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@ -1,3 +1,5 @@
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import operator
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import numpy as np
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import numpy as np
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import pandas as pd
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import pandas as pd
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import copy
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import copy
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@ -13,6 +15,8 @@ def is_minimal(md, md_list, target_col):
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return True
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return True
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minimal = True
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minimal = True
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for _ in md_list:
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for _ in md_list:
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if isinstance(_, tuple):
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_ = _[0]
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if _ != md:
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if _ != md:
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other_cols = list(set(_.keys()) - {target_col})
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other_cols = list(set(_.keys()) - {target_col})
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# 假设列表中每一个md都使当前md不minimal
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# 假设列表中每一个md都使当前md不minimal
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@ -34,6 +38,7 @@ def is_minimal(md, md_list, target_col):
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def pairs_inference(path, target_col, conf: Configuration):
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def pairs_inference(path, target_col, conf: Configuration):
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simt = conf["similarity_thresh"]
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simt = conf["similarity_thresh"]
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# simt = round(simt, ndigits=3)
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supt = conf["support_thresh"]
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supt = conf["support_thresh"]
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cont = conf["confidence_thresh"]
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cont = conf["confidence_thresh"]
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data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
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data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
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@ -55,6 +60,7 @@ def pairs_inference(path, target_col, conf: Configuration):
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table_tensor = torch.stack(split_embedding, dim=0, out=None)
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table_tensor = torch.stack(split_embedding, dim=0, out=None)
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norm_table_tensor = torch.nn.functional.normalize(table_tensor, dim=2)
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norm_table_tensor = torch.nn.functional.normalize(table_tensor, dim=2)
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sim_tensor = torch.matmul(norm_table_tensor, norm_table_tensor.transpose(1, 2))
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sim_tensor = torch.matmul(norm_table_tensor, norm_table_tensor.transpose(1, 2))
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# sim_tensor = torch.round(sim_tensor, decimals=3)
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# torch.save(sim_tensor, md_output_dir + "tensor.pt")
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# torch.save(sim_tensor, md_output_dir + "tensor.pt")
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@ -65,7 +71,7 @@ def pairs_inference(path, target_col, conf: Configuration):
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init_md[col] = 1 if col == target_col else -1
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init_md[col] = 1 if col == target_col else -1
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md_list.append(init_md)
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md_list.append(init_md)
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for row1 in range(0, length - 1):
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for row1 in tqdm(range(0, length - 1)):
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terminate = False
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terminate = False
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for row2 in range(row1 + 1, length):
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for row2 in range(row1 + 1, length):
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violated_mds = []
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violated_mds = []
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@ -126,13 +132,11 @@ def pairs_inference(path, target_col, conf: Configuration):
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break
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break
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if len(minimal_vio) > 0:
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if len(minimal_vio) > 0:
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remove_list = []
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for md in minimal_vio[:]:
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for md in minimal_vio:
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support, confidence = get_metrics(md, data, sim_tensor, target_col, target_index)
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support, confidence = get_metrics(md, data, sim_tensor, target_col, target_index)
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if confidence < cont:
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if support >= supt and confidence >= cont:
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remove_list.append(md)
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minimal_vio.append((md, support, confidence))
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for _ in remove_list:
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minimal_vio.remove(md)
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minimal_vio.remove(_)
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if len(md_list) > 0:
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if len(md_list) > 0:
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# 去除重复MD
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# 去除重复MD
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@ -142,32 +146,32 @@ def pairs_inference(path, target_col, conf: Configuration):
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tmp.append(_)
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tmp.append(_)
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md_list = tmp
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md_list = tmp
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# 去除support小于阈值MD
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# 去除support小于阈值MD
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md_rm_list = []
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for _ in md_list[:]:
