def __init__(self, features_dir, detdup_data_model):
self.features_dir = features_dir
self.model = detdup_data_model
self.features = [DefaultFeatures()]
self.features_map = dict()
self.storage_type = ['memory', 'disk'][0]
self.is_logger = True
self.is_inspect_detail = False
self.buffer_logger = BufferLogger(os.path.join(self.features_dir, 'process.log'))
self.result = ItemsGroupAndIndexes()
self.count = 0
self.candidate_dup_count = None
class DetDupCore(object):
"""
Detect duplicated items, use decision tree.
Usage:
-----------
"""
similarity_rate = 0.90
def __init__(self, features_dir, detdup_data_model):
self.features_dir = features_dir
self.model = detdup_data_model
self.features = [DefaultFeatures()]
self.features_map = dict()
self.storage_type = ['memory', 'disk'][0]
self.is_logger = True
self.is_inspect_detail = False
self.buffer_logger = BufferLogger(os.path.join(self.features_dir, 'process.log'))
self.result = ItemsGroupAndIndexes()
self.count = 0
self.candidate_dup_count = None
def select_feature(self, item1):
f1 = item1.typename
if not isinstance(f1, str) and not isinstance(f1, unicode): f1 = f1()
return self.features_map[f1].insert_item(item1)
def feeded(self):
for feature1 in self.features:
# 这个Feature是否有效
if not feature1.link_to_detdup:
continue
# 之前已经导出数据库啦?!
if os.path.exists(feature1.sqlite3db_path()):
return True
return False
def load_features_from_db(self):
for feature1 in self.features: feature1.load_features_tree()
def dump_features_from_memory(self):
for feature1 in self.features: feature1.dump_features_tree()
def feed_items(self, obj, persist=True):
""" Feed items to features """
# 1. insert it into memory
[self.select_feature(item1).feed_item() for item1 in process_notifier(obj)]
# 2. backup into files fully!
if persist:
self.dump_features_from_memory()
return self
def plug_features(self, features1):
"""
1. Plug features, and bind typename to classify items
2. init features tree, memory or disk
"""
if not isinstance(features1, list): features1 = [features1]
self.features.extend(features1)
for f1 in self.features:
f1.link_to_detdup = self
f1.build_features_tree()
for f1 in self.features:
self.features_map[f1.typename] = f1
return self
time_sql = 0
time_calculate_text_similarity = 0
time_fetch_content = 0
def detect_duplicated_items(self, item1):
feature1 = self.select_feature(item1)
speed = Speed()
t1 = datetime.now()
item_ids = feature1.fetch_matched_item_ids()
t2 = datetime.now(); self.time_sql += (t2 - t1).total_seconds();
# 4. 看看题目相似度
# 相似度得大于 95%
new_ids = list()
for item_id1 in item_ids:
# 2. 排除自己
if item_id1 == unicode(item1.item_id): continue
if item_id1 not in self.model:
# 删除不一致数据, 以在self.model里为准
feature1.delete_item_ids([item_id1])
continue
t11 = datetime.now()
content1 = self.model[item_id1].item_content
t12 = datetime.now(); self.time_fetch_content += (t12 - t11).total_seconds();
t11 = datetime.now()
res1 = String.calculate_text_similarity(item1.item_content,
content1,
inspect=True,
skip_special_chars=True,
similar_rate_baseline=self.similarity_rate)
t12 = datetime.now(); self.time_calculate_text_similarity += (t12 - t11).total_seconds();
if res1['similarity_rate'] > self.similarity_rate:
new_ids.append(item_id1)
self.buffer_logger.append(res1['info'])
self.buffer_logger.inspect()
print "字符串相似度 [前]", (len(item_ids) - 1), "个，[后]", len(new_ids), "个"
item_ids = new_ids
# 如果要排除已处理过为排重的
speed.tick().inspect()
print "self.time_sql", self.time_sql
print "self.time_calculate_text_similarity", self.time_calculate_text_similarity
print "self.time_fetch_content", self.time_fetch_content
return item_ids
def detect_duplicated_items_verbose(self, item_id1, verbose=False):
self.count += 1
print "\n"*5, "从", self.candidate_dup_count, "个候选题目中 排重第", self.count, "个题目。", item_id1
# 如果结果已经计算出来
if self.result.exists(item_id1):
return self.result.find(item_id1)
self.buffer_logger.append("-"*80)
self.buffer_logger.append("要处理的记录")
item1 = self.model[item_id1]
if verbose: item1.inspect()
self.buffer_logger.append("")
item_ids = self.detect_duplicated_items(item1)
self.buffer_logger.append("疑似和", item1.item_id, "重复的条目有", len(item_ids), "个")
for item_id1 in item_ids:
if verbose: self.model[item_id1].inspect()
self.buffer_logger.append("")
# 输出日志
if (len(item_ids) > 0) and self.is_logger:
self.buffer_logger.inspect()
else:
self.buffer_logger.clear()
item_ids.append(unicode(item1.item_id))
# 有重复结果，就存储一下
if len(item_ids) > 1:
self.result.add([i1 for i1 in item_ids])
return item_ids
