def grep_articles(self):
for docid in self.docid_set:
self.logger.info('%s', docid)
cmd = '/usr/bin/grep'
param1 = docid
param2 = FileLocations.get_dropbox_datasets_path(
) + 'washingtonpost/WashingtonPost/data/*.txt'
self.logger.info('%s %s %s ', cmd, param1, param2)
full_cmd = cmd + ' ' + param1 + ' ' + param2 + ' >> wp.txt'
process = subprocess.Popen(full_cmd,
shell=True,
stdout=subprocess.PIPE)
process.wait()
self.logger.info('return code %d ', process.returncode)
'%(asctime)s %(name)-12s %(levelname)-8s %(message)s'))
logger = logging.getLogger(__name__)
logger.addHandler(handler)
logger.propagate = False
logger.setLevel(logging.INFO)
smb = BaseDocToSentiment()
datasetDexter = DatasetDexter
wikipediaDataset = WikipediaDataset()
if use_dexter_dataset:
document_list = datasetDexter.get_dexter_dataset(
path=FileLocations.get_dropbox_dexter_path())
if use_wahington_post_dataset:
document_list = datasetDexter.get_dexter_dataset(
path=FileLocations.get_dropbox_datasets_path() + 'washingtonpost/',
filename="washington_post.json")
spotter = GoldenSpotter(document_list, wikipediaDataset)
golden_saliency_by_entid_by_docid = datasetDexter.get_golden_saliency_by_entid_by_docid(
document_list, wikipediaDataset)
if train_model:
salience_by_entity_by_doc_id = smb.build_output_using_dexter_dataset(
spotter,
golden_saliency_by_entid_by_docid,
output_filename,
document_to_feature_converter,
None,
train_docid_set,
wikipediaDataset,
aws_util = AWSUtil()
smb = SelModelBuilder()
# if build_model:
# sentiment_processor = smb.train_and_save_model(filename)
# else:
# sentiment_processor = SentimentProcessor()
# sentiment_processor.load_model(filename)
dd = smb.get_dexter_datset()
wikipediaDataset = WikipediaDataset()
document_list = dd.get_dexter_dataset(path=FileLocations.get_dropbox_datasets_path()+'washingtonpost/', filename="washington_post.json")
spotter = GoldenSpotter(document_list, wikipediaDataset)
golden_saliency_by_entid_by_docid = dd.get_golden_saliency_by_entid_by_docid(document_list, wikipediaDataset)
output_filename = FileLocations.get_dropbox_intermediate_path() + 'sel_all_features_golden_spotter.washington_post.docnum.'+ str(min_number) + '-' + str(max_number) + '.txt'
heavy_feature_filename = FileLocations.get_temp_path() + 'sel_heavy_features_golden_spotter.washington_post.docnum.'+ str(min_number) + '-' + str(max_number) + '.txt'
light_feature_filename = FileLocations.get_temp_path() + 'sel_light_features_golden_spotter.washington_post.docnum.'+ str(min_number) + '-' + str(max_number) + '.txt'
document_to_feature_converter = SelFeatureExtractor(spotter, binary_classifier_threshold=0.5,
min_candidates_to_pass_through = 5000,
binary_classifier=None,
light_feature_filename = light_feature_filename, heavy_feature_filename = heavy_feature_filename, num_light_features = 23, break_early = break_early)
