def cv_mode(testing_mode,
multiclass,
predict_lemmas,
paradigm_file,
infile,
fraction,
nfolds=0,
order=3):
lemma_descriptions_list = process_lemmas_file(infile)
data, labels_with_vars = read_lemmas(lemma_descriptions_list,
multiclass=multiclass,
return_joint=True)
paradigm_descriptions_list = process_codes_file(paradigm_file)
paradigm_table, pattern_counts = read_paradigms(paradigm_descriptions_list)
if predict_lemmas:
paradigm_handlers = {
code: ParadigmSubstitutor(descr)
for descr, code in paradigm_table.items()
}
else:
test_lemmas, pred_lemmas = None, None
# подготовка для кросс-валидации
classes = sorted(
set(chain(*((x[0] for x in elem) for elem in labels_with_vars))))
if nfolds == 0:
train_data_length = int(fraction * len(data))
train_data, test_data = [data[:train_data_length]
], [data[train_data_length:]]
train_labels_with_vars, test_labels_with_vars =\
[labels_with_vars[:train_data_length]], [labels_with_vars[train_data_length:]]
nfolds = 1
else:
test_data, train_data = [None] * nfolds, [None] * nfolds
test_labels_with_vars, train_labels_with_vars = [None] * nfolds, [
None
] * nfolds
for fold in range(nfolds):
train_data[fold], test_data[fold], train_labels_with_vars[fold], test_labels_with_vars[fold] =\
skcv.train_test_split(data, labels_with_vars, test_size = 1.0 - fraction,
random_state = 100 * fold + 13)
if predict_lemmas:
test_lemmas = [None] * nfolds
for fold in range(nfolds):
test_lemmas[fold] = make_lemmas(paradigm_handlers,
test_labels_with_vars[fold])
predictions = [None] * nfolds
prediction_probs = [None] * nfolds
classes_by_cls = [None] * nfolds
if predict_lemmas:
pred_lemmas = [None] * nfolds
# задаём классификатор
lm_classifier = LMParadigmClassifier(paradigm_table,
pattern_counts,
lm_order=order)
for i, (train_sample, train_labels_sample, test_sample, test_labels_sample) in\
enumerate(zip(train_data, train_labels_with_vars, test_data, test_labels_with_vars)):
lm_classifier.fit(train_sample, train_labels_sample)
lm_classifier.test()
def cv_mode(testing_mode, multiclass, predict_lemmas, paradigm_file, infile,
fraction, nfolds=0, order=3):
lemma_descriptions_list = process_lemmas_file(infile)
data, labels_with_vars = read_lemmas(lemma_descriptions_list, multiclass=multiclass, return_joint=True)
paradigm_descriptions_list = process_codes_file(paradigm_file)
paradigm_table, pattern_counts = read_paradigms(paradigm_descriptions_list)
if predict_lemmas:
paradigm_handlers = {code: ParadigmSubstitutor(descr)
for descr, code in paradigm_table.items()}
else:
test_lemmas, pred_lemmas = None, None
# подготовка для кросс-валидации
classes = sorted(set(chain(*((x[0] for x in elem) for elem in labels_with_vars))))
if nfolds == 0:
train_data_length = int(fraction * len(data))
train_data, test_data = [data[:train_data_length]], [data[train_data_length:]]
train_labels_with_vars, test_labels_with_vars =\
[labels_with_vars[:train_data_length]], [labels_with_vars[train_data_length:]]
nfolds = 1
else:
test_data, train_data = [None] * nfolds, [None] * nfolds
test_labels_with_vars, train_labels_with_vars = [None] * nfolds, [None] * nfolds
for fold in range(nfolds):
train_data[fold], test_data[fold], train_labels_with_vars[fold], test_labels_with_vars[fold] =\
skcv.train_test_split(data, labels_with_vars, test_size = 1.0 - fraction,
random_state = 100 * fold + 13)
if predict_lemmas:
test_lemmas = [None] * nfolds
for fold in range(nfolds):
test_lemmas[fold] = make_lemmas(paradigm_handlers, test_labels_with_vars[fold])
predictions = [None] * nfolds
