def _convert_to_export(self):
destination = cache_path('{}{}.hdf'.format(
self.mp.preprocessing_name(), csv_extension()))
if os.path.exists(destination):
return pandas.read_hdf(destination, 'export')
training_data = self._training_data().copy()
training_data['use_for_classifier_training'] = True
training_data['use_for_score_calculation'] = False
training_data['predict_sentiment'] = False
training_data['predicted_sentiment'] = None
unlabeled_training_data = self._unlabeled_training_data().copy()
unlabeled_training_data['use_for_classifier_training'] = False
unlabeled_training_data['use_for_score_calculation'] = False
unlabeled_training_data['sentiment'] = None
unlabeled_training_data['predict_sentiment'] = False
unlabeled_training_data['predicted_sentiment'] = None
if not use_different_source():
self._preprocess_test_data()
testing_data = self._testing_data()
testing_data['use_for_classifier_training'] = False
testing_data['use_for_score_calculation'] = True
testing_data['predict_sentiment'] = True
testing_data['predicted_sentiment'] = None
reviews = self._post_process(
[training_data, unlabeled_training_data, testing_data])
reviews.to_hdf(destination, 'export', mode='w')
return reviews
def _convert_to_export(self):
destination = cache_path('{}{}.hdf'.format(self.mp.preprocessing_name(), csv_extension()))
if os.path.exists(destination):
return pandas.read_hdf(destination, 'export')
training_data = self._training_data().copy()
training_data['use_for_classifier_training'] = True
training_data['use_for_score_calculation'] = False
training_data['predict_sentiment'] = False
training_data['predicted_sentiment'] = None
unlabeled_training_data = self._unlabeled_training_data().copy()
unlabeled_training_data['use_for_classifier_training'] = False
unlabeled_training_data['use_for_score_calculation'] = False
unlabeled_training_data['sentiment'] = None
unlabeled_training_data['predict_sentiment'] = False
unlabeled_training_data['predicted_sentiment'] = None
if not use_different_source():
self._preprocess_test_data()
testing_data = self._testing_data()
testing_data['use_for_classifier_training'] = False
testing_data['use_for_score_calculation'] = True
testing_data['predict_sentiment'] = True
testing_data['predicted_sentiment'] = None
reviews = self._post_process([training_data, unlabeled_training_data, testing_data])
reviews.to_hdf(destination, 'export', mode='w')
return reviews
def _testing_data(self):
if use_different_source():
return self._load_all_data('testing')
return self._load_data('cheatTestData{}'.format(csv_extension()))
def _training_data(self):
if use_different_source():
return self._load_all_data('training')
return self._load_data('labeledTrainData{}'.format(csv_extension()))
def _unlabeled_training_data(self):
if use_different_source():
return self._load_all_data('unlabeled')
return self._load_data('unlabeledTrainData{}'.format(csv_extension()))
def _testing_data(self):
if use_different_source():
return self._load_all_data('testing')
return self._load_data('cheatTestData{}'.format(csv_extension()))
