Change paths to loading/conversion functions now in bob.bio.base (previously in bob.measure)

bob/bio/base/script/collect_results.py

@@ -29,8 +29,9 @@ import sys, os,  glob
 import argparse
 import numpy
 
-import bob.measure
 import bob.core
+from .. import score
+
 logger = bob.core.log.setup("bob.bio.base")
 
 def command_line_arguments(command_line_parameters):
@@ -80,9 +81,9 @@ class Result:
   def _calculate(self, dev_file, eval_file = None):
     """Calculates the EER and HTER or FRR based on the threshold criterion."""
     if self.m_args.criterion in ("RR", "DIR"):
-      scores_dev = bob.measure.load.cmc(dev_file)
+      scores_dev = score.cmc(dev_file)
       if eval_file is not None:
-        scores_eval = bob.measure.load.cmc(eval_file)
+        scores_eval = score.cmc(eval_file)
 
       if self.m_args.criterion == "DIR":
         # get negatives without positives
@@ -110,7 +111,7 @@ class Result:
 
     else:
 
-      dev_neg, dev_pos = bob.measure.load.split(dev_file)
+      dev_neg, dev_pos = score.split(dev_file)
 
       # switch which threshold function to use
       if self.m_args.criterion == 'EER':
@@ -127,7 +128,7 @@ class Result:
       dev_hter = (dev_far + dev_frr)/2.0
 
       if eval_file:
-        eval_neg, eval_pos = bob.measure.load.split(eval_file)
+        eval_neg, eval_pos = score.split(eval_file)
         eval_far, eval_frr = bob.measure.farfrr(eval_neg, eval_pos, threshold)
         eval_hter = (eval_far + eval_frr)/2.0
       else:

bob/bio/base/script/fuse_scores.py

@@ -17,6 +17,7 @@ import bob, os, sys
 import bob.learn.linear
 
 import bob.core
+from .. import score
 logger = bob.core.log.setup("bob.bio.base")
 
 def parse_command_line(command_line_options):
@@ -66,7 +67,7 @@ def main(command_line_options = None):
   for i in range(n_systems):
     logger.info("Loading development set score file '%s'", args.dev_files[i])
     # pythonic way: create inline dictionary "{...}", index with desired value "[...]", execute function "(...)"
-    data.append({'4column' : bob.measure.load.split_four_column, '5column' : bob.measure.load.split_five_column}[args.parser](args.dev_files[i]))
+    data.append({'4column' : score.split_four_column, '5column' : score.split_five_column}[args.parser](args.dev_files[i]))
   import numpy
 
   trainer = bob.learn.linear.CGLogRegTrainer(0.5, args.convergence_threshold, args.max_iterations, mean_std_norm=not args.no_whitening)
@@ -78,7 +79,7 @@ def main(command_line_options = None):
   gen_data_dev = []
   for i in range(n_systems):
     logger.info("Loading development set score file '%s'", args.dev_files[i])
-    gen_data_dev.append({'4column' : bob.measure.load.four_column, '5column' : bob.measure.load.five_column}[args.parser](args.dev_files[i]))
+    gen_data_dev.append({'4column' : score.four_column, '5column' : score.five_column}[args.parser](args.dev_files[i]))
 
   logger.info("Writing fused development set score file '%s'", args.fused_dev_file)
   outf = open(args.fused_dev_file, 'w')
@@ -99,7 +100,7 @@ def main(command_line_options = None):
     gen_data_eval = []
     for i in range(n_systems):
       logger.info("Loading evaluation set score file '%s'", args.eval_files[i])
-      gen_data_eval.append({'4column' : bob.measure.load.four_column, '5column' : bob.measure.load.five_column}[args.parser](args.eval_files[i]))
+      gen_data_eval.append({'4column' : score.four_column, '5column' : score.five_column}[args.parser](args.eval_files[i]))
 
     logger.info("Writing fused evaluation set score file '%s'", args.fused_eval_file)
     outf = open(args.fused_eval_file, 'w')

bob/bio/base/test/test_scripts.py

@@ -8,6 +8,7 @@ import nose
 import bob.io.image
 import bob.bio.base
 from . import utils
+from .. import score
 
 from nose.plugins.skip import SkipTest
 
@@ -20,7 +21,6 @@ data_dir = pkg_resources.resource_filename('bob.bio.base', 'test/data')
 
 def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('scores',''), counts=3, check_zt=True):
   from bob.bio.base.script.verify import main
-  import bob.measure
   try:
     main(parameters)
 
@@ -42,7 +42,7 @@ def _verify(parameters, test_dir, sub_dir, ref_modifier="", score_modifier=('sco
       d = []
       # read reference and new data
       for score_file in (score_files[i], reference_files[i]):
-        f = bob.measure.load.open_file(score_file)
+        f = score.open_file(score_file)
         d_ = []
         for line in f:
           if isinstance(line, bytes): line = line.decode('utf-8')
@@ -278,7 +278,6 @@ def test_verify_filelist():
   ]
 
   from bob.bio.base.script.verify import main
-  import bob.measure
   try:
     main(parameters)
 
