def build_file_train_model_produce_output(
self, feature_names, n_gram_length, sentiment_processor, spotter,
golden_saliency_by_entid_by_docid, dexter_dataset,
wikipedia_dataset):
feature_filename = FileLocations.get_dropbox_intermediate_path(
) + 'sentiment_simple_ngram_' + str(n_gram_length) + '.txt'
document_to_feature_converter = SimpleSentiment(
sentiment_processor, n_gram_length=n_gram_length)
model_filename = FileLocations.get_dropbox_intermediate_path(
) + 'simple_sentiment_model_ngram_' + str(n_gram_length) + '.pickle'
tosent_converter = SimpleGBRT(model_filename)
test_docid_set = set(Const.TESTSET_DOCID_LIST)
train_docid_set = set(Const.TRAINSET_DOCID_LIST)
salience_by_entity_by_doc_id = smb.build_output_using_dexter_dataset(
spotter, golden_saliency_by_entid_by_docid, feature_filename,
document_to_feature_converter, tosent_converter, test_docid_set,
train_docid_set)
# if not os.path.isfile(model_filename):
# build model
self.train_model(feature_filename, feature_names, dexter_dataset,
wikipedia_dataset, model_filename)
trc = TrecReferenceCreator()
prefix = str(n_gram_length) + '_n_gram_x_temp'
trc.create_results_file(salience_by_entity_by_doc_id, prefix)
report, ndcg, trec_by_id = trc.get_report(
FileLocations.get_dropbox_intermediate_path() +
'trec_ground_truth.txt', prefix)
trc.logger.info('\nTrec Eval Results:\n%s', report)
return salience_by_entity_by_doc_id, ndcg, trec_by_id
def get_ndcg_and_trec_eval(self, feature_filename, model_filename, feature_names, docid_set, wikipediaDataset , dexterDataset, per_document_ndcg):
self.logger.info('loading model %s', model_filename)
with open(model_filename, 'rb') as handle:
model = pickle.load(handle)
salience_by_entity_by_doc_id = self.get_salience_by_entity_by_doc_id(feature_filename, model, docid_set, feature_names, dexterDataset,
wikipediaDataset, filter_for_interesting=False)
trc = TrecReferenceCreator()
prefix = 'model_runner_x_temp'
trc.create_results_file(salience_by_entity_by_doc_id, prefix)
overall_report, overall_ndcg, overall_trec_val_by_name = trc.get_report(FileLocations.get_dropbox_intermediate_path() + 'trec_ground_truth.txt', prefix)
ndcg_by_docid = {}
trec_val_by_name_by_docid = {}
if per_document_ndcg:
skipped = []
for docid in docid_set:
salience_by_entity_by_doc_id_b = {}
if docid in salience_by_entity_by_doc_id:
salience_by_entity_by_doc_id_b[docid] = salience_by_entity_by_doc_id[docid]
trc = TrecReferenceCreator()
prefix = 'model_runner_x_temp'
trc.create_results_file(salience_by_entity_by_doc_id_b, prefix)
report, ndcg, trec_val_by_name = trc.get_report(FileLocations.get_dropbox_intermediate_path() + 'trec_ground_truth.txt', prefix)
trc.logger.info('\nTrec Eval Results:\n%s', report)
ndcg_by_docid[docid] = ndcg
trec_val_by_name_by_docid[docid] = trec_val_by_name
else:
self.logger.warning('No data for docid %d, skipping',docid)
skipped.append(docid)
self.logger.info('per doc ndcg : %s ', ndcg_by_docid)
self.logger.info('skipped in the per doc ndcg : %s ', skipped)
trc.logger.info('\n_____________________________________\nTrec Eval Results Overall:\n%s', overall_report)
return overall_ndcg, ndcg_by_docid, overall_trec_val_by_name, trec_val_by_name_by_docid
def create_reference_file(self, zero_less_than_2):
# load the data
dd = DatasetDexter()
document_list = dd.get_dexter_dataset(
path=FileLocations.get_dropbox_dexter_path())
results = ''
# process the data
result_count = 0
doc_count = 0
for document in document_list:
