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Bole Ma
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xgb_local_example.py
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xgb_local_example.py
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#!/usr/bin/env python3
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"""
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XGBoost Multi-Label Inference Usage Examples
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===================================================
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This script demonstrates how to use the XGBoostMultiLabelPredictor class
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for multi-label classification with confidence scores.
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Sample data is from real HPC workloads extracted from roofline_features.h5:
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- TurTLE: Turbulence simulation (memory-bound, low arithmetic intensity ~0.84)
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- SCALEXA: Scaling benchmarks (high bw-flops correlation ~0.995)
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- Chroma: Lattice QCD (compute-intensive, high arithmetic intensity ~2.6)
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"""
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import json
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from xgb_local import XGBoostMultiLabelPredictor
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# ============================================================================
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# Realistic Sample Data from roofline_features.h5
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# ============================================================================
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# TurTLE application - turbulence simulation workload
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SAMPLE_TURTLE = {
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"bandwidth_raw_p10": 186.33,
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"bandwidth_raw_median": 205.14,
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"bandwidth_raw_p90": 210.83,
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"bandwidth_raw_mad": 3.57,
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"bandwidth_raw_range": 24.5,
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"bandwidth_raw_iqr": 12.075,
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"flops_raw_p10": 162.024,
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"flops_raw_median": 171.45,
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"flops_raw_p90": 176.48,
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"flops_raw_mad": 3.08,
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"flops_raw_range": 14.456,
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"flops_raw_iqr": 8.29,
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"arith_intensity_p10": 0.7906,
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"arith_intensity_median": 0.837,
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"arith_intensity_p90": 0.9109,
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"arith_intensity_mad": 0.02,
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"arith_intensity_range": 0.12,
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"arith_intensity_iqr": 0.0425,
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"bw_flops_covariance": 60.86,
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"bw_flops_correlation": 0.16,
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"avg_performance_gflops": 168.1,
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"median_performance_gflops": 171.45,
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"performance_gflops_mad": 3.08,
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"avg_memory_bw_gbs": 350.0,
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"scalar_peak_gflops": 432.0,
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"simd_peak_gflops": 9216.0,
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"node_num": 0,
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"duration": 19366,
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}
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# SCALEXA application - scaling benchmark workload
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SAMPLE_SCALEXA = {
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"bandwidth_raw_p10": 13.474,
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"bandwidth_raw_median": 32.57,
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"bandwidth_raw_p90": 51.466,
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"bandwidth_raw_mad": 23.62,
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"bandwidth_raw_range": 37.992,
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"bandwidth_raw_iqr": 23.745,
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"flops_raw_p10": 4.24,
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"flops_raw_median": 16.16,
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"flops_raw_p90": 24.584,
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"flops_raw_mad": 10.53,
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"flops_raw_range": 20.344,
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"flops_raw_iqr": 12.715,
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"arith_intensity_p10": 0.211,
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"arith_intensity_median": 0.475,
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"arith_intensity_p90": 0.492,
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"arith_intensity_mad": 0.021,
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"arith_intensity_range": 0.281,
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"arith_intensity_iqr": 0.176,
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"bw_flops_covariance": 302.0,
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"bw_flops_correlation": 0.995,
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"avg_performance_gflops": 14.7,
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"median_performance_gflops": 16.16,
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"performance_gflops_mad": 10.53,
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"avg_memory_bw_gbs": 350.0,
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"scalar_peak_gflops": 432.0,
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"simd_peak_gflops": 9216.0,
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"node_num": 18,
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"duration": 165,
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}
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# Chroma application - lattice QCD workload (compute-intensive)
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SAMPLE_CHROMA = {
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"bandwidth_raw_p10": 154.176,
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"bandwidth_raw_median": 200.57,
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"bandwidth_raw_p90": 259.952,
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"bandwidth_raw_mad": 5.12,
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"bandwidth_raw_range": 105.776,
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"bandwidth_raw_iqr": 10.215,
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"flops_raw_p10": 327.966,
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"flops_raw_median": 519.8,
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"flops_raw_p90": 654.422,
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"flops_raw_mad": 16.97,
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"flops_raw_range": 326.456,
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"flops_raw_iqr": 34.88,
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"arith_intensity_p10": 1.55,
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"arith_intensity_median": 2.595,
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"arith_intensity_p90": 3.445,
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"arith_intensity_mad": 0.254,
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"arith_intensity_range": 1.894,
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"arith_intensity_iqr": 0.512,
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"bw_flops_covariance": 382.76,
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"bw_flops_correlation": 0.063,
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"avg_performance_gflops": 503.26,
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"median_performance_gflops": 519.8,
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"performance_gflops_mad": 16.97,