@@ -292,8 +291,8 @@ def test_verify_filelist():
 
     for i in (0,1):
       # load scores
-      a1, b1 = bob.measure.load.split_four_column(score_files[i])
-      a2, b2 = bob.measure.load.split_four_column(reference_files[i])
+      a1, b1 = score.split_four_column(score_files[i])
+      a2, b2 = score.split_four_column(reference_files[i])
       # sort scores
       a1 = sorted(a1); a2 = sorted(a2); b1 = sorted(b1); b2 = sorted(b2)
 
@@ -323,7 +322,6 @@ def test_verify_missing():
   ]
 
   from bob.bio.base.script.verify import main
-  import bob.measure
   try:
     main(parameters)
 
@@ -336,7 +334,7 @@ def test_verify_missing():
 
     for i in (0,1):
       # load scores
-      a, b = bob.measure.load.split_four_column(score_files[i])
+      a, b = score.split_four_column(score_files[i])
 
       assert numpy.all(numpy.isnan(a))
       assert numpy.all(numpy.isnan(b))
@@ -479,15 +477,14 @@ def test_fusion():
 
   # execute the script
   from bob.bio.base.script.fuse_scores import main
-  import bob.measure
   try:
     main(parameters)
 
     # assert that we can read the two files, and that they contain the same number of lines as the original file
     for i in (0,1):
       assert os.path.exists(output_files[i])
-      r = bob.measure.load.four_column(reference_files[i])
-      o = bob.measure.load.four_column(output_files[i])
+      r = score.four_column(reference_files[i])
+      o = score.four_column(output_files[i])
       assert len(list(r)) == len(list(o))
   finally:
     shutil.rmtree(test_dir)

bob/bio/base/tools/scoring.py

 import bob.io.base
 import bob.learn.em
 import bob.learn.linear
-import bob.measure
 import numpy
 import os, sys
 import tarfile
@@ -12,6 +11,7 @@ logger = logging.getLogger("bob.bio.base")
 
 from .FileSelector import FileSelector
 from .. import utils
+from .. import score
 
 def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
   """Compute scores for the given model and a list of probes.
@@ -62,12 +62,12 @@ def _scores(algorithm, reader, model, probe_objects, allow_missing_files):
 
 
 def _open_to_read(score_file):
-  """Checks for the existence of the normal and the compressed version of the file, and calls :py:func:`bob.measure.load.open_file` for the existing one."""
+  """Checks for the existence of the normal and the compressed version of the file, and calls :py:func:`score.open_file` for the existing one."""
   if not os.path.exists(score_file):
     score_file += '.tar.bz2'
     if not os.path.exists(score_file):
       raise IOError("The score file '%s' cannot be found. Aborting!" % score_file)
-  return bob.measure.load.open_file(score_file)
+  return score.open_file(score_file)
 
 
 def _open_to_write(score_file, write_compressed):
@@ -115,7 +115,7 @@ def _delete(score_file, write_compressed):
 
 
 def _save_scores(score_file, scores, probe_objects, client_id, write_compressed):
-  """Saves the scores of one model into a text file that can be interpreted by :py:func:`bob.measure.load.split_four_column`."""
+  """Saves the scores of one model into a text file that can be interpreted by :py:func:`score.split_four_column`."""
   assert len(probe_objects) == scores.shape[1]
 
   # open file for writing
@@ -493,7 +493,7 @@ def _concat(score_files, output, write_compressed, model_ids):
 def concatenate(compute_zt_norm, groups = ['dev', 'eval'], write_compressed = False, add_model_id = False):
   """Concatenates all results into one (or two) score files per group.
 
-  Score files, which were generated per model, are concatenated into a single score file, which can be interpreter by :py:func:`bob.measure.load.split_four_column`.
+  Score files, which were generated per model, are concatenated into a single score file, which can be interpreter by :py:func:`score.split_four_column`.
   The score files are always re-computed, regardless if they exist or not.
 
   **Parameters:**
@@ -563,7 +563,7 @@ def calibrate(compute_zt_norm, groups = ['dev', 'eval'], prior = 0.5, write_comp
     logger.info(" - Calibration: Training calibration for type %s from group %s", norm, groups[0])
     llr_trainer = bob.learn.linear.CGLogRegTrainer(prior, 1e-16, 100000)
 
-    training_scores = list(bob.measure.load.split_four_column(training_score_file))
+    training_scores = list(score.split_four_column(training_score_file))
     for i in (0,1):
       h = numpy.array(training_scores[i])
       # remove NaN's
@@ -582,7 +582,7 @@ def calibrate(compute_zt_norm, groups = ['dev', 'eval'], prior = 0.5, write_comp
       logger.info(" - Calibration: calibrating scores from '%s' to '%s'", score_file, calibrated_file)
 
       # iterate through the score file and calibrate scores
-      scores = bob.measure.load.four_column(_open_to_read(score_file))
+      scores = score.four_column(_open_to_read(score_file))
 
       f = _open_to_write(calibrated_file, write_compressed)