diff --git a/src/bob/bio/base/score/load.py b/src/bob/bio/base/score/load.py
index 47043f85dfebb77a2eb940fdd33e4ebb7abe650c..8daf48abc6414d47f311299f721e897bd194df90 100644
--- a/src/bob/bio/base/score/load.py
+++ b/src/bob/bio/base/score/load.py
@@ -2,14 +2,14 @@
# vim: set fileencoding=utf-8 :
# Mon 23 May 2011 16:23:05 CEST
-"""A set of utilities to load score files with different formats.
-"""
+"""A set of utilities to load score files with different formats."""
import csv
import logging
import os
import tarfile
+from collections import defaultdict
from pathlib import Path
import dask.dataframe
@@ -94,8 +94,8 @@ def four_column(filename):
str: The claimed identity -- the client name of the model that was used in
the comparison
- str: The real identity -- the client name of the probe that was used in the
- comparison
+ str: The real identity -- the client name of the probe that was used in
+ the comparison
str: A label of the probe -- usually the probe file name, or the probe id
@@ -153,15 +153,19 @@ def get_split_dataframe(filename):
-------
dataframe: negatives, contains the list of scores (and metadata) for which
- the fields of the ``bio_ref_subject_id`` and ``probe_subject_id`` columns are
- different. (see :ref:`bob.bio.base.pipeline_simple_advanced_features`)
+ the fields of the ``bio_ref_subject_id`` and ``probe_subject_id``
+ columns are different. (see
+ :ref:`bob.bio.base.pipeline_simple_advanced_features`)
dataframe: positives, contains the list of scores (and metadata) for which
- the fields of the ``bio_ref_subject_id`` and ``probe_subject_id`` columns are
- identical. (see :ref:`bob.bio.base.pipeline_simple_advanced_features`)
+ the fields of the ``bio_ref_subject_id`` and ``probe_subject_id``
+ columns are identical. (see
+ :ref:`bob.bio.base.pipeline_simple_advanced_features`)
"""
- df = dask.dataframe.read_csv(filename)
+ df = dask.dataframe.read_csv(
+ filename, dtype=defaultdict(lambda: str, {"score": float})
+ )
genuines = df[df.probe_subject_id == df.bio_ref_subject_id]
impostors = df[df.probe_subject_id != df.bio_ref_subject_id]
@@ -184,15 +188,19 @@ def split_csv_scores(filename, score_column: str = "score"):
-------
array: negatives, 1D float array containing the list of scores, for which
- the fields of the ``bio_ref_subject_id`` and ``probe_subject_id`` columns are
- different. (see :ref:`bob.bio.base.pipeline_simple_advanced_features`)
+ the fields of the ``bio_ref_subject_id`` and ``probe_subject_id``
+ columns are different. (see
+ :ref:`bob.bio.base.pipeline_simple_advanced_features`)
array: positives, 1D float array containing the list of scores, for which
- the fields of the ``bio_ref_subject_id`` and ``probe_subject_id`` columns are
- identical. (see :ref:`bob.bio.base.pipeline_simple_advanced_features`)
+ the fields of the ``bio_ref_subject_id`` and ``probe_subject_id``
+ columns are identical. (see
+ :ref:`bob.bio.base.pipeline_simple_advanced_features`)
"""
- df = dask.dataframe.read_csv(filename)
+ df = dask.dataframe.read_csv(
+ filename, dtype=defaultdict(lambda: str, {"score": float})
+ )
genuines = df[df.probe_subject_id == df.bio_ref_subject_id]
impostors = df[df.probe_subject_id != df.bio_ref_subject_id]
@@ -262,8 +270,8 @@ def five_column(filename):
str: A label for the model -- usually the model file name, or the model id
- str: The real identity -- the client name of the probe that was used in the
- comparison
+ str: The real identity -- the client name of the probe that was used in
+ the comparison
str: A label of the probe -- usually the probe file name, or the probe id
@@ -346,7 +354,8 @@ def scores(filename, ncolumns=None):
Parameters:
filename: :py:class:`str`, ``file-like``:
- The file object that will be opened with :py:func:`open_file` containing the scores.
+ The file object that will be opened with :py:func:`open_file` containing
+ the scores.
ncolumns: any
ignored
@@ -461,8 +470,8 @@ def load_score(filename, ncolumns=None, minimal=False, **kwargs):
specifying the number of columns in the score file. If None is provided,
the number of columns will be guessed.
- minimal (:py:class:`bool`, optional): If True, only loads ``claimed_id``, ``real_id``,
- and ``scores``.
+ minimal (:py:class:`bool`, optional): If True, only loads ``claimed_id``,
+ ``real_id``, and ``scores``.
**kwargs: Keyword arguments passed to :py:func:`numpy.genfromtxt`
@@ -624,9 +633,10 @@ def _estimate_score_file_format(filename, ncolumns=None):
def _iterate_score_file(filename, csv_score_column: str = "score"):
- """Opens the score file for reading and yields the score file line by line in a tuple/list.
+ """Opens the score file and yields the score file lines in a tuple/list.
- The last element of the line (which is the score) will be transformed to float, the other elements will be str
+ The last element of the line (which is the score) will be transformed to
+ float, the other elements will be str.
"""
if iscsv(filename):
for row in _iterate_csv_score_file(
@@ -635,7 +645,7 @@ def _iterate_score_file(filename, csv_score_column: str = "score"):
yield [
row["bio_ref_subject_id"],
row["probe_subject_id"],
- row["probe_key"],
+ row["probe_template_id"],
row[csv_score_column],
]
else:
@@ -667,7 +677,9 @@ def _iterate_csv_score_file(filename, score_column: str = "score"):
def _split_scores(
score_lines, real_id_index, claimed_id_index=0, score_index=-1
):
- """Take the output of :py:func:`four_column` or :py:func:`five_column` and return negatives and positives."""
+ """Take the output of :py:func:`four_column` or :py:func:`five_column` and
+ return negatives and positives.
+ """
positives, negatives = [], []
for line in score_lines:
which = (
@@ -687,7 +699,9 @@ def _split_cmc_scores(
claimed_id_index=0,
score_index=-1,
):
- """Takes the output of :py:func:`four_column` or :py:func:`five_column` and return cmc scores."""
+ """Takes the output of :py:func:`four_column` or :py:func:`five_column` and
+ return cmc scores.
+ """
if probe_name_index is None:
probe_name_index = real_id_index + 1
# extract positives and negatives
@@ -712,12 +726,16 @@ def _split_cmc_scores(
# get all scores in the desired format
return [
(
- numpy.array(neg_dict[probe_name], numpy.float64)
- if probe_name in neg_dict
- else None,
- numpy.array(pos_dict[probe_name], numpy.float64)
- if probe_name in pos_dict
- else None,
+ (
+ numpy.array(neg_dict[probe_name], numpy.float64)
+ if probe_name in neg_dict
+ else None
+ ),
+ (
+ numpy.array(pos_dict[probe_name], numpy.float64)
+ if probe_name in pos_dict
+ else None
+ ),
)
for probe_name in probe_names
]