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import pandas as pd
import numpy as np
from tqdm import tqdm
from copy import copy
from collections import Counter
import torch
from zipfile import ZipFile
import pickle
from io import BytesIO
from .match_groups import MatchGroups
class Embeddings(torch.nn.Module):
"""
Stores embeddings for a fixed array of strings and provides methods for
clustering the strings to create MatchGroups objects according to different
algorithms.
"""
def __init__(self,strings,V,score_model,weighting_function,counts,device='cpu'):
super().__init__()
self.strings = np.array(list(strings))
self.string_map = {s:i for i,s in enumerate(strings)}
self.V = V
self.counts = counts
self.w = weighting_function(counts)
self.score_model = score_model
self.weighting_function = weighting_function
self.device = device
self.to(device)
def __repr__(self):
return f'<nama.Embeddings containing {self.V.shape[1]}-d vectors for {len(self)} strings'
def to(self,device):
super().to(device)
self.V = self.V.to(device)
self.counts = self.counts.to(device)
self.w = self.w.to(device)
self.score_model.to(device)
self.device = device
def save(self,f):
"""
Save embeddings in a simple custom zipped archive format (torch.save
works too, but it requires huge amounts of memory to serialize large
embeddings objects).
"""
with ZipFile(f,'w') as zip:
# Write score model
zip.writestr('score_model.pkl',pickle.dumps(self.score_model))
# Write score model
zip.writestr('weighting_function.pkl',pickle.dumps(self.weighting_function))
# Write string info
strings_df = pd.DataFrame().assign(
string=self.strings,
count=self.counts.to('cpu').numpy())
zip.writestr('strings.csv',strings_df.to_csv(index=False))
# Write embedding vectors
byte_io = BytesIO()
np.save(byte_io,self.V.to('cpu').numpy(),allow_pickle=False)
zip.writestr('V.npy',byte_io.getvalue())
def __getitem__(self,arg):
"""
Slice a Match Groups object
"""
if isinstance(arg,slice):
i = arg
elif isinstance(arg, MatchGroups):
return self[arg.strings()]
elif hasattr(arg,'__iter__'):
# Return a subset of the embeddings and their weights
string_map = self.string_map
i = [string_map[s] for s in arg]
if i == list(range(len(self))):
# Just selecting the whole match groups object - no need to slice the embedding
return copy(self)
else:
raise ValueError(f'Unknown slice input type ({type(input)}). Can only slice Embedding with a slice, match group, or iterable.')
new = copy(self)
new.strings = self.strings[i]
new.V = self.V[i]
new.counts = self.counts[i]
new.w = self.w[i]
new.string_map = {s:i for i,s in enumerate(new.strings)}
return new
def embed(self,grouping):
"""
Construct updated Embeddings with counts from the input MatchGroups
"""
new = self[grouping]
new.counts = torch.tensor([grouping.counts[s] for s in new.strings],device=self.device)
new.w = new.weighting_function(new.counts)
return new
def __len__(self):
return len(self.strings)
def _group_to_ids(self,grouping):
group_id_map = {g:i for i,g in enumerate(grouping.groups.keys())}
group_ids = torch.tensor([group_id_map[grouping[s]] for s in self.strings]).to(self.device)
return group_ids
def _ids_to_group(self,group_ids):
if isinstance(group_ids,torch.Tensor):
group_ids = group_ids.to('cpu').numpy()
strings = self.strings
counts = self.counts.to('cpu').numpy()
# Sort by group and string count
g_sort = np.lexsort((counts,group_ids))
group_ids = group_ids[g_sort]
strings = strings[g_sort]
counts = counts[g_sort]
# Identify group boundaries and split locations
split_locs = np.nonzero(group_ids[1:] != group_ids[:-1])[0] + 1
# Get grouped strings as separate arrays
groups = np.split(strings,split_locs)
# Build the groupings
grouping = MatchGroups()
grouping.counts = Counter({s:int(c) for s,c in zip(strings,counts)})
grouping.labels = {s:g[-1] for g in groups for s in g}
grouping.groups = {g[-1]:list(g) for g in groups}
return grouping
@torch.no_grad()
def _fast_unite_similar(self,group_ids,threshold=0.5,progress_bar=True,batch_size=64):
V = self.V
cos_threshold = self.score_model.score_to_cos(threshold)
for batch_start in tqdm(range(0,len(self),batch_size),
delay=1,desc='Predicting matches',disable=not progress_bar):
i_slice = slice(batch_start,batch_start+batch_size)
j_slice = slice(batch_start+1,None)
g_i = group_ids[i_slice]
g_j = group_ids[j_slice]
# Find j's with jaccard > threshold ("matches")
batch_matched = (V[i_slice]@V[j_slice].T >= cos_threshold) \
* (g_i[:,None] != g_j[None,:])
