  font-family: inherit;
+  font-size: 0.9rem;
+  transition: border-color 0.2s, box-shadow 0.2s;
+}
+
+.playground-field input:focus {
+  outline: none;
+  border-color: var(--primary);
+  box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2);
+}
+
+.playground-output {
+  position: sticky;
+  top: 100px;
+  height: fit-content;
+}
+
+.output-card {
+  background: linear-gradient(145deg, var(--surface2), var(--surface));
+  border: 1px solid var(--border);
+  padding: 30px;
+  border-radius: 16px;
+  text-align: center;
+  box-shadow: var(--shadow-lg);
+}
+
+.output-label {
+  font-size: 0.7rem;
+  text-transform: uppercase;
+  letter-spacing: 0.1em;
+  color: var(--text-muted);
+  margin-bottom: 15px;
+}
+
+.prediction-main {
+  font-size: 2.8rem;
+  font-weight: 800;
+  color: var(--primary);
+  margin-bottom: 8px;
+  font-family: 'JetBrains Mono', monospace;
+  text-shadow: 0 0 20px rgba(99, 102, 241, 0.3);
+}
+
+.prediction-sub {
+  font-size: 0.85rem;
+  color: var(--text-muted);
+  margin-bottom: 20px;
+}
+
+.prob-container {
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+  text-align: left;
+}
+
+.prob-row {
+  display: flex;
+  flex-direction: column;
+  gap: 4px;
+}
+
+.prob-meta {
+  display: flex;
+  justify-content: space-between;
+  font-size: 0.75rem;
+}
+
+.prob-bar-bg {
+  height: 6px;
+  background: var(--border);
+  border-radius: 3px;
+  overflow: hidden;
+}
+
+.prob-bar-fill {
+  height: 100%;
+  background: var(--primary);
+  transition: width 0.3s ease;
+}
+
+.footer {
+  text-align: center;
+  padding: 24px;
+  color: #2a3a5a;
+  font-size: .78rem;
+  border-top: 1px solid var(--border);
+}
+
+/* ── Utilities ────────────────────────────────────────────────────────────── */
+[hidden] { display: none !important; }
+
+/* Landing Page Specific Overrides & Additions */
+.landing-hero {
+  min-height: 80vh;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  text-align: center;
+  position: relative;
+  overflow: hidden;
+  padding: 4rem 2rem;
+  background: var(--hero-gradient);
+}
+.landing-hero-glow {
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  width: 800px;
+  height: 800px;
+  background: radial-gradient(circle, rgba(162, 59, 255, 0.25) 0%, rgba(255, 94, 98, 0.15) 40%, transparent 70%);
+  transform: translate(-50%, -50%);
+  filter: blur(80px);
+  z-index: 0;
+  animation: pulseGlow 8s infinite alternate ease-in-out;
+}
+@keyframes pulseGlow {
+  0% { transform: translate(-50%, -50%) scale(1); opacity: 0.8; }
+  100% { transform: translate(-50%, -50%) scale(1.1); opacity: 1; }
+}
+.landing-content {
+  position: relative;
+  z-index: 1;
+  max-width: 900px;
+}
+.landing-title {
+  font-size: 4.5rem;
+  font-weight: 900;
+  line-height: 1.1;
+  letter-spacing: -0.04em;
+  margin-bottom: 1.5rem;
+  color: var(--text);
+}
+.landing-title .gradient-text {
+  background: linear-gradient(135deg, #a23bff, #ff5e62, #ff9966);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+  background-clip: text;
+  animation: gradientShift 5s ease infinite;
+  background-size: 200% 200%;
+}
+@keyframes gradientShift {
+  0% { background-position: 0% 50%; }
+  50% { background-position: 100% 50%; }
+  100% { background-position: 0% 50%; }
+}
+.landing-subtitle {
+  font-size: 1.25rem;
+  color: var(--text-dim);
+  max-width: 700px;
+  margin: 0 auto 3rem auto;
+  line-height: 1.6;
+}
+.cta-container {
+  display: flex;
+  gap: 1.5rem;
+  justify-content: center;
+  margin-bottom: 4rem;
+}
+.btn-cta {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  padding: 1.1rem 2.8rem;