data = json.loads(document)
saliency_by_ent_id_golden = self.extract_saliency_by_ent_id_golden(
data)
docid = data['docId']
sorted_list = self.get_ordered_list_from_dictionary(
saliency_by_ent_id_golden)
for item in sorted_list:
entity_id = item[0]
salience = item[1]
if zero_less_than_2:
if salience < 2.0:
salience = 0.0
results = results + str(docid) + ' 0 ' + str(
entity_id) + ' ' + str(salience) + '\n'
result_count += 1
self.logger.info('Documents Processed %d Entities Processed %d ',
doc_count, result_count)
doc_count += 1
fn = FileLocations.get_dropbox_intermediate_path(
) + "trec_ground_truth.txt"
self.logger.info('writing to %s ', fn)
file = open(fn, "w")
file.write(results)
file.close()
from sellibrary.text_file_loader import join_feature_matrix
from sellibrary.text_file_loader import load_feature_matrix
from sellibrary.locations import FileLocations
from sellibrary.sel.dexter_dataset import DatasetDexter
from sellibrary.wiki.wikipedia_datasets import WikipediaDataset
from sellibrary.filter_only_golden import FilterGolden
from sellibrary.util.const import Const
if __name__ == "__main__":
const = Const()
dropbox_intermediate_path = FileLocations.get_dropbox_intermediate_path()
file_A_feature_names = const.get_sel_feature_names()
filename_A = dropbox_intermediate_path + 'wp/wp.txt'
file_B_feature_names = const.sent_feature_names
filename_B = dropbox_intermediate_path + 'wp_sentiment_simple.txt' #'base_tf_simple_v2.txt'
output_filename = dropbox_intermediate_path + 'wp_joined.txt' #'joined_sel_sent_and_tf.txt'
# Load File A
X1, y1, docid_array1, entity_id_array1 = load_feature_matrix(
feature_filename=filename_A,
feature_names=file_A_feature_names,
entity_id_index=1,
y_feature_index=2,
first_feature_index=4,
number_features_per_line=len(file_A_feature_names) + 4,
tmp_filename='/tmp/temp_conversion_file.txt')
s = '%d %d 0 0 [ %f ]' % (docid, entity_id, sent)
self.logger.info(s)
file_contents = file_contents + s + '\n'
file = open(output_filename, "w")
file.write(file_contents)
file.close()
self.logger.info('processing complete')
# def train_and_save_model(self, filename):
# spotter = SpotlightCachingSpotter(False)
# afinn_filename = '../sellibrary/resources/AFINN-111.txt'
# sentiment_processor = SentimentProcessor()
# self.train_model_using_dexter_dataset(sentiment_processor, spotter, afinn_filename)
# sentiment_processor.save_model(filename)
# return sentiment_processor
if __name__ == "__main__":
filename = FileLocations.get_dropbox_intermediate_path(
) + 'sentiment.pickle'
dexter_feeder = DexterFeeder()
spotter = SpotlightCachingSpotter(False)
sentiment_processor = SentimentProcessor()
sentiment_processor.load_model(filename)
dexter_feeder.dexter_dataset_sentiment(sentiment_processor, spotter,
'/tmp/sentiment_output.txt')
import logging
from sklearn.ensemble import GradientBoostingRegressor
from sellibrary.gbrt import GBRTWrapper
from sellibrary.text_file_loader import load_feature_matrix
from sellibrary.filter_only_golden import FilterGolden
from sellibrary.sel.dexter_dataset import DatasetDexter
from sellibrary.wiki.wikipedia_datasets import WikipediaDataset
from sellibrary.locations import FileLocations
INTERMEDIATE_PATH = FileLocations.get_dropbox_intermediate_path()
# setup logging
handler = logging.StreamHandler()
handler.setFormatter(
logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s'))
logger = logging.getLogger(__name__)
logger.addHandler(handler)
logger.propagate = False
