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from utils import *
import faiss
import numpy as np
from collections import defaultdict
from time import time
from scipy.spatial import KDTree
import tqdm
from sklearn.preprocessing import RobustScaler
class Blocker:
def __init__(self, dataset_name, object_dict, property_dict, feature_importance_scores, property_ratios,
blocking_method, sdr_factor, bkafi_criterion, train_or_test):
self.dataset_name = dataset_name
self.blocking_method = blocking_method
self.nn_param = config.Blocking.nn_param
self.cand_pairs_per_item_list = config.Blocking.cand_pairs_per_item_list
self.bkafi_dim_list = config.Blocking.bkafi_dim_list
self.bkafi_criterion = bkafi_criterion
self.nbits = config.Blocking.nbits
self.object_dict = object_dict
self.property_dict = property_dict
self.feature_importance_scores = feature_importance_scores
self.property_ratios = property_ratios
self.sdr_factor = sdr_factor
self.train_or_test = train_or_test
self.centroids_dict = self._get_centroids()
self.cands_mapping = {ind: orig_ind for ind, orig_ind in enumerate(self.object_dict['cands'].keys())}
self.index_mapping = {ind: orig_ind for ind, orig_ind in enumerate(self.object_dict['index'].keys())}
self.blocking_method_dict = self._get_blocking_method_dict()
self.nn_dict, self.dist_dict, self.blocking_execution_time = self._run_blocking()
self.pos_pairs_dict, self.neg_pairs_dict = self._get_candidate_pairs()
def _get_centroids(self):
centroids_dict = defaultdict(dict)
for objects_type in ['cands', 'index']:
centroids_dict[objects_type] = {obj_ind: obj_data['centroid'] for obj_ind, obj_data in
self.object_dict[objects_type].items()}
centroids_dict[objects_type] = dict(sorted(centroids_dict[objects_type].items()))
return centroids_dict
@staticmethod
def _get_ind_mapping(centroids):
return {ind: orig_ind for ind, orig_ind in enumerate(sorted(centroids))}
def _get_blocking_method_dict(self):
blocking_method_dict = {'bkafi': self._run_bkafi,
'ViT-B_32': self._run_vit,
'ViT-L_14': self._run_vit,
'centroid': self._run_exhaustive,
'centroid_with_transform': self._run_exhaustive,
# 'lsh': self._run_lsh,
# 'kdtree': self._run_kdtree,
}
return blocking_method_dict
def _run_blocking(self):
nn_dict, dists_dict, blocking_execution_time = self.blocking_method_dict[self.blocking_method]()
return nn_dict, dists_dict, blocking_execution_time
def _run_exhaustive(self):
nn_dict, dists_dict = {}, {}
cands_centroids_np = np.array(list(self.centroids_dict['cands'].values()), dtype=np.float32)
index_centroids_np = np.array(list(self.centroids_dict['index'].values()), dtype=np.float32)
if self.blocking_method == 'centroid_with_transform':
cands_centroids_np = self._transform_centroids(cands_centroids_np, index_centroids_np)
index = faiss.IndexFlatL2(index_centroids_np.shape[1])
index.add(index_centroids_np)
start = time()
for i, query_centroid in enumerate(cands_centroids_np):
dists, neighbors = index.search(np.array([query_centroid]), self.nn_param)
nn_dict[self.cands_mapping[i]] = [self.index_mapping[ind] for ind in neighbors.flatten()]
dists_dict[self.cands_mapping[i]] = [dist for dist in dists.flatten()]
end = time()
return nn_dict, dists_dict, round(end - start, 3)
def _transform_centroids(self, cands_centroids_np, index_centroids_np):
index_mean = np.mean(index_centroids_np, axis=0)
cands_mean = np.mean(cands_centroids_np, axis=0)
index_centered = index_centroids_np - index_mean
cands_centered = cands_centroids_np - cands_mean
H = np.dot(index_centered, cands_centered.T)
U, S, Vt = np.linalg.svd(H)
rotation_matrix = np.dot(Vt.T, U.T)
if np.linalg.det(rotation_matrix) < 0:
Vt[-1, :] *= -1
rotation_matrix = np.dot(Vt.T, U.T)
translation_vector = cands_mean - np.dot(index_mean, rotation_matrix)
scaling_factor = np.linalg.norm(cands_centered) / np.linalg.norm(index_centered)
cands_centroids_np = scaling_factor * np.dot(index_centroids_np, rotation_matrix) + translation_vector
