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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,44 +1,67 @@ | ||
| import pandas as pd | ||
| import time | ||
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| import pandas as pd | ||
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| import lotus | ||
| from lotus.models import LM | ||
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| def main(): | ||
| lm = LM(model="gpt-4o-mini") | ||
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| lotus.settings.configure(lm=lm) | ||
| lotus.settings.configure(enable_multithreading=True) | ||
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| data = { | ||
| "Department": ["Math", "Physics", "Computer Science", "Biology"] * 7, | ||
| "Course Name": [ | ||
| "Calculus", "Quantum Mechanics", "Data Structures", "Genetics", | ||
| "Linear Algebra", "Thermodynamics", "Algorithms", "Ecology", | ||
| "Statistics", "Optics", "Machine Learning", "Molecular Biology", | ||
| "Number Theory", "Relativity", "Computer Networks", "Evolutionary Biology", | ||
| "Differential Equations", "Particle Physics", "Operating Systems", "Biochemistry", | ||
| "Complex Analysis", "Fluid Dynamics", "Artificial Intelligence", "Microbiology", | ||
| "Topology", "Astrophysics", "Cybersecurity", "Immunology" | ||
| ] | ||
| "Calculus", | ||
| "Quantum Mechanics", | ||
| "Data Structures", | ||
| "Genetics", | ||
| "Linear Algebra", | ||
| "Thermodynamics", | ||
| "Algorithms", | ||
| "Ecology", | ||
| "Statistics", | ||
| "Optics", | ||
| "Machine Learning", | ||
| "Molecular Biology", | ||
| "Number Theory", | ||
| "Relativity", | ||
| "Computer Networks", | ||
| "Evolutionary Biology", | ||
| "Differential Equations", | ||
| "Particle Physics", | ||
| "Operating Systems", | ||
| "Biochemistry", | ||
| "Complex Analysis", | ||
| "Fluid Dynamics", | ||
| "Artificial Intelligence", | ||
| "Microbiology", | ||
| "Topology", | ||
| "Astrophysics", | ||
| "Cybersecurity", | ||
| "Immunology", | ||
| ], | ||
| } | ||
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| df = pd.DataFrame(data) | ||
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| for method in ["quick", "heap", "naive"]: | ||
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| start_time = time.time() | ||
| sorted_df, stats = df.sem_topk( | ||
| "Which {Course Name} is the most challenging?", | ||
| K=2, | ||
| method=method, | ||
| return_stats=True, | ||
| group_by=["Department"] | ||
| group_by=["Department"], | ||
| ) | ||
| end_time = time.time() | ||
| print(sorted_df) | ||
| print(stats) | ||
| print(f"Total execution time: {end_time - start_time:.2f} seconds") | ||
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| if __name__ == '__main__': | ||
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| if __name__ == "__main__": | ||
| main() |
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