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# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from os.path import exists
from os.path import join as pjoin
import plotly.graph_objects as go
import torch
import transformers
from datasets import load_from_disk
from tqdm import tqdm
from .dataset_utils import EMBEDDING_FIELD, OUR_TEXT_FIELD
def sentence_mean_pooling(model_output, attention_mask):
token_embeddings = model_output[
0
] # First element of model_output contains all token embeddings
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
class Embeddings:
def __init__(self, dstats, use_cache=False):
"""Item embeddings and clustering"""
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.node_list = None
self.nid_map = None
self.embeddings_dset = None
self.fig_tree = None
self.cached_clusters = {}
self.dstats = dstats
self.cache_path = dstats.cache_path
self.node_list_fid = pjoin(self.cache_path, "node_list.th")
self.use_cache = use_cache
self.tokenizer = transformers.AutoTokenizer.from_pretrained(
"sentence-transformers/all-mpnet-base-v2"
)
self.model = transformers.AutoModel.from_pretrained(
"sentence-transformers/all-mpnet-base-v2"
).to(self.device)
def make_text_embeddings(self):
embeddings_dset_fid = pjoin(self.cache_path, "embeddings_dset")
if self.use_cache and exists(embeddings_dset_fid):
self.embeddings_dset = load_from_disk(embeddings_dset_fid)
else:
self.embeddings_dset = self.make_embeddings()
self.embeddings_dset.save_to_disk(embeddings_dset_fid)
def make_hierarchical_clustering(self):
if self.use_cache and exists(self.node_list_fid):
self.node_list = torch.load(self.node_list_fid)
else:
self.make_text_embeddings()
self.node_list = self.fast_cluster(self.embeddings_dset, EMBEDDING_FIELD)
torch.save(self.node_list, self.node_list_fid)
self.nid_map = dict(
[(node["nid"], nid) for nid, node in enumerate(self.node_list)]
)
self.fig_tree = make_tree_plot(self.node_list, self.dstats.text_dset)
def compute_sentence_embeddings(self, sentences):
batch = self.tokenizer(
sentences, padding=True, truncation=True, return_tensors="pt"
)
batch = {k: v.to(self.device) for k, v in batch.items()}
with torch.no_grad():
model_output = self.model(**batch)
sentence_embeds = sentence_mean_pooling(
model_output, batch["attention_mask"]
)
sentence_embeds /= sentence_embeds.norm(dim=-1, keepdim=True)
return sentence_embeds
def make_embeddings(self):
def batch_embed_sentences(sentences):
return {
EMBEDDING_FIELD: [
embed.tolist()
for embed in self.compute_sentence_embeddings(
sentences[OUR_TEXT_FIELD]
)
]
}
text_dset_embeds = self.dstats.text_dset.map(
batch_embed_sentences,
batched=True,
batch_size=32,
remove_columns=[self.dstats.our_text_field],
)
return text_dset_embeds
@staticmethod
def prepare_merges(embeddings, batch_size, low_thres=0.5):
top_idx_pre = torch.cat(
[torch.LongTensor(range(embeddings.shape[0]))[:, None]] * batch_size, dim=1
)
top_val_all = torch.Tensor(0, batch_size)
top_idx_all = torch.LongTensor(0, batch_size)
n_batches = math.ceil(len(embeddings) / batch_size)
for b in tqdm(range(n_batches)):
cos_scores = torch.mm(
embeddings[b * batch_size : (b + 1) * batch_size], embeddings.t()
)
for i in range(cos_scores.shape[0]):
cos_scores[i, (b * batch_size) + i :] = -1
top_val_large, top_idx_large = cos_scores.topk(
k=batch_size, dim=-1, largest=True
)
top_val_all = torch.cat([top_val_all, top_val_large], dim=0)
top_idx_all = torch.cat([top_idx_all, top_idx_large], dim=0)
all_merges = torch.cat(
[
top_idx_pre[top_val_all > low_thres][:, None],
top_idx_all[top_val_all > low_thres][:, None],
],
dim=1,
)
