示例#1
0
文件: test.py 项目: UFAL-DSG/flect
def test_models(file_in, file_out, model_files, source_attr, target_attr,
                oov_test_file, oov_part, pos_attr, test_indiv):
    """\
    Test all the given models on the selected file and save the target.

    If oov_test_file is set, performs also OOV evaluation.
    If test_pos is True, prints detailed results for various POSs.
    """
    # load testing data
    log_info('Loading data: ' + file_in)
    data = DataSet()
    data.load_from_arff(file_in)
    forms = data[source_attr]
    # apply all models
    for model_num, model_file in enumerate(model_files, start=1):
        model = Model.load_from_file(model_file)
        log_info('Applying model: ' + model_file)
        rules = model.classify(data)
        output_attr = 'OUTPUT_M' + str(model_num)
        data.add_attrib(Attribute(output_attr, 'string'), rules)
        if test_indiv:
            good = count_correct(data, model.class_attr, output_attr)
            print_score(good, len(data), 'Model accuracy')
        forms = [inflect(form, rule) for form, rule in zip(forms, rules)]
        forms_attr = 'FORMS_M' + str(model_num)
        data.add_attrib(Attribute(forms_attr, 'string'), forms)
    # test the final performance
    log_info('Evaluating...')
    good = count_correct(data, target_attr, forms_attr)
    print_score(good, len(data), 'ALL')
    # evaluate without punctuation
    evaluate_nopunct(data, source_attr, target_attr, forms_attr)
    # evaluate forms different from lemma
    evaluate_nolemma(data, source_attr, target_attr, forms_attr)
    # load training data for OOV tests, evaluate on OOV
    if oov_test_file:
        evaluate_oov(data, source_attr, target_attr, forms_attr,
                     oov_test_file, oov_part)
    # test on different POSes
    if pos_attr:
        evaluate_poses(data, target_attr, forms_attr, pos_attr)
    # save the classification results
    log_info('Saving data: ' + file_out)
    data.save_to_arff(file_out)
示例#2
0
文件: test.py 项目: mkorvas/flect
def test_models(file_in, file_out, model_files, source_attr, target_attr,
                oov_test_file, oov_part, pos_attr, test_indiv):
    """\
    Test all the given models on the selected file and save the target.

    If oov_test_file is set, performs also OOV evaluation.
    If test_pos is True, prints detailed results for various POSs.
    """
    # load testing data
    log_info('Loading data: ' + file_in)
    data = DataSet()
    data.load_from_arff(file_in)
    forms = data[source_attr]
    # apply all models
    for model_num, model_file in enumerate(model_files, start=1):
        model = Model.load_from_file(model_file)
        log_info('Applying model: ' + model_file)
        rules = model.classify(data)
        output_attr = 'OUTPUT_M' + str(model_num)
        data.add_attrib(Attribute(output_attr, 'string'), rules)
        if test_indiv:
            good = count_correct(data, model.class_attr, output_attr)
            print_score(good, len(data), 'Model accuracy')
        forms = [inflect(form, rule) for form, rule in zip(forms, rules)]
        forms_attr = 'FORMS_M' + str(model_num)
        data.add_attrib(Attribute(forms_attr, 'string'), forms)
    # test the final performance
    log_info('Evaluating...')
    good = count_correct(data, target_attr, forms_attr)
    print_score(good, len(data), 'ALL')
    # evaluate without punctuation
    evaluate_nopunct(data, source_attr, target_attr, forms_attr)
    # evaluate forms different from lemma
    evaluate_nolemma(data, source_attr, target_attr, forms_attr)
    # load training data for OOV tests, evaluate on OOV
    if oov_test_file:
        evaluate_oov(data, source_attr, target_attr, forms_attr, oov_test_file,
                     oov_part)
    # test on different POSes
    if pos_attr:
        evaluate_poses(data, target_attr, forms_attr, pos_attr)
    # save the classification results
    log_info('Saving data: ' + file_out)
    data.save_to_arff(file_out)
示例#3
0
def main():
    """\
    Main application entry: parse command line and run the test.
    """
    opts, filenames = getopt.getopt(sys.argv[1:], 'g:p:ai')
    show_help = False
    annot_errors = False
    gold = None
    predicted = 'PREDICTED'
    ignore_case = False
    for opt, arg in opts:
        if opt == '-g':
            gold = arg
        elif opt == '-p':
            predicted = arg
        elif opt == '-a':
            annot_errors = True
        elif opt == '-i':
            ignore_case = True
    # display help and exit
    if len(filenames) != 2 or not gold or show_help:
        display_usage()
        sys.exit(1)
    # run the training
    filename_in, filename_out = filenames
    data = DataSet()
    log_info('Loading data: ' + filename_in)
    data.load_from_arff(filename_in)
    if ignore_case:
        cmp_func = lambda a, b: a.lower() != b.lower()
    else:
        cmp_func = lambda a, b: a != b
    if annot_errors:
        log_info('Annotating errors...')
        err_ind = [
            'ERR' if cmp_func(i[gold], i[predicted]) else '' for i in data
        ]
        data.add_attrib(Attribute('ERROR_IND', 'string'), err_ind)
    else:
        log_info('Selecting errors...')
        data = data[lambda _, i: cmp_func(i[gold], i[predicted])]
    log_info('Saving data: ' + filename_out)
    data.save_to_arff(filename_out)
示例#4
0
def main():
    """\
    Main application entry: parse command line and run the test.
    """
    opts, filenames = getopt.getopt(sys.argv[1:], "g:p:ai")
    show_help = False
    annot_errors = False
    gold = None
    predicted = "PREDICTED"
    ignore_case = False
    for opt, arg in opts:
        if opt == "-g":
            gold = arg
        elif opt == "-p":
            predicted = arg
        elif opt == "-a":
            annot_errors = True
        elif opt == "-i":
            ignore_case = True
    # display help and exit
    if len(filenames) != 2 or not gold or show_help:
        display_usage()
        sys.exit(1)
    # run the training
    filename_in, filename_out = filenames
    data = DataSet()
    log_info("Loading data: " + filename_in)
    data.load_from_arff(filename_in)
    if ignore_case:
        cmp_func = lambda a, b: a.lower() != b.lower()
    else:
        cmp_func = lambda a, b: a != b
    if annot_errors:
        log_info("Annotating errors...")
        err_ind = ["ERR" if cmp_func(i[gold], i[predicted]) else "" for i in data]
        data.add_attrib(Attribute("ERROR_IND", "string"), err_ind)
    else:
        log_info("Selecting errors...")
        data = data[lambda _, i: cmp_func(i[gold], i[predicted])]
    log_info("Saving data: " + filename_out)
    data.save_to_arff(filename_out)