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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FBMC Chronos-2 Zero-Shot Inference - Full Production Forecast\n",
"\n",
"**Production run**: 38 borders × 14 days (336 hours)\n",
"\n",
"This notebook runs complete zero-shot forecasts for all FBMC borders on HuggingFace Space with GPU."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Environment Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import os\n",
"import polars as pl\n",
"import torch\n",
"from datetime import datetime, timedelta\n",
"from datasets import load_dataset\n",
"from chronos import ChronosPipeline\n",
"import altair as alt\n",
"from pathlib import Path\n",
"\n",
"# Add src to path for imports\n",
"import sys\n",
"sys.path.append('/home/user/app/src') # HF Space path\n",
"\n",
"from forecasting.dynamic_forecast import DynamicForecast\n",
"from forecasting.feature_availability import FeatureAvailability\n",
"\n",
"print(\"Environment setup complete\")\n",
"print(f\"PyTorch version: {torch.__version__}\")\n",
"print(f\"GPU available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU device: {torch.cuda.get_device_name(0)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Load Extended Dataset from HuggingFace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Loading dataset from HuggingFace...\")\n",
"start_time = time.time()\n",
"\n",
"# Load dataset\n",
"hf_token = os.getenv(\"HF_TOKEN\")\n",
"dataset = load_dataset(\n",
" \"evgueni-p/fbmc-features-24month\",\n",
" split=\"train\",\n",
" token=hf_token\n",
")\n",
"\n",
"# Convert to Polars\n",
"df = pl.from_arrow(dataset.data.table)\n",
"\n",
"print(f\"✓ Loaded: {df.shape}\")\n",
"print(f\" Date range: {df['timestamp'].min()} to {df['timestamp'].max()}\")\n",
"print(f\" Load time: {time.time() - start_time:.1f}s\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Configure Dynamic Forecast System"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Categorize features by availability\n",
"categories = FeatureAvailability.categorize_features(df.columns)\n",
"\n",
"print(\"Feature categorization:\")\n",
"print(f\" Full-horizon D+14: {len(categories['full_horizon_d14'])} features\")\n",
"print(f\" Partial D+1: {len(categories['partial_d1'])} features\")\n",
"print(f\" Historical only: {len(categories['historical'])} features\")\n",
"print(f\" Total: {sum(len(v) for v in categories.values())} features\")\n",
"\n",
"# Identify target borders\n",
"target_cols = [col for col in df.columns if col.startswith('target_border_')]\n",
"borders = [col.replace('target_border_', '') for col in target_cols]\n",
"print(f\"\\n✓ Found {len(borders)} borders\")\n",
"print(f\" Borders: {', '.join(borders[:5])}...\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Load Chronos-2 Model on GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Loading Chronos-2 Large model...\")\n",
"start_time = time.time()\n",
"\n",
"pipeline = ChronosPipeline.from_pretrained(\n",
" \"amazon/chronos-t5-large\",\n",
" device_map=\"cuda\",\n",
" torch_dtype=torch.bfloat16\n",
")\n",
"\n",
"print(f\"✓ Model loaded in {time.time() - start_time:.1f}s\")\n",
"print(f\" Device: {next(pipeline.model.parameters()).device}\")\n",
"print(f\" Dtype: {next(pipeline.model.parameters()).dtype}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Run Zero-Shot Inference for All Borders"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Production configuration\n",
"prediction_hours = 336 # 14 days\n",
"context_hours = 512 # Context window\n",
"run_date = datetime(2025, 9, 30, 23, 0) # Sept 30 11 PM\n",
"\n",
"print(\"Production forecast configuration:\")\n",
"print(f\" Run date: {run_date}\")\n",
"print(f\" Context: {context_hours} hours\")\n",
"print(f\" Forecast: {prediction_hours} hours (14 days)\")\n",
"print(f\" Forecast range: Oct 1 00:00 to Oct 14 23:00\")\n",
"print(f\" Borders: {len(borders)}\")\n",
"print()\n",
"\n",
"# Initialize dynamic forecast\n",
"forecaster = DynamicForecast(\n",
" df=df,\n",
" feature_categories=categories\n",
")\n",
"\n",
"# Storage for all forecasts\n",
"all_forecasts = {}\n",
"inference_times = {}\n",
"\n",
"# Run inference for each border\n",
"total_start = time.time()\n",
"\n",
"for i, border in enumerate(borders, 1):\n",
" print(f\"[{i}/{len(borders)}] Processing {border}...\", end=\" \")\n",
" \n",
" try:\n",
" # Extract data\n",
" context_data, future_data = forecaster.prepare_forecast_data(\n",
" run_date=run_date,\n",
" border=border\n",
" )\n",
" \n",
" # Get context (last 512 hours)\n",