for k,matched in enumerate(batch_matched):
if matched.any():
# Get the group ids of the matched j's
matched_groups = g_j[matched]
# Identify all embeddings in these groups
ids_to_group = torch.isin(group_ids,matched_groups)
# Assign all matched embeddings to the same group
group_ids[ids_to_group] = g_i[k].clone()
return self._ids_to_group(group_ids)
@torch.no_grad()
def unite_similar(self,
threshold=0.5,
group_threshold=None,
always_match=None,
never_match=None,
batch_size=64,
progress_bar=True,
always_never_conflicts='warn',
return_united=False):
"""
Unite embedding strings according to predicted pairwise similarity.
- "theshold" sets the minimimum match similarity required to unite two strings.
- Note that strings with similarity<threshold can end up matched if they are
linked by a chain of sufficiently similar strings (matching is transitive).
"group_threshold" can be used to add an additional constraing on the minimum
similarity within each group.
- "group_threshold" sets the minimum similarity required within a single group.
- "always_match" takes any argument that can be used to unite strings. These
strings will always be matched.
- "never_match" takes a set, or a list of sets, where each set indicates two or
more strings that should never be united with each other (these strings may
still be united with other strings).
- "always_never_conflicts" determines how to handle conflicts between
"always_match" and "never_match":
- always_never_conflicts="warn": Check for conflicts and print a warning
if any are found (default)
- always_never_conflicts="raise": Check for conflicts and raise an error
if any are found
- always_never_conflicts="ignore": Do not check for conflicts ("always_match"
will take precedence)
If "group_threshold" or "never_match" arguments are supplied, strings pairs are
united in order of similarity. Highest similarity strings are matched first, and
before each time a new pair of strings is united, the function checks if this will
result in grouping any two strings with similarity<group_threshold. If so, this
pair is skipped. This version of the algorithm requires more memory and processing
time, but guaruntees deterministic output that is consistent with the constraints.
returns: MatchGroups object
"""
if group_threshold and group_threshold < threshold:
raise ValueError('group_threshold must be greater than or equal to threshold')
group_ids = torch.arange(len(self)).to(self.device)
if always_match is not None:
always_grouping = (MatchGroups(self.strings)
.unite(always_match))
always_match_labels = always_grouping.labels
# Use a simpler, faster prediction algorithm if possible
if not (return_united or group_threshold or (never_match is not None)):
if always_match is not None:
group_ids = self._group_to_ids(always_grouping)
return self._fast_unite_similar(
group_ids=group_ids,
threshold=threshold,
batch_size=batch_size,
progress_bar=progress_bar)
if never_match is not None:
# Ensure never_match is a nested list
if all(isinstance(s,str) for s in never_match):
never_match = [never_match]
if always_match is not None:
assert always_never_conflicts in ['raise','warn','ignore']
if always_never_conflicts != 'ignore':
# Find conflicts between never_match and always_match groups
conflicts = []
for i,g in enumerate(never_match):
g = sorted(list(g))
g_labels = [always_match_labels.get(s,s) for s in g]
if len(set(g_labels)) < len(g):
df = (pd.DataFrame()
.assign(
string=g,
never_match_group=i,
always_match_group=g_labels
))
conflicts.append(df)
if conflicts:
conflicts_df = pd.concat(conflicts)
if always_never_conflicts == 'warn':
print(f'Warning: The following never_match groups are in conflict with always_match groups:\n{conflicts_df}')
print('Conflicted never_match relationships will be ignored')
else:
raise ValueError(f'The following never_match groups are in conflict with always_match groups\n{conflicts_df}')
# If always_match, collapse to group labels that should not match
# Note: Implicitly letting always_match over-ride never_match here
never_match = [{always_match_labels[s] for s in g if s in always_match_labels} for g in never_match]
else:
# Otherwise just use the strings themselves as labels
never_match = [set(s) for s in never_match]
# Convert thresholds from scores to raw cosine distances
V = self.V
cos_threshold = self.score_model.score_to_cos(threshold)
if group_threshold is not None:
separate_cos = self.score_model.score_to_cos(group_threshold)