all_merge_scores = top_val_all[top_val_all > low_thres]
return (all_merges, all_merge_scores)
@staticmethod
def merge_nodes(nodes, current_thres, previous_thres, all_merges, all_merge_scores):
merge_ids = (all_merge_scores <= previous_thres) * (
all_merge_scores > current_thres
)
merges = all_merges[merge_ids]
for a, b in merges.tolist():
node_a = nodes[a]
while node_a["parent_id"] != -1:
node_a = nodes[node_a["parent_id"]]
node_b = nodes[b]
while node_b["parent_id"] != -1:
node_b = nodes[node_b["parent_id"]]
if node_a["nid"] == node_b["nid"]:
continue
else:
# merge if threshold allows
if (node_a["depth"] + node_b["depth"]) > 0 and min(
node_a["merge_threshold"], node_b["merge_threshold"]
) == current_thres:
merge_to = None
merge_from = None
if node_a["nid"] < node_b["nid"]:
merge_from = node_a
merge_to = node_b
if node_a["nid"] > node_b["nid"]:
merge_from = node_b
merge_to = node_a
merge_to["depth"] = max(merge_to["depth"], merge_from["depth"])
merge_to["weight"] += merge_from["weight"]
merge_to["children_ids"] += (
merge_from["children_ids"]
if merge_from["depth"] > 0
else [merge_from["nid"]]
)
for cid in merge_from["children_ids"]:
nodes[cid]["parent_id"] = merge_to["nid"]
merge_from["parent_id"] = merge_to["nid"]
# else new node
else:
new_nid = len(nodes)
new_node = {
"nid": new_nid,
"parent_id": -1,
"depth": max(node_a["depth"], node_b["depth"]) + 1,
"weight": node_a["weight"] + node_b["weight"],
"children": [],
"children_ids": [node_a["nid"], node_b["nid"]],
"example_ids": [],
"merge_threshold": current_thres,
}
node_a["parent_id"] = new_nid
node_b["parent_id"] = new_nid
nodes += [new_node]
return nodes
def finalize_node(self, node, nodes, min_cluster_size):
node["children"] = sorted(
[
self.finalize_node(nodes[cid], nodes, min_cluster_size)
for cid in node["children_ids"]
],
key=lambda x: x["weight"],
reverse=True,
)
if node["depth"] > 0:
node["example_ids"] = [
eid for child in node["children"] for eid in child["example_ids"]
]
node["children"] = [
child for child in node["children"] if child["weight"] >= min_cluster_size
]
assert node["weight"] == len(node["example_ids"]), print(node)
return node
def fast_cluster(
self,
text_dset_embeds,
embedding_field,
batch_size=1000,
min_cluster_size=10,
low_thres=0.5,
):
embeddings = torch.Tensor(text_dset_embeds[embedding_field])
batch_size = min(embeddings.shape[0], batch_size)
all_merges, all_merge_scores = self.prepare_merges(
embeddings, batch_size, low_thres
)
# prepare leaves
nodes = [
{
"nid": nid,
"parent_id": -1,
"depth": 0,
"weight": 1,
"children": [],
"children_ids": [],
"example_ids": [nid],
"merge_threshold": 1.0,
}
for nid in range(embeddings.shape[0])
]
# one level per threshold range
for i in range(10):
p_thres = 1 - i * 0.05
c_thres = 0.95 - i * 0.05
nodes = self.merge_nodes(
nodes, c_thres, p_thres, all_merges, all_merge_scores
)
# make root
root_children = [
node
for node in nodes
if node["parent_id"] == -1 and node["weight"] >= min_cluster_size
]
root = {
"nid": len(nodes),
"parent_id": -1,
"depth": max([node["depth"] for node in root_children]) + 1,
"weight": sum([node["weight"] for node in root_children]),
"children": [],
"children_ids": [node["nid"] for node in root_children],
"example_ids": [],
"merge_threshold": -1.0,
}
nodes += [root]
for node in root_children:
node["parent_id"] = root["nid"]
# finalize tree
tree = self.finalize_node(root, nodes, min_cluster_size)
node_list = []
def rec_map_nodes(node, node_list):
node_list += [node]
for child in node["children"]:
rec_map_nodes(child, node_list)
rec_map_nodes(tree, node_list)
# get centroids and distances
for node in node_list:
