Python savePredictionsToCSV Examples

Example #1

File: BigramHMMTagger.py Project: gkeswani92/Named-Entity-Recognition

def main():
    train_word_list, train_pos_list, train_ne_list = getTrainingData(HMM=True)
    test_word_list, test_pos_list, test_idx_list = getTestData(HMM=True)

    # tag_seq_preds = getTestPreds(train_pos_list, train_ne_list, test_pos_list, smooth=True)
    # tag_seq_preds = getTestPreds(train_word_list, train_ne_list, test_word_list, smooth=True)
    tag_seq_preds = getTestPreds(train_word_list, train_ne_list, test_word_list, smooth=True, similarity=True)
    formatted_preds = formatTestPreds(tag_seq_preds, test_idx_list)
    savePredictionsToCSV(formatted_preds)

Example #2

File: HMMTagger.py Project: gkeswani92/Named-Entity-Recognition

def main():
    train_word_list, _, train_ne_list = getTrainingData(HMM=True)
    test_word_list, test_pos_list, test_idx_list = getTestData(HMM=True)
    
    feature_type = 'text_features'
    low_frequency_probabilities = json.load(open(dir_path + 'Training_Test_Data/{0}'.format(feature_type)))

    tag_seq_preds = getTestPreds(train_word_list, train_ne_list, test_word_list, low_frequency_probabilities, smooth=None, similarity_based=True, test_pos_list=test_pos_list)
    formatted_preds = formatTestPreds(tag_seq_preds, test_idx_list)
    savePredictionsToCSV(formatted_preds)

Example #3

File: BigramHMMTagger.py Project: gkeswani92/Named-Entity-Recognition

def main():
    train_word_list, train_pos_list, train_ne_list = getTrainingData(HMM=True)
    test_word_list, test_pos_list, test_idx_list = getTestData(HMM=True)

    # tag_seq_preds = getTestPreds(train_pos_list, train_ne_list, test_pos_list, smooth=True)
    # tag_seq_preds = getTestPreds(train_word_list, train_ne_list, test_word_list, smooth=True)
    tag_seq_preds = getTestPreds(train_word_list,
                                 train_ne_list,
                                 test_word_list,
                                 smooth=True,
                                 similarity=True)
    formatted_preds = formatTestPreds(tag_seq_preds, test_idx_list)
    savePredictionsToCSV(formatted_preds)

Example #4

File: HMMTagger.py Project: cfan2012/Named-Entity-Recognition

def main():
    train_word_list, _, train_ne_list = getTrainingData(HMM=True)
    test_word_list, test_pos_list, test_idx_list = getTestData(HMM=True)

    feature_type = 'text_features'
    low_frequency_probabilities = json.load(
        open(dir_path + 'Training_Test_Data/{0}'.format(feature_type)))

    tag_seq_preds = getTestPreds(train_word_list,
                                 train_ne_list,
                                 test_word_list,
                                 low_frequency_probabilities,
                                 smooth=None,
                                 similarity_based=True,
                                 test_pos_list=test_pos_list)
    formatted_preds = formatTestPreds(tag_seq_preds, test_idx_list)
    savePredictionsToCSV(formatted_preds)

Example #5

File: DynamicTagger.py Project: gkeswani92/Named-Entity-Recognition

def main():
    training_data, largest_key_size = getTrainingData()
    context, pos, index = getTestData()
    predictions = makePredictions(training_data, context, pos, index, largest_key_size)
    savePredictionsToCSV(predictions)

Example #6

File: UnigramTagger.py Project: gkeswani92/Named-Entity-Recognition

def main():
    training_data = getTrainingData()
    context, pos, index = getTestData()
    predictions = makePredictions(training_data, context, pos, index)
    savePredictionsToCSV(predictions)

Example #7

def main():
    training_data = getTrainingData()
    context, pos, index = getTestData()
    predictions = makePredictions(training_data, context, pos, index)
    savePredictionsToCSV(predictions)

Example #8

File: DynamicTagger.py Project: gkeswani92/Named-Entity-Recognition

def main():
    training_data, largest_key_size = getTrainingData()
    context, pos, index = getTestData()
    predictions = makePredictions(training_data, context, pos, index,
                                  largest_key_size)
    savePredictionsToCSV(predictions)