72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
import os
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import pandas as pd
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# Directory where CSVs are located
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csv_dir = './'
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# List of CSV file names as you described
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csv_files = [
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'bt_randon_o=100_cp=10_ap=10_MAE=0_time=20240506 115257.csv',
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'bt_randon_o=100_cp=10_ap=10_MAE=5_time=20240506 172702.csv',
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'bt_randon_o=100_cp=10_ap=50_MAE=0_time=20240506 115600.csv',
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'bt_randon_o=100_cp=30_ap=10_MAE=5_time=20240508 222143.csv',
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'bt_randon_o=100_cp=50_ap=10_MAE=5_time=20240508 222622.csv',
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'bt_randon_o=100_cp=50_ap=30_MAE=5_time=20240508 223352.csv',
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'bt_randon_o=100_cp=50_ap=50_MAE=5_time=20240508 224801.csv',
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'bt_randon_o=300_cp=50_ap=50_MAE=5_time=20240508 232751.csv',
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'bt_randon_o=500_cp=50_ap=50_MAE=0_time=20240506 124308.csv',
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'bt_randon_o=500_cp=50_ap=50_MAE=5_time=20240120 020115.csv'
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]
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# Prepare a list to accumulate the results
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results = []
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# Loop through all CSV files
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for file in csv_files:
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# Extract the scenario details from the file name
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parts = file.split('_')
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scenario = {
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'Objects': int(parts[2].split('=')[1]),
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'Pc': int(parts[3].split('=')[1]),
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'Pa': int(parts[4].split('=')[1]),
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'MAE': int(parts[5].split('=')[1]),
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'Time': parts[6].split('=')[1] + '_' + parts[6].split('.')[0]
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}
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# Load CSV into DataFrame
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df = pd.read_csv(os.path.join(csv_dir, file))
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# Extract results for each algorithm
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obtea = df.iloc[0]
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baseline = df.iloc[1]
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# Collect relevant data in a single row
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result = {
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'Objects': scenario['Objects'],
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'Pc': scenario['Pc'],
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'Pa': scenario['Pa'],
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'MAE': scenario['MAE'],
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'Literals': round(obtea['literals_obj_count'], 1),
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'States Avg': round(obtea['state_avg'], 1),
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'Actions Avg': round(obtea['act_avg'], 1),
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'Baseline Cost Avg': round(baseline['cost_avg'], 1),
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'OBTEA Cost Avg': round(obtea['cost_avg'], 1),
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'Baseline Cond Tick Avg': round(baseline['cond_tick_avg'], 1),
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'OBTEA Cond Tick Avg': round(obtea['cond_tick_avg'], 1),
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'OBTEA WM Cond Tick Avg': round(obtea['wm_cond_tick_avg'], 1)
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}
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# Add this result to the list
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results.append(result)
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# Convert the list of results into a DataFrame
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final_results = pd.DataFrame(results)
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# Sort the DataFrame
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final_results.sort_values(by=['MAE', 'Objects', 'Pc', 'Pa'], inplace=True)
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# Display the final results
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# Output the entire DataFrame
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pd.set_option('display.max_columns', None)
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pd.set_option('display.width', None)
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print(final_results)
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# 输出的表格，每行的内容是 Objects、Pc、Pa、MAE，csv中的 literals_obj_count、state_avg、act_avg，
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# Baseline的cost_avg，OBTEA的cost_avg，Baseline的cond_tick_avg，OBTEA的cond_tick_avg，OBTEA的wm_cond_tick_avg |