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import glob |
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import pandas as pd |
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from collections import defaultdict |
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from argparse import ArgumentParser |
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parser = ArgumentParser() |
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parser.add_argument('--path_to_folder', required=True, type=str, default='loggings/eval_uncond/ours/', |
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help='Path (absolute) to the first dataset (folder)') |
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args = parser.parse_args() |
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metric = 'OA' |
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attributes = ['total_used_pitch', |
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'avg_IOI', |
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'total_pitch_class_histogram', |
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'pitch_range', |
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'mean_note_velocity', |
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'mean_note_duration', |
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'note_density', |
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'avg' |
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] |
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csv_files = glob.glob(args.path_to_folder + '*.csv') |
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results_mean = defaultdict(list) |
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results_std = defaultdict(list) |
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for file_path in csv_files: |
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file_name = file_path.split('/')[-1] |
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dataset = file_name.split('.')[0] |
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method = file_name.split('.')[1] |
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stat_type = file_name.split('.')[2] |
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df = pd.read_csv(file_path) |
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if stat_type == 'mean': |
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results_mean["dataset"].append(dataset) |
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results_mean["method"].append(method) |
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for attr in attributes: |
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results_mean[attr].append(df[df.attribute == attr][metric].item()) |
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else: |
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results_std["dataset"].append(dataset) |
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results_std["method"].append(method) |
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for attr in attributes: |
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results_std[attr].append(df[df.attribute == attr][metric].item()) |
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df_results_mean = pd.DataFrame(results_mean) |
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sorted_df = df_results_mean.sort_values(by=['dataset', 'method']) |
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sorted_df.to_csv(args.path_to_folder + 'results_mean.csv', index=False) |
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df_results_std = pd.DataFrame(results_std) |
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sorted_df = df_results_std.sort_values(by=['dataset', 'method']) |
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sorted_df.to_csv(args.path_to_folder + 'results_std.csv', index=False) |
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print("done") |
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