requirements

util/metric_reader.py

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+import csv
+from collections import defaultdict
+from pathlib import Path
+import numpy as np
+from util.config import MConfig
+
+
+outpath = Path('..', 'output')
+metric_file_name = 'metrics.csv'
+config_file_name = 'config.ini'
+
+
+if __name__ == '__main__':
+    for model_path in outpath.iterdir():
+        out_file = (model_path / metric_file_name)
+        for paramter_configuration in model_path.iterdir():
+            uar_scores = defaultdict(list)
+            for metric_file in paramter_configuration.rglob(metric_file_name):
+                with metric_file.open('r') as f:
+                    config = MConfig()
+                    with (metric_file.parent / config_file_name).open('r') as c:
+                        config.read_file(c)
+                    for key, val in config.data.__dict__.items():
+                        uar_scores[key].append(val)
+
+                    headers = f.readline().split(',')
+                    metric_dict = defaultdict(list)
+                    for line in f:
+                        values = line.split(',')
+                        for header, value in zip(headers, values):
+                            if value:
+                                try:
+                                    metric_dict[header].append(float(value))
+                                except ValueError:
+                                    metric_dict[header].append(value)
+                for score, func in zip(['mean', 'max', 'median', 'std'], [np.mean, np.max, np.median, np.std]):
+                    try:
+                        uar_scores[score].append(func(np.asarray(metric_dict['uar_score'])).round(2))
+                    except ValueError as e:
+                        print(e)
+                        pass
+            file_existed = out_file.exists()
+            with out_file.open('a+') as f:
+                headers = list(uar_scores.keys())
+
+                writer = csv.DictWriter(f, delimiter=',', lineterminator='\n', fieldnames=headers)
+                if not file_existed:
+                    writer.writeheader()  # file doesn't exist yet, write a header
+                for row_idx in range(len(uar_scores['mean'])):
+                    writer.writerow({key: uar_scores[key][row_idx] for key in headers})