prediction_probs = [None] * nfolds
classes_by_cls = [None] * nfolds
if predict_lemmas:
pred_lemmas = [None] * nfolds
# задаём классификатор
lm_classifier = LMParadigmClassifier(paradigm_table, pattern_counts, lm_order=order)
for i, (train_sample, train_labels_sample, test_sample, test_labels_sample) in\
enumerate(zip(train_data, train_labels_with_vars, test_data, test_labels_with_vars)):
lm_classifier.fit(train_sample, train_labels_sample)
lm_classifier.test()
def cv_mode(testing_mode, multiclass, predict_lemmas, find_flection,
paradigm_file, infile, max_length, fraction, nfolds=0,
selection_method=None, feature_fraction=None,
output_train_dir=None, output_pred_dir=None):
'''
Определяет качество классификатора с заданными параметрами
по скользящему контролю на обучающей выборке
Параметры:
-----------
testing_mode: str ('predict' or 'predict_proba'), режим использования
multiclass: bool, может ли одно слово иметь несколько парадигм
find_flection: bool, выполняется ли предварительный поиск флексии
для того, чтобы использовать в качестве признаков суффиксы основы,
а не всего слова. Оказалось, что качество ухудшается
paradigm_file: str, путь к файлу с парадигмами
infile: str, путь к файлу с обучающей выборкой
fraction: float, доля обучающей выборки
nfolds: int, optional(default=0)
число разбиений, по которым производится усреднение при скользящем контроле
nfolds=0 --- в обучающую выборку попадает соответствующее число лексем
из начала файла
selection_method: str or None, optional (default=None),
метод отбора признаков
feature_fraction: float or None, optional (default=None),
доля признаков, которые следует оставить при отборе признаков
(при этом nfeatures должно быть не задано)
output_train_dir: str or None, optional(default=None),
директория для сохранения тестовых данных,
в случае output_train_dir=None сохранение не производится
output_pred_dir: str or None, optional(default=None),
директория для сохранения результатов классификации,
в случае output_train_dir=None сохранение не производится
'''
# чтение входных файлов
lemma_descriptions_list = process_lemmas_file(infile)
data, labels_with_vars = read_lemmas(lemma_descriptions_list, multiclass=multiclass, return_joint=True)
paradigm_descriptions_list = process_codes_file(paradigm_file)
paradigm_table, pattern_counts = read_paradigms(paradigm_descriptions_list)
if predict_lemmas:
paradigm_handlers = {code: ParadigmSubstitutor(descr)
for descr, code in paradigm_table.items()}
else:
test_lemmas, pred_lemmas = None, None
# подготовка для кросс-валидации
classes = sorted(set(chain(*((x[0] for x in elem) for elem in labels_with_vars))))
if selection_method is None:
selection_method = 'ambiguity'
if nfolds == 0:
train_data_length = int(fraction * len(data))
train_data, test_data = [data[:train_data_length]], [data[train_data_length:]]
train_labels_with_vars, test_labels_with_vars =\
[labels_with_vars[:train_data_length]], [labels_with_vars[train_data_length:]]
nfolds = 1
else:
test_data, train_data = [None] * nfolds, [None] * nfolds
test_labels_with_vars, train_labels_with_vars = [None] * nfolds, [None] * nfolds
for fold in range(nfolds):
train_data[fold], test_data[fold], train_labels_with_vars[fold], test_labels_with_vars[fold] =\
skcv.train_test_split(data, labels_with_vars, test_size = 1.0 - fraction,
random_state = 100 * fold + 13)
if predict_lemmas:
test_lemmas = [None] * nfolds
for fold in range(nfolds):
test_lemmas[fold] = make_lemmas(paradigm_handlers, test_labels_with_vars[fold])