logger.setLevel(logging.INFO)
feature_names = [
'v1_graph_size', 'v1_graph_diameter', 'v1_node_degree',
'v1_degree_mean_median_ratio', 'v1_out_degree_mean_median_ratio',
'v1_degree_mean_median_ratio', 'v1_farness', 'v1_closeness',
'v1_centrality', 'v1_minus_low_relatedness_graph_size',
'v1_minus_low_relatedness_graph_diameter',
'v1_minus_low_relatedness_node_degree',
'v1_minus_low_relatedness_degree_mean_median_ratio',
'v1_minus_low_relatedness_out_degree_mean_median_ratio',
'v1_minus_low_relatedness_degree_mean_median_ratio',
self.logger.info('%s %d', docid, new_id)
cmd = '/usr/bin/sed'
param1 = '-i'
param2 = ".old"
param3 = "'s/" + docid + "/" + str(new_id) + "/g'"
param4 = filename
self.logger.info('%s %s %s %s %s', cmd, param1, param2, param3,
param4)
full_cmd = cmd + ' ' + param1 + ' ' + param2 + ' ' + param3 + ' ' + param4
process = subprocess.Popen(full_cmd,
shell=True,
stdout=subprocess.PIPE)
process.wait()
self.logger.info('return code %d ', process.returncode)
if __name__ == "__main__":
# greps all the documents in the corpus against an ID list to build a file. This
# file needs to be manually copied to the washingtonpost directory
app = WashingtonPostSedder()
app.replace_docid_articles(FileLocations.get_dropbox_intermediate_path() +
'wp/wp_minus_0.txt')
app.replace_docid_articles(FileLocations.get_dropbox_intermediate_path() +
'wp_sentiment_simple.txt')
app.replace_docid_articles(FileLocations.get_dropbox_intermediate_path() +
'wp_base_tf_simple_v2.txt')
verbose=0,
warm_start=False)
forest = forest.fit(X_train, y_train)
print('oob score ' + str(forest.oob_score_))
with open(model_filename, 'wb') as handle:
pickle.dump(forest, handle, protocol=pickle.HIGHEST_PROTOCOL)
if __name__ == "__main__":
use_dexter_dataset = False
use_wahington_post_dataset = True
output_filename = 'base_tf_simple_v2.txt'
model_filename = FileLocations.get_dropbox_intermediate_path(
) + 'simple_tf.pickle'
train_model = False
filter_for_interesting = False
train_docid_set = None # == ALL - filtered later
train_docid_set = set(Const.TRAINSET_DOCID_LIST).union(
Const.TESTSET_DOCID_LIST)
report_docid_set = None #set(Const.TESTSET_DOCID_LIST).union(Const.TRAINSET_DOCID_LIST) # filters the outputfile - add Train data to get a full output files
if (use_wahington_post_dataset):
output_filename = 'wp_' + output_filename
output_filename = FileLocations.get_dropbox_intermediate_path(
) + output_filename
document_to_feature_converter = DocToTermFreqConverter()
handler = logging.StreamHandler()
handler.setFormatter(
return salience_by_entity_by_doc_id
if __name__ == "__main__":
min_docid = int(sys.argv[1])
max_docid = int(sys.argv[2])
break_early = False
if len(sys.argv) >= 4:
break_early = sys.argv[3].lower() == 'break_early'
if break_early:
SelModelBuilder.logger.warning('Break early is set to True')
filename = FileLocations.get_dropbox_intermediate_path() + 'sel.pickle'
build_model = False
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_dexter_path())
spotter = GoldenSpotter(document_list, wikipediaDataset)
file.write('\n')
sentiment = features_by_entity_id[entity_id][0]
salience_by_entity_by_doc_id[docid][entity_id] = sentiment
self.logger.debug('sent %f', sentiment)
if file is not None:
file.close()
self.logger.info('written to %s', output_filename)
self.logger.info('processing complete')
return salience_by_entity_by_doc_id
if __name__ == "__main__":
filename = FileLocations.get_dropbox_intermediate_path(
) + 'sentiment.pickle'