# First collect all pairs to match (can be memory intensive!)
matches = []
cos_scores = []
for batch_start in tqdm(range(0,len(self),batch_size),
desc='Scoring pairs',
delay=1,disable=not progress_bar):
i_slice = slice(batch_start,batch_start+batch_size)
j_slice = slice(batch_start+1,None)
# Find j's with jaccard > threshold ("matches")
batch_cos = V[i_slice]@V[j_slice].T
# Search upper diagonal entries only
# (note j_slice starting index is offset by one)
batch_cos = torch.triu(batch_cos)
bi,bj = torch.nonzero(batch_cos >= cos_threshold,as_tuple=True)
if len(bi):
# Convert batch index locations to global index locations
i = bi + batch_start
j = bj + batch_start + 1
cos = batch_cos[bi,bj]
# Can skip strings that are already matched in the base grouping
unmatched = group_ids[i] != group_ids[j]
i = i[unmatched]
j = j[unmatched]
cos = cos[unmatched]
if len(i):
batch_matches = torch.hstack([i[:,None],j[:,None]])
matches.append(batch_matches.to('cpu').numpy())
cos_scores.append(cos.to('cpu').numpy())
# Unite potential match pairs in priority order, while respecting
# the group_threshold and never_match arguments
united = []
if matches:
matches = np.vstack(matches)
cos_scores = np.hstack(cos_scores).T
# Sort matches in descending order of score
m_sort = cos_scores.argsort()[::-1]
matches = matches[m_sort]
if return_united:
# Save cos scores for later return
cos_scores_df = pd.DataFrame(matches,columns=['i','j'])
cos_scores_df['cos'] = cos_scores[m_sort]
# Set up tensors
matches = torch.tensor(matches).to(self.device)
# Set-up per-string tracking of never-match relationships
if never_match is not None:
never_match_map = {s:sep for sep in never_match for s in sep}
if always_match is not None:
# If always_match, we use group labels instead of the strings themselves
never_match_array = np.array([never_match_map.get(always_match_labels[s],set()) for s in self.strings])
else:
never_match_array = np.array([never_match_map.get(s,set()) for s in self.strings])
n_matches = matches.shape[0]
with tqdm(total=n_matches,desc='Uniting matches',
delay=1,disable=not progress_bar) as p_bar:
while len(matches):
# Select the current match pair and remove it from the queue
match_pair = matches[0]
matches = matches[1:]
# Get the groups of the current match pair
g = group_ids[match_pair]
g0 = group_ids == g[0]
g1 = group_ids == g[1]
# Identify which strings should be united
to_unite = g0 | g1
# Flag whether the new group will have three or more strings
singletons = to_unite.sum() < 3
# Start by asuming that we can match this pair
unite_ok = True
# Check whether uniting this pair will unite any never_match strings/labels
if never_match is not None:
never_0 = never_match_array[match_pair[0]]
never_1 = never_match_array[match_pair[1]]
if never_0 and never_1 and (never_0 & never_1):
# Here we make use of the fact that any pair of never_match strings/labels
# will appear in both never_0 and never_1 if one string/label is in each group
unite_ok = False
# Check whether the uniting the pair will violate the group_threshold
# (impossible if the strings are singletons)
if unite_ok and group_threshold and not singletons:
V0 = V[g0,:]
V1 = V[g1,:]
unite_ok = ([email protected]).min() >= separate_cos
if unite_ok:
# Unite groups
group_ids[to_unite] = g[0]
if never_match and (never_0 or never_1):