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"avg_memory_bw_gbs": 350.0,
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"scalar_peak_gflops": 432.0,
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"simd_peak_gflops": 9216.0,
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"node_num": 3,
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"duration": 31133,
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}
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# Raw JSON roofline data (before aggregation, as produced by monitoring)
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SAMPLE_JSON_ROOFLINE = json.dumps([
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{"node_num": 1, "bandwidth_raw": 150.5, "flops_raw": 2500.0, "arith_intensity": 16.6,
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"performance_gflops": 1200.0, "memory_bw_gbs": 450, "scalar_peak_gflops": 600, "duration": 3600},
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{"node_num": 1, "bandwidth_raw": 155.2, "flops_raw": 2600.0, "arith_intensity": 16.8,
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"performance_gflops": 1250.0, "memory_bw_gbs": 450, "scalar_peak_gflops": 600, "duration": 3600},
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{"node_num": 1, "bandwidth_raw": 148.0, "flops_raw": 2450.0, "arith_intensity": 16.5,
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"performance_gflops": 1180.0, "memory_bw_gbs": 450, "scalar_peak_gflops": 600, "duration": 3600},
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])
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def main():
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print("XGBoost Multi-Label Inference Examples")
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print("=" * 50)
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# Initialize the predictor
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predictor = XGBoostMultiLabelPredictor()
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# =========================================================================
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# Example 1: Single prediction with aggregated features
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# =========================================================================
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print("\n=== Example 1: Single Prediction (TurTLE workload) ===")
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result = predictor.predict(SAMPLE_TURTLE, threshold=0.3)
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print(f"Predictions: {result['predictions']}")
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print(f"Confidences: {result['confidences']}")
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print("\nTop 5 probabilities:")
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sorted_probs = sorted(result['probabilities'].items(), key=lambda x: x[1], reverse=True)
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for cls, prob in sorted_probs[:5]:
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print(f" {cls}: {prob:.4f}")
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# =========================================================================
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# Example 2: Compare different workload types
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# =========================================================================
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print("\n=== Example 2: Compare Different Workloads ===")
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workloads = [
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("TurTLE (turbulence)", SAMPLE_TURTLE),
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("SCALEXA (benchmark)", SAMPLE_SCALEXA),
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("Chroma (lattice QCD)", SAMPLE_CHROMA),
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]
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for name, features in workloads:
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result = predictor.predict(features, threshold=0.3)
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top_pred = result['predictions'][0] if result['predictions'] else "None"
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top_prob = max(result['probabilities'].values()) if result['probabilities'] else 0
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print(f"{name:25} -> Top prediction: {top_pred:20} (prob: {top_prob:.4f})")
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# =========================================================================
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# Example 3: Top-K predictions
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# =========================================================================
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print("\n=== Example 3: Top-5 Predictions (Chroma workload) ===")
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top_k_result = predictor.predict_top_k(SAMPLE_CHROMA, k=5)
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for i, cls in enumerate(top_k_result['top_predictions'], 1):
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prob = top_k_result['top_probabilities'][cls]
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print(f" {i}. {cls}: {prob:.4f}")
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# =========================================================================
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# Example 4: Batch prediction
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# =========================================================================
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print("\n=== Example 4: Batch Prediction ===")
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batch_data = [SAMPLE_TURTLE, SAMPLE_SCALEXA, SAMPLE_CHROMA]
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batch_results = predictor.batch_predict(batch_data, threshold=0.3)
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for i, result in enumerate(batch_results, 1):
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if 'error' not in result:
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preds = result['predictions'][:2] # Show top 2
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print(f" Sample {i}: {preds}")
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else:
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print(f" Sample {i}: Error - {result['error']}")
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# =========================================================================
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# Example 5: Prediction from raw JSON roofline data
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# =========================================================================
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print("\n=== Example 5: Prediction from Raw JSON Data ===")
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result = predictor.predict(
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SAMPLE_JSON_ROOFLINE,
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is_json=True,
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job_id="example_job_001",
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threshold=0.3
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)
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print(f"Predictions: {result['predictions'][:3]}")
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print(f"(Aggregated from {len(json.loads(SAMPLE_JSON_ROOFLINE))} roofline samples)")
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# =========================================================================
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# Example 6: Model information
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# =========================================================================
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print("\n=== Example 6: Model Information ===")
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info = predictor.get_class_info()
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print(f"Number of classes: {info['n_classes']}")
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print(f"Number of features: {info['n_features']}")
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print(f"Sample classes: {info['classes'][:5]}...")
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print(f"Sample features: {info['feature_columns'][:3]}...")
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if __name__ == "__main__":
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main()
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