smb = PWModelBuilder()
sentiment_processor = SentimentProcessor()
sentiment_processor.load_model(filename)
phrase = ' one iraq three'
# sent = sentiment_processor.get_doc_sentiment(phrase)
# print(sent, phrase)
smb.get_feature_list(sentiment_processor, ' one iraq three')
smb.get_feature_list(sentiment_processor, 'abandon')
smb.get_feature_list(sentiment_processor, 'outstanding')
smb.get_feature_list(sentiment_processor, 'appeases')
smb.get_feature_list(sentiment_processor, 'superb')
smb.get_feature_list(sentiment_processor, 'prick')
'freq in last 3 sentences of body ', # 4
'freq in title ', # 4
'one occurrence capitalised', # 5
'maximum fraction of uppercase letters', # 6
'average spot length in words', # 8.1 :
'average spot length in characters', # 8.2 :
'is in title', # 11 :
'unambiguous entity frequency', # 14 : 1 entity frequency feature
'entity in_degree in wikipeada', # 20 :
'entity out_degree in wikipeada', # 20 :
'entity degree in wikipeada', # 20 :
'document length', # 22 :
]
X, y, docid_array, entity_id_array = load_feature_matrix(
feature_filename=FileLocations.get_dropbox_intermediate_path() + 'dexter_fset_02__1_to_604_light_output_all.txt',
feature_names=feature_names,
entity_id_index=1,
y_feature_index=2, first_feature_index=4, number_features_per_line=27,
tmp_filename='/tmp/temp_conversion_file.txt'
)
# train only on records we have a golden salience for
fg = FilterGolden()
X2, y2, docid2, entityid2 = fg.get_only_golden_rows(X, y, docid_array, entity_id_array, dexterDataset= , wikipediaDataset=)
wrapper = GBRTWrapper()
splitter = DataSplitter()
wrapper.train_model(X2, y2, entityid2, 0, n_estimators=40)
from sellibrary.locations import FileLocations
from sellibrary.sel.dexter_dataset import DatasetDexter
from sellibrary.sentiment.sentiment import SentimentProcessor
from sellibrary.wiki.wikipedia_datasets import WikipediaDataset
from sellibrary.converters.tosentiment.simple_gbrt import SimpleGBRT
from sellibrary.converters.tosentiment.sel_features_to_sentiment import SelFeatToSent
from sellibrary.trec.trec_util import TrecReferenceCreator
from sellibrary.util.s3_util import AWSUtil
from sellibrary.main.main_build_sel_model import SelModelBuilder
if __name__ == "__main__":
min_number = int(sys.argv[1])
max_number = int(sys.argv[2])
filename = FileLocations.get_dropbox_intermediate_path() + 'sel.pickle'
build_model = False
break_early = False
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()
salience_by_entity_by_doc_id[docid][entity_id] = sentiment
self.logger.info('sent %f', sentiment)
if file is not None:
file.close()
self.logger.info('written to %s', output_filename)
self.logger.info('processing complete')
return salience_by_entity_by_doc_id
if __name__ == "__main__":
filename = FileLocations.get_dropbox_intermediate_path(
) + 'sentiment.pickle'
tosent_converter = SimpleGBRT(
FileLocations.get_dropbox_intermediate_path() +
'simple_sentiment_GradientBoostingRegressor.pickle')
build_model = False
output_filename = FileLocations.get_dropbox_intermediate_path(
) + 'wp_sentiment_simple.txt'
use_dexter_dataset = False
use_wshington_post_dataset = True
smb = SentimentModelBuilder()
if build_model:
sentiment_processor = smb.train_and_save_model(filename)
else:
sentiment_processor = SentimentProcessor()
class Const:
light_feature_names = [
'min_normalised_position', # 1
'max_normalised_position', # 1
'mean_normalised_position', # 1
'normalised_position_std_dev', # 1
