def process(self):
log.info("Commencing execution")
tagged_docs = TaggedLineDocument(self.labeled_articles_file_path)
log.info("Training Doc2Vec model")
doc2vec_model = doc2vec_helper.init_model(tagged_docs)
doc2vec_model.save(self.doc2vec_model_file_path)
log.info("Learnt vocab from training set and saved doc2vec model")
x_train = list()
with open(self.labeled_articles_file_path) as training_set:
for line in training_set:
x_train.append(doc2vec_model.infer_vector(line))
y_train = [0] * self.samples_per_class_train
y_train.extend([1] * self.samples_per_class_train)
x_test = list()
with open(self.articles_source_file_path) as test_set:
for line in test_set:
x_test.append(doc2vec_model.infer_vector(line))
y_true = [1] * self.samples_per_class_test
y_true.extend([0] * self.samples_per_class_test)
ml_model_logreg = scikit_ml_helper.train_logistic_reg_classifier(x_train, y_train)
scikit_ml_helper.persist_model_to_disk(ml_model_logreg, self.ml_model_file_path)
y_pred = ml_model_logreg.predict(x_test)
log.info("Logistic Regression")
log.info("Precision: " + str(metrics.precision_score(y_pred=y_pred, y_true=y_true)))
log.info("Recall: " + str(metrics.recall_score(y_pred=y_pred, y_true=y_true)))
log.info("Accuracy: " + str(metrics.accuracy_score(y_pred=y_pred, y_true=y_true)))
ml_model_svm = scikit_ml_helper.train_svm_classifier(x_train, y_train)
y_pred = ml_model_svm.predict(x_test)
log.info("SVM")
log.info("Precision: " + str(metrics.precision_score(y_pred=y_pred, y_true=y_true)))
log.info("Recall: " + str(metrics.recall_score(y_pred=y_pred, y_true=y_true)))
log.info("Accuracy: " + str(metrics.accuracy_score(y_pred=y_pred, y_true=y_true)))
ml_model_nb = scikit_ml_helper.train_gnb_classifier(x_train, y_train)
y_pred = ml_model_nb.predict(x_test)
log.info("Naive Bayes")
log.info("Precision: " + str(metrics.precision_score(y_pred=y_pred, y_true=y_true)))
log.info("Recall: " + str(metrics.recall_score(y_pred=y_pred, y_true=y_true)))
log.info("Accuracy: " + str(metrics.accuracy_score(y_pred=y_pred, y_true=y_true)))
log.info("Completed execution")
def process(self):
log.info("Began Processing")
fpb_training_docs = FPBTaggedLineDocument(
self.options.fpb_sentences_file_path)
doc2vec_model = \
doc2vec_helper.init_model(
fpb_training_docs, self.options.docvec_dimension_size, self.options.docvec_iteration_count
)
log.info("Doc2vec model initialized with " +
str(self.options.docvec_dimension_size) + " dimensions and " +
str(self.options.docvec_iteration_count) + " iterations")
label_list = fpb_training_docs.get_label_list()
log.info("Re-training document vectors")
x_train = list()
for i in xrange(len(label_list)):
x_vector = doc2vec_model.infer_vector(
fpb_training_docs.get_phrases())
x_train.append(x_vector)
log.info("Training ML model")
linear_regression_model = ml_helper.train_linear_regressor(
x_train, label_list)
log.info("Predicting test set")
x_test_articles, y_true = file_helper.get_article_details(
self.options.test_headlines_data_path)
x_test = list()
for article in x_test_articles:
x_vector = doc2vec_model.infer_vector(article)
x_test.append(x_vector)
y_pred = linear_regression_model.predict(x_test)
test_result_dict = dict()
test_result_dict['dimension_size'] = self.options.docvec_dimension_size
test_result_dict[
'iteration_count'] = self.options.docvec_iteration_count
test_result_dict['r2_score'] = metrics.r2_score(y_true, y_pred)
test_result_dict[
'semeval_score'] = evaluation_helper.evaluate_task_score(
y_pred, y_true)
log.info("Test result: " + str(test_result_dict))
log.info("Completed Processing")
def process(self):
log.info("Began Processing")
semeval_train_docs = SemevalTaggedLineDocument(
self.options.train_headlines_data_path)
doc2vec_model = \
doc2vec_helper.init_model(
semeval_train_docs, self.options.docvec_dimension_size, self.options.docvec_iteration_count
)
log.info("Doc2vec model initialized with " +
str(self.options.docvec_dimension_size) + " dimensions and " +