return cands_centroids_np
def _run_lsh(self):
nn_dict, dists_dict = {}, {}
cands_centroids_np = np.array(list(self.centroids_dict['cands'].values()), dtype=np.float32)
index_centroids_np = np.array(list(self.centroids_dict['index'].values()), dtype=np.float32)
index = faiss.IndexLSH(index_centroids_np.shape[1], self.nbits)
index.add(index_centroids_np)
for i, query_centroid in enumerate(cands_centroids_np):
dists, neighbors = index.search(np.array([query_centroid]), self.nn_param)
nn_dict[self.cands_mapping[i]] = [self.index_mapping[ind] for ind in neighbors.flatten()]
dists_dict[self.cands_mapping[i]] = [dist for dist in dists.flatten()]
return nn_dict, dists_dict
def _run_kdtree(self, search_dict):
robust_scaler = RobustScaler()
nn_dict, dists_dict = {}, {}
cands_vectors_np = np.array(list(search_dict['cands'].values()), dtype=np.float32)
index_vectors_np = np.array(list(search_dict['index'].values()), dtype=np.float32)
cands_vectors_np = robust_scaler.fit_transform(cands_vectors_np)
index_vectors_np = robust_scaler.transform(index_vectors_np)
index = KDTree(index_vectors_np)
dists, neighbors = index.query(cands_vectors_np, self.nn_param)
for i, query_centroid in enumerate(cands_vectors_np):
nn_dict[self.cands_mapping[i]] = [self.index_mapping[ind] for ind in neighbors[i]]
dists_dict[self.cands_mapping[i]] = [round(dist, 3) for dist in dists[i]]
return nn_dict, dists_dict
def _run_bkafi(self):
if self.train_or_test == 'train':
return self._run_bkafi_train()
nn_dict, dists_dict = {}, {}
model_name = config.Models.blocking_model
execution_time_dict = {}
for bkafi_dim in self.bkafi_dim_list:
target_blocking_features = self._get_target_blocking_features(model_name, bkafi_dim)
bkafi_dict = self._get_bkafi_dict(target_blocking_features)
start_time = time()
nn_dict[bkafi_dim], dists_dict[bkafi_dim] = self._run_kdtree(bkafi_dict)
end_time = time()
execution_time_dict[bkafi_dim] = round(end_time - start_time, 3)
return nn_dict, dists_dict, execution_time_dict
def _get_target_blocking_features(self, model_name, bkafi_dim):
if self.bkafi_criterion == 'std':
target_blocking_features = {prop: prop_information for prop, prop_information in
list(self.property_ratios.items())[:bkafi_dim]}
else: # feature_importance
target_blocking_features = {feature: self.property_ratios[feature.split('_ratio')[0]]
for feature, _ in self.feature_importance_scores[model_name][:bkafi_dim]}
return target_blocking_features
def _run_bkafi_train(self):
nn_dict, dists_dict = {}, {}
model_name = config.Models.blocking_model
bkafi_dim = len(self.feature_importance_scores[model_name])
target_blocking_features = {feature: self.property_ratios[feature.split('_ratio')[0]]
for feature, _ in self.feature_importance_scores[model_name][:bkafi_dim]}
bkafi_dict = self._get_bkafi_dict(target_blocking_features)
nn_dict[bkafi_dim], dists_dict[bkafi_dim] = self._run_kdtree(bkafi_dict)
return nn_dict, dists_dict, None
def _get_bkafi_dict(self, target_blocking_features):
bkafi_dict = defaultdict(dict)
factor_dict = self._get_bkafi_factor_dict(target_blocking_features)
for obj_type in ['cands', 'index']:
for obj_ind in self.object_dict[obj_type].keys():
bkafi_dict[obj_type][obj_ind] = []
for feature in target_blocking_features:
property_val = self.property_dict[feature.split('_ratio')[0]][obj_type][obj_ind]
bkafi_dict[obj_type][obj_ind].append(property_val * factor_dict[obj_type][feature])
bkafi_dict[obj_type][obj_ind] = np.array(bkafi_dict[obj_type][obj_ind])
return bkafi_dict
def _get_bkafi_factor_dict(self, target_blocking_features):
factor_dict = defaultdict(dict)
if self.sdr_factor:
factor_dict['cands'] = {feature: target_blocking_features[feature]['mean']
for feature in target_blocking_features}
else:
factor_dict['cands'] = {feature: 1.0 for feature in target_blocking_features}
factor_dict['index'] = {feature: 1.0 for feature in target_blocking_features}
return factor_dict
def _run_vit(self):
