def main():
print("Starting app...")
SEED = 56427
random.seed(SEED)
data = DataLoader()
data.load_data()
# Create baseline model predict, print accuracy
print('\n Baseline Model')
print('---------------------------------------------------')
most_freq_tag_baseline = MostFrequentTag(data.train_data)
most_freq_tag_baseline.calculate_the_most_frequent_tag_per_word()
predicted_labels, given_labels = most_freq_tag_baseline.predict(
data.test_data)
eval = Evaluation()
baseline_accuracy = eval.computeSeqAccuracy(predicted_labels, given_labels)
print('The accuracy of Most Frequent Tag baseline is: ' +
str(round(baseline_accuracy, 2)) + '%')
baseline_labels = []
for sentence_tags in given_labels:
for tag in sentence_tags:
if tag not in baseline_labels:
baseline_labels.append(tag)
baseline_f1_score_train = metrics.flat_classification_report(
given_labels, predicted_labels, labels=baseline_labels)
print(baseline_f1_score_train)
# Create Hmm model, train, predict, print accuracy
hmm_tagger = HMMTagger()
hmm_tagger.train_tagger(data.train_data)
dev_tags, dev_pred = hmm_tagger.predict(data.dev_data)
hmmeval = HmmEvaluation(data.train_data, data.dev_data)
hmmeval.calculate_overall_accuracy_and_f1_score_per_pos()
hmmeval.plot_learning_curves(
'The learning curves for hmm pos tagger model')
hmmeval.plot_f1_curves()
hmm_accuracy = hmmeval.computeSeqAccuracy(dev_pred, dev_tags)
print('The accuracy of hmm Pos Tagger is: ' +
str(round(hmm_accuracy, 2) * 100) + '%')
# Create crf model, train, predict, print accuracy
print('\n Crf Pos Tagger')
print('---------------------------------------------------')
print(
'Create the features for crf pos tagger model for train, dev and test set.\n'
)
crf_pos = CrfPosTagger()
trainSeqFeatures, trainSeqLabels = crf_pos.transformDatasetSequence(
data.train_data)
devSeqFeatures, devSeqLabels = crf_pos.transformDatasetSequence(
data.dev_data)
testSeqFeatures, testSeqLabels = crf_pos.transformDatasetSequence(
data.test_data)
print(
'Tune the hyper-parameters c1 and c2 of crf model on a held-out part of the training data, the dev set.\n'
)
crf_hyperparameters = tune_the_hyperparameters_on_held_out_data(
trainSeqFeatures, trainSeqLabels, devSeqFeatures, devSeqLabels)
print('The best value for c1 is ' + str(crf_hyperparameters[0]) +
' anc c2 = ' + str(crf_hyperparameters[1]) + '. \n')
print('Train crf using the best hyperparameters. \n')
crf_model = crf_pos.trainCRF(trainSeqFeatures, trainSeqLabels,
crf_hyperparameters)
crfeval = CrfEvaluation(trainSeqFeatures, trainSeqLabels, devSeqFeatures,
devSeqLabels)
crfeval.calculate_overall_accuracy_and_f1_score_per_pos(
crf_hyperparameters)
crfeval.plot_learning_curves(
'The learning curves for crf pos tagger model')
crfeval.plot_f1_curves()
print('Predict the labels on test set. \n')
pred_labels = crf_model.predict(testSeqFeatures)
crf_accuracy = crfeval.computeSeqAccuracy(pred_labels, testSeqLabels)
print('The accuracy of crf Pos Tagger is: ' +
str(round(crf_accuracy, 2) * 100) + '%')
labels = list(crf_model.classes_)
f1_score_train = metrics.flat_classification_report(testSeqLabels,
pred_labels,
labels=labels)
print(f1_score_train)
