Calculate confidence interval for benchmark measurements
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@ -78,6 +78,7 @@ class BenchmarkRunner:
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raise Exception(f"Unknown benchmark: {benchmark_name}")
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def calculate_confidence_interval(data):
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print('Data: ', data.shape)
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assert len(data) == 5
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mean = np.mean(data)
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std_err = np.std(data, ddof=1) / np.sqrt(len(data))
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@ -89,7 +90,7 @@ def main():
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parser.add_argument('--host', type=str, required=True, help='Host URL')
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parser.add_argument('--method', type=str, required=True, choices=['route', 'table', 'match', 'nearest', 'trip'], help='Benchmark method')
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parser.add_argument('--num_requests', type=int, required=True, help='Number of requests to perform')
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parser.add_argument('--iterations', type=int, default=5, required=True, help='Number of iterations to run the benchmark')
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parser.add_argument('--iterations', type=int, required=True, help='Number of iterations to run the benchmark')
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parser.add_argument('--gps_traces_file_path', type=str, required=True, help='Path to the GPS traces file')
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args = parser.parse_args()
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@ -104,6 +105,8 @@ def main():
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all_times.append(times)
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all_times = np.asarray(all_times)
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print('Shape: ', all_times.shape)
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total_time, total_ci = calculate_confidence_interval(np.sum(all_times, axis=0))
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min_time, min_ci = calculate_confidence_interval(np.min(all_times, axis=0))
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mean_time, mean_ci = calculate_confidence_interval(np.mean(all_times, axis=0))
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