str(self.options.docvec_iteration_count) + " iterations")
x_articles, y_train = file_helper.get_article_details(
self.options.train_headlines_data_path)
x_train = list()
for article in x_articles:
x_vector = doc2vec_model.infer_vector(article)
x_train.append(x_vector)
x_test_articles, y_true = file_helper.get_article_details(
self.options.test_headlines_data_path)
custom_scorer = make_scorer(evaluate_task_score)
x_test = list()
for article in x_test_articles:
x_vector = doc2vec_model.infer_vector(article)
x_test.append(x_vector)
x_train.extend(x_test)
y_train.extend(y_true)
scores = model_selection.cross_val_score(svm.LinearSVR(),
x_train,
y_train,
cv=10,
scoring=custom_scorer)
log.info("Accuracy: %0.2f (+/- %0.2f)" %
(scores.mean(), scores.std() * 2))
log.info("Completed Processing")
def process(self):
log.info("Commencing execution")
# Get tagged articles from Veriday
log.info("Getting tagged Veriday articles ... ")
veriday_articles_raw = file_helper.get_articles_list(
self.articles_source_file_path)
veriday_tagged_articles = doc2vec_helper.get_tagged_articles_veriday(
veriday_articles_raw)
log.info("Getting tagged Amazon reviews ... ")
tagged_articles, sentiment_scores_dict = \
doc2vec_helper.get_tagged_amazon_reviews(self.labeled_articles_file_path)
# combine both article sets
tagged_articles.extend(veriday_tagged_articles)
# model initialization and vocab building
log.info("Initializing the doc2vec model ...")
doc2vec_model = doc2vec_helper.init_model(tagged_articles)
# shuffling and training the model
log.info("Training the doc2vec model ...")
for i in range(self.shuffle_count):
log.info("Shuffles remaining: " + str(self.shuffle_count - i))
doc2vec_helper.shuffle_and_train_articles(doc2vec_model,
tagged_articles)
# saving the doc2vec model to disk
doc2vec_model.save(self.doc2vec_model_file_path)
# Extracting parameters for and training the ML model
x_docvecs, y_scores = scikit_ml_helper.extract_training_parameters(
doc2vec_model, sentiment_scores_dict)
log.info("Training the ML model ...")
ml_model = scikit_ml_helper.train_linear_model(x_docvecs, y_scores)
# saving the ml model to disk
scikit_ml_helper.persist_model_to_disk(ml_model,
self.ml_model_file_path)
log.info("Completed execution")
def process(self):
log.info("Commencing execution")
combined_iterator = file_helper.get_reviews_iterator(
self.classification_sources_file_path)
sentences = []
doc_count = 0
for tagged_doc in combined_iterator:
doc_count += 1
sentences.append(tagged_doc)
doc2vec_model = doc2vec_helper.init_model(sentences)
log.info("Learnt vocab from training set")
# saving the doc2vec model to disk
doc2vec_model.save(self.doc2vec_model_file_path)
# Extracting parameters for and training the ML model
x_docvecs, y_scores = doc2vec_helper.extract_classification_parameters(
doc2vec_model, doc_count)
log.info("Training the ML models")
ml_model_logreg = scikit_ml_helper.train_logistic_reg_classifier(
x_docvecs, y_scores)
ml_model_nb = scikit_ml_helper.train_gnb_classifier(
x_docvecs, y_scores)
ml_model_svm_linear = scikit_ml_helper.train_svm_classifier(
x_docvecs, y_scores)
log.info("Saving the ML models to disk")
scikit_ml_helper.persist_model_to_disk(
ml_model_logreg, self.ml_model_file_path + ".docvec.log_reg")
scikit_ml_helper.persist_model_to_disk(
ml_model_nb, self.ml_model_file_path + ".docvec.nb")
scikit_ml_helper.persist_model_to_disk(
ml_model_svm_linear,
self.ml_model_file_path + ".docvec.svm_linear")
log.info("Completed execution")
def process(self):
log.info("Commencing execution")
# Get tagged articles from Semeval
log.info("Getting Semeval articles ... ")
semeval_articles_raw = file_helper.get_articles_list(
self.labeled_articles_file_path)
semeval_tagged_articles, document_sentiment_classes = \
doc2vec_helper.get_tagged_semeval_articles(semeval_articles_raw)
# model initialization and vocab building
log.info("Initializing the doc2vec model ...")