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for _ in md_list:
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support, confidence = get_metrics(_, data, sim_tensor, target_col, target_index)
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support, confidence = get_metrics(_, data, sim_tensor, target_col, target_index)
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if support < supt:
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if support >= supt and confidence >= cont:
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md_rm_list.append(_)
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md_list.append((_, support, confidence))
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for _ in md_rm_list:
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md_list.remove(_)
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md_list.remove(_)
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# 去除不minimal的MD
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# 去除不minimal的MD
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for _ in md_list[:]:
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for md_tuple in md_list[:]:
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if not is_minimal(_, md_list, target_col):
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if not is_minimal(md_tuple[0], md_list, target_col) and md_tuple[2] < 0.5:
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md_list.remove(_)
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md_list.remove(md_tuple)
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if len(minimal_vio) > 0:
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if len(minimal_vio) > 0:
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for vio in minimal_vio[:]:
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for vio_tuple in minimal_vio[:]:
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if not is_minimal(vio, md_list, target_col):
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if not is_minimal(vio_tuple[0], md_list, target_col) and vio_tuple[2] < 0.5:
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minimal_vio.remove(vio)
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minimal_vio.remove(vio_tuple)
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if len(minimal_vio) > 0:
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if len(minimal_vio) > 0:
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for _ in minimal_vio[:]:
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for vio_tuple in minimal_vio[:]:
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if not is_minimal(_, minimal_vio, target_col):
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if not is_minimal(vio_tuple[0], minimal_vio, target_col) and vio_tuple[2] < 0.5:
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minimal_vio.remove(_)
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minimal_vio.remove(vio_tuple)
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print(f'\033[33mList Length: {len(md_list)}\033[0m')
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result = []
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print(f'\033[33mVio Length: {len(minimal_vio)}\033[0m')
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result.extend(md_list)
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result.extend(minimal_vio)
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result.sort(key=operator.itemgetter(2), reverse=True)
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print(f'\033[33mList Length: {len(result)}\033[0m')
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print(f'\033[33mSupport: {supt}\tConfidence: {cont}\033[0m')
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print(f'\033[33mSupport: {supt}\tConfidence: {cont}\033[0m')
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return result
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return md_list, minimal_vio
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def get_metrics(current_md, data, sim_tensor, target_col, target_index):
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def get_metrics(current_md, data, sim_tensor, target_col, target_index):
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@ -181,6 +185,7 @@ def get_metrics(current_md, data, sim_tensor, target_col, target_index):
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md_tensor_3d = md_tensor_2d.unsqueeze(2)
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md_tensor_3d = md_tensor_2d.unsqueeze(2)
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md_tensor_3d = md_tensor_3d.repeat(1, length, length)
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md_tensor_3d = md_tensor_3d.repeat(1, length, length)
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sim_tensor = torch.round(sim_tensor, decimals=4)
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sup_tensor = torch.ge(sim_tensor, md_tensor_3d)
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sup_tensor = torch.ge(sim_tensor, md_tensor_3d)
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ini_slice = torch.ones((length, length), dtype=torch.bool, device='cuda')
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ini_slice = torch.ones((length, length), dtype=torch.bool, device='cuda')
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for i in range(0, width):
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for i in range(0, width):
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@ -189,10 +194,12 @@ def get_metrics(current_md, data, sim_tensor, target_col, target_index):
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ini_slice = torch.logical_and(ini_slice, sup_tensor_slice)
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ini_slice = torch.logical_and(ini_slice, sup_tensor_slice)
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sup_tensor_int = ini_slice.int()
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sup_tensor_int = ini_slice.int()
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support_Naumann = torch.count_nonzero(sup_tensor_int).item()
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support_Naumann = torch.count_nonzero(sup_tensor_int).item()
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support_Naumann = (support_Naumann - length) / 2
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ini_slice = torch.logical_and(ini_slice, sup_tensor[target_index])
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ini_slice = torch.logical_and(ini_slice, sup_tensor[target_index])
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conf_tensor_int = ini_slice.int()
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conf_tensor_int = ini_slice.int()
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support_Fan = torch.count_nonzero(conf_tensor_int).item()
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support_Fan = torch.count_nonzero(conf_tensor_int).item()
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confidence = support_Fan / support_Naumann
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support_Fan = (support_Fan - length) / 2
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confidence = support_Fan / support_Naumann if support_Naumann > 0 else 0
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return support_Fan, confidence
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return support_Fan, confidence
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