predictions = [None] * nfolds
prediction_probs = [None] * nfolds
classes_by_cls = [None] * nfolds
if predict_lemmas:
pred_lemmas = [None] * nfolds
# задаём классификатор
paradigm_classifier = ParadigmClassifier(paradigm_table)
paradigm_classifier_params = {'multiclass': multiclass, 'find_flection': find_flection,
'max_length': max_length, 'use_prefixes': True,
'classifier_params': None, 'selection_method': selection_method,
'nfeatures': feature_fraction, 'smallest_prob': 0.01}
transformation_handler = TransformationsHandler(paradigm_table, pattern_counts)
transformation_classifier_params = {'select_features': 'ambiguity',
'selection_params': {'nfeatures': 0.1, 'min_count': 2}}
# statprof.start()
cls = JointParadigmClassifier(paradigm_table, paradigm_classifier_params,
transformation_handler, transformation_classifier_params)
# cls = CombinedParadigmClassifier(paradigm_classifier, transformation_handler,
# paradigm_classifier_params, transformation_classifier_params)
# сохраняем тестовые данные
# if output_train_dir is not None:
# if not os.path.exists(output_train_dir):
# os.makedirs(output_train_dir)
# for i, (train_sample, train_labels_sample) in\
# enumerate(zip(train_data, train_labels_with_vars), 1):
# write_joint_data(os.path.join(output_train_dir, "{0}.data".format(i)),
# train_sample, train_labels_sample)
# применяем классификатор к данным
for i, (train_sample, train_labels_sample, test_sample, test_labels_sample) in\
enumerate(zip(train_data, train_labels_with_vars, test_data, test_labels_with_vars)):
cls.fit(train_sample, train_labels_sample)
classes_by_cls[i] = cls.classes_
if testing_mode == 'predict':
predictions[i] = cls.predict(test_sample)
elif testing_mode == 'predict_proba':
prediction_probs[i] = cls.predict_probs(test_sample)
# в случае, если мы вернули вероятности,
# то надо ещё извлечь классы
if not multiclass:
predictions[i] = [[elem[0][0]] for elem in prediction_probs[i]]
else:
raise NotImplementedError()
if predict_lemmas:
pred_lemmas[i] = make_lemmas(paradigm_handlers, predictions[i])
descrs_by_codes = {code: descr for descr, code in paradigm_table.items()}
if output_pred_dir:
test_words = test_data
if testing_mode == 'predict_proba':
prediction_probs_for_output = prediction_probs
else:
prediction_probs_for_output = None
else:
test_words, prediction_probs_for_output = None, None
if not predict_lemmas:
label_precisions, variable_precisions, form_precisions =\
output_accuracies(classes, test_labels_with_vars, predictions, multiclass,
outfile=output_pred_dir, paradigm_descrs=descrs_by_codes,
test_words=test_words, predicted_probs=prediction_probs_for_output)
print("{0}\t{1:<.2f}\t{2}\t{3:<.2f}\t{4:<.2f}\t{5:<.2f}".format(
max_length, fraction, cls.paradigm_classifier.nfeatures,
100 * np.mean(label_precisions), 100 * np.mean(variable_precisions),
100 * np.mean(form_precisions)))
else:
label_precisions, variable_precisions, lemma_precisions, form_precisions =\
output_accuracies(classes, test_labels_with_vars, predictions,
multiclass, test_lemmas, pred_lemmas,
outfile=output_pred_dir, paradigm_descrs=descrs_by_codes,
test_words=test_words, predicted_probs=prediction_probs_for_output,
save_confusion_matrices=True)
print("{0}\t{1:<.2f}\t{2}\t{3:<.2f}\t{4:<.2f}\t{5:<.2f}\t{6:<.2f}".format(
max_length, fraction, cls.paradigm_classifier.nfeatures,
100 * np.mean(label_precisions), 100 * np.mean(variable_precisions),
100 * np.mean(lemma_precisions), 100 * np.mean(form_precisions)))
