Refactor meta to follow drop of bob-devel

This follows the refactoring of bob's conda logic to drop bob-devel.

bob/med/tb/scripts/predtojson.py

@@ -2,21 +2,22 @@
 # coding=utf-8
 
 import os
-import click
-
-from bob.extension.scripts.click_helper import (
-    verbosity_option,
-    AliasedGroup,
-)
-
+import re
 import shutil
+
+import click
+import numpy
 import torch
-import re
 import pandas
 
 import logging
 logger = logging.getLogger(__name__)
 
+from bob.extension.scripts.click_helper import (
+    verbosity_option,
+    AliasedGroup,
+)
+
 
 def _load(data):
     """Load prediction.csv files
@@ -36,11 +37,17 @@ def _load(data):
         A dict in which keys are the names of the systems and the values are
         dictionaries that contain two keys:
 
-        * ``df``: A :py:class:`pandas.DataFrame` with the predictions data 
+        * ``df``: A :py:class:`pandas.DataFrame` with the predictions data
           loaded to
 
     """
 
+    def _to_double_tensor(col):
+        """Converts a column in a dataframe to a tensor array"""
+
+        pattern = re.compile(" +")
+        return col.apply(lambda cell: numpy.array(eval(pattern.sub(",", cell))))
+
     # loads all data
     retval = {}
     for name, predictions_path in data.items():
@@ -48,16 +55,8 @@ def _load(data):
         # Load predictions
         logger.info(f"Loading predictions from {predictions_path}...")
         pred_data = pandas.read_csv(predictions_path)
-        pred = torch.Tensor([eval(re.sub(' +', ' ', x.replace('\n', '')).replace(' ', ',')) for x in pred_data['likelihood'].values]).double()
-        gt = torch.Tensor([eval(re.sub(' +', ' ', x.replace('\n', '')).replace(' ', ',')) for x in pred_data['ground_truth'].values]).double()
-
-        if pred.shape[1] == 1 and gt.shape[1] == 1:
-            pred = torch.flatten(pred)
-            gt = torch.flatten(gt)
-
-        pred_data['likelihood'] = pred
-        pred_data['ground_truth'] = gt
-
+        pred_data['likelihood'] = _to_double_tensor(pred_data['likelihood'])
+        pred_data['ground_truth'] = _to_double_tensor(pred_data['ground_truth'])
         retval[name] = dict(df=pred_data)
 
     return retval
@@ -109,12 +108,12 @@ def predtojson(label_path, output_folder, **kwargs):
 
     logger.info("Saving JSON file...")
     with open(output_file, "a+", newline="") as f:
-        
+
         f.write('{')
         for i, (name, value) in enumerate(data.items()):
             if i > 0:
                 f.write(',')
-                
+
             df = value["df"]
             f.write('"'+name+'": [')
             for index, row in df.iterrows():
@@ -127,4 +126,4 @@ def predtojson(label_path, output_folder, **kwargs):
                 f.write(']')
             f.write(']')
         f.write('}')
-            
+