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How to use filtered_by method in Selene

Best Python code snippet using selene_python

test_scores.py

Source:test_scores.py

...24    assert scores.length() == 1249025@pytest.mark.unit26@pytest.mark.parametrize("scores", scores_approaches_test)27def test_should_load_scores_filter_sex(scores):28    assert len(scores.filtered_by(Filter(sex=Sex.MALE))) == 1710  # 559129    assert len(scores.filtered_by(Filter(sex=Sex.FEMALE))) == 669  # 193830@pytest.mark.unit31@pytest.mark.parametrize("scores", scores_approaches_test)32def test_should_load_scores_filter_age(scores):33    assert len(scores.filtered_by(Filter(age=Age.YOUNG))) == 221434    assert len(scores.filtered_by(Filter(age=Age.ADULT))) == 5291  # 515335    assert len(scores.filtered_by(Filter(age=Age.SENIOR))) == 6336@pytest.mark.unit37@pytest.mark.parametrize("scores", scores_approaches_test)38def test_should_load_scores_filter_skin_tone(scores):39    assert len(scores.filtered_by(Filter(skin_tone=SkinTone.LIGHT_PINK))) == 25640    assert len(scores.filtered_by(Filter(skin_tone=SkinTone.MEDIUM_PINK_BROWN))) == 212141    assert len(scores.filtered_by(Filter(skin_tone=SkinTone.LIGHT_YELLOW))) == 78642    assert (43        len(scores.filtered_by(Filter(skin_tone=SkinTone.MEDIUM_YELLOW_BROWN))) == 378244    )45    assert len(scores.filtered_by(Filter(skin_tone=SkinTone.MEDIUM_DARK_BROWN))) == 40246    assert len(scores.filtered_by(Filter(skin_tone=SkinTone.DARK_BROWN))) == 22247@pytest.mark.unit48@pytest.mark.parametrize("scores", scores_approaches_test)49def test_should_load_quality_rbf_scores_filter_dataset(scores):50    assert len(scores.filtered_by(Filter(dataset=Dataset.CASIA_FASD))) == 36051    assert len(scores.filtered_by(Filter(dataset=Dataset.THREEDMAD))) == 7352    assert len(scores.filtered_by(Filter(dataset=Dataset.UVAD))) == 485653    assert len(scores.filtered_by(Filter(dataset=Dataset.SIW_M))) == 45654    assert len(scores.filtered_by(Filter(dataset=Dataset.SIW))) == 204255    assert len(scores.filtered_by(Filter(dataset=Dataset.ROSE_YOUTU))) == 174056    assert len(scores.filtered_by(Filter(dataset=Dataset.REPLAY_MOBILE))) == 30157    assert len(scores.filtered_by(Filter(dataset=Dataset.REPLAY_ATTACK))) == 47558    assert len(scores.filtered_by(Filter(dataset=Dataset.OULU_NPU))) == 179859    assert len(scores.filtered_by(Filter(dataset=Dataset.MSU_MFSD))) == 11860    assert len(scores.filtered_by(Filter(dataset=Dataset.HKBU_V2))) == 16861    assert len(scores.filtered_by(Filter(dataset=Dataset.HKBU))) == 3062    assert len(scores.filtered_by(Filter(dataset=Dataset.CSMAD))) == 7363@pytest.mark.unit64@pytest.mark.parametrize("scores", scores_approaches_test)65def test_should_load_scores_filter_dataset_and_sex(scores):66    assert (67        len(scores.filtered_by(Filter(sex=Sex.MALE, dataset=Dataset.CASIA_FASD))) == 30068    )69    assert (70        len(scores.filtered_by(Filter(sex=Sex.FEMALE, dataset=Dataset.CASIA_FASD)))71        == 6072    )73@pytest.mark.unit74@pytest.mark.parametrize("scores", scores_approaches_test)75def test_should_load_scores_with_fair_sex_subset(scores):76    fair_sex_subset = scores.get_fair_sex_subset()77    for gender in Sex.options():78        assert len(fair_sex_subset.get(gender.name)) == 137  # 581  # 188779@pytest.mark.unit80@pytest.mark.parametrize("scores", scores_approaches_test)81def test_should_load_scores_with_fair_age_subset(scores):82    fair_age_subset = scores.get_fair_age_subset()83    for age in Age.options():84        assert len(fair_age_subset.get(age.name)) == 42  # 6385@pytest.mark.unit86@pytest.mark.parametrize("scores", scores_approaches_test)87def test_should_load_scores_with_fair_skin_tone_subset(scores):88    fair_skin_tone_subset = scores.get_fair_skin_tone_subset()89    for skin_tone in SkinTone.options():90        assert len(fair_skin_tone_subset.get(skin_tone.name)) == 34  # 11391@pytest.mark.unit92@pytest.mark.parametrize("scores", scores_approaches_test)93def test_should_load_scores_filter_with_random_values(scores):94    assert len(scores.filtered_by(Filter(random_values=10))) == 1095@pytest.mark.unit96@pytest.mark.parametrize("scores", scores_approaches_test)97def test_should_load_scores_filter_with_pseudo_random_values(scores):98    assert len(scores.filtered_by(Filter(pseudo_random_values=10))) == 1099@pytest.mark.unit100@pytest.mark.parametrize("scores", scores_approaches_test)101def test_should_load_scores_get_genuine(scores):102    assert len(scores.get_genuine()) == 2281103@pytest.mark.unit104@pytest.mark.parametrize("scores", scores_approaches_test)105def test_should_load_scores_get_attacks(scores):106    assert len(scores.get_attacks()) == 10209107@pytest.mark.unit108@pytest.mark.parametrize("scores", scores_approaches_test)109def test_should_load_scores_get_numpy_scores_and_labels_filtered_by_labels(scores):110    scores, labels = scores.get_numpy_scores_and_labels_filtered_by_labels()111    assert len(scores) == 12490112    assert len(labels) == 12490