# Propagate never_match information to the whole group
never_match_array[to_unite.detach().cpu().numpy()] = never_0 | never_1
# If we are uniting more than two strings, we can eliminate
# some redundant matches in the queue
if not singletons:
# Removed queued matches that are now in the same group
matches = matches[group_ids[matches[:,0]] != group_ids[matches[:,1]]]
if return_united:
match_record = np.empty(4,dtype=int)
match_record[:2] = match_pair.cpu().numpy().ravel()
match_record[2] = self.counts[g0].sum().item()
match_record[3] = self.counts[g1].sum().item()
united.append(match_record)
else:
# Remove queued matches connecting these groups
matches = matches[torch.isin(group_ids[matches[:,0]],g,invert=True) \
| torch.isin(group_ids[matches[:,1]],g,invert=True)]
# Update progress bar
p_bar.update(n_matches - matches.shape[0])
n_matches = matches.shape[0]
predicted_grouping = self.ids_to_group(group_ids)
if always_match is not None:
predicted_grouping = predicted_grouping.unite(always_grouping)
if return_united:
united_df = pd.DataFrame(np.vstack(united),columns=['i','j','n_i','n_j'])
united_df = pd.merge(united_df,cos_scores_df,how='inner',on=['i','j'])
united_df['score'] = self.score_model(
torch.tensor(united_df['cos'].values).to(self.device)
).cpu().numpy()
united_df = united_df.drop('cos',axis=1)
for c in ['i','j']:
united_df[c] = [self.strings[i] for i in united_df[c]]
if always_match is not None:
united_df['always_match'] = [always_grouping[i] == always_grouping[j]
for i,j in united_df[['i','j']].values]
return predicted_grouping,united_df
else:
return predicted_grouping
@torch.no_grad()
def unite_nearest(self,target_strings,threshold=0,always_grouping=None,progress_bar=True,batch_size=64):
"""
Unite embedding strings with each string's most similar target string.
- "always_grouping" will be used to inialize the group_ids before uniting new matches
- "theshold" sets the minimimum match similarity required between a string and target string
for the string to be matched. (i.e., setting theshold=0 will result in every embedding
string to be matched its nearest target string, while setting threshold=0.9 will leave
strings that have similarity<0.9 with their nearest target string unaffected)
returns: MatchGroups object
"""
if always_grouping is not None:
# self = self.embed(always_grouping)
group_ids = self._group_to_ids(always_grouping)
else:
group_ids = torch.arange(len(self)).to(self.device)
V = self.V
cos_threshold = self.score_model.score_to_cos(threshold)
seed_ids = torch.tensor([self.string_map[s] for s in target_strings]).to(self.device)
V_seed = V[seed_ids]
g_seed = group_ids[seed_ids]
is_seed = torch.zeros(V.shape[0],dtype=torch.bool).to(self.device)
is_seed[g_seed] = True
for batch_start in tqdm(range(0,len(self),batch_size),
delay=1,desc='Predicting matches',disable=not progress_bar):
batch_slice = slice(batch_start,batch_start+batch_size)
batch_cos = V[batch_slice]@V_seed.T
max_cos,max_seed = torch.max(batch_cos,dim=1)
# Get batch index locations where score > threshold
batch_i = torch.nonzero(max_cos > cos_threshold)
if len(batch_i):
# Drop target strings from matches (otherwise numerical precision
# issues can allow target strings to match to other strings)
batch_i = batch_i[~is_seed[batch_slice][batch_i]]
if len(batch_i):
# Get indices of matched strings
i = batch_i + batch_start
# Assign matched strings to the target string's group
group_ids[i] = g_seed[max_seed[batch_i]]