" context = context_data.select([border]).to_numpy()[-context_hours:].flatten()\n",
" \n",
" # Run inference\n",
" start_time = time.time()\n",
" forecast = pipeline.predict(\n",
" context=context,\n",
" prediction_length=prediction_hours,\n",
" num_samples=20\n",
" )\n",
" elapsed = time.time() - start_time\n",
" \n",
" # Store median forecast\n",
" forecast_median = forecast.numpy().median(axis=0)\n",
" all_forecasts[border] = forecast_median\n",
" inference_times[border] = elapsed\n",
" \n",
" print(f\"✓ {elapsed:.1f}s\")\n",
" \n",
" except Exception as e:\n",
" print(f\"✗ ERROR: {str(e)}\")\n",
" all_forecasts[border] = None\n",
" inference_times[border] = 0.0\n",
"\n",
"total_time = time.time() - total_start\n",
"\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"INFERENCE COMPLETE\")\n",
"print(\"=\"*60)\n",
"print(f\"Total time: {total_time/60:.1f} minutes\")\n",
"print(f\"Avg per border: {total_time/len(borders):.1f}s\")\n",
"print(f\"Successful: {sum(1 for v in all_forecasts.values() if v is not None)}/{len(borders)}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Save Forecasts to Parquet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create timestamp range for forecasts\n",
"forecast_timestamps = pl.datetime_range(\n",
" datetime(2025, 10, 1, 0, 0),\n",
" datetime(2025, 10, 14, 23, 0),\n",
" interval='1h',\n",
" eager=True\n",
")\n",
"\n",
"# Build forecast DataFrame\n",
"forecast_data = {'timestamp': forecast_timestamps}\n",
"for border, forecast in all_forecasts.items():\n",
" if forecast is not None:\n",
" forecast_data[f'forecast_{border}'] = forecast.tolist()\n",
" else:\n",
" forecast_data[f'forecast_{border}'] = [None] * len(forecast_timestamps)\n",
"\n",
"forecast_df = pl.DataFrame(forecast_data)\n",
"\n",
"# Save to parquet\n",
"output_path = Path('/home/user/app/forecasts_14day.parquet')\n",
"forecast_df.write_parquet(output_path)\n",
"\n",
"print(f\"✓ Forecasts saved: {forecast_df.shape}\")\n",
"print(f\" File: {output_path}\")\n",
"print(f\" Size: {output_path.stat().st_size / 1024 / 1024:.1f} MB\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Visualize Sample Borders"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Select 4 representative borders for visualization\n",
"sample_borders = borders[:4]\n",
"\n",
"charts = []\n",
"for border in sample_borders:\n",
" if all_forecasts[border] is not None:\n",
" viz_data = pl.DataFrame({\n",
" 'timestamp': forecast_timestamps,\n",
" 'forecast': all_forecasts[border].tolist()\n",
" })\n",
" \n",
" chart = alt.Chart(viz_data.to_pandas()).mark_line().encode(\n",
" x=alt.X('timestamp:T', title='Date'),\n",
" y=alt.Y('forecast:Q', title='Flow (MW)'),\n",
" tooltip=['timestamp:T', alt.Tooltip('forecast:Q', format='.0f')]\n",
" ).properties(\n",
" width=400,\n",
" height=200,\n",
" title=f'{border}'\n",
" )\n",
" charts.append(chart)\n",
"\n",
"# Combine into 2x2 grid\n",
"combined = alt.vconcat(\n",
" alt.hconcat(charts[0], charts[1]),\n",
" alt.hconcat(charts[2], charts[3])\n",
").properties(\n",
" title='Sample Zero-Shot Forecasts (Oct 1-14, 2025)'\n",
")\n",
"\n",
"combined"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Performance Summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create performance summary\n",
"perf_data = pl.DataFrame({\n",
" 'border': list(inference_times.keys()),\n",
" 'inference_time_s': list(inference_times.values()),\n",
" 'status': ['SUCCESS' if all_forecasts[b] is not None else 'FAILED' for b in inference_times.keys()]\n",
"}).sort('inference_time_s', descending=True)\n",
"\n",
"print(\"\\nTop 10 Slowest Borders:\")\n",
"print(perf_data.head(10))\n",
"\n",
"print(\"\\nPerformance Statistics:\")\n",
"print(f\" Mean: {perf_data['inference_time_s'].mean():.1f}s\")\n",
"print(f\" Median: {perf_data['inference_time_s'].median():.1f}s\")\n",
"print(f\" Min: {perf_data['inference_time_s'].min():.1f}s\")\n",
"print(f\" Max: {perf_data['inference_time_s'].max():.1f}s\")\n",
"\n",
"print(\"\\n\" + \"=\"*60)\n",
"print(\"PRODUCTION FORECAST COMPLETE\")\n",
"print(\"=\"*60)\n",
"print(f\"Borders processed: {len(borders)}\")\n",
"print(f\"Forecast horizon: 14 days (336 hours)\")\n",
"print(f\"Total runtime: {total_time/60:.1f} minutes\")\n",
"print(f\"Output: forecasts_14day.parquet\")\n",
"print(f\"\\n✓ Ready for evaluation against Oct 1-14 actuals\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.0"
}
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"nbformat": 4,
"nbformat_minor": 4
}
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