utils.py

Source:utils.py

1from __future__ import division2import pickle3import io4import os5from collections import Counter, defaultdict6from magpie.base.document import Document7def save_to_disk(path_to_disk, obj, overwrite=False):8  """ Pickle an object to disk """9  dirname = os.path.dirname(path_to_disk)10  if not os.path.exists(dirname):11    raise ValueError("Path " + str(dirname) + " does not exist")12  if not overwrite and os.path.exists(path_to_disk):13    raise ValueError("File " + str(path_to_disk) + "already exists")14  pickle.dump(obj, open(path_to_disk, 'wb'))15def load_from_disk(path_to_disk):16  """ Load a pickle from disk to memory """17  if not os.path.exists(path_to_disk):18    raise ValueError("File " + path_to_disk + " does not exist")19  return pickle.load(open(path_to_disk, 'rb'))20def get_all_answers(data_dir, filtered_by=None):21  """22  Extract ground truth answers from *.lab files in a given directory23  :param data_dir: path to the directory with .lab files24  :param filtered_by: whether to filter the answers.25  :return: dictionary of the form e.g. {'101231': set('lab1', 'lab2') etc.}26  """27  answers = dict()28  files = {filename[:-4] for filename in os.listdir(data_dir)}29  for f in files:30    answers[f] = get_answers_for_doc(f + '.txt',31                                     data_dir,32                                     filtered_by=filtered_by)33  return answers34def get_answers_for_doc(doc_name, data_dir, filtered_by=None):35  """36  Read ground_truth answers from a .lab file corresponding to the doc_name37  :param doc_name: the name of the document, should end with .txt38  :param data_dir: directory in which the documents and answer files are39  :param filtered_by: whether to filter the answers.40  :return: set of unicodes containing answers for this particular document41  """42  filename = os.path.join(data_dir, doc_name[:-4] + '.lab')43  if not os.path.exists(filename):44    raise ValueError("Answer file " + filename + " does not exist")45  with io.open(filename, 'r') as f:46    answers = {line.rstrip('\n') for line in f}47  if filtered_by:48    answers = {kw for kw in answers if kw in filtered_by}49  return answers50def calculate_label_distribution(data_dir, filtered_by=None):51  """52  Calculate the distribution of labels in a directory. Function can be used53  to find the most frequent and not used labels, so that the target54  vocabulary can be trimmed accordingly.55  :param data_dir: directory path with the .lab files56  :param filtered_by: a set of labels that defines the vocabulary57  :return: list of KV pairs of the form (14, ['lab1', 'lab2']), which means58           that both lab1 and lab2 were labels in 14 documents59  """60  answers = [61      kw for v in get_all_answers(62          data_dir,63          filtered_by=filtered_by).values() for kw in v]64  counts = Counter(answers)65  histogram = defaultdict(list)66  for kw, cnt in counts.items():67    histogram[cnt].append(kw)68  return histogram69def calculate_number_of_labels_distribution(data_dir, filtered_by=None):70  """ Look how many papers are there with 3 labels, 4 labels etc.71   Return a histogram. """72  answers = get_all_answers(data_dir, filtered_by=filtered_by).values()73  lengths = [len(ans_set) for ans_set in answers]...

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