'norm_first_position_within_first 3 sentences', # 2
'norm first positon within body middle', # 2
'norm_first_position_within last 3 sentences', # 2
'normed first position within title', # 2
'averaged normed position within sentences', # 3
'freq in first 3 sentences of body ', # 4
'freq in middle of body ', # 4
'freq in last 3 sentences of body ', # 4
'freq in title ', # 4
'one occurrence capitalised', # 5
'maximum fraction of uppercase letters', # 6
'average spot length in words', # 8.1 :
'average spot length in characters', # 8.2 :
'is in title', # 11 :
'unambiguous entity frequency', # 14 : 1 entity frequency feature
'entity in_degree in wikipeada', # 20 :
'entity out_degree in wikipeada', # 20 :
'entity degree in wikipeada', # 20 :
'document length', # 22 :
]
heavy_feature_names = [
'v1_graph_size', 'v1_graph_diameter', 'v1_node_degree',
'v1_degree_mean_median_ratio', 'v1_out_degree_mean_median_ratio',
'v1_degree_mean_median_ratio', 'v1_farness', 'v1_closeness',
'v1_centrality', 'v1_minus_low_relatedness_graph_size',
'v1_minus_low_relatedness_graph_diameter',
'v1_minus_low_relatedness_node_degree',
'v1_minus_low_relatedness_degree_mean_median_ratio',
'v1_minus_low_relatedness_out_degree_mean_median_ratio',
'v1_minus_low_relatedness_degree_mean_median_ratio',
'v1_minus_low_relatedness_farness',
'v1_minus_low_relatedness_closeness',
'v1_minus_low_relatedness_centrality', 'v0_graph_size',
'v0_graph_diameter', 'v0_node_degree', 'v0_degree_mean_median_ratio',
'v0_out_degree_mean_median_ratio', 'v0_degree_mean_median_ratio',
'v0_farness', 'v0_closeness', 'v0_centrality',
'v0_minus_low_relatedness_graph_size',
'v0_minus_low_relatedness_graph_diameter',
'v0_minus_low_relatedness_node_degree',
'v0_minus_low_relatedness_degree_mean_median_ratio',
'v0_minus_low_relatedness_out_degree_mean_median_ratio',
'v0_minus_low_relatedness_degree_mean_median_ratio',
'v0_minus_low_relatedness_farness',
'v0_minus_low_relatedness_closeness',
'v0_minus_low_relatedness_centrality'
]
tf_feature_names = ['doc_freg', 'title_freq', 'body_freq']
def get_sel_feature_names(self):
l = []
l.extend(Const.light_feature_names)
l.extend(Const.heavy_feature_names)
return l
INTERMEDIATE_PATH = FileLocations.get_dropbox_intermediate_path()
TEMP_PATH = FileLocations.get_temp_path()
sent_feature_names = [
'title_sentiment_ngram_20', 'title_neg_sent_ngram_20',
'title_pos_sent_ngram_20', 'body_sentiment_ngram_20',
'body_neg_sent_ngram_20', 'body_pos_sent_ngram_20'
]
def get_joined_feature_names(self):
l = []
l.extend(self.get_sel_feature_names())
l.extend(self.sent_feature_names)
return l
def get_joined_sel_sent_and_tf_feature_names(self):
l = []
l.extend(self.get_sel_feature_names())
l.extend(self.sent_feature_names)
l.extend(self.tf_feature_names)
return l
efficient_1_feature_names = [
'freq in first 3 sentences of body ', 'title_freq', 'freq in title ',
'norm_first_position_within_first 3 sentences',
'normed first position within title', 'title_sentiment_ngram_20',
'title_neg_sent_ngram_20', 'title_pos_sent_ngram_20',
'normalised_position_std_dev', 'min_normalised_position',
'norm first positon within body middle', 'freq in middle of body ',
'body_neg_sent_ngram_20', 'body_sentiment_ngram_20',
'body_pos_sent_ngram_20', 'max_normalised_position',
'mean_normalised_position',
'norm_first_position_within last 3 sentences',
'freq in last 3 sentences of body ',
'averaged normed position within sentences',
'entity out_degree in wikipeada', 'average spot length in words',
