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import multiprocessing
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import pandas as pd
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import Levenshtein
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import copy
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conf_thresh = 0.8
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def my_Levenshtein_ratio(str1, str2):
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return 1 - Levenshtein.distance(str1, str2) / max(len(str1), len(str2))
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def if_minimal(md, md_list, target_col):
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# 假设这个md是minimal
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minimal = True
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for _ in md_list:
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if _ != md:
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# 假设列表中每一个md都使当前md不minimal
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exist = True
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# 如果左边任何一个大于，则假设不成立
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for col in list(set(_.keys()) - {target_col}):
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if _[col] > md[col]:
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exist = False
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# 如果右边小于，假设也不成立
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if _[target_col] < md[target_col]:
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exist = False
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# 任何一次假设成立，当前md不minimal
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if exist:
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minimal = False
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break
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return minimal
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def remove_by_confidence(md, l, relation, target_col, lock):
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support, confidence = get_one_md_metadata(md, relation, target_col)
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# todo: replace constant 0.8
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if confidence < 0.8:
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with lock:
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l.remove(md)
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# def remove_by_confidence(md, l, relation, target_col):
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# boolean, conf = satisfy_confidence(md, relation, 0.8, target_col)
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# if not boolean:
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# l.remove(md)
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# print(md, '\t', conf)
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def inference_from_record_pairs(path, threshold, target_col):
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data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
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data = data.astype(str)
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columns = data.columns.values.tolist()
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md_list = []
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minimal_vio = []
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init_md = {}
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for col in columns:
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init_md[col] = 1 if col == target_col else 0
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md_list.append(init_md)
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for row1 in data.itertuples():
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# 获取当前行的索引，从后一行开始切片
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i = row1[0]
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data1 = data[i + 1:]
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for row2 in data1.itertuples():
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violated_mds = []
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# sims是两行的相似度
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sims = {}
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for col in columns:
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similarity = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
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sims[col] = similarity
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# 寻找violated md,从md列表中删除并加入vio列表
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for md in md_list:
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lhs_satis = True
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rhs_satis = True
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for col in list(set(columns) - {target_col}):
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if sims[col] < md[col]:
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lhs_satis = False
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if sims[target_col] < md[target_col]:
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rhs_satis = False
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if lhs_satis == True and rhs_satis == False:
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md_list.remove(md)
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violated_mds.append(md)
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minimal_vio.extend(violated_mds)
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for vio_md in violated_mds:
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# 特殊化右侧,我们需要右侧百分百相似，其实不需要降低右侧阈值
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# if sims[target_col] >= threshold:
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# new_rhs = sims[target_col]
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# spec_r_md = copy.deepcopy(vio_md)
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# spec_r_md[target_col] = new_rhs
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# if if_minimal(spec_r_md, md_list, target_col):
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# md_list.append(spec_r_md)
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# 特殊化左侧
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for col in list(set(columns) - {target_col}):
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if sims[col] + 0.001 <= 1:
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spec_l_md = copy.deepcopy(vio_md)
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spec_l_md[col] = threshold if sims[col] < threshold else sims[col] + 0.001
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if if_minimal(spec_l_md, md_list, target_col):
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md_list.append(spec_l_md)
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for vio in minimal_vio:
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if not if_minimal(vio, md_list, target_col):
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minimal_vio.remove(vio)
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manager = multiprocessing.Manager()
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lock = manager.Lock()
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if len(minimal_vio) == 0:
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return [], []
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pool = multiprocessing.Pool(len(minimal_vio))
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tmp = copy.deepcopy(minimal_vio)
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with manager:
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proxy_minimal_vio = manager.list(minimal_vio)
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for _ in tmp:
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pool.apply_async(remove_by_confidence, args=(_, proxy_minimal_vio, data, target_col, lock))
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pool.close()
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pool.join()
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minimal_vio = list(proxy_minimal_vio)
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for _ in tmp:
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if not if_minimal(_, minimal_vio, target_col):
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minimal_vio.remove(_)
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return md_list, minimal_vio
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def get_mds_metadata(md_list, dataset_path, target_col):
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data = pd.read_csv(dataset_path, low_memory=False, encoding='ISO-8859-1')
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data = data.astype(str)
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manager = multiprocessing.Manager()
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if len(md_list) == 0:
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return []
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pool = multiprocessing.Pool(len(md_list))
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result = []
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with manager:
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for _ in md_list:
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task = pool.apply_async(get_one_md_metadata, args=(_, data, target_col))
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support, confidence = task.get()
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result.append({"md": _, "support": support, "confidence": confidence})
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pool.close()
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pool.join()
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return result
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def get_one_md_metadata(md, dataframe, target_col):
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support = 0
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pre_confidence = 0
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for row1 in dataframe.itertuples():
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i = row1[0]
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df_slice = dataframe[i + 1:]
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for row2 in df_slice.itertuples():
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left_satisfy = True
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both_satisfy = True
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for col in dataframe.columns.values.tolist():
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sim = my_Levenshtein_ratio(getattr(row1, col), getattr(row2, col))
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if col == target_col:
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if sim < 1:
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both_satisfy = False
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else:
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if sim < md[col]:
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left_satisfy = False
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both_satisfy = False
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if left_satisfy:
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support += 1
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if both_satisfy:
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pre_confidence += 1
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confidence = 0 if support == 0 else pre_confidence / support
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# return {"md": md, "support": support, "confidence": confidence}
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return support, confidence
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