vit_model_name = self.blocking_method
embeddings_dict = get_embeddings_wrapper(self.dataset_name, self.object_dict, vit_model_name)
faiss_embds_dict, mapping_dict = get_faiss_embeddings(embeddings_dict)
dim = faiss_embds_dict['index'].shape[1]
index = faiss.IndexFlatIP(dim)
index.add(faiss_embds_dict['index'])
nn_dict, dists_dict = {}, {}
start_time = time()
for i, cand_query in enumerate(faiss_embds_dict['cands']):
query = cand_query.reshape(1, -1)
dists, neighbors = index.search(query, self.nn_param)
nn_dict[mapping_dict['cands'][i]] = [mapping_dict['index'][ind] for ind in neighbors[0]]
dists_dict[mapping_dict['cands'][i]] = [dist for dist in dists[0]]
end_time = time()
return nn_dict, dists_dict, round(end_time - start_time, 3)
@staticmethod
def _get_start_ind4nn(nn_inds, cand_ind):
if nn_inds[0] + 1 == cand_ind:
return 1
else:
return 0
def _get_candidate_pairs(self):
if self.train_or_test == 'train':
cand_pairs_per_item_list = [self.cand_pairs_per_item_list[0]]
else:
cand_pairs_per_item_list = self.cand_pairs_per_item_list
if 'bkafi' in self.blocking_method:
return self._get_candidate_pairs_bkafi(cand_pairs_per_item_list)
else:
return self._get_candidate_pairs_not_bkafi(cand_pairs_per_item_list)
def _get_candidate_pairs_bkafi(self, cand_pairs_per_item_list):
pos_pairs_dict, neg_pairs_dict = defaultdict(dict), defaultdict(dict)
for bkafi_dim in self.nn_dict.keys():
for list_ind, cand_pairs_per_item in enumerate(cand_pairs_per_item_list):
if list_ind == 0:
pos_pairs_dict[bkafi_dim][cand_pairs_per_item] = []
neg_pairs_dict[bkafi_dim][cand_pairs_per_item] = []
else:
previous_val = self.cand_pairs_per_item_list[list_ind - 1]
pos_pairs_dict[bkafi_dim][cand_pairs_per_item] = pos_pairs_dict[bkafi_dim][previous_val].copy()
neg_pairs_dict[bkafi_dim][cand_pairs_per_item] = neg_pairs_dict[bkafi_dim][previous_val].copy()
for cand_ind, nn_inds in self.nn_dict[bkafi_dim].items():
start_ind = self.cand_pairs_per_item_list[list_ind - 1] if list_ind > 0 else 0
# cand_ind = str(cand_ind)
for nn_ind in nn_inds[start_ind:cand_pairs_per_item]:
if cand_ind == nn_ind:
pos_pairs_dict[bkafi_dim][cand_pairs_per_item].append((cand_ind, nn_ind))
else:
neg_pairs_dict[bkafi_dim][cand_pairs_per_item].append((cand_ind, nn_ind))
return pos_pairs_dict, neg_pairs_dict
def _get_candidate_pairs_not_bkafi(self, cand_pairs_per_item_list):
pos_pairs_dict, neg_pairs_dict = dict(), dict()
for list_ind, cand_pairs_per_item in enumerate(cand_pairs_per_item_list):
if list_ind == 0:
pos_pairs_dict[cand_pairs_per_item] = []
neg_pairs_dict[cand_pairs_per_item] = []
else:
previous_val = self.cand_pairs_per_item_list[list_ind - 1]
pos_pairs_dict[cand_pairs_per_item] = pos_pairs_dict[previous_val].copy()
neg_pairs_dict[cand_pairs_per_item] = neg_pairs_dict[previous_val].copy()
for cand_ind, nn_inds in self.nn_dict.items():
start_ind = self.cand_pairs_per_item_list[list_ind - 1] if list_ind > 0 else 0
cand_ind = str(cand_ind)
for nn_ind in nn_inds[start_ind:cand_pairs_per_item]:
if cand_ind == nn_ind:
pos_pairs_dict[cand_pairs_per_item].append((cand_ind, nn_ind))
else:
neg_pairs_dict[cand_pairs_per_item].append((cand_ind, nn_ind))
return pos_pairs_dict, neg_pairs_dict
def _get_local_mapping_dict(self):
"""
This function returns the mapping dictionary of the objects in the current object_dict.
The mapping is required because in some datasets object file names are recognized as integers and in some
datasets as uids
"""
local_mapping_dict = defaultdict(dict)
if 'mapping_dict' not in self.object_dict.keys():
local_mapping_dict['cands'] = {ind: ind for ind in self.object_dict['cands'].keys()}
local_mapping_dict['index'] = {ind: ind for ind in self.object_dict['index'].keys()}
else:
local_mapping_dict['cands'] = self.object_dict['mapping_dict']['cands']
local_mapping_dict['index'] = self.object_dict['mapping_dict']['index']
return local_mapping_dict
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