Minor improvements

Now the user can select the folder where the metrics.csv file is using the flag --metrics-folder

Brain_study/ABSTRACT/format_tables_abstract.py

@@ -1,6 +1,7 @@
 import warnings
 
 import pandas as pd
+pd.options.display.max_columns = 10
 import os
 import argparse
 import re
@@ -18,21 +19,33 @@ DICT_METRICS_NAMES = {'NCC': 'N',
 
 
 def row_name(in_path: str):
-    model = re.search('((UW|SEGGUIDED|BASELINE).*)_\d', in_path).group(1).rstrip('_')
-    model = model.replace('_Lsim', '')
-    model = model.replace('_Lseg', '')
-    model = model.replace('_L', '')
-    model = model.replace('_', ' ')
-    model = model.upper()
-    elements = model.split()
-    model = elements[0]
-    metrics = list()
-    model = DICT_MODEL_NAMES[model]
-    for m in elements[1:]:
-        if m != 'MACRO':
-            metrics.append(DICT_METRICS_NAMES[m])
-
-    return '{}-{}'.format(model, ''.join(metrics))
+    model = re.search('((UW|SEGGUIDED|BASELINE).*)_\d', in_path)
+    ret_val = None
+    if model:
+        model = model.group(1).rstrip('_')
+        model = model.replace('_Lsim', '')
+        model = model.replace('_Lseg', '')
+        model = model.replace('_L', '')
+        model = model.replace('_', ' ')
+        model = model.upper()
+        elements = model.split()
+        model = elements[0]
+        metrics = list()
+        model = DICT_MODEL_NAMES[model]
+        for m in elements[1:]:
+            if m != 'MACRO':
+                metrics.append(DICT_METRICS_NAMES[m])
+
+        ret_val = '{}-{}'.format(model, ''.join(metrics))
+    elif re.search('((COMET|IXI).*)', in_path):
+        model = re.search('((COMET|IXI).*)', in_path)
+        ret_val = model.group(1).split('_')[0]
+    else:
+        try:
+            ret_val = re.search('(SyNCC|SyN)', in_path).group(1)
+        except AttributeError:
+            raise ValueError('Unknown folder name/model: '+ in_path)
+    return ret_val
 
 
 if __name__ == '__main__':
@@ -44,32 +57,43 @@ if __name__ == '__main__':
     parser.add_argument('--overwrite', type=bool, default=True)
     parser.add_argument('--filename', type=str, help='Output file name', default='metrics')
     parser.add_argument('--removemetrics', nargs='+', type=str, default=None)
+    parser.add_argument('--metrics-folder', type=str, default=None)
     args = parser.parse_args()
     assert args.dir is not None, "No directories provided. Stopping"
 
     if len(args.dir) == 1:
         list_files = list()
+        if args.metrics_folder:
+            file_found_condition = lambda name: 'metrics.csv' == name and args.metrics_folder in r.split(os.sep)
+        else:
+            file_found_condition = lambda name: 'metrics.csv' == name
+        starting_level = args.dir[0].count(os.sep)
         for r, d, f in os.walk(args.dir[0]):
-            for name in f:
-                if 'metrics.csv' == name:  # and os.path.split(r)[1] == 'Evaluation_paper':
-                    list_files.append(os.path.join(r, name))
+            level = r.count(os.sep) - starting_level
+            if level < 3:
+                for name in f:
+                    if file_found_condition(name):
+                        list_files.append(os.path.join(r, name))
     else:
         list_files = [os.path.join(d, 'metrics.csv') for d in args.dir]
 
         for d in list_files:
             assert os.path.exists(d), "Missing metrics.csv file in: " + os.path.split(d)[0]
-
-    print('Metric files found: {}'.format(list_files))
+    list_files.sort()
+    print('Metric files found ({}):\n\t{}'.format(len(list_files), '\n\t'.join(list_files)))
 
     dataframes = list()
     if len(list_files):
         for d in list_files:
-            df = pd.read_csv(d, sep=';', header=0)
+            df = pd.read_csv(d, sep=';', header=0, dtype={'TRE':float})
             model = row_name(d)
 
             df.insert(0, "Model", model)
             df.drop(columns=list(df.filter(regex='Unnamed')), inplace=True)
-            df.drop(columns=['File', 'MSE', 'No_missing_lbls'], inplace=True)
+            if not 'SyN' in model:
+                df.drop(columns=['File', 'MSE', 'No_missing_lbls'], inplace=True)
+            else:
+                df.drop(columns=['File', 'MSE'], inplace=True)
             dataframes.append(df)
 
         full_table = pd.concat(dataframes)
@@ -79,7 +103,7 @@ if __name__ == '__main__':
         # mean_table.insert(column='Type', value='Avg.', loc=1)
         # mean_table = mean_table.groupby(['Type', 'Model']).mean().round(3)
         mean_table = mean_table.groupby(['Model'])
-        hd95 = mean_table.HD.quantile(0.95).map('{:.2f}'.format)
+        # hd95 = mean_table.HD.quantile(0.95).map('{:.2f}'.format)
         mean_table = mean_table.mean().round(3)
 
         std_table = full_table.copy()
@@ -91,21 +115,25 @@ if __name__ == '__main__':
         metrics_table = mean_table.applymap('{:.2f}'.format) + u"\u00B1" + std_table.applymap('{:.2f}'.format)
         time_col = metrics_table.pop('Time')
         metrics_table.insert(len(metrics_table.columns), 'Time', time_col)
-        metrics_table.insert(5, 'HD 95%ile', hd95)
+        # metrics_table.insert(4, 'HD95', hd95)
+        metrics_table.rename(columns={'DICE_MACRO': 'DSC', 'Time': 'Runtime'}, inplace=True)
 
         metrics_file = os.path.join(args.output, args.filename + '.tex')
         if os.path.exists(metrics_file) and args.overwrite:
             shutil.rmtree(metrics_file, ignore_errors=True)
             metrics_table.to_latex(metrics_file,
-                                   column_format='l' + 'c' * len(metrics_table.columns),
-                                   caption='Average and standard deviation of the metrics: MSE, NCC, SSIM, DICE and HD. As well as the number of missing labels in the predicted images.')
+                                   bold_rows=True,
+                                   column_format='r' + 'c' * len(metrics_table.columns),
+                                   caption='Average and standard deviation of the metrics: MSE, NCC, SSIM, DSC and HD.')
         elif os.path.exists(metrics_file):
             warnings.warn('File {} already exists. Skipping'.format(metrics_file))
         else:
             metrics_table.to_latex(metrics_file,
-                                   column_format='l' + 'c' * len(metrics_table.columns),
-                                   caption='Average and standard deviation of the metrics: MSE, NCC, SSIM, DICE and HD. As well as the number of missing labels in the predicted images.')
+                                   bold_rows=True,
+                                   column_format='r' + 'c' * len(metrics_table.columns),
+                                   caption='Average and standard deviation of the metrics: MSE, NCC, SSIM, DSC and HD.')
 
+        print(metrics_table)
         print('Done')
     else:
         print('No files found in {}!'.format(args.dir))