node_embeds = embeddings[node["example_ids"]]
node["centroid"] = node_embeds.sum(dim=0)
node["centroid"] /= node["centroid"].norm()
node["centroid_dot_prods"] = torch.mv(node_embeds, node["centroid"])
node["sorted_examples_centroid"] = sorted(
[
(eid, edp.item())
for eid, edp in zip(node["example_ids"], node["centroid_dot_prods"])
],
key=lambda x: x[1],
reverse=True,
)
return node_list
def find_cluster_beam(self, sentence, beam_size=20):
"""
This function finds the `beam_size` lef clusters that are closest to the
proposed sentence and returns the full path from the root to the cluster
along with the dot product between the sentence embedding and the
cluster centroid
Args:
sentence (string): input sentence for which to find clusters
beam_size (int): this is a beam size algorithm to explore the tree
Returns:
[([int], float)]: list of (path_from_root, score) sorted by score
"""
embed = self.compute_sentence_embeddings([sentence])[0].to("cpu")
active_paths = [([0], torch.dot(embed, self.node_list[0]["centroid"]).item())]
finished_paths = []
children_ids_list = [
[
self.nid_map[nid]
for nid in self.node_list[path[-1]]["children_ids"]
if nid in self.nid_map
]
for path, score in active_paths
]
while len(active_paths) > 0:
next_ids = sorted(
[
(
beam_id,
nid,
torch.dot(embed, self.node_list[nid]["centroid"]).item(),
)
for beam_id, children_ids in enumerate(children_ids_list)
for nid in children_ids
],
key=lambda x: x[2],
reverse=True,
)[:beam_size]
paths = [
(active_paths[beam_id][0] + [next_id], score)
for beam_id, next_id, score in next_ids
]
active_paths = []
for path, score in paths:
if (
len(
[
nid
for nid in self.node_list[path[-1]]["children_ids"]
if nid in self.nid_map
]
)
> 0
):
active_paths += [(path, score)]
else:
finished_paths += [(path, score)]
children_ids_list = [
[
self.nid_map[nid]
for nid in self.node_list[path[-1]]["children_ids"]
if nid in self.nid_map
]
for path, score in active_paths
]
return sorted(
finished_paths,
key=lambda x: x[-1],
reverse=True,
)[:beam_size]
def make_tree_plot(node_list, text_dset):
nid_map = dict([(node["nid"], nid) for nid, node in enumerate(node_list)])
for nid, node in enumerate(node_list):
node["label"] = node.get(
"label",
f"{nid:2d} - {node['weight']:5d} items <br>"
+ "<br>".join(
[
"> " + txt[:64] + ("..." if len(txt) >= 63 else "")
for txt in list(
set(text_dset.select(node["example_ids"])[OUR_TEXT_FIELD])
)[:5]
]
),
)
# make plot nodes
# TODO: something more efficient than set to remove duplicates
labels = [node["label"] for node in node_list]
root = node_list[0]
root["X"] = 0
root["Y"] = 0
def rec_make_coordinates(node):
total_weight = 0
add_weight = len(node["example_ids"]) - sum(
[child["weight"] for child in node["children"]]
)
for child in node["children"]:
child["X"] = node["X"] + total_weight
child["Y"] = node["Y"] - 1
total_weight += child["weight"] + add_weight / len(node["children"])
rec_make_coordinates(child)
rec_make_coordinates(root)
E = [] # list of edges
Xn = []
Yn = []
Xe = []
Ye = []
for nid, node in enumerate(node_list):
Xn += [node["X"]]
Yn += [node["Y"]]
for child in node["children"]:
E += [(nid, nid_map[child["nid"]])]
Xe += [node["X"], child["X"], None]
Ye += [node["Y"], child["Y"], None]
# make figure
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=Xe,
y=Ye,
mode="lines",
line=dict(color="rgb(210,210,210)", width=1),
hoverinfo="none",
)
)
fig.add_trace(
go.Scatter(
x=Xn,
y=Yn,
mode="markers",
name="nodes",
marker=dict(
symbol="circle-dot",
size=18,
color="#6175c1",
line=dict(color="rgb(50,50,50)", width=1)
# '#DB4551',
),
text=labels,
hoverinfo="text",
opacity=0.8,
)
)
return fig