+  font-size: 1.125rem;
+  font-weight: 700;
+  color: #fff;
+  background: var(--accent);
+  border: 1px solid rgba(255, 255, 255, 0.1);
+  border-radius: 9999px;
+  text-decoration: none;
+  transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+  box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
+  cursor: pointer;
+}
+.btn-cta:hover {
+  background: var(--accent2);
+  transform: translateY(-3px) scale(1.02);
+  box-shadow: 0 20px 40px rgba(0, 0, 0, 0.3);
+}
+.btn-secondary {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  padding: 1rem 2.5rem;
+  font-size: 1.125rem;
+  font-weight: 600;
+  color: var(--text);
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: 9999px;
+  text-decoration: none;
+  transition: all 0.3s ease;
+  backdrop-filter: blur(10px);
+}
+.btn-secondary:hover {
+  background: var(--bg-alt);
+  transform: translateY(-3px);
+}
+.features-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
+  gap: 2rem;
+  max-width: 1200px;
+  margin: 0 auto;
+  padding: 0 2rem 5rem 2rem;
+  position: relative;
+  z-index: 1;
+}
+.feature-card {
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: 20px;
+  padding: 2.5rem 2rem;
+  text-align: left;
+  backdrop-filter: blur(16px);
+  transition: all 0.4s ease;
+}
+.feature-card:hover {
+  transform: translateY(-10px);
+  background: var(--surface2);
+  border-color: var(--accent);
+  box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
+}
+.feature-icon {
+  font-size: 2.5rem;
+  margin-bottom: 1.5rem;
+  display: inline-block;
+  background: linear-gradient(135deg, #a23bff, #ff5e62);
+  -webkit-background-clip: text;
+  -webkit-text-fill-color: transparent;
+}
+.feature-card h3 {
+  font-size: 1.25rem;
+  font-weight: 700;
+  color: var(--text);
+  margin-bottom: 1rem;
+}
+.feature-card p {
+  color: var(--text-dim);
+  line-height: 1.6;
+  font-size: 0.95rem;
+}
+.models-banner {
+  padding: 3rem 0;
+  border-top: 1px solid var(--border);
+  border-bottom: 1px solid var(--border);
+  background: var(--bg-alt);
+  text-align: center;
+  margin-bottom: 2rem;
+}
+.models-list {
+  display: flex;
+  flex-wrap: wrap;
+  justify-content: center;
+  gap: 3rem;
+  align-items: center;
+  opacity: 0.7;
+}
+.model-item {
+  font-size: 1.5rem;
+  font-weight: 800;
+  letter-spacing: -0.02em;
+  color: var(--text-muted);
+}
+
+@media (max-width: 768px) {
+  .landing-title { font-size: 3rem; }
+  .cta-container { flex-direction: column; }
+}
+
+/* ── How It Works ───────────────────────────────────────────────────────── */
+.how-it-works {
+  padding: 4rem 2rem 8rem;
+  text-align: center;
+  background: var(--bg);
+  position: relative;
+  overflow: hidden;
+}
+
+.workflow-container {
+  display: flex;
+  justify-content: center;
+  align-items: stretch;
+  gap: 1rem;
+  max-width: 1400px;
+  margin: 4rem auto 0;
+  flex-wrap: nowrap;
+}
+
+.workflow-step {
+  flex: 1;
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-radius: 20px;
+  padding: 2.5rem 1.5rem;
+  text-align: center;
+  position: relative;
+  transition: all 0.4s cubic-bezier(0.175, 0.885, 0.32, 1.275);
+  min-width: 180px;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+}
+
+.workflow-step:hover {
+  transform: translateY(-10px);
+  border-color: var(--accent);
+  background: var(--surface2);
+  box-shadow: 0 20px 40px rgba(0,0,0,0.2);
+}
+
+.step-icon {
+  font-size: 2.5rem;
+  margin-bottom: 1.5rem;
+  filter: drop-shadow(0 0 10px rgba(99, 102, 241, 0.3));
+}
+
+.step-num {
+  font-size: 0.85rem;
+  font-weight: 900;
+  color: var(--accent);
+  margin-bottom: 0.75rem;