# statprof.stop()
# with open("statprof_{0:.1f}_{1:.1f}.stat".format(fraction, feature_fraction), "w") as fout:
# with redirect_stdout(fout):
# statprof.display()
# вычисляем точность и обрабатываем результаты
# for curr_test_values, curr_pred_values in zip(test_values_with_codes, pred_values_with_codes):
# print(len(curr_test_values), len(curr_pred_values))
# for first, second in zip(curr_test_values, curr_pred_values):
# first_code, first_vars = first[0].split('_')[0], tuple(first[0].split('_')[1:])
# second_code, second_vars = second[0].split('_')[0], tuple(second[0].split('_')[1:])
# if first_code == second_code and first_vars != second_vars:
# print('{0}\t{1}'.format(first, second))
# if not multiclass:
# confusion_matrices = [skm.confusion_matrix(first, second, labels=classes)
# for first, second in zip(firsts, seconds)]
# сохраняем результаты классификации
# if output_pred_dir is not None:
# if not os.path.exists(output_pred_dir):
# os.makedirs(output_pred_dir)
# if testing_mode == 'predict':
# for i, (test_sample, pred_labels_sample, true_labels_sample) in\
# enumerate(zip(test_data, predictions, test_labels), 1):
# write_data(os.path.join(output_pred_dir, "{0}.data".format(i)),
# test_sample, pred_labels_sample, true_labels_sample)
# elif testing_mode == 'predict_proba':
# for i, (test_sample, pred_probs_sample, labels_sample, cls_classes) in\
# enumerate(zip(test_data, prediction_probs, test_labels, classes_by_cls), 1):
# write_probs_data(os.path.join(output_pred_dir, "{0}.prob".format(i)),
# test_sample, pred_probs_sample, cls_classes, labels_sample)
# сохраняем матрицы ошибок классификации
# if not multiclass and nfolds <= 1:
# confusion_matrices_folder = "confusion_matrices"
# if not os.path.exists(confusion_matrices_folder):
# os.makedirs(confusion_matrices_folder)
# dest = os.path.join(confusion_matrices_folder,
# "confusion_matrix_{0}_{1:<.2f}_{2:<.2f}.out".format(
# max_length, fraction, feature_fraction))
# with open(dest, "w", encoding="utf8") as fout:
# fout.write("{0:<4}".format("") +
# "".join("{0:>4}".format(label) for label in classes) + "\n")
# for label, elem in zip(cls.classes_, confusion_matrices[0]):
# nonzero_positions = np.nonzero(elem)
# nonzero_counts = np.take(elem, nonzero_positions)[0]
# nonzero_labels = np.take(classes, nonzero_positions)[0]
# fout.write("{0:<4}\t".format(label))
# fout.write("\t".join("{0}:{1}".format(*pair)
# for pair in sorted(zip(nonzero_labels, nonzero_counts),
# key=(lambda x: x[1]), reverse=True))
# + "\n")
return
def cv_mode(testing_mode, language_code, multiclass, predict_lemmas,
paradigm_file, counts_file, infile,
train_fraction, feature_fraction, paradigm_counts_threshold,
nfolds, selection_method, binarization_method, max_feature_length,
output_train_dir=None, output_pred_dir=None):
lemma_descriptions_list = process_lemmas_file(infile)
data, labels_with_vars = read_lemmas(lemma_descriptions_list, multiclass=multiclass, return_joint=True)
paradigm_descriptions_list = process_codes_file(paradigm_file)
paradigm_table, paradigm_counts = read_paradigms(paradigm_descriptions_list)
word_counts_table = read_counts(counts_file)
if predict_lemmas:
paradigm_handlers = {code: ParadigmSubstitutor(descr)
for descr, code in paradigm_table.items()}
else:
test_lemmas, pred_lemmas = None, None
# подготовка для кросс-валидации
classes = sorted(set(chain(*((x[0] for x in elem) for elem in labels_with_vars))))
active_paradigm_codes = [i for i, count in paradigm_counts.items()
if count >= paradigm_counts_threshold]