doc2vec_model = doc2vec_helper.init_model(semeval_tagged_articles)
# shuffling and training the model
log.info("Training the doc2vec model ...")
for i in range(self.shuffle_count):
log.info("Shuffles remaining: " + str(self.shuffle_count - i))
doc2vec_helper.shuffle_and_train_articles(doc2vec_model,
semeval_tagged_articles)
# saving the doc2vec model to disk
doc2vec_model.save(self.doc2vec_model_file_path)
# Extracting parameters for and training the ML model
x_docvecs, y_scores = scikit_ml_helper.extract_training_parameters(
doc2vec_model, document_sentiment_classes)
log.info("Training the ML model ...")
# ml_model = scikit_ml_helper.train_linear_model(x_docvecs, y_scores)
ml_model = scikit_ml_helper.train_gnb_classifier(x_docvecs, y_scores)
# saving the ml model to disk
scikit_ml_helper.persist_model_to_disk(ml_model,
self.ml_model_file_path)
log.info("Completed execution")
def process(self):
log.info("Began Processing")
if self.options.validate:
semeval_train_docs = SemevalTaggedLineDocument(self.options.train_headlines_data_path)
doc2vec_model = \
doc2vec_helper.init_model(
semeval_train_docs, self.options.docvec_dimension_size, self.options.docvec_iteration_count
)
log.info("Doc2vec model initialized with " + str(self.options.docvec_dimension_size) +
" dimensions and " + str(self.options.docvec_iteration_count) + " iterations")
x_articles, y_train = file_helper.get_article_details(self.options.train_headlines_data_path)
x_train = list()
for article in x_articles:
x_vector = doc2vec_model.infer_vector(article)
x_train.append(x_vector)
linear_regression_model = ml_helper.train_linear_regressor(x_train, y_train)
x_test_articles, y_true = file_helper.get_article_details(self.options.test_headlines_data_path)
x_test = list()
for article in x_test_articles:
x_vector = doc2vec_model.infer_vector(article)
x_test.append(x_vector)
y_pred = linear_regression_model.predict(x_test)
test_result_dict = dict()
test_result_dict['dimension_size'] = self.options.docvec_dimension_size
test_result_dict['iteration_count'] = self.options.docvec_iteration_count
test_result_dict['r2_score'] = metrics.r2_score(y_true, y_pred)
test_result_dict['semeval_score'] = evaluation_helper.evaluate_task_score(y_pred, y_true)
log.info(test_result_dict)
# with open(self.options.results_file, 'a') as results_file:
# results_file.write(str(json.dumps(test_result_dict)) + "\n")
elif self.options.annotate:
semeval_train_docs = SemevalTaggedLineDocument(self.options.train_headlines_data_path)
doc2vec_model = \
doc2vec_helper.init_model(
semeval_train_docs, self.options.docvec_dimension_size, self.options.docvec_iteration_count
)
log.info("Doc2vec model initialized with " + str(self.options.docvec_dimension_size) +
" dimensions and " + str(self.options.docvec_iteration_count) + " iterations")
x_articles, y_train = file_helper.get_article_details(self.options.train_headlines_data_path)
x_train = list()
for article in x_articles:
x_vector = doc2vec_model.infer_vector(article)
x_train.append(x_vector)
linear_regression_model = ml_helper.train_linear_regressor(x_train, y_train)
x_test_articles, y_true = file_helper.get_article_details(self.options.test_headlines_data_path)
x_test = list()
for article in x_test_articles:
x_vector = doc2vec_model.infer_vector(article)
x_test.append(x_vector)
y_pred = linear_regression_model.predict(x_test)
log.info("Annotating test set")
file_helper.annotate_test_set(self.options.test_headlines_data_path, y_pred)
else:
raise RuntimeError("Invalid run mode. Valid modes are 'validate' and 'annotate'")
log.info("Completed Processing")