return self._ids_to_group(group_ids)
@torch.no_grad()
def score_pairs(self,string_pairs,batch_size=64,progress_bar=True):
string_pairs = np.array(string_pairs)
scores = []
for batch_start in tqdm(range(0,string_pairs.shape[0],batch_size),desc='Scoring pairs',disable=not progress_bar):
V0 = self[string_pairs[batch_start:batch_start+batch_size,0]].V
V1 = self[string_pairs[batch_start:batch_start+batch_size,1]].V
batch_cos = (V0*V1).sum(dim=1).ravel()
batch_scores = self.score_model(batch_cos)
scores.append(batch_scores.cpu().numpy())
return np.concatenate(scores)
@torch.no_grad()
def _batch_scores(self,group_ids,batch_start,batch_size,
is_match=None,
min_score=None,max_score=None,
min_loss=None,max_loss=None):
strings = self.strings
V = self.V
w = self.w
# Create simple slice objects to avoid creating copies with advanced indexing
i_slice = slice(batch_start,batch_start+batch_size)
j_slice = slice(batch_start+1,None)
X = V[i_slice]@V[j_slice].T
Y = (group_ids[i_slice,None] == group_ids[None,j_slice]).float()
if w is not None:
W = w[i_slice,None]*w[None,j_slice]
else:
W = None
scores = self.score_model(X)
loss = self.score_model.loss(X,Y,weights=W)
# Search upper diagonal entries only
# (note j_slice starting index is offset by one)
scores = torch.triu(scores)
# Filter by match type
if is_match is not None:
if is_match:
scores *= Y
else:
scores *= (1 - Y)
# Filter by min score
if min_score is not None:
scores *= (scores >= min_score)
# Filter by max score
if max_score is not None:
scores *= (scores <= max_score)
# Filter by min loss
if min_loss is not None:
scores *= (loss >= min_loss)
# Filter by max loss
if max_loss is not None:
scores *= (loss <= max_loss)
# Collect scored pairs
i,j = torch.nonzero(scores,as_tuple=True)
pairs = np.hstack([
strings[i.cpu().numpy() + batch_start][:,None],
strings[j.cpu().numpy() + (batch_start + 1)][:,None]
])
pair_groups = np.hstack([
strings[group_ids[i + batch_start].cpu().numpy()][:,None],
strings[group_ids[j + (batch_start + 1)].cpu().numpy()][:,None]
])
pair_scores = scores[i,j].cpu().numpy()
pair_losses = loss[i,j].cpu().numpy()
return pairs,pair_groups,pair_scores,pair_losses
def iter_scores(self,grouping=None,batch_size=64,progress_bar=True,**kwargs):
if grouping is not None:
self = self.embed(grouping)
group_ids = self._group_to_ids(grouping)
else:
group_ids = torch.arange(len(self)).to(self.device)
for batch_start in tqdm(range(0,len(self),batch_size),desc='Scoring pairs',disable=not progress_bar):
pairs,pair_groups,scores,losses = self._batch_scored_pairs(self,group_ids,batch_start,batch_size,**kwargs)
for (s0,s1),(g0,g1),score,loss in zip(pairs,pair_groups,scores,losses):
yield {
'string0':s0,
'string1':s1,
'group0':g0,
'group1':g1,
'score':score,
'loss':loss,
}
def load_embeddings(f):
"""
Load embeddings from custom zipped archive format
"""
with ZipFile(f,'r') as zip:
score_model = pickle.loads(zip.read('score_model.pkl'))
weighting_function = pickle.loads(zip.read('weighting_function.pkl'))
strings_df = pd.read_csv(zip.open('strings.csv'),na_filter=False)
V = np.load(zip.open('V.npy'))
return Embeddings(
strings=strings_df['string'].values,
counts=torch.tensor(strings_df['count'].values),
score_model=score_model,
weighting_function=weighting_function,
V=torch.tensor(V)
)
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