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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FBMC Chronos-2 Zero-Shot Inference - Smoke Test\n",
"\n",
"**Quick validation**: 1 border × 7 days (168 hours)\n",
"\n",
"This notebook tests the complete inference pipeline on HuggingFace Space with GPU acceleration."
]
},
{
"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",
"\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\" Test border: {borders[0]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Prepare Test Data with Time-Aware Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Test configuration\n",
"test_border = borders[0]\n",
"prediction_hours = 168 # 7 days\n",
"context_hours = 512 # Context window\n",
"\n",
"# Use Sept 30 as run date (requires Oct 1-7 future covariates)\n",
"run_date = datetime(2025, 9, 30, 23, 0)\n",
"\n",
"print(f\"Test configuration:\")\n",
"print(f\" Run date: {run_date}\")\n",
"print(f\" Context: {context_hours} hours (historical)\")\n",
"print(f\" Forecast: {prediction_hours} hours (7 days)\")\n",
"print(f\" Forecast range: Oct 1 00:00 to Oct 7 23:00\")\n",
"\n",
"# Initialize dynamic forecast\n",
"forecaster = DynamicForecast(\n",
" df=df,\n",
" feature_categories=categories\n",
")\n",
"\n",
"# Extract data with leakage prevention\n",
"context_data, future_data = forecaster.prepare_forecast_data(\n",
" run_date=run_date,\n",
" border=test_border\n",
")\n",
"\n",
"print(f\"\\n✓ Data extracted:\")\n",
"print(f\" Context: {context_data.shape}\")\n",
"print(f\" Future: {future_data.shape}\")\n",
"print(f\" Leakage check: PASSED\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. 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": [
"## 6. Run Zero-Shot Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Running zero-shot inference...\")\n",
"start_time = time.time()\n",
"\n",
"# Take last 512 hours of context\n",
"context = context_data.select([test_border]).to_numpy()[-context_hours:].flatten()\n",
"\n",
"# Run forecast\n",
"forecast = pipeline.predict(\n",
" context=context,\n",
" prediction_length=prediction_hours,\n",
" num_samples=20\n",
")\n",
"\n",
"# Get median forecast\n",
"forecast_median = forecast.numpy().median(axis=0)\n",
"\n",
"inference_time = time.time() - start_time\n",
"print(f\"✓ Inference complete in {inference_time:.1f}s\")\n",
"print(f\" Forecast shape: {forecast.shape}\")\n",
"print(f\" Median forecast range: [{forecast_median.min():.0f}, {forecast_median.max():.0f}] MW\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Visualize Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prepare data for visualization\n",
"forecast_timestamps = pl.datetime_range(\n",
" datetime(2025, 10, 1, 0, 0),\n",
" datetime(2025, 10, 7, 23, 0),\n",
" interval='1h',\n",
" eager=True\n",
")\n",
"\n",
"viz_data = pl.DataFrame({\n",
" 'timestamp': forecast_timestamps,\n",
" 'forecast': forecast_median.tolist()\n",
"})\n",
"\n",
"# Create chart\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=800,\n",
" height=400,\n",
" title=f'Zero-Shot Forecast: {test_border} (Oct 1-7, 2025)'\n",
")\n",
"\n",
"chart"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"=\"*60)\n",
"print(\"SMOKE TEST COMPLETE\")\n",
"print(\"=\"*60)\n",
"print(f\"Border: {test_border}\")\n",
"print(f\"Forecast period: Oct 1-7, 2025 (168 hours)\")\n",
"print(f\"Inference time: {inference_time:.1f}s\")\n",
"print(f\"GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A'}\")\n",
"print(f\"\\n✓ Zero-shot forecasting working on HuggingFace Space!\")"
]
}
],
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