# зачем нужны метки???
marks = [LEMMA_KEY] + [",".join(x) for x in get_categories_marks(language_code)]
paradigms_by_codes = {code: descr for descr, code in paradigm_table.items()}
# подготовка данных для локальных трансформаций
if selection_method is None:
selection_method = 'ambiguity'
if nfolds == 0:
train_data_length = int(train_fraction * len(data))
train_data, test_data = [data[:train_data_length]], [data[train_data_length:]]
train_labels_with_vars, test_labels_with_vars =\
[labels_with_vars[:train_data_length]], [labels_with_vars[train_data_length:]]
nfolds = 1
else:
test_data, train_data = [None] * nfolds, [None] * nfolds
test_labels_with_vars, train_labels_with_vars = [None] * nfolds, [None] * nfolds
for fold in range(nfolds):
train_data[fold], test_data[fold], train_labels_with_vars[fold], test_labels_with_vars[fold] =\
skcv.train_test_split(data, labels_with_vars, test_size = 1.0 - train_fraction,
random_state = 100 * fold + 13)
if predict_lemmas:
test_lemmas = [None] * nfolds
for fold in range(nfolds):
test_lemmas[fold] = make_lemmas(paradigm_handlers, test_labels_with_vars[fold])
predictions = [None] * nfolds
prediction_probs = [None] * nfolds
classes_by_cls = [None] * nfolds
if predict_lemmas:
pred_lemmas = [None] * nfolds
# задаём классификатор
# cls = ParadigmCorporaClassifier(marks, paradigm_table, word_counts_table,
# multiclass=multiclass, selection_method=selection_method,
# binarization_method=binarization_method,
# inner_feature_fraction=feature_fraction,
# active_paradigm_codes=active_paradigm_codes,
# paradigm_counts=paradigm_counts , smallest_prob=0.01)
cls = ParadigmCorporaClassifier(paradigm_table, word_counts_table,
multiclass=multiclass, selection_method=selection_method,
binarization_method=binarization_method,
inner_feature_fraction=feature_fraction,
active_paradigm_codes=active_paradigm_codes,
paradigm_counts=paradigm_counts , smallest_prob=0.001)
cls_params = {'max_length': max_feature_length}
transformation_handler = TransformationsHandler(paradigm_table, paradigm_counts)
transformation_classifier_params = {'select_features': 'ambiguity',
'selection_params': {'nfeatures': 0.1, 'min_count': 2}}
# statprof.start()
cls = JointParadigmClassifier(cls, transformation_handler, cls_params,
transformation_classifier_params)
# cls = CombinedParadigmClassifier(cls, transformation_handler, cls_params,
# transformation_classifier_params)
# сохраняем тестовые данные
# if output_train_dir is not None:
# if not os.path.exists(output_train_dir):
# os.makedirs(output_train_dir)
# for i, (train_sample, train_labels_sample) in\
# enumerate(zip(train_data, train_labels_with_vars), 1):
# write_joint_data(os.path.join(output_train_dir, "{0}.data".format(i)),
# train_sample, train_labels_sample)
# применяем классификатор к данным
for i, (train_sample, train_labels_sample, test_sample, test_labels_sample) in\
enumerate(zip(train_data, train_labels_with_vars, test_data, test_labels_with_vars)):
cls.fit(train_sample, train_labels_sample)
classes_by_cls[i] = cls.classes_
if testing_mode == 'predict':
predictions[i] = cls.predict(test_sample)
elif testing_mode == 'predict_proba':
prediction_probs[i] = cls.predict_probs(test_sample)
# в случае, если мы вернули вероятности,
# то надо ещё извлечь классы
if not multiclass:
predictions[i] = [[elem[0][0]] for elem in prediction_probs[i]]
else:
raise NotImplementedError()
if predict_lemmas:
pred_lemmas[i] = make_lemmas(paradigm_handlers, predictions[i])
# statprof.stop()
# with open("statprof_{0:.1f}_{1:.1f}.stat".format(train_fraction,
# feature_fraction), "w") as fout:
# with redirect_stdout(fout):
# statprof.display()
if output_pred_dir:
descrs_by_codes = {code: descr for descr, code in paradigm_table.items()}
test_words = test_data
if testing_mode == 'predict_proba':
prediction_probs_for_output = prediction_probs
else:
prediction_probs_for_output = None
else:
descrs_by_codes, test_words, prediction_probs_for_output = None, None, None
if not predict_lemmas:
label_precisions, variable_precisions, form_precisions =\
output_accuracies(classes, test_labels_with_vars, predictions, multiclass,
outfile=output_pred_dir, paradigm_descrs=descrs_by_codes,
test_words=test_words, predicted_probs=prediction_probs_for_output,
save_confusion_matrices=True)
print("{0:<.2f}\t{1}\t{2:<.2f}\t{3:<.2f}\t{4:<.2f}".format(
train_fraction, cls.paradigm_classifier.nfeatures,
100 * np.mean(label_precisions), 100 * np.mean(variable_precisions),
100 * np.mean(form_precisions)))
else:
label_precisions, variable_precisions, lemma_precisions, form_precisions =\
output_accuracies(classes, test_labels_with_vars, predictions,
multiclass, test_lemmas, pred_lemmas,
outfile=output_pred_dir, paradigm_descrs=descrs_by_codes,
test_words=test_words, predicted_probs=prediction_probs_for_output,
save_confusion_matrices=True)
print("{0:<.2f}\t{1}\t{2:<.2f}\t{3:<.2f}\t{4:<.2f}\t{5:<.2f}".format(
train_fraction, cls.paradigm_classifier.nfeatures,
100 * np.mean(label_precisions), 100 * np.mean(variable_precisions),
100 * np.mean(lemma_precisions), 100 * np.mean(form_precisions)))
# statprof.stop()
# with open("statprof_{0:.1f}_{1:.1f}.stat".format(fraction, feature_fraction), "w") as fout:
# with redirect_stdout(fout):
# statprof.display()
# вычисляем точность и обрабатываем результаты
# for curr_test_values, curr_pred_values in zip(test_values_with_codes, pred_values_with_codes):
# for first, second in zip(curr_test_values, curr_pred_values):
# first_code, first_vars = first[0].split('_')[0], tuple(first[0].split('_')[1:])
# second_code, second_vars = second[0].split('_')[0], tuple(second[0].split('_')[1:])
# if first_code == second_code and first_vars != second_vars:
# print('{0}\t{1}'.format(first, second))
# if not multiclass:
# confusion_matrices = [skm.confusion_matrix(first, second, labels=classes)
# for first, second in zip(firsts, seconds)]
# сохраняем результаты классификации
# if output_pred_dir is not None:
# if not os.path.exists(output_pred_dir):
# os.makedirs(output_pred_dir)
# if testing_mode == 'predict':
# for i, (test_sample, pred_labels_sample, true_labels_sample) in\
# enumerate(zip(test_data, predictions, test_labels), 1):
# write_data(os.path.join(output_pred_dir, "{0}.data".format(i)),
# test_sample, pred_labels_sample, true_labels_sample)
# elif testing_mode == 'predict_proba':
# for i, (test_sample, pred_probs_sample, labels_sample, cls_classes) in\
# enumerate(zip(test_data, prediction_probs, test_labels, classes_by_cls), 1):
# write_probs_data(os.path.join(output_pred_dir, "{0}.prob".format(i)),
# test_sample, pred_probs_sample, cls_classes, labels_sample)
# сохраняем матрицы ошибок классификации
# if not multiclass and nfolds <= 1:
# confusion_matrices_folder = "confusion_matrices"
# if not os.path.exists(confusion_matrices_folder):
# os.makedirs(confusion_matrices_folder)
# dest = os.path.join(confusion_matrices_folder,
# "confusion_matrix_{0}_{1:<.2f}_{2:<.2f}.out".format(
# max_length, fraction, feature_fraction))
# with open(dest, "w", encoding="utf8") as fout:
# fout.write("{0:<4}".format("") +
# "".join("{0:>4}".format(label) for label in classes) + "\n")
# for label, elem in zip(cls.classes_, confusion_matrices[0]):
# nonzero_positions = np.nonzero(elem)
# nonzero_counts = np.take(elem, nonzero_positions)[0]
# nonzero_labels = np.take(classes, nonzero_positions)[0]
# fout.write("{0:<4}\t".format(label))
# fout.write("\t".join("{0}:{1}".format(*pair)
# for pair in sorted(zip(nonzero_labels, nonzero_counts),
# key=(lambda x: x[1]), reverse=True))
# + "\n")
return