'average spot length in characters', 'unambiguous entity frequency',
'entity degree in wikipeada', 'document length'
]
efficient_2_feature_names = [
'freq in first 3 sentences of body ', 'title_freq', 'freq in title ',
'norm_first_position_within_first 3 sentences',
'normed first position within title', 'title_sentiment_ngram_20',
'title_neg_sent_ngram_20', 'title_pos_sent_ngram_20',
'normalised_position_std_dev', 'min_normalised_position',
'norm first positon within body middle', 'freq in middle of body ',
'body_neg_sent_ngram_20', 'body_sentiment_ngram_20',
'body_pos_sent_ngram_20', 'max_normalised_position',
'mean_normalised_position',
'norm_first_position_within last 3 sentences',
'freq in last 3 sentences of body ',
'averaged normed position within sentences',
'entity out_degree in wikipeada', 'average spot length in words',
'average spot length in characters', 'unambiguous entity frequency',
'entity degree in wikipeada', 'document length', 'v0_node_degree',
'v0_closeness', 'v0_centrality', 'v0_farness',
'v0_minus_low_relatedness_node_degree',
'v0_minus_low_relatedness_farness',
'v0_minus_low_relatedness_closeness',
'v0_minus_low_relatedness_centrality', 'entity in_degree in wikipeada',
'v0_graph_diameter', 'v0_minus_low_relatedness_graph_size',
'v0_minus_low_relatedness_graph_diameter',
'v0_minus_low_relatedness_degree_mean_median_ratio', 'v0_graph_size',
'v0_degree_mean_median_ratio', 'v0_out_degree_mean_median_ratio',
'v0_minus_low_relatedness_out_degree_mean_median_ratio'
]
TRAINSET_DOCID_LIST = [
1, 2, 4, 6, 7, 9, 10, 11, 12, 13, 16, 17, 22, 24, 26, 27, 28, 29, 31,
34, 36, 37, 38, 40, 41, 43, 44, 49, 54, 56, 57, 60, 62, 63, 71, 75, 78,
81, 86, 87, 89, 90, 91, 93, 94, 96, 98, 99, 101, 103, 108, 109, 110,
113, 114, 115, 116, 118, 123, 124, 125, 126, 136, 141, 144, 145, 148,
149, 150, 151, 153, 154, 155, 156, 157, 158, 159, 162, 163, 165, 166,
167, 170, 171, 172, 173, 174, 175, 178, 179, 182, 184, 189, 190, 191,
195, 196, 204, 206, 207, 208, 209, 210, 213, 217, 218, 219, 221, 222,
226, 229, 235, 236, 238, 241, 242, 245, 246, 247, 250, 254, 256, 257,
262, 265, 266, 267, 268, 270, 271, 272, 274, 275, 278, 283, 284, 285,
288, 290, 291, 294, 299, 301, 302, 308, 315, 317, 323, 327, 329, 330,
331, 332, 333, 338, 341, 530, 532, 534, 540, 541, 543, 544, 545, 548,
551, 552, 553, 561, 568, 569, 570, 572, 573, 574, 577, 578, 582, 583,
587, 588, 592, 594, 596, 597, 600, 601, 603, 604
]
TESTSET_DOCID_LIST = [
3, 5, 8, 14, 18, 20, 21, 23, 25, 30, 32, 33, 35, 39, 42, 45, 46, 50,
51, 52, 53, 55, 58, 64, 66, 68, 70, 72, 73, 74, 76, 77, 80, 82, 83, 84,
85, 88, 95, 100, 104, 105, 106, 107, 111, 112, 119, 127, 129, 131, 132,
133, 134, 137, 138, 139, 140, 146, 160, 161, 168, 169, 177, 180, 181,
183, 185, 186, 187, 188, 192, 193, 194, 197, 198, 199, 200, 202, 203,
205, 211, 212, 214, 215, 216, 223, 224, 225, 227, 228, 230, 232, 233,
234, 237, 239, 240, 243, 244, 248, 249, 251, 252, 253, 255, 258, 259,
260, 261, 263, 264, 269, 273, 276, 277, 279, 280, 281, 282, 289, 292,
293, 295, 296, 298, 300, 303, 304, 305, 306, 307, 311, 312, 313, 314,
316, 318, 319, 320, 321, 324, 325, 326, 334, 336, 337, 339, 340, 527,
528, 531, 536, 538, 542, 546, 550, 555, 560, 562, 566, 567, 571, 575,
576, 579, 580, 581, 586, 589, 590, 591, 593, 595, 598, 599, 602
]
DOCID_BY_REASONBLE_ENTITY = {
9282173: [
1, 32, 66, 85, 95, 106, 107, 108, 126, 136, 149, 154, 155, 157,
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