+  letter-spacing: 0.1em;
+}
+
+.workflow-step h4 {
+  font-size: 1.2rem;
+  font-weight: 700;
+  color: var(--text);
+  margin-bottom: 1rem;
+}
+
+.workflow-step p {
+  font-size: 0.9rem;
+  color: var(--text-dim);
+  line-height: 1.6;
+}
+
+.workflow-arrow {
+  display: flex;
+  align-items: center;
+  color: var(--text-muted);
+  font-size: 1.5rem;
+  opacity: 0.3;
+  transition: all 0.3s;
+}
+
+.workflow-step:hover + .workflow-arrow {
+  opacity: 0.8;
+  color: var(--accent);
+  transform: scale(1.2);
+}
+
+@media (max-width: 1200px) {
+  .workflow-container { flex-wrap: wrap; gap: 2rem; }
+  .workflow-arrow { display: none; }
+  .workflow-step { min-width: 280px; }
+}
+
+/* ── Statistical Rigor ───────────────────────────────────────────────────── */
+.rigor-card {
+  background: var(--card-bg);
+  border: 1px solid var(--border);
+  border-radius: 16px;
+  padding: 32px;
+  margin-bottom: 48px;
+  box-shadow: var(--shadow);
+}
+
+.rigor-header {
+  display: flex;
+  align-items: center;
+  gap: 16px;
+  margin-bottom: 24px;
+  flex-wrap: wrap;
+}
+
+.rigor-meta {
+  font-size: 0.9rem;
+  color: var(--text-muted);
+}
+
+.rigor-table-wrapper {
+  overflow-x: auto;
+  border-radius: 12px;
+  border: 1px solid var(--border);
+}
+
+.rigor-table {
+  width: 100%;
+  border-collapse: collapse;
+  text-align: left;
+  font-size: 0.95rem;
+}
+
+.rigor-table th {
+  background: var(--bg-alt);
+  padding: 16px;
+  font-weight: 600;
+  color: var(--text-dim);
+  text-transform: uppercase;
+  font-size: 0.75rem;
+  letter-spacing: 0.05em;
+}
+
+.rigor-table td {
+  padding: 16px;
+  border-top: 1px solid var(--border);
+}
+
+.rigor-table tr:hover {
+  background: var(--accent-soft);
+}
+
+.rank-pill {
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  width: 28px;
+  height: 28px;
+  border-radius: 50%;
+  font-weight: 800;
+  font-size: 0.85rem;
+  margin-right: 12px;
+}
+
+.rank-1 { background: var(--accent); color: white; box-shadow: 0 0 10px var(--accent-soft); }
+.rank-2 { background: var(--bg-alt); color: var(--text); }
+
+.stability-bar {
+  height: 6px;
+  width: 100px;
+  background: var(--bg-alt);
+  border-radius: 10px;
+  overflow: hidden;
+  display: inline-block;
+  vertical-align: middle;
+  margin-right: 8px;
+}
+
+.stability-fill {
+  height: 100%;
+  background: var(--accent);
+}
+
+.p-value-badge {
+  padding: 4px 12px;
+  border-radius: 99px;
+  font-weight: 700;
+  font-size: 0.75rem;
+  text-transform: uppercase;
+}
+
+.p-value-badge.significant {
+  background: var(--green);
+  color: white;
+}
+
+.p-value-badge.not-significant {
+  background: #64748b;
+  color: white;
+}

webapp/static/uploader.html

@@ -0,0 +1,133 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8"/>
+  <meta name="viewport" content="width=device-width,initial-scale=1"/>
+  <title>SAP RPT-1 OSS Benchmarking — Model Arena</title>
+  <meta name="description" content="Upload your CSV and instantly benchmark XGBoost, LightGBM, CatBoost and SAP RPT-1 OSS. Get a detailed model recommendation for your use case."/>
+  <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap" rel="stylesheet"/>
+  <link rel="stylesheet" href="/static/style.css?v=2"/>
+  <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.2/dist/chart.umd.min.js"></script>
+</head>
+<body>
+
+<nav class="navbar">
+  <div class="nav-container">
+    <a href="/static/landing.html" class="nav-brand" id="nav-logo">ModelMatrix</a>
+    <div class="nav-actions">
+      <a href="/static/uploader.html" class="nav-btn-upload">Upload</a>
+      <button class="nav-toggle" id="theme-toggle" aria-label="Toggle theme">
+        <svg id="theme-icon-dark" class="theme-icon" viewBox="0 0 24 24" width="20" height="20" fill="none" stroke="currentColor" stroke-width="2" style="display: none;"><path d="M21 12.79A9 9 0 1 1 11.21 3 7 7 0 0 0 21 12.79z"/></svg>
+        <svg id="theme-icon-light" class="theme-icon" viewBox="0 0 24 24" width="20" height="20" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="5"/><path d="M12 1v2m0 18v2M4.22 4.22l1.42 1.42m12.72 12.72l1.42 1.42M1 12h2m18 12h2M4.22 19.78l1.42-1.42m12.72-12.72l1.42-1.42"/></svg>
+      </button>
+    </div>
+  </div>
+</nav>
+
+<!-- HEADER SECTION -->
+<header class="hero">
+  <div class="hero-glow"></div>
+  <div class="hero-content">
+    <div class="hero-badge">🔬 ML Model Arena</div>
+    <h1>Upload. Benchmark. <span class="gradient-text">Decide.</span></h1>
+    <p>Drop your CSV dataset and we'll automatically run <strong>XGBoost, LightGBM, CatBoost &amp; SAP RPT-1 OSS</strong> in parallel — then tell you exactly which model wins for your use case.</p>
+    <div class="hero-chips">
+      <span class="chip">5-Fold Cross-Validation</span>
+      <span class="chip">Auto Task Detection</span>
+      <span class="chip">Smart Recommendation</span>
+      <span class="chip">Max 5 MB CSV</span>
+    </div>
+  </div>
+</header>
+
+<main class="container">
+
+  <!-- ░░ RESUME SECTION (Shown if navigating back) ░░ -->
+  <section id="resume-section" class="section" hidden>
+    <div class="resume-card">
+      <div class="resume-icon">📁</div>
+      <div class="resume-content">
+        <h3>Resume Previous Session?</h3>
+        <p>Found results for <strong id="resume-filename">dataset.csv</strong>.</p>
+        <div class="resume-actions">
+          <button id="resume-clear-btn" class="btn-ghost">🗑️ Clear Previous Upload</button>
+          <button id="resume-go-btn" class="btn-primary">📊 Go to Results</button>
+        </div>
+      </div>
+    </div>
+  </section>
+
+<!-- UPLOAD AREA -->
+<section id="upload-section" class="section">
+    <div id="drop-zone" class="drop-zone" role="button" tabindex="0" aria-label="Upload CSV file">
+      <div class="drop-icon">
+        <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.5">
+          <path d="M12 16V4m0 0L8 8m4-4l4 4"/>
+          <path d="M4 16v2a2 2 0 002 2h12a2 2 0 002-2v-2"/>
+        </svg>
+      </div>
+      <div class="drop-text">
+        <p class="drop-title">Drag &amp; drop your CSV file</p>
+        <p class="drop-sub">or <span class="drop-link">click to browse</span></p>
+      </div>
+      <input type="file" id="file-input" accept=".csv" hidden/>
+    </div>
+    <p id="upload-error" class="error-msg" hidden></p>
+  </section>
+
+  <!-- ░░ FILE PREVIEW + TARGET SELECTOR ░░ -->
+  <section id="preview-section" class="section" hidden>
+    <div class="preview-header">
+      <div class="preview-meta" id="preview-meta"></div>
+      <button id="change-file-btn" class="btn-ghost">🗑️ Clear Upload</button>
+    </div>
+
+    <div class="target-picker">
+      <label for="target-select" class="picker-label">
+        <span class="picker-icon">🎯</span>
+        Select Target Column <span class="picker-hint">(the column you want to predict)</span>
+      </label>
+      <select id="target-select" class="target-select"></select>
+    </div>
+
+    <div class="preview-table-wrap">
+      <p class="table-label">Dataset Preview (first 5 rows)</p>
+      <div class="table-scroll">
+        <table id="preview-table" class="preview-table"></table>
+      </div>
+    </div>
+
+    <button id="run-btn" class="btn-primary">
+      <span class="btn-icon">⚡</span> Run Benchmark
+    </button>
+  </section>
+
+  <!-- ░░ LOADING ░░ -->
+  <section id="loading-section" class="section" hidden>
+    <div class="loader-card">
+      <div class="spinner-ring"></div>
+      <h2 class="loader-title">Running Benchmark</h2>
+      <p class="loader-sub">Training &amp; evaluating all 4 models across 5 folds…</p>
+      <div class="loader-steps" id="loader-steps">
+        <div class="step active" id="step-xgb">🟡 XGBoost</div>
+        <div class="step" id="step-lgb">🟢 LightGBM</div>
+        <div class="step" id="step-cat">🟣 CatBoost</div>
+        <div class="step" id="step-tabpfn">🟦 TabPFN</div>
+        <div class="step" id="step-sap">🩷 SAP RPT-1 OSS</div>
+        <div class="step" id="step-vote">🏆 Voting Ensemble</div>
+        <div class="step" id="step-stack">✨ Stacking Ensemble</div>
+      </div>
+    </div>
+  </section>
+
+
+
+</main>
+
+<footer class="footer">
+  SAP RPT-1 OSS Benchmarking · Built with FastAPI &amp; Chart.js
+</footer>
+
+<script src="/static/app.js?v=2"></script>
+</body>
+</html>

webapp/test_api.py

@@ -0,0 +1,40 @@
+import requests, json, time
+
+print("Running benchmark on breast_cancer (569 rows, 30 features)...")
+t0 = time.time()
+
+with open("webapp/test_upload.csv", "rb") as f:
+    r = requests.post(
+        "http://localhost:8000/benchmark",
+        files={"file": ("test.csv", f, "text/csv")},
+        data={"target_col": "target"},
+        timeout=300
+    )
+
+elapsed = time.time() - t0
+
+if r.status_code == 200:
+    d = r.json()
+    task = d["task"]
+    pk   = "roc_auc" if task == "classification" else "r2"
+    print(f"Task: {task}  |  Time: {elapsed:.1f}s\n")
+
+    for model, res in d["results"].items():
+        if "error" in res:
+            err = res["error"]
+            print(f"  {model:15s}  ERROR: {err}")
+        else:
+            score = res["mean"].get(pk, res["mean"].get("accuracy", 0))
+            ft    = res["mean"]["fit_time"]
+            print(f"  {model:15s}  {pk}={score:.4f}  fit_time={ft:.3f}s")
+
+    print()
+    rec = d["recommendation"]["recommendations"]
+    print("RECOMMENDATION:")
+    print(f"  Best Overall:     {rec['best_overall']['model']}")
+    print(f"  Best Accuracy:    {rec['best_accuracy']['model']}")
+    print(f"  Fastest:          {rec['best_speed']['model']}")
+    print(f"  Most Consistent:  {rec['best_consistency']['model']}")
+    print(f"  Production:       {rec['production']['model']}")
+else:
+    print("ERROR", r.status_code, r.text[:500])

webapp/test_ensemble.py

@@ -0,0 +1,32 @@
+import sys
+sys.path.insert(0, "webapp")
+import pandas as pd
+from sklearn.datasets import load_breast_cancer
+from benchmark import run_benchmark
+
+d = load_breast_cancer(as_frame=True)
+df = d.data.copy()
+df["target"] = d.target
+
+print("Running benchmark with ensembles...")
+result = run_benchmark(df, "target")
+
+print("Task:", result["task"])
+print()
+
+for name, r in result["results"].items():
+    if "error" in r:
+        msg = r["error"][:60]
+        print(f"  {name:22s}  ERROR: {msg}")
+    else:
+        auc = r["mean"].get("roc_auc", 0)
+        print(f"  {name:22s}  ROC-AUC={auc:.4f}")
+
+print()
+print("Ensemble info:")
+for name, info in result["ensemble_info"].items():
+    print(f"  {name}: type={info['type']}, components={info['components']}")
+
+print()
+best = result["recommendation"]["recommendations"]["best_overall"]
+print("Best Overall:", best["model"], "| score